Cell Reports

Acute respiratory distress syndrome (ARDS) is a devastating complication of respiratory infections; however, the biological mechanisms that initiate its onset are poorly defined. Here we show that TNFRSF13B polymorphisms increase the risk of ARDS following SARS-CoV-2 infection up to 7.4-fold compared to the WT genotype. The increased risk was not due to immune-deficiency or impaired virus neutralization. On the contrary, TNFRSF13B mutant subjects mounted better antibody neutralization compared to subjects with WT TNFRSF13B. However, IgG from subjects expressing TNFRSF13B variants had less sialic acid, terminal galactose, and fucose than IgG from subjects with a WT genotype. Moreover, IgG from TNFRSF13B mutant subjects exhibited increased recruitment of complement factors. Thus, besides well-known actions governing plasma cell differentiation, TNFRSF13B impacts both affinity maturation and effector functions of IgG in ways that independently govern complement activation controlling inflammatory responses known to trigger ARDS.

Amyotrophic lateral sclerosis (ALS) is increasingly recognized as a multisystem neurodegenerative disorder in which motor-neuron degeneration is accompanied by widespread alterations in cortical dynamics. Among its most reproducible neurophysiological signatures is cortical hyperexcitability, yet how this local excitability imbalance shapes distributed whole-brain activity remains poorly understood. Here, we combined source-reconstructed resting-state MEG data, tractography-informed whole-brain modeling, and simulation-based inference to investigate whether ALS-related alterations in large-scale brain dynamics can be mechanistically explained by changes in cortical excitability. First, we characterized empirical brain dynamics using complementary features spanning regional activity amplitude and variability, functional connectivity, and avalanche-based metrics. These analyses revealed significant alterations in ALS patients relative to healthy controls, as well as associations with clinical impairment and disease staging. To mechanistically interpret these changes, we employed a reduced Wong-Wang whole-brain model in which local recurrent excitation modulates emergent large-scale neural dynamics. Simulations showed that increasing excitability systematically reproduced the empirical dynamical signatures observed in ALS. We then applied a simulation-based inference framework to estimate latent excitability parameters directly from empirical observations. Whole-brain model inversion revealed increased excitability in ALS patients compared with controls. The recovered excitability parameter was associated with disease staging, supporting its clinical relevance as a model-derived descriptor of ALS progression. Finally, by extending the model to estimate frontal and non-frontal excitability separately, we found that ALS-related alterations were predominantly associated with increased frontal excitability, whereas non-frontal regions appeared comparatively less affected. The recovered parameters related to disease staging. Together, these findings provide a mechanistic framework linking altered large-scale brain dynamics in ALS to selective cortical hyperexcitability, explaining how local excitability changes can give rise to global network reorganization. More broadly, they show how computational model inversion can recover latent multiscale pathophysiological processes from empirical neural recordings, offering a non-perturbative alternative to complex experimental paradigms typically required to causally probe local-to-global mechanisms.

Biological systems evolve as continuous dynamical processes, but at organ-scale and across human lifespans they are rarely observed longitudinally--population data typically exist instead as sparse, cross-sectional snapshots. Inferring lifespan dynamics from such data requires methods distinct from those used at cellular and tissue scales where dense observations are accessible. We address this problem in the thoracic aorta, where surgical decisions currently rest on static, age- and sex-agnostic diameter thresholds that