阿尔兹海默；口腔微生物

No abstract available

Abstract Background Evidence suggest an association between periodontal disease (PerioD) and Alzheimer's disease (AD), with PerioD‐associated microbial ecosystems driving oral and systemic inflammation that may activate or accelerate neuroinflammation, a hallmark of AD. Social determinants of health (SDoH) are critical factors influencing both oral health and AD risk yet are often overlooked, and rarely investigated together. This study aims to characterize and compare the oral microbiome of age‐ and education‐matched individuals at high risk for AD by virtue of family history, with and without PerioD, and to investigate the relationships between PerioD‐associated microbiome features, SDoH, systemic inflammation and brain inflammation, and AD biomarkers (in cerebrospinal fluid [CSF]). Method This two‐year NINDS‐funded study collects oral microbiome samples, blood, and CSF annually in a cognitively normal, racially diverse cohort (n = 165). Metagenomic sequencing will be used to investigate cross‐kingdom microbial communities and their association with inflammatory and systemic markers. Surveys and interviews investigate behaviors and SDoH influencing PerioD and AD risk. Result To date, 55 participants have been recruited. Participants are 62 years of age on average, predominantly white (70%), female (63.3%), with Stage 1–2 periodontitis (85.7%). Preliminary analyses found no significant relationships between bleeding on probing, behavioral factors, SDoH variables, and Montreal Cognitive Assessment (MoCA) scores, which was expected given the small sample size. As recruitment continues, we anticipate identifying associations between oral microbiome features, inflammatory markers, AD biomarkers and cognitive outcomes. SDoH, such as access to dental care and oral hygiene behaviors, may mediate these relationships, offering insights into the interplay between periodontal disease, systemic inflammation, and AD risk. Conclusion By leveraging longitudinal data and exploring upstream sociocultural factors, this research addresses critical gaps in understanding PerioD's contribution to AD risk. Findings will provide novel insights into the interplay between the oral microbiome, systemic inflammation, brain inflammation, and AD risk.

Recent studies have suggested that periodontal disease and alterations in the oral microbiome may be associated with cognitive decline and Alzheimer’s disease (AD) development. Here, we report a case-control study of oral microbiota diversity in AD patients compared to healthy seniors from Central Asia. We have characterized the bacterial taxonomic composition of the oral microbiome from AD patients (n = 64) compared to the healthy group (n = 71) using 16S ribosomal RNA sequencing. According to our results, the oral microbiome of AD has a higher microbial diversity, with an increase in Firmicutes and a decrease in Bacteroidetes in the AD group. LEfSe analysis showed specific differences at the genus level in both study groups. A region-based analysis of the oral microbiome compartment in AD was also performed, and specific differences were identified, along with the absence of differences in bacterial richness and on the functional side. Noteworthy findings demonstrated the decrease in periodontitis-associated bacteria in the AD group. Distinct differences were revealed in the distribution of metabolic pathways between the two study groups. Our study confirms that the oral microbiome is altered in AD. However, a comprehensive picture of the complete composition of the oral microbiome in patients with AD requires further investigation.

Several studies have found that oral and gut microbiome and their byproducts can impact Alzheimer’s Disease (AD). The objective of our study is to analyze metagenomic sequencing data from paired oral and fecal microbiomes, along with clinical variables, to identify communities of bacteria associated with AD. This research aims to improve our understanding of the microbiome community matrix, and how these communities interact and correlate with AD status compared to healthy controls (HC) through an oral‐gut microbial axis.

The human oral microbiota is a community of microorganisms that reside in the oral cavity, including lingual, buccal, and saliva, each niche with a distinct microbial composition. Alterations in oral microbiota have been associated with an increased risk of Alzheimer’s disease (AD). This study used data from 143 older adults in the MIND trial to evaluate the association between oral microbiome and cognitive function. Oral niche-specific differences (saliva, buccal, and lingual), as well as the microbiome composition differences (α and β diversity), were associated with cognitive function. A lower abundance of Gemella and a higher abundance of anaerobic pro-inflammatory bacteria (e.g., Parvimonas, Treponema, Dialister) were linked to a lower Cognitive Z Score. Porphyromonas, previously linked to AD, was not associated with cognition. The outcomes suggest that oral microbiota may be a biomarker for cognitive function. Further research is required to assess whether oral microbiota-directed strategies can positively impact cognitive decline.

Recent studies have suggested that periodontal disease and alterations in the oral microbiome may be associated with cognitive decline and the development of Alzheimer’s disease (AD). Here, we report a pilot case‐control study of oral microbiota diversity in AD patients in comparison with healthy seniors from the Central Asian region.

The involvement of the oral microbiome (OM) in the pathophysiology of Alzheimer's disease and vascular dementia has been recognized epidemiologically, but the molecular mechanisms remain elusive. In this study, we uncovered the presence of OM-derived proteins (OMdPs) in brain extracellular vesicles (bEVs) from post-mortem Alzheimer's disease and vascular dementia subjects using unbiased metaproteomics. OMdP circulation in blood EVs was also confirmed in an independent cohort. Our findings also reveal that specific OMdPs are present in bEVs, with their levels varying with disease progression. Peptidome-wide correlation analyses further explored their exchange dynamics and composition within bEVs. In addition, we validated the ability of OM-derived EVs to cross the blood–brain barrier using a blood–brain barrier–on-a-chip model, confirming a potential route for bacterial-derived molecules to reach the central nervous system. Bioinformatics-driven interaction analyses indicated that OMdPs engage with key neuropathological proteins, including amyloid-beta and tau, suggesting a novel mechanism linking dysbiotic OM to dementia. These results provide new insights into the role of the OM in neurodegeneration and highlight OMdPs as potential biomarkers and therapeutic targets.

OBJECTIVE To investigate the association between Alzheimer's disease (AD) and periodontitis in the aspects of periodontal status, serological markers and oral microbiome. MATERIALS AND METHODS Twenty AD and 20 healthy subjects were enrolled in this age- and gender-matched case-control study. Clinical periodontal parameters and serum biomarkers, including amyloid β42 (Aβ42 ), Tau, phosphorylated Tau (pTau), triglyceride, pro-inflammatory cytokines and anti-P. gingivalis lipopolysaccharide (LPS) antibody were examined. The saliva samples were analyzed for oral microbiome composition. RESULTS AD patients with Clinical Dementia Rating (CDR) >1 exhibited significantly more clinical attachment loss (CAL) than those with lower CDR. The levels of serum Tau protein, hsCRP and anti-P. gingivalis LPS antibody were markedly elevated in the AD group compared to the control group. Serum pTau protein level was positively correlated with anti-P. gingivalis LPS antibody titer. Moreover, the increased abundances of Capnocytophaga sp ora clone DZ074, Eubacterium infirmum, Prevotella buccae and Selenomonas artemidis were detected in the AD group. Interestingly, serum levels of Aβ42, pTau, and anti-P. gingivalis LPS antibody were strongly related to the gene upregulation in human pathogen septicemia. CONCLUSIONS Our study suggested the association of periodontal infection and oral microbiome with AD. Further large-scale studies with longitudinal follow-up are warranted.

Abstract Background Over recent decades, growing evidence has highlighted the pivotal role of the microbiome in Alzheimer's disease (AD) and dementia. Studies suggests the disruptions in the gut microbiome may contribute to cognitive impairment, but the association between the oral microbiome and cognitive impairment remains unclear. This study aims to characterize the oral microbiome and investigate its role in cognitive decline among elderly participants of MiaGB cohort. Method Whole‐genome metagenomics sequencing was performed on 368 samples (Controls: 236, MCI: 107, and Dementia: 25) collected from the MiaGB (Microbiome in Aging Gut and Brain) consortium, a multi‐site, clinical research study. The data was processed and analyzed using KneadData, MetaPhlAn, and HUMAnNnn tools. Result Taxonomic analysis revealed an increasing abundance of the genus Porphyromonas, and species Neisseria subflava, Neisseria sicca, and Streptococcus australis from controls to MCI to dementia participants. Random forest (RF) and LEfSe analysis identified significant increase in abundance of species N. subflava, Veillonella parvula, N. sicca, and Neisseria flavescens in MCI and dementia participants compared to controls. Additionally, Lautropia mirabilis, Eubacterium sulci, and Gemella sanguinis species were enriched in MCI compared to Controls and Dementia participants. Genera Porphyromonas are associated with cognitive impairment in other studies. Also, S. australis and V. parvula and Gemella sanguinis has been linked to neurodegenerative diseases and infective endocarditis. Distinct microbial profiles specific to each group could serve as biomarkers to identify the risk of cognitive impairment. Conclusion This study revealed a strong link between oral microbiome alterations and cognitive impairment. Further analysis will provide a more comprehensive understanding about the role of these microbes in cognitively impaired participants. These findings offer new insights into early biomarkers for cognitive impairment and the development of potential therapeutic approaches for the prevention and intervention of Alzheimer's disease (AD).

No abstract available

Background An association between the gut microbiome and cognitive function has been demonstrated in prior studies. However, whether the oral microbiome, the second largest microbial habitant in humans, has a role in cognition remains unclear. Design, setting, participants Using weighted data from the 2011 to 2012 National Health and Nutrition Examination Survey, we examined the association between oral microbial composition and cognitive function in older adults. The oral microbiome was characterized by 16S ribosomal RNA gene sequencing. Cognitive status was assessed using the Consortium to Establish a Registry for Alzheimer’s Disease immediate recall and delayed recall, Animal Fluency Test, and Digit Symbol Substitution Test (DSST). Subjective memory changes over 12 months were also assessed. Linear and logistic regression models were conducted to quantify the association of α-diversity with different cognitive measurements controlling for potential confounding variables. Differences in β-diversity were analyzed using permutational analysis of variance. Results A total of 605 participants aged 60–69 years were included in the analysis. Oral microbial α-diversity was significantly and positively correlated with DSST (β, 2.92; 95% CI, 1.01–4.84). Participants with higher oral microbial α-diversity were more likely to have better cognitive performance status based on DSST (adjusted odds ratio, 2.35; 95% CI, 1.28–4.30) and were less likely to experience subjective memory changes (adjusted odds ratio, 0.43; 95% CI, 0.25–0.74). In addition, β-diversity was statistically significant for the cognitive performance status based on DSST (P = 0.031) and subjective memory changes (P = 0.023). Conclusions Oral microbial composition was associated with executive function and subjective memory changes among older adults among older U.S. adults in a nationally representative population sample. Oral dysbiosis is a potential biomarker or therapeutic target for cognitive decline. Further work is needed to elucidate the mechanisms underpinning the association between the oral microbiome and cognitive function.

The diversity of bacterial species in the oral cavity makes it a key site for research. The close proximity of the oral cavity to the brain and the blood brain barrier enhances the interest to study this site. Changes in the oral microbiome are linked to multiple systemic diseases. Alcohol is shown to cause a shift in the microbiome composition. This change, particularly in the oral cavity, may lead to neurological diseases. Alzheimer’s disease (AD) is a common neurodegenerative disorder that may cause irreversible memory loss. This study uses the meta-analysis method to establish the link between binge drinking, the oral microbiome and AD. The QIAGEN Ingenuity Pathway Analysis (IPA) shows that high levels of ethanol in binge drinkers cause a shift in the microbiome that leads to the development of AD through the activation of eIF2, regulation of eIF4 and p70S6K signaling, and mTOR signaling pathways. The pathways associated with both binge drinkers and AD are also analyzed. This study provides a foundation that shows how binge drinking and the oral microbiome dysbiosis lead to permeability changes in the blood brain barrier (BBB), which may eventually result in the pathogenesis of AD.

Background Investigating human oral microbiota is now of great interest, being clinically significant for general and oral health. Many research studies have started to focus on the link between oral microbial dysbiosis and Alzheimer's disease. However, little is known about North African populations. Objective We aimed to distinguish the dissimilarity in the structure of microbial oral flora between the Alzheimer's disease patients and healthy controls in a Tunisian population. Methods We investigated the salivary microbiota using next-generation shotgun sequencing. Results The overall structure of the oral microbial community of the Alzheimer's disease patient group was obviously different from the healthy control group. Significantly higher levels of Haemophilus (25.26%) were noticed in the AD group. However, Neisseria (10.17%) showed lower levels compared to the HC group. Considering the disease severity, Selenomonas and Aggregatibacter showed gradually higher levels as the disease progressed. Porphyromonas showed the highest levels in the mild stage of the disease, while Treponema, Selenomonas, and Peptostreptococcus were associated with severe stage. The presence of key taxa, Aggregatibacter and Selenomonas may constitute a dysbiosis signature in individuals with AD. Conclusions These findings may be of high relevance for orienting further studies on evaluating the physio-pathological process, confirming the implication of oral microbiota in AD and opening diagnostic and therapeutic avenues.

Inflammation and immune mechanisms are believed to play important roles in Alzheimer's disease pathogenesis. Research supports the link between poor oral health and Alzheimer's disease. Periodontal disease and dental caries represent the two most common infections of the oral cavity. This study focused on a precursor to Alzheimer's disease, mild cognitive impairment (MCI). Using 16S rRNA sequencing, we characterized and compared the oral microbiome of 68 older adults who met the criteria for MCI or were cognitively normal, then explored relationships between the oral microbiome, diagnostic markers of MCI, and blood markers of systemic inflammation. Two taxa, Pasteurellacae and Lautropia mirabilis were identified to be differentially abundant in this cohort. Although systemic inflammatory markers did not differentiate the two groups, differences in five cerebrospinal fluid inflammatory mediators were identified and had significant associations with MCI. Because inflammatory markers may reflect CNS changes, pursuing this line of research could provide opportunities for new diagnostic tools and illuminate mechanisms for prevention and mitigation of Alzheimer's disease.

New studies have linked epidemiological and pathophysiological relationships between oral microbiota and Alzheimer’s disease (AD), a neurodegenerative disease more prevalent in the Puerto Rican population. Dysbiosis of the oral microbiome induces periodontal disease, which increases systemic chronic inflammation, an important component in the multifactorial pathogenesis of AD. This project aims to characterize the oral microbiota’s composition and diversity in AD patients compared to healthy controls, and explore the potential role of oral dysbiosis in dementia.

