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PubMed2026

Telomere Length Dynamics as a Biomarker of Individual Radiation Sensitivity and Pneumonitis in Lung Cancer Patients Receiving Thoracic Radiation Therapy.

PURPOSE: Telomere shortening is a biomarker for genome instability and aging, and the vulnerability of telomeric DNA to oxidative damage suggests its potential role in mediating radiation therapy (RT) side effects. This study evaluates telomere length (TL) as a biomarker for clinical radiosensitivity and adverse outcomes in thoracic RT-treated patients. METHODS AND MATERIALS: Patients with cancer receiving thoracic RT (2019-2022) were prospectively enrolled at Brigham and Women's Hospital, Boston, Massachusetts. Peripheral blood mononuclear cells (PBMCs) were collected pre-RT and ≤12 months post-RT. TL was measured using quantitative PCR, and multipathway DNA repair capacity (DRC) was simultaneously assessed by fluorescence multiplex host cell reactivation assays. RT outcomes included patient-reported quality of life and radiation pneumonitis. Linear mixed-effects models were used to analyze TL dynamics; risk prediction models for RT outcomes were evaluated using area under the curve. RESULTS: Pre-RT TL decreased with age (0.44% lower per year; 95% CI, 0.12%-0.77%) and advanced cancer stage (6.87% lower per step increase of stage; 95% CI, 3.45%-10.16%). Radical RT was associated with telomere shortening (3.7% lower; 95% CI, 0.27%-7.07%) in PBMCs, detectable ≤6 months post-RT. Pre-RT TL strongly predicted post-RT changes, and TL dynamics outperformed static measures in predicting symptom burden and radiation pneumonitis. Positive associations were observed between TL and DRC against oxidative lesions, with A:8-oxoG repair capacity mediating 12.8% of RT-induced TL shortening. CONCLUSIONS: Lymphocyte TL can reflect individual radiosensitivity and interact with oxidative damage repair. Longitudinal assessment of TL dynamics provides additional predictive value for adverse RT outcomes compared with static measures. Further studies are needed to fully determine the clinical utility of TL.

Humans↗

PubMed2026

Emerging multidimensional biomarker system for cardiovascular-kidney-metabolic syndrome: from multi-omics integration to clinical artificial intelligence.

Cardiovascular-kidney-metabolic (CKM) syndrome is an emerging clinical entity that highlights the complex, bidirectional interplay among cardiovascular disease, chronic kidney disease, and metabolic disorders, representing a substantial and growing global health burden. This conceptualization marks a paradigm shift from viewing these conditions in isolation to understanding them as an interconnected disease continuum. Traditional biomarkers face significant limitations in the early detection, risk stratification, and precise management of CKM, necessitating a transition towards an integrated framework that captures its multisystem nature. This review systematically outlines an emerging multidimensional biomarker system encompassing key pathological axes such as metabolism, immuno-inflammation, oxidative stress, and biological aging, offering refined risk assessment beyond conventional metrics. The development of this system is propelled by revolutionary platforms, including accessible sampling techniques (e.g., dried blood spots), advanced in vitro models (e.g., multi-organ-on-a-chip), and multi-omics technologies. These platforms not only facilitate a deeper dissection of the heterogeneous origins and inter-organ crosstalk in CKM but also accelerate the discovery and validation of novel biomarkers. Concurrently, artificial intelligence serves as a pivotal tool for clinical translation, effectively integrating high-dimensional data to transform complex molecular profiles into actionable clinical insights. By enabling the construction of dynamic risk prediction and decision-support systems, this review charts a pathway toward proactive, individualized, and precise prevention and management of CKM syndrome.

Humans↗

PubMed2026

Toward a Better Paradigm for Head and Neck Cancer Treatment Applying AI (HNC-TACTIC): Protocol for an International Cohort Study of Electronic Health Records.

