Genome Medicine

Gut microbial structural variation captures strain-level genomic diversity beyond species abundance, yet its contribution to rheumatoid arthritis (RA) remains unclear. Integrating gut metagenomic datasets from four independent cohorts (n = 491), we identified reproducible RA-associated structural variants (SVs), most of which occurred in species without differential abundance. Incorporating SVs into machine learning models consistently improved disease discrimination across independent validation cohorts. Functional analyses identified a core deletion SV in Agathobacter rectalis that removes an XRE-family transcriptional regulator. Motif discovery and 3D structural modeling demonstrated sequence-specific binding of the regulator to the promoter of a short-chain fatty acid biosynthetic gene, supporting a strain-level regulatory mechanism. Together, these findings establish microbial structural variation as a complementary functional layer beyond taxonomy for RA discrimination and mechanistic interpretation.

Tandem repeat expansions cause more than 60 Mendelian disorders but are difficult to detect and size accurately with short-read sequencing because of their length and repetitive structure. Long-read sequencing (LRS) resolves these loci directly, yet analytic tooling for nanopore sequencing data has lagged behind that available for other long-read platforms. Here we present Mahi-mahi, a tandem repeat genotyper designed for nanopore reads that reports allele-level repeat length, motif composition, interruptions, and read-level flags for systematic basecalling artifacts. Across six Genome in a Bottle samples, whole-genome and adaptive sampling (real-time targeted) data returned calls within one repeat unit of each other for 829 of 832 alleles, and Mahi-mahi calls were within one repeat unit of orthogonal HiFi data at 95.6% of compared alleles after excluding three loci at which the two tools define the repeat differently. In 245 samples from the 1000 Genomes Project, Mahi-mahi calls were within one repeat unit of assembly-derived calls for 98.6% of 10,927 alleles at 57 loci, and Mahi-mahi distinguished pathogenic from nonpathogenic RFC1 alleles of overlapping size by motif. Across 14 Coriell reference lines, Mahi-mahi identified the expected expansion in every line and agreed with orthogonal assays to within two repeat units at HTT, ATXN1, DMPK, and PABPN1. Finally, in 103 samples from three laboratories, Mahi-mahi detected an expanded allele in all samples in which prior testing had reported a pathogenic expansion, assigned the same category as the referring laboratory for 98% of 180 genotypes, and identified known ATXN1 interruption structure. Our results demonstrate that Mahi-mahi accurately identifies repeat expansions at disease-associated loci and could serve as a single assay to evaluate repeat expansions.

Background A growing body of work has highlighted advantages of long-read genome sequencing (LR-GS) over short-read genome sequencing (SR-GS) with regards to the detection and interpretation of pathogenic variants. However, the incremental diagnostic yield of LR-GS over SR-GS for patients with suspected Mendelian conditions has not been systematically characterized. Methods We performed LR-GS on 144 cases that previously had undergone SR-GS through the Pediatric Mendelian Genomics Research Center at the University of California, Irvine (PMGRC-UCI, a founding center of the GREGoR Consortium), of which 107 remained undiagnosed after SR-GS. Results We identified new molecular diagnoses in 13 cases, five of which could not have been detected with SR-GS, corresponding to an incremental diagnostic yield of 4.7%. These five diagnoses leveraged distinct capabilities of LR-GS: the detection of variants in short-read dark regions, the detection of structural variants and tandem repeat expansions, and the identification of de novo variants using only one biological parent. In two of these five cases, confirmation of the diagnosis also relied critically on identifying aberrant transcript production via RNA-seq, while in one other case LR-GS-based CpG methylation profiling further supported the diagnosis through detection of abnormal methylation at episignature-associated CpGs. Conclusions In this paired comparison of LR-GS and SR-GS, LR-GS provided an incremental diagnostic yield of 4.7% among cases that remained undiagnosed after SR-GS. Together, our findings demonstrate that LR-GS can resolve a meaningful subset of cases beyond the reach of SR-GS while simultaneously expanding the range of genomic and epigenomic mechanisms accessible to a single sequencing assay.