AIM Periodontitis is caused by dysbiosis of oral microbes and is associated with increased cognitive decline in Alzheimer's disease (AD), and recently, a potential functional link was proposed between oral microbes and AD. We compared the oral microbiomes of patients with or without AD to evaluate the association between oral microbes and AD in periodontitis. MATERIALS AND METHODS Periodontitis patients with AD (n = 15) and cognitively unimpaired periodontitis patients (CU) (n = 14) were recruited for this study. Each patient underwent an oral examination and neuropsychological evaluation. Buccal, supragingival and subgingival plaque samples were collected, and microbiomes were analysed by next-generation sequencing. Alpha diversity, beta diversity, linear discriminant analysis effect size, analysis of variance-like differential expression analysis and network analysis were used to compare group oral microbiomes. RESULTS All 29 participants had moderate to severe periodontitis. Group buccal and supragingival samples were indistinguishable, but subgingival samples demonstrated significant alpha and beta diversity differences. Differential analysis showed subgingival samples of the AD group had higher prevalence of Atopobium rimae, Dialister pneumosintes, Olsenella sp. HMT 807, Saccharibacteria (TM7) sp. HMT 348 and several species of Prevotella than the CU group. Furthermore, subgingival microbiome network analysis revealed a distinct, closely connected network in the AD group comprised of various Prevotella spp. and several anaerobic bacteria. CONCLUSIONS A unique microbial composition was discovered in the subgingival region in the AD group. Specifically, potential periodontal pathogens were found to be more prevalent in the subgingival plaque samples of the AD group. These bacteria may possess a potential to worsen periodontitis and other systemic diseases. We recommend that AD patients receive regular, careful dental check-ups to ensure proper oral hygiene management.

Introduction Periodontitis-related oral microbial dysbiosis is thought to contribute to Alzheimer's disease (AD) neuroinflammation and brain amyloid production. Since probiotics can modulate periodontitis/oral dysbiosis, this study examined the effects of a probiotic/lantibiotic, nisin, in modulating brain pathology triggered by periodontitis. Methods A polymicrobial mouse model of periodontal disease was used to evaluate the effects of this disease on brain microbiome dysbiosis, neuroinflammation, Alzheimer’s-related changes, and nisin’s therapeutic potential in this context. Results 16S sequencing and real-time PCR data revealed that Nisin treatment mitigated the changes in the brain microbiome composition, diversity, and community structure, and reduced the levels of periodontal pathogen DNA in the brain induced by periodontal disease. Nisin treatment significantly decreased the mRNA expression of pro-inflammatory cytokines (Interleukin-1β/IL-1 β, Interleukin 6/IL-6, and Tumor Necrosis Factor α/TNF-α) in the brain that were elevated by periodontal infection. In addition, the concentrations of amyloid-β 42 (Aβ42), total Tau, and Tau (pS199) (445.69 ± 120.03, 1420.85 ± 331.40, 137.20 ± 36.01) were significantly higher in the infection group compared to the control group (193.01 ± 31.82, 384.27 ± 363.93, 6.09 ± 10.85), respectively. Nisin treatment markedly reduced the Aβ42 (261.80 ± 52.50), total Tau (865.37 ± 304.93), and phosphorylated Tau (82.53 ± 15.77) deposition in the brain of the infection group. Discussion Nisin abrogation of brain microbiome dysbiosis induces beneficial effects on AD-like pathogenic changes and neuroinflammation, and thereby may serve as a potential therapeutic for periodontal–dysbiosis-related AD.

Microbial communities in the intestinal tract have been suggested to impact the ethiopathogenesis of Alzheimer 's disease (AD). The human microbiome might modulate neuroinflammatory processes and thus contribute to neurodegeneration in AD. However, the microbial compositions in AD patients at different stages of the disease are still not fully characterized. We used 16S rRNA analyses to investigate the oral and fecal microbiota in patients with AD and mild cognitive impairment (MCI), a cohort of at-risk individuals (APOE4 carriers) and healthy controls, and investigated the relationship of microbial communities and disease specific markers. We found a slightly decreased diversity in the fecal microbiota of AD patients and identified differences in bacterial abundances including Bacteroidetes, Ruminococcus, Sutterella, Porphyromonadaceae. The diversity in the oral microbiota was increased in AD patients and at-risk individuals. Gram-negative pro-inflammatory bacteria including Haemophilus, Neisseria, Actinobacillus and Porphyromonas were dominant oral bacteria in AD and MCI patients and the abundance correlated with the cerebrospinal fluid (CSF) biomarker. Taken together, we observed a strong shift in the fecal and the oral communities of patients with AD already prominent in prodromal and, in case of the oral microbiota, in at-risk stages. This indicates stage-dependent alterations in oral and fecal microbiota in AD which may contribute to the pathogenesis via a facilitated intestinal and systemic inflammation leading to neuroinflammation and neurodegeneration.

Gingipains from Porphyromonas gingivalis drive Alzheimer’s pathology and can be blocked with small-molecule inhibitors. Porphyromonas gingivalis, the keystone pathogen in chronic periodontitis, was identified in the brain of Alzheimer’s disease patients. Toxic proteases from the bacterium called gingipains were also identified in the brain of Alzheimer’s patients, and levels correlated with tau and ubiquitin pathology. Oral P. gingivalis infection in mice resulted in brain colonization and increased production of Aβ1–42, a component of amyloid plaques. Further, gingipains were neurotoxic in vivo and in vitro, exerting detrimental effects on tau, a protein needed for normal neuronal function. To block this neurotoxicity, we designed and synthesized small-molecule inhibitors targeting gingipains. Gingipain inhibition reduced the bacterial load of an established P. gingivalis brain infection, blocked Aβ1–42 production, reduced neuroinflammation, and rescued neurons in the hippocampus. These data suggest that gingipain inhibitors could be valuable for treating P. gingivalis brain colonization and neurodegeneration in Alzheimer’s disease.

Oral infection with Porphyromonas gingivalis (P. gingivalis), a kind of pathogenic bacteria causing periodontitis, can increase the risk of Alzheimer's disease (AD) and cause cognitive decline. Therefore, precise intracerebral antimicrobial therapy to reduce the load of P. gingivalis in brain may serve as a potential therapeutic approach to improve AD-like cognitive impairment. A kind of nano-delivery system precisely targets bacteria in the brain through coating P. gingivalis stimulated macrophage membrane onto the surface of platinum nanoclusters (Pg-M-PtNCs). Approximate 50 nm spherical Pg-M-PtNCs demonstrate good biocompatibility and the pretreated macrophage membranes can inhibit macrophages phagocytosis and increase the adherence to bacteria. Pg-M-PtNCs can significantly inhibit the growth of P.gingivalis in vitro, and are effectively delivered and remain at the infection site in the mice brain to reduce the bacterial load and neuronal damage, and then improve the AD-like cognitive dysfunction in the chronic periodontitis mice. Platinum nanoclusters coated with P. gingivalis pretreated macrophage membrane play an important role in targeting bacteria in the brain, and effectively improve AD-like cognitive function disorder caused by P. gingivalis infection in the brain.

Introduction Porphyromonas gingivalis and Treponema species have been found to invade the central nervous system through virulence factors, causing inflammation and influencing the host immune response. P. gingivalis interacts with astrocytes, microglia, and neurons, leading to neuroinflammation. Aggregatibacter actinomycetemcomitans and Fusobacterium nucleatum may also play a role in the development of Alzheimer's disease. Interactomic hub genes, central to protein-protein interaction networks, are vulnerable to perturbations, leading to diseases such as cancer, neurodegenerative disorders, and cardiovascular diseases. Machine learning can identify differentially expressed hub genes in specific conditions or diseases, providing insights into disease mechanisms and developing new therapeutic approaches. This study compares the performance of light gradient boosting and logistic regression in identifying interactomic hub genes in P. gingivalis and F. nucleatum-induced periodontitis with those in Alzheimer's disease. Methods Using the GSE222136 dataset, we analyzed differential gene expression in periodontitis and Alzheimer's disease. The GEO2R tool was used to identify differentially expressed genes under different conditions, providing insights into molecular mechanisms. Bioinformatics tools such as Cytoscape and CytoHubba were employed to create gene expression networks to identify hub genes. Logistic regression and light gradient boosting were used to predict interactomic hub genes, with outliers removed and machine learning algorithms applied. Results The data were cross-validated and divided into training and testing segments. The top hub genes identified were TNFRSF9, LZIC, TNFRSF8, SLC45A1, GPR157, and SLC25A33, which are induced by P. gingivalis and F. nucleatum and are responsible for endothelial dysfunction in brain cells. The accuracy of logistic regression and light gradient boosting was 67% and 60%, respectively. Discussion The logistic regression model demonstrated superior accuracy and balance compared to the light gradient boosting model, indicating its potential for future improvements in predicting hub genes in periodontal and Alzheimer's diseases.

Chronic periodontitis, driven by the keystone pathogen Porphyromonas gingivalis, has been increasingly associated with Alzheimer’s disease (AD) and AD-related dementias (ADRDs). However, the mechanisms through which P. gingivalis-lipopolysaccharide (LPS)-induced release of neuroinflammatory proteins contribute to the pathogenesis of AD and ADRD remain inadequately understood. Caspase-4, a critical mediator of neuroinflammation, plays a pivotal role in these processes following exposure to P. gingivalis-LPS. In this study, we investigated the mechanistic role of caspase-4 in P. gingivalis-LPS-induced IL-1β production, neuroinflammation, oxidative stress, and mitochondrial alterations in human neuronal and microglial cell lines. Silencing of caspase-4 significantly attenuated IL-1β secretion by inhibiting the activation of the caspase-4-NLRP3-caspase-1-gasdermin D inflammasome pathway, confirming its role in neuroinflammation. Moreover, caspase-4 silencing reduced the activation of amyloid precursor protein and presenilin-1, as well as the secretion of amyloid-β peptides, suggesting a role for caspase-4 in amyloidogenesis. Caspase-4 inhibition also restored the expression of key neuroinflammatory markers, such as total tau, VEGF, TGF, and IL-6, highlighting its central role in regulating neuroinflammatory processes. Furthermore, caspase-4 modulated oxidative stress by regulating reactive oxygen species production and reducing oxidative stress markers like inducible nitric oxide synthase and 4-hydroxynonenal. Additionally, caspase-4 influenced mitochondrial membrane potential, mitochondrial biogenesis, fission, fusion, mitochondrial respiration, and ATP production, all of which were impaired by P. gingivalis-LPS but restored with caspase-4 inhibition. These findings provide novel insights into the role of caspase-4 in P. gingivalis-LPS-induced neuroinflammation, oxidative stress, and mitochondrial dysfunction, demonstrating caspase-4 as a potential therapeutic target for neurodegenerative conditions associated with AD and related dementias.

Porphyromonas gingivalis is one of the major pathogens of chronic periodontitis. P. gingivalis can cause systemic inflammation, amyloid β protein deposition, and hyperphosphorylation of tau protein, leading to Alzheimer’s disease (AD)–like lesions. P. gingivalis oral infection causes gut microbiota alteration, gut barrier dysfunction, and intestinal immune response and inflammation. The microbiota-gut-brain axis has a potential role in the pathogenesis of AD. Whether P. gingivalis affects AD-like lesions via the gut-brain axis needs more study. In this study, orally administered P. gingivalis induced alveolar resorption, intestinal barrier impairment, and AD-like lesions. Oral infection with P. gingivalis induced oral and gut microflora dysbiosis, imbalance of the tryptophan metabolism pathway of gut microbiota, and elevated levels of 3-hydroxykynurenine in the sera and hippocampi. The key metabolite, 3-hydroxykynurenine, suppressed Bcl2 gene expression, leading to neuronal apoptosis and promoting AD-like lesions in vivo and in vitro. These findings suggest that P. gingivalis can induce AD pathogenesis through the gut-brain axis, providing new ideas for the prevention and treatment of AD.

Background Lipopolysaccharides from Porphyromonas gingivalis (P.gLPS) are involved in the pathology of Alzheimer's disease (AD). However, the effect of P.gLPS on synaptic defects remains unclear. Objective In this study, we tested our hypothesis that P.gLPS induces synaptic defects in a low-amyloid-beta (Aβ)-concentration environment. Methods MG6 microglia or N2a neurons was treated with P.gLPS (0.1 μg/mL), soluble Aβ42 (0.1 μM) or AL (combined P.gLPS and soluble Aβ42 at 0.1 μM). Results In cultured MG6 microglia, increased the mRNA expression of TNF-α, IL-1β and IL-6 and the TNF-α release in parallel with increased NF-κB activation. In cultured N2a neurons, treatment with Aβ42, P.gLPS, and AL did not affect the mRNA expression of synapsin1 (SYN1) or post-synaptic density protein-95 (PSD-95). However, the treatment with conditioned medium from AL-exposed MG6 microglia (AL-MCM) significantly reduced the mRNA and protein expression of SYN1, PSD-95, and nuclear translocation of repressor element-1 silencing transcription factor (REST) but significantly increased the mRNA expression of TNF receptor type I (at 48 h) and glycogen synthase kinase (GSK)3β (at 24 h). TWS119 pretreatment (5 μM), a GSK3β specific inhibitor, significantly reversed the AL-MCM-induced reduction in the mRNA expression of SYN1 and PSD-95 and nuclear translocation of REST in cultured N2a neurons. In APPNL-F/NL-F mice, the immunofluorescence intensity of SYN1 and PSD-95 in cortical neurons was positively correlated with the index of the memory test but negatively correlated with that of TNF-α-positive microglia. Conclusions These observations demonstrate that P.gLPS induces neuronal GSK3β-dependent synaptic defects in a low-Aβ concentration environment via microglial activation.