BACKGROUND: Head and neck squamous cell carcinomas (HNSCCs) cause considerable morbidity and mortality. Multimodal treatment strategies can cause significant toxicity, and therapy options are limited for recurrent disease. Immunotherapy has emerged as a promising approach. However, patient response variability underscores the need for better predictive markers. OBJECTIVE: This study aims to use artificial intelligence to develop two predictive models in patients with HNSCC to assess (1) progression or recurrence following primary curative treatment and (2) long-term survival after immunotherapy schemes in recurrent and metastatic disease. This study will also describe the characteristics of patients with early, locally advanced, and recurrent or metastatic cancers. METHODS: This is a retrospective, observational study of data captured in electronic health records (EHRs) from participating hospitals between January 1, 2014, and December 31, 2021. This study's population comprises adults diagnosed with HNSCC at any stage. Study variables, including demographics, comorbidities, clinical variables, treatments, and outcomes, will be extracted using EHRead, a technology that applies natural language processing and machine learning to extract and analyze structured and unstructured clinical information in deidentified EHRs. Predictive models based on dynamic risk stratification for treatment response and progression or recurrence will be developed using multivariable logistic regressions, decision tree classifiers, and random forest approaches. Descriptive and outcome analyses will be shown for different anatomic subsites and stratified by stage and treatment. RESULTS: This study began enrolling sites in July 2021 and is currently ongoing. By December 2025, data from 10 centers has been collected, comprising a total of 151,934,990 EHRs from 2,159,719 patients. CONCLUSIONS: Development of predictive models using artificial intelligence will advance clinical understanding of HNSCC to improve patient outcomes.

Humans↗

PubMed2026

Dual β-lactam therapy against high-risk Pseudomonas aeruginosa isolates: a dynamic in-vitro infection model study integrating population genomics with quantitative systems pharmacology modelling and simulations.

BACKGROUND: Pseudomonas aeruginosa has an extraordinary capacity for resistance emergence during treatment, even with newer antipseudomonals. There is a gap in understanding how resistance mechanisms affect the time-course of bacterial response to these newer agents. Traditional approaches for predicting pathogen response to an antibiotic do not apply to combination therapy. We aimed to develop a modelling framework to predict treatment response based on resistome information, using isolates of the worldwide-disseminated high-risk clone sequence type (ST) 235 and β-lactam antibiotics as the example. METHODS: In this hollow-fibre in-vitro infection study, we used three extensively drug-resistant ST235 clinical isolates from the national collection of the Clinical Microbiology Department of the Hospital Son Espases (Palma de Mallorca, Spain) that were hospital-acquired, were isolated following routine microbiological procedures from different patients between 2017 and 2022, were susceptible to ceftolozane-tazobactam, and had different levels of meropenem resistance. The selected isolates (ST235-05, ST235-09, and ST235-10) showed classical β-lactam resistance mechanisms pre-treatment. The isolates were investigated in 240-h dynamic hollow-fibre in-vitro infection models (HFIMs). The studies exposed the isolates to pharmacokinetic profiles of ceftolozane-tazobactam (simulating 1 g of ceftolozane and 0·5 g of tazobactam as a 3-h infusion every 8 h) and meropenem (simulating 6 g per day continuous infusion) as observed in hospitalised patients, as monotherapy and in combination. Treatment response was assessed through the quantification of the time-courses of viable total and resistant bacteria. Whole-genome sequencing identified the mechanisms of emerging resistance. A quantitative systems pharmacology (QSP) approach was used to model total and resistant bacterial counts and corresponding pharmacokinetic data from the HFIM. Monte Carlo simulations were used to predict treatment responses in 1000 virtual infected patients treated with ceftolozane-tazobactam and meropenem as monotherapies or in combination over 10 days. FINDINGS: In the HFIMs, each antibiotic alone amplified resistance by approximately 48 h for all isolates; that is, monotherapies resulted in a higher concentration of resistant bacteria compared with the control treatment at the respective time, except ceftolozane-tazobactam against ST235-10. Combination of ceftolozane-tazobactam and meropenem was synergistic (bacterial counts ≥2 log10 colony forming units [CFU] per mL lower than the best performing monotherapy and initial inoculum) against all isolates and suppressed resistance. Against ST235-10, ceftolozane-tazobactam monotherapy reduced counts to less than 1 log10 CFU per mL from 192 h onwards, whereas the combination reached less than 1 log10 CFU per mL by 24 h. Across strains, population genomics confirmed monotherapy failures were associated with emerging resistance mechanisms (ceftolozane-tazobactam: ampC Ω-loop mutations; meropenem: ftsl mutation). The developed QSP model incorporated baseline resistance mechanisms and those emerging in resistant mutant subpopulations. The model explained and predicted the monotherapy failures involving amplification of these subpopulations, and synergistic killing and resistance suppression by the combination. Simulations using the model predicted bacterial regrowth above the initial inoculum for more than 90% of patients after 0 to approximately 3 days for meropenem monotherapy across all strains and for ceftolozane-tazobactam monotherapy against ST235-05 and ST235-09. For ceftolozane-tazobactam monotherapy against ST235-10, regrowth was predicted for approximately 30% of patients. In contrast, the simulations predicted sustained bacterial killing of at least 2 log10 CFU per mL compared with the initial inoculum by the combination for more than 89% of patients across all strains. INTERPRETATION: To our knowledge, this model is the first to characterise and predict the time-course of responses of clinical isolates to antibiotics only by the resistance mechanisms present and their complex interplay, representing a step towards pathogen-specific, personalised medicine. FUNDING: Australian National Health and Medical Research Council.

