ZNF362

Egg production by female schistosomes drives both transmission and pathology of schistosomiasis, affecting over 200 million people. Female maturation relies on the male-derived pheromone β-alanyl-tryptamine (BATT), but underlying molecular mechanisms are unclear. We identified the BATT-responsive transcription factor gene znf362 as a key regulator of female reproductive development. Functional studies showed that znf362.1, but not znf362.2, is essential for BATT-induced ovary and vitellaria maturation. Single-cell transcriptomics and in situ hybridization revealed up-regulation of znf362.1 in oocytes and vitellaria S1 cells after BATT exposure. Multiomics analysis showed ZNF362.1 directly activates Smp_349410, a female gonad-specific gene encoding a CPEB1 homolog. Loss of znf362.1 or Smp_349410 impaired oocyte and vitellocyte differentiation without affecting progenitors. Mechanistically, SmCPEB1 promotes female ovary development by regulating polyadenylation of cyclin B1 mRNA and drives S1 cell differentiation in the vitellaria. These findings define a transcriptional and post-transcriptional axis, BATT-znf362.1-cpeb1, that initiates female sexual maturation, offering mechanistic insight into schistosome reproduction and potential targets for schistosomiasis control.

Introduction: Since 2020, we have successfully developed a targeted blood cancer NGS panel and have been using it efficiently in clinical settings. However, it needed to be upgraded due to recent new blood cancer diagnosis and classification guidelines by the World Health Organization and the International Collaboration for Cancer Classification (ICC) in 2022. Therefore, in this study, an updated targeted NGS panel that covers the updated guidelines was evaluated using clinical samples. Methods: To adhere to the recent guidelines, the currently used targeted NGS panel (KBB DNA/RNAseq NGS Leukemia PHB; KBlueBio Inc., Hwasun, Korea) was reviewed. Its analytical performance was validated using standard NA12878 and clinical samples. The clinical samples consisted of 19 genomic DNA and 20 total RNA samples extracted from the bone marrow or peripheral blood of patients with hematologic malignancies, including acute myeloid leukemia, acute lymphocytic leukemia, myelodysplastic syndrome/myeloproliferative neoplasm, multiple myeloma, and lymphoma. Results: The updated DNA panel comprises 125 genes including 6 new genes ( BCORL1, BLM, GNB1, PRPF8, SAMD9 and SAMD9L). The updated RNA panel has 116 genes including 31 new genes ( FLT3, CBFA2T3, RPN1, BCL11B, ZNF362, HLF, NUTM1, UBRF, CDX2, ZEB2, CDK6, ZMYM2, AFDN, ELL, NSD1, HOXA9, KDM5A, ZBTB16, GLIS2, PRDM16, NPM1, KAT6A, MNX1, TAF15, MLLT1, P2RY8, HNRNPUL1, ECM1, ENAM, JCHAIN (IGJ) and MDFIC). The accuracy, repeatability, reproducibility, sensitivity, and detection limit of the updated NGS panels were evaluated using standard materials, and the results met the predefined criteria. A comparison of the updated DNA panel with the old panel using 19 clinical samples revealed an overall concordance rate of 100% between the two panels for all mutations (95% confidence interval (CI): 99.72-100.00%). Similarly, the updated RNA panel also met the predefined criteria. The overall concordance rate between the updated RNA panel and the old panel was 99.72% in 20 clinical samples (95% CI: 99.00-99.72%). Conclusions: The old targeted DNA/RNA NGS panel was successfully updated according to the 2022 WHO and ICC guidelines, and can be used to accurately and efficiently detect the genetic variants of blood cancers.

Abstract Purpose Duchenne muscular dystrophy (DMD) is currently the most commonly diagnosed form of muscular dystrophy due to mutations in the dystrophin gene. However, its pathological process remains unknown and there is a lack of specific molecular biomarkers. The aim of our study is to explore key regulatory connections underlying the progression of DMD. Materials and methods The gene expression profile dataset GSE38417 of DMD was obtained from the Gene Expression Omnibus (GEO) database. The differentially expressed genes (DEGs) between DMD patients and healthy controls were screened using geo2R, followed by Kyoto Encyclopedia of Genes and Genomes (KEGG) and Gene Ontology (GO) pathway enrichment analyses. Then a protein–protein interaction (PPI) network and sub-network of modules were constructed. To investigate the regulatory network underlying DMD, a global triple network including miRNAs, mRNAs and transcription factors (TFs) was constructed. Results A total of 1811 DEGs were found between the DMD and control groups, among which HERC5, SKP2 and FBXW5 were defined as hub genes with a degree of connectivity >35 in the PPI network. Furthermore, the five TFs ZNF362, ATAT1, SPI1, TCF12 and ABCF2, as well as the eight miRNAs miR-124a, miR-200b/200c/429, miR-19a/b, miR-23a/b, miR-182, miR-144, miR-498 and miR-18a/b were identified as playing crucial roles in the molecular pathogenesis of DMD. Conclusions This paper provides a comprehensive perspective on the miRNA–TF–mRNA co-regulatory network underlying DMD, although the bioinformatic findings need further validation in future studies.

