research on Superbug from 2020 to 2026, challenges and future trend

Abstract Background Increasing antimicrobial resistance (AMR) among common bacteria combined with the slow development of new antibiotics has posed a challenge to clinicians. Aim and objective To demonstrate whether antimicrobial and diagnostic stewardship program (ASP and DSP)-related interventions improve antibiotic susceptibilities among common bacteria causing bloodstream infections (BSI) in patients admitted to the intensive care unit (ICU) and whether these resulted in changes in the volume of antimicrobial consumption. Materials and methods We compared the susceptibility patterns of gram-negative bacteria (GNB) and gram-positive cocci (GPC) causing BSI and changes in the volume of antibiotics prescribed for the same before and after 2017 by a retrospective analysis. Results Postintervention, there was increased susceptibility of all GNBs to aminoglycosides; Escherichia coli and Klebsiella spp. to beta-lactambeta-lactamase inhibitors (BLBLI) combinations; and Klebsiella spp. and Pseudomonas spp. to carbapenems. Acinetobacter spp., Klebsiella spp., and Pseudomonas spp. showed improved susceptibility to doxycycline, whereas E. coli and Klebsiella spp. showed significantly improved susceptibility to fluoroquinolones. Among GPCs, there was increased susceptibility of Staphylococcus aureus (levofloxacin, clindamycin, and aminoglycoside), coagulase-negative S. aureus (CoNS) (chloramphenicol, levofloxacin, clindamycin, and aminoglycoside), and enterococci (chloramphenicol, levofloxacin, and clindamycin). There was a significant reduction in usage of antimicrobials for the treatment of GPCs (linezolid, doxycycline, chloramphenicol, levofloxacin, BLBLI, macrolide, and cephalosporin) and GNBs (levofloxacin, cephalosporin, carbapenem, and colistin), which caused BSI. Conclusion The present study illustrated that combined ASP and DSP interventions successfully reversed the resistance pattern of organisms causing BSI and resulted in a reduction in antibiotic utilization. How to cite this article Agarwal J, Singh V, Das A, Nath SS, Kumar R, Sen M. Reversing the Trend of Antimicrobial Resistance in ICU: Role of Antimicrobial and Diagnostic Stewardship. Indian J Crit Care Med 2021;25(6):635–641.

Bacterial keratitis (BK) is a severe ocular infection that can lead to vision loss, with antimicrobial resistance (AMR) posing a growing challenge. This study retrospectively analyzed 1071 bacterial isolates from corneal infections over a 10-year period (2014–2024) at a tertiary ophthalmic center in Beijing, categorizing them into three distinct phases: pre-COVID-19, during COVID-19, and post-COVID-19. The results indicated significant changes in pathogen distribution, including a marked decrease in Gram-positive cocci (from 69.8% pre-COVID-19 to 49.3% in post-COVID-19, p < 0.001), particularly in Staphylococcus epidermidis. In contrast, Gram-positive bacilli, particularly Corynebacterium spp., increased from 4.2% to 16.1% (p < 0.001). The susceptibility to gatifloxacin, moxifloxacin, and ciprofloxacin significantly declined in both Gram-positive cocci and bacilli during the COVID-19 period (all p < 0.01). Gatifloxacin resistance in Staphylococcus rose from pre-COVID-19 (15.2%) to COVID-19 (32.7%), remaining high post-COVID-19 (29.7%). A similar trend was observed in Streptococcus and Corynebacterium, where resistance rose sharply from 12.0% and 22.2% pre-COVID-19 to 42.9% during COVID-19, and remained elevated at 40.0% and 46.4% post-COVID-19, respectively (p < 0.01). These findings emphasize the rapid rise of fluoroquinolone resistance in several bacterial groups, underscoring the urgent need for continuous surveillance and improved antimicrobial stewardship to enhance treatment outcomes.

The rise of antimicrobial resistance (AMR) and the challenge of developing safe and effective antibacterial strategies pose growing public health threats. Bioinspired nanostructured surfaces with mechano-bactericidal activity provide a purely physical antibacterial strategy without the risk of inducing AMR. However, their antibacterial performance is often limited, particularly regarding long-term effectiveness and varying bactericidal efficacy against different strains. Generally, these nanostructured surfaces are combined with other antibacterial strategies to enhance their performance. Among these, physically enhanced methods can achieve satisfactory antibacterial effects while completely circumventing AMR, making them a safer and more sustainable way to assist these nanostructured surfaces. Herein, we highlight recent advances in bioinspired nanostructured bactericidal surfaces with physically enhanced performance, delving into their design principles and mechanisms of physical enhancement and summarizing related trends. These insights provide theoretical support for designing novel nanostructured bactericidal surfaces and purely physical antibacterial strategies, offering innovative solutions for bacterial infection control while effectively mitigating AMR.

Abstract Introduction/Background Antimicrobial resistance (AMR) remains a critical global health issue, particularly in low- and middle-income countries like Nigeria. Colistin, a last-resort antibiotic for multidrug-resistant Gram-negative infections, has seen rising resistance, posing a significant challenge for neonatal sepsis management. This narrative review focuses on colistin resistance in neonates in Nigeria, addressing a critical public health threat. With rising antimicrobial resistance, understanding its epidemiology in vulnerable populations is essential for effective interventions. Methods A narrative mini-review was conducted, focusing on literature, systematic reviews, and global and national reports on colistin resistance in neonates. Data were synthesized from studies across Africa, with an emphasis on epidemiological insights and implications for public health in Nigeria. Results The review identified an increasing trend of colistin resistance in Gram-negative bacteria in neonates across Nigeria. Key findings highlight the presence of mobile colistin resistance (MCR) genes, such as mcr-1, in clinical isolates from neonates, despite limited exposure to colistin. The analysis also emphasized the limitations in screening practices and gaps in neonatal AMR surveillance in Nigeria. The results suggest that inadequate antimicrobial stewardship, overuse of antibiotics, and poor healthcare infrastructure contribute to the rapid emergence of colistin resistance in neonates. Conclusion Colistin resistance in neonates poses a grave threat to public health. Addressing this issue requires urgent improvements in antimicrobial stewardship, neonatal care, and AMR surveillance systems. Strengthening laboratory capacities, improving infection prevention practices, and global cooperation are critical to mitigating the spread of colistin-resistant infections in neonates and reducing mortality in low-resource settings.

Abstract Background Antimicrobial resistance (AMR) presents a critical challenge to global healthcare systems, necessitating vigilant surveillance efforts. The European Centre for Disease Prevention and Control (ECDC) conducts point prevalence surveys (PPSs) to monitor AMR trends across European hospitals. This study focuses on comparing AMR patterns in Italian hospitals using data from two surveys: PPS2 (2016-2017) and PPS3 (2022-2023). Methods A total of 140 hospitals enrolling 28991 patients participated in PPS2, while 325 hospitals with 58506 patients contributed data to PPS3. Microbiological samples were collected and tested for antimicrobial susceptibility. Prevalence ratios (PR) with corresponding 95% confidence intervals (CI) were calculated to assess changes in resistance patterns between PPS2 and PPS3. Statistical significance was determined with p-value <0.05. Results Staphylococcus aureus showed a trend towards decreased oxacillin resistance, (PR: 0.77, 95% CI: 0.60 - 0.99). Enterococcus faecalis exhibited a non-significant decrease in glycopeptide resistance. Pseudomonas aeruginosa and Acinetobacter baumannii maintained stable carbapenem resistance between the two surveys. Escherichia coli and Klebsiella pneumoniae showed a decreasing trend in resistance to carbapenems and third-generation cephalosporins, but only Klebsiella pneumoniae was found to be significant (PR: 0.69, 95% CI: 0.55 - 0.88, p 0.004 and PR: 0.78, 95% CI: 0.66 - 0.94, p 0.01, respectively). Conclusions These findings underscore the importance of surveillance in monitoring AMR trends. Observed decreases, especially in Klebsiella pneumoniae, suggest potential improvements. However, the overall stability of resistance highlights the ongoing challenge of combating AMR. A multifaceted approach, including antimicrobial stewardship and One Health initiatives, is crucial to address this global health threat effectively. Key messages • Surveillance efforts are crucial to understand antimicrobial resistance (AMR) trends, as demonstrated by comparing data from two surveys in Italian hospital. • Results highlight the need for data-driven interventions, guiding future strategies within a One Health framework to address antimicrobial resistance comprehensively.

Background Antimicrobial resistance (AMR) is a major public health challenge, particularly in sub-Saharan Africa (SSA). This study aimed to investigate the evolution and predict the future outlook of AMR in SSA over a 12-year period. By analysing the trends and patterns of AMR, the study sought to enhance our understanding of this pressing issue in the region and provide valuable insights for effective interventions and control measures to mitigate the impact of AMR on public health in SSA. Results The study found that general medicine patients had the highest proportion of samples with AMR. Different types of samples showed varying levels of AMR. Across the studied locations, the highest resistance was consistently observed against ceftaroline (ranging from 68 to 84%), while the lowest resistance was consistently observed against ceftazidime avibactam, imipenem, meropenem, and meropenem vaborbactam (ranging from 92 to 93%). Notably, the predictive analysis showed a significant increasing trend in resistance to amoxicillin-clavulanate, cefepime, ceftazidime, ceftaroline, imipenem, meropenem, piperacillin-tazobactam, and aztreonam over time. Conclusions These findings suggest the need for coordinated efforts and interventions to control and prevent the spread of AMR in SSA. Targeted surveillance based on local resistance patterns, sample types, and patient populations is crucial for effective monitoring and control of AMR. The study also highlights the urgent need for action, including judicious use of antibiotics and the development of alternative treatment options to combat the growing problem of AMR in SSA.

Background: Pseudomonas aeruginosa is one of the commonest organism causing different infections like wound infections, Lower Respiratory Tract Infection, Urinary tract infection, infections in burn patient in hospital setting. The increasing trend of antibiotic resistance in Pseudomonas aeruginosa poses a challenge to their empiric treatment with conventional agents. So, the objective of this study was to determine the prevalence and antimicrobial resistance pattern of Pseudomonas aeruginosa isolated from different clinical samples. Methods: This was descriptive cross-sectional study carried out in the Microbiology laboratory, BPKIHS from March–August 2022. Identification of Pseudomonas aeruginosa was done by standard protocol and antimicrobial susceptibility testing was done by Kirby-Bauer disc diffusion method following Clinical Laboratory Standard Institute guidelines Results: A total of 16,950 clinical samples were processed of which 198 isolates of Pseudomonas aeruginosa were isolated mainly from urine, pus, blood, sputum, wound swab, BAL (broncho-alveolar lavage). Of  the total Pseudomonas aeruginosa isolated 78.3% were resistant to ceftazidime, 71.2% were resistant to cefepime, 62.1% were resistant to ceftriaxone followed by Piperacillin 59%, ciprofloxacin 43.4%, levofloxacin 39.3%, Gentamicin 36.3%, Imipenem 31.3%. None of the isolates were resistant to colistin. This study shows that the organism was highly sensitive to Amikacin (76.7%), Tobramycin (74.7%) and Piperacillin+tazobactam (PIT-71.7%) which could be the good choice for the treatment of this organism. Conclusion: Periodic antimicrobial surveillance is essential to update the data for the prevalence and changing susceptibility pattern of the antibiotics over the period of time as this will help in choosing appropriate antibiotics for the treatment.

Introduction Bloodstream infections in the intensive care unit have always been a global healthcare challenge. The present study was conducted with the aim to evaluate the yearly trend of antibiotic resistance in non-fermenting Gram-negative bacilli (NFGNB) causing septicemia in intensive care units. Methods Blood samples were collected from the patients admitted in various intensive care units and processed for isolation and identification of non-fermenting Gram-negative bacilli. The isolated bacterial strains were subjected to antibiotic susceptibility testing as per standard operating procedures. Results Out of 3632 blood samples, 977 (26.9%) samples showed microbial growth, of which 10.1% were Gram positive cocci, 8.7% were Gram negative bacilli (Enterobacterales), 7% were NFGNB and 1% were Candida spp. Increasing resistance among Acinetobacter baumannii complex was observed to ceftazidime, cefepime, amikacin, ciprofloxacin, levofloxacin, meropenem and trimethoprim-sulfamethoxazole. Moreover, Pseudomonas aeruginosa strains were found to be associated with increased resistance to ciprofloxacin, levofloxacin, ceftazidime and meropenem. A substantial increase in resistance levels was observed among Stenotrophomonas maltophilia and Sphingomonas paucimobilis as well. Conclusions An increasing trend of antimicrobial resistance in NFGNB envisages the worst consequences in ICUs in the coming years. Therefore, reviewing and strict implementation of the antimicrobial policies including 'rational use of antibiotics' is recommended.

INTRODUCTION The increasing resistance to antibiotics is a public health problem and an imminent therapeutic challenge in hospitals. In this report we aimed to analyze the relationship between antimicrobial resistance and antibiotic consumption in a third-level pediatric hospital. METHODOLOGY A cross-sectional analysis was conducted using the information from the microbiology and pharmacy databases of the Pediatric Hospital "Doctor Silvestre Frenk Freund", during the period 2015-2018. Prevalence of antimicrobial resistance by microorganisms and dispensed grams of selected antibiotics were calculated annually. Antibiotic resistance trend over the time was evaluated using the Chi-square trends test and to assess the correlation between the dispensed grams of antibiotics with their antimicrobial resistance prevalence, we calculated the Pearson's coefficient (r). RESULTS A total of 4,327 isolated bacterial samples were analyzed (56.5% Gram-positive and 44.5% Gram-negative). Most frequently isolated microorganisms were coagulase-negative staphylococci (CoNS), E. coli, K. pneumoniae, P. aeruginosa and S. aureus. We found a significant increase in resistance to clindamycin and oxacillin for CoNS and significant decrease in nitrofurantoin and amikacin resistance for E. coli and K. pneumoniae. We observed a strong positive and statistically significant correlation between amikacin resistance prevalence and amikacin dispensed grams for P. aeruginosa (r = 0.95, p = 0.05). CONCLUSIONS The antibiotic resistance profile showed by our study highlights the need of an appropriate antibiotic control use in the Hospital setting.

BACKGROUND/PURPOSE The rapid emergence of Pseudomonas aeruginosa resistance made selecting antibiotics more challenge. Antimicrobial stewardship programs (ASPs) are urging to implant to control the P. aeruginosa resistance. The purpose of this study is to evaluate the relationship between antimicrobial consumption and P. aeruginosa resistance, the impact of ASPs implemented during the 14-year study period. METHODS A total 14,852 P. aeruginosa isolates were included in our study. The resistant rate and antimicrobial consumption were investigated every six months. Linear regression analysis was conducted to examine the trends in antibiotics consumption and antimicrobial resistance over time. The relationship between P. aeruginosa resistance and antimicrobial consumption were using Pearson correlation coefficient to analysis. The trend of resistance before and after ASPs implanted is evaluated by segment regression analysis. RESULTS P. aeruginosa resistance to ceftazidime, gentamicin, amikacin, ciprofloxacin and levofloxacin significantly decreased during the study period; piperacillin/tazobactam (PTZ), cefepime, imipenem/cilastatin and meropenem remained stable. The P. aeruginosa resistance to ciprofloxacin and levofloxacin increasing initial then decreased after strictly controlled the use of levofloxacin since 2007. As the first choice antibiotic to treat P. aeruginosa, the consumption and resistance to PTZ increase yearly and resistance became stable since extended-infusion therapy policy implant in 2009. CONCLUSION Our ASP intervention strategy, which included extended infusion of PTZ and restrict use of levofloxacin, may be used to control antimicrobial resistance of P. aeruginosa in medical practice.

Antimicrobial resistance (AMR) is a global health challenge, with carbapenemase-producing organisms (CPOs) posing a significant concern in British Columbia, Canada. Traditional surveillance focuses on hospital-associated data, overlooking community-level trends. Monitoring carbapenemase genes in communities can provide early insights into resistance trends and support antimicrobial stewardship efforts. This study employs wastewater surveillance to track antimicrobial resistance genes (ARGs) at the community level. Four ARGs, blaNDM, blaKPC, blaOXA−48, and mcr-1, were detected and quantified in wastewater samples from five treatment plants across British Columbia. The results revealed year-round ARG presence in wastewater, with blaOXA−48 being the most prevalent, followed by blaNDM, blaKPC, and mcr-1. Seasonal fluctuations were observed, with most ARGs peaking in the winter and spring, a trend not reflected in clinical data. Notably, mcr-1 was detected in wastewater despite its limited clinical presence. These results highlight the value of integrating wastewater surveillance with traditional methods to enhance AMR monitoring.

Background Shared Multidrug resistance (sMDR) is a major challenge in antimicrobial therapy, particularly among ESKAPEE pathogens, as these bacteria develop resistance to multiple antimicrobial agents via mutations, recombination, or gene transfer. These antimicrobial resistance (AMR) mechanisms evolve and require constant surveillance. Understanding sMDR patterns in large antimicrobial surveillance datasets is complex because of the multifaceted nature of the data. Methods We explored Pfizer-Atlas (2004 – 2022) and Venatorx-GEARS (2018-2022) datasets from the Vivli platform, focusing on ESKAPEE pathogens. Descriptive data analysis, time-trend analysis, and association rule generation through the a priori algorithm were performed using Python 3.10 libraries pandas, matplotlib, seaborn, scipy.stats, and mlxtend libraries. The best rule set that explored sMDR patterns was visualized in a network format using NetworkX: 3.2 package. Results “MERIT- Multidrug ESKAPEE Resistance Insights and Tracker” dashboard was created for user-friendly visualization of antimicrobial surveillance datasets, trends in antimicrobial susceptibility profile (ASP) over years, interactive widgets to see the ASP by country and by pathogens, and Network to see significant rules as a result of association rule mining in categories by age, year and country. Conclusion Time trend analysis revealed a decline in meropenem and piperacillin-tazobactam resistance to Enterococcus faecium and doripenem for Pseudomonas aeruginosa, while resistance to imipenem (Klebsiella pneumoniae, Acinetobacter baumannii, and Enterobacter species) and meropenem (Staphylococcus aureus, A. baumannii, and Enterobacter species) increased. Association rule mining identified sMDR patterns, such as meropenem and levofloxacin resistance, in S. aureus, K. pneumoniae, P. aeruginosa and A. baumannii. Thus, our findings from the data challenge could aid healthcare professionals in making informed decisions regarding antibiotic use.

Background The world's governments have agreed both global and national actions to address the challenge of antimicrobial resistance. This raises the importance of understanding to what degree national action so far has been effective. Answering this question is challenged by variation in data availability and quality as well as disruptive events such as the COVID-19 pandemic. We investigate the association between a survey of self-reported action based on the first Global Database for Tracking Antimicrobial Resistance (TrACSS) survey and trends in multiple indicators related to the DPSEEA framework leading up to the survey. Methods and findings We apply regression methods across 73 countries between actions in 2016 and the trend in indicators of health system development (drivers), antibiotic use (pressures, ABU), absolute rates of resistance (state, ABR) and relative rates of resistance (exposure, Drug Resistance Index, DRI) from 2000 to 2016. We find that action is consistently associated with improved linear and categorical trend in health systems, ABU, ABR and DRI. Reductions are associated with relatively high levels of action (0-4) for ABU (median 2.8, 25-75% quartile 2.6-3.3), ABR (3.0, 2.4-3.4), and DRI (3.5, 3.1-3.6). These associations are robust to the inclusion of other contextual factors such as health system and socio-economic status, human population density, animal production and climate. Since 2016, a majority of both Low-Middle Income Countries (LMICs) and High-Income Countries (HICs) report increased action on repeated questions, while one third of countries report reduced action. The main limitations in interpretation are heterogeneity in data availability and the recency of action. Conclusions Our findings highlight the importance of national action to address the domestic situation related to antibiotic resistance and indicate the value of both incremental changes in reducing adverse outcomes and the need for high levels of action in delivering improvements.

BACKGROUND Quinolone-resistant Shigella has emerged as a significant global public health challenge. METHODS We assembled a global collection of 8325 plasmid-mediated quinolone resistance (PMQR)-positive Shigella isolates (1998-2025) and subjected them to comprehensive genomic analysis. RESULTS Geographically, the isolates spanned 37 countries, with the majority sourced from the United States (47.02%) and United Kingdom (32.28%). Eight distinct PMQR genes-aac(6')-Ib-cr, oqxAB, qepA, qnrA, qnrB, qnrD, qnrS, and qnrVC-were identified in the Shigella analyzed. qnrS was the most predominant PMQR gene (62.05%), followed by qnrB (38.76%). One hundred and eight sequence types (STs) were identified among the PMQR-positive Shigella isolates, with ST152 predominating (59.51%). Notably, multiple antibiotic resistance genes (ARGs) were universal in PMQR-positive Shigella, with aph(6)-Id (5825/8325), tet(B) (2398/8325), and blaTEM-1 (2232/8325) most prevalent. PMQR-positive Shigella from developed countries displayed a significant decreasing trend in ARGs abundance as collection year and the opposite trend in the abundance of virulence factors (VFs) between developed and developing countries (P < 0.001). Correlation analysis demonstrated the mobile genetic elements constitute principal vectors for PMQR genes spread. Plasmid replicons abundance positively correlated with ARGs abundance (P < 0.001), demonstrating plasmid-driven ARGs spread in PMQR-positive Shigella. In addition, high genetic similarity among geographically dispersed PMQR-positive Shigella isolates implies intercountry dissemination. CONCLUSIONS These findings elucidate PMQR-positive Shigella genomic characteristics and transmission dynamics, necessitating global surveillance reinforcement against this antimicrobial resistance threat.

UTIs are quite a common infection in outpatient care; however, the rise of antimicrobial resistance raises considerable challenge. This study determines the trend of resistance among UTI pathogens and considers factors contributing to it, such as prescribing, which often occurs in an outpatient setting. It was a single-setting retrospective analysis of 80 outpatient UTI cases. This involved bacterial isolation, antimicrobial susceptibility testing and analysis of potential factors that may have led to resistance, such as antibiotic prescribing and patient comorbidities. Descriptive statistics were therefore applied in SPSS for data analysis. The most common pathogen was Escherichia coli (70%) and exhibited significant resistance to trimethoprim-sulfamethoxazole at 30% and to fluoroquinolones at 22%. Extended-spectrum β-lactamase (ESBL)-producing strains comprised 8% of E. coli isolates. Higher resistance rates were associated with inappropriate antibiotic use (p = 0.001), frequent use of antibiotics (p = 0.004) and comorbid conditions such as diabetes (p = 0.002). The levels of resistance to antimicrobials in outpatient UTIs are rising, especially due to the inappropriate prescribing and health conditions. Improvement of stewardship of antibiotics and accuracy of diagnosis are required in controlling trends in resistance seen in outpatient care.

The importance of obtaining new compounds with improved antimicrobial activity is a current trend and challenge. Some polymers such as chitosan have shown promising bactericidal properties when they are structurally modified, which is due to the binding versatility provided by their free amines. Likewise, antimicrobial peptides (AMPs) have received attention in recent years because of their bactericidal activity that is similar to or even better than that of conventional drugs, and they exhibit a low induction rate of antimicrobial resistance. Herein, the modified AMP Ctx(Ile21)-Ha-Ahx-Cys was conjugated to chitosan using N-acetylcysteine as an intermediate by the carbodiimide method. Films were prepared using protonated chitosan in 1% acetic acid and Ctx(Ile21)-Ha-Ahx-Cys AMP dissolved in N-acetylcysteine-chitosan; 1.6 mmol of ethylcarbodiimide hydrochloride, 1.2 mmol of N-hydroxysulfosucchimide, and 0.1 mol L –1of N-morpholino)ethanesulfonic acid buffer at pH 6.5 by continuous stirring at 100 × g for 10 min at 37 °C. Physicochemical properties were evaluated by Fourier-transform infrared spectroscopy, differential scanning calorimetry/thermogravimetric analysis, and X-ray diffraction to determine the mechanical properties, solubility, morphology, and thickness. Furthermore, the antimicrobial activities of chitosan-based conjugated films were evaluated againstStaphylococcus aureus,Pseudomonas aeruginosa,SalmonellaTyphimurium, andEscherichia coli. The results showed that the conjugation of a potent AMP could further increase its antibacterial activity and maintain its stable physicochemical properties. Therefore, the developed peptide–chitosan conjugate could be applied as an additive in surgical procedures to prevent and combat bacterial infection.

BACKGROUND: Diabetic foot infection (DFI) is a major challenge in podiatric surgery. Initiation of an appropriate antimicrobial drug is the determining factor in the prognosis in DFI. AIM: This study was undertaken to analyse the changing trends in the clinical profile of patients and the bacterial profile causing DFI for over a decade. MATERIALS AND METHODS: This was a retrospective observational study conducted at the department of microbiology and surgery of the tertiary teaching hospital. The clinical and bacterial profile of patients with DFI in 2009 and 2019 was collected and analysed for the changing trend. RESULTS: Amongst the 203 patients (95 in 2009 and 108 in 2019) included in the study, 113 (56%) were male patients. There was a shift in the age group of patients from 51–60 to 61–70 years over a decade. There was a change of most common pathogens from Pseudomonas aeruginosa to Klebsiella species. The resistance rates to third-generation cephalosporins decreased amongst the Gram-negative isolates such as P. aeruginosa and Escherichia coli, but an increase in resistance to carbapenems was observed during the study period. There was reduced incidence of infection with methicillin-resistant Staphylococcus aureus during the study period. CONCLUSION: There was a change in the demographic and bacteriological flora in the DFI patients over a decade. The antimicrobial resistance rate varied for various antimicrobial agents over a decade for a particular pathogen. Regular surveillance of the change in resistance pattern amongst the pathogens is essential for the initiation of appropriate empirical therapy to reduce the morbidity in DFI.

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The COVID-19 pandemic has increased relationships and interactions between human and companion animals, supported by widespread social distancing and isolation measures. Additionally, the COVID-19 pandemic has led to an exponential growth in antibiotic and biocide use worldwide, possibly inducing further pressure, contributing to the selection of antibiotic-resistant bacteria, including WHO critical priority pathogens. While data from global surveillance studies reveal a linear trend of increasing carbapenem resistance among Gram-negative pathogens from companion animals, the acquisition of carbapenemase-producing Enterobacterales through direct contact with colonized hosts and contaminated veterinary hospital environments has been documented. This article highlights the rapid spread of WHO critical priority carbapenemase-producing pathogens in companion animals, which is a One Health challenge for a post-pandemic world.