Pseudomonas aeruginosa↗

PubMed2026

Toward real-time quantification of driving risks: a systematic review and research agenda of risk field theory.

In complex traffic systems, driving risk often evolves in a continuous and progressive manner prior to crash occurrence. How to effectively represent and analyze such latent risk states remains a central challenge in traffic safety research. In recent years, risk field-based approaches have introduced spatial and spatiotemporal continuous modeling paradigms, providing new perspectives for characterizing the distribution of traffic risk and its dynamic evolution. Motivated by the rapid growth of this research area and the lack of a systematic synthesis, this paper presents a comprehensive review of studies applying risk field theory to driving safety and traffic risk analysis. Following the PRISMA guidelines, relevant literature was collected through multi-database searches and analyzed using a combination of bibliometric analysis and qualitative review. The review systematically summarizes the theoretical foundations, modeling elements, data sources, analytical methods, and application domains of risk field-related research. Particular attention is given to studies that conceptualize traffic risk as a continuous field, complemented by a broader review of traffic risk factor literature to identify key elements and analytical dimensions involved in risk field modeling. On this basis, the paper synthesizes research progress in major application areas, including traffic safety state representation, driving behavior analysis, traffic conflict assessment, and autonomous driving and human-machine cooperative systems. Differences and commonalities among existing studies are compared in terms of modeling strategies, data support, and application scenarios. Through this systematic review, the paper clarifies the main research themes and methodological trends of risk field-based studies, providing a structured framework for understanding the evolution and application of this approach and offering methodological insights for risk perception modeling and safety-oriented decision support in intelligent transportation systems (ITS).

Humans↗

PubMed2025

Dynamic neuro-immune regulation of psychiatric risk loci in human neurons.

The prenatal environment influences neurodevelopment and subsequent clinical trajectories for psychiatric outcomes in childhood and adolescence. Yet it remains unclear if the impact of maternal and fetal immune activation varies with distinct polygenic risk profiles. Therefore, here we catalogue genotype and environment (GxE) interactions, contrasting allele-specific regulatory activity between inflammatory contexts. We report a cue-specific neuronal massively parallel reporter assay (MPRA) of 220 loci from genome-wide association study (GWAS) linked to ten brain traits/disorders, empirically dissecting the impact of interleukin-6 (IL-6) and interferon-alpha (IFNα) on transcriptional activity. Of 1,469 active candidate regulatory risk elements (MPRA-active CRSs) across three conditions, we identify 316 with dynamic variant-specific effects (MPRA-QTLs) in human induced pluripotent stem cell (hiPSC)-derived glutamatergic neurons. Broadly, across hundreds of variants, neuronal immune-mediated regulatory activity is driven by differences in transcription factor binding and chromatin accessibility, the gene targets of which show pleiotropic enrichments for brain, metabolic, and immune disorders. Dynamic genetic regulation mediates immune effects, informing our understanding of mechanisms governing pleiotropy and variable penetrance. Understanding neurodevelopmental GxE interactions will inform mental health trajectories and resolve mechanisms mediating prenatal risk.

dynamic expression quantitative trait loci↗

PubMed2026

In vitro fertilization-conceived offspring exhibit altered Long Interspersed Nuclear Elements-1 retrotransposition dynamics associated with long-term disease risks.