Background Fusion genes are major drivers of acute leukemia. Conventional diagnostics are limited in detecting the diverse fusions included in recently updated acute leukemia classifications. We evaluated the fusion detection performance of RNA sequencing (RNA-seq) compared with that of conventional diagnostics in patients with acute leukemia. Methods We retrospectively obtained the data of 101 patients with acute leukemia who underwent conventional diagnostics (i.e., karyotyping, FISH, or multiplex reverse transcription PCR) at diagnosis at Samsung Medical Center, Seoul, Korea, between September 2022 and September 2023. Whole RNA-seq was performed using the Illumina Stranded mRNA Prep kit (Illumina, San Diego, CA, USA). The concordance, sensitivity, and specificity of RNA-seq for fusion gene detection were compared with those of conventional diagnostics. Results RNA-seq helped identify 52 fusion genes in 51 (50.5%) of 101 patients, with detection rates of 40.7%, 70.3%, 37.5%, and 50% in acute myeloid leukemia, B-cell acute lymphoblastic leukemia, T-cell acute lymphoblastic leukemia, and mixed-phenotype acute leukemia, respectively. RNA-seq showed 83.3% sensitivity and 80.8% concordance with conventional diagnostics; it missed eight fusions, likely because of low transcript abundance or enhancer hijacking. RNA-seq also helped clarify three previously unspecified rearrangements and detected 12 fusions (21.4%) in 56 cases that tested negative with conventional diagnostics, including four novel (KMT2ATHAP12, RUNX1PRPF19, MLLT10UBE2L6, and FUSZNF362) and three rare (HNRNPH1ERG, RUNX1USP42, and ETV6NCOA2) fusions. Conclusions This was the first study to evaluate the performance of whole RNA-seq in fusion detection in patients with acute leukemia in Korea. Incorporating RNA-seq into diagnostic workflows may facilitate earlier and more precise therapeutic decisions and improve prognostic assessment in patients with acute leukemia.

… Genetic investigation revealed two novel fusion genes within this cohort: ETV6-ZNF385A and ZNF362-TCF4. Our study highlighted the clinical aspects of genomic features of ALL in …

Significance In BCP ALL, molecular classification is used for risk stratification and influences treatment strategies. We reanalyzed the transcriptomic landscape of 1,223 BCP ALLs and identified 14 subgroups based on their transcriptional profiles. Eight of these (G1 to G8) are previously well-known subgroups, harboring specific genetic abnormalities. The sample size allowed the identification of six previously undescribed subgroups, consisting of cases harboring PAX5 or CRLF2 fusions (G9), PAX5 (p.P80R) mutations (G10), IKZF1 (p.N159Y) mutations (G11), either ZEB2 (p.H1038R) mutations or IGH–CEBPE fusions (G12), HLF rearrangements (G13), or NUTM rearrangements (G14). In addition, this study allowed us to determine the prognostic impact of several recently defined subgroups. This study suggests that RNA sequencing should be a valuable tool in the routine diagnostic workup for ALL. Most B cell precursor acute lymphoblastic leukemia (BCP ALL) can be classified into known major genetic subtypes, while a substantial proportion of BCP ALL remains poorly characterized in relation to its underlying genomic abnormalities. We therefore initiated a large-scale international study to reanalyze and delineate the transcriptome landscape of 1,223 BCP ALL cases using RNA sequencing. Fourteen BCP ALL gene expression subgroups (G1 to G14) were identified. Apart from extending eight previously described subgroups (G1 to G8 associated with MEF2D fusions, TCF3–PBX1 fusions, ETV6–RUNX1–positive/ETV6–RUNX1–like, DUX4 fusions, ZNF384 fusions, BCR–ABL1/Ph–like, high hyperdiploidy, and KMT2A fusions), we defined six additional gene expression subgroups: G9 was associated with both PAX5 and CRLF2 fusions; G10 and G11 with mutations in PAX5 (p.P80R) and IKZF1 (p.N159Y), respectively; G12 with IGH–CEBPE fusion and mutations in ZEB2 (p.H1038R); and G13 and G14 with TCF3/4–HLF and NUTM1 fusions, respectively. In pediatric BCP ALL, subgroups G2 to G5 and G7 (51 to 65/67 chromosomes) were associated with low-risk, G7 (with ≤50 chromosomes) and G9 were intermediate-risk, whereas G1, G6, and G8 were defined as high-risk subgroups. In adult BCP ALL, G1, G2, G6, and G8 were associated with high risk, while G4, G5, and G7 had relatively favorable outcomes. This large-scale transcriptome sequence analysis of BCP ALL revealed distinct molecular subgroups that reflect discrete pathways of BCP ALL, informing disease classification and prognostic stratification. The combined results strongly advocate that RNA sequencing be introduced into the clinical diagnostic workup of BCP ALL.