Background The apolipoprotein E allele ε4 is the most well-known predisposing genetic risk factor for Alzheimer's disease (AD). Objective To identify AD genes in apolipoprotein E−/− (ApoE−/−) mice brains with confirmed entry of Porphyromonas gingivalis. Methods TaqMan™ Mouse AD arrays were performed on orally infected ApoE−/− mice with confirmed P. gingivalis entry and compared with sham infected mice brains (N = 4) at 12- and 24-weeks post infection. Results Gene expression by qPCR demonstrated that in the P. gingivalis 12-weeks post oral infection, two genes were statistically significantly changed in their expression. These were cyclin dependent kinase 5 regulatory subunit 1 (Cdk5r1, 0.15 logfold change, p = 0.05) and Interleukin 1 alpha, (IL1a, −0.10 log fold change, p = 0.012). In the P. gingivalis 24-weeks post oral infection, three genes were statistically significantly changed in their expression. These were cholinergic receptor nicotinic alpha 7 subunit or Chrna7 (0.10 log fold change, p = 0.02), mitogen-activated protein kinase 1 or Mapk1 (0.10 log fold change, p = 0.05) and visinin like 1 or Vnsl1 (0.01 log fold change, p = 0.04). 87 out of 92 AD target genes demonstrated no difference between infected and sham mice brains. Conclusions Five genes, from a recognized AD panel had statistically significantly altered expression in the ApoE−/− mouse AD model following P. gingivalis entry into the brain. This suggests the ApoE−/− genetic variation may control the biological activity of specific genes relevant to inflammation and neuronal plasticity following P. gingivalis infection.

The etiology of Alzheimer's disease (AD) remains under active debate. In this perspective, we explore the hypothesis that a primarily infection‐caused chronic dysregulation and weakening of human innate immunity via the underexpression, degradation, and inactivation of innate immune proteins necessary for direct antimicrobial effects and regulation of host defense and autophagy could lead to AD. Key evidence relates to the fact that important innate immune proteins such as LL‐37—which can bind Aβ and block amyloid formation—as well as Apolipoprotein E, antiviral interferons, and TNF‐α can be degraded and deactivated by enzymes produced by the common oral anaerobic pathogen Porphyromonas gingivalis (Pg). Pg produces numerous virulence factors; of particular importance for AD are Pg’s gingipain cysteine proteases. Deleterious effects of chronic Pg infection and gingipains include a systemic downregulation and paralysis of the interferon response, particularly the antiviral interferon‐lambda response, which enables replication of endemic herpesviruses. The result is a chronic, low‐level viral infectious assault on gut, nerves, and brain causing the production of Aβ antimicrobial peptides, accumulation of Aβ plaques, phosphorylation of Tau, progressive neuroinflammation, and neurodegeneration. The resultant innate immune system dysregulation, as an AD etiology, ties together the well‐known amyloid cascade hypothesis and the infectious theory of AD into a unified explanation of the pathology and cause of AD. If this theory holds true, it suggests preventative approaches: (1) test for and eradicate Pg from oral flora, and/or directly deactivate the gingipains; and (2) reduce Herpesvirus exacerbations by the use of antiviral drugs and/or vaccines (e.g., Bacillus Calmette–Guérin).

Background Porphyromonas gingivalis (P. gingivalis) has been found to enter the brain and induce inflammation, contributing to Alzheimer's disease (AD). P. gingivalis is also closely linked to gut dysbiosis. However, does P. gingivalis induce AD-like pathology through the microbiota-gut-brain axis? There is limited literature on this topic. Objective To determine the precise causal link among P. gingivalis, intestinal inflammation, and AD-related pathology. Methods 12- to 13-month-old female C57BL/6J mice were subjected to ligature placement and oral administration of P. gingivalis over a 24-week period. Then, cognitive performance was evaluated with behavioral tests, while AD neuropathological changes, neuroinflammation, and intestinal inflammation were assessed through qPCR, immunofluorescence, and western blot, and gut microbiota was analyzed by 16S rRNA. Results Mice exposed to P. gingivalis showed impaired behavior in open field test, novel object recognition, and Y-maze tests. The bacterium infiltrated their brains, increasing Aβ42, AβPP, and Aβ fragments, promoting tau phosphorylation and microglial activation, and reducing levels of ZO-1, PSD95, SYP, and NeuN proteins. Inflammatory factors like NLRP3, caspase-1, IL-1β, IL-6, and TNF-α were elevated in both brains and intestine, while ZO-1 and occludin levels decreased in intestine. P. gingivalis also altered gut microbial compositions. Conclusions P. gingivalis induced gut dysbiosis and activated the NLRP3 inflammasome in the intestine and brains of mice. This led to impairment of both the intestinal and brain-blood barriers, triggering neuroinflammation and promoting the progression of AD. These findings highlight the critical role of NLRP3 inflammasome activation in the microbiota-gut-brain axis in the AD-like pathology induced by P. gingivalis.

No abstract available

Porphyromonas gingivalis (P. gingivalis) is one of the pathogens involved in gingival inflammation, which may trigger neuroinflammatory diseases such as Alzheimer’s disease (AD). This study aimed to investigate the protective (preventive and treatment) effects of a lactobacilli mixture combining Lactobacillus reuteri PTCC1655, Lactobacillus brevis CD0817, Lacticaseibacillus rhamnosus PTCC1637, and Lactobacillus plantarum PTCC1058 against P. gingivalis-induced gingival inflammation and AD-like pathology in rats. These probiotic strains exhibited cognitive enhancement effects, but this study proposed to assess their activity in a mixture. To propose a probable mechanism for P. gingivalis cognitive impairments, the TEs balance were analyzed in hippocampus and cortex tissues. Animals were divided into five groups: the control, lactobacilli, P. gingivalis, lactobacilli + P. gingivalis (prevention), and P. gingivalis + lactobacilli group (treatment) groups. The behavioral and histopathological changes were compared among them. Finally, The Trace elements (TEs) levels in the hippocampus and cortex tissues were analyzed. The palatal tissue sections of the P. gingivalis infected rats showed moderate inflammation with dense infiltration of inflammatory cells, a limited area of tissue edema, and vascular congestion. Additionally, passive avoidance learning and spatial memory were impaired. Histopathological tests revealed the presence of Aβ-positive cells in the P. gingivalis group. While the Aβ-positive cells decreased in the treatment group, their formation was inhibited in the preventive group. Administration of a mixture of lactobacilli (orally) effectively mitigated the gingival inflammation, Aβ production, and improved learning and memory functions. Moreover, Zn, Cu, and Mn levels in the hippocampus were dramatically elevated by P. gingivalis infection, whereas lactobacilli mixture mitigated these disruptive effects. The lactobacilli mixture significantly prevented the disruptive effects of P. gingivalis on gingival and brain tissues in rats. Therefore, new formulated combination of lactobacilli may be a good candidate for inhibiting the P. gingivalis infection and its subsequent cognitive effects. The current study aimed to evaluate the effects of a lactobacilli mixture to manage the disruptive effects of P. gingivalis infection on memory.

AIM Porphyromonas gingivalis, a consensus periodontal pathogen, is thought to be involved in Alzheimer's disease (AD) progression, and P. gingivalis-derived outer membrane vesicles (PgOMVs) are a key toxic factor in inducing AD pathology. This study aimed to clarify the regulatory mechanism underlying the PgOMV-induced AD-like phenotype. MATERIALS AND METHODS We intraperitoneally injected PgOMVs into the periphery of wild-type and CatB knockout mice for 4 or 8 weeks to assess the effect of CatB on PgOMV-induced AD pathology. Mice were evaluated for cognitive change, tau phosphorylation, microglial activation, neuroinflammation and synapse loss. Microglial and primary neuron culture were prepared to verify the in vivo results. RESULTS CatB deficiency significantly alleviated PgOMV-induced cognitive dysfunction, microglia-mediated neuroinflammation, tau hyperphosphorylation and synapse loss. Subsequent transcriptomic analysis, immunofluorescence and immunoblotting suggested that CatB modulates microglia-mediated neuroinflammation through stress-activated protein kinases (SAPK)/Jun amino-terminal kinases (JNK) signals after administration of PgOMVs, which in turn regulates neuronal tau phosphorylation and synapse loss in a SAPK/JNK-dependent manner. CONCLUSION Our study unveils a previously unknown role of CatB in regulating PgOMV-induced AD pathology.

BACKGROUND Alzheimer's disease (AD) is the most prevalent neurodegenerative disorder, with a significant burden on global health. AD is characterized by a progressive cognitive decline and memory loss. Emerging research suggests a potential link between periodontitis, specifically the presence of oral bacteria such as Porphyromonas gingivalis (P. gingivalis), and AD progression. P. gingivalis produces an enzyme, Agmatine deiminase (AgD), which converts agmatine to N-carbamoyl putrescine (NCP), serving as a precursor to essential polyamines. Recent studies have confirmed the correlation between disruptions in polyamine metabolism and cognitive impairment. OBJECTIVE This study aims to investigate the dysregulation of P. gingivalis Agmatine deiminase (PgAgD) in the context of AD. METHODS Saliva samples were collected from a total of 54 individuals, including 27 AD patients and 27 healthy controls. The expression of the PgAgD gene was analyzed using quantitative Real-- Time PCR. RESULTS The results showed a significant decrease in PgAgD gene expression in the saliva samples of AD patients compared to healthy controls. This downregulation was found in AD patients with advanced stages of periodontitis. Additionally, a correlation was observed between the decrease in PgAgD expression and the 30-item Mini-Mental State Examination (MMSE) score. CONCLUSION These findings suggest that measuring PgAgD expression in saliva could be a noninvasive tool for monitoring AD progression and aid in the early diagnosis of patients with periodontitis. Further research is needed to validate our results and explore the underlying mechanisms linking periodontitis, PgAgD expression, and AD pathophysiology.

: Gingipain K, a virulence protein from Porphyromonas gingivalis , is involved in the pathogenesis of Alzheimer’s disease. Hence, this research aimed to investigate the potential of methanolic extract of Cratoxylum cochinchinese leaves (CME) and the pure compound, mangiferin, in inhibiting this protein. The inhibition of gingipain K activity was measured based on the cleaving potential of this enzyme towards Ac-Lys-pNA, a synthetic peptide substrate containing a chromogenic leaving group. Phytocompounds present in CME were then used as ligands in a simulated docking study with gingipain K. Results indicated that the CME was a potential inhibitor of gingipain K, reducing the protein activity in a dose dependent manner compared with the untreated control. Molecular docking analysis of the phytocompounds revealed mangiferin as the best inhibitor with the highest docking score. The studies showed that mangiferin engaged in H-bonding and π - π interactions with important active site residues in vicinity, such as Asp388, Gly445, Cys477, Trp391, and Trp513. The compound, when tested in the in vitro gingipain K inhibition assay, produced an IC 50 of 134.20 µ M, which was close to the IC 50 of the positive control, TLCK (IC 50 = 108.40 µ M). The additional bioactivity of mangiferin as gingipain K inhibitor as reported here together with its known neuroprotective activity shall encourage further investigation of this molecule in the treatment of such a debilitating illness, the Alzheimer’s disease.

The association between Porphyromonas gingivalis (P. gingivalis) and Alzheimer’s disease (AD) remains unclear. The major aim of this study was to elucidate the role of genes and molecular targets in P. gingivalis-associated AD. Two Gene Expression Omnibus (GEO) datasets, GSE5281 for AD (n = 84 Alzheimer’s, n = 74 control) and GSE9723 (n = 4 P. gingivalis, n = 4 control), were downloaded from the GEO database. Differentially expressed genes (DEGs) were obtained, and genes common to both diseases were drawn. Additionally, Kyoto Encyclopedia of Genes and Genomes (KEGG) and Gene Ontology (GO) analysis was performed from the top 100 genes (50 upregulated and 50 downregulated genes). We then proceeded with CMap analysis to screen for possible small drug molecules targeting these genes. Subsequently, we performed molecular dynamics simulations. A total of 10 common genes (CALD1, HES1, ID3, PLK2, PPP2R2D, RASGRF1, SUN1, VPS33B, WTH3DI/RAB6A, and ZFP36L1) were identified with a p-value < 0.05. The PPI network of the top 100 genes showed UCHL1, SST, CHGB, CALY, and INA to be common in the MCC, DMNC, and MNC domains. Out of the 10 common genes identified, only 1 was mapped in CMap. We found three candidate small drug molecules to be a fit for PLK2, namely PubChem ID: 24971422, 11364421, and 49792852. We then performed molecular docking of PLK2 with PubChem ID: 24971422, 11364421, and 49792852. The best target, 11364421, was used to conduct the molecular dynamics simulations. The results of this study unravel novel genes to P. gingivalis-associated AD that warrant further validation.

Alzheimer's disease (AD) is the most common chronic neurodegenerative disorder, with neuroinflammation playing an important role in its progression to become a major research focus. The role of Porphyromonas gingivalis (Pg) and its outer membrane vesicles (Pg OMVs) in AD development is uncertain, particularly regarding their effects on neuroinflammation.

Infection with Porphyromonas gingivalis (Pg), which is a major periodontal pathogen, causes a large number of systemic diseases based on chronic inflammation such as diabetes and Alzheimer’s disease (AD). However, it is not yet fully understood how Pg can augment local systemic immune and inflammatory responses during progression of AD. There is a strong association between depression and elevated levels of inflammation. Noradrenaline (NA) is a key neurotransmitter that modulates microglial activation during stress conditions. In this study, we have thus investigated the regulatory mechanisms of NA on the production of interleukin-1β (IL-1β) by microglia following stimulation with Pg virulence factors, lipopolysaccharide (LPS), and outer membrane vesicles (OMVs). NA (30–1000 nM) significantly enhanced the mRNA level, promoter activity, and protein level of IL-1β up to 20-fold in BV-2 microglia following treatment with Pg LPS (10 μg/mL) and OMVs (150 μg of protein/mL) in a dose-dependent manner. Pharmacological studies have suggested that NA synergistically augments the responses induced by Pg LPS and OMVs through different mechanisms. AP-1 is activated by the β2 adrenergic receptor (Aβ2R)-mediated pathway. NF-κB, which is activated by the Pg LPS/toll-like receptor 2-mediated pathway, is required for the synergistic effect of NA on the Pg LPS-induced IL-1β production by BV-2 microglia. Co-immunoprecipitation combined with Western blotting and the structural models generated by AlphaFold2 suggested that cross-coupling of NF-κB p65 and AP-1 c-Fos transcription factors enhances the binding of NF-κB p65 to the IκB site, resulting in the synergistic augmentation of the IL-1β promoter activity. In contrast, OMVs were phagocytosed by BV-2 microglia and then activated the TLR9/p52/RelB-mediated pathway. The Aβ2R/Epac-mediated pathway, which promotes phagosome maturation, may be responsible for the synergistic effect of NA on the OMV-induced production of IL-1β in BV-2 microglia. Our study provides the first evidence that NA synergistically enhances the production of IL-1β in response to Pg LPS and OMVs through distinct mechanisms.