BACKGROUND: In vitro fertilization has transformed reproductive medicine, yet offspring conceived through in vitro fertilization display elevated risks for diverse long-term health conditions, with underlying mechanisms unclear. Long Interspersed Nuclear Elements-1, a mobile genetic element responsive to environmental stress, represents a potential mediator. OBJECTIVE: This study aimed to test the hypothesis that in vitro fertilization procedures may act as an embryonic stressor that alters Long Interspersed Nuclear Elements-1 dynamics, potentially contributing to genomic instability associated with long-term disease susceptibility. STUDY DESIGN: Umbilical cord blood or peripheral blood from 33 in vitro fertilization and 42 naturally conceived neonates were collected for whole-genome sequencing. Total Long Interspersed Nuclear Elements-1 proportion in individual genome was counted with Bowtie2 software. De novo Long Interspersed Nuclear Elements-1 insertion and Long Interspersed Nuclear Elements-1 deletion were detected with Mobile Element Locator Tool. Three parent-matched in vitro fertilization-naturally conceived sibling pairs were included to control for genetic background. Disease association analysis was performed for genes within 500 kb of differential Long Interspersed Nuclear Elements-1 sites in The Database for Annotation, Visualization and Integrated Discovery (DAVID). Statistical analysis was performed using the R language. RESULTS: In vitro fertilization offspring demonstrate elevated global Long Interspersed Nuclear Elements-1 content compared to naturally conceived controls (P=.04). This finding was corroborated in 3 sibling pairs from identical genetic backgrounds, where in vitro fertilization-conceived children consistently exhibited higher Long Interspersed Nuclear Elements-1 levels than their naturally conceived siblings. Eleven genomic loci with differential Long Interspersed Nuclear Elements-1 insertion frequencies and 14 loci with differential Long Interspersed Nuclear Elements-1 deletion frequencies between in vitro fertilization offspring and naturally conceived controls were identified. Notably, these differential Long Interspersed Nuclear Elements-1 sites demonstrated significant enrichment near genes implicated in metabolic, cardiovascular, neuropsychiatric, and neoplastic diseases, conditions associated with in vitro fertilization conception. CONCLUSION: These findings provide preliminary evidence that in vitro fertilization conception is associated with increased Long Interspersed Nuclear Elements-1 content and altered genomic distribution of Long Interspersed Nuclear Elements-1 elements. The proximity of these differential Long Interspersed Nuclear Elements-1 sites to disease-associated genes suggests a plausible genomic mechanism linking in vitro fertilization-associated embryonic stress to elevated disease risk. This work provides valuable molecular insights that may inform the ongoing discussion about assisted reproductive technology safety and suggests that continued attention to genomic integrity in in vitro fertilization-conceived individuals would be beneficial.

Humans↗

PubMed2026

Temporal mismatch in allogeneic iPSC therapies: biological risks and implications for clinical translation.

INTRODUCTION: The clinical translation of pluripotent stem cell-derived therapies has entered a new phase following conditional approval of first-in-class allogeneic induced pluripotent stem cell (iPSC)-derived products in Japan. These approvals highlight both the therapeutic promise of iPSC technologies and regulatory challenges associated with evaluating complex cell-based interventions. AREAS COVERED: This report examines the evidentiary basis supporting recent approvals and reviews key biological characteristics of allogeneic iPSC-derived therapies, including pluripotency-associated instability, immunological constraints, and manufacturing-related genomic variability. Drawing on recent clinical studies and relevant experimental literature, we analyze how these multilayered risks evolve over extended time horizons and assess their implications for the interpretation of early-phase clinical data and current regulatory frameworks. EXPERT OPINION: We argue that the central challenge extends beyond limited clinical evidence to a fundamental mismatch between the temporal dynamics of biological risk and the duration of conventional clinical evaluation. As a result, early clinical observations may systematically underestimate long-term risks. Conditional approval pathways should therefore incorporate safeguards aligned with this temporal uncertainty, including long-term follow-up, rigorous post-approval evaluation, and enhanced transparency in biological and manufacturing data. Aligning regulatory design with intrinsic properties of pluripotent stem cell-derived therapies will be essential for ensuring safe and responsible clinical translation.

Humans↗

PubMed2026

Beyond data and technology: the need for new thinking to enable the era of precision prevention.

BACKGROUND: Global flagship initiatives increasingly advocate for proactive health maintenance to alleviate the growing burden on reactive, disease-focused healthcare systems. Precision prevention is conceived as the targeted modulation of causal pathways across the disease continuum, from latent risk and pre-disease states to clinical manifestation, surpassing conventional public health prevention strategies that prioritise managing population-level risk factors. Traditional discovery and implementation models, however, remain poorly aligned with the pace and breadth of scientific and technological advances. This review outlines key barriers to scaling precision prevention and argues for the integration of conceptual, methodological, and policy perspectives into a single implementation‑oriented framework. MAIN: Individualised risk stratification lies at the core of precision prevention. Genomics serves as a stable substrate for lifetime susceptibility assessment, while meaningful prediction in multifactorial chronic disease requires additional risk monitoring using dynamic intermediate molecular markers and high-resolution exposomic data. Machine learning and other artificial intelligence (AI) methods are increasingly helpful tools for integrating large, heterogeneous and temporally structured real-world data to generate personalised predictions of health trajectories. Trustworthy AI-enabled risk prediction or decision-support systems are expected to provide transparency about model logic, assumptions and performance. In discovery, existing diagnostic classifications and conventional case-control designs can obscure mechanistic heterogeneity. Shifting toward precision phenotyping and biologically grounded disease redefinition could reveal a new layer of molecular understanding. Evidence generation strategies that reflect the temporal change of disease, including high‑risk enrichment, surrogate endpoints, and adaptive, trajectory-based monitoring, are particularly important for common conditions with prolonged latency periods (e.g., cancer, cardiovascular disease). Features often dismissed as "noise", such as stochastic molecular variation and minimal exposures, may in fact encode meaningful individual-level signals and thus merit investigation. CONCLUSION: To shift healthcare from reactive treatment toward proactive health maintenance requires coordinated action from stakeholders to reshape the pillars of discovery, reform outcome assessments and modernise implementation strategies.