Early- and average-onset colorectal cancer (CRC) are separated at age 50, but whether this defines a biological threshold remains unclear. To clarify this, we identified molecular profiles in nine harmonised cBioPortal CRC cohorts (4,609 patients) by fitting Bernoulli mixture models to 31 repair-state, genomic-burden and gene-alteration features, excluding age, sex and tumour site, and compared their prevalence using <50/[&ge;]50 and decade-resolved groups. Four profiles captured conventional/CIN-like (P1), intermediate MSS (P2), KRAS/PI3K/APC-rich (P3) and hypermutated/MSI-high (P4) states along a left-to-right gradient. Although molecular identities remained stable, profile prevalence followed non-linear P1/P4 and opposing linear P2/P3 age trajectories. Profile-prevalence patterns did not track chronological proximity: profile composition at 30-39 differed from 50-59 but not clearly from 60-69. The age-50 threshold captured only 17.8% of decade-resolved deviance, whereas the optimal age-70 cut-off retained only 51.3%. Validation in 2,579 non-overlapping MSK-IMPACT patients (2,476 age-evaluable) reproduced molecular-feature patterns (r=0.97-0.98), age trajectories (r=0.92) and limited binary-threshold performance: age 50 and the optimal age-66 cut-off retained 12.7% and 45.1%, respectively. Thus, age reorganizes the prevalence of shared CRC states rather than defining a biological threshold at age 50.

Background: Short-read whole-genome sequencing (sWGS) has limitations in characterizing tandem-repeat expansions and variants in highly homologous or pseudogene-associated regions. Long-read sequencing can address some of these challenges but often benefits from high-molecular-weight DNA. TruPath Genome incorporates spatial proximity information into a short-read sequencing framework. This study evaluated its performance in characterizing selected complex genomic variants using archived clinical DNA specimens. Methods: Ten archived peripheral blood DNA specimens were analyzed, comprising seven cases with disease-associated short tandem repeat (STR) expansions and three cases involving homologous or pseudogene-associated loci (STRC, SMN1/SMN2, and CYP21A2). TruPath results were compared with previously available orthogonal assay results, including triplet repeat-primed PCR (TP-PCR), multiplex ligation-dependent probe amplification (MLPA), quantitative PCR (qPCR), and sWGS-based ExpansionHunter analysis. Results: TruPath generated repeat-length estimates across the evaluated STR loci. For two FMR1 full-mutation alleles, TruPath estimated 284 and 295 CGG repeats, respectively, while TP-PCR reported expansions exceeding 200 repeats. In a DMPK case, TruPath estimated an expanded allele of 1,203 CTG repeats, compared with a TP-PCR result exceeding 100 repeats. At the STRC and SMN1/SMN2 loci, TruPath findings were consistent with available orthogonal results for deletion and copy-number status. At the CYP21A2 locus, TruPath reconstructed the RCCX gene arrangement and assigned the I2G variant and a chimeric allele to different haplotypes. Conclusions: In this small series of archived clinical DNA specimens, TruPath Genome enabled characterization of selected repeat expansions and homologous or pseudogene-associated variants. The findings support further evaluation of proximity-mapped sequencing as a complementary approach for complex genomic regions, particularly when conventional short-read sequencing is limited.

To evaluate the clinical implementation of long-read genome sequencing (lrGS) and establish the population reference resources needed to support its use in diagnostics, we integrate two complementary initiatives: Genome of Sweden (GoS), Sweden's contribution to Genome of Europe (GoE), and the Genomic Medicine Sweden - Rare Diseases (GMS-RD) long-read sequencing pilot. The initial GoS release of 576 individuals provides population-scale maps of genetic and epigenetic variation. Among 485 GMS-RD probands, 161 (33.2%) receive a molecular diagnosis, with lrGS providing added clinical value in 40 (24.8%). As a first-line test, lrGS could have avoided at least 42 follow-up analyses and an additional testing cycle in 7.2% of probands, reducing costs and the burden on patients. By integrating population and clinical long-read genomics, Sweden establishes a national ecosystem for genomic research and clinical diagnostics. Our findings support the clinical implementation of lrGS and offer a scalable model for national collaboration.