Porphyromonas gingivalis (Pg) is a primary oral pathogen in the widespread biofilm‐induced “chronic” multi‐systems inflammatory disease(s) including Alzheimer’s disease (AD). It is possibly the only second identified unique example of a biological extremophile in the human body. Having a better understanding of the key microbiological and genetic mechanisms of its pathogenesis and disease induction are central to its future diagnosis, treatment, and possible prevention.

Abstract Background Periodontitis, a chronic disease that progresses over years, is a modifiable risk factor for Alzheimer’s disease (AD). Periodontitis is caused by endogenous oral pathobionts, including Porphyromonas gingivalis (Pg) and Treponema denticola (Td), that have a synergistic relationship resulting in increased virulence. Pg and Td proteins and DNA have been detected in post-mortem brains of AD patients. However it is unclear if these bacteria infect the brain or if the detected bacterial products are on membrane vesicles (MVs); spherical nanostructures released from bacteria. The aims of this work were to determine whether intact bacterial cells were in brains of mice after chronic oral inoculation with these organisms; whether Td enhanced the ability of Pg products to penetrate the brain and whether Td alone induced AD biomarkers Methods C57BL/6 mice were orally inoculated 3 times/week for 12 weeks with either Pg, Td, or Pg + Td cells in a 2:1 ratio, or sham inoculated as a negative control. Mice were culled and their brains dissected, with one hemisphere prepared for immunohistochemistry (IHC) as FFPE tissue and probed with antibodies to amyloid beta (Aβ), phospho-Tau (p-Tau) and the Pg surface protein RgpA. The other hemisphere had the hippocampus removed for transmission electron microscopy (TEM) analysis. Results Pg alone and Pg + Td inoculated mice showed significant increases in alveolar bone loss compared with the uninoculated control. No whole bacterial cells were detected in any of the brains of inoculated mice including those that were IHC positive for RgpA. RgpA immunoactivity was not significantly different in brains of mice that received Pg alone or the Pg+Td combination. Aβ and p-Tau were detected in all mice with an increasing magnitude in uninoculated < Td inoculated ≤ Pg inoculated ≤ Pg+Td inoculated. Conclusion Conclusions: Repeated oral inoculation of mice with Pg alone and Pg + Td resulted in periodontitis, as determined by alveolar bone loss. The lack of whole bacterial cells in the brain argues against a direct infection mechanism of AD initiation by the oral bacteria Pg and Td. Our data are consistent with the observed AD-like pathology in the brain resulting from a focal infection of Pg and/or Td in the mouth mediated by MV penetration of the brain. Disclosures All Authors: No reported disclosures

Addressing novel mechanisms and effective therapeutic treatments for cognitive decline, Dementia and Alzheimer’s Disease is a major public health need. Keystone Bio have identified that specific virulent strains of Porphyromonas gingivalis (Pg) and the release of specific virulence factors/toxins in the oral cavity as the primary driver/causation of systemic/neuro‐vascular inflammation/degenerative diseases i.e., cognitive decline/dementias/Alzheimer’s disease (Nara et. al 2021). A recent landmark finding was reported in a sub‐cohort analysis from a Phase 2/3 GAIN trial (n = 238) demonstrated that lowering the load of Pg in the mouth leads to a significant improvement of cognitive slowing at both 24 and 48 weeks in participants with mild to moderate AD. Another study showed lowering the load of Pg in the mouth had a favorable effect on AD‐related brain atrophy (https://doi.org/10.1002/alz.12378). Pg OMVs and systemic system diseases has many well‐defined examples such as: cardiometabolic diseases‐ Pg OMVs attenuate insulin induced Akt/GSK‐3_ signaling in hepatic HepG2 cells, thereby causing changes in glucose metabolism in the liver and promoting the development of diabetes and increase vascular permeability by cleaving endothelial cell connexins such as PECAM‐1, thereby promoting cardiovascular diseases.

Chronic bacterial infections exert profound systemic effects beyond their primary infection sites, influencing a range of inflammatory, metabolic, and neurodegenerative diseases. Helicobacter pylori (Hp) and Porphyromonas gingivalis (Pg) are two highly prevalent pathogens that, despite occupying distinct niches, share remarkable pathogenic similarities. Both bacteria, connected with metabolic syndrome, employ immune evasion strategies, induce chronic inflammation, and contribute to systemic diseases such as metabolic-associated steatotic liver disease, cardiovascular disease, and neurodegeneration, such as Alzheimer's disease. A key unifying factor in their pathogenicity is the role of gingipains-cysteine proteases produced by Pg-which facilitate bacterial invasion, immune modulation, and tissue destruction. Emerging evidence suggests that Hp possesses analogous proteolytic enzymes, further supporting their potential synergistic impact on host health. Moreover, both pathogens have been implicated in metabolic syndrome-related blood-brain barrier disruption, chronic (smoldering) systemic inflammation, and lipid metabolism dysregulation, contributing to progressive neurodegenerative and cardiovascular disorders. The role of galectins, particularly galectin-3, in modulating microglial activation and inflammatory pathways further highlights their involvement in neuroinflammatory diseases. Targeting gingipains presents a promising therapeutic avenue, with bismuth compounds and novel inhibitors showing potential in disrupting these proteases and mitigating their systemic effects. Understanding the interactions between Hp and metabolic syndrome-related Pg is crucial for developing comprehensive treatment strategies, integrating gastroenterology, periodontology, and neurology. Addressing these infections at both local and systemic levels may improve long-term health outcomes and reduce the burden of chronic inflammatory diseases linked to microbial persistence.

Porphyromonas gingivalis (Pg), a keystone pathogen in chronic periodontitis, has been identified as an emerging risk factor for Alzheimer’s disease (AD). Pg can promote the accumulation of amyloid β protein (Aβ), a characteristic feature of AD pathology. However, the underlying mechanism, particularly in Aβ clearance, remains poorly understood. Here, by using 3 different strains of Pg, ATCC33277, W50, and W83, we discovered that APP/PS1 mice infected with all 3 Pg strains showed decreased microglial Aβ internalization, increased Aβ deposition in the brain, and impaired cognitive function. Using in vitro experiments, we further demonstrated that all 3 Pg strains inhibited microglial Aβ clearance, where gingipains, a group of toxic proteases derived from Pg, were involved. Gingipains were shown to hydrolyze CD14, subsequently impeding the CD14-mediated Vav-Rac/Cdc42 signaling cascade, which ultimately suppressed phagocytosis. Gingipain inhibitor could effectively restore microglial Aβ clearance and diminish Aβ deposition, leading to improved cognitive function in Pg-infected APP/PS1 mice. These findings may provide new insights into the mechanism through which Pg impairs microglial Aβ clearance to aggravate AD phenotypes, suggesting that gingipain inhibitors could be potential therapeutics for treating Pg-associated AD.

Alzheimer's disease is characterized by declining memory and the presence of insoluble amyloid-β (Aβ) plaques and neurofibrillary tangles in the brain. Gui et al. 1 studied the effects of low levels of Porphyromonas gingivalis-lipopolysaccharide (P. gingivalis-LPS) and soluble Aβ on synaptic proteins, synapsin1 (SYN1) and post-synaptic density protein-95 (PSD-95). Their study revealed increased proinflammatory cytokine production in microglial cells (MG6) treated with P. gingivalis-LPS and Aβ, while neuronal cells, N2a, exposed to MG6-conditioned medium showed SYN1 and PSD-95 loss. This suggests that excessive neuroinflammation may contribute to synaptic protein and memory loss, offering mechanistic insights into P. gingivalis-LPS-mediated inflammatory pathways in periodontitis.

OBJECTIVES Advanced periodontitis potentially contributes to Alzheimer's disease (AD) development and progression by altering the blood-brain barrier microenvironment in the cerebral microvascular endothelium. This results, in cytotoxicity, cell cycle disruption, and increased pro-inflammatory cytokine expression, allowing pathogens to enter the brain and damage the central nervous system (CNS). This study evaluated the effects of Porphyromonas gingivalis W83 infection on pro-inflammatory response, cell viability, and cell cycle regulation in mouse brain endothelial cells (mBECs). METHODS mBECs were stimulated with live P. gingivalis at different multiplicity of infection (MOI) values (1:5, 1:10, 1:50, 1:100, 1:200) for 6, 12, 24, and 48 h. Cell viability, cell cycle regulation, and pro-inflammatory cytokine mRNA expression were assessed using the alamarBlue assay, flow cytometry, and reverse transcription quantitative polymerase chain reaction (RT-qPCR), respectively. RESULTS P. gingivalis reduced cell viability, induced morphological changes in mBECs by >50% after 48 h (p < 0.05) and caused concentration-dependent arrest in the S and G0/G1 phases of the cell cycle at MOI=1:100 and 1:200. The Il6, Il1b, and tumor necrosis factor alpha (Tnf) mRNA expression increased significantly compared to that of the controls (p < 0.05). CONCLUSIONS P. gingivalis reduced cellular metabolism and induced early cell cycle arrest at the G0/G1 phase in MBEC cells. It also increased the pro-inflammatory response, which could be associated with cell death and possible senescence of brain endothelial cells. These results suggested a possible role for P. gingivalis in the pathogenesis of AD. Further studies are required to elucidate these underlying mechanisms.

Porphyromonas gingivalis (Pg) is a primary oral pathogen in the widespread biofilm-induced “chronic” multi-systems inflammatory disease(s) including Alzheimer’s disease (AD). It is possibly the only second identified unique example of a biological extremophile in the human body. Having a better understanding of the key microbiological and genetic mechanisms of its pathogenesis and disease induction are central to its future diagnosis, treatment, and possible prevention. The published literature around the role of Pg in AD highlights the bacteria’s direct role within the brain to cause disease. The available evidence, although somewhat adopted, does not fully support this as the major process. There are alternative pathogenic/virulence features associated with Pg that have been overlooked and may better explain the pathogenic processes found in the “infection hypothesis” of AD. A better explanation is offered here for the discrepancy in the relatively low amounts of “Pg bacteria” residing in the brain compared to the rather florid amounts and broad distribution of one or more of its major bacterial protein toxins. Related to this, the “Gingipains Hypothesis”, AD-related iron dyshomeostasis, and the early reduced salivary lactoferrin, along with the resurrection of the Cholinergic Hypothesis may now be integrated into one working model. The current paper suggests the highly evolved and developed Type IX secretory cargo system of Pg producing outer membrane vesicles may better explain the observed diseases. Thus it is hoped this paper can provide a unifying model for the sporadic form of AD and guide the direction of research, treatment, and possible prevention.

Background: Porphyromonas gingivalis (P. gingivalis) and its gingipain virulence factors have been identified as pathogenic effectors in Alzheimer’s disease (AD). In a recent study we demonstrated the presence of gingipains in over 90% of postmortem AD brains, with gingipains localizing to the cytoplasm of neurons. However, infection of neurons by P. gingivalis has not been previously reported. Objective: To demonstrate intraneuronal P. gingivalis and gingipain expression in vitro after infecting neurons derived from human inducible pluripotent stem cells (iPSC) with P. gingivalis for 24, 48, and 72 h. Methods: Infection was characterized by transmission electron microscopy, confocal microscopy, and bacterial colony forming unit assays. Gingipain expression was monitored by immunofluorescence and RT-qPCR, and protease activity monitored with activity-based probes. Neurodegenerative endpoints were assessed by immunofluorescence, western blot, and ELISA. Results: Neurons survived the initial infection and showed time dependent, infection induced cell death. P. gingivalis was found free in the cytoplasm or in lysosomes. Infected neurons displayed an accumulation of autophagic vacuoles and multivesicular bodies. Tau protein was strongly degraded, and phosphorylation increased at T231. Over time, the density of presynaptic boutons was decreased. Conclusion: P. gingivalis can invade and persist in mature neurons. Infected neurons display signs of AD-like neuropathology including the accumulation of autophagic vacuoles and multivesicular bodies, cytoskeleton disruption, an increase in phospho-tau/tau ratio, and synapse loss. Infection of iPSC-derived mature neurons by P. gingivalis provides a novel model system to study the cellular mechanisms leading to AD and to investigate the potential of new therapeutic approaches.

Porphyromonas gingivalis has been associated with diseases such as atherosclerosis and Alzheimer's Disease and found in affected tissue, suggesting they can pass from the oral cavity into the bloodstream while evading the immune system. A possible mechanism is via interaction with Candida albicans that facilitates P. gingivalis survival and virulence in aerobic conditions and attracts macrophages, which could be a carrier for the pathogen to distant organs. The aim was to study the macrophage response upon exposure by P. gingivalis and C. albicans. Chemotaxis towards either P. gingivalis or C. albicans and association with P. gingivalis by macrophages was studied using time-lapse microscopy. Macrophages were exposed to both microorganisms to determine expression of pro-inflammatory M1 or anti-inflammatory M2 cytokines and genes using a cytokine bead array and qPCR. The results showed that macrophages were attracted towards C. albicans, not P. gingivalis. Both M1 and M2 macrophages could take up P. gingivalis from the hyphae of C. albicans. Exposure to both microorganisms led to polarization towards M1, as shown by a higher IL-6, IL-1β and TNFα release and lower expression of CD206 and CD209. In conclusion, C. albicans attracts macrophages to associate with P. gingivalis from the hyphae. A combined exposure predominantly triggers a pro-inflammatory response. By attracting macrophages and stimulating their immune response, C. albicans could facilitate phagocytosis of P. gingivalis by macrophages. P. gingivalis could use this to enter the body.