Humans↗

PubMed2026

Metagenomics Reveals Microbial Community Shifts Associated With Contrasting Anthropogenic Impacts in Freshwater Sources of A Coastal Protected Area in Southeastern Brazil.

This study aimed to characterize freshwater microbial communities, environmental drivers, and anthropogenic impact patterns across three sites on Marambaia Island (southeastern Brazil) using metagenomics. Samples collected from freshwater sources used for human consumption were processed through concentration, nucleic acid extraction, and sequencing on the Illumina NextSeq 2000 platform. A total of 67.2 million reads were assembled into 89,230 bacterial contigs, mostly attributed to Gammaproteobacteria, Alphaproteobacteria, and Betaproteobacteria. Sites under lower anthropogenic influence exhibited higher microbial diversity, whereas impacted sites showed enrichment of opportunistic and fecal-associated genera. A heterogeneous anthropogenic impact profile was observed across sites, corroborated by the proposed Anthropogenic Impact Index (AII). Fourteen antimicrobial resistance genes conferring resistance to beta-lactams, quinolones, sulfonamides, tetracyclines, and macrolides were detected predominantly in sewage-impacted areas, indicating potential diffuse contamination. Redundancy analysis revealed that environmental variables explained 88.1% of microbial community variation, with conductivity, salinity, and turbidity as key drivers. These findings demonstrate the applicability of metagenomics as a powerful tool for assessing microbial diversity, ecological dynamics, and contamination risks in vulnerable freshwater systems.

Brazil↗

PubMed2025

SARS-CoV-2 genomic diversity and within-host evolution in individuals with persistent infection in the UK: an observational, longitudinal, population-based surveillance study.

BACKGROUND: Persistent SARS-CoV-2 infections in hospitalised immunocompromised individuals are known to facilitate accelerated within-host viral evolution, potentially contributing to the emergence of highly divergent variants. However, little is known about the evolutionary dynamics and transmission risks of persistent infections in the general population. We aimed to characterise the within-host evolution of SARS-CoV-2 during persistent infections identified through a large community surveillance study. METHODS: We used data from the Office for National Statistics COVID-19 Infection Survey (ONS-CIS), a large-scale, longitudinal, population-based surveillance study conducted in the UK from April, 2020, to March, 2023. For this analysis, we focused on infections with high viral load (cycle threshold ≤30) and available genome sequences, from seven major SARS-CoV-2 lineages (alpha, delta, BA.1, BA.2, BA.4, BA.5, and XBB). ONS-CIS participants were randomly selected from the general population and tested regularly by RT-PCR, regardless of symptoms. We defined persistent infections as those with sustained or rebounding high viral RNA titres for 26 days or longer. We examined associated host characteristics and used raw sequence data to identify de novo mutations and estimate within-host synonymous and non-synonymous evolutionary rates across the SARS-CoV-2 genome. FINDINGS: Between Nov 2, 2020, and March 21, 2023, we identified 576 persistent infections with at least two sequences, including 11 alpha, 106 delta, 102 BA.1, 204 BA.2, 16 BA.4, 133 BA.5, and 4 XBB. Persistent infections were more common in males than females (p<0·0001) and individuals older than 60 years (p=0·0027). The median within-host genome-wide evolutionary rate was 7·9 × 10-4 substitutions per site per year (IQR 7·0-9·0 × 10-4), with high inter-individual variability driven largely by non-synonymous mutations, particularly in the N-terminal and receptor-binding domains of the spike protein. Longer infection duration was associated with higher evolutionary rates, while no associations were found with age, sex, vaccination status, previous infection, or virus lineage. We found no clear evidence of transmission beyond the first month of infection in any of the 84 persistent infections lasting 56 days or longer. In total, we identified 379 recurrent mutations, including many with known or predicted negative fitness effects and low prevalence at the population level, as well as de novo reversions to the Wuhan-Hu-1 reference sequence, which were likely under positive selection within those individuals. INTERPRETATION: This study highlights the heterogeneous nature of within-host SARS-CoV-2 evolution in individuals with persistent infection in the community. Notably, a small subset of persistent infections with high viral loads underwent accelerated viral evolution or recurrently acquired hallmark mutations found in novel variants. In addition, onward transmission from a persistent infection during the later stages of infection is likely to be rare. These insights have important implications for prioritising genomic surveillance and managing patients with persistent infections. FUNDING: Department of Health and Social Care.