Targeted gene panels (TGPs) dominate clinical sequencing, yet most copy-number (CN) signature studies rely on genome-wide assays. Whether signatures can be recovered from TGPs and retain clinical relevance remains unclear. We analyzed two real-world TGP cohorts comprising 1,726 patients across 62 tumor types, including 825 with clinical annotations, and made the underlying data publicly available. TGP-derived signatures recapitulated established biological associations, including links to homologous recombination deficiency and TP53 alterations, concordant with published genome-wide assay-derived CN signatures. Using our detailed clinical data, we found TGP-derived CN signatures associated with overall survival under standard therapies in ovarian, pancreatic, and colorectal cancer. In ovarian cancer, CN2 was associated with CCNE1 amplification and shorter survival under paclitaxel/carboplatin, with the survival association validated in an independent SNP-array cohort. These findings demonstrate that routine TGPs yield biologically and clinically relevant CN signatures with potential as biomarkers for therapy stratification.

Background: The circadian clock regulates cellular homeostasis, and its disruption has been implicated in aggressive neuroblastoma, particularly in tumours harbouring MYCN- amplification. However, it remains unclear whether alterations are restricted to individual clock genes or extend to circadian gene network coordination. We therefore examined circadian clock network disruption in adverse neuroblastoma across multiple biological contexts. Methods: We estimated circadian gene network dysregulation using Delta-CCD in tumours from two neuroblastoma cohorts (SEQC n=498 and Kocak n=649), comparing clinical features associated with outcome across canonical, adrenal-tissue matched, and developmental references. Robustness was assessed by cross-cohort meta-analysis and leave-one-gene-out analyses. Cox proportional hazards models adjusted for clinical covariates assessed association between individual clock gene expression patient outcome. Results: Delta-CCD was highest in tumours classified as high-risk (study-specific definition) across reference contexts in both cohorts. MYCN-amplified tumours showed a more reference-dependent pattern, strongest in adrenal context, while stage 4 tumours showed a similar but weaker pattern. Additional analyses supported the high-risk signal as a distributed network-level alteration rather than a single-gene phenomenon. Conclusions: High-risk neuroblastoma is characterised by robust disruption of coordinated clock gene network organisation across canonical and tissue-matched references, extending beyond individual clock genes. The extent of circadian dysregulation depends on the reference state used.

Background: DNA damage response (DDR) pathways are central regulators of genome maintenance and major determinants of cancer cell survival. The extensive genomic instability and high replicative stress that characterize glioblastoma render tumor cells highly dependent on DDR pathways to preserve genome integrity and sustain proliferation. This reliance creates potential therapeutic vulnerabilities, making the systematic identification of essential DDR genes a promising strategy for uncovering novel therapeutic targets. Methods: We developed DNA Damage Response KnockOut Library (DDRKOL), a custom CRISPR/Cas9 sgRNA library targeting 819 DDR genes with approximately 10 sgRNAs per gene, together with positive (essential), negative (non-essential) and non-targeting controls. Parallel depletion screens were performed in Cas9-expressing U87-MG and A172 cells cultured for 15 population doublings. Hits were prioritized utilizing TCGA and DepMap databases and validated by viability, clonogenic, apoptosis and GFP competition assays. Clinically relevant patient-derived glioblastoma spheroids and an orthotopic xenograft model was employed to characterize the effects of hit genes. Results: Sequencing confirmed near-complete recovery of the designed sgRNAs from the plasmid pool, with uniform representation across the library and complexity preserved through transduction and selection. Essential-gene controls depleted strongly while non-targeting controls remained neutral, confirming screen performance in both cell lines. The screens identified DDR dependencies in each line and defined a shared core composed of 20 genes belonging to homologous recombination, nucleotide excision repair and ATM/DSB signaling pathways. This shared dependency landscape highlighted four high-confidence candidate genes (TOP2A, CDK1, XRCC6, and RAD21), which were successfully validated across multiple orthogonal assays. These genes displayed grade-associated expression and their expressions were positively correlated with proliferation markers in TCGA. Individual knockouts reduced viability, colony formation and competitive fitness, induced apoptosis, and impaired growth of patient-derived glioblastoma spheroids. Both genetic depletion and pharmacological inhibition of TOP2A induced S/G2-M cell cycle arrest. In orthotopic xenografts, TOP2A depletion prevented tumor progression, and led to significantly prolonged survival. Conclusion: DDRKOL represents a robust and versatile focused CRISPR platform for systematic functional interrogation of the DDR associated genes. Using glioblastoma, we demonstrate that the library reliably identifies biologically significant and clinically relevant genetic dependencies through multiple orthogonal validation approaches. As a reusable platform rather than a disease-specific tool, DDRKOL can be broadly applied across diverse biological contexts to discover context-dependent DDR vulnerabilities, therapeutic targets, and mechanisms of treatment resistance.