ABSTRACT The oral cavity is the initial chamber of digestive tract; the saliva swallowed daily contains an estimated 1.5 × 1012 oral bacteria. Increasing evidence indicates that periodontal pathogens and subsequent inflammatory responses to them contribute to the pathogenesis of Alzheimer’s disease (AD). The intestine and central nervous system jointly engage in crosstalk; microbiota-mediated immunity significantly impacts AD via the gut-brain axis. However, the exact mechanism linking periodontitis to AD remains unclear. In this study, we explored the influence of periodontitis-related salivary microbiota on AD based on the gut-brain crosstalk in APPswe/PS1ΔE9 (PAP) transgenic mice. Saliva samples were collected from patients with periodontitis and healthy individuals. The salivary microbiota was gavaged into PAP mice for two months. Continuous gavage of periodontitis-related salivary microbiota in PAP mice impaired cognitive function and increased β-amyloid accumulation and neuroinflammation. Moreover, these AD-related pathologies were consistent with gut microbial dysbiosis, intestinal pro-inflammatory responses, intestinal barrier impairment, and subsequent exacerbation of systemic inflammation, suggesting that the periodontitis-related salivary microbiota may aggravate AD pathogenesis through crosstalk of the gut-brain axis. In this study, we demonstrated that periodontitis might participate in the pathogenesis of AD by swallowing salivary microbiota, verifying the role of periodontitis in AD progression and providing a novel perspective on the etiology and intervention strategies of AD.

Background The relationship between periodontitis and Alzheimer’s disease (AD) has attracted more attention recently, whereas profiles of subgingival microbiomes and gingival crevicular fluid (GCF) metabolic signatures in AD patients have rarely been characterized; thus, little evidence exists to support the oral-brain axis hypothesis. Therefore, our study aimed to characterize both the microbial community of subgingival plaque and the metabolomic profiles of GCF in patients with AD and amnestic mild cognitive impairment (aMCI) for the first time. Methods This was a cross-sectional study. Clinical examinations were performed on all participants. The microbial community of subgingival plaque and the metabolomic profiles of GCF were characterized using the 16S ribosomal RNA (rRNA) gene high-throughput sequencing and liquid chromatography linked to tandem mass spectrometry (LC–MS/MS) analysis, respectively. Results Thirty-two patients with AD, 32 patients with aMCI, and 32 cognitively normal people were enrolled. The severity of periodontitis was significantly increased in AD patients compared with aMCI patients and cognitively normal people. The 16S rRNA gene sequencing results showed that the relative abundances of 16 species in subgingival plaque were significantly correlated with cognitive function, and LC–MS/MS analysis identified a total of 165 differentially abundant metabolites in GCF. Moreover, multiomics Data Integration Analysis for Biomarker discovery using Latent cOmponents (DIABLO) analysis revealed that 19 differentially abundant metabolites were significantly correlated with Veillonella parvula , Dialister pneumosintes , Leptotrichia buccalis , Pseudoleptotrichia goodfellowii , and Actinomyces massiliensis , in which galactinol, sn-glycerol 3-phosphoethanolamine, D-mannitol, 1 h-indole-1-pentanoic acid, 3-(1-naphthalenylcarbonyl)- and L-iditol yielded satisfactory accuracy for the predictive diagnosis of AD progression. Conclusions This is the first combined subgingival microbiome and GCF metabolome study in patients with AD and aMCI, which revealed that periodontal microbial dysbiosis and metabolic disorders may be involved in the etiology and progression of AD, and the differential abundance of the microbiota and metabolites may be useful as potential markers for AD in the future.

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Alzheimer’s disease (AD) is a progressive neurodegenerative disorder that affects millions of people worldwide, but its etiology remains largely unknown, and methods to control its onset and progression have not been established. If risk factors for dementia can be identified and effective countermeasures are taken against them, they may contribute to the prevention of dementia and extension of healthy life expectancy. Deposition of amyloid-β protein (Aβ) and hyperphosphorylation of tau protein are thought to be causes of AD, and there is a theory that those phenomena are induced by microorganisms invading the brain. In recent years, the association between infections caused by oral bacteria and AD has been pointed out, and the association between AD and Porphyromonas gingivalis, a type of periodontal disease-associated bacteria, has attracted attention. P. gingivalis and its virulence factors LPS and gingipain penetrate the blood-brain barrier (BBB) and enter the brain. They then enhance Aβ production and tau protein phosphorylation, leading to neuronal cell death. In addition, they may cooperate with Aβ to activate microglia and induce inflammatory reactions, which may exacerbate the pathogenesis of AD. Elucidation of the causal relationship between oral bacteria and AD may help in the development of treatments for this still incurable disease.

Abstract Epidemiological studies have identified an association between periodontitis and Alzheimer disease (AD); however, the nature of this association has been unclear. Recent work suggests that brain colonization by the periodontal pathogen Porphyromonas gingivalis may link these two inflammatory and degenerative conditions. Evidence of P. gingivalis infiltration has been detected in autopsy specimens from the brains of people with AD and in cerebrospinal fluid of individuals diagnosed with AD. Gingipains, a class of P. gingivalis proteases, are found in association with neurons, tau tangles, and beta‐amyloid in specimens from the brains of individuals with AD. The brains of mice orally infected with P. gingivalis show evidence of P. gingivalis infiltration, along with various neuropathological hallmarks of AD. Oral administration of gingipain inhibitors to mice with established brain infections decreases the abundance of P. gingivalis DNA in brain and mitigates the neurotoxic effects of P. gingivalis infection. Thus, gingipain inhibition could provide a potential approach to the treatment of both periodontitis and AD.

Background Among different types of sphingolipids produced by human cells, the possible engagement of ceramide species in the pathogenesis of Alzheimer’s disease (AD) has attracted recent attention. While ceramides are primarily generated by de novo synthesis in mammalian cells, only a limited number of bacterial species, produce ceramides, including phosphoglycerol dihydroceramide (PGDHC) that is produced by the key periodontal pathogen Porphyromonas gingivalis. Emerging evidence indicates that virulence factors produced by P. gingivalis, such as lipopolysaccharide and gingipain, may be engaged in the initiation and/or progression of AD. However, the potential role of PGDHC in the pathogenesis of AD remains unknown. Therefore, the aim of this study was to evaluate the influence of PGDHC on hallmark findings in AD. Material and Methods CHO-7WD10 and SH-SY-5Y cells were exposed to PGDHC and lipopolysaccharide (LPS) isolated from P. gingivalis. Soluble Aβ42 peptide, amyloid precursor protein (APP), phosphorylated tau and senescence-associated secretory phenotype (SASP) factors were quantified using ELISA and Western blot assays. Results Our results indicate that P. gingivalis (Pg)-derived PGDHC, but not Pg-LPS, upregulated secretion of soluble Aβ42 peptide and expression of APP in CHO-7WD10 cells. Furthermore, hyperphosphorylation of tau protein was observed in SH-SY-5Y cells in response to PGDHC lipid. In contrast, Pg-LPS had little, or no significant effect on the tau phosphorylation induced in SH-SY-5Y cells. However, both PGDHC and Pg-LPS contributed to the senescence of SH-SY5Y cells as indicated by the production of senescence-associated secretory phenotype (SASP) markers, including beta-galactosidase, cathepsin B (CtsB), and pro-inflammatory cytokines TNF-α, and IL-6. Additionally, PGDHC diminished expression of the senescence-protection marker sirtuin-1 in SH-SY-5Y cells. Conclusions Altogether, our results indicate that P. gingivalis-derived PGDHC ceramide promotes amyloidogenesis and hyperphosphorylation, as well as the production of SASP factors. Thus, PGDHC may represent a novel class of bacterial-derived virulence factors for AD associated with periodontitis.

Alzheimer's Disease Analysis Model (ADAM) is a multi-agent reasoning large language model (LLM) framework designed to integrate and analyze multimodal data, including microbiome profiles, clinical datasets, and external knowledge bases, to enhance the understanding and classification of Alzheimer's disease (AD). By leveraging the agentic system with LLM, ADAM produces insights from diverse data sources and contextualizes the findings with literature-driven evidence. A comparative evaluation with XGBoost revealed a significantly improved mean F1 score and significantly reduced variance for ADAM, highlighting its robustness and consistency, particularly when utilizing human biological data. Although currently tailored for binary classification tasks with two data modalities, future iterations will aim to incorporate additional data types, such as neuroimaging and peripheral biomarkers, and expand them to predict disease progression, thereby broadening ADAM's scalability and applicability in AD research and diagnostic applications.

Discriminative analysis in neuroimaging by means of deep/machine learning techniques is usually tested with validation techniques, whereas the associated statistical significance remains largely under-developed due to their computational complexity. In this work, a non-parametric framework is proposed that estimates the statistical significance of classifications using deep learning architectures. In particular, a combination of autoencoders (AE) and support vector machines (SVM) is applied to: (i) a one-condition, within-group designs often of normal controls (NC) and; (ii) a two-condition, between-group designs which contrast, for example, Alzheimer's disease (AD) patients with NC (the extension to multi-class analyses is also included). A random-effects inference based on a label permutation test is proposed in both studies using cross-validation (CV) and resubstitution with upper bound correction (RUB) as validation methods. This allows both false positives and classifier overfitting to be detected as well as estimating the statistical power of the test. Several experiments were carried out using the Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset, the Dominantly Inherited Alzheimer Network (DIAN) dataset, and a MCI prediction dataset. We found in the permutation test that CV and RUB methods offer a false positive rate close to the significance level and an acceptable statistical power (although lower using cross-validation). A large separation between training and test accuracies using CV was observed, especially in one-condition designs. This implies a low generalization ability as the model fitted in training is not informative with respect to the test set. We propose as solution by applying RUB, whereby similar results are obtained to those of the CV test set, but considering the whole set and with a lower computational cost per iteration.

Many studies have been performed to characterize the dynamics and stability of the microbiome across a range of environmental contexts [Costello et al., 2012, Faust et al., 2015]. For example, it is often of interest to identify time intervals within which certain subsets of taxa have an interesting pattern of behavior. Viewed abstractly, these problems often have a flavor not just of time series modeling but also of regime detection, a problem with a rich history across a variety of applications, including speech recognition [Fox et al., 2011], finance [Lee, 2009], EEG analysis [Camilleri et al., 2014], and geophysics [Weatherley and Mora, 2002]. In spite of the parallels, regime detection methods are rarely used in microbiome analysis, most likely due to the fact that references for these methods are scattered across several literatures, descriptions are inaccessible outside limited research communities, and implementations are difficult to come across. We distill the core ideas of different regime detection methods, provide example applications, and share reproducible code, making these techniques more accessible to microbiome researchers. We re-analyze data of Dethlefsen and Relman [2011], a study of the effects of antibiotics on the microbiome, using Classification and Regression Trees (CART) [Breiman et al., 1984], Hidden Markov Models (HMMs) [Rabiner and Juang, 1986], Bayesian nonparametric HMMs [Teh and Jordan, 2010, Fox et al., 2008], mixtures of Gaussian Processes (GPs) [Rasmussen and Ghahramani, 2002], switching dynamical systems [Linderman et al., 2016], and multiple changepoint detection [Fan and Mackey, 2015]. Along the way, we summarize each method, their relevance to the microbiome, and tradeoffs associated with using them. Ultimately, our goal is to describe types of temporal or regime switching structure that can be incorporated into studies of microbiome dynamics.

The human microbiome is the ensemble of genes in the microbes that live inside and on the surface of humans. Because microbial sequencing information is now much easier to come by than phenotypic information, there has been an explosion of sequencing and genetic analysis of microbiome samples. Much of the analytical work for these sequences involves phylogenetics, at least indirectly, but methodology has developed in a somewhat different direction than for other applications of phylogenetics. In this paper I review the field and its methods from the perspective of a phylogeneticist, as well as describing current challenges for phylogenetics coming from this type of work.

Microbiome interventions provide valuable data about microbial ecosystem structure and dynamics. Despite their ubiquity in microbiome research, few rigorous data analysis approaches are available. In this study, we extend transfer function-based intervention analysis to the microbiome setting, drawing from advances in statistical learning and selective inference. Our proposal supports the simulation of hypothetical intervention trajectories and False Discovery Rate-guaranteed selection of significantly perturbed taxa. We explore the properties of our approach through simulation and re-analyze three contrasting microbiome studies. An R package, mbtransfer, is available at https://go.wisc.edu/crj6k6. Notebooks to reproduce the simulation and case studies can be found at https://go.wisc.edu/dxuibh and https://go.wisc.edu/emxv33.

Microbiome studies have recently transitioned from experimental designs with a few hundred samples to designs spanning tens of thousands of samples. Modern studies such as the Earth Microbiome Project (EMP) afford the statistics crucial for untangling the many factors that influence microbial community composition. Analyzing those data used to require access to a compute cluster, making it both expensive and inconvenient. We show that recent improvements in both hardware and software now allow to compute key bioinformatics tasks on EMP-sized data in minutes using a gaming-class laptop, enabling much faster and broader microbiome science insights.

Recent advances in bioinformatics have made high-throughput microbiome data widely available, and new statistical tools are required to maximize the information gained from these data. For example, analysis of high-dimensional microbiome data from designed experiments remains an open area in microbiome research. Contemporary analyses work on metrics that summarize collective properties of the microbiome, but such reductions preclude inference on the fine-scale effects of environmental stimuli on individual microbial taxa. Other approaches model the proportions or counts of individual taxa as response variables in mixed models, but these methods fail to account for complex correlation patterns among microbial communities. In this paper, we propose a novel Bayesian mixed-effects model that exploits cross-taxa correlations within the microbiome, a model we call MIMIX (MIcrobiome MIXed model). MIMIX offers global tests for treatment effects, local tests and estimation of treatment effects on individual taxa, quantification of the relative contribution from heterogeneous sources to microbiome variability, and identification of latent ecological subcommunities in the microbiome. MIMIX is tailored to large microbiome experiments using a combination of Bayesian factor analysis to efficiently represent dependence between taxa and Bayesian variable selection methods to achieve sparsity. We demonstrate the model using a simulation experiment and on a 2x2 factorial experiment of the effects of nutrient supplement and herbivore exclusion on the foliar fungal microbiome of $\textit{Andropogon gerardii}$, a perennial bunchgrass, as part of the global Nutrient Network research initiative.