Humans↗

PubMed2026

Perfluorooctane sulfonate drives the synergistic dissemination of antimicrobial resistance and pathogenicity during sludge anaerobic digestion.

Per- and polyfluoroalkyl substances, one of the most prevalent and persistent emerging contaminants in sludge, may drive the dissemination of antimicrobial resistance and pathogenicity during sludge treatment. However, the mechanisms underlying perfluorooctane sulfonate (PFOS)-mediated propagation of antibiotic resistance genes (ARGs) and virulence factors (VFs) remain poorly understood. This study investigated the effects of PFOS (1 and 10 μg/g-dw) on ARGs dynamics and virulence risks. Quantitative PCR and metagenomic analysis revealed that PFOS stress led to the widespread enrichment of ARGs, the total abundance of mobile genetic elements (MGEs) and VFs also increased by 33.22-37.62% and 6.71-8.41%, respectively. Metagenomic binning results demonstrated that most metagenome-assembled genomes carrying ARGs or VFs simultaneously harbored MGEs. Mechanistically, excessive reactive oxygen species production and enhanced substrate-level phosphorylation for ATP generation may contribute to the increased horizontal transfer potential of ARGs under PFOS stress, which further facilitated the convergence of antimicrobial resistance and virulence traits within pathogens. Furthermore, PFOS may have hindered the negative regulation of the RhlI/RhlR quorum sensing system on the Type III secretion system, stimulating the secretion of VFs. This study elucidates the mechanisms by which PFOS promotes the dissemination of ARGs and pathogenicity during anaerobic digestion, highlighting the potentially overlooked environmental health risks of PFOS during sludge disposal.

Alkanesulfonic Acids↗

PubMed2026

Long-read proteogenomic atlas of human neuronal differentiation reveals isoform diversity informing neurodevelopmental risk mechanisms.

RNA splicing shapes neuronal identity and disease risk, yet current maps lack the developmental resolution and depth to resolve this complexity. Here, we integrate deep long-read RNA sequencing and proteomics in induced pluripotent stem cell-derived cortical neurons to generate a high-resolution proteogenomic atlas of human neuron development. We identify 182,371 mRNA isoforms (over half previously unknown) and provide direct peptide evidence for the translation of hundreds of novel protein-coding sequences. Population genetics demonstrates that variants affecting novel exons and splice sites are under negative selection, underscoring the potential significance of these isoforms. During neuronal maturation, we observe that autism risk genes undergo dynamic isoform switching, including microexon inclusion and intron retention, that remodel key protein domains and regulatory regions. Furthermore, we uncover widespread, long-range coordination between alternative transcript processing events, including transcription start sites, exon splicing, and polyadenylation. Finally, our atlas enables variant reinterpretation in autism, highlighting the value of an isoform-centric view for interpreting pathogenic variation in neurodevelopment.

Humans↗

PubMed2025

GRUMB: a genome-resolved metagenomic framework for monitoring urban microbiomes and diagnosing pathogen risk.

SUMMARY: Urban infrastructure hosts dynamic microbial communities that complicate biosurveillance and AMR monitoring. Existing tools rarely combine genome-resolved reconstruction with ecological modeling and batch-aware analytics tailored to infrastructure-scale studies. We present GRUMB (Genome-Resolved Urban Microbiome Biosurveillance), an open-source, SLURM-compatible pipeline that reconstructs high-quality metagenome-assembled genomes (MAGs) from shotgun sequencing reads and integrates taxonomic/functional annotation (CARD, VFDB), batch-aware normalization, ecological diagnostics and machine learning classification of environment types with uncertainty and risk scoring. GRUMB accepts either SRA project accessions or paired-end FASTQ files with metadata, and produces assemblies, MAGs, taxonomic and functional profiles, ecological outputs and risk-informed classification. Its modular design enables reproducible, infrastructure-scale biosurveillance across diverse environments. AVAILABILITY AND IMPLEMENTATION: GRUMB is freely available under the MIT License at: https://github.com/SuleimanAminu/genome-resolved-urban-microbiome-biosurveillance; Zenodo DOI: https://doi.org/10.5281/zenodo.15505402. Requirements: Linux (Ubuntu 20.04+), Python 3.11, R 4.2+, SLURM. Issues and feature requests are tracked on GitHub.