Colorectal cancer (CRC) remains a leading cause of cancer-related mortality worldwide, yet the molecular mechanisms driving tumor initiation and progression are incompletely understood. Here, we integrate single-cell long-read sequencing, proteomics, and DNA methylation data to identify tumor-specific targets from unannotated isoforms in CRC. Analysis of 29,429 isoforms across 3,262 single cells revealed 3,338 novel isoforms, comprising 2,779 Novel In Catalog (NIC) and 559 Novel Not in Catalog (NNC). Cross-referencing with 68 normal tissues from GTEx identified 3 tumor-specific candidates absent from normal colon. Among these, CAST (Calpastatin) emerged as a tumor-specific suppressor, with a 63.1-fold higher expression in tumor (2.62) versus normal colon (0.03). CAST protein was confirmed in 100 tumor samples by mass spectrometry (mean expression: 0.22). Mutational analysis of 100 CRC samples revealed 8 deleterious mutations, including 2 nonsense, 2 splice-site, 2 missense, 1 frameshift, and 1 in-frame variant, consistent with a tumor-suppressor role. Promoter methylation analysis of 393 tumor samples showed that 89.3% (351/393) were hypomethylated ({beta} < 0.1), whereas only 1.5% (6/393) were hypermethylated ({beta} > 0.3), indicating demethylation-mediated reactivation. Furthermore, CAST expression was negatively correlated with promoter methylation and regulated by the miR-200 family (miR-200c: 13.48, miR-141: 10.13, miR-429: 8.09). Our findings establish CAST as a tumor-specific suppressor in CRC and provide a generalizable framework for discovering therapeutic targets from unannotated isoforms.

Host genetic variation influences heterogeneity in infectious disease severity. Genome-wide association studies (GWAS) have identified 69 lead single nucleotide polymorphisms (SNPs) associated with critical COVID-19, but most lack known regulatory mechanisms. To date, the genetic cis-regulatory effects of critical COVID-19 GWAS lead SNPs have not been systematically characterized across acute illness and convalescent phases in a COVID-19 specific cohort. Here, performing severity-stratified, longitudinal cis-expression quantitative trait loci (cis-eQTL) mapping and colocalization with critical COVID-19 GWAS signals, we find that host cis-regulatory effects vary across clinical severity and disease phase. We analyzed peripheral blood mononuclear cells from 1,031 patients in the Immunophenotyping Assessment in a COVID-19 Cohort (IMPACC), a prospective multicenter study of adults hospitalized with COVID-19, profiled across 10 timepoints spanning acute illness and convalescence. We identified 23 GWAS lead SNPs that colocalized with cis-eQTLs at 26 candidate causal genes, with 16 detectable only when severity stratification and longitudinal sampling were incorporated. A colocalized, protective allele (chr19:4,717,660) at the TMIGD2 locus upregulated TMIGD2 expression exclusively during the acute phase in the prolonged-critical/fatal cohort, with no detectable effect in the rapid-recovery cohort. Our results show that host genetic effects on critical COVID-19 can be context-dependent, with cis-regulatory effects varying across clinical severity and disease phase. Severity-stratified, longitudinal cis-eQTL designs have greater sensitivity to identify candidate causal genes for critical COVID-19 GWAS associations than single-timepoint cross-sectional studies.