Tremendous strides have been made in the molecular and cytogenetic classification of acute lymphoblastic leukemia based on gene expression profiling data, leading to an expansion of entities in the recent International Consensus Classification (ICC) of myeloid neoplasms and acute leukemias and 2022 WHO Classification of Tumours: Haematolymphoid Tumors, 5th edition. This increased diagnostic and therapeutic complexity can be overwhelming, and this review compares nomenclature differences between the ICC and WHO 5th edition publications, compiles key features of each entity, and provides a diagnostic algorithmic approach. In covering B-lymphoblastic leukemia (B-ALL), we divided the entities into established (those present in the revised 4th edition WHO) and novel (those added to either the ICC or WHO 5th edition) groups. The established B-ALL entities include B-ALL with BCR::ABL1 fusion, BCR::ABL1-like features, KMT2A rearrangement, ETV6::RUNX1 rearrangement, high hyperdiploidy, hypodiploidy (focusing on near haploid and low hypodiploid), IGH::IL3 rearrangement, TCF3::PBX1 rearrangement, and iAMP21. The novel B-ALL entities include B-ALL with MYC rearrangement; DUX4 rearrangement; MEF2D rearrangement; ZNF384 or ZNF362 rearrangement, NUTM1 rearrangement; HLF rearrangement; UBTF::ATXN7L3/PAN3,CDX2; mutated IKZF1 N159Y; mutated PAX5 P80R; ETV6::RUNX1-like features; PAX5 alteration; mutated ZEB2 (p.H1038R)/IGH::CEBPE; ZNF384 rearranged-like; KMT2A-rearranged-like; and CRLF2 rearrangement (non-Ph-like). Classification of T-ALL is complex with some variability in how the subtypes are defined in recent literature. It was classified as early T-precursor lymphoblastic leukemia/lymphoma and T-ALL, NOS in the WHO revised 4th edition and WHO 5th edition. The ICC added an entity into early T-cell precursor ALL, BCL11B-activated, and also added provisional entities subclassified based on transcription factor families that are aberrantly activated.

Acute lymphoblastic leukemia (ALL) is the most successful paradigm of how risk-adapted therapy and detailed understanding of the genetic alterations driving leukemogenesis and therapeutic response may dramatically improve treatment outcomes, with cure rates now exceeding 90% in children. However, ALL still represents a leading cause of cancer-related death in the young, and the outcome for older adolescents and young adults with ALL remains poor. In the past decade, next generation sequencing has enabled critical advances in our understanding of leukemogenesis. These include the identification of risk-associated ALL subtypes (e.g., those with rearrangements of MEF2D, DUX4, NUTM1, ZNF384 and BCL11B; the PAX5 P80R and IKZF1 N159Y mutations; and genomic phenocopies such as Ph-like ALL) and the genomic basis of disease evolution. These advances have been complemented by the development of novel therapeutic approaches, including those that are of mutation-specific, such as tyrosine kinase inhibitors, and those that are mutation-agnostic, including antibody and cellular immunotherapies, and protein degradation strategies such as proteolysis-targeting chimeras. Herein, we review the genetic taxonomy of ALL with a focus on clinical implications and the implementation of genomic diagnostic approaches.

The functional impact of a large portion of the human genome known as “dark matter DNA”, which is composed mainly of repeat sequences, remains enigmatic. The genome also encodes hundreds of putative and poorly characterized transcription factors (TFs). Here, we determined genomic binding locations of 166 poorly characterized human TFs in living cells. Nearly half of them associate strongly with known regulatory regions such as promoters and enhancers, frequently co-localizing with each other at conserved motif matches. The other half often associate with genomic dark matter, however, at largely non-overlapping (i.e., unique) sites, via intrinsic sequence recognition. Fifty-four of the latter half, which we term “Dark TFs”, mainly bind within regions of closed chromatin, with each recognizing a unique set of repeat sequences. The Dark TFs include many KZNFs, which are known to bind and silence TEs, and other TFs with apparent repressive functions. By contrast, some may be pioneers: we find that induction of TPRX1, a known regulator of zygotic preimplantation, leads to chromatin opening at many of its binding sites in the dark matter genome. Altogether, our results shed light on a large fraction of poorly characterized human TFs and simultaneously illuminate the diversity of function within the dark matter genome.

… ) and zinc finger protein 384/zinc finger protein 362 (ZNF384/ZNF362) as well as ETS variant transcription factor 6-runt-related transcription factor 1 fusion gene (TV6-RUNX1)-like gene …

… Of note, B-ALL with ZNF362 rearrangements were originally included in this category in the initial report of the updated ICC, but further data suggest that they may be best considered in …