Microbiota↗

PubMed2026

Dynamic Fusion of Genomics and Functional Network Connectivity in UK Biobank Reveals Schizophrenia-Related SNP Manifolds.

Many mental disorders show strong genetic influence. In parallel, dynamic functional network connectivity (dFNC) has shown high sensitivity to brain changes related to mental disorders. However, previous studies linking dFNC to genetics largely follow a paradigm to identify associations between one set of genetic factors and multiple sets of connectivity features from different dFNC states, ignoring the potential variability in genetic correlates across states. We propose a novel joint ICA (jICA)-based "dynamic fusion" framework to identify dynamically tuned genetic manifolds. A sliding window approach was utilized to estimate four dFNC states and compute subject-level state-average dFNC (sa-dFNC) features. The sa-dFNC features of each state were combined with schizophrenia risk single nucleotide polymorphisms (SNPs) within a jICA fusion framework, resulting in four parallel fusions in 32,861 individuals of the UK Biobank cohort. The extracted four sets of joint SNP-dFNC components were further validated for clinical relevance in a combined schizophrenia cohort of 820 individuals (348 patients). The similarity of SNP-dFNC components across four parallel fusions was evaluated as a measure of state variability. We observed a mixture of "state-invariant" and "state-variant" components for SNP and dFNC modalities. Particularly, the schizophrenia-related state-variant SNP components, or manifolds, complemented each other by capturing different SNPs involved in the same biological functions, revealing a partition of genomic risk particularly elicited by the dynamics of brain function. By augmenting the SNP factors to state-variant manifolds, this dynamic fusion framework promises additional insights into the underlying genetic risk of disease-related alterations in dynamic brain function.

Humans↗

PubMed2024

A methylation risk score for chronic kidney disease: a HyperGEN study.

Chronic kidney disease (CKD) impacts about 1 in 7 adults in the United States, but African Americans (AAs) carry a disproportionately higher burden of disease. Epigenetic modifications, such as DNA methylation at cytosine-phosphate-guanine (CpG) sites, have been linked to kidney function and may have clinical utility in predicting the risk of CKD. Given the dynamic relationship between the epigenome, environment, and disease, AAs may be especially sensitive to environment-driven methylation alterations. Moreover, risk models incorporating CpG methylation have been shown to predict disease across multiple racial groups. In this study, we developed a methylation risk score (MRS) for CKD in cohorts of AAs. We selected nine CpG sites that were previously reported to be associated with estimated glomerular filtration rate (eGFR) in epigenome-wide association studies to construct a MRS in the Hypertension Genetic Epidemiology Network (HyperGEN). In logistic mixed models, the MRS was significantly associated with prevalent CKD and was robust to multiple sensitivity analyses, including CKD risk factors. There was modest replication in validation cohorts. In summary, we demonstrated that an eGFR-based CpG score is an independent predictor of prevalent CKD, suggesting that MRS should be further investigated for clinical utility in evaluating CKD risk and progression.

Humans↗

PubMed2025

Global emergence and transmission dynamics of carbapenemase-producing Citrobacter freundii sequence type 22 high-risk international clone: a retrospective, genomic, epidemiological study.