While progress has been made in identifying the true risk-driving single nucleotide polymorphisms (SNPS) on juvenile idiopathic arthritis (JIA) risk haplotypes, the affected cells and target genes largely remain unknown. We used data from a previously published massively parallel reporter assay (MPRA) to query human data in the Database of Immune Cell eQTLs (DICE) and the Gene-Tissue Expression (GTEx) database to identify affected cells and target genes of MPRA-identified SNPs in immune cells and relevant tissues. SNPs identified on MPRA were associated with gene expression levels in a broad range of immune cells in the DICE database, including CD4+ and CD8+ T lymphocytes, monocytes, NK cells, and B cells. MPRA-identified SNPs showed strong associations with gene expression in GTEx whole blood, spleen, and/or EBV-stimulated lymphocytes. Our data show the efficacy of combining MPRA and using human cells/tissue expression data to elucidate complex mechanisms driving genetic risk for JIA.

Polygenic risk scores (PRS) assume additive SNP effects, yet genetic risk also arises from interactions between loci and environmental factors that contribute to broad-sense heritability. We developed an extended PRS (ePRS) framework for type 2 diabetes (T2D) that incorporates locus-by-locus non-additive effects beyond those captured by additive single-locus PRS or linkage disequilibrium (LD) tagging. These were modelled as cumulative burden (G+G; summed allele counts), statistical epistasis (GxG; allele count products), and gene-environment effects derived from cardiometabolic variables in electronic health records. Across 235,000 UK Biobank participants, five complementary ePRS models captured largely non-overlapping high-risk individuals, suggesting that a key to individual risk predictions comprise the inclusion of multiple interaction-driven biological components rather than a single signal. A composite score improved case detection beyond clinical predictors, including individuals within clinically normal ranges. These findings were generalized to celiac disease, with similar complementarity across models, with potential for clinical use pending prospective validation.

Based on recent epidemiologic studies, Streptococcus agalactiae (Group B Streptococcus; GBS) sequence type (ST) 1010 is an emerging lineage now identified in multiple countries. We report the phylogenetic and genomic characteristics of a set of 55 GBS sequence type (ST) 1010 strains, as well as two newly described single-locus variants of ST1010. A core genome phylogeny suggests that ST1010 is closely related to both ST452 and the hypervirulent clonal complex (CC) 17 GBS lineage. Notably, we demonstrate that genes encoding two virulence determinants previously described as specific to CC17 GBS, the HvgA adhesin and the serine-rich repeat protein Srr2, are both present in ST1010 genomes. Srr2 is shared with members of ST452. High-level gentamicin resistance (HLGR) encoded on an IS256 mobile element, previously described in a small number of ST1010 isolates, is present in a distinct ST1010 subclade encompassing the majority of ST1010 isolates. The relationship between ST452 (serotype IV), ST1010 (serotype IV), and ST17 (serotype III) strains suggests that ST17 may have arisen from a serotype IV ancestor and later acquired the type III capsule locus. Taken together, these findings clarify the phylogenetic position of ST1010 and suggest sequential acquisition of virulence determinants and HLGR prior to its international emergence. IMPACT STATEMENTST1010 GBS has emerged internationally, with colonizing and invasive isolates described in the United States, Dominican Republic, Netherlands, and Italy. Using a core genome phylogeny and targeted detection of genomic regions, we demonstrate that ST1010 shares specific virulence determinants with the CC17 hypervirulent GBS lineage and that HLGR is confined to a specific numerically dominant subclade of ST1010. Our work spotlights the importance of future epidemiologic and genomic surveillance of ST1010 and related lineages. DATA SUMMARYPublicly available genomic data were used from three previously published studies (Laycock KM et al., McGee L et al., Khan UB et al.), as well as a set of newly sequenced GBS genomes from clinical strains originating in New York City (NYC). The corresponding accession numbers and detailed information for all strains are provided in the Table.