Massive Multi-Omics Microbiome Database (M3DB) is a data warehousing and analytics solution designed to handle diverse, complex, and unprecedented volumes of sequence and taxonomic classification data obtained in a typical microbiome project using NGS technologies. M3DB is a platform developed on Apache Hadoop, Apache Hive and PostgreSQL technologies. It enables users to store, analyze and manage high volumes of data, and also provides them the ability to query it in a fast and efficient manner. The M3DB framework includes command line tools to process and store microbiome data, along with an easy-to-use web-interface for uploading, querying, analyzing and visualizing the data and/or results. Availability: The source-code of M3DB is freely available for download at http://www.github.com/nisheth/M3DB.

Microbiomes, which are collections of interacting microbes in an environment, often substantially impact the environmental patches or living hosts that they occupy. In microbiome models, it is important to consider both the local dynamics within an environment and exchanges of microbiomes between environments. One way to incorporate these and other interactions across multiple scales is to employ metacommunity theory. Metacommunity models commonly assume continuous microbiome dispersal between the environments in which local microbiome dynamics occur. Under this assumption, a single parameter between each pair of environments controls the dispersal rate between those environments. This metacommunity framework is well-suited to abiotic environmental patches, but it fails to capture an essential aspect of the microbiomes of living hosts, which generally do not interact continuously with each other. Instead, living hosts interact with each other in discrete time intervals. In this paper, we develop a modeling framework that encodes such discrete interactions and uses two parameters to separately control the interaction frequencies between hosts and the amount of microbiome exchange during each interaction. We derive analytical approximations of models in our framework in three parameter regimes and prove that they are accurate in those regimes. We compare these approximations to numerical simulations for an illustrative model. We demonstrate that both parameters in our modeling framework are necessary to determine microbiome dynamics. Key features of the dynamics, such as microbiome convergence across hosts, depend sensitively on the interplay between interaction frequency and strength.

The human microbiome plays an important role in human health and disease status. Next generating sequencing technologies allow for quantifying the composition of the human microbiome. Clustering these microbiome data can provide valuable information by identifying underlying patterns across samples. Recently, Fang and Subedi (2020) proposed a logistic normal multinomial mixture model (LNM-MM) for clustering microbiome data. As microbiome data tends to be high dimensional, here, we develop a family of logistic normal multinomial factor analyzers (LNM-FA) by incorporating a factor analyzer structure in the LNM-MM. This family of models is more suitable for high-dimensional data as the number of parameters in LNM-FA can be greatly reduced by assuming that the number of latent factors is small. Parameter estimation is done using a computationally efficient variant of the alternating expectation conditional maximization algorithm that utilizes variational Gaussian approximations. The proposed method is illustrated using simulated and real datasets.

Microbial ecology serves as a foundation for a wide range of scientific and biomedical studies. Rapidly-evolving high-throughput sequencing technology enables the comprehensive search for microbial biomarkers using longitudinal experiments. Such experiments consist of repeated biological observations from each subject over time and are essential in accounting for the high between-subject and within-subject variability. Unfortunately, many of the statistical tests based on parametric models rely on correctly specifying temporal dependence structure which is unavailable in most microbiome data. In this paper, we propose an extension of the nonparametric bootstrap method that enables inference on these types longitudinal data. The proposed moving block bootstrap (MBB) method accounts for within-subject dependency by using overlapping blocks of repeated observations within each subject to draw valid inferences based on approximately pivotal statistics. Our simulation studies show an increase in power compared to merge-by-subject (MBS) strategies. We also show that compared to tests that presume independent samples (PIS), our proposed method reduces false microbial biomarker discovery rates. In this paper, we illustrated the MBB method using three different pregnancy data and an oral microbiome data. We provide an open-source R package https://github.com/PratheepaJ/bootLong to make our method accessible and the study in this paper reproducible.

We present BrainPainter, a software that automatically generates images of highlighted brain structures given a list of numbers corresponding to the output colours of each region. Compared to existing visualisation software (i.e. Freesurfer, SPM, 3D Slicer), BrainPainter has three key advantages: (1) it does not require the input data to be in a specialised format, allowing BrainPainter to be used in combination with any neuroimaging analysis tools, (2) it can visualise both cortical and subcortical structures and (3) it can be used to generate movies showing dynamic processes, e.g. propagation of pathology on the brain. We highlight three use cases where BrainPainter was used in existing neuroimaging studies: (1) visualisation of the degree of atrophy through interpolation along a user-defined gradient of colours, (2) visualisation of the progression of pathology in Alzheimer's disease as well as (3) visualisation of pathology in subcortical regions in Huntington's disease. Moreover, through the design of BrainPainter we demonstrate the possibility of using a powerful 3D computer graphics engine such as Blender to generate brain visualisations for the neuroscience community. Blender's capabilities, e.g. particle simulations, motion graphics, UV unwrapping, raster graphics editing, raytracing and illumination effects, open a wealth of possibilities for brain visualisation not available in current neuroimaging software. BrainPainter is customisable, easy to use, and can run straight from the web browser: https://brainpainter.csail.mit.edu , as well as from source-code packaged in a docker container: https://github.com/mrazvan22/brain-coloring . It can be used to visualise biomarker data from any brain imaging modality, or simply to highlight a particular brain structure for e.g. anatomy courses.

Normative modelling is an emerging method for understanding the underlying heterogeneity within brain disorders like Alzheimer Disease (AD) by quantifying how each patient deviates from the expected normative pattern that has been learned from a healthy control distribution. Since AD is a multifactorial disease with more than one biological pathways, multimodal magnetic resonance imaging (MRI) neuroimaging data can provide complementary information about the disease heterogeneity. However, existing deep learning based normative models on multimodal MRI data use unimodal autoencoders with a single encoder and decoder that may fail to capture the relationship between brain measurements extracted from different MRI modalities. In this work, we propose multi-modal variational autoencoder (mmVAE) based normative modelling framework that can capture the joint distribution between different modalities to identify abnormal brain structural patterns in AD. Our multi-modal framework takes as input Freesurfer processed brain region volumes from T1-weighted (cortical and subcortical) and T2-weighed (hippocampal) scans of cognitively normal participants to learn the morphological characteristics of the healthy brain. The estimated normative model is then applied on Alzheimer Disease (AD) patients to quantify the deviation in brain volumes and identify the abnormal brain structural patterns due to the effect of the different AD stages. Our experimental results show that modeling joint distribution between the multiple MRI modalities generates deviation maps that are more sensitive to disease staging within AD, have a better correlation with patient cognition and result in higher number of brain regions with statistically significant deviations compared to a unimodal baseline model with all modalities concatenated as a single input.

Background: Microbiota have evolved to acclimate themselves to many environments. Humanity is become ever increasingly medicated and many of those medications are antibiotics. Sadly, Microbiota are adapting to medication and with each passing generation they become more difficult to subdue. The 16S small subunit of bacterial ribosomal rRNA provides a wealth of information for classifying the species level taxonomy of bacteria. Methodology/Principal Findings: Experiments were collected utilizing broad and narrow spectrum antibiotics, which act primarily on DNA. In each experiment a statistically significant, unique and predictable pattern of sequential and thermodynamic stability or instability was found to correlate to antibiotic resistance. Conclusions/Significance: Classification of antibiotic resistance is possible for some species and antibiotic combinations using the 16S rRNA sequential and thermodynamic properties.

The intra- and inter-species genetic diversity of bacteria and the absence of 'reference', or the most representative, sequences of individual species present a significant challenge for sequence-based identification. The aims of this study were to determine the utility, and compare the performance of several clustering and classification algorithms to identify the species of 364 sequences of 16S rRNA gene with a defined species in GenBank, and 110 sequences of 16S rRNA gene with no defined species, all within the genus Nocardia. A total of 364 16S rRNA gene sequences of Nocardia species were studied. In addition, 110 16S rRNA gene sequences assigned only to the Nocardia genus level at the time of submission to GenBank were used for machine learning classification experiments. Different clustering algorithms were compared with a novel algorithm or the linear mapping (LM) of the distance matrix. Principal Components Analysis was used for the dimensionality reduction and visualization. Results: The LM algorithm achieved the highest performance and classified the set of 364 16S rRNA sequences into 80 clusters, the majority of which (83.52%) corresponded with the original species. The most representative 16S rRNA sequences for individual Nocardia species have been identified as 'centroids' in respective clusters from which the distances to all other sequences were minimized; 110 16S rRNA gene sequences with identifications recorded only at the genus level were classified using machine learning methods. Simple kNN machine learning demonstrated the highest performance and classified Nocardia species sequences with an accuracy of 92.7% and a mean frequency of 0.578.

The high throughput and cost-effectiveness afforded by short-read sequencing technologies, in principle, enable researchers to perform 16S rRNA profiling of complex microbial communities at unprecedented depth and resolution. Existing Illumina sequencing protocols are, however, limited by the fraction of the 16S rRNA gene that is interrogated and therefore limit the resolution and quality of the profiling. To address this, we present the design of a novel protocol for shotgun Illumina sequencing of the bacterial 16S rRNA gene, optimized to capture more than 90% of sequences in the Greengenes database and with nearly twice the resolution of existing protocols. Using several in silico and experimental datasets, we demonstrate that despite the presence of multiple variable and conserved regions, the resulting shotgun sequences can be used to accurately quantify the diversity of complex microbial communities. The reconstruction of a significant fraction of the 16S rRNA gene also enabled high precision (>90%) in species-level identification thereby opening up potential application of this approach for clinical microbial characterization.

The standard approach to analyzing 16S tag sequence data, which relies on clustering reads by sequence similarity into Operational Taxonomic Units (OTUs), underexploits the accuracy of modern sequencing technology. We present a clustering-free approach to multi-sample Illumina datasets that can identify independent bacterial subpopulations regardless of the similarity of their 16S tag sequences. Using published data from a longitudinal time-series study of human tongue microbiota, we are able to resolve within standard 97% similarity OTUs up to 20 distinct subpopulations, all ecologically distinct but with 16S tags differing by as little as 1 nucleotide (99.2% similarity). A comparative analysis of oral communities of two cohabiting individuals reveals that most such subpopulations are shared between the two communities at 100% sequence identity, and that dynamical similarity between subpopulations in one host is strongly predictive of dynamical similarity between the same subpopulations in the other host. Our method can also be applied to samples collected in cross-sectional studies and can be used with the 454 sequencing platform. We discuss how the sub-OTU resolution of our approach can provide new insight into factors shaping community assembly.

The bacterial microbiome is increasingly being recognised as a key factor in human health, driven in large part by datasets collected using 16S rRNA (ribosomal ribonucleic acid) gene sequencing, which enable cost-effective quantification of the composition of an individual's bacterial community. One of the defining characteristics of 16S rRNA datasets is the evolutionary relationships that exist between taxa (phylogeny). Here, we demonstrate the utility of modelling these phylogenetic relationships in two statistical tasks (the two sample test and host trait prediction) and propose a novel family of kernels for analysing microbiome datasets by leveraging string kernels from the natural language processing literature. We show via simulation studies that a kernel two-sample test using the proposed kernel is sensitive to the phylogenetic scale of the difference between the two populations. In a second set of simulations we also show how Gaussian process modelling with string kernels can infer the distribution of bacterial-host effects across the phylogenetic tree \new{and apply this approach to a real host-trait prediction task.} The results in the paper can be reproduced by running the code at https://github.com/jonathanishhorowicz/modelling_phylogeny_in_16srrna_using_string_kernels.

Recent advances in next-generation sequencing have revolutionized genomic research. 16S rRNA amplicon sequencing using paired-end sequencing on the MiSeq platform from Illumina, Inc., is being used to characterize the composition and dynamics of extremely complex/diverse microbial communities. For this analysis on the Illumina platform, merging and quality filtering of paired-end reads are essential first steps in data analysis to ensure the accuracy and reliability of downstream analysis. We have developed the Merging and Filtering Tool (MeFiT) to combine these pre-processing steps into one simple, intuitive pipeline. MeFiT provides an open-source solution that permits users to merge and filter paired end illumina reads based on user-selected quality parameters. The tool has been implemented in python and the source-code is freely available at https://github.com/nisheth/MeFiT.

The Composition Vector Tree (CVTree) method, developed under the leadership of Professor Hao Bailin, is an alignment-free algorithm for constructing phylogenetic trees. Although initially designed for studying prokaryotic evolution based on whole-genome, it has demonstrated broad applicability across diverse biological systems and gene sequences. In this study, we employed two methods, InterList and Hao, of CVTree to investigate the phylogeny and taxonomy of prokaryote based on the 16S rRNA sequences from All-Species Living Tree Project. We have established a comprehensive phylogenetic tree that incorporates the majority of species documented in human scientific knowledge and compared it with the taxonomy of prokaryotes. And the performance of CVTree were also compared with multiple sequence alignment-based approaches. Our results revealed that CVTree methods achieve computational speeds 1-3 orders of magnitude faster than conventional alignment methods while maintaining high consistency with established taxonomic relationships, even outperforming some multiple sequence alignment methods. These findings confirm CVTree's effectiveness and efficiency not only for whole-genome evolutionary studies but also for phylogenetic and taxonomic investigations based on genes.