BACKGROUND: Carbapenemase-producing Citrobacter (CPC) species have recently been recognised as emerging pathogens associated with nosocomial infections in humans. The increased rate of Citrobacter freundii infections is a public health concern and there is a paucity of genomic data regarding its global transmission dynamics. We aimed to characterise the genetic features of CPC species, and their associated carbapenemase-encoding plasmids, obtained from hospitalised patients in China and from publicly available global data, with a particular focus on high-risk clones. METHODS: This was a retrospective, genomic epidemiological study of CPC species obtained from a tertiary hospital in Zhejiang Province, China, from March 5, 2013, to March 5, 2023. We used antimicrobial susceptibility testing, short-read and long-read whole-genome sequencing, phylogenomic analysis, and plasmid structure analysis. A global dataset of complete plasmid sequences encoding blaKPC, blaNDM, and blaIMP was constructed from the National Center for Biotechnology Information (NCBI) RefSeq database to provide insights into their diversity and distribution. All carbapenemase-producing Citrobacter freundii genomes from the NCBI GenBank database were incorporated in the comparative genomic analyses. Bayesian phylogeographical analysis and growth rate assays were carried out to characterise the high-risk C freundii sequence type (ST) 22 clone. FINDINGS: 1724 Citrobacter species isolates were collected from diverse clinical specimens, with 48 identified as CPC species. Citrobacter koseri (22 [46%] of 48) and C freundii (20 [42%]) were the predominant CPC species. Comparative analysis found C freundii carried significantly higher median numbers of plasmid replicons (5·0 [IQR 3·3-6·0] vs 2·0 [2·0-3·0]; p<0·0001) and acquired antimicrobial resistance genes (12·0 [7·3-15·8] vs 3·0 [3·0-5·3]; p<0·0001) than did C koseri. Molecular characterisation identified Inc-type plasmids, In823::Kl.pn.I3/In1589-like/In837-like integrons, Tn6296/Tn125/Tn5060 transposons, and insertion sequences (eg, IS26, IS3000, IS5, ISAba125, ISCR1), collectively facilitating the dissemination of carbapenemase genes. Global analysis of 3126 carbapenemase-encoding plasmids found epidemic plasmids with broad host ranges and global diversity. Phylogenetic investigation of predominant carbapenemase-encoding plasmids showed their persistence across geographical regions, temporal spans, and Enterobacterales species, exhibiting high genetic similarity to our clinical plasmids. A phylogenetic tree of 726 global carbapenemase-producing C freundii genomes showed that ST22 (227 [31·3%]) represents the predominant multidrug-resistant clone across community, health-care, and environmental niches. Transmission across continents contributes to the global predominance of the ST22 clone, which carries a high load of resistance genes (median 15·0 [IQR 11·0-17·0] vs 12·0 [3·0-16·0]; p<0·0001) and enhanced plasmid maintenance capacity (median replicons 5·0 [IQR 4·0-7·0] vs 4·0 [3·0-6·0]; p<0·0001) relative to non-ST22 clones. INTERPRETATION: Our study provides evidence to suggest that Citrobacter species are emerging carriers of carbapenem-resistance genes. These findings provide insight into the population structure of CPC species and highlight C freundii ST22 as a prominent high-risk international clone. FUNDING: National Natural Science Foundation of China, National Health Commission Scientific Research Fund-Zhejiang Provincial Major Health Science and Technology Plan Project, Zhejiang Province Natural Science Foundation Project, Outstanding Youth Foundation of Jiangsu Province of China, the Priority Academic Program Development of Jiangsu Higher Education Institutions, and Postgraduate Research and Practice Innovation Program of Jiangsu Province.

Citrobacter freundii↗

PubMed2026

Longitudinal analysis of circulating tumor DNA and CA19-9 dynamics in predicting disease relapse and monitoring treatment response in stage I-III pancreatic ductal adenocarcinoma: An interim analysis of a prospective observational study.

INTRODUCTION: Postoperative recurrence is the leading cause of mortality in resected pancreatic ductal adenocarcinoma (PDAC), yet reliable tools for early relapse detection and treatment response assessment remain lacking. METHODS: In a prospective cohort of 136 patients with resected stage I-III PDAC receiving adjuvant chemotherapy, we evaluated circulating tumor DNA (ctDNA) and CA19-9 as longitudinal biomarkers across multiple postoperative time windows. RESULTS: ctDNA consistently outperformed CA19-9 as an independent prognostic factor; ctDNA positivity at on-treatment and surveillance assessments achieved a positive predictive value of 91.7%, while persistent negativity identified the lowest-risk patients. Integrating CA19-9 with ctDNA resolved the ctDNA-alone gap in distinguishing treatment clearance from conversion, improving discrimination of responders from non-responders (HR 3.72; P = .008). A time-weighted dynamic ctDNA risk score (MinerVa-dynamic) further stratified ctDNA-negative patients into clinically distinct prognostic subgroups, achieving an area under the curve of 0.87 and 0.82 for one- and two-year disease-free survival prediction, respectively. CONCLUSIONS: These findings support a dual-biomarker longitudinal monitoring framework as a practical, individualized approach to postoperative surveillance and early therapeutic decision-making in PDAC.

CA19-9↗