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Background Neonatal sepsis is the most common cause of mortality and morbidity. It is a major global public health challenge, particularly in developing countries. Therefore, knowing the current status of bacterial isolates and their antimicrobial resistance profile is essential to physicians and health workers to implement appropriate intervention. The aims of this study was to assess a ten-year trend of bacterial prevalence isolates from blood culture among neonates (<1 month of age). Method A hospital-based retrospective study was conducted on 1854 neonatal patients who were admitted at University of Gondar Specialized Comprehensive Hospital between 2010 and 2020. Sociodemographical and laboratory data were collected from medical records. Quality of the data was assured through standard operational procedures. Data were entered and analysed using SPSS version 20. Bivariate analysis was employed to determine strings of association between the outcome variable and sociodemographic variables. A P value less than 0.05 will be considered to be statically significant. Results In a total of 1854 patients, 538 (29%) were culture positive. The overall neonatal sepsis infection rate was 287 (53.5%) for male and 249 (46.5%) for female. The highest proportion of neonatal sepsis infection rate was observed among the patients in the age range between 3 and 28 days and gestational at birth <37 weeks, 461(86%) and 278 (52%), respectively. Gestational at birth (P ≤ 0.001, AOR = 5.81, CI: 4.63–7.29) is significantly associated with bacterial isolates. The predominant pathogens were Staphylococcus aureus, 18 (76.6%), Klebsiella pneumoniae, 146 (38%), and E. coli, 45 (11.7%) among the age range less than one weak. Klebsiella spp, S. aureus, and E. coli showed a high level of resistance to most tested antimicrobials. Amikacin, ciprofloxacin and norfloxacin, and erythromycin were the most effective antibiotics whereas ampicillin, amoxicillin, and cotrimoxazole were the least effective antibiotics for isolates. Conclusion Neonatal sepsis infection is common in the 3–28 days of age range. S. aureus, E. coli, and K. pneumonia were the most common isolates. Most the bacterial pathogens were resistant to commonly prescribed antibiotics. Therefore, an antimicrobial sensitivity test for bacterial isolates is recommended to provide updated data for the physician in choosing the appropriate antibiotic for better patient treatment outcome.

The design of new antimicrobial agents is an important challenge due to the growing resistance of microorganisms to existing antibiotics. In recent years, the trend towards the development of compounds and materials with (bio)degradable properties has emerged. In this work, we propose and develop a method for the synthesis of new peptidomimetics, i.e., water-soluble macrocyclic quaternary ammonium salts containing L-tyrosine fragments based on p-tert-butylthiacalix[4]arene in various stereoisomeric forms (cone, partial cone, and 1,3-alternate). These compounds have low cytotoxicity (IC50 = 80-267 μM) and high antibacterial activity (MIC = 0.5-15.6 μM) against Gram-positive bacterial strains including methicillin-resistant Staphylococcus aureus (MRSA). The obtained peptidomimetics can bind α-chymotrypsin with the formation of supramolecular systems and their subsequent degradation. Our results demonstrate the first example of multi-action thiacalixarene derivatives with antibacterial activity, protein binding ability and degradation induced by binding to α-chymotrypsin. The obtained results open the possibility of creating multi-action peptidomimetic systems with antimicrobial and biodegradable effect.

Background: The challenge of emerging antimicrobial resistance and variation in antibiotic use across provinces in China call for knowledge on antibiotic utilization at the regional level. This study aims to evaluate the long-term trends and patterns of antibiotic usage in Xinjiang Province, the largest provincial-level division located in the northwest of China, aiming to provide evidence in enhancing provincial antimicrobial stewardship (AMS) and developing policy measures to optimize regional antimicrobial use. Methods: This was an ecological study with temporal trend analysis on inpatient antibiotic utilization, with antibiotic use data from 92 public hospitals covered by Xinjiang’s Center for Antibacterial Surveillance from 2012 to 2022. Antibiotic use was measured by the number of daily defined doses per 100 patient days (DDDs/100 pds). Patterns of antibiotic use were described by Anatomical Therapeutic Chemical (ATC) subgroups and the Access, Watch, Reserve (AWaRe) classification. The Average Annual Percent Change (AAPC) of antibiotic use and the corresponding 95% confidence intervals (CIs) were calculated to describe the trend of antibiotic use over time. Joinpoint regression was performed using the Weighted Bayesian Information Criteria (WBIC) model with a parametric method. A pairwise comparison between secondary and tertiary hospitals was conducted to explore disparities in antibiotic use across hospital levels. The most commonly used antibiotics were also analyzed. Results: The total inpatient antibiotic use in Xinjiang was 27.6 DDDs/100 patient days in 2022, with a significant decreasing trend during 2012–2022 (AAPC, −2.0%; 95% CI, −3.6% to −0.4%). The Watch group antibiotics were the most used AWaRe category, with the Access-to-Watch ratio decreasing significantly from 46.4% to 24.4% (AAPC, −6.8%; 95% CI, −8.4% to −5.1%). No significant difference was found in the trend of total antibiotic use between secondary and tertiary hospitals, but there were disparities across hospital levels in subgroups. Third-generation cephalosporins, second-generation cephalosporins, and fluoroquinolones remained the top three antibiotic class throughout the study period. The number of antibiotics accounting for 90% of the total antibiotic use decreased from 34 antibiotics in 2012 to 18 antibiotics in 2022. Conclusions: The decreasing trend of inpatient antibiotic use in Xinjiang’s public hospitals reflects the effects of continuous AMS implementation. Patterns of antibiotic use underscore the need for further efforts on evidence-based antibiotic selection and for analyses on the appropriateness of antibiotic use.

Antimicrobial resistance (AMR), a serious global public health challenge, may have accelerated development during the COVID-19 pandemic because antibiotics were prescribed for COVID-19. This study aimed to assess antibiotics use before and during the pandemic and correlate the results with the rate of resistant microorganisms detected in hospitalized patients during the study period. This single-center study looked retrospectively at four years of data (2018–2021) from Royal Hospital, Muscat, which is the biggest hospital in Oman with approximately 60,000 hospital admissions yearly. The consumption rate of ceftriaxone, piperacillin tazobactam, meropenem, and vancomycin was presented as the antibiotic consumption index, the ratio of defined daily dose (DDD) per 100 bed days. Analyses were performed using the nonparametric test for trend across the study period. Correlation between antibiotic consumption indexes and the isolated microorganisms in the four-year study period was performed using Spearman’s rank correlation coefficient. We compared data from the pre-COVID-19 to the COVID-19 period. Though more patients were admitted pre-COVID-19 (132,828 versus 119,191 during COVID-19), more antibiotics were consumed during the pandemic (7350 versus 7915); vancomycin and ceftriaxone had higher consumption during than before the pandemic (p-values 0.001 and 0.036, respectively). Vancomycin-resistant Enterococcus (VRE) and Candida auris were detected more during the COVID-19 period with p-values of 0.026 and 0.004, respectively. Carbapenem-resistant Enterobacterales (CRE), vancomycin-resistant Enterococcus spp., and C. auris were detected more often during the pandemic with p-values of 0.011, 0.002, and 0.03, respectively. Significant positive correlations between antibiotic consumption and drug-resistant isolates were noted. This study confirms that the overuse of antibiotics triggers the development of bacterial resistance; our results emphasize the importance of antibiotic control.

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Background Since the beginning of the war in Ukraine in February 2022, Ukrainians have been seeking shelter in other European countries. Aim We aimed to investigate the prevalence and the molecular epidemiology of multidrug-resistant Gram-negative (MDRGN) bacteria and meticillin-resistant Staphylococcus aureus (MRSA) in Ukrainian patients at admittance to the University Hospital Frankfurt, Germany. Methods We performed screening and observational analysis of all patients from March until June 2022. Genomes of MDRGN isolates were analysed for antimicrobial resistance, virulence genes and phylogenetic relatedness. Results We included 103 patients (median age: 39 ± 23.7 years), 57 of whom were female (55.3%; 95% confidence interval (CI): 45.2–5.1). Patients were most frequently admitted to the Department of Paediatrics (29/103; 28.2%; 95% CI: 19.7–37.9). We found 34 MDRGN isolates in 17 of 103 patients (16.5%; 95% CI: 9.9–25.1). Ten patients carried 21 carbapenem-resistant (CR) bacteria, five of them more than one CR isolate. Four of six patients with war-related injuries carried eight CR isolates. In six of 10 patients, CR isolates caused infections. Genomic characterisation revealed that the CR isolates harboured at least one carbapenemase gene, bla NDM-1 being the most frequent (n = 10). Core genome and plasmid analysis revealed no epidemiological connection between most of these isolates. Hypervirulence marker genes were found in five of six Klebsiella pneumoniae CR isolates. No MRSA was found. Conclusion Hospitals should consider infection control strategies to cover the elevated prevalence of MDRGN bacteria in Ukrainian patients with war-related injuries and/or hospital pre-treatment and to prevent the spread of hypervirulent CR isolates.

In the past years infections caused by multidrug-resistant Gram-negative bacteria have dramatically increased in all parts of the world. This consensus paper is based on presentations, subsequent discussions and an appraisal of current literature by a panel of international experts invited by the Rudolf Schülke Stiftung, Hamburg. It deals with the epidemiology and the inherent properties of Gram-negative bacteria, elucidating the patterns of the spread of antibiotic resistance, highlighting reservoirs as well as transmission pathways and risk factors for infection, mortality, treatment and prevention options as well as the consequences of their prevalence in livestock. Following a global, One Health approach and based on the evaluation of the existing knowledge about these pathogens, this paper gives recommendations for prevention and infection control measures as well as proposals for various target groups to tackle the threats posed by Gram-negative bacteria and prevent the spread and emergence of new antibiotic resistances.

The increasing incidence of bacterial infections caused by multidrug-resistant (MDR) Gram-negative bacteria has deepened the need for new effective treatments. Antibiotic adjuvant strategy is a more effective and economical approach to expand the lifespan of currently used antibiotics. Herein, we uncover that alcohol-abuse drug disulfiram (DSF) and derivatives thereof are potent antibiotic adjuvants, which dramatically potentiate the antibacterial activity of carbapenems and colistin against New Delhi metallo-β-lactamase (NDM)- and mobilized colistin resistance (MCR)-expressing Gram-negative pathogens, respectively. Mechanistic studies indicate that DSF improves meropenem efficacy by specifically inhibiting NDM activity. Moreover, the robust potentiation of DSF to colistin is due to its ability to exacerbate the membrane-damaging effects of colistin and disrupt bacterial metabolism. Notably, the passage and conjugation assays reveal that DSF minimizes the evolution and spread of meropenem and colistin resistance in clinical pathogens. Finally, their synergistic efficacy in animal models was evaluated and DSF-colistin/meropenem combination could effectively treat MDR bacterial infections in vivo. Taken together, our works demonstrate that DSF and its derivatives are versatile and potent colistin and carbapenems adjuvants, opening a new horizon for the treatment of difficult-to-treat infections. Disulfiram and derivatives are adjuvants that aid treatment against antibiotic-resistant gram-negative bacteria by inhibiting New Delhi metallo-β-lactamase and enhancing membrane-damaging effects of antibiotics.

Peptide antibiotics are an abundant and synthetically tractable source of molecular diversity, but they are often cationic and can be cytotoxic, nephrotoxic and/or ototoxic, which has limited their clinical development. Here we report structure-guided optimization of an amphipathic peptide, arenicin-3, originally isolated from the marine lugworm Arenicola marina . The peptide induces bacterial membrane permeability and ATP release, with serial passaging resulting in a mutation in mlaC , a phospholipid transport gene. Structure-based design led to AA139, an antibiotic with broad-spectrum in vitro activity against multidrug-resistant and extensively drug-resistant bacteria, including ESBL, carbapenem- and colistin-resistant clinical isolates. The antibiotic induces a 3–4 log reduction in bacterial burden in mouse models of peritonitis, pneumonia and urinary tract infection. Cytotoxicity and haemolysis of the progenitor peptide is ameliorated with AA139, and the ‘no observable adverse effect level’ (NOAEL) dose in mice is ~10-fold greater than the dose generally required for efficacy in the infection models. Peptide antibiotics often display a very narrow therapeutic index. Here, the authors present an optimized peptide antibiotic with broad-spectrum in vitro activities, in vivo efficacy in multiple disease models against multidrug-resistant Gram-negative infections, and reduced toxicity.

Cefiderocol is a parenteral siderophore cephalosporin with a catechol-containing 3′ substituent. We evaluated its MICs against Gram-negative bacteria, using iron-depleted Mueller-Hinton broth. The panel comprised 305 isolates of Enterobacterales, 111 of Pseudomonas aeruginosa, and 99 of Acinetobacter baumannii, all selected for carbapenem resistance and multidrug resistance to other agents. At 2 and 4 μg/ml, cefiderocol inhibited 78. ABSTRACT Cefiderocol is a parenteral siderophore cephalosporin with a catechol-containing 3′ substituent. We evaluated its MICs against Gram-negative bacteria, using iron-depleted Mueller-Hinton broth. The panel comprised 305 isolates of Enterobacterales, 111 of Pseudomonas aeruginosa, and 99 of Acinetobacter baumannii, all selected for carbapenem resistance and multidrug resistance to other agents. At 2 and 4 μg/ml, cefiderocol inhibited 78.7 and 92.1%, respectively, of all Enterobacterales isolates tested, with rates of 80 to 100% for isolates with all modes of carbapenem resistance except NDM enzymes (41.0% inhibited at 2 μg/ml and 72.1% at 4 μg/ml) or combinations of extended-spectrum β-lactamase (ESBL) and porin loss (61.5% inhibited at 2 μg/ml and 88.5% at 4 μg/ml). Cefiderocol also inhibited 81.1 and 86.5% of all P. aeruginosa isolates at 2 and 4 μg/ml, respectively, with rates of 80 to 100% for isolates with VIM, IMP, GES, or VEB β-lactamases and slightly lower rates for those with NDM (45.5% at 2 μg/ml and 72.7% at 4 μg/ml) and PER (66.7% at 2 μg/ml and 73.3% at 4 μg/ml) enzymes; 63.3% of P. aeruginosa isolates were inhibited at the FDA’s 1-μg/ml breakpoint. Lastly, cefiderocol at 2 and 4 μg/ml inhibited 80.8 and 88.9% of the A. baumannii isolates, respectively, with rates of >85% for isolates with OXA-51-like, -23, -24, or -58 enzymes and 50% at 2 μg/ml and 80% at 4 μg/ml for those with NDM carbapenemases. Dipicolinic acid and avibactam weakly potentiated cefiderocol against Enterobacterales isolates with metallo-β-lactamases (MBLs) and serine carbapenemase, respectively, indicating incomplete β-lactamase stability.

Patients with burn injuries are at high risk for infectious complications, and infections are the most common cause of death after the first 72 h of hospitalization. Hospital-acquired infections caused by multidrug resistant (MDR) Gram-negative bacteria (GNB) in this population are concerning. ABSTRACT Patients with burn injuries are at high risk for infectious complications, and infections are the most common cause of death after the first 72 h of hospitalization. Hospital-acquired infections caused by multidrug resistant (MDR) Gram-negative bacteria (GNB) in this population are concerning. Here, we evaluated carriage with MDR GNB in patients in a large tertiary-care burn intensive care unit. Twenty-nine patients in the burn unit were screened for intestinal carriage. Samples were cultured on selective media. Median time from admission to the burn unit to first sample collection was 9 days (IQR 5 – 17 days). In 21 (72%) patients, MDR GNB were recovered; the most common bacterial species isolated was Pseudomonas aeruginosa, which was found in 11/29 (38%) of patients. Two of these patients later developed bloodstream infections with P. aeruginosa. Transmission of KPC-31-producing ST22 Citrobacter freundii was detected. Samples from two patients grew genetically similar C. freundii isolates that were resistant to ceftazidime-avibactam. On analysis of whole-genome sequencing, blaKPC-31 was part of a Tn4401b transposon that was present on two different plasmids in each C. freundii isolate. Plasmid curing experiments showed that removal of both copies of blaKPC-31 was required to restore susceptibility to ceftazidime-avibactam. In summary, MDR GNB colonization is common in burn patients and patient-to-patient transmission of highly resistant GNB occurs. These results emphasize the ongoing need for infection prevention and antimicrobial stewardship efforts in this highly vulnerable population.

Gram-negative bacteria (GNB), including multidrug-resistant (MDR) pathogens, are gaining importance in the aetiology of prosthetic joint infection (PJI). This retrospective observational study identified independent risk factors (RFs) associated with MDR-GNB PJI and their influence on treatment outcomes. We assessed MDR bacteria causing hip and knee PJIs diagnosed at a Brazilian tertiary hospital from January 2014 to July 2018. RFs associated with MDR-GNB PJI were estimated by bivariate and multivariate analyses using prevalence ratios (PRs) with significance at p < 0.05. Kaplan–Meier analysis was performed to evaluate treatment outcomes. Overall, 98 PJI patients were analysed, including 56 with MDR-GNB and 42 with other bacteria. Independent RFs associated with MDR-GNB PJI were revision arthroplasty (p = 0.002), postoperative hematoma (p < 0.001), previous orthopaedic infection (p = 0.002) and early infection (p = 0.001). Extensively drug-resistant GNB (p = 0.044) and comorbidities (p = 0.044) were independently associated with MDR-GNB PJI treatment failure. In sum, MDR-GNB PJI was independently associated with previous orthopaedic surgery, postoperative local complications and pre-existing infections and was possibly related to selective pressure on bacterial skin colonisation by antibiotics prescribed for early PJI. Infections due to MDR-GNB and comorbidities were associated with higher treatment failure rates.

Novel aprosamine derivatives were synthesized for the development of aminoglycoside antibiotics active against multidrug-resistant Gram-negative bacteria. The synthesis of aprosamine derivatives involved glycosylation at the C-8' position and subsequent modification (epimerization and deoxygenation at the C-5 position and 1-N-acylation) of the 2-deoxystreptamine moiety. All 8'-β-glycosylated aprosamine derivatives (3a-h) showed excellent antibacterial activity against carbapenem-resistant Enterobacteriaceae and 16S ribosomal RNA methyltransferase-producing multidrug-resistant Gram-negative bacteria compared to the clinical drug, arbekacin. The antibacterial activity of 5-epi (6a-d) and 5-deoxy derivatives (8a,b and 8h) of β-glycosylated aprosamine was further enhanced. On the other hand, the derivatives (10a,b and 10h) in which the amino group at the C-1 position was acylated with (S)-4-amino-2-hydroxybutyric acid showed excellent activity (MICs 0.25-0.5 μg/mL) against resistant bacteria that produce the aminoglycoside-modifying enzyme, aminoglycoside 3-N-acetyltransferase IV, which induces high resistance against parent apramycin (MIC > 64 μg/mL). In particular, 8b and 8h showed approximately 2- to 8-fold antibacterial activity against carbapenem-resistant Enterobacteriaceae and 8- to 16-fold antibacterial activity against resistant Gram-positive bacteria, such as methicillin-resistant Staphylococcus aureus and vancomycin-resistant enterococci, compared to apramycin. Our results showed that aprosamine derivatives have immense potential in the development of therapeutic agents for multidrug-resistant bacteria.

Background: Experience in real clinical practice with ceftazidime-avibactam for the treatment of serious infections due to gram−negative bacteria (GNB) other than carbapenem-resistant Enterobacterales (CRE) is very limited. Methods: We carried out a retrospective multicenter study of patients hospitalized in 13 Italian hospitals who received ≥72 h of ceftazidime-avibactam for GNB other than CRE to assess the rates of clinical success, resistance development, and occurrence of adverse events. Results: Ceftazidime-avibactam was used to treat 41 patients with GNB infections other than CRE. Median age was 62 years and 68% of them were male. The main causative agents were P. aeruginosa (33/41; 80.5%) and extended spectrum beta lactamase (ESBL)-producing Enterobacterales (4/41, 9.8%). Four patients had polymicrobial infections. All strains were susceptible to ceftazidime-avibactam. The most common primary infection was nosocomial pneumonia (n = 20; 48.8%), primary bacteremia (n = 7; 17.1%), intra-abdominal infection (n = 4; 9.8%), and bone infection (n = 4; 9.8%). Ceftazidime-avibactam was mainly administered as a combination treatment (n = 33; 80.5%) and the median length of therapy was 13 days. Clinical success at the end of the follow-up period was 90.5%, and the only risk factor for treatment failure at multivariate analysis was receiving continuous renal replacement therapy during ceftazidime-avibactam. There was no association between clinical failures and type of primary infection, microbiological isolates, and monotherapy with ceftazidime-avibactam. Only one patient experienced recurrent infection 5 days after the end of treatment. Development of resistance to ceftazidime-avibactam was not detected in any case during the whole follow-up period. No adverse events related to ceftazidime-avibactam were observed in the study population. Conclusions: Ceftazidime-avibactam may be a valuable therapeutic option for serious infections due to GNB other than CRE.

New antimicrobial agents are urgently needed, especially to eliminate multidrug resistant Gram-negative bacteria that stand for most antibiotic-resistant threats. In the following study, we present superior properties of an engineered antimicrobial peptide, OMN6, a 40-amino acid cyclic peptide based on Cecropin A, that presents high efficacy against Gram-negative bacteria with a bactericidal mechanism of action. The target of OMN6 is assumed to be the bacterial membrane in contrast to small molecule-based agents which bind to a specific enzyme or bacterial site. Moreover, OMN6 mechanism of action is effective on Acinetobacter baumannii laboratory strains and clinical isolates, regardless of the bacteria genotype or resistance-phenotype, thus, is by orders-of-magnitude, less likely for mutation-driven development of resistance, recrudescence, or tolerance. OMN6 displays an increase in stability and a significant decrease in proteolytic degradation with full safety margin on erythrocytes and HEK293T cells. Taken together, these results strongly suggest that OMN6 is an efficient, stable, and non-toxic novel antimicrobial agent with the potential to become a therapy for humans.

Background Infectious diseases caused by multidrug-resistant (MDR) bacteria, especially MDR Gram-negative strains, have become a global public health challenge. Multifunctional nanomaterials for controlling MDR bacterial infections via eradication of planktonic bacteria and their biofilms are of great interest. Results In this study, we developed a multifunctional platform (TG-NO-B) with single NIR laser-triggered PTT and NO release for synergistic therapy against MDR Gram-negative bacteria and their biofilms. When located at the infected sites, TG-NO-B was able to selectively bind to the surfaces of Gram-negative bacterial cells and their biofilm matrix through covalent coupling between the BA groups of TG-NO-B and the bacterial LPS units, which could greatly improve the antibacterial efficiency, and reduce side damages to ambient normal tissues. Upon single NIR laser irradiation, TG-NO-B could generate hyperthermia and simultaneously release NO, which would synergistically disrupt bacterial cell membrane, further cause leakage and damage of intracellular components, and finally induce bacteria death. On one hand, the combination of NO and PTT could largely improve the antibacterial efficiency. On the other hand, the bacterial cell membrane damage could improve the permeability and sensitivity to heat, decrease the photothermal temperature and avoid damages caused by high temperature. Moreover, TG-NO-B could be effectively utilized for synergistic therapy against the in vivo infections of MDR Gram-negative bacteria and their biofilms and accelerate wound healing as well as exhibit excellent biocompatibility both in vitro and in vivo. Conclusions Our study demonstrates that TG-NO-B can be considered as a promising alternative for treating infections caused by MDR Gram-negative bacteria and their biofilms.

BackgroundBloodstream infections (BSI) are associated with high morbidity and mortality. This scenario worsens with the emergence of drug-resistant pathogens, resulting in infections which are difficult to treat or even untreatable with conventional antimicrobials. The aim of this study is to describe the epidemiological aspects of BSI caused by multiresistant gram-negative bacilli (MDR-GNB).MethodsWe conducted a laboratory-based surveillance for gram-negative bacteremia over a 1-year period. The bacterial isolates were identified by MALDI-TOF/MS and the antimicrobial susceptibility testing was performed by VITEK®2. Resistance genes were identified through PCR assays.ResultsOf the 143 patients, 28.7% had infections caused by MDR-GNB. The risk factors for MDR bacteremia were male sex, age ≥ 60, previous antimicrobial use, liver disease and bacteremia caused by K. pneumoniae. K. pneumoniae was the most frequently observed causative agent and had the highest resistance level. Regarding the resistance determinants, SHV, TEM, OXA-1-like and CTX-M-gp1 were predominant enzymatic variants, whereas CTX-M-gp9, CTX-M-gp2, KPC, VIM, GES, OXA-48-like, NDM and OXA-23-like were considered emerging enzymes.ConclusionsHere we demonstrate that clinically relevant antibiotic resistance genes are prevalent in this setting. We hope our findings support the development of intervention measures by policy makers and healthcare professionals to face antibiotic resistance.

Abstract Background Carriage of multidrug resistant (MDR) Gram-negative bacteria (GN) in hospitalized neonates may increase the risk of difficult-to-treat invasive infections at neonatal intensive care units (NICUs). Data on MDRGN carriage among hospitalized newborns in Africa are limited. Methods We conducted a cross-sectional study at the NICUs of 2 tertiary hospitals in Ghana. Swabs from the axilla, groin, perianal region, and the environment were cultured, GN were identified, and antibiotic susceptibility was tested. We obtained blood culture isolates from neonates with sepsis. Whole-genome sequencing was used to characterize carbapenemase-producing Klebsiella pneumoniae. Typing was done by multilocus sequence typing (MLST) and single nucleotide polymorphism (SNP) analysis. Results A total of 276 GN were isolated from 228 screened neonates. Pathogenic GN were cultured in 76.8% (175 of 228) of neonates. Klebsiella spp (41.7%; 115 of 276) and Escherichia coli (26.4%; 73 of 276) were the commonest organisms. Carriage rates of MDRGN and third-generation cephalosporin resistant organisms were 49.6% (113 of 228) and 46.1% (105 of 228), respectively. Among Klebsiella spp, 75.6% (87 of 115) phenotypically expressed extended-spectrum β-lactamase activity, whereas 15.6% expressed carbapenemase and harbored bla-OXA-181 and bla-CTX-M-15. Overall, 7.0% (16 of 228) of neonates developed GN bloodstream infection. In 2 of 11 neonates, sequencing showed the same identity between carriage and the bloodstream isolate. Length of stay before specimen collection and antibiotic use were independently associated with carriage rates, which increased from 13% at admission to 42% by day 2 and reached a plateau at 91% by day 15. Conclusions High carriage rates of MDRGN, including carbapenemase-producing enterobacterales may be an emerging problem in NICUs in Africa.

No abstract available

Medicinal leads that are also compatible with imaging technologies are attractive, as they facilitate the development of therapeutics through direct mechanistic observations at the molecular level. In this context, the uptake and antimicrobial activities of several luminescent dinuclear RuII complexes against E. coli were assessed and compared to results obtained for another ESKAPE pathogen, the Gram-positive major opportunistic pathogen Enterococcus faecalis, V583. The most promising lead displays potent activity, particularly against the Gram-negative bacteria, and potency is retained in the uropathogenic multidrug resistant EC958 ST131 strain. Exploiting the inherent luminescent properties of this complex, super-resolution STED nanoscopy was used to image its initial localization at/in cellular membranes and its subsequent transfer to the cell poles. Membrane damage assays confirm that the complex disrupts the bacterial membrane structure before internalization. Mammalian cell culture and animal model studies indicate that the complex is not toxic to eukaryotes, even at concentrations that are several orders of magnitude higher than its minimum inhibitory concentration (MIC). Taken together, these results have identified a lead molecular architecture for hard-to-treat, multiresistant, Gram-negative bacteria, which displays activities that are already comparable to optimized natural product-based leads.

The development of new antibacterial agents and therapeutic approaches is of high importance to combat the global problem of antibiotic resistance. Although antimicrobial peptides offer insights as adjuvant partners to existing antibiotics, their clinical application is limited by their systemic toxicity, protease instability, and high production cost. To overcome these problems, nine dilipid ultrashort tetrabasic peptidomimetics (dUSTBPs) were prepared consisting of three basic amino acids separated by the molecular scaffold, bis(3-aminopropyl)glycine, and are ligated to two fatty acids. Several non-hemolytic dUSTBPs were shown to potentiate activity against Gram-negative bacteria of several antibiotics. More importantly, compound 8, consisting of three L-arginine units and a dilipid of 8 carbons-long, potentiated rifampicin and novobiocin consistently against multidrug-resistant (MDR) clinical isolates of Pseudomonas aeruginosa, Acinetobacter baumannii, and Enterobacteriaceae. Preliminary studies showed that dUSTBPs were likely to potentiate antibiotics through outer membrane permeabilization and/or disruption of active efflux, and that dUSTBP 8 exhibited enhanced resistance to trypsin in comparison to di-C9-KKKK-NH2. Our data revealed that the degree of potentiation of rifampicin and novobiocin by dUSTBP 8 was comparable to other known outer membrane permeabilizing agents including the gold standard polymyxin B nonapeptide. Our results indicate that polybasic peptidomimetic-based adjuvants repurpose novobiocin and rifampicin as potent agents against priority MDR Gram-negative pathogens.

Cefiderocol is a novel catechol-substituted siderophore cephalosporin that binds to the extracellular free iron, and uses the bacterial active iron transport channels to penetrate in the periplasmic space of Gram-negative bacteria (GNB). Cefiderocol overcomes many resistance mechanisms of these bacteria. Cefiderocol is approved for the treatment of complicated urinary tract infections, hospital-acquired bacterial pneumonia and ventilator-associated bacterial pneumonia in the case of adults with limited treatment options, based on the clinical data from the APEKS-cUTI, APEKS-NP and CREDIBLE-CR trials. In the CREDIBLE-CR trial, a higher all-cause mortality was observed in the group of patients who received cefiderocol, especially those with severe infections due to Acinetobacter spp. Further phase III clinical studies are necessary in order to evaluate cefiderocol´s efficacy in the treatment of serious infections.

Infections caused by multi-drug resistant gram-negative bacterial infections are the principle threats to the critically ill patients of intensive care units. Increasing reports of these infections from the Nepalese intensive care unit underline the clinical importance of these pathogens. However, the impact of these infections on the patient’s clinical outcome has not yet been clearly evaluated. The objective of our study was to determine the incidence and associated clinical outcome of multi-drug resistant gram-negative bacterial infections in intensive care unit from a tertiary care center of Nepal. A prospective cohort study was conducted among adult patients admitted in intensive care unit of B. P Koirala Institute of Health Sciences from July to December 2017. Patients infected with multi-drug resistant gram-negative bacteria, non-multi-drug resistant gram-negative bacteria and those without infection were included. Identification of gram-negative bacteria and their antibiotic susceptibility pattern was performed with standard microbiological methods. Demographic, clinical profiles and outcomes (in-hospital-mortality, intensive care unit and hospital length of stay) were documented. The incidence rate of multi-drug resistant gram-negative bacteria infections was 47 per 100 admitted patients (64/137) with 128 episodes. Acinetobacter species (41%, 52/128) was the commonest followed by Klebsiella pneumoniae (28%, 36/128) and Pseudomonas spp (21%, 27/128). Patients with multi-drug resistant gram-negative bacteria in comparison to non-multi-drug resistant gram-negative bacteria had high healthcare-associated infections (95%, 61/64 versus 20%, 2/10; p = < 0.001). In-hospital-mortality was 38% (24/64), 20% (2/10) and 10% (4/41) in multi-drug resistant, non-multi-drug resistant and uninfected group respectively (p = 0.007). After adjustment for independent risk factors, compared to uninfected patients, the odds ratio (CI) for in-hospital-mortality in multi-drug resistant and non-multi-drug resistant group was (4.7[1.4–15.5], p = 0.01) and 2.60 [0.38–17.8], p = 0.32) respectively. Multi-drug resistant patients also had longer intensive care unit and hospital stay, however, it was statistically insignificant. The incidence of multi-drug resistant gram-negative bacterial infections was remarkably high in our intensive care unit and showed a significant association with healthcare-associated infections and in-hospital-mortality.

Risks for subsequent multidrug-resistant gram-negative bacteria (MDRGNB) infection and long-term outcome after hospitalization among patients with MDRGNB colonization remain unknown. This observational study enrolled 817 patients who were hospitalized in the study hospital in 2009. We defined MDRGNB as a GNB resistant to at least three different antimicrobial classes. Patients were classified into MDRGNB culture-positive (MDRGNB-CP; 125 patients) and culture-negative (MDRGNB-CN; 692 patients) groups based on the presence or absence of any MDRGNB identified from either active surveillance or clinical cultures during index hospitalization. Subsequent MDRGNB infection and mortality within 12 months after index hospitalization were recorded. We determined the frequency and risk factors for subsequent MDRGNB infection and mortality associated with previous MDRGNB culture status. In total, 129 patients had at least one subsequent MDRGNB infection (MDRGNB-CP, 48.0%; MDRGNB-CN, 10.0%), and 148 patients died (MDRGNB-CP, 31.2%; MDRGNB-CN, 15.9%) during the follow-up period. MDR Escherichia coli and Acinetobacter baumannii were the predominant colonization microorganisms; patients with Proteus mirabilis and Pseudomonas aeruginosa had the highest hazard risk for developing subsequent infection. After controlling for other confounders, MDRGNB-CP during hospitalization independently predicted subsequent MDRGNB infection (hazard ratio [HR], 5.35; 95% confidence interval [CI], 3.72–7.71), all-cause mortality (HR, 2.42; 95% CI, 1.67–3.50), and subsequent MDRGNB infection-associated mortality (HR, 4.88; 95% CI, 2.79–8.52) after hospitalization. Harboring MDRGNB significantly increases patients’ risk for subsequent MDRGNB infection and mortality after hospitalization, justifying the urgent need for developing effective strategies to prevent and eradicate MDRGNB colonization.

No abstract available

Aim: To characterize extensively drug-resistant Pseudomonas aeruginosa from a patient with diarrhea. Materials & methods: Antimicrobial susceptibility was tested by the disk diffusion method. The P. aeruginosa genome was sequenced to identify virulence, antibiotic resistance and prophages encoding genes. Results: P. aeruginosa had a wide spectrum of resistance to antibiotics. Genomic analysis of P. aeruginosa revealed 76 genes associated with antimicrobial resistance, xenobiotic degradation and the type three secretion system. Conclusion: This is the first report on diarrhea associated with P. aeruginosa. Since no other organism was identified, the authors assume that the patient had dysbiosis due to antibiotic exposure, leading to antibiotic-associated diarrhea. The in vivo toxicity expressed by the pathogen may be associated with T3SS.

Extensively produced by members of the genus Streptomyces, piericidins are a large family of microbial metabolites, which consist of main skeleton of 4‐pyridinol with methylated polyketide side chain. Nonetheless, these metabolites show differences in their bioactive potentials against micro‐organisms, insects and tumour cells. Due to its close structural similarity with coenzyme Q, piericidins also possess an inhibitory activity against NADH dehydrogenase as well as Photosystem II. This review studied the latest research progress of piericidins, covering the chemical structure and physical properties of newly identified members, bioactivities, biosynthetic pathway with gene clusters and future prospect. With the increasing incidence of drug‐resistant human pathogen strains and cancers, this review aimed to provide clues for the development of either new potential antibiotics or anti‐tumour agents.

ABSTRACT Introduction Acinetobacter baumannii complex (Abc) is currently a significant cause of difficult-to-treat pneumonia. Due to the high prevalence rates of carbapenem- and extensively drug-resistant (CR, XDR) phenotypes, limited antibiotic options are available for the effective treatment of pneumonia caused by CR/XDR-Abc. Areas covered In vitro susceptibility data, relevant pharmacokinetic profiles (especially the penetration ratios from plasma into epithelial-lining fluid), and pharmacodynamic indices of key antibiotics against CR/XDR-Abc are reviewed. Expert opinion Doubling the routine intravenous maintenance dosages of conventional tigecycline (100 mg every 12 h) and minocycline (200 mg every 12 h) might be recommended for the effective treatment of pneumonia caused by CR/XDR-Abc. Nebulized polymyxin E, novel parenteral rifabutin BV100, and new polymyxin derivatives (SPR206, MRX-8, and QPX9003) could be considered supplementary combination options with other antibiotic classes. Regarding other novel antibiotics, the potency of sulbactam-durlobactam (1 g/1 g infused over 3 h every 6 h intravenously) combined with imipenem-cilastatin, and the β-lactamase inhibitor xeruborbactam, is promising. Continuous infusion of full-dose cefiderocol is likely an effective treatment regimen for CR/XDR-Abc pneumonia. Zosurabalpin exhibits potent anti-CR/XDR-Abc activity in vitro, but its practical use in clinical therapy remains to be evaluated. The clinical application of antimicrobial peptides and bacteriophages requires validation.

With the surge of antibiotic resistance in bacteria, the need for a larger arsenal of effective antibiotics and vaccines has drastically increased in the past decades. Antibiotics like vaccines can benefit from significant potentiation when used in combination with adjuvants. Antibiotic adjuvants can allow for gram-positive bacteria (GPB) specific treatments to be used against gram-negative bacteria (GNB) infections, with minimal antimicrobial resistance (AMR). In the case of vaccines, they allow for modulation and increase of the immune response. Lipopeptides are molecules of choice because of their ability to activate specific cell surface receptors, penetrate the outer membrane of GNB, safety and ease of synthesis. This review explores the recent developments in lipopeptide adjuvants for antibiotics and vaccines, providing a roadmap on how to develop adjuvants to efficiently combat AMR. After a brief overview of bacterial resistance, lipopeptide adjuvants for antibiotics and vaccines are discussed, providing insights into stability, sources, and delivery methods. Findings discussed in this review could be applied to the development of safer, more effective adjuvants, that could expand the use or repurpose current antibiotics or improve vaccination results in future clinical trials.

ABSTRACT Antibiotic resistance in typhoid fever poses a critical public health problem due to the emergence of extensively drug-resistant (XDR) Salmonella, resulting in prolonged illness and treatment failure. Salmonella enterica serovar Typhi is the most predominant among all serotypes and can acquire resistance. The emergence of XDR Salmonella in various regions globally, particularly Pakistan, presents a concerning trend. However, limited data availability impedes a comprehensive understanding of the outbreaks and hinders the development of real-time solutions. Here, we have provided an updated overview of the current outbreaks of XDR Salmonella in epidemic and endemic regions. Treatments of XDR Salmonella infections are challenging, as there are records of treatment failure in humans and animals. However, intensive prevention techniques can be implemented pending the advent of novel antibiotics. Emphasis on antimicrobial stewardship and frequent surveillance of the pathogen should be made to keep track of potential outbreaks in both human and animal populations. Although progress is being made to combat XDR Salmonella within some regions, a unified and efficient effort on an international scale is required to curtail the XDR outbreak before it escalates and leads us back to the pre-antibiotic era.

Purpose Acinetobacter baumannii has evolved to become a major pathogen of nosocomial infections, resulting in increased morbidity and mortality. This study aimed to investigate the risk factors, outcomes, and predictions of extensively drug-resistant (XDR)-A. baumannii nosocomial infections in patients with nervous system diseases (NSDs). Methods A retrospective study of patients infected with XDR-A. baumannii admitted to the Affiliated Hospital of Southwest Medical University (Luzhou, China) from January 2021 to December 2022 was conducted. Three multivariate regression models were used to assess the risk factors and predictive value for specific diagnostic and prognostic subgroups. Results A total of 190 patients were included, of which 84 were diagnosed with NSDs and 80% of those were due to stroke. The overall rates of all-cause mortality for XDR-A. baumannii nosocomial infections and those in NSDs were 38.9% and 40.5%, respectively. Firstly, hypertension, indwelling gastric tube, tracheotomy, deep puncture, bladder irrigation, and pulmonary infections were independent risk factors for XDR-A. baumannii nosocomial infections in patients with NSDs. Moreover, pulmonary infections, the aspartate aminotransferase (AST) to alanine aminotransferase (ALT) ratio, and the neutrophil-to-lymphocyte ratio (NLR) were significantly associated with increased mortality rates in patients with nosocomial infections caused by XDR-A. baumannii. Thirdly, NLR and cardiovascular diseases accounted for a high risk of mortality for XDR-A. baumannii nosocomial infections in patients with NSDs. The area under the curves of results from each multivariate regression model were 0.827, 0.811, and 0.853, respectively. Conclusion This study reveals the risk factors of XDR-A. baumannii nosocomial infections in patients with NSDs, and proves their reliable predictive value. Early recognition of patients at high risk, sterilizing medical tools, and regular blood monitoring are all critical aspects for minimizing the nosocomial spread and mortality of A. baumannii infections.

Haemophilus parainfluenzae is a commensal organism which, due to the rise of multidrug-resistant (MDR) strains, has become an emerging opportunistic pathogen with increasing clinical relevance in urogenital infection. This work aimed to identify and characterize the molecular mechanisms of resistance associated with four cephalosporin-resistant H. parainfluenzae strains collected from patients with urethritis. Antimicrobial resistance was determined by microdilution following European Committee on Antimicrobial Susceptibility Testing criteria. Strains were then analyzed by whole genome sequencing to determine their clonal relationship and the molecular basis of antimicrobial resistance. Finally, a phylogenetic analysis was performed on all urogenital MDR strains of H. parainfluenzae previously isolated in our hospital. All strains were resistant to β-lactams, macrolides, tetracycline, fluoroquinolones, chloramphenicol, cotrimoxazole, and aminoglycosides. The resistance profile was compatible with the presence of an extended-spectrum β-lactamase (ESBL). Whole genome sequencing detected blaCTX-M-15 that conferred high minimum inhibitory concentrations to cephalosporins in two novel integrative and conjugative elements (ICEHpaHUB6 and ICEHpaHUB7) that also harbored a blaTEM-1 β-lactamase. We report a novel blaCTX-M-15 ESBL carried in an integrative conjugative element in four extensively drug resistant H. parainfluenzae strains. The possibility that this resistance determinant could be transmitted to other sexually transmitted pathogens is a cause for concern.

Since its emergence in 2016, extensively drug resistant (XDR) Salmonella enterica serovar Typhi (S. Typhi) has become the dominant cause of typhoid fever in Pakistan. The establishment of sustained XDR S. Typhi transmission in other countries represents a major public health threat. We show that the annual volume of air travel from Pakistan strongly discriminates between countries that have and have not imported XDR S. Typhi in the past, and identify a significant association between air travel volume and the rate of between-country movement of the H58 haplotype of S. Typhi from fitted phylogeographic models. Applying these insights, we analyze flight itinerary data cross-referenced with model-based estimates of typhoid fever incidence to identify the countries at highest risk of importation and sustained onward transmission of XDR S. Typhi. Future outbreaks of XDR typhoid are most likely to occur in countries that can support efficient local S. Typhi transmission and have strong travel links to regions with ongoing XDR typhoid outbreaks (currently Pakistan). Public health activities to track and mitigate the spread of XDR S. Typhi should be prioritized in these countries.

ABSTRACT Acinetobacter baumannii is a critical priority gram-negative bacterial species featured with multidrug resistance and biofilm formation. This study screened 46 indole derivative agents for their antimicrobial activities against clinical isolates of extensively drug-resistant A. baumannii (XDRAB) with various degrees of biofilm production. Three selected indole agents—5-iodoindole, 3-methylindole, and 7-hydroxyindole—were revealed to display potent antimicrobial and antibiofilm activity, including synergistic interplay with anti-A. baumannii antimicrobial drugs against XDRAB. Sub-inhibitory concentrations of these agents (particularly 7-hydroxyindole at 1/64 of MIC) not only inhibited XDRAB biofilm formation but also eradicated the mature biofilm. The survival rate of XDRAB-infected Galleria mellonella was improved with the treatment of 7-hydroxyindole. Mechanistically, 7-hydroxyindole was found to reduce the expression of quorum sensing/biofilm-implicated genes abaI and abaR. Together, the findings highlight the potential of indole derivatives against A. baumannii infections. IMPORTANCE Extensively drug-resistant Acinetobacter baumannii (XDRAB) isolates pose a major public health threat to antimicrobial therapy and are highly prevalent in hospital settings. This study identified and characterized indole derivative agents for their antimicrobial and antibiofilm activities against XDRAB. Sub-inhibitory indole agents such as 7-hydroxyindole can both inhibit XDRAB biofilm formation and eradicate the mature biofilm. Indole agents warrant further investigation against hard-to-treat antimicrobial-resistant pathogens. Extensively drug-resistant Acinetobacter baumannii (XDRAB) isolates pose a major public health threat to antimicrobial therapy and are highly prevalent in hospital settings. This study identified and characterized indole derivative agents for their antimicrobial and antibiofilm activities against XDRAB. Sub-inhibitory indole agents such as 7-hydroxyindole can both inhibit XDRAB biofilm formation and eradicate the mature biofilm. Indole agents warrant further investigation against hard-to-treat antimicrobial-resistant pathogens.

Surveillance of COVID-19 is challenging but critical for mitigating disease, particularly if predictive of future disease burden. We report a robust multiyear lead-lag association between internet

For decades, the spread of multidrug-resistant (MDR) Acinetobacter baumannii has been rampant in critically ill, hospitalized patients. Traditional antibiotic therapies against this pathogen have been failing, leading to rising concerns over management options for patients. Two new antibiotics, eravacycline and omadacycline, were introduced to the market and have shown promising results in the treatment of Gram-negative infections. Since these drugs are newly available, there is limited in vitro data about their effectiveness against MDR A. baumannii or even susceptible strains. Here, we examined the effectiveness of 22 standard-of-care antibiotics, eravacycline, and omadacycline against susceptible and extensively drug-resistant (XDR) A. baumannii patient isolates from Cooper University Hospital. Furthermore, we examined selected combinations of eravacycline or omadacycline with other antibiotics against an XDR strain. We demonstrated that this collection of strains is largely resistant to monotherapies of carbapenems, fluoroquinolones, folate pathway antagonists, cephalosporins, and most tetracyclines. While clinical breakpoint data are not available for eravacycline or omadacycline, based on minimum inhibitory concentrations, eravacycline was highly effective against these strains. The aminoglycoside amikacin alone and in combination with eravacycline or omadacycline yielded the most promising results. Our comprehensive characterization offers direction in the treatment of this deadly infection in hospitalized patients.

Carbapenem-resistant Pseudomonas aeruginosa (CRPA) producing the Verona integron‒encoded metallo-β-lactamase (VIM) are highly antimicrobial drug-resistant pathogens that are uncommon in the United States. We investigated the source of VIM-CRPA among US medical tourists who underwent bariatric surgery in Tijuana, Mexico. Cases were defined as isolation of VIM-CRPA or CRPA from a patient who had an elective invasive medical procedure in Mexico during January 2018‒December 2019 and within 45 days before specimen collection. Whole-genome sequencing of isolates was performed. Thirty-eight case-patients were identified in 18 states; 31 were operated on by surgeon 1, most frequently at facility A (27/31 patients). Whole-genome sequencing identified isolates linked to surgeon 1 were closely related and distinct from isolates linked to other surgeons in Tijuana. Facility A closed in March 2019. US patients and providers should acknowledge the risk for colonization or infection after medical tourism with highly drug-resistant pathogens uncommon in the United States.

Pseudomonas aeruginosa is a Gram-negative bacterium which is capable of developing a high level of antibiotic resistance. It has been placed on the WHO’s critical priority pathogen list and it is commonly found in ventilator-associated pneumonia infections, blood stream infections and other largely hospital-acquired illnesses. These infections are difficult to effectively treat due to their increasing antibiotic resistance and as such patients are often treated with antibiotic combination regimens. Methods: We conducted a systematic search with screening criteria using the Ovid search engine and the Embase, Ovid Medline, and APA PsycInfo databases. Results: It was found that in many cases the combination therapies were able to match or outperform the monotherapies and none performed noticeably worse than the monotherapies. However, the clinical studies were mostly small, only a few were prospective randomized clinical trials and statistical significance was lacking. Conclusions: It was concluded that combination therapies have a place in the treatment of these highly resistant bacteria and, in some cases, there is some evidence to suggest that they provide a more effective treatment than monotherapies.

A nosocomial salmonellosis outbreak caused by carbapenem-resistant Salmonella enterica serovar Goldcoast occurred in a respiratory care ward (RCW) of a hospital in central Taiwan between December 24, 2020, and January 21, 2021. Ten RCW residents had positive Salmonella culture tests. The route of transmission for the outbreak was not resolved. All isolates were resistant to extended-spectrum cephalosporins. Whole-genome sequencing indicated that each outbreak isolate harbored an IncHI2 plasmid that carried 15 antimicrobial resistance genes aac(3)-IId, aadA22, aph(3')-Ia, aph(6)-Id, arr-2, blaCTX-M-55, blaLAP-2, blaTEM-1, dfrA14, floR, lnu(F), qnrS13, sul2, sul3, tet(A), and ramAp, an efflux pump regulatory gene, and an IncL plasmid that carried a blaOXA-48. The outbreak isolates were likely derived from an extensively drug-resistant S. enterica Goldcoast strain, which has become a major pathogen in Taiwan since 2018, through acquiring a blaOXA-48-carrying plasmid. The outbreak strains were expected to be resistant to numerous antimicrobials, including aminoglycosides, β-lactams, β-lactam/β-lactamase inhibitor mixtures, tetracycline, rifamycin, lincosamide, sulfonamides, trimethoprim, phenicols, fluoroquinolones, and carbapenems. Two outbreak isolates displayed higher minimum inhibitory concentrations than the other eight isolates to cefmetazole and carbapenems. The higher resistance in the two isolates could be associated with a frameshift mutation in a major facilitator superfamily transporter. Therefore, special efforts are needed in Taiwan to monitor the spread of extremely resistant strains.

OBJECTIVES Acinetobacter baumannii is a significant opportunistic pathogen causing nosocomial infections. Infections caused by A. baumannii are often difficult to treat because this bacterium is often multidrug-resistant and shows high environmental adaptability. Here, we report on the analysis of three A. baumannii strains isolated from hospital effluents in South Africa. METHOD Strains were isolated on Leeds Acinetobacter agar and were identified using VITEK®2 platform. Antibiotic susceptibility testing was performed using the Kirby-Bauer Disk diffusion method. Whole genome sequencing was performed. The assembled contigs were annotated. Multilocus sequence type, antimicrobial resistance, and virulence genes were identified. RESULT The strains showed two multilocus sequences types, ST231 (FA34) and ST1552 (PL448, FG116). Based on their antibiotic susceptibility profiles, PL448 and FG116 were classified as extensively drug-resistant and FA34 as pandrug-resistant. FA34 harbored mutations in LpxA, LpxC and PmrB conferring resistance to colistin, but not mcr genes. All three strains encoded virulence genes for immune evasion (capsule, lipopolysaccharide (LPS)), iron uptake and biofilm formation. FA34 was related to human strains from South Africa, PL448 and FG116 were related to a strain isolated in the USA from a human wound. CONCLUSION The detection of extensively drug- and pandrug-resistant A. baumannii strains in hospital effluents is of particular concern. It indicates that wastewater might play a role in the spread of these bacteria. Our data provide insight into the molecular epidemiology, resistance, pathogenicity, and distribution of A. baumannii in South Africa.

Acinetobacter baumannii is an aggressive opportunistic bacterial pathogen that causes severe nosocomial infections, especially among burn patients. An increasing number of hospitals-acquired infections have been reported all over the world. However, little attention has been paid to the relatedness between A. baumannii isolates from different hospital environments and patients. In this study, 27 isolates were collected from the Burn and Plastic Surgery Hospital of Al Sulaymaniyah City, Iraq, from January through December 2019 (11 from patients and 16 from the wards environment), identified to species level as A. baumannii using Vitek 2 system and molecular detection of 16S rRNA gene, and then confirmed by targeting the blaOXA-51 gene. Moreover, the isolates were characterized by means of automated antimicrobial susceptibility assay, antimicrobial-resistant patterns, a phenotypic method using a combined disk test, and molecular methods for the detection of class A and C β-lactamase genes, and finally, the genetic relatedness was classified. Antimicrobial susceptibility testing showed that 63% (17/27) of the retrieved A. baumannii isolates were extensively drug-resistant to 8/9 antimicrobial classes. Furthermore, 37% (10/27) of the isolates were classified as multidrug-resistant; 8 isolates exhibited similar resistant patterns and the other two isolates showed 2 different patterns, while resistance was greater in isolates from patients than from the ward environment. Combined disk test showed that two isolates contained extended-spectrum β-lactamase. All isolates carried blaTEM-1, and two copies of the blaCTX-1 gene were indicated in one isolate, while blaSHV was absent in all isolates. Twenty-four isolates carried the blaAmpC gene; among them, 3 isolates harbored the insertion sequence ISAba-1 upstream to the gene. Using Enterobacterial Repetitive Intergenic Consensus PCR, the isolates were clustered into 6 distinct types; among them, two clusters, each of four strains, were classified to contain isolates from both patients and environments. The clusters of similar genotypes were found in inpatients as well as the environments of different wards during time periods, suggesting transmission within the hospital. Identification of possible infection sources and controlling the transmission of these aggressive resistance strains should be strictly conducted.

Antibiotic resistance is recognised as a global threat to human health by national healthcare agencies, governments and medical societies, as well as the World Health Organization. Increasing resistance to available antimicrobial agents is of concern for bacterial, fungal, viral and parasitic pathogens. One of the greatest concerns is the continuing escalation of antimicrobial resistance among Gram-negative bacteria resulting in the endemic presence of multidrug-resistant (MDR) and extremely drug-resistant (XDR) pathogens. This concern is heightened by the identification of such MDR/XDR Gram-negative bacteria in water and food sources, as colonisers of the intestine and other locations in both hospitalised patients and individuals in the community, and as agents of all types of infections. Pneumonia and other types of respiratory infections are among the most common infections caused by MDR/XDR Gram-negative bacteria and are associated with high rates of mortality. Future concerns are already heightened due to emergence of resistance to all existing antimicrobial agents developed in the past decade to treat MDR/XDR Gram-negative bacteria and a scarcity of novel agents in the developmental pipeline. This clinical scenario increases the likelihood of a future pandemic caused by MDR/XDR Gram-negative bacteria. Antimicrobial resistance continues to rise among Gram-negative bacteria, leading to greater morbidity, mortality, lengths of stay and costs. The level of resistance is approaching pandemic proportions, requiring an urgent call to address this problem. https://bit.ly/3NTnDqK

Salmonella Typhi is a Gram-negative pathogen that causes typhoid fever in humans. The use of antibiotics to treat typhoid has considerably mitigated its fatality risk, but rising multidrug-resistant (MDR) and extensively drug-resistant (XDR) resistance in Pakistan threatens effective treatment. This study determined the prevalence of MDR and XDR S. Typhi at a local hospital in Lahore. Blood samples (n = 3000) were obtained and processed for bacterial identification. Antibiotic susceptibility test was performed using VITEK® 2 Compound 30 System. Statistical data analysis was performed using a Mann–Whitney U and Kruskal–Wallis H test, respectively. The results revealed 600 positive cultures, of which the majority were found to be XDR S. Typhi (46.1%) and MDR S. Typhi (24.5%) strains. The disease burden of resistant Salmonella strains was greater in males (60.67%) than females (39.33%), with the most affected age group being 0–10 years old (70.4 %). In both the outpatient department (OPD) and general ward, the prevalence of XDR S. Typhi cases was found to be alarmingly high (48.24%), followed by MDR S. Typhi (25.04 %). The results of the statistical analysis demonstrated that the incidence of resistance in MDR and XDR S. Typhi strains was not affected by the age as well as the gender of patients (p > 0.05). The occurrence of resistant strains against four tested antibiotics (azithromycin, ciprofloxacin, imipenem, and meropenem) was found to be similar in different wards and among hospitalized and OPD patients (p > 0.05). Maximum resistance was observed against chloramphenicol and ampicillin in the OPD and pediatric ward. Piperacillin/Tazobactam was observed to be the most effective antibiotic, followed by co-amoxiclav (p < 0.001). This study is effective in validating the existence of MDR and XDR S. Typhi in Lahore, where stringent methods should be applied for controlling its spread.

ABSTRACT Global challenges presented by multidrug-resistant Acinetobacter baumannii infections have stimulated the development of new treatment strategies. We reported that outer membrane protein W (OmpW) is a potential therapeutic target in A. baumannii. Here, a library of 11,648 natural compounds was subjected to a primary screening using quantitative structure-activity relationship (QSAR) models generated from a ChEMBL data set with >7,000 compounds with their reported minimal inhibitory concentration (MIC) values against A. baumannii followed by a structure-based virtual screening against OmpW. In silico pharmacokinetic evaluation was conducted to assess the drug-likeness of these compounds. The ten highest-ranking compounds were found to bind with an energy score ranging from −7.8 to −7.0 kcal/mol where most of them belonged to curcuminoids. To validate these findings, one lead compound exhibiting promising binding stability as well as favorable pharmacokinetics properties, namely demethoxycurcumin, was tested against a panel of A. baumannii strains to determine its antibacterial activity using microdilution and time-kill curve assays. To validate whether the compound binds to the selected target, an OmpW-deficient mutant was studied and compared with the wild type. Our results demonstrate that demethoxycurcumin in monotherapy and in combination with colistin is active against all A. baumannii strains. Finally, the compound was found to significantly reduce the A. baumannii interaction with host cells, suggesting its anti-virulence properties. Collectively, this study demonstrates machine learning as a promising strategy for the discovery of curcuminoids as antimicrobial agents for combating A. baumannii infections. IMPORTANCE Acinetobacter baumannii presents a severe global health threat, with alarming levels of antimicrobial resistance rates resulting in significant morbidity and mortality in the USA, ranging from 26% to 68%, as reported by the Centers for Disease Control and Prevention (CDC). To address this threat, novel strategies beyond traditional antibiotics are imperative. Computational approaches, such as QSAR models leverage molecular structures to predict biological effects, expediting drug discovery. We identified OmpW as a potential therapeutic target in A. baumannii and screened 11,648 natural compounds. We employed QSAR models from a ChEMBL bioactivity data set and conducted structure-based virtual screening against OmpW. Demethoxycurcumin, a lead compound, exhibited promising antibacterial activity against A. baumannii, including multidrug-resistant strains. Additionally, demethoxycurcumin demonstrated anti-virulence properties by reducing A. baumannii interaction with host cells. The findings highlight the potential of artificial intelligence in discovering curcuminoids as effective antimicrobial agents against A. baumannii infections, offering a promising strategy to address antibiotic resistance. Acinetobacter baumannii presents a severe global health threat, with alarming levels of antimicrobial resistance rates resulting in significant morbidity and mortality in the USA, ranging from 26% to 68%, as reported by the Centers for Disease Control and Prevention (CDC). To address this threat, novel strategies beyond traditional antibiotics are imperative. Computational approaches, such as QSAR models leverage molecular structures to predict biological effects, expediting drug discovery. We identified OmpW as a potential therapeutic target in A. baumannii and screened 11,648 natural compounds. We employed QSAR models from a ChEMBL bioactivity data set and conducted structure-based virtual screening against OmpW. Demethoxycurcumin, a lead compound, exhibited promising antibacterial activity against A. baumannii, including multidrug-resistant strains. Additionally, demethoxycurcumin demonstrated anti-virulence properties by reducing A. baumannii interaction with host cells. The findings highlight the potential of artificial intelligence in discovering curcuminoids as effective antimicrobial agents against A. baumannii infections, offering a promising strategy to address antibiotic resistance.

The rise of antibiotic-resistant pathogens, particularly gram-negative bacteria, highlights the urgent need for novel therapeutics. Drug-resistant infections now contribute to approximately 5 million deaths annually, yet traditional antibiotic discovery has significantly stagnated. Venoms form an immense and largely untapped reservoir of bioactive molecules with antimicrobial potential. In this study, we mined global venomics datasets to identify new antimicrobial candidates. Using deep learning, we explored 16,123 venom proteins, generating 40,626,260 venom-encrypted peptides. From these, we identified 386 candidates that are structurally and functionally distinct from known antimicrobial peptides. They display high net charge and elevated hydrophobicity, characteristics conducive to bacterial-membrane disruption. Structural studies revealed that many of these peptides adopt flexible conformations that transition to α-helical conformations in membrane-mimicking environments, supporting their antimicrobial potential. Of the 58 peptides selected for experimental validation, 53 display potent antimicrobial activity. Mechanistic assays indicated that they primarily exert their effects through bacterial-membrane depolarization, mirroring AMP-like mechanisms. In a murine model of Acinetobacter baumannii infection, lead peptides significantly reduced bacterial burden without observable toxicity. Our findings demonstrate that venoms are a rich source of previously hidden antimicrobial scaffolds, and that integrating large-scale computational mining with experimental validation can accelerate the discovery of urgently needed antibiotics. Researchers used artificial intelligence to mine global venom proteomes and discovered novel peptides with antimicrobial activity. Several candidates showed efficacy against drug-resistant bacteria in laboratory and animal tests.

No abstract available

The discovery of novel antimicrobial peptides (AMPs) against clinical superbugs is urgently needed to address the ongoing antibiotic resistance crisis. AMPs are promising candidates due to their broad-spectrum activity, rapid bactericidal mechanisms and reduced likelihood of inducing resistance compared with conventional antibiotics. Here, a pre-trained protein large language model (LLM), ProteoGPT, was established and further developed into multiple specialized subLLMs to assemble a sequential pipeline. This pipeline enables rapid screening across hundreds of millions of peptide sequences, ensuring potent antimicrobial activity and minimizing cytotoxic risks. Through transfer learning, we endowed the LLMs with different domain-specific knowledge to achieve high-throughput mining and generation of AMPs within a unified methodological framework. Notably, both mined and generated AMPs exhibited reduced susceptibility to resistance development in ICU-derived carbapenem-resistant Acinetobacter baumannii (CRAB) and methicillin-resistant Staphylococcus aureus (MRSA) in vitro. The AMPs also showed comparable or superior therapeutic efficacy in in vivo thigh infection mouse models compared with clinical antibiotics, without causing organ damage and disrupting gut microbiota. The mechanisms of action of these AMPs involve disruption of the cytoplasmic membrane and membrane depolarization. Overall, this study presents a generative artificial intelligence approach for the discovery of novel antimicrobials against multidrug-resistant bacteria, enabling efficient and extensive exploration of AMP space. This study presents a generative artificial intelligence approach for the high-throughput discovery of antimicrobials against multidrug-resistant bacteria.

Antibiotic discovery and antibiotic prescribing represent two domains that both stand to benefit from artificial intelligence (AI)-driven progress in the near future. In this article, we discuss these parallel advances and the potential future synergy between AI-enabled antibiotic discovery and AI-assisted antibiotic prescribing. Although multiple challenges remain before these two domains meaningfully converge, their integration could amplify the strengths of each: discovery pipelines generating broader, more diverse classes of antibacterial agents, and prescribing tools capable of matching these agents to individual patients with unprecedented precision. Such a scenario could transform antibiotic therapy by enabling AI-supported, patient-specific treatment decisions while reinforcing the principles of precision medicine and antimicrobial stewardship.

Antimicrobial resistance (AMR) poses a critical global health threat, underscoring the urgent need for innovative antibiotic discovery strategies. While recent advances in peptide design have yielded numerous antimicrobial agents, optimizing these molecules experimentally remains challenging due to unpredictable and resource-intensive trial-and-error approaches. Here, we introduce APEX Generative Optimization (APEXGO), a generative artificial intelligence (AI) framework that integrates a transformer-based variational autoencoder with Bayesian optimization to design and optimize antimicrobial peptides. Unlike traditional supervised learning approaches that screen fixed databases of existing molecules, APEXGO generates entirely novel peptide sequences through arbitrary modifications of template peptides, representing a paradigm shift in peptide design and antibiotic discovery. Our framework introduces a new peptide variational autoencoder with design and diversity constraints to maintain similarity to specific templates while enabling sequence innovation. This work represents the first in vitro and in vivo experimental validation of generative Bayesian optimization in any setting. Using ten de-extinct peptides as templates, APEXGO generated optimized derivatives with enhanced antimicrobial properties. We synthesized 100 of these optimized peptides and conducted comprehensive in vitro characterizations, including assessments of antimicrobial activity, mechanism of action, secondary structure, and cytotoxicity. Notably, APEXGO achieved an outstanding 85% ground-truth experimental hit rate and a 72% success rate in enhancing antimicrobial activity against clinically relevant Gram-negative pathogens, outperforming previously reported methods for antibiotic discovery and optimization. In preclinical mouse models of Acinetobacter baumannii infection, several AI-optimized molecules—most notably derivatives of mammuthusin-3 and mylodonin-2—exhibited potent anti-infective activity comparable to or exceeding that of polymyxin B, a widely used last-resort antibiotic. These findings highlight the potential of APEXGO as a novel generative AI approach for peptide design and antibiotic optimization, offering a powerful tool to accelerate antibiotic discovery and address the escalating challenge of AMR.

Today, resistance developed by bacteria to common antibiotics that were otherwise regarded as effective is posing a serious challenge. It is believed that without any different efforts, this perennial problem will undermine all the ongoing efforts in antibiotic discovery and therapy development. In Nigeria, antibiotics are frequently prescribed in hospitals. However, issues like multidrug resistance (MDR) and inappropriate use and misuse of antibiotics, including incorrect dosages and use of broad-spectrum antibiotics for targeted infections, have precipitated the rise of MDR bacteria. Consequently, this leads to higher healthcare costs, mainly due to prolonged hospital stays and additional medications as well as increased patient mortality. The prospects of artificial intelligence (AI)-enabled antibiotic prescribing hold significant promise in transforming the current health-care practices. AI has the potential to enhance the precision and efficiency of antibiotic treatment through advanced algorithms and data analytics. This technology can contribute to improved diagnostic accuracy, providing real-time clinical support, optimizing dosage recommendations, personalized treatment plans, and streamlined antimicrobial stewardship, ultimately aiding the global fight against antibiotic resistance and optimizing patient outcomes. The integration of AI in antibiotic prescribing reflects a cutting-edge approach with the potential to revolutionize how antibiotics are prescribed to address challenges in antimicrobial stewardship, clinical decision-making, and combating antibiotic resistance. One of the key impediments to integrating AI into Nigeria’s health-care system is budgetary constraints. Addressing these constraints through strategic investments, improved budgetary allocation to research and development, and leveraging the opportunities presented by AI technologies can significantly enhance antibiotic prescribing and health-care practices, leading to improved public health outcomes.

Neisseria gonorrhoeae (Ng) causes the sexually transmitted disease gonorrhoea. There are no vaccines and infections are treated principally with antibiotics. However, gonococci rapidly develop resistance to every antibiotic class used and there is a need for developing new antimicrobial treatments. In this study we focused on two gonococcal enzymes as potential antimicrobial targets, namely the serine protease L,D-carboxypeptidase LdcA (NgO1274/NEIS1546) and the lytic transglycosylase LtgD (NgO0626/NEIS1212). To identify compounds that could interact with these enzymes as potential antimicrobials, we used the AtomNet virtual high-throughput screening technology. We then did a computational modelling study to examine the interactions of the most bioactive compounds with their target enzymes. The identified compounds were tested against gonococci to determine minimum inhibitory and bactericidal concentrations (MIC/MBC), specificity, and compound toxicity in vitro. AtomNet identified 74 compounds that could potentially interact with Ng-LdcA and 84 compounds that could potentially interact with Ng-LtgD. Through MIC and MBC assays, we selected the three best performing compounds for both enzymes. Compound 16 was the most active against Ng-LdcA, with a MIC50 value < 1.56 µM and MBC50/90 values between 0.195 and 0.39 µM. In general, the Ng-LdcA compounds showed higher activity than the compounds directed against Ng-LtgD, of which compound 45 had MIC50 values of 1.56–3.125 µM and MBC50/90 values between 3.125 and 6.25 µM. The compounds were specific for gonococci and did not kill other bacteria. They were also non-toxic for human conjunctival epithelial cells as judged by a resazurin assay. To support our biological data, in-depth computational modelling study detailed the interactions of the compounds with their target enzymes. Protein models were generated in silico and validated, the active binding sites and amino acids involved elucidated, and the interactions of the compounds interacting with the enzymes visualised through molecular docking and Molecular Dynamics Simulations for 50 ns and Molecular Mechanics Poisson-Boltzmann Surface Area (MM-PBSA). We have identified bioactive compounds that appear to target the N. gonorrhoeae LdcA and LtgD enzymes. By using a reductionist approach involving biological and computational data, we propose that compound Ng-LdcA-16 and Ng-LtgD-45 are promising anti-gonococcal compounds for further development.

With the explosive increase in genome sequence data, perhaps the major challenge in natural-product-based drug discovery is the identification of gene clusters most likely to specify new chemistry and bioactivities. We discuss the challenges and state-of-the-art of antibiotic discovery based on ecological principles, genome mining and artificial intelligence.

No abstract available

Due to the evolutionary development of bacterial antibiotic resistance and the global spread of super-resistant bacteria, there is a growing need for novel approaches to antibiotic discovery. Machine learning for molecular property prediction offers a powerful approach to chemical sciences and drug discovery. However, there are two main challenges in predicting molecular antibiotic activity: scarcity of data and lack of interpretable work. To address these challenges, we propose a novel molecular property prediction method. Multimodal neural network and transfer learning are included to mitigate the issue of insufficient data. Additionally, we introduced the Grad-CAM method to interpret the predictions made by the image-ViT model. We conducted experiments on two Escherichia coli (E. coli) inhibition datasets: ECI-ChemDiv constructed by ourselves and the public dataset published by Stokes et al. We validated that introducing multimodal neural network and transfer learning had significantly improvements on the performance of model. Moreover, through analyzing the heat maps of the image-ViT model, we revealed that the model exhibited a particular attention to the key structures in the molecules when making predictions.

Antibiotic resistance presents a growing global health crisis, demanding new therapeutic strategies that target novel bacterial mechanisms. Recent advances in protein structure prediction and machine learning-driven molecule generation offer a promising opportunity to accelerate drug discovery. However, practical guidance on selecting and integrating these models into real-world pipelines remains limited. In this study, we develop an end-to-end, artificial intelligence-guided antibiotic discovery pipeline that spans target identification to compound realization. We leverage structure-based clustering across predicted proteomes of multiple pathogens to identify conserved, essential, and non-human-homologous targets. We then systematically evaluate six leading 3D-structure-aware generative models$\unicode{x2014}$spanning diffusion, autoregressive, graph neural network, and language model architectures$\unicode{x2014}$on their usability, chemical validity, and biological relevance. Rigorous post-processing filters and commercial analogue searches reduce over 100 000 generated compounds to a focused, synthesizable set. Our results highlight DeepBlock and TamGen as top performers across diverse criteria, while also revealing critical trade-offs between model complexity, usability, and output quality. This work provides a comparative benchmark and blueprint for deploying artificial intelligence in early-stage antibiotic development.

Antimicrobial resistance is a critical global health challenge and one of the World Health Organization's top ten public health threats. The alarming rise of drug-resistant pathogens threatens to usher in a postantibiotic era where common infections could once again become fatal. Despite the urgency, traditional discovery methods are time-consuming, expensive, and insufficient to keep pace with rapidly evolving resistance. Recent advances in machine learning (ML) and artificial intelligence (AI) present a transformative alternative, enabling the rapid identification of potential candidate antibiotics in a fraction of the time required by conventional methods. In this Essay, we discuss how ML and AI significantly accelerate antibiotic discovery, drawing on previous works and insights in this emerging field. We also consider the promises and challenges of this emerging area and speculate on its evolution in the coming years, highlighting the potential contributions from the physics community. Part of a series of Essays in Physical Review Letters which concisely present author visions for the future of their field.

The escalating threat of antibiotic resistance demands a concerted effort to revitalize antibiotic discovery, a field that has stagnated in recent decades. These abstract outlines a comprehensive scientific roadmap for antibiotic discovery, aimed at addressing the pressing global health challenge posed by resistant bacterial infections. The proposed roadmap integrates cutting-edge technologies and innovative strategies to navigate the complex terrain of antibiotic discovery. Starting with a thorough understanding of microbial ecology, this roadmap emphasizes the identification of novel bacterial strains through metagenomics and functional screening. Harnessing the power of genomics, transcriptomics, and proteomics, researchers can unravel the intricate mechanisms of bacterial resistance and identify potential drug targets. In silicone approaches, including artificial intelligence and machine learning, play a pivotal role in predicting and optimizing antibiotic candidates. These computational tools expedite the screening process, reducing the time and resources traditionally required for drug discovery. Moreover, exploring untapped natural sources, such as extremophiles and uncultivable microorganisms, unveils a treasure trove of bioactive compounds with unexplored therapeutic potential. The integration of synthetic biology allows for the design and engineering of novel antibiotics, overcoming traditional limitations. Furthermore, this roadmap advocates for collaborative initiatives, encouraging data sharing and open-access platforms to accelerate the pace of discovery. Ethical considerations are paramount in antibiotic discovery, and the road map emphasizes responsible research practices, mindful of the potential ecological consequences of widespread antibiotic use. Finally, regulatory frameworks need to adapt to the dynamic nature of antibiotic discovery, incentivizing innovation while ensuring patient safety.

Multidrug-resistant bacterial infections are a rising threat to human health and currently account for 1.3 million deaths annually. Notably, 70% of these deaths are due to gram-negative pathogens, and no new classes of gram-negative-active antibiotics have been approved by the US Food and Drug Administration in the past 55 years. The challenges of converting compounds with in vitro biochemical activity to whole cell gram-negative antibacterial activity are significant, as the outer membrane and promiscuous efflux pumps thwart the potential of most antibiotic candidates. Significant strides have been made toward understanding compound penetration and accumulation in gram-negative bacteria, but efflux remains a major obstacle for antibiotic drug discovery. Recent advances in machine learning (ML) algorithms and increased accessibility of code and programs for the nonexpert suggest artificial intelligence could help address the efflux problem. Here, we discuss work toward understanding efflux and cast a vision for how ML can be utilized to address compound efflux from gram-negative bacteria.

Antibiotic resistance is a worldwide public health problem due to the costs and mortality rates it generates. However, the large pharmaceutical industries have stopped searching for new antibiotics because of their low profitability, given the rapid replacement rates imposed by the increasingly observed resistance acquired by microorganisms. Alternatively, antimicrobial peptides (AMPs) have emerged as potent molecules with a much lower rate of resistance generation. The discovery of these peptides is carried out through extensive in vitro screenings of either rational or non-rational libraries. These processes are tedious and expensive and generate only a few AMP candidates, most of which fail to show the required activity and physicochemical properties for practical applications. This work proposes implementing an artificial intelligence algorithm to reduce the required experimentation and increase the efficiency of high-activity AMP discovery. Our deep learning (DL) model, called AMPs-Net, outperforms the state-of-the-art method by 8.8% in average precision. Furthermore, it is highly accurate to predict the antibacterial and antiviral capacity of a large number of AMPs. Our search led to identifying two unreported antimicrobial motifs and two novel antimicrobial peptides related to them. Moreover, by coupling DL with molecular dynamics (MD) simulations, we were able to find a multifunctional peptide with promising therapeutic effects. Our work validates our previously proposed pipeline for a more efficient rational discovery of novel AMPs.

Discovery and exploratory research can identify new antibiotics and biological targets. However, failure rates are high, and funding is insufficient to solve the scientific challenges and attract researchers to antibacterial R&D. Novel methods, including artificial intelligence, have been applied to early-stage research, but these have yet to deliver new antibiotics. The Global Antibiotic Research & Development Partnership (GARDP) is investing in discovery and exploratory research and an R&D education and outreach program. GARDP's efforts, including application of novel R&D methods and new global networks of R&D researchers to develop new antibiotics, is helping address antimicrobial resistance sustainably over the long-term.

The rise of antibiotic resistance calls for innovative solutions. The realization that biology can be mined digitally using artificial intelligence has revealed a new paradigm for antibiotic discovery, offering hope in the fight against superbugs.

Antibiotic resistance poses a significant threat to global public health due to complex interactions between bacterial genetic factors and external influences such as antibiotic misuse. Artificial intelligence (AI) offers innovative strategies to address this crisis. For example, AI can analyze genomic data to detect resistance markers early on, enabling early interventions. In addition, AI-powered decision support systems can optimize antibiotic use by recommending the most effective treatments based on patient data and local resistance patterns. AI can accelerate drug discovery by predicting the efficacy of new compounds and identifying potential antibacterial agents. Although progress has been made, challenges persist, including data quality, model interpretability, and real-world implementation. A multidisciplinary approach that integrates AI with other emerging technologies, such as synthetic biology and nanomedicine, could pave the way for effective prevention and mitigation of antimicrobial resistance, preserving the efficacy of antibiotics for future generations.

Over the past year, the world's attention has focused on combating COVID-19 disease, but the other threat waiting at the door—antimicrobial resistance should not be forgotten. Although making the diagnosis rapidly and accurately is crucial in preventing antibiotic resistance development, bacterial identification techniques include some challenging processes. To address this challenge, we proposed a deep neural network (DNN) that can discriminate antibiotic-resistant bacteria using surface-enhanced Raman spectroscopy (SERS). Stacked autoencoder (SAE)-based DNN was used for the rapid identification of methicillin-resistant Staphylococcus aureus (MRSA) and methicillin-sensitive S. aureus (MSSA) bacteria using a label-free SERS technique. The performance of the DNN was compared with traditional classifiers. Since the SERS technique provides high signal-to-noise ratio (SNR) data, some subtle differences were found between MRSA and MSSA in relative band intensities. SAE-based DNN can learn features from raw data and classify them with an accuracy of 97.66%. Moreover, the model discriminates bacteria with an area under curve (AUC) of 0.99. Compared to traditional classifiers, SAE-based DNN was found superior in accuracy and AUC values. The obtained results are also supported by statistical analysis. These results demonstrate that deep learning has great potential to characterize and detect antibiotic-resistant bacteria by using SERS spectral data.

The emergence of multidrug-resistant bacteria has led to an urgent need for novel antimicrobial agents. Antimicrobial peptides (AMPs) exhibit broad-spectrum and highly effective antibacterial activity and are less prone to resistance, making them potential candidates for the next generation of antimicrobial drugs. However, screening for AMPs from a vast library of peptides through wet lab experiments is a slow and laborious process. By leveraging large datasets of labeled peptides, researchers utilize deep learning algorithms to train models that capture complex patterns and features associated with antimicrobial activity, which advance the discovery and development of novel AMPs. Since the discovery of certain lengths of AMPs has been rarely reported, we applied deep learning to accelerate the discovery of AMPs consisting of 15 amino acids and developed a model named AMPPRED15 in this article. Wet lab experiments were also conducted to evaluate the performance of the model. Fortunately, we successfully identified two AMPs, one of which demonstrated antibacterial activities comparable to the marketed antibiotic cefoperazone sodium.

The escalating crisis of multiresistant bacteria demands the rapid discovery of novel antibiotics that transcend the limitations imposed by the biased chemical space of current libraries. To address this challenge, we introduce an innovative deep learning-driven pipeline for de novo antibiotic design. Our unique approach leverages a chemical language model to generate structurally unprecedented antibiotic candidates. The model was trained on a diverse chemical space of drug-like molecules and natural products. We then applied transfer learning using a data set of diverse antibiotic scaffolds to refine its generative capabilities. Using predictive modeling and expert curation, we prioritized the most promising compounds for synthesis. This pipeline identified a lead candidate with potent activity against methicillin-resistant Staphylococcus aureus. We then performed iterative refinement by synthesizing 40 derivatives of the lead compound. This effort produced a suite of active compounds, with 30 showing activity against S. aureus and 17 against Escherichia coli. Among these, lead compound D8 exhibited remarkable submicromolar and single-digit micromolar potency against the aforementioned pathogens, respectively. Mechanistic investigations point to the reductive generation of reactive species as its primary mode of action. This work validates a deep-learning pipeline that explores chemical space to generate antibiotic candidates. This process yields a potent nitrofuran derivative and a set of experimentally validated scaffolds to seed future antibiotic development.

Efficient tools for rapid antibiotic susceptibility testing (AST) are crucial for appropriate use of antibiotics, especially colistin, which is now often considered a last resort therapy with extremely drug resistant Gram-negative bacteria. Here, we developed a rapid, easy and miniaturized colistin susceptibility assay based on microfluidics, which allows for culture and high-throughput analysis of bacterial samples. Specifically, a simple microfluidic platform that can easily be operated was designed to encapsulate bacteria in nanoliter droplets and perform a fast and automated bacterial growth detection in 2 h, using standardized samples. Direct bright-field imaging of compartmentalized samples proved to be a faster and more accurate detection method as compared to fluorescence-based analysis. A deep learning powered approach was implemented for the sensitive detection of the growth of several strains in droplets. The DropDeepL AST method (Droplet and Deep learning-based method for AST) developed here allowed the determination of the colistin susceptibility profiles of 21 fast-growing Enterobacterales (E. coli and K. pneumoniae), including clinical isolates with different resistance mechanisms, showing 100 % categorical agreement with the reference broth microdilution (BMD) method performed simultaneously. Direct AST of bacteria in urine samples on chip also provided accurate results in 2 h, without the need of complex sample preparation procedures. This method can easily be implemented in clinical microbiology laboratories, and has the potential to be adapted to a variety of antibiotics, especially for last-line antibiotics to optimize treatment of patients infected with multi-drug resistant strains.

Drug-resistant bacteria pose a significant global health threat, driving the need for innovative antibiotic development. The efficacy of these antibiotics is evaluated through biological potency assays that measure their ability to elicit targeted responses. Beta-lactam bacteria produce beta-lactamases, enzymes that hydrolyze the beta-lactam ring, rendering the antibiotics ineffective. To counteract this mechanism, beta-lactamase inhibitors play a pivotal role by preventing the enzymatic degradation of beta-lactam antibiotics. This study focuses on predicting the chemical compositions and molecular motifs that characterize active beta-lactamase inhibitors. Using k-means clustering, active small-molecule beta-lactamase inhibitors were categorized based on their unique molecular structures. A graph-based modeling approach was employed to represent these molecular structures, then leveraging Graph Attention Networks (GAT) to identify and predict substructural features associated with each distinct cluster. Molecular graph representations served as inputs for the GAT model, enabling precise classification of compounds into distinct clusters. The GAT model's performance in multiclass classification was benchmarked against traditional approaches, demonstrating superior accuracy in identifying key substructures that differentiate active beta-lactamase inhibitors. Additionally, the attention mechanism within the GAT model facilitated the identification of specific molecular motifs by focusing on relevant structural features during the learning process. The findings highlight the effectiveness of the graph-based approach in advancing the understanding and prediction of active betalactamase inhibitors, with implications for drug discovery and combating antibiotic resistance.

No abstract available

Drugs must accumulate at their target site to be effective, and inadequate uptake of drugs is a substantial barrier to the design of potent therapies. This is particularly true in the development of antibiotics, as bacteria possess numerous barriers to prevent chemical uptake. Designing compounds that circumvent bacterial barriers and accumulate to high levels in cells could dramatically improve the success rate of antibiotic candidates. However, a comprehensive understanding of which chemical structures promote or prevent drug uptake is currently lacking. Here we use liquid chromatography-mass spectrometry to measure accumulation of 1528 approved drugs in Mycobacterium abscessus, a highly drug-resistant, opportunistic pathogen. We find that simple chemical properties fail to effectively predict drug accumulation in mycobacteria. Instead, we use our data to train deep learning models that predict drug accumulation in M. abscessus with high accuracy, including for chemically diverse compounds not included in our original drug library. We find that differential drug uptake is a critical determinant of the efficacy of drugs currently in development and can identify compounds which accumulate well and have antibacterial activity in M. abscessus. These predictive algorithms can be an important complement to chemical synthesis and accumulation assays in the evaluation of drug candidates.

Due to the rapid emergence of antibiotic-resistant bacteria, there is a growing need to discover new antibiotics. To address this challenge, we trained a deep neural network capable of predicting molecules with antibacterial activity. We performed predictions on multiple chemical libraries and discovered a molecule from the Drug Repurposing Hub-halicin-that is structurally divergent from conventional antibiotics and displays bactericidal activity against a wide phylogenetic spectrum of pathogens including Mycobacterium tuberculosis and carbapenem-resistant Enterobacteriaceae. Halicin also effectively treated Clostridioides difficile and pan-resistant Acinetobacter baumannii infections in murine models. Additionally, from a discrete set of 23 empirically tested predictions from >107 million molecules curated from the ZINC15 database, our model identified eight antibacterial compounds that are structurally distant from known antibiotics. This work highlights the utility of deep learning approaches to expand our antibiotic arsenal through the discovery of structurally distinct antibacterial molecules.

Antimicrobial resistance (AMR) is a significant global health challenge caused by the misuse and overuse of antibiotics in various sectors, leading to the development of resistant bacteria. In such infections, the first-line antibiotics intended for specific diseases become ineffective, necessitating the repurposing of other antibiotics for treatment. To address this, we have developed a new algorithm for general-purpose drug repositioning based on a matrix completion framework on graphs. Our probabilistic approach combines deep matrix factorization with graph learning to achieve precise drug repurposing. In this study, we curated a new dataset on antibiotic-bacteria associations. Applying our proposed method to this dataset demonstrates that our approach outperforms benchmarks in both general-purpose drug repositioning and three specific AMR case studies.

Pathogenic bacteria, including drug-resistant variants such as methicillin-resistant Staphylococcus aureus (MRSA), can cause severe infections in the human body. Early detection of MRSA is essential for clinical diagnosis and proper treatment, considering the distinct therapeutic strategies for methicillin-sensitive S. aureus (MSSA) and MRSA infections. However, the similarities between MRSA and MSSA properties present a challenge in promptly and accurately distinguishing between them. This work introduces an approach to differentiate MRSA from MSSA utilizing matrix-assisted laser desorption ionization mass spectrometry (MALDI-MS) in conjunction with a neural network-based classification model. Four distinct strains of S. aureus were utilized, comprising three MSSA strains and one MRSA strain. The classification accuracy of our model ranges from ~ 92 to ~ 97% for each strain. We used deep SHapley Additive exPlanations to reveal the unique feature peaks for each bacterial strain. Furthermore, Fe3O4 MNPs were used as affinity probes for sample enrichment to eliminate the overnight culture and reduce the time in sample preparation. The limit of detection of the MNP-based affinity approach toward S. aureus combined with our machine learning strategy was as low as ~ 8 × 103 CFU mL−1. The feasibility of using the current approach for the identification of S. aureus in juice samples was also demonstrated.

Antibiotic resistance is a growing global health concern prompting researchers to seek alternatives to conventional antibiotics. Antimicrobial peptides (AMPs) are attracting attention again as therapeutic agents with promising utility in this domain, and using in silico methods to discover novel AMPs is a strategy that is gaining interest. Such methods can sift through large volumes of candidate sequences and reduce lab screening costs. Here we introduce AMPlify, an attentive deep learning model for AMP prediction, and demonstrate its utility in prioritizing peptide sequences derived from the Rana [Lithobates] catesbeiana (bullfrog) genome. We tested the bioactivity of our predicted peptides against a panel of bacterial species, including representatives from the World Health Organization’s priority pathogens list. Four of our novel AMPs were active against multiple species of bacteria, including a multi-drug resistant isolate of carbapenemase-producing Escherichia coli. We demonstrate the utility of deep learning based tools like AMPlify in our fight against antibiotic resistance. We expect such tools to play a significant role in discovering novel candidates of peptide-based alternatives to classical antibiotics.

Rapid antimicrobial susceptibility testing (AST) is an integral tool to mitigate the unnecessary use of powerful and broad-spectrum antibiotics that leads to the proliferation of multi-drug-resistant bacteria. Using a sensor platform composed of surface-enhanced Raman scattering (SERS) sensors with control of nanogap chemistry and machine learning algorithms for analysis of complex spectral data, bacteria metabolic profiles post antibiotic exposure are correlated with susceptibility. Deep neural network models are able to discriminate the responses of Escherichia coli and Pseudomonas aeruginosa to antibiotics from untreated cells in SERS data in 10 min after antibiotic exposure with greater than 99% accuracy. Deep learning analysis is also able to differentiate responses from untreated cells with antibiotic dosages up to 10-fold lower than the minimum inhibitory concentration observed in conventional growth assays. In addition, analysis of SERS data using a generative model, a variational autoencoder, identifies spectral features in the P. aeruginosa lysate data associated with antibiotic efficacy. From this insight, a combinatorial dataset of metabolites is selected to extend the latent space of the variational autoencoder. This culture-free dataset dramatically improves classification accuracy to select effective antibiotic treatment in 30 min. Unsupervised Bayesian Gaussian mixture analysis achieves 99.3% accuracy in discriminating between susceptible versus resistant to antibiotic cultures in SERS using the extended latent space. Discriminative and generative models rapidly provide high classification accuracy with small sets of labeled data, which enormously reduces the amount of time needed to validate phenotypic AST with conventional growth assays. Thus, this work outlines a promising approach toward practical rapid AST.

The emergence of infectious disease and antibiotic resistance in bacteria like Escherichia coli (E. coli) shows the necessity for novel computational techniques for identifying essential genes that contribute to resistance. The task of identifying resistant strains and multi-drug patterns in E. coli is a major challenge with whole genome sequencing (WGS) and next-generation sequencing (NGS) data. To address this issue, we suggest ARGai 1.0 a deep learning architecture enhanced with generative adversarial networks (GANs). We mitigate data scarcity difficulties by augmenting limited experimental datasets with synthetic data generated by GANs. Our in-silico method (augmentation with feature selection) improves the identification of resistance genes in E. coli by using feature extraction techniques to identify valuable features from actual and GAN-generated data. Employing comprehensive validation, we exhibit the effectiveness of our ARGai 1.0 in precisely identifying the informative and resistant genes. In addition, our ARGai 1.0 identifies the resistant strains with a classification accuracy of 98.96 % on Deep Convolutional Generative Adversarial Network (DCGAN) augmented data. Additionally, ARGai 1.0 achieves more than 98 % of sensitivity and specificity. We also benchmark our ARGai 1.0 with several state-of-the-art AI models for resistant strain classification. In the fight against antibiotic resistance, ARGai 1.0 offers a promising avenue for computational genomics. With implications for research and clinical practice, this work shows the potential of deep networks with GAN augmentation as a practical and successful method for gene identification in E. coli.

Antimicrobial peptides (AMPs) have exhibited unprecedented potential as biomaterials in combating multidrug-resistant bacteria, prompting the proposal of many excellent generative models. However, the multiobjective nature of AMP discovery is often overlooked, contributing to the high attrition rate of drug candidates. Here, we propose a novel approach termed hypervolume-driven multiobjective AMP design (HMAMP), which prioritizes the simultaneous optimization of multiattribute AMPs. By synergizing reinforcement learning and a gradient descent algorithm rooted in the hypervolume maximization concept, HMAMP effectively biases generative processes and mitigates the pattern collapse issue. Comparative experiments show that HMAMP significantly outperforms state-of-the-art methods in effectiveness and diversity. A knee-based decision strategy is then employed to fast screen candidates with favorable physicochemical properties, aligning with the enhanced antimicrobial activity and reduced side effects. Molecular visualization further elucidates structural and functional properties of the AMPs. Overall, HMAMP is an effective approach to traverse large and complex exploration spaces to search for idealism-realism trade-off AMPs.

No abstract available

In the last decades, antibiotic resistance has been considered a severe problem worldwide. Antimicrobial peptides (AMPs) are molecules that have shown potential for the development of new drugs against antibiotic-resistant bacteria. Nowadays, medicinal drug researchers use supervised learning methods to screen new peptides with antimicrobial potency to save time and resources. In this work, we consolidate a database with 15945 AMPs and 12535 non-AMPs taken as the base to train a pool of supervised learning models to recognize peptides with antimicrobial activity. Results show that the proposed tool (AmpClass) outperforms classical state-of-the-art prediction models and achieves similar results compared with deep learning models.

In the past decade, advancements in artificial intelligence (AI) and machine learning (ML) have revolutionized pharmaceutical research and development, powered by enhanced computational capabilities and data management. Deep learning techniques facilitate rapid analysis of extensive datasets, enabling the prediction and validation of peptide efficacy, critical for developing antimicrobial agents. AI excels at analyzing molecular structures to identify potential antibiotics, achieving over 85% success in early trials against resistant bacteria. Compared to traditional methods, AI accelerates drug development by 40–50% and cuts costs by approximately 35%, addressing the urgent threat of multidrugresistant bacteria that affect 700,000 individuals annually. Furthermore, AI models significantly enhance drug properties, including solubility and binding affinity, with 80–95% precision. Despite challenges with data consistency and model reliability, AI continues to reshape pharmaceutical innovation.

Many organisms and cell types, from bacteria to cancer cells, exhibit a remarkable ability to adapt to fluctuating environments. Additionally, cells can leverage a memory of past environments to better survive previously-encountered stressors. From a control perspective, this adaptability poses significant challenges in driving cell populations toward extinction, and thus poses an open question with great clinical significance. In this work, we focus on drug dosing in cell populations exhibiting phenotypic plasticity. For specific dynamical models switching between resistant and susceptible states, exact solutions are known. However, when the underlying system parameters are unknown, and for complex memory-based systems, obtaining the optimal solution is currently intractable. To address this challenge, we apply reinforcement learning (RL) to identify informed dosing strategies to control cell populations evolving under novel non-Markovian dynamics. We find that model-free deep RL is able to recover exact solutions and control cell populations even in the presence of long-range temporal dynamics. To further test our approach in more realistic settings, we demonstrate robust RL-based control strategies in environments with measurement noise and dynamic memory strength.

Antimicrobial peptides (AMPs), which are parts of the innate immune response found among all classes of life, are promising in broad-spectrum antibiotics and drug-resistant infection treatments. Although AMPs effectively kill bacteria, numerous AMPs widely distributed in the sequence space remain unknown to humans. Therefore, the de novo design of AMPs involves the exploration of vast sequence space to identify peptides with high antimicrobial activity and good diversity among the known AMPs. Computational intelligence approaches have successfully identified some AMPs; however, most of them fail to address the diversity of the obtained AMPs. This paper reports an evolutionary multi-objective approach for AMP design to optimize both the antimicrobial activity and diversity among identified AMPs. Our approach employs a deep learning model to predict a peptide’s antimicrobial activity and a niche sharing method to estimate a peptide’s density. Then, an evolutionary multi-objective algorithm is presented to simultaneously optimize the objectives of antimicrobial activity and diversity. The algorithm takes the advantage of a decomposition-based framework to search for AMPs with good diversity. These AMPs are collected by an elite archive during the evolution process. Moreover, a local search strategy is applied to enhance the quality of the identified AMPs. The experimental results show that the proposed approach outperforms the state-of-the-art designs in searching for various AMPs. The AMPs generated by the proposed approach have high antimicrobial activities and are distinct from each other and among the AMPs in the datasets.

Background: The emergence and prevalence of antibiotic-resistant bacteria (ARBs) have become a serious global threat, as the morbidity and mortality associated with ARB infections are continuously rising. The activation of quorum sensing (QS) genes can promote biofilm formation, which contributes to the acquisition of drug resistance and increases virulence. Therefore, there is an urgent need to develop new antimicrobial agents to control ARB and prevent further development. Antimicrobial peptides (AMPs) are naturally occurring defense molecules in organisms known to suppress pathogens through a broad range of antimicrobial mechanisms. Methods: In this study, we utilized a previously developed deep-learning model to identify AMP candidates from the venom gland transcriptome of the spider Pardosa astrigera, followed by experimental validation. Results: PA-Win2 was among the top-scoring predicted peptides and was selected based on physiochemical features. Subsequent experimental validation demonstrated that PA-Win2 inhibits the growth of Bacillus subtilis, Escherichia coli, Staphylococcus aureus, Staphylococcus epidermidis, Pseudomonas aeruginosa, and multidrug-resistant P. aeruginosa (MRPA) strain CCARM 2095. The peptide exhibited strong bactericidal activity against P. aeruginosa, and MRPA CCARM 2095 through the depolarization of bacterial cytoplasmic membranes and alteration of gene expression associated with bacterial survival. In addition, PA-Win2 effectively inhibited biofilm formation and degraded pre-formed biofilms of P. aeruginosa. The gene expression study showed that the peptide treatment led to the downregulation of QS genes in the Las, Pqs, and Rhl systems. Conclusions: These findings suggest PA-Win2 as a promising drug candidate against ARB and demonstrate the potential of in silico methods in discovering functional peptides from biological data.

The relentless emergence of antibiotic-resistant pathogens, particularly Gram-negative bacteria, highlights the urgent need for novel therapeutic interventions. Drug-resistant infections account for approximately 5 million deaths annually, yet the antibiotic development pipeline has largely stagnated. Venoms, representing a remarkably diverse reservoir of bioactive molecules, remain an underexploited source of potential antimicrobials. Venom-derived peptides, in particular, hold promise for antibiotic discovery due to their evolutionary diversity and unique pharmacological profiles. In this study, we mined comprehensive global venomics datasets to identify new antimicrobial candidates. Using machine learning, we explored 16,123 venom proteins, generating 40,626,260 venom-encrypted peptides (VEPs). Using APEX, a deep learning model combining a peptide-sequence encoder with neural networks for antimicrobial activity prediction, we identified 386 VEPs structurally and functionally distinct from known antimicrobial peptides. Our analyses showed that these VEPs possess high net charge and elevated hydrophobicity, characteristics conducive to bacterial membrane disruption. Structural studies revealed considerable conformational flexibility, with many VEPs transitioning to α-helical conformations in membrane-mimicking environments, indicative of their antimicrobial potential. Of the 58 VEPs selected for experimental validation, 53 displayed potent antimicrobial activity. Mechanistic assays indicated that VEPs primarily exert their effects through bacterial membrane depolarization, mirroring AMP-like mechanisms. In vivo studies using a mouse model of Acinetobacter baumannii infection demonstrated that lead VEPs significantly reduced bacterial burdens without notable toxicity. This study highlights the value of venoms as a resource for new antibiotics. By integrating computational approaches and experimental validation, venom-derived peptides emerge as promising candidates to combat the global challenge of antibiotic resistance.

No abstract available

No abstract available

Infections caused by persistent, drug-resistant bacteria pose significant challenges in inflammation treatment, often leading to severe morbidity and mortality. Herein, the photosensitizer rhodamine derivatives are selected as the light-trapping dye and the electron-rich substituent N-nitrosoaminophen as the nitric oxide (NO)-releasing component to develop a multifunctional (deep) red-light activatable NO photocage/photodynamic prodrug for efficient treatment of wounds and diabetic foot infections. The prodrug, RhB-NO-2 integrates antimicrobial photodynamic therapy (aPDT), NO sterilization, and NO-mediated anti-inflammatory properties within a small organic molecule and is capable of releasing NO and generating Reactive oxygen species (ROS) when exposed to (deep) red laser (660 nm). This strategy overcomes the limitation of using a single photosensitizer, which is often inadequate for eliminating drug-resistant bacteria. Additionally, it demonstrates that NO released from the prodrug can interact with superoxide anions (O2 •-) generated by PDT to form a more reactive and oxidative agent, peroxynitrite (ONOO-). These three components act synergistically to enhance the antimicrobial effects. Furthermore, the released NO can inhibit the NF-κB pathway by regulating the expression of toll-like receptor 2 (TRL2) and tumor necrosis factor-α (TNF-α), thereby alleviating tissue inflammation. The developed prodrug ， RhB-NO-2 has the potential to expedite the healing of superficial infected wounds and offer a promising approach for treating diabetic foot ulcers (DFUs).

No abstract available

Urinary tract infection (UTI), the most common bacterial infection among renal transplant recipients (RTRs), remains a challenge, particularly given the increased incidence of MDR organisms, including Carbapenem-resistant Enterobacteriaceae (CRE). CRE infections were associated with inferior patient and graft outcomes compared to other bacterial infections. Paradoxically, there have been no guidelines on managing CRE in RTRs besides multiple challenges, including the bacteriostatic nature, poor urinary concentration, and dose-limiting adverse effects of various antibiotics. The current study designed and assessed the outcomes of protocol-based therapy consisting of high-dose Meropenem in combination with other antibiotics followed by prolonged oral administration. This is a single-centre retrospective study conducted in the department of Nephrology and Renal transplantation, Sanjay Gandhi Post Graduate Institute of Medical Sciences, India, between 1st August 2016 till 31st July 2022. All the RTRs aged $ \ge $ 18 years admitted between 1st August 2016 and 31st July 2018 with symptomatic UTI and a urine culture positive for CRE were included in the study. Patients who received various antibiotics conventionally based on the treating physician's decision from August 2016 to July 2017 were considered under the Best available treatment (BAT) group, while the Standardized Therapeutic Antibiotic Regimen (STAR) group included patients treated from August 2017 to July 2018. The treatment of the STAR group consists of a therapeutic phase for at least six weeks (IV therapeutic phase for two weeks total or one week after the clinical recovery, whichever is later + Oral Therapeutic phase for four weeks) followed by an oral chemoprophylaxis phase for three months. Following the treatment of index UTI episodes due to CRE, follow-up both groups’ follow-up data until a minimum of four years were collected. Appropriate statistical tools were applied, and analyses were performed by SPSS software, version 25. A total of 37 patients fulfilling the inclusion criteria were included in the study, of which 13 patients were under the BAT group, and 24 were treated by the STAR-based protocol. The mean age of the study population was 37.6 $ \pm \ $12.3 years, and all were males. Most patients (70.2%) had the UTI due to CRE within one-month post-transplant, and the median duration of UTI post-transplant was 6.1 days (IQR: 4.5 – 33). The primary outcome, recurrence rates of UTI at 48-month follow-up among the patients in the STAR group, was significantly lower than those in the BAT group (30.4% vs 77.8%, p = 0.01). The death-censored graft survival was also significantly better among the STAR group than the BAT group (100% vs 75%, p = 0.03) after 48 months. Graft function at 48 months was also better in the STAR group (Serum creatinine- 1.4 ± 0.8 mg/dl vs 2.9 ± 2.2 mg/dl, p = 0.007). The patient survival, however, was similar among the two groups (95.8% vs 88.9%, p = 0.47). Prolonged and combination antibiotic therapy followed by long-term antibiotic prophylaxis significantly reduced the recurrence of UTI due to CRE among the RTRs. Graft function and death-censored graft survival were also considerably better. Hence, the current study may pave the path for future RCTs based on combination antibiotic therapy as a solution to combat the challenge of CRE in RTRs.

Carbapenem-resistant Enterobacteriaceae (CRE) represent a critical antimicrobial resistance threat due to their resistance to last-resort antibiotics and high transmission potential. While conventional strategies—such as infection control, antimicrobial stewardship, and novel antibiotic development—remain essential, growing attention has shifted toward the gut microbiome, which plays a central role in mediating colonization resistance against CRE. Disruption of the intestinal microbiota—primarily driven by antibiotic exposure and further exacerbated by non-antibiotic drugs such as proton pump inhibitors—reduces microbial diversity and impairs functional integrity, facilitating CRE acquisition, prolonged carriage, and horizontal transmission. In response, microbiome-based strategies—including microbiome disruption indices (MDIs), fecal microbiota transplantation (FMT), and rationally designed symbiotic microbial consortia—are being explored as novel approaches for CRE prevention and decolonization. Mechanistic studies have shown that colonization resistance is mediated by both direct mechanisms (e.g., nutrient competition, short-chain fatty acid production) and indirect mechanisms (e.g., immune modulation via IL-36 signaling). Advances in metagenomics, metabolomics, and culturomics have enabled high-resolution profiling of gut microbial communities and their functional roles. Emerging preclinical and clinical evidence supports the potential of microbiome-informed interventions to predict infection risk, enhance antimicrobial stewardship, and guide the development of next-generation probiotics targeting CRE. Longitudinal studies continue to evaluate the efficacy of FMT and synthetic microbial consortia in eradicating intestinal CRE colonization. Collectively, these insights underscore the promise of gut microbiome science as a complementary and innovative strategy for CRE control in the post-antibiotic era.

Background: Current antibiotic options for carbapenem-resistant Enterobacteriaceae (CRE) are limited. However, anagement of patients with descending necrotizing mediastinitis (DNM) colonized with CRE is urgently required, as it is associated with significant post-operative morbidity and mortality. Ceftazidime-avibactam represents a novel combination therapy for CRE. Case Presentation: A 50-year-old male patient with clinical DNM and prolonged intensice care. The patient underwent repeated surgeries and experienced infections with K. Pneumoniae, Acinetobacter Sp., and Pseudomonas Sp. resistant to carbapenems. The patient was administered ceftazidime-avibactam for 6 days. However, the patient was declared death after 30-days. Discussion: The administration of ceftazidime-avibactam yielded good clinical outcomes in CRE infections. Combination therapy had a higher survival rate compared to monotherapy and lower rates of resistance. Conclusion: Ceftazidime-avibactam demonstrates effective and safe potential for the management of CRE infections.

Carbapenem-resistant Enterobacteriaceae (CRE), particularly Klebsiella pneumoniae, are major causes of severe systemic infections due to their resistance to most antibiotics and the high associated mortality, representing a growing global health concern. In this study, we report the in vivo efficacy of a novel probiotic strain, Lactiplantibacillus plantarum PMC105, against systemic CRE infections. In a mouse model characterized by neutropenia and antibiotic-induced gut dysbiosis, infection with carbapenem-resistant K. pneumoniae (CRKP) resulted in 60% mortality within two weeks. However, oral administration of PMC105 significantly reduced intestinal CRKP colonization, minimized body weight loss, and resulted in 100% survival. This therapeutic effect is presumed to result from enhanced gut barrier function, driven by upregulation of the tight junction protein ZO-1 in the ileum, thereby preventing bacterial translocation and subsequent systemic dissemination. In a therapeutic model of systemic infection following translocation, intranasal administration of PMC105 reduced bacterial loads in the stool, liver, kidneys, and lungs, improved clinical symptoms, and maintained body weight, thereby increasing survival rates. Comprehensive safety evaluations, including antibiotic susceptibility testing, hemolysis, bile salt deconjugation, D-lactate production, and cytotoxicity assays, confirmed the strain's safety. These findings support the potential of PMC105 as a dual-route microbiome-based therapeutic candidate for the treatment of systemic CRE infections and warrant further clinical investigation.

Background Carbapenem-resistant Enterobacteriaceae (CRE) present substantial challenges to clinical intervention, necessitating the formulation of novel antimicrobial strategies to counteract them. Nanomaterials offer a distinctive avenue for eradicating bacteria by employing mechanisms divergent from traditional antibiotic resistance pathways and exhibiting reduced susceptibility to drug resistance development. Non-caloric artificial sweeteners, commonly utilized in the food sector, such as saccharin, sucralose, acesulfame, and aspartame, possess structures amenable to nanomaterial formation. In this investigation, we synthesized gold nanoparticles decorated with non-caloric artificial sweeteners and evaluated their antimicrobial efficacy against clinical CRE strains. Results Among these, gold nanoparticles decorated with aspartame (ASP_Au NPs) exhibited the most potent antimicrobial effect, displaying minimum inhibitory concentrations ranging from 4 to 16 µg/mL. As a result, ASP_Au NPs were chosen for further experimentation. Elucidation of the antimicrobial mechanism unveiled that ASP_Au NPs substantially elevated bacterial reactive oxygen species (ROS) levels, which dissipated upon ROS scavenger treatment, indicating ROS accumulation within bacteria as the fundamental antimicrobial modality. Furthermore, findings from membrane permeability assessments suggested that ASP_Au NPs may represent a secondary antimicrobial modality via enhancing inner membrane permeability. In addition, experiments involving crystal violet and confocal live/dead staining demonstrated effective suppression of bacterial biofilm formation by ASP_Au NPs. Moreover, ASP_Au NPs demonstrated notable efficacy in the treatment of Galleria mellonella bacterial infection and acute abdominal infection in mice, concurrently mitigating the organism's inflammatory response. Crucially, evaluation of in vivo safety and biocompatibility established that ASP_Au NPs exhibited negligible toxicity at bactericidal concentrations. Conclusions Our results demonstrated that ASP_Au NPs exhibit promise as innovative antimicrobial agents against clinical CRE. Graphical Abstract

Carbapenem-resistant Enterobacteriaceae (CRE), known for their extensive antibiotic resistance, pose a severe global medical threat. Therefore, developing novel therapeutics beyond conventional antibiotics is urgently needed, and the importance of microbiome therapeutics is increasingly being recognized. This study explores the expanded systemic efficacy of PMC101, a microbiome therapeutic, beyond intestinal CRE infections and investigates its mechanism of action from a microbiome perspective. First, the genetic characteristics of the novel strain were identified through whole-genome analysis, and a scalable cultivation process was established as part of the overall development of this microbiome therapeutic. PMC101 increased the survival rate to 100%, significantly reduced disease severity scores, and prevented weight loss in CRE-infected mice treated with antibiotics. These effects are attributed to the inhibition of CRE growth in stool and the reduced detection of CRE in the lungs and kidneys, indicating suppression of systemic translocation. Metagenomic analysis revealed that PMC101 prevented the reduction in microbial population caused by antibiotics and CRE infection, restored species diversity indices, and mitigated dysbiosis while promoting eubiosis. This CRE translocation suppression was closely associated with increased CRE translocation-microbiome index, defined as the ratio of Bacteroidetes to Proteobacteria. This relationship was further confirmed through simulations using a human intestinal microbial ecosystem model. Additionally, increases in short-chain fatty acids, reductions in excessive inflammatory responses, and decreases in tissue damage were observed, all of which contribute to preventing CRE translocation. Finally, pathogen inhibition effects and safety tests were conducted, confirming the prophylactic potential of PMC101 as a microbiome therapeutic. These findings strongly support PMC101 as a promising candidate for future microbiome-based therapies against CRE infections. Restoring gut eubiosis using microbiome therapeutic PMC101 to mitigate dysbiosis and combat CRE infections. The graphical abstract illustrates the role of microbiome therapeutic PMC101 in maintaining and restoring gut eubiosis. The diagram illustrates how the administration of antibiotics and CRE disrupts the gut microbiota, leading to dysbiosis characterized by reduced microbial diversity and increased severity of infections. PMC101 mitigates microbial disruption and promotes the restoration of eubiosis, thereby reducing the severity of infections and restoring gut health. The left-to-right flow of the illustration highlights the progression from a healthy eubiotic state to dysbiosis caused by external factors and the prophylactic intervention of PMC101 to reverse these adverse effects. Key indicators include eubiosis state on the left y-axis and infection severity on the right y-axis. The transition arrows represent the changes in microbial balance with or without the intervention of PMC101.

ABSTRACT Carbapenem-resistant Enterobacteriaceae have become widely prevalent globally because of antibiotic misuse and the spread of drug-resistant plasmids, where carbapenem-resistant Escherichia coli (CREC) is one of the most common and prevalent pathogens. Furthermore, E. coli has been identified as a member of normal gut flora and does not cause disease under normal circumstances. However, certain strains of E. coli, due to the expression of virulence genes, can cause severe intestinal and extra-intestinal infections. Therefore, clinically, drug resistance and pathogenic E. coli strains are significantly challenging to treat. In this study, a novel CREC strain DC8855 was isolated from the ascites of a patient with intestinal perforation, identified as a novel sequence type 12531 (ST12531) and an unreported serotype O8:H7. It was revealed that the resistance of ST12531 CREC was predominantly conferred by an IncFII(K) plasmid carrying blaNDM-4. Furthermore, phylogenetic analysis indicated that this is the first discovery of such plasmids in China and the first identification in E. coli. Moreover, regarding virulence, the swimming assays, qRT-PCR, and in vitro intestinal barrier model indicated that DC8855 had significantly higher motility, flagella gene expression, and intestinal epithelial cell barrier migration ability than the other sequence types CREC strains (ST167 and ST410). In conclusion, this study identified novel CREC which was multidrug resistant as well as enteropathogenic and therefore requires continuous monitoring.

The discovery of safe and efficient inhibitors against efflux pumps as well as metallo-β-lactamases (MBL) is one of the main challenges in the development of multidrug-resistant (MDR) reversal agents which can be utilized in the treatment of carbapenem-resistant Gram-negative bacteria. In this study, we have identified that introduction of an ethylene-linked sterically demanding group at the 3-OH position of the previously reported MDR reversal agent di-F-Q endows the resulting compounds with hereto unknown multitarget inhibitory activity against both efflux pumps and broad-spectrum β-lactamases including difficult-to-inhibit MBLs. A molecular docking study of the multitarget inhibitors against efflux pump, as well as various classes of β-lactamases, revealed that the 3-O-alkyl substituents occupy the novel binding sites in efflux pumps as well as carbapenemases. Not surprisingly, the multitarget inhibitors rescued the antibiotic activity of a carbapenem antibiotic, meropenem (MEM), in NDM-1 (New Delhi Metallo-β-lactamase-1)-producing carbapenem-resistant Enterobacteriaceae (CRE), and they reduced MICs of MEM more than four-fold (synergistic effect) in 8-9 out of 14 clinical strains. The antibiotic-potentiating activity of the multitarget inhibitors was also demonstrated in CRE-infected mouse model. Taken together, these results suggest that combining inhibitory activity against two critical targets in MDR Gram-negative bacteria, efflux pumps, and β-lactamases, in one molecule is possible, and the multitarget inhibitors may provide new avenues for the discovery of safe and efficient MDR reversal agents.

LYS228 is a novel monobactam with potent activity against Enterobacteriaceae. LYS228 is stable to metallo-β-lactamases (MBLs) and serine carbapenemases, including Klebsiella pneumoniae carbapenemases (KPCs), resulting in potency against the majority of extended-spectrum β-lactamase (ESBL)-producing and carbapenem-resistant Enterobacteriaceae strains tested. ABSTRACT LYS228 is a novel monobactam with potent activity against Enterobacteriaceae. LYS228 is stable to metallo-β-lactamases (MBLs) and serine carbapenemases, including Klebsiella pneumoniae carbapenemases (KPCs), resulting in potency against the majority of extended-spectrum β-lactamase (ESBL)-producing and carbapenem-resistant Enterobacteriaceae strains tested. Overall, LYS228 demonstrated potent activity against 271 Enterobacteriaceae strains, including multidrug-resistant isolates. Based on MIC90 values, LYS228 (MIC90, 1 μg/ml) was ≥32-fold more active against those strains than were aztreonam, ceftazidime, ceftazidime-avibactam, cefepime, and meropenem. The tigecycline MIC90 was 4 μg/ml against the strains tested. Against Enterobacteriaceae isolates expressing ESBLs (n = 37) or displaying carbapenem resistance (n = 77), LYS228 had MIC90 values of 1 and 4 μg/ml, respectively. LYS228 exhibited potent bactericidal activity, as indicated by low minimal bactericidal concentration (MBC) to MIC ratios (MBC/MIC ratios of ≤4) against 97.4% of the Enterobacteriaceae strains tested (264/271 strains). In time-kill studies, LYS228 consistently achieved reductions in CFU per milliliter of 3 log10 units (≥99.9% killing) at concentrations ≥4× MIC for Escherichia coli and K. pneumoniae reference strains, as well as isolates encoding TEM-1, SHV-1, CTX-M-14, CTX-M-15, KPC-2, KPC-3, and NDM-1 β-lactamases.

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In response to the threat of increasing antimicrobial resistance, we must increase the amount of available high-quality genomic data gathered on antibiotic-resistant bacteria. To this end, we developed an integrated pipeline for high-throughput long-read sequencing, assembly, annotation and analysis of bacterial isolates and used it to generate a large genomic data set of carbapenemase-producing Enterobacterales (CPE) isolates collected in Spain. The set of 461 isolates were sequenced with a combination of both Illumina and Oxford Nanopore Technologies (ONT) DNA sequencing technologies in order to provide genomic context for chromosomal loci and, most importantly, structural resolution of plasmids, important determinants for transmission of antimicrobial resistance. We developed an informatics pipeline called Assembly and Annotation of Carbapenem-Resistant Enterobacteriaceae (AACRE) for the full assembly and annotation of the bacterial genomes and their complement of plasmids. To explore the resulting genomic data set, we developed a new database called inCREDBle that not only stores the genomic data, but provides unique ways to filter and compare data, enabling comparative genomic analyses at the level of chromosomes, plasmids and individual genes. We identified a new sequence type, ST5000, and discovered a genomic locus unique to ST15 that may be linked to its increased spread in the population. In addition to our major objective of generating a large regional data set, we took the opportunity to compare the effects of sample quality and sequencing methods, including R9 versus R10 nanopore chemistry, on genome assembly and annotation quality. We conclude that converting short-read and hybrid microbial sequencing and assembly workflows to the latest nanopore chemistry will further reduce processing time and cost, truly enabling the routine monitoring of resistance transmission patterns at the resolution of complete chromosomes and plasmids.

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Untreated wastewater, particularly from hospitals and other healthcare facilities, is considered to be a reservoir for multidrug-resistant bacteria. However, its role in the spread of antibiotic resistances in the human population remains poorly investigated. We used whole genome sequencing to analyze 25 KPC-2-producing Enterobacteriaceae isolates from sewage water collected during a 3-year period and three clinical Citrobacter freundii isolates from a tertiary hospital in the same collection area in Spain. We detected a common, recently described, IncP-6 plasmid carrying the gene blaKPC-2 in 21 isolates from both sources. The plasmid was present in diverse environmental bacterial species of opportunistic pathogens such as C. freundii, Enterobacter cloacae, Klebsiella oxytoca, and Raoultella ornithinolytica. The 40,186 bp IncP-6 plasmid encoded 52 coding sequences and was composed of three uniquely combined regions that were derived from other plasmids recently reported in different countries of South America. The region harboring the carbapenem resistance gene (14 kb) contained a Tn3 transposon disrupted by an ISApu-flanked element and the core sequence composed by ISKpn6/blaKPC-2/ΔblaTEM-1/ISKpn27. We document here the presence of a novel promiscuous blaKPC-2 plasmid circulating in environmental bacteria in wastewater and human populations.

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KPC is currently the most common carbapenemase identified in the United States. More than 40 KPC variants have been described, of which KPC-2 and KPC-3 are the most frequent clinical variants. However, our understanding of the genetic structures and β-lactam resistance profiles of other novel KPC variants remains incomplete. Here, we report a novel blaKPC variant (blaKPC-14) and the complete genome sequence of blaKPC-14-harboring K. pneumoniae strain BK13048, which is susceptible to carbapenems but resistant to ceftazidime-avibactam. To the best of our knowledge, this is one of the earliest KPC-producing K. pneumoniae strains exhibiting resistance to ceftazidime-avibactam. ABSTRACT Ceftazidime-avibactam is a potent antibiotic combination against Klebsiella pneumoniae carbapenemase (KPC)-producing Enterobacteriaceae. Here, we describe a unique ceftazidime-avibactam-resistant and carbapenem-susceptible K. pneumoniae strain harboring a novel blaKPC-14 variant. This strain was isolated from a New York City patient in 2003, which predates the introduction of avibactam. Despite resistance to ceftazidime-avibactam, the strain was susceptible to imipenem-relebactam and meropenem-vaborbactam. Comprehensive genomic sequencing revealed that blaKPC-14 is harbored on an ST6 IncN plasmid associated with the early spread of blaKPC. IMPORTANCE KPC is currently the most common carbapenemase identified in the United States. More than 40 KPC variants have been described, of which KPC-2 and KPC-3 are the most frequent clinical variants. However, our understanding of the genetic structures and β-lactam resistance profiles of other novel KPC variants remains incomplete. Here, we report a novel blaKPC variant (blaKPC-14) and the complete genome sequence of blaKPC-14-harboring K. pneumoniae strain BK13048, which is susceptible to carbapenems but resistant to ceftazidime-avibactam. To the best of our knowledge, this is one of the earliest KPC-producing K. pneumoniae strains exhibiting resistance to ceftazidime-avibactam.

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Carbapenem-resistant Enterobacteriaceae infections are a considerable challenge for clinicians. In recent years, novel antibiotic options have resulted in a tremendous advance in medical therapy; however, current treatment options are primarily effective for resistance derived from serine-based carbapenemases. The Ambler class B metallo-β-lactamases (MBLs) remain a critical challenge with decidedly fewer effective options. One intriguing option for these MBL pathogens is the combination of ceftazidime-avibactam with aztreonam. While clinical experience with this regimen is limited, in vitro studies are promising, and limited case reports describe success with this regimen; however, significant challenges preclude widespread adoption of this novel treatment regimen. A systemic literature review was performed to offer recommendations based on current evidence for a practical strategy on how to best integrate the use of aztreonam with avibactam combination therapy.

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Dear Editor, Antibiotic resistance is one of the major global health concerns that poses a threat on both individual and societal levels. As a result of the emerging novel bacterial pathogen and resistance to several antibiotic classes, many antibiotics have been futile in coping with these infections. In the United States, it is estimated that 23 000 people with antibiotic-resistant infections die due to the limited treatment options and severe symptoms caused by drug-resistant organisms. It is interesting to note that carbapenems, a class of antibiotics including doripenem, ertapenem, meropenem, and imipenem, have a significant impact in treating multidrug resistance and hospital-acquired extended-spectrum β-lactamase infections. However, the usage of carbapenem antibiotics around the world is risky due to the evolution of resistance to them. Enzyme synthesis, efflux pumps, and porin mutations are the three main methods by which Enterobacteriaceae acquire carbapenem resistance. The primary mode of resistance among them is enzyme synthesis. The enzymes responsible for hydrolyzing carbapenems in resistant bacteria are called carbapenemases, which are β-lactamases that can hydrolyze carbapenems, penicillin, cephalosporins, and monobactam. Four primary classes of carbapenemases have been classified based on amino acid homology (classes A, B, C, and D). β-lactamases which are found in molecular classes A, C, and D, contain serine at their active sites, whereas molecular class B βlactamases are metalloenzymes with an active site containing zinc. Metallo-β-lactamases (MBL), Klebsiella pneumonia carbapenemase (KPC), and oxacillinase (OXA-48)-like are the three primary categories of enzymes that cause the majority of carbapenem resistance. More details are shown in Figure 1. Nevertheless, a significant health issue is an alarming rise in resistance to these final resource agents. The primary risk factors for the emergence of resistance are the abuse of antibiotics, genetic mobile elements, worldwide travel, and poor infection control techniques. Saudi Arabia, like other Gulf nations, is under pressure from the emergence of multiple resistant pathogens like carbapenem-resistant Enterobacteriaceae (CRE). Traveling both inside and outside of the Gulf region poses a significant risk for the spread of resistant Enterobacteriaceae strains. Enterobacteriaceae multiple resistant variants, which typically produce extended-spectrum β-lactamases and carbapenemases like KPC and New Delhi MBL-1 (NDM-1), are resistant to cephalosporins and have recently spread across the globe. However, there are only a few medications, including polymyxins, tigecycline, fosfomycin, and aminoglycosides, either alone or in combinationwith other antibiotics, that can be used to treat CRE infections. Compared to monotherapy, combination therapy had a better clinical outcome. In the United States, Colombia, Argentina, Greece, and Italy, KPC-producing Enterobacteriaceae are widespread. However, in Pakistan, India, and Sri Lanka, the MBL NDM-1 is the major carbapenemase-producing resistance, and OXA48-like enzyme producers are widespread in North Africa, the Middle East, Turkey, and Malta. Figure 2 demonstrates the demographical distribution of carbapenem-resistance ambler classes endemicity. Because all three groups of enzymes are plasmid-mediated, the horizontal transmission will likely be simpler, and that carbapenem resistance will spread more quickly throughout the world. Due to the few available alternatives for treatment and inadequate early therapy, CRE infections are associated with poor results. Additionally, evidence suggested that CRE infections have worse outcomes than those caused by sensitive Enterobacterales. Moreover, a CRE infection is linked to lengthier hospital stays and higher medical expenses. There is little information about the molecular epidemiology of CRE in the Middle East and Gulf countries in particular. Based on the available research, OXA-48 and NDM are the two most common carbapenemase strains in Saudi Arabia. The extensive geographic range of the Arab region and the ongoing influx of both native and foreign pilgrims are possible influences on the dissemination of CRE strains. There is still a lack of clarity regarding the precise molecular epidemiology and consequences of CRE. Self-medication is one of the factors that is acknowledged on a global scale as being the most prevalent and obvious contributor to bacteria that are resistant to antibiotics. Selfmedication is the phrase used to describe taking medications on one’s own will or at the suggestion of someone who is not a licensed medical expert. Advertisements on television, radio, and print media, as well as recommendations from family and aFaculty of Medicine, The Hashemite University, Zarqa, bDepartment of Pathology, Microbiology and Forensic Medicine, School of Medicine, The University of Jordan, Amman, Jordan and cFaculty of Medicine, Elrazi University, Khartoum, Sudan

Antibiotic resistance has increased markedly in gram-negative bacteria over the last two decades, and in many cases has been associated with increased mortality and healthcare costs. The adoption of genotyping and next generation whole genome sequencing of large sets of clinical bacterial isolates has greatly expanded our understanding of how antibiotic resistance develops and transmits among bacteria and between patients. Diverse mechanisms of resistance, including antibiotic degradation, antibiotic target modification, and modulation of permeability through the bacterial membrane have been demonstrated. These fundamental insights into the mechanisms of gram-negative antibiotic resistance have influenced the development of novel antibiotics and treatment practices in highly resistant infections. Here, we review the mechanisms and global epidemiology of antibiotic resistance in some of the most clinically important resistance phenotypes, including carbapenem resistant Enterobacteriaceae, extensively drug resistant (XDR) Pseudomonas aeruginosa, and XDR Acinetobacter baumannii. Understanding the resistance mechanisms and epidemiology of these pathogens is critical for the development of novel antibacterials and for individual treatment decisions, which often involve alternatives to β-lactam antibiotics.

Stenotrophomonas maltophilia is an urgent global threat due to its increasing incidence and intrinsic antibiotic resistance. Antibiotic development has focused on carbapenem-resistant Enterobacteriaceae, Pseudomonas, and Acinetobacter, with approved antibiotics in recent years having limited activity for Stenotrophomonas. Accordingly, novel treatment strategies for Stenotrophomonas are desperately needed. We conducted a systemic literature review and offer recommendations based on current evidence for a treatment strategy of Stenotrophomonas infection.

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Treatment options for carbapenem-resistant Enterobacteriaceae (CRE) are limited. While Klebsiella pneumoniae strains harboring blaKPC account for most CRE, recent evidence points to increasing diversification of CRE. ABSTRACT Treatment options for carbapenem-resistant Enterobacteriaceae (CRE) are limited. While Klebsiella pneumoniae strains harboring blaKPC account for most CRE, recent evidence points to increasing diversification of CRE. We determined whether the CRE species and antibiotic resistance genotype influence the response to relebactam (REL), a novel beta-lactamase inhibitor with class A/C activity, combined with imipenem-cilastatin (IMI). We carried out broth microdilution testing with IMI alone or in the presence of 4 μg/ml REL against 154 clinical isolates collected at a New York City hospital with a high prevalence of organisms carrying blaKPC, including Enterobacter spp. (n = 96), K. pneumoniae (n = 44), Escherichia coli (n = 1), Serratia marcescens (n = 9), and Citrobacter spp. (n = 4). Resistance gene profiles and the presence of major porin gene disruptions were ascertained by whole-genome sequencing. Addition of REL decreased the IMI MIC to the susceptible range (≤1 μg/ml) against 88% of isolates. However, S. marcescens IMI-REL MICs were 4- to 8-fold higher than those for other organisms. Most blaKPC-positive isolates had IMI-REL MICs of ≤1 μg/ml (88%), including isolates of Enterobacter cloacae ST171 (93%) and K. pneumoniae ST258 (82%). Nineteen isolates had IMI-REL MICs of ≥2 μg/ml, among which 84% harbored blaKPC and one was blaNDM-1 positive. Isolates with IMI-REL MICs of ≥2 μg/ml versus those with MICs of ≤1 μg/ml were significantly more likely to demonstrate disruption of at least one porin gene (42% versus 19%; P = 0.04), although most S. marcescens isolates (67%) had intact porin genes. In conclusion, while REL reduced IMI MICs in a majority of diverse CRE isolates, including high-risk clones, chromosomal factors had an impact on IMI-REL susceptibilities and may contribute to elevated MICs for S. marcescens.

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Klebsiella pneumoniae is a gram-negative bacterium that is known for causing infection in nosocomial settings. As reported by the World Health Organization, carbapenem-resistant Enterobacteriaceae, a category that includes K. pneumoniae, are classified as an urgent threat, and the greatest concern is that these bacterial pathogens may acquire genetic traits that make them resistant towards antibiotics. The last class of antibiotics, carbapenems, are not able to combat these bacterial pathogens, allowing them to clonally expand antibiotic-resistant strains. Most antibiotics target essential pathways of bacterial cells; however, these targets are no longer susceptible to antibiotics. Hence, in our study, we focused on a hypothetical protein in K. pneumoniae that contains a DNA methylation protein domain, suggesting a new potential site as a drug target. DNA methylation regulates the attenuation of bacterial virulence. We integrated computational-aided drug design by using a bioinformatics approach to perform subtractive genomics, virtual screening, and fingerprint similarity search. We identified a new potential drug, koenimbine, which could be a novel antibiotic.

Meropenem-vaborbactam (MEV) is a novel carbapenem–beta-lactamase inhibitor combination antibiotic approved by the U.S. Food and Drug Administration (FDA) for treatment of complicated urinary tract infections, including pyelonephritis, in adults. In this study, we evaluated the performance of Etest MEV (bioMérieux, Marcy l’Etoile, France) compared to that of broth microdilution for 629 Enterobacterales and 163 Pseudomonas aeruginosa isolates. ABSTRACT Meropenem-vaborbactam (MEV) is a novel carbapenem–beta-lactamase inhibitor combination antibiotic approved by the U.S. Food and Drug Administration (FDA) for treatment of complicated urinary tract infections, including pyelonephritis, in adults. In this study, we evaluated the performance of Etest MEV (bioMérieux, Marcy l’Etoile, France) compared to that of broth microdilution for 629 Enterobacterales and 163 Pseudomonas aeruginosa isolates. According to CLSI/FDA breakpoints, 13 Enterobacterales isolates (12 clinical and 1 challenge) were resistant to MEV. Overall, Etest MEV demonstrated 92.4% essential agreement (EA), 99.2% category agreement (CA), 0% very major errors (VME), 0% major errors (ME), and 0.8% minor errors (mE) with clinical and challenge isolates of Enterobacterales. Individual species demonstrated EA rates of ≥80%, with the exception of Proteus mirabilis, for which clinical and challenge isolates demonstrated 34.3% EA, 97.1% CA, 0% ME, and 2.9% mE, precluding the use of Etest MEV with this species. Excluding P. mirabilis, MEV Etest MEV demonstrated 95.8% EA, 99.3% CA, 0% VME, 0% ME, and 0.7% mE with Enterobacterales isolates. When evaluated using European Committee on Antimicrobial Susceptibility Testing (EUCAST) breakpoints, Etest MEV performance with clinical (16 MEV resistant) and challenge (12 MEV resistant) isolates of Enterobacterales (excluding P. mirabilis) and P. aeruginosa demonstrated an unacceptably high VME rate of 7.1% despite 95.2% EA, 99.2% CA, and 0.5% ME compared to the reference method. In conclusion, we report that Etest MEV is accurate and reproducible for MEV susceptibility testing for P. aeruginosa and Enterobacterales, with the exception of P. mirabilis, using CLSI/FDA breakpoints. Etest MEV should not be used with P. mirabilis due to unacceptable analytical performance.

Abstract Antibiotic resistance is recognized as a key determinant of outcome in patients with serious infections influencing empiric antibiotic practices especially for critically ill patients. Within the intensive care unit (ICU), nosocomial infections and increasingly community-onset infections are caused by multidrug-resistant bacteria. Escalating rates of antibiotic resistance adds substantially to the morbidity, mortality, and cost related to infections treated in the ICU. Both gram-positive organisms, such as methicillin-resistant Staphylococcus aureus and vancomycin-resistant enterococci, and gram-negative bacteria, including Pseudomonas aeruginosa, Acinetobacter species, carbapenem-resistant Enterobacteriaceae, and extended spectrum β-lactamase producing organisms, are urgent threats. The rising rates of antimicrobial resistance have resulted in routine empiric administration of broad-spectrum antibiotics by clinicians to critically ill patients even when bacterial infection is microbiologically absent. Moreover, new broad-spectrum antibiotics are a challenge to use effectively while avoiding emergence of further resistance. Use of rapid diagnostic technologies (RDTs) will likely provide an important methodology for achieving this important balance. There is an urgent need for integrating the administration of new and existing antibiotics with RDTs in a way that is safe, cost-effective, applicable in all countries, and sustainable.

Antibacterial activity screening of a collection of Xenorhabdus strains led to the discovery of the odilorhabdins, a new antibiotic class with broad-spectrum activity against Gram-positive and Gram-negative pathogens. Odilorhabdins inhibit bacterial translation by a new mechanism of action on ribosomes. ABSTRACT Antibacterial activity screening of a collection of Xenorhabdus strains led to the discovery of the odilorhabdins, a new antibiotic class with broad-spectrum activity against Gram-positive and Gram-negative pathogens. Odilorhabdins inhibit bacterial translation by a new mechanism of action on ribosomes. A lead optimization program identified NOSO-502 as a promising candidate. NOSO-502 has MIC values ranging from 0.5 to 4 μg/ml against standard Enterobacteriaceae strains and carbapenem-resistant Enterobacteriaceae (CRE) isolates that produce KPC, AmpC, or OXA enzymes and metallo-β-lactamases. In addition, this compound overcomes multiple chromosome-encoded or plasmid-mediated resistance mechanisms of acquired resistance to colistin. It is effective in mouse systemic infection models against Escherichia coli EN122 (extended-spectrum β-lactamase [ESBL]) or E. coli ATCC BAA-2469 (NDM-1), achieving a 50% effective dose (ED50) of 3.5 mg/kg of body weight and 1-, 2-, and 3-log reductions in blood burden at 2.6, 3.8, and 5.9 mg/kg, respectively, in the first model and 100% survival in the second, starting with a dose as low as 4 mg/kg. In a urinary tract infection (UTI) model with E. coli UTI89, urine, bladder, and kidney burdens were reduced by 2.39, 1.96, and 1.36 log10 CFU/ml, respectively, after injection of 24 mg/kg. There was no cytotoxicity against HepG2, HK-2, or human renal proximal tubular epithelial cells (HRPTEpiC), no inhibition of hERG-CHO or Nav 1.5-HEK current, and no increase of micronuclei at 512 μM. NOSO-502, a compound with a new mechanism of action, is active against Enterobacteriaceae, including all classes of CRE, has a low potential for resistance development, shows efficacy in several mouse models, and has a favorable in vitro safety profile.

Objectives The neutropenic murine thigh infection model and a dose-fractionation approach were used to determine the pharmacokinetic/pharmacodynamic (PK/PD) relationship of LYS228, a novel monobactam antibiotic with activity against Enterobacteriaceae including carbapenem-resistant strains. Methods Mice (n = 4 per group) were inoculated with Enterobacteriaceae strains via intramuscular injection. Two hours post-bacterial inoculation, treatment with LYS228 was initiated. Animals were euthanized with CO2 24 h after the start of therapy and bacterial counts (log10 cfu) per thigh were determined. PK parameters were calculated using free (f) plasma drug levels. Results Following a dose-fractionation study, non-linear regression analysis determined that the predominant PK/PD parameter associated with antibacterial efficacy of LYS228 was the percentage of the dosing interval that free drug concentrations remained above the MIC (%fT>MIC). In a dose-dependent manner, LYS228 reduced the thigh bacterial burden in models established with Enterobacteriaceae producing β-lactamase enzymes of all classes (e.g. ESBLs, NDM-1, KPC, CMY-2 and OXA-48). The range of the calculated static dose was 86-649 mg/kg/day for the isolates tested, and the magnitude of the driver of efficacy was 37-83 %fT>MIC. %fT>MIC was confirmed as the parameter predominantly driving efficacy as evidenced by a strong coefficient of determination (r2 = 0.68). Neutrophils had minimal impact on the effect of LYS228 in the murine thigh infection model. Conclusions LYS228 is efficacious in murine thigh infection models using β-lactamase-producing strains of Enterobacteriaceae, including those expressing metallo-β-lactamases, ESBLs and serine carbapenemases, with the PK/PD driver of efficacy identified as %T>MIC.

Infections caused by carbapenem-resistant Enterobacteriaceae are associated with high therapeutic failure and mortality rates. Thus, it is critical to rapidly identify clinical isolates expressing KPC β-lactamases to facilitate administration of the correct antibiotic treatment and initiate infection control strategies. To address this problem, we developed a protein-based, KPC-specific binding assay in combination with a cell lysate inhibition assay that provided a 100% identification rate of KPC from clinical isolates of known genomic sequence. In addition, this protein sensor was adapted to the Carba-NP assay to provide a rapid strategy to detect KPC-producing isolates that will facilitate informed treatment of critically ill patients. ABSTRACT Carbapenemases confer resistance to nearly all β-lactam antibiotics. The extensive spread of carbapenemase-producing multidrug-resistant bacteria contributes significantly to hospital-acquired infections. We have developed a novel protein-based binding assay that identifies KPC β-lactamases from clinical isolates. We used the protein-protein interaction between KPCs and a soluble β-lactamase inhibitory protein (BLIP) variant, BLIPK74T/W112D, which specifically inhibits KPCs but not other β-lactamases. In this assay, BLIPK74T/W112D was allowed to form complexes with KPC-2 in bacterial cell lysates and then extracted using His tag binding resins. We demonstrated the presence of KPC-2 by monitoring the hydrolysis of a colorimetric β-lactam substrate. Also, to further increase the accuracy of the method, a BLIPK74T/W112D-mediated inhibition assay was developed. The binding and inhibition assays were validated by testing 127 Klebsiella pneumoniae clinical isolates with known genome sequences for the presence of KPC. Our assays identified a total of 32 strains as KPC-2 producers, a result in 100% concordance with genome sequencing predictions. To further simplify the assay and decrease the time to obtain results, the BLIPK74T/W112D protein was tested in combination with the widely used Carba-NP assay. For this purpose, the genome-sequenced K. pneumoniae strains were tested for the presence of carbapenemases with the Carba-NP test with and without the addition of BLIPK74T/W122D. The test accurately identified carbapenemase-producing strains and the addition of BLIPK74T/W112D allowed a further determination that the strains contain KPC carbapenemase. Thus, the BLIPK74T/W112D protein is an effective sensor to specifically detect KPC β-lactamases produced by clinical isolates. IMPORTANCE Infections caused by carbapenem-resistant Enterobacteriaceae are associated with high therapeutic failure and mortality rates. Thus, it is critical to rapidly identify clinical isolates expressing KPC β-lactamases to facilitate administration of the correct antibiotic treatment and initiate infection control strategies. To address this problem, we developed a protein-based, KPC-specific binding assay in combination with a cell lysate inhibition assay that provided a 100% identification rate of KPC from clinical isolates of known genomic sequence. In addition, this protein sensor was adapted to the Carba-NP assay to provide a rapid strategy to detect KPC-producing isolates that will facilitate informed treatment of critically ill patients.

Simple Summary Campylobacter spp. are among the most important causes of bacterial gastroenteritis around the world. In addition to poultry, pigs are also considered an important source of this pathogen. Antimicrobial resistance (AMR) in Campylobacter is a serious public health concern. A supervised machine learning model was developed and validated in this study to predict MDR status in Campylobacter isolates from swine, using publicly available phenotypic AMR data collected by NARMS from 2013 to 2023. Among five evaluated machine learning algorithms, Random Forest showed the highest performance (accuracy = 99.87%, Kappa = 0.9962), achieving high balanced accuracy, sensitivity, and specificity in both training and external validation. The feature importance analysis found that erythromycin, azithromycin, and clindamycin were the most influential predictors of MDR among Campylobacter isolates from swine. Our temporally validated, interpretable model offers a robust and cost-effective approach for predicting MDR in Campylobacter spp., facilitating surveillance and early detection in food animal production systems.

Abstract Antimicrobial resistance, a major threat to human health, is mainly driven by the overuse of antimicrobials. The purpose of this study was to further investigate the relationship between antimicrobial use and resistance with a 15-year record in Southwest hospital, one of the largest hospitals in Southwest China and a university affiliated hospital, thus to further predict the antimicrobial resistance in an autoregressive integrated moving average (ARIMA) manner. Kirby-Bauer tests were carried out to figure out the drug sensitivity of Gram-negative bacterial. Antimicrobials (β-lactamase inhibitor complex, aminoglycosides, quinolones, third and fourth-generation cephalosporins, carbapenems, cephamycins, oxacephems, and sulfonamides) consumption were calculated according to World Health Organization (WHO) anatomical therapeutic chemical classification index and expressed as annual defined daily dose (DDD) or DDD per 1000 out patients. Resistance rates of levofloxacin-resistant Escherichia coli, ceftazidime-resistant Klebsiella pneumoniae, amikacin-resistant Bacterium levans, imipenem-resistant Pseudomonas aeruginosa is positively correlated with the usage of aminoglycosides and quinolones; resistance rates of imipenem-resistant Acinetobacter baumanii is positively correlated with the usage of carbapenemes (P-value between the drug resistance of levofloxacin-resistant E. coli, ceftazidime-resistant K. pneumoniae and the usage of aminoglycosides is under .05, the other P-value are under .01); resistance rates of the drug resistance of levofloxacin-resistant E. coli is positively correlated with the usage of oxacephems (P < .01); resistance rates of imipenem-resistant P. aeruginosa is positively correlated with the usage of oxacephems and sulfonamides (P < .01). The present study presents one of the largest and longest retrospective analyses in China between antimicrobial consumption and antimicrobial resistance. Change of the usage of several antibacterial drugs has great influence on the drug resistance of Gram-negative bacterial. Of particular, ARIMA forecasting revealed that carbapenem related bacterial resistance should be closely watched.

No abstract available

Antimicrobial resistance (AMR) poses a significant threat to global health, particularly in Western sub-Saharan Africa where 27.3 deaths per 100,000 lives are affected, and surveillance and control measures are often limited. Genomics research plays a crucial role in understanding the emergence, spread and containment measures of AMR. However, its implementation in such settings is particularly challenging due to limited human capacity. This manuscript outlines a three-day bioinformatics workshop in Cameroon, highlighting efforts to build human capacity for genomics research to support AMR surveillance using readily accessible and user-friendly web-based tools. The workshop introduced participants to basic next-generation sequencing concepts, data file formats used in bacterial genomics, data sharing procedures and considerations, as well as the use of web-based bioinformatics software to analyse genomic data, including in silico prediction of AMR, phylogenetics analyses, and a quick introduction to Linux© command line. Briefly, a substantial increase in participants’ confidence in bioinformatics knowledge and skills was observed before and after the workshop. Notably, before the workshop most participants lacked confidence in their ability to identify next-generation sequencing technologies or workflows (64%) and analyse genetic data using web-based bioinformatics tools (81%). After the workshop, majority of participants were extremely confident using NCBI BLAST and other web-based bioinformatics tools for data analysis with a score ≥ 5 among which 45%, 9% and 18% had a score of 8, 9, and 10, respectively. Our findings highlight the effectiveness of this training approach in empowering local researchers and bridging the bioinformatics gap in genomics surveillance of AMR in resource-constrained settings. We provide a detailed description of the relevant training approaches used, including workshop structure, the selection and planning, and utilization of freely available web-based tools, and the evaluation methods employed. Our approach aimed to overcome limitations such as inadequate infrastructure, limited access to computational resources, and scarcity of expertise. By leveraging the power of freely available web-based tools, we demonstrated how participants can acquire fundamental bioinformatics skills, enhance their understanding of biological data analysis, and contribute to the field, even in an underprivileged environment. Building human capacity for genomics research globally, and especially in resource-constrained settings, is imperative for ensuring global health and sustainable containment of AMR.

Antimicrobial resistance (AMR) poses a growing threat to human health. Increasingly, genome sequencing is being applied for the surveillance of bacterial pathogens, producing a wealth of data to train machine learning (ML) applications to predict AMR and identify resistance determinants. However, bacterial populations are highly structured, and sampling is biased towards human disease isolates, violating ML assumptions of independence between samples. This is rarely considered in applications of ML to AMR. Here, we demonstrate the confounding effects of sample structure by analyzing over 24,000 whole genome sequences and AMR phenotypes from five diverse pathogens, using pathological training data where resistance is confounded with phylogeny. We show the resulting ML models perform poorly and that increasing the training sample size fails to rescue performance. A comprehensive analysis of 6,740 models identifies species- and drug-specific effects on model accuracy. These findings highlight the limitations of current ML approaches in the face of realistic sampling biases and underscore the need for population structure-aware methods and more diverse datasets to improve AMR prediction and surveillance.

Monitoring and surveillance of antimicrobial resistance (AMR) is important procedure in clinically patient management and epidemiologically public health. Conventionally such culture based tools as disk diffusion method or broth dilution method for antibiotic susceptibility test has been used. While recently such culture independent approaches as PICRUSt2, Tax4Fun, or MicFunPred have been tried based on predictive functional profiling using 16S rRNA marker gene, evaluation of the tools regarding AMR is little. A total of 20 E. coli strains (Carbapenem resistant (CRE) positive: 10, CRE negative: 10) were used. The AMR phenotype was based on Vitek2 (biomerieux). DNA was extracted from the 20 strains and 16S rRNA (V3-V4 region) and shotgun sequencing were done. The bioinformatic pieplines were QIIM2 for 16S rRNA and MetaPhlAn4 for shotgun. Functional prediction tools were PICRUSt2, Tax4Fun, and MicFunPred for 16S rRNA and AMRFinderPlus for shotgun. The presence/absence of 25 KEGG numbers regarding AMR in PICRUSt2, Tax4Fun, and MicFunPred were compared to shotgun AMR profiles. The F1 scores were calculated according to each 16S marker gene based prediction tools using confusion matrix. A total of 14 class of antibiotics including carbapenem were analyzed. The F1 scores of 16S predictive functional profilers regarding AMR were 0.22 for Tax4Fun, 0.12 for PICRUSt2, and 0.08 for MicFunPred. While Tax4Fun showed the highest F1 score among three 16S predictive functional profilers, the F1 scores were generally low. The strength of Tax4Fun is the incorporation of user-defined data and the enhanced accuracy of the predictions is expected through incorporation of AMR-specific data in engine training.

Simple Summary While 16S rRNA-based predictive functional profiling has proven useful for broad ecological studies, its utility for AMR surveillance remains limited. The results of our study highlight the necessity of integrating specialized AMR databases and improving algorithmic approaches to achieve meaningful accuracy in resistance prediction. These advancements will be essential if marker gene-based tools are to complement or substitute for shotgun metagenomics in the context of clinical or epidemiological AMR monitoring. Abstract The monitoring and surveillance of antimicrobial resistance (AMR) is an important procedure in clinical patient management and epidemiological public health. Conventionally, culture-based tools such as disk diffusion methods or broth dilution methods for antibiotic susceptibility tests are used. While culture-independent approaches, such as PICRUSt2, Tax4Fun, or MicFunPred, have recently been tried based on predictive functional profiling using the 16S rRNA marker gene, evaluations of AMR tools are scarce. A total of 20 E. coli strains (Carbapenem-resistant (CRE) positive: 10, CRE negative: 10) were used. The AMR phenotype was based on Vitek2 (bioMerieux). DNA was extracted from the 20 strains, and 16S rRNA (V3-V4 region) and shotgun sequencing was carried out. The bioinformatic pipelines were QIIM2 for 16S rRNA and MetaPhlAn4 for shotgun. The functional prediction tools were PICRUSt2, Tax4Fun, and MicFunPred for 16S rRNA and AMRFinderPlus for shotgun. The presence/absence of 23 KEGG numbers regarding AMR in PICRUSt2, Tax4Fun, and MicFunPred were compared to shotgun AMR profiles. The F1 scores were calculated according to each 16S marker gene-based prediction tool using a confusion matrix. A total of 12 classes of antibiotics, including carbapenem, were analyzed. The F1 scores of 16S predictive functional profilers regarding AMR were 0.22 for Tax4Fun, 0.12 for PICRUSt2, and 0.08 for MicFunPred. While Tax4Fun showed the highest F1 score of the three 16S predictive functional profilers, the F1 scores were generally low. Our study highlights the necessity of integrating specialized AMR databases and improving algorithmic approaches to achieve meaningful accuracy in resistance prediction.

Antimicrobial resistance (AMR) is a serious threat to global public health, necessitating rapid and precise diagnostic tools. The prevalence of novel antibiotic resistance genes (ARGs) has increased due to microbial sequencing, resulting in the need to extract vital information from vast amounts of data. Although many AMR prediction tools exist, only a few are accurate and scalable. We examined 20 widely used AMR prediction tools and chose 4 web-based tools for antimicrobial resistance surveillance over standalone software due to their easy accessibility, portability, and centralized data management, eliminating the need for complex installation and maintenance. CGE (Center for Genomic Epidemiology) provides bioinformatics tools and promotes open data sharing. At the same time, CARD (Comprehensive Antibiotic Resistance Database) is a valuable resource for antibiotic resistance gene information, collectively contributing to our understanding and management of antibiotic resistance. We highlighted web-based AMR prediction tools and performed a case study using the Pseudomonas aeruginosa complete plasmid sequence (CPS) to identify strengths and weaknesses in the system. Our study explored four web-based antibiotic resistance gene prediction tools: ResFinder, KmerResistance, ResFinderFG, and RGI. ResFinder excelled at finding acquired antimicrobial resistance genes as well as maintaining a database up to date. KmerResistance identified resistance genes using k-mer analysis. esFinderFG offered a unique perspective, excelling in detecting a broad range of resistant phenotypes, due to its inclusion of sequences discovered through functional metagenomics. RGI was versatile in detecting a wide range of resistance genes and provided extensive resistance mechanism information. Researchers must understand the capabilities and trade-offs of these tools to make well-informed choices for efficient resistance gene identification and surveillance as the antibiotic resistance landscape evolves.

Multiantigen sequence typing in combination with single-nucleotide polymorphism assay and/or whole-genome sequencing can be used as a surrogate to predict antibiotic resistance in Neisseria gonorrhoeae. Background The aims of this study was to describe molecular surveillance of Neisseria gonorrhoeae in the North Zone of Alberta (NZ) and to determine its value in predicting antimicrobial resistance. Methods Sequence types (STs) and single-nucleotide polymorphism (SNP) assays were performed on nucleic acid amplification testing (NAAT) samples. Sequence types of NAATs were matched to ST of cultures from across Alberta. Antimicrobial resistance prediction of NAATs for cephalosporins, azithromycin, and ciprofloxacin using SNP was compared with matching ST culture results using agar dilution and whole-genome sequencing. Results Of 2755 eligible specimens (2492 cases), 61.9% (1646 specimens) were sent for sequence typing, identifying 196 unique ST. Antimicrobial resistance data for 1307 additional cases were available using matching cultures. Decreased susceptibility (DS) to antimicrobials used for gonorrhea treatment was rare in the NZ; according to the SNP assay, none of the specimens had predicted DS to cephalosporins or azithromycin resistance. However, of the NZ NAAT samples tested in this study, 10.7% (131 of 1220) were predicted to have intermediate cephalosporin minimum inhibitory concentrations and 9.6% (115 of 1204) were resistant to ciprofloxacin. Based on cultures, the proportions of resistance in all of Alberta were as follows: DS to cephalosporins, 0.6% (20 of 3373); DS to intermediate cephalosporin, 16.9% (570 of 3373); azithromycin resistance, 1.2% (41 of 3373); and ciprofloxacin resistance, 32.2% (1087 of 3373). Conclusions Our results highlight our ability to use culture-independent methods to predict antimicrobial resistance in N. gonorrhoeae.

Antimicrobial resistance is a growing global health threat, and artificial intelligence offers a promising avenue for developing advanced tools to address this challenge. In this study, we applied various machine learning techniques to predict bacterial antibiotic resistance using the Pfizer ATLAS Antibiotics dataset. This comprehensive dataset includes patient demographic data, sample collection details, antibiotic susceptibility test results, and resistance phenotypes for 917,049 bacterial isolates. The dataset was divided into two subsets: Phenotype-Only and Phenotype + Genotype, excluding and including 589,998 isolates with genotype data, respectively. Both subsets underwent exploratory data analysis, preprocessing, machine learning model training, validation, and optimization. XGBoost consistently outperformed other models, achieving AUC values of 0.96 and 0.95 for the Phenotype-Only and Phenotype + Genotype sets, respectively. Hyperparameter tuning yielded slight accuracy improvements, while data balancing techniques notably increased recall. Across all models, the antibiotic used emerged as the most influential feature in predicting resistance outcomes. The SHAP summary plots generated provide insights into model interpretability. Our findings provide valuable insights into global AMR patterns and demonstrate the potential of AI-driven approaches for resistance prediction to help inform clinical decision-making and support the formulation of effective AMR mitigation policies, subject to the availability of highly granular datasets.

Background Antimicrobial resistance (AMR) is rising at an alarming rate and complicating the management of infectious diseases including lower respiratory tract infections (LRTI). Metagenomic next-generation sequencing (mNGS) is a recently established method for culture-independent LRTI diagnosis, but its utility for predicting AMR has remained unclear. We aimed to assess the performance of mNGS for AMR prediction in bacterial LRTI and demonstrate proof of concept for epidemiological AMR surveillance and rapid AMR gene detection using Cas9 enrichment and nanopore sequencing. Methods We studied 88 patients with acute respiratory failure between 07/2013 and 9/2018, enrolled through a previous observational study of LRTI. Inclusion criteria were age ≥ 18, need for mechanical ventilation, and respiratory specimen collection within 72 h of intubation. Exclusion criteria were decline of study participation, unclear LRTI status, or no matched RNA and DNA mNGS data from a respiratory specimen. Patients with LRTI were identified by clinical adjudication. mNGS was performed on lower respiratory tract specimens. The primary outcome was mNGS performance for predicting phenotypic antimicrobial susceptibility and was assessed in patients with LRTI from culture-confirmed bacterial pathogens with clinical antimicrobial susceptibility testing ( n  = 27 patients, n  = 32 pathogens). Secondary outcomes included the association between hospital exposure and AMR gene burden in the respiratory microbiome ( n  = 88 patients), and AMR gene detection using Cas9 targeted enrichment and nanopore sequencing ( n  = 10 patients). Results Compared to clinical antimicrobial susceptibility testing, the performance of respiratory mNGS for predicting AMR varied by pathogen, antimicrobial, and nucleic acid type sequenced. For gram-positive bacteria, a combination of RNA + DNA mNGS achieved a sensitivity of 70% (95% confidence interval (CI) 47–87%) and specificity of 95% (CI 85–99%). For gram-negative bacteria, sensitivity was 100% (CI 87–100%) and specificity 64% (CI 48–78%). Patients with hospital-onset LRTI had a greater AMR gene burden in their respiratory microbiome versus those with community-onset LRTI ( p  = 0.00030), or those without LRTI ( p  = 0.0024). We found that Cas9 targeted sequencing could enrich for low abundance AMR genes by > 2500-fold and enabled their rapid detection using a nanopore platform. Conclusions mNGS has utility for the detection and surveillance of resistant bacterial LRTI pathogens.

No abstract available

Antimicrobial-resistant (AMR) Neisseria gonorrhoeae is an urgent threat to public health, as strains resistant to at least one of the two last-line antibiotics used in empiric therapy of gonorrhoea, ceftriaxone and azithromycin, have spread internationally. Whole genome sequencing (WGS) data can be used to identify new AMR clones and transmission networks and inform the development of point-of-care tests for antimicrobial susceptibility, novel antimicrobials and vaccines. Community-driven tools that provide an easy access to and analysis of genomic and epidemiological data is the way forward for public health surveillance. Here we present a public health-focussed scheme for genomic epidemiology of N. gonorrhoeae at Pathogenwatch (https://pathogen.watch/ngonorrhoeae). An international advisory group of experts in epidemiology, public health, genetics and genomics of N. gonorrhoeae was convened to inform on the utility of current and future analytics in the platform. We implement backwards compatibility with MLST, NG-MAST and NG-STAR typing schemes as well as an exhaustive library of genetic AMR determinants linked to a genotypic prediction of resistance to eight antibiotics. A collection of over 12,000 N. gonorrhoeae genome sequences from public archives has been quality-checked, assembled and made public together with available metadata for contextualization. AMR prediction from genome data revealed specificity values over 99% for azithromycin, ciprofloxacin and ceftriaxone and sensitivity values around 99% for benzylpenicillin and tetracycline. A case study using the Pathogenwatch collection of N. gonorrhoeae public genomes showed the global expansion of an azithromycin-resistant lineage carrying a mosaic mtr over at least the last 10 years, emphasising the power of Pathogenwatch to explore and evaluate genomic epidemiology questions of public health concern. The N. gonorrhoeae scheme in Pathogenwatch provides customised bioinformatic pipelines guided by expert opinion that can be adapted to public health agencies and departments with little expertise in bioinformatics and lower-resourced settings with internet connection but limited computational infrastructure. The advisory group will assess and identify ongoing public health needs in the field of gonorrhoea, particularly regarding gonococcal AMR, in order to further enhance utility with modified or new analytic methods.

Antimicrobial resistance prediction from whole genome sequencing data (WGS) is an emerging application of machine learning, promising to improve antimicrobial resistance surveillance and outbreak monitoring. Despite significant reductions in sequencing cost, the availability and sampling diversity of WGS data with matched antimicrobial susceptibility testing (AST) profiles required for training of WGS-AST prediction models remains limited. Best practice machine learning techniques are required to ensure trained models generalize to independent data for optimal predictive performance. Limited data restricts the choice of machine learning training and evaluation methods and can result in overestimation of model performance. We demonstrate that the widely used random k-fold cross-validation method is ill-suited for application to small bacterial genomics datasets and offer an alternative cross-validation method based on genomic distance. We benchmarked three machine learning architectures previously applied to the WGS-AST problem on a set of 8,704 genome assemblies from five clinically relevant pathogens across 77 species-compound combinations collated from public databases. We show that individual models can be effectively ensembled to improve model performance. By combining models via stacked generalization with cross-validation, a model ensembling technique suitable for small datasets, we improved average sensitivity and specificity of individual models by 1.77% and 3.20%, respectively. Furthermore, stacked models exhibited improved robustness and were thus less prone to outlier performance drops than individual component models. In this study, we highlight best practice techniques for antimicrobial resistance prediction from WGS data and introduce the combination of genome distance aware cross-validation and stacked generalization for robust and accurate WGS-AST.

No abstract available

Abstract Rising antimicrobial resistance (AMR) in Escherichia coli bloodstream infections (BSIs) in high-income settings has typically been dominated by one clone, the sequence type (ST)131. More specifically, ST131 clade C (ST131-C) is associated with fluoroquinolone resistance and extended-spectrum β-lactamases (ESBLs). Even though urinary tract infections (UTIs) are a known common precursor to BSIs, there is currently limited knowledge on the longitudinal prevalence of ST131-C in UTIs and, therefore, the temporal link between the two infection types. Leveraging available genomic and antimicrobial susceptibility test (AST) data for ciprofloxacin, gentamicin and ceftazidime in 2,790 E. coli BSI isolates, we trained Random Forest and extreme gradient boosting (XGBoost) classifiers to predict if an E. coli isolate belongs to ST131-C using only AST data. These models were used to predict the yearly prevalence of ST131-C in 22942 UTI and 24866 BSI isolates from Norway. The XGBoost classifier achieved a prediction F1-score of over 70% on a highly unbalanced dataset where only 4.3% of the genomic BSI isolates belonged to ST131-C. The predicted prevalence of ST131-C in UTIs exhibited a similar annual trend to that of BSIs, with a stable infection burden for 8 years after its rapid expansion, confirming that the persistence of ST131-C in BSIs is largely driven by ST131-C UTIs. However, a higher prevalence of ST131-C in BSIs (~7 %) compared to UTIs (~4 %) suggests a subsequent enrichment of ST131-C. Our study highlights how existing epidemiological knowledge can be supplemented by utilizing extensive data from AMR surveillance efforts without genomic markers.

Antimicrobial resistance (AMR) is a critical global health threat and artificial intelligence (AI) presents new opportunities for our response. However, research priorities at the AI-AMR intersection remain undefined. This study aimed to identify and prioritise key areas for future investigation. Using a modified James Lind Alliance approach, we conducted semi-structured interviews with eight experts in AI and AMR between February and June 2024. Analysis of 338 coded responses revealed 44 distinct themes. Major barriers included fragmented data access, integration challenges and economic disincentives. The top ten priorities identified were: Combination Therapy, Novel Therapeutics, Data Acquisition, AMR Public Health Policy, Prioritisation, Economic Resource Allocation, Diagnostics, Modelling Microbial Evolution, AMR Prediction and Surveillance. A notable limitation was the underrepresentation of data from high-burden regions, limiting the generalisability of findings. To address these gaps, we propose the novel BARDI framework: Brokered Data-sharing, AI-driven Modelling, Rapid Diagnostics, Drug Discovery and Integrated Economic Prevention.

Methicillin-resistant Staphylococcus aureus (MRSA) surveillance in regions with mass gatherings presents unique challenges for public health systems. Saudi Arabia, hosting millions of pilgrims annually, provides a distinctive setting for studying how human mobility shapes bacterial populations, yet comprehensive genomic surveillance data from this region remains limited. Here, we present an integrated analysis of S. aureus isolates collected across seven Saudi Arabian regions, combining whole-genome sequencing with extensive antimicrobial susceptibility testing and standardized metadata following FAIR data principles. Our analysis revealed striking differences between pilgrimage and non-pilgrimage cities. Pilgrimage cities showed significantly higher genetic diversity and antimicrobial resistance rates, harboring numerous international strains including recognized clones from diverse geographic origins. Reported lineage dynamics is changing, expanding toward community clones. While genomic prediction of antimicrobial resistance showed high accuracy for some antibiotics, particularly beta-lactams, with varying performance for others, highlighting the necessity for phenotypic testing in clinical settings. Our findings demonstrate how mass gatherings drive bacterial population structures and emphasize the importance of integrated surveillance approaches in regions with significant global connectivity and travel. Importance Genomic data enables the tracking of pathogens by revealing clonal expansions within populations and identifying successful lineages. However, comprehensive national-level data from Saudi Arabia remains limited on a large scale. The adoption of FAIR principles and reproducible workflows ensures robust, consistent analysis, fostering effective data sharing. The OneHealth approach’s success depends on the integration and collaboration across diverse domains in today’s digital landscape.

Abstract Motivation Antimicrobial resistance is increasingly recognized as one of the most significant global health threats, with profound implications for human, animal, and environmental health. Genome analysis represents a very useful tool that provides accurate and reproducible results allowing for the advancement of knowledge regarding antimicrobial resistance diagnosis, therapeutics, surveillance, transmission, and evolution. However, due to increasing complexity of bacterial genome analysis and computational power required for genomic approaches, there is a continuous need for comprehensive, user-friendly tools for data analysis. We developed Pangenome and Genomic Analysis Suite (PeGAS), to address some of these challenges by offering an all-in-one pipeline that performs a range of analyses. Results PeGAS integrates key genomic analysis features of bacteria whole genome sequencing, including the prediction of antimicrobial resistance profiles, sorted by various categories of antibiotics, VF detection, and plasmid replicon assignment. The pipeline also performs pangenome analysis, multilocus sequence typing, genome assembly quality control (by reporting statistics such as GC content, contig length, the number of contigs, as well as variation from certain GC thresholds) providing a comprehensive genomic overview. PeGAS also offers the ability to restart seamlessly from any sporadic interruptions that might occur during long or resource-intensive runs. Availability and implementation PeGAS is available at: https://github.com/liviurotiul/PeGAS

Campylobacter jejuni is recognised as the leading cause of bacterial gastroenteritis in industrialised countries. Although the majority of Campylobacter infections are self-limiting, antimicrobial treatment is necessary in severe cases. Therefore, the development of antimicrobial resistance (AMR) in Campylobacter is a growing public health challenge and surveillance of AMR is important for bacterial disease control. The aim of this study was to predict antimicrobial resistance in C. jejuni from whole-genome sequencing data. A total of 516 clinical C. jejuni isolates collected between 2014 and 2017 were subjected to WGS. Resistance phenotypes were determined by standard broth dilution, categorising isolates as either susceptible or resistant based on epidemiological cutoffs for six antimicrobials: ciprofloxacin, nalidixic acid, erythromycin, gentamicin, streptomycin, and tetracycline. Resistance genotypes were identified using an in-house database containing reference genes with known point mutations and the presence of resistance genes was determined using the ResFinder database and four bioinformatical methods (modified KMA, ABRicate, ARIBA, and ResFinder Batch Upload). We identified seven resistance genes including tet(O), tet(O/32/O), ant(6)-Ia, aph(2″)-If, blaOXA, aph(3′)-III, and cat as well as mutations in three genes: gyrA, 23S rRNA, and rpsL. There was a high correlation between phenotypic resistance and the presence of known resistance genes and/or point mutations. A correlation above 98% was seen for all antimicrobials except streptomycin with a correlation of 92%. In conclusion, we found that WGS can predict antimicrobial resistance with a high degree of accuracy and have the potential to be a powerful tool for AMR surveillance.

BACKGROUND Accurate molecular assays for prediction of antimicrobial resistance (AMR)/susceptibility in Neisseria gonorrhoeae (Ng) can offer individualized treatment of gonorrhoea and enhanced AMR surveillance. OBJECTIVES We evaluated the new ResistancePlus® GC assay and the GC 23S 2611 (beta) assay (SpeeDx), for prediction of resistance/susceptibility to ciprofloxacin and azithromycin, respectively. METHODS Nine hundred and sixty-seven whole-genome-sequenced Ng isolates from 20 European countries, 143 Ng-positive (37 with paired Ng isolates) and 167 Ng-negative clinical Aptima Combo 2 (AC2) samples, and 143 non-gonococcal Neisseria isolates and closely related species were examined with both SpeeDx assays. RESULTS The sensitivity and specificity of the ResistancePlus® GC assay to detect Ng in AC2 samples were 98.6% and 100%, respectively. ResistancePlus® GC showed 100% sensitivity and specificity for GyrA S91 WT/S91F detection and 99.8% sensitivity and specificity in predicting phenotypic ciprofloxacin resistance. The sensitivity and specificity of the GC 23S 2611 (beta) assay for Ng detection in AC2 samples were 95.8% and 100%, respectively. GC 23S 2611 (beta) showed 100% sensitivity and 99.9% specificity for 23S rRNA C2611 WT/C2611T detection and 64.3% sensitivity and 99.9% specificity for predicting phenotypic azithromycin resistance. Cross-reactions with non-gonococcal Neisseria species were observed with both assays, but the analysis software solved most cross-reactions. CONCLUSIONS The new SpeeDx ResistancePlus® GC assay performed well in the detection of Ng and AMR determinants, especially in urogenital samples. The GC 23S 2611 (beta) assay performed relatively well, but its sensitivity, especially for predicting phenotypic azithromycin resistance, was suboptimal and further optimizations are required, including detection of additional macrolide resistance determinant(s).

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Surveillance of antimicrobial resistance (AMR) in non-typhoidal Salmonella enterica (NTS), is essential for monitoring transmission of resistance from the food chain to humans, and for establishing effective treatment protocols. We evaluated the prediction of phenotypic resistance in NTS from genotypic profiles derived from whole genome sequencing (WGS). Genes and chromosomal mutations responsible for phenotypic resistance were sought in WGS data from 3,491 NTS isolates received by Public Health England’s Gastrointestinal Bacteria Reference Unit between April 2014 and March 2015. Inferred genotypic AMR profiles were compared with phenotypic susceptibilities determined for fifteen antimicrobials using EUCAST guidelines. Discrepancies between phenotypic and genotypic profiles for one or more antimicrobials were detected for 76 isolates (2.18%) although only 88/52,365 (0.17%) isolate/antimicrobial combinations were discordant. Of the discrepant results, the largest number were associated with streptomycin (67.05%, n = 59). Pan-susceptibility was observed in 2,190 isolates (62.73%). Overall, resistance to tetracyclines was most common (26.27% of isolates, n = 917) followed by sulphonamides (23.72%, n = 828) and ampicillin (21.43%, n = 748). Multidrug resistance (MDR), i.e., resistance to three or more antimicrobial classes, was detected in 848 isolates (24.29%) with resistance to ampicillin, streptomycin, sulphonamides and tetracyclines being the most common MDR profile (n = 231; 27.24%). For isolates with this profile, all but one were S. Typhimurium and 94.81% (n = 219) had the resistance determinants blaTEM-1, strA-strB, sul2 and tet(A). Extended-spectrum β-lactamase genes were identified in 41 isolates (1.17%) and multiple mutations in chromosomal genes associated with ciprofloxacin resistance in 82 isolates (2.35%). This study showed that WGS is suitable as a rapid means of determining AMR patterns of NTS for public health surveillance.

BACKGROUND Surveillance and prediction of antibiotic resistance in Escherichia coli relies on curated databases of genes and mutations. We aimed to quantify the effect of acquiring specific genetic elements on minimum inhibitory concentrations (MICs) for particular antibiotic-species combinations, addressing the current scarcity of such data in existing databases. METHODS For this observational study, we evaluated a collection of E coli isolates with linked whole-genome sequencing and MIC data, originating from human urinary or bloodstream infections obtained from the Oxford University Hospitals National Health Service Foundation Trust in Oxfordshire, UK. We used multivariable interval regression models to estimate the change in MIC (with 95% CIs) for specific antibiotics associated with the acquisition of antibiotic resistance genes and associated mutations in the National Center for Biotechnology Information AMRFinder database, with and without an adjustment for population structure. We then tested the ability of these models to predict MIC and binary resistance or susceptibility using leave-one-out cross-validation. FINDINGS We evaluated 2875 E coli isolates obtained during 2013-2018 and 2020. Although most ARGs and resistance mutations (89 [80%] of 111) were associated with an increased MIC, a much smaller number (27 [24%] of 111) was found to be putatively independently resistance-conferring (ie, associated with an MIC above the European Committee on Antimicrobial Susceptibility Testing breakpoint) when acquired in isolation. We found evidence of differential effects of acquired ARGs and resistance mutations between different generations of cephalosporin antibiotics and showed that sub-breakpoint variation in MIC can be linked to genetic mechanisms of resistance. 20 697 (83·3%; range 52·9-97·7 across all antibiotics) of 24 858 MICs were correctly exactly predicted and 23 677 (95·2%; 87·3-97·7) of 24 858 MICs were predicted to within one doubling dilution. INTERPRETATION Quantitative estimates of the independent effect of the acquisition of ARGs on MIC add to the interpretability and utility of existing databases. Compared with approaches using machine learning models, the use of these estimates yields similar or better performance in the prediction of antibiotic resistance phenotype with more readily interpretable results. The methods outlined here could be readily applied to other antibiotic-pathogen combinations. FUNDING The National Institute for Health and Care Research (NIHR) and the Medical Research Council (MRC).

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Abstract Antimicrobial resistance (AMR) remains a critical global health threat, with significant impacts on individuals and healthcare systems, particularly in low-income countries. By 2019, AMR was responsible for >4.9 million fatalities globally, and projections suggest this could rise to 10 million annually by 2050 without effective interventions. Sub-Saharan Africa (SSA) faces considerable challenges in managing AMR due to insufficient surveillance systems, resulting in fragmented data. Technological advancements, notably artificial intelligence (AI), offer promising avenues to enhance AMR biosurveillance. AI can improve the detection, tracking and prediction of resistant strains through advanced machine learning and deep learning algorithms, which analyze large datasets to identify resistance patterns and develop predictive models. AI's role in genomic analysis can pinpoint genetic markers and AMR determinants, aiding in precise treatment strategies. Despite the potential, SSA's implementation of AI in AMR surveillance is hindered by data scarcity, infrastructural limitations and ethical concerns. This review explores what is known about the integration and applicability of AI-enhanced biosurveillance methodologies in SSA, emphasizing the need for comprehensive data collection, interdisciplinary collaboration and the establishment of ethical frameworks. By leveraging AI, SSA can significantly enhance its AMR surveillance capabilities, ultimately improving public health outcomes.

With antimicrobial resistance (AMR) rapidly evolving in pathogens, quick and accurate identification of genetic determinants of phenotypic resistance is essential for improving surveillance, stewardship, and clinical mitigation. Machine learning (ML) models show promise for AMR prediction in diagnostics but require a deep understanding of internal processes to use effectively. Our study utilized AMR gene, pangenomic, and predicted plasmid features from 647 Enterococcus faecium and Enterococcus faecalis genomes across the One Health continuum, along with corresponding resistance phenotypes, to develop interpretive ML classifiers. Vancomycin resistance could be predicted with 99% accuracy with AMR gene features, 98% with pangenome features, and 96% with plasmid clusters. Top pangenome features overlapped with the resistance genes of the vanA operon, which are often laterally transmitted via plasmids. Doxycycline resistance prediction achieved approximately 92% accuracy with pangenome features, with the top feature being elements of Tn916 conjugative transposon, a tet(M) carrier. Erythromycin resistance prediction models achieved about 90% accuracy, but top features were negatively correlated with resistance due to the confounding effect of population structure. This work demonstrates the importance of reviewing ML models' features to discern biological relevance even when achieving high-performance metrics. Our workflow offers the potential to propose hypotheses for experimental testing, enhancing the understanding of AMR mechanisms, which are crucial for combating the AMR crisis.

OBJECTIVES Antimicrobial susceptibility testing of multiple antimicrobial agents within the same class remains challenging for clinical microbiology laboratories. The evidence behind the use of tetracycline hydrochloride (HCl) susceptibility as a predictor for minocycline, doxycycline, and tigecycline susceptibility was evaluated in clinical Gram-negative and Gram-positive bacterial isolates, analysing the SENTRY Antimicrobial Surveillance Programme, an extensive and representative global antimicrobial resistance dataset. METHODS Tetracycline hydrochloride to predict susceptibility to doxycycline, minocycline, and tigecycline was studied in 69 259 bacterial isolates of Escherichia spp., Klebsiella spp., Enterobacter spp., Haemophilus influenzae, Acinetobacter spp., Staphylococcus spp., Streptococcus spp., and Enterococcus spp. collected from 123 medical centres in the United States and Europe between 2017 and 2019 or between 2013 and 2014. Antimicrobial susceptibility testing was performed using the broth microdilution method. Clinical and Laboratory Standards Institute breakpoints were applied when available; in their absence, the European Committee on Antimicrobial Susceptibility Testing and U.S. Food and Drug Administration breakpoints were used. To be considered a reliable surrogate for susceptibility, the very major error (VME) rate comparing tetracycline HCl with the tetracycline derivatives should be ≤ 1.5%, and the category agreement (CA) should be ≥ 90%. RESULTS Tetracycline HCl is a surrogate for doxycycline susceptibility for Klebsiella spp., Enterobacter spp., Staphylococcus aureus, β-hemolytic Streptococcus, and Streptococcus pneumoniae (high CA >90% and low VME ≤1.5%). Tetracycline HCl could not reliably predict susceptibility to minocycline and tigecycline in any of the tested bacteria except S. aureus (CA = 94.4% and 93.8%, respectively, and VME = 0% for both). CONCLUSIONS Tetracycline HCl should not be universally used to predict susceptibility to tetracycline derivatives, especially to minocycline and tigecycline. Tetracycline derivatives in vitro susceptibility testing remains crucial to optimizing their clinical use.

Abstract The Comprehensive Antibiotic Resistance Database (CARD; https://card.mcmaster.ca) is a curated resource providing reference DNA and protein sequences, detection models and bioinformatics tools on the molecular basis of bacterial antimicrobial resistance (AMR). CARD focuses on providing high-quality reference data and molecular sequences within a controlled vocabulary, the Antibiotic Resistance Ontology (ARO), designed by the CARD biocuration team to integrate with software development efforts for resistome analysis and prediction, such as CARD’s Resistance Gene Identifier (RGI) software. Since 2017, CARD has expanded through extensive curation of reference sequences, revision of the ontological structure, curation of over 500 new AMR detection models, development of a new classification paradigm and expansion of analytical tools. Most notably, a new Resistomes & Variants module provides analysis and statistical summary of in silico predicted resistance variants from 82 pathogens and over 100 000 genomes. By adding these resistance variants to CARD, we are able to summarize predicted resistance using the information included in CARD, identify trends in AMR mobility and determine previously undescribed and novel resistance variants. Here, we describe updates and recent expansions to CARD and its biocuration process, including new resources for community biocuration of AMR molecular reference data.

Genome-based diagnostics provides relevant information to guide patient treatment and support pathogen and resistance surveillance. Recently, Coll et al. introduced a curated database for predicting antimicrobial resistance (AMR) from Enterococcus faecium genomics data, offering excellent predictive values for susceptibility to important antimicrobials. Challenges to predict resistance to last-resort antimicrobials remain.

Antimicrobial resistance (AMR) is arguably one of the major health and economic challenges in our society. A key aspect of tackling AMR is rapid and accurate detection of the emergence and spread of AMR in food animal production, which requires routine AMR surveillance. However, AMR detection can be expensive and time-consuming considering the growth rate of the bacteria and the most commonly used analytical procedures, such as Minimum Inhibitory Concentration (MIC) testing. To mitigate this issue, we utilized machine learning to predict the future AMR burden of bacterial pathogens. We collected pathogen and antimicrobial data from >600 farms in the United States from 2010 to 2021 to generate AMR time series data. Our prediction focused on five bacterial pathogens (Escherichia coli, Streptococcus suis, Salmonella sp., Pasteurella multocida, and Bordetella bronchiseptica). We found that Seasonal Auto-Regressive Integrated Moving Average (SARIMA) outperformed five baselines, including Auto-Regressive Moving Average (ARMA) and Auto-Regressive Integrated Moving Average (ARIMA). We hope this study provides valuable tools to predict the AMR burden not only of the pathogens assessed in this study but also of other bacterial pathogens.

ABSTRACT Determination of antimicrobial resistance (AMR) in pneumococcal isolates is important for surveillance purposes and in a clinical context. Antimicrobial susceptibility testing (AST) of pneumococci is complicated by the need for exact minimal inhibitory concentrations (MICs) of beta-lactam antibiotics. Two next-generation sequencing (NGS) analysis tools have implemented the prediction of AMR in their analysis workflow, including the prediction of MICs: Pathogenwatch (https://pathogen.watch/) and AREScloud (OpGen). The performance of these tools in comparison to phenotypic AST following EUCAST guidelines is unknown. A total of 538 Streptococcus pneumoniae isolates were used to compare both tools with phenotypic AST for penicillin, amoxicillin, cefotaxime/ceftriaxone, erythromycin, trimethoprim-sulfamethoxazole, and tetracycline. Disk diffusion was performed for all isolates, and broth microdilution was performed for isolates with reduced beta-lactam susceptibility. Demultiplexed FASTQ files from Illumina sequencing, covering the whole genome of pneumococci, were used as input for the NGS tools. Categorical agreement (CA), major error (ME), and very major error (VME) rates were calculated. For beta-lactam antibiotics, CA was high (>94%) associated with none or only one ME and VME (<1%). For erythromycin and tetracycline, CA was >93% for predictions by AREScloud, while for Pathogenwatch, this ranged around 88%. For trimethoprim-sulfamethoxazole, CA was for both tools <86%. High VME rates were observed for erythromycin and tetracycline, higher for Pathogenwatch (53.6% and 47.0%, respectively) compared to AREScloud (14.3% and 19.1%, respectively). Both tools performed excellently despite the complexity of predicting beta-lactam resistance in pneumococci. Further optimization and validation are needed for non-beta-lactams since high (very) major error rates were observed.

BackgroundChina is one of ten countries with the highest prevalence rate of pneumococcal infections. However, there is limited serotype surveillance data for Streptococcus pneumoniae, especially from the community or rural regions, partly due to limited serotyping capacity because Quellung serotyping is only available in few centers in China. The aim of this study was to develop a simple, practical and economic pneumococcal serotype prediction strategy suitable for future serotype surveillance in China.MethodsIn this study, 193 S. pneumoniae isolates were collected from hospitalized children, 96.9 % of whom were < 5 years old. The cpsB sequetyping, complemented by selective and modified USA CDC sequential multiplex-PCR, was performed on all the isolates, and serotypes 6A-6D specific PCRs were done on all serogroup 6 isolates. Based on systematic analysis of available GenBank cpsB sequences, we established a more comprehensive cpsB sequence database than originally published for cpsB sequetyping. Antibiotic susceptibility of all isolates was determined using the disk diffusion or E-test assays.ResultsWe built up a comprehensive S. pneumoniae serotype cpsB sequetyping database for all the 95 described serotypes first, and then developed a simple strategy for serotype prediction based on the improved cpsB sequetyping and selective multiplex-PCR. Using the developed serotype prediction strategy, 191 of 193 isolates were successfully “serotyped”, and only two isolates were “non-serotypeable”. Sixteen serotypes were identified among the 191 “serotypeable” isolates. The serotype distribution of the isolates from high to low was: 19 F (34.7 %), 23 F (17.1 %), 19A (11.9 %), 14 (7.3 %), 15B/15C (6.7 %), 6B (6.7 %), 6A (6.2 %), 9 V/9A (1.6 %); serotypes 6C, 3, 15 F/15A, 23A and 20 (each 1.1 %); serotypes 10B, 28 F/28A and 34 (each 0.5 %). The prevalence of parenteral penicillin resistance was 1.0 % in the non-meningitis isolates and 88.6 % in meningitis isolates. The total rate of multidrug resistance was 86.8 %.ConclusionsThe integrated cpsB sequetyping supplemented with selective mPCR and serotypes 6A-6D specific PCRs “cocktail” strategy is practical, simple and cost-effective for use in pneumococcal infection serotype surveillance in China. For hospitalized children with non-meningitis penicillin-susceptible pneumococcal infections, clinicians still can use narrow-spectrum and cheaper penicillin, using the parenteral route, rather than using broader-spectrum and more expensive antimicrobials.

Abstract Background β-Lactam/β-lactamase inhibitor combinations (BLICs) are commonly used in the UK and are a fertile area of drug development. BLICs such as ceftazidime/avibactam (CZA) and meropenem/vaborbactam (MEV) were developed as vital therapeutic options for KPC-producing carbapenem-resistant Enterobacterales (CRE). However, resistance to BLICs is increasingly reported, often mediated by mutations within β-lactamase genes that alter enzyme-inhibitor interactions. Currently, genomic surveillance fails to detect uncharacterized inhibitor-resistant mutants, leading to discordance between the phenotype and genotype. This limits our ability to predict treatment success and to select the most appropriate antibiotic, as well as limiting the treatment options available. Objectives Using a molecular biology approach, we aimed to identify novel mutations in blaKPC-2 that confer resistance to CZA and MEV to improve resistance prediction using genomic data. Methods We used random mutagenesis to generate a mutant library of blaKPC-2, cloned into the low-copy plasmid pBR322 and screened for resistance to CZA and MEV. We tested three concentrations: 2× MIC, 4× MIC and the clinical breakpoint. Resistant clones were extracted for whole-plasmid sequencing using Illumina short-read sequencing to identify single nucleotide polymorphisms (SNPs) within the gene. Mutations were then mapped to the WT blaKPC-2 and assessed for their potential impact on enzyme function bioinformatically. Results We found that while random mutagenesis produced CZA-resistant mutations within blaKPC-2, we were unable to identify SNPs that confer resistance to MEV. The mutants exhibited resistance to 2× MIC; a subset also showed resistance to 4× MIC, and to the clinical breakpoint concentration. Analysis of 45 SNPs revealed that 58% were located outside the characterized active site loops, although there was notable clustering in the β3-β4 loop (5 SNPs), β5-α11 loop (4 SNPs) and omega loop (10 SNPs). Importantly, all mutagenesis reactions reproduced the clinically significant D178 mutations in the omega loop. Approximately 45 % (39) of the non-synonymous mutations identified 39 are predicted as intolerant, therefore affecting enzyme function, and 16 of which confer resistance at the clinical breakpoint. Additionally, comparison with β-lactamase databases showed that only 20% of the identified SNPs have been reported in clinical isolates, indicating that 80% represent novel mutations with potential clinical relevance for CZA resistance. Conclusions We identified a substantial reservoir of uncharacterized blaKPC-2 mutations predicted to be capable of conferring resistance to CZA. These findings have immediate implications for genomic surveillance, diagnostics and antimicrobial stewardship, as incorporation of these novel variants into resistance databases will enhance the accuracy of molecular diagnostics and guide more effective antimicrobial prescribing. Future phenotypic characterization of these mutations through site-directed mutagenesis will provide essential data for improving resistance prediction.

Acinetobacter baumannii is a multidrug-resistant opportunistic pathogen that poses critical challenges in hospital settings due to its environmental resilience and high resistance to antibiotics. Genomic surveillance has become essential for identifying transmission patterns, guiding antimicrobial stewardship, and informing infection control policies. We conducted whole-genome sequencing on 44 A. baumannii isolates collected between 2022 and 2023 from diverse wards in an Italian hospital. Illumina-based sequencing was followed by a comprehensive bioinformatics pipeline, including genome assembly, taxonomic validation, MLST, SNP-based phylogeny, pan-genome analysis, antimicrobial resistance (AMR) gene profiling, and virulence factor prediction. Most isolates were classified as ST2; SAMPLE-34 was ST1 and genetically distinct. Phylogenetic analysis revealed four clonal clusters with cluster-specific AMR and accessory gene content. The pan-genome included 5050 genes, with notable variation linked to hospital ward origin. ICU and internal medicine strains carried higher loads of AMR genes, especially against aminoglycosides, β-lactams, and quinolones. Virulence profiling highlighted widespread immune evasion mechanisms; “Acenovactin” was predominant, while some isolates lacked key adhesion or toxin factors. Our findings underscore the clinical relevance of integrating genomic epidemiology into routine hospital surveillance. Identifying clonal clusters and resistance signatures supports real-time outbreak detection, risk stratification, and targeted infection prevention strategies.

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