Pre-clinical evidence implicates oral bacteria in the pathogenesis of Alzheimer's disease (AD), while clinical studies show diverse results. To comprehensively assess the association between oral bacteria and AD with clinical evidence. Studies investigating the association between oral bacteria and AD were identified through a systematic search of six databases PubMed, Embase, Cochrane Central Library, Scopus, ScienceDirect, and Web of Science. Methodological quality ratings of the included studies were performed. A best evidence synthesis was employed to integrate the results. When applicable, a meta-analysis was conducted using a random-effect model. Of the 16 studies included, ten investigated periodontal pathobionts and six were microbiome-wide association studies. Samples from the brain, serum, and oral cavity were tested. We found over a ten-fold and six-fold increased risk of AD when there were oral bacteria (OR = 10.68 95% CI: 4.48-25.43; p < 0.00001, I2 = 0%) and Porphyromonas gingivalis (OR = 6.84 95% CI: 2.70-17.31; p < 0.0001, I2 = 0%) respectively in the brain. While AD patients exhibited lower alpha diversity of oral microbiota than healthy controls, the findings of bacterial communities were inconsistent among studies. The best evidence synthesis suggested a moderate level of evidence for an overall association between oral bacteria and AD and for oral bacteria being a risk factor for AD. Current evidence moderately supports the association between oral bacteria and AD, while the association was strong when oral bacteria were detectable in the brain. Further evidence is needed to clarify the interrelationship between both individual species and bacterial communities and the development of AD.

Frailty is a critical intermediate status of the aging process with a multidimensional and multisystem nature and at higher risk for adverse health-related outcomes, including falls, disability, hospitalizations, institutionalization, mortality, dementia, and Alzheimer's disease. Among different frailty phenotypes, oral frailty has been recently suggested as a novel construct defined as a decrease in oral function with a coexisting decline in cognitive and physical functions. We briefly reviewed existing evidence on operational definitions of oral frailty, assessment and screening tools, and possible relationships among oral frailty, oral microbiota, and Alzheimer's disease neurodegeneration. Several underlying mechanism may explain the oral health-frailty links including undernutrition, sarcopenia linked to both poor nutrition and frailty, psychosocial factors, and the chronic inflammation typical of oral disease. Oral microbiota may influence Alzheimer's disease risk through circulatory or neural access to the brain and the interplay with periodontal disease, often causing tooth loss also linked to an increased Alzheimer's disease risk. On this bases, COR388, a bacterial protease inhibitor targeting Porphyromonas gingivalis implicated in periodontal disease, is now being tested in a double-blind, placebo-controlled Phase II/III study in mild-to-moderate Alzheimer's disease. Therefore, oral status may be an important contributor to general health, including Alzheimer's disease and late-life cognitive disorders, suggesting the central role of preventive strategies targeting the novel oral frailty phenotype and including maintenance and improvement of oral function and nutritional status to reduce the burden of both oral dysfunction and frailty.

In the initiation or exacerbation of Alzheimer disease, the dissemination of oral microorganisms into the brain tissue or the low-level systemic inflammation have been speculated to play a role. However, the impact of oral microorganisms, such as Porphyromonas gingivalis, on the pathogenesis of Alzheimer disease and the potential causative relationship is still unclear. The present review has critically reviewed the literature by examining the following aspects: (a) the oral microbiome and the immune response in the elderly population, (b) human studies on the association between periodontal and gut microorganisms and Alzheimer disease, (c) animal and in vitro studies on microorganisms and Alzheimer disease, and (d) preventive and therapeutic approaches. Factors contributing to microbial dysbiosis seem to be aging, local inflammation, systemic diseases, wearing of dentures, living in nursing homes and no access to adequate oral hygiene measures. Porphyromonas gingivalis was detectable in post-mortem brain samples. Microbiome analyses of saliva samples or oral biofilms showed a decreased microbial diversity and a different composition in Alzheimer disease compared to cognitively healthy subjects. Many in-vitro and animal studies underline the potential of P gingivalis to induce Alzheimer disease-related alterations. In animal models, recurring applications of P gingivalis or its components increased pro-inflammatory mediators and β-amyloid in the brain and deteriorated the animals' cognitive performance. Since periodontitis is the result of a disturbed microbial homoeostasis, an effect of periodontal therapy on the oral microbiome and host response related to cognitive parameters may be suggested and should be elucidated in further clinical trials.

Periodontitis, a major inflammatory disease of the oral mucosa, is epidemiologically associated with other chronic inflammation-driven disorders, including cardio-metabolic, neurodegenerative and autoimmune diseases and cancer. Emerging evidence from interventional studies indicates that local treatment of periodontitis ameliorates surrogate markers of comorbid conditions. The potential causal link between periodontitis and its comorbidities is further strengthened by recent experimental animal studies establishing biologically plausible and clinically consistent mechanisms whereby periodontitis could initiate or aggravate a comorbid condition. This multi-faceted 'mechanistic causality' aspect of the link between periodontitis and comorbidities is the focus of this Review. Understanding how certain extra-oral pathologies are affected by disseminated periodontal pathogens and periodontitis-associated systemic inflammation, including adaptation of bone marrow haematopoietic progenitors, may provide new therapeutic options to reduce the risk of periodontitis-associated comorbidities.

"Chronic" periodontitis and its keystone pathogen

Porphyromonas gingivalis (PG)-infected periodontitis is in close connection with the development of Alzheimer's disease (AD). PG-derived extracellular vesicles (pEVs) contain inflammation-inducing virulence factors, including gingipains (GPs) and lipopolysaccharide (LPS). To understand how PG could cause cognitive decline, we investigated the effects of PG and pEVs on the etiology of periodontitis and cognitive impairment in mice. Cognitive behaviors were measured in the Y-maze and novel object recognition tasks. Biomarkers were measured using ELISA, qPCR, immunofluorescence assay, and pyrosequencing. pEVs contained neurotoxic GPs and inflammation-inducible fimbria protein and LPS. Gingivally exposed, but not orally gavaged, PG or pEVs caused periodontitis and induced memory impairment-like behaviors. Gingival exposure to PG or pEVs increased TNF-α expression in the periodontal and hippocampus tissues. They also increased hippocampal GP Gingivally infected PG, particularly pEVs, may cause cognitive decline with periodontitis. PG products pEVs and LPS may be translocated into the brain through the trigeminal nerve and periodontal blood pathways, respectively, resulting in the cognitive decline, which may cause colitis and gut dysbiosis. Therefore, pEVs may be a remarkable risk factor for dementia.

Periodontal disease is a chronic infectious disease associated with a variety of bacteria, which can cause damage to the periodontal support structure and affect a variety of systemic system diseases such as cancer, cardiovascular disease, diabetes, rheumatoid arthritis, non-alcoholic fatty liver, and Alzheimer's disease.

In line with the strong association between periodontitis and Alzheimer's disease (AD) clinically, preclinical studies have shown that systemic exposure to Porphyromonas gingivalis (Pg) initiates AD pathologies. However, the involvement of periodontitis in promoting AD pathologies is unclear. In the present study, we provided evidence that chronic systemic exposure to lipopolysaccharide derived from Pg (PgLPS, 1 mg/kg, daily, intraperitoneally) prompted neuroinflammation and tau hyperphosphorylation in 10-month-old of amyloid precursor protein (APP) knock-in mice, a model of AD, carrying the Swedish and Beyreuther/Iberian mutation (APP

Bacteremia induced by periodontal infection is an important factor for periodontitis to threaten general health. P. gingivalis DNA/virulence factors have been found in the brain tissues from patients with Alzheimer's disease (AD). The blood-brain barrier (BBB) is essential for keeping toxic substances from entering brain tissues. However, the effect of P. gingivalis bacteremia on BBB permeability and its underlying mechanism remains unclear. In the present study, rats were injected by tail vein with P. gingivalis three times a week for eight weeks to induce bacteremia. An in vitro BBB model infected with P. gingivalis was also established. We found that the infiltration of Evans blue dye and Albumin protein deposition in the rat brain tissues were increased in the rat brain tissues with P. gingivalis bacteremia and P. gingivalis could pass through the in vitro BBB model. Caveolae were detected after P. gingivalis infection in BMECs both in vivo and in vitro. Caveolin-1 (Cav-1) expression was enhanced after P. gingivalis infection. Downregulation of Cav-1 rescued P. gingivalis-enhanced BMECs permeability. We further found P. gingivalis-gingipain could be colocalized with Cav-1 and the strong hydrogen bonding between Cav-1 and arg-specific-gingipain (RgpA) were detected. Moreover, P. gingivalis significantly inhibited the major facilitator superfamily domain containing 2a (Mfsd2a) expression. Mfsd2a overexpression reversed P. gingivalis-increased BMECs permeability and Cav-1 expression. These results revealed that Mfsd2a/Cav-1 mediated transcytosis is a key pathway governing BBB BMECs permeability induced by P. gingivalis, which may contribute to P. gingivalis/virulence factors entrance and the subsequent neurological impairments.

The outer membrane vesicles of Pg OMVs impaired memory and learning ability of mice and decreased tight junction-related gene expression ZO-1, occludin, claudin-5, and occludin protein expression in the hippocampus. Pg OMVs could be detected in the hippocampus and cortex three days after oral gavage. Furthermore, Pg OMVs activated both astrocytes and microglia and elevated IL-1β, tau phosphorylation on the Thr231 site, and NLRP3 inflammasome-related protein expression in the hippocampus. In These results indicate that Pg OMVs prompt memory dysfunction, neuroinflammation, and tau phosphorylation and trigger NLRP3 inflammasome in the brain of middle-aged mice. We propose that Pg OMVs play an important role in activating neuroinflammation in the AD-like pathology triggered by

No abstract

Periodontitis is considered a non-communicable chronic disease caused by a dysbiotic microbiota, which generates a low-grade systemic inflammation that chronically damages the organism. Several studies have associated periodontitis with other chronic non-communicable diseases, such as cardiovascular or neurodegenerative diseases. Besides, the oral bacteria considered a keystone pathogen,

Periodontitis is a risk factor linked to Alzheimer's disease (AD), and characterized by amyloid-beta (Aβ) pathology. Mounting evidence suggests a contributory role of periodontitis in the onset and progression of AD. Type I interferons are upregulated in Porphyromonas gingivalis (Pg)-induced periodontitis in murine models. Colonization of Pg has been identified in the brains of patients with AD. Recently, interferon-induced transmembrane protein 3 (IFITM3), an inflammation-induced innate immunity protein, was identified as a novel γ-secretase modulatory protein for Aβ production in AD. However, whether periodontitis triggers an increase in type I interferons in the brain, subsequently inducing AD-like pathology by eliciting the innate immune response of glial cells and activating the IFITM3-Aβ axis, remains unclear. Additionally, the question of whether colonization of Pg in brain induces innate immune in astrocytes and microglia remains unanswered. We assessed the impact of Pg-induced periodontitis on cognitive impairment in C57BL/6J and APP/PS1 mice using behavioral tests. The effects of Periodontitis/Pg on microglia and astrocytes were measured using quantitative reverse transcriptase PCR (qRT-PCR), western blotting, and histological staining. Pg-induced periodontitis led to cognitive impairment in C57BL/6J mice and exacerbated a cognitive decline in APP/PS1 mice. Furthermore, Pg-induced periodontitis elevated the levels of interferon (IFN)-β, IFITM3, and Aβ deposition in the brains of both C57BL/6J and APP/PS1 mice. We also identified Pg DNA, glial activation, and the expression of inflammatory mediators in the brain of a Pg-induced periodontitis model. Additionally, our findings confirmed astrocytes as the primary responders to Pg-induced innate immunity and inflammation both in vitro and in vivo. Periodontitis also induces an increase in IFITM3 expression in periodontal tissue, salivary glands. We define a previously unidentified link between periodontitis and cognitive decline, and provide new evidence linking oral pathogenic bacteria-induced innate immunity and neuroinflammation to AD pathogenesis and cognitive decline, partly through increased blood-brain barrier (BBB) permeability, triggered neuroinflammation, and elevated IFITM3 in glial cells for Aβ deposition. Moreover, periodontitis exacerbates innate immunity and cognitive impairment in AD mice, underscoring the importance of preventive and therapeutic strategies for periodontal disease in AD patients.

Our research into Alzheimer's disease (AD) focuses on the oral cavity and the brain, from which key evaluations of prospective and retrospective population-based data have shown that chronic periodontal disease existing for ten-years or over doubles the risk for the sporadic form of AD. Furthermore,

The results from cross sectional and longitudinal studies show that periodontitis is closely associated with cognitive impairment (CI) and Alzhemer's Disease (AD). Further, studies using animal model of periodontitis and human post-mortem brain tissues from subjects with AD strongly suggest that a gram-negative periodontal pathogen, Porphyromonas gingivalis (Pg) and/or its product gingipain is/are translocated to the brain. However, neuropathology resulting from Pg oral application is not known. In this work, we tested the hypothesis that repeated exposure of wild type C57BL/6 mice to orally administered Pg results in neuroinflammation, neurodegeneration, microgliosis, astrogliosis and formation of intra- and extracellular amyloid plaque and neurofibrillary tangles (NFTs) which are pathognomonic signs of AD. Experimental chronic periodontitis was induced in ten wild type 8-week old C57BL/6 WT mice by repeated oral application (MWF/week) of Pg/gingipain for 22 weeks (experimental group). Another 10 wild type 8-week old C57BL/6 mice received vehicle alone (control group) MWF per week for 22 weeks. Brain tissues were collected and the presence of Pg/gingipain was determined by immunofluorescence (IF) microscopy, confocal microscopy, and quantitative PCR (qPCR). The hippocampi were examined for the signs of neuropathology related to AD: TNFα, IL1β, and IL6 expression (neuroinflammation), NeuN and Fluoro Jade C staining (neurodegeneration) and amyloid beta1-42 (Aβ42) production and phosphorylation of tau protein at Ser396 were assessed by IF and confocal microscopy. Further, gene expression of amyloid precursor protein (APP), beta-site APP cleaving enzyme 1 (BACE1), a disintegrin and metalloproteinase domain-containing protein10 (ADAM10) for α-secretase and presenilin1 (PSEN1) for ɣ-secretase, and NeuN (rbFox3) were determined by RT-qPCR. Microgliosis and astrogliosis were also determined by IF microscopy. Pg/gingipain was detected in the hippocampi of mice in the experimental group by immunohistochemistry, confocal microscopy, and qPCR confirming the translocation of orally applied Pg to the brain. Pg/gingipain was localized intra-nuclearly and peri-nuclearly in microglia (Iba1+), astrocytes (GFAP+), neurons (NeuN+) and was evident extracellularly. Significantly greater levels of expression of IL6, TNFα and IL1β were evident in experimental as compared to control group (p<0.01, p<0.00001, p<0.00001 respectively). In addition, microgliosis and astrogliosis were evident in the experimental but not in control group (p <0.01, p<0.0001 respectively). Neurodegeneration was evident in the experimental group based on a fewer number of intact neuronal cells assessed by NeuN positivity and rbFOX3 gene expression, and there was a greater number of degenerating neurons in the hippocampi of experimental mice assessed by Fluoro Jade C positivity. APP and BACE1 gene expression were increased in experimental group compared with control group (p<0.05, p<0.001 respectively). PSEN1 gene expression was higher in experimental than control group but the difference was not statistically significant (p = 0.07). ADAM10 gene expression was significantly decreased in experimental group compared with control group (p<0.01). Extracellular Aβ42 was detected in the parenchyma in the experimental but not in the control group (p< 0.00001). Finally, phospho-Tau (Ser396) protein was detected and NFTs were evident in experimental but not in the control group (p<0.00001). This study is the first to show neurodegeneration and the formation of extracellular Aβ42 in young adult WT mice after repeated oral application of Pg. The neuropathological features observed in this study strongly suggest that low grade chronic periodontal pathogen infection can result in the development of neuropathology that is consistent with that of AD.