Background and aims. Inflammatory Bowel Disease (IBD) is a complex immune-mediated condition with a strong genetic contribution. The genetics of IBD are highly complex, with inflammation, autoimmunity, and mucosal immunity pathways involved in its pathogenicity. GenePy is a gene-level disease burden metric. The integration of clinical and genetic data and the application of machine learning methods can aid patient stratification for personalised care. Methods. Lists of consensus IBD pathway genes were curated. GenePy scores were computed from whole exome sequencing data. A K-Means algorithm was optimised using clustering quality and stability metrics on the GenePy scores of genes in each IBD pathway, and the results replicated in three independent IBD cohorts (SPARC IBD, MSCCR IBD, and Southampton IBD). Chi-square tests assessed the association between cluster assignment and phenotypic features, and were multiple test corrected via Benjamini-Hochberg FDR. Results. A strong association was replicated for NOD signalling genes driving IBD subtype, Crohns disease behaviour, and IBD surgery across all cohorts, as well as granuloma in Southampton IBD. This was mainly driven by NOD2. Similar genetic clustering patterns were observed across all cohorts, with additional phenotypic enrichment in clusters driven by NLRP1 (surgery in MSCCR IBD), and nominally significant enrichment for disease behaviour linked to BIRC2 (B2 in Southampton IBD and B2B3 in SPARC IBD). Conclusions. This analysis identified a common genetic architecture across NOD signalling genes across IBD cohorts, replicated a signal for NOD2 as a monogenic driver of disease with a strong phenotypic enrichment, and identified other putative genetic drivers of IBD, such as the NLRP1 gene.

Abstract INTRODUCTION Despite major advances in genomic sequencing and analytical pipelines for rare disorders, more than 50% of affected individuals remain without a molecular diagnosis. One challenge is the interpretation of inherited variants exhibiting incomplete penetrance (IP). MATERIALS AND METHODS We developed a pipeline to systematically investigate inherited variants as the cause of rare disease in 4,325 families, consisting of affected probands and unaffected parents, from the Broad Center for Mendelian Genomics, part of the GREGoR Consortium. As a proof-of-concept of further omics follow-up, epigenomic and transcriptomic analyses were performed in one family harboring an inherited disease-causing frameshift variant in CHD8. RESULTS We identified 81 families where the affected proband harbored a putative disease-causing variant inherited from a reportedly unaffected parent. Clinical re-evaluation identified 30 families in which an inherited variant was considered diagnostic, including six previously missed diagnoses. In this cohort, inherited IP variants account for 1.6% of the diagnosed cases, with an overall diagnostic yield of 0.4% in the cohort. Methylation analysis of the CHD8 family confirmed a positive CHD8 clinical episignature in the male proband, but also in the reportedly unaffected mother. Transcriptomic analysis showed that despite similar CHD8 haploinsufficiency, the proband and mother displayed divergent downstream transcriptional effects. DISCUSSION This study highlights the importance of considering parentally inherited IP variants in rare disease, with at least 1 in 60 diagnostic variants inherited from a reportedly unaffected parent, and demonstrates the potential of integrating omics evaluation to improve variant interpretation.

Despite metabolomics transforming our understanding of risk factors and aetiology of metabolic diseases, profiling is rarely performed for people of non-European ancestries, on whom much of metabolic disease burden falls. Metabolome-wide association studies (MWAS) can be performed using genetic scores to predict metabolomic traits, helping address these inequities; however, their performance in populations of admixed American (AMR) ancestries is unexplored. We evaluated 141 genetic scores, developed in an INTERVAL Study sample of European (EUR) genetic ancestries, in the Mexico City Prospective Study (MCPS; n=132,336), obtaining a median predictive R2 of 0.027. Training Bayesian ridge models within MCPS substantially improved performance, with a median R2 of 0.083 on a withheld 20% subset. MCPS-trained models also outperformed INTERVAL-trained models among UK Biobank participants of AMR ancestries (n=600; median R2: 0.070 vs. 0.046). Finally, among AMR participants of the All of Us cohort, using MCPS-trained (vs. INTERVAL-trained) models to predict metabolomic traits yielded five times as many significant associations (FDR-corrected P<0.05) across three cardiometabolic diseases: ischaemic heart disease, type 2 diabetes, and chronic kidney disease. The genetic scores are openly available at the OmicsPred portal (www.OmicsPred.org), enabling better-powered analyses in diverse AMR cohorts and helping reduce global inequities in omics research.

Genetic prediction of clinically defined disease subtypes could advance precision medicine, but polygenic risk scores (PRSs) often show limited ability to distinguish subtypes, either because the subtypes share genetic architecture or because available PRSs are inaccurate. We introduce GenSep, a statistical framework that estimates the oracle case-case AUC attainable if the true additive genetic values of both subtypes were known, and the proportion of this genetic separation recovered by subtype-specific PRSs. Across 18 subtype pairs in UK Biobank, oracle AUCs ranged from 0.605 to 1.000, whereas current PRSs achieved a median observed case-case AUC of only 0.530 (range, 0.510-0.754), corresponding to recovery of a median of 4.1% (range, 0.3-15.5%) of the genetic separation variance available to a perfect predictor. Oracle AUCs were generally consistent across UK Biobank, All of Us, FinnGen and the Million Veteran Program, and across European, African and Admixed American ancestry groups. FinnGen-trained PRSs, despite 2.6-to 3.9-fold larger discovery samples, performed no better than internally trained PRSs, and substantially larger consortium GWAS raised case-case AUC by 0.06 on average (range, 0.003-0.089). GenSep separates subtype pairs limited by intrinsic genetic overlap from those limited by current PRS accuracy, identifying where larger studies and better predictors could improve genetic discrimination.

Accurate discrimination between viral, bacterial, and inflammatory diseases in febrile children remains a major clinical challenge that contributes to diagnostic uncertainty, inappropriate antimicrobial use, and suboptimal clinical management. Host blood transcriptomics offer a promising strategy to improve diagnostic precision. The present study represents the largest integrative multi-cohort pediatric study of transcriptomic biomarker discovery, validation, and confirmation reported to date, integrating harmonized public transcriptomic datasets with an independent confirmation cohort comprising well-phenotyped patients to identify parsimonious host-response signatures for differentiating viral, bacterial, and inflammatory diseases. Transcriptomic signatures were derived from an integrated retrospective microarray multi-cohort (n=1,683), independently validated in a retrospective RNA-seq cohort (n=767), and confirmed by digital PCR in an independent cohort (n=29), demonstrating reproducibility across patient populations, transcriptomic technologies, and analytical platforms. The analysis identified binary signatures and a unified multiclass classifier that consistently achieved high diagnostic accuracy across all three study phases and outperformed more than 30 published host transcriptomic signatures. Decision curve analysis showed substantially greater clinical net benefit than C-reactive protein across clinically relevant decision thresholds. These findings provide a strong foundation for clinically deployable molecular diagnostics to improve patient triage, antimicrobial stewardship, and precision medicine in childhood infections.

OXA-48-like carbapenemases have been historically rare, however steady increases both locally and globally have warranted further investigation into their spread. Here we present the largest genomic analysis of blaOXA-181-producing bacteria in Australia to date, focusing on a single jurisdiction over seven years (2017 -- 2024). The initial investigation was prompted by an outbreak in 2017, where enhanced genomic surveillance in a single hospital identified 85 outbreak isolates related to an imported Escherichia coli ST38, carrying blaOXA-181 on an IncX3/colKP3 plasmid (previously reported as pOXA181). After four months of intensive infection control, the initial outbreak strain was eliminated. To confirm the outbreak plasmid was also contained, we collected all blaOXA-181-positive isolates from the same jurisdiction over subsequent years and sequenced with both Illumina and Oxford Nanopore Technologies to investigate clonal and mobile genetic element mediated spread. While continued surveillance post-2017 did not identify the same E. coli strain following the outbreak, pOXA181 plasmids were identified in >70% of surveillance isolates, with minimal genetic changes, which initially suggested local plasmid-mediated spread. Additional comparison to a global collection of pOXA181 plasmids found that epidemiologically unrelated pOXA181 plasmids were near identical, with no rearrangements and low, or no, single nucleotide polymorphisms. This suggests the mutation rate of pOXA-181 is incompatible with recent genomic transmission inference. This study highlights the current genomic epidemiology and drivers of blaOXA-181 and further demonstrates the necessity for detailed understanding of plasmid evolutionary rates to inform genomic surveillance.