The traditional identification of bacteria on the basis of phenotypic characteristics is generally not as accurate as identification based on genotypic methods. Comparison of the bacterial 16S rRNA gene sequence has emerged as a preferred genetic technique. 16S rRNA gene sequence analysis can better identify poorly described, rarely isolated, or phenotypically aberrant strains, can be routinely used for identification of mycobacteria, and can lead to the recognition of novel pathogens and noncultured bacteria. Problems remain in that the sequences in some databases are not accurate, there is no consensus quantitative definition of genus or species based on 16S rRNA gene sequence data, the proliferation of species names based on minimal genetic and phenotypic differences raises communication difficulties, and microheterogeneity in 16S rRNA gene sequence within a species is common. Despite its accuracy, 16S rRNA gene sequence analysis lacks widespread use beyond the large and reference laboratories because of technical and cost considerations. Thus, a future challenge is to translate information from 16S rRNA gene sequencing into convenient biochemical testing schemes, making the accuracy of the genotypic identification available to the smaller and routine clinical microbiology laboratories.

16S rRNA gene sequence is the most common housekeeping genetic marker to study bacterial phylogeny and taxonomy. Therefore, 16S rRNA gene sequencing has the potential to identify novel bacteria and diagnose bacteria. This study compared 16S rRNA gene sequencing with conventional PCR for bacterial identification and disease diagnosis. The bacterial community in healthy and diseased hosts was analyzed by 16S rRNA gene sequencing. 16S rRNA gene sequencing is more sensitive than conventional PCR in detecting bacteria. Moreover, 16S rRNA gene sequencing is adequate to identify novel bacteria. 16S rRNA gene sequencing demonstrated that most pathogenic bacteria persist in diseased or healthy hosts in different abundance. Pathogenic bacteria, such as well-known chicken pathogen Avibacterium paragallinarum, Ornithobacterium rhinotracheale, and Gallibacterium anatis, were identified as indicator species of diseased samples. Alpha diversity analysis showed that the healthy group species is significantly higher than in the diseased groups. Beta diversity analysis also demonstrated differences between healthy and infected groups. The study concluded that 16S rRNA gene sequencing is a more sensitive method for detecting pathogens, and microbiota analysis can distinguish between healthy and diseased samples. Eventually, 16S rRNA gene sequencing has represented the potential in human and animal clinical diagnosis and novel bacterial identification.

16S rRNA gene sequences are commonly analyzed for taxonomic and phylogenetic studies because they contain variable regions that can help distinguish different genera. However, intra-genus distinction using variable region homology is often impossible due to the high overall sequence identities among closely related species, even though some residues may be conserved within respective species. Using a computational method that included the allelic diversity within individual genomes, we discovered that certain Escherichia and

The full-length 16S rRNA sequencing can better improve the taxonomic and phylogenetic resolution compared to the partial 16S rRNA gene sequencing. The 16S-FAS-NGS (16S rRNA full-length amplicon sequencing based on a next-generation sequencing platform) technology can generate high-quality, full-length 16S rRNA gene sequences using short-read sequencers, together with assembly procedures. However there is a lack of a data analysis suite that can help process and analyze the synthetic long read data. Herein, we developed software named 16S-FASAS (16S full-length amplicon sequencing data analysis software) for 16S-FAS-NGS data analysis, which provided high-fidelity species-level microbiome data. 16S-FASAS consists of data quality control, 16S-FASAS is a valuable tool that helps researchers process and interpret the results of full-length 16S rRNA gene sequencing. Depending on the full-length amplicon sequencing technology, the 16S-FASAS pipeline enables a more accurate report on the bacterial complexity of microbiome samples. 16S-FASAS is freely available for use at https://github.com/capitalbio-bioinfo/FASAS.

16S rRNA gene amplicon sequencing is routinely used in environmental surveys to identify microbial diversity and composition of the samples of interest. The dominant sequencing technology of the past decade (Illumina) is based on the sequencing of 16S rRNA hypervariable regions. Online sequence data repositories, which represent an invaluable resource for investigating microbial distributional patterns across spatial, environmental or temporal scales, contain amplicon datasets from diverse 16S rRNA gene variable regions. However, the utility of these sequence datasets is potentially reduced by the use of different 16S rRNA gene amplified regions. By comparing 10 Antarctic soil samples sequenced for five different 16S rRNA amplicons, we explore whether sequence data derived from diverse 16S rRNA variable regions can be validly used as a resource for biogeographical studies. Patterns of shared and unique taxa differed among samples as a result of variable taxonomic resolutions of the assessed 16S rRNA variable regions. However, our analyses also suggest that the use of multi-primer datasets for biogeographical studies of the domain Bacteria is a valid approach to explore bacterial biogeographical patterns due to the preservation of bacterial taxonomic and diversity patterns across different variable region datasets. We deem composite datasets useful for biogeographical studies.

Precise identification of species is fundamental in microbial genomics and is crucial for understanding the microbial communities. While the 16S rRNA gene, particularly its V3-V4 regions, has been extensively employed for microbial identification, however has limitations in achieving species-level resolution. Advancements in long-read sequencing technologies have highlighted the rRNA operon as a more accurate marker for microbial classification and analysis than the 16S rRNA gene. This study aims to compare the accuracy of species classification and microbial community analysis using the rRNA operon versus the 16S rRNA gene. We evaluated the species classification accuracy of the rRNA operon,16S rRNA gene, and 16S rRNA V3-V4 regions using a BLAST-based method and a k-mer matching-based method with public data available from NCBI. We further performed simulations to model microbial community analysis. We accessed the performance using each marker in community composition estimation and differential abundance analysis. Our findings demonstrate that the rRNA operon offers an advantage over the 16S rRNA gene and its V3-V4 regions for species-level classification within the genus. When applied to microbial community analysis, the rRNA operon enables a more accurate determination of composition. Using the rRNA operon yielded more reliable results in differential abundance analysis as well. We quantitatively demonstrated that the rRNA operon outperformed the 16S rRNA and its V3-V4 regions in accuracy for both individual species identification and species-level microbial community analysis. Our findings can provide guidelines for selecting appropriate markers in the field of microbial research.

Culture-independent 16S rRNA gene metabarcoding is a commonly used method for microbiome profiling. To achieve more quantitative cell fraction estimates, it is important to account for the 16S rRNA gene copy number (hereafter 16S GCN) of different community members. Currently, there are several bioinformatic tools available to estimate the 16S GCN values, either based on taxonomy assignment or phylogeny. Here we present a novel approach ANNA16, Artificial Neural Network Approximator for 16S rRNA gene copy number, a deep learning-based method that estimates the 16S GCN values directly from the 16S gene sequence strings. Based on 27,579 16S rRNA gene sequences and gene copy number data from the rrnDB database, we show that ANNA16 outperforms the commonly used 16S GCN prediction algorithms. Interestingly, Shapley Additive exPlanations (SHAP) shows that ANNA16 can identify unexpected informative positions in 16S rRNA gene sequences without any prior phylogenetic knowledge, which suggests potential applications beyond 16S GCN prediction.

Next-generation sequencing technologies have impressively unlocked capacities to depict the complexity of microbial communities. Microbial community structure is for now routinely monitored by sequencing of 16S rRNA gene, a phylogenetic marker almost conserved among bacteria and archaea. Nevertheless, amplicon sequencing, the most popular used approach, suffers from several biases impacting the picture of microbial communities. Here, we describe an innovative method based on gene capture by hybridization for the targeted enrichment of 16S rDNA biomarker from metagenomic samples. Coupled to near full-length 16S rDNA reconstruction, this approach enables an exhaustive and accurate description of microbial communities by enhancing taxonomic and phylogenetic resolutions. Furthermore, access of captured 16S flanking regions opens link between structure and function in microbial communities.

Currently, diagnosis of bacterial infections is based on culture, possibly followed by the amplification and sequencing (Sanger method) of the 16S rDNA - encoding gene when cultures are negative. Clinical metagenomics (CMg), i.e. the sequencing of a sample's entire nucleic acids, may allow for the identification of bacteria not detected by conventional methods. Here, we tested the performance of CMg compared to 16S rDNA sequencing (Sanger) in 50 patients with suspected bacterial infection but negative cultures. This is a prospective cohort study. Fifty patients (73 samples) with negative culture and a 16S rDNA sequencing demand (Sanger) were recruited from two sites. On the same samples, CMg (Illumina NextSeq) was also performed and compared to 16S rDNA Sanger sequencing. Bacteria were identified using MetaPhlAn4. Among the 73 samples, 20 (27 %, 17 patients) had a clinically relevant 16S rDNA Sanger sequencing result (used for patient management) while 11 (15 %, 9 patients) were considered contaminants. At the patient level, the sensitivity of CMg was 70 % (12/17) compared to 16S rDNA. In samples negative for 16S rDNA Sanger sequencing (n = 53), CMg identified clinically-relevant bacteria in 10 samples (19 %, 10 patients) with 14 additional bacteria. CMg was not 100 % sensitive when compared to 16S, supporting that it may not be a suitable replacement. However, CMg did find additional bacteria in samples negative for 16S rDNA Sanger. CMg could therefore be positioned as a complementary to 16S rDNA Sanger sequencing.

The 16S rRNA gene works as a rapid and effective marker for the identification of microorganisms in complex communities; hence, a huge number of microbiomes have been surveyed by 16S amplicon-based sequencing. The resolution of the 16S rRNA gene is always considered only at the genus level; however, it has not been verified on a wide range of microbes yet. To fully explore the ability and potential of the 16S rRNA gene in microbial profiling, here, we propose Qscore, a comprehensive method to evaluate the performance of amplicons by integrating the amplification rate, multitier taxonomic annotation, sequence type, and length. Our

Analyzing 16S ribosomal RNA (rRNA) sequences allows researchers to elucidate the prokaryotic composition of an environment. In recent years, third-generation sequencing technology has provided opportunities for researchers to perform full-length sequence analysis of bacterial 16S rRNA. RDP, SILVA, and Greengenes are the most widely used 16S rRNA databases. Many 16S rRNA classifiers have used these databases as a reference for taxonomic assignment tasks. However, some of the prokaryotic taxonomies only exist in one of the three databases. Furthermore, Greengenes and SILVA include a considerable number of taxonomies that do not have the resolution to the species level, which has limited the classifiers' performance. In order to improve the accuracy of taxonomic assignment at the species level for full-length 16S rRNA sequences, we manually curated the three databases and removed the sequences that did not have a species name. We then established a taxonomy-based integrated database by considering both taxonomies and sequences from all three 16S rRNA databases and validated it by a mock community. Results showed that our taxonomy-based integrated database had improved taxonomic resolution to the species level. The integrated database and the related datasets are available at https://github.com/yphsieh/ItgDB.

Metagenome-assembled genomes (MAGs) have substantially extended our understanding of microbial functionality. However, 16S rRNA genes, which are commonly used in phylogenetic analysis and environmental surveys, are often missing from MAGs. Here, we developed MarkerMAG, a pipeline that links 16S rRNA genes to MAGs using paired-end sequencing reads. Assessment of MarkerMAG on three benchmarking metagenomic datasets with various degrees of complexity shows substantial increases in the number of MAGs with 16S rRNA genes and a 100% assignment accuracy. MarkerMAG also estimates the copy number of 16S rRNA genes in MAGs with high accuracy. Assessments on three real metagenomic datasets demonstrate 1.1- to 14.2-fold increases in the number of MAGs with 16S rRNA genes. We also show that MarkerMAG-improved MAGs increase the accuracy of functional prediction from 16S rRNA gene amplicon data. MarkerMAG is helpful in connecting information in MAG databases with those in 16S rRNA databases and surveys and hence contributes to our increasing understanding of microbial diversity, function and phylogeny. MarkerMAG is implemented in Python3 and freely available at https://github.com/songweizhi/MarkerMAG. Supplementary data are available at Bioinformatics online.

High-throughput sequencing of 16S rRNA gene amplicons (16S-seq) has become a widely deployed method for profiling complex microbial communities but technical pitfalls related to data reliability and quantification remain to be fully addressed. In this work, we have developed and implemented a set of synthetic 16S rRNA genes to serve as universal spike-in standards for 16S-seq experiments. The spike-ins represent full-length 16S rRNA genes containing artificial variable regions with negligible identity to known nucleotide sequences, permitting unambiguous identification of spike-in sequences in 16S-seq read data from any microbiome sample. Using defined mock communities and environmental microbiota, we characterized the performance of the spike-in standards and demonstrated their utility for evaluating data quality on a per-sample basis. Further, we showed that staggered spike-in mixtures added at the point of DNA extraction enable concurrent estimation of absolute microbial abundances suitable for comparative analysis. Results also underscored that template-specific Illumina sequencing artifacts may lead to biases in the perceived abundance of certain taxa. Taken together, the spike-in standards represent a novel bioanalytical tool that can substantially improve 16S-seq-based microbiome studies by enabling comprehensive quality control along with absolute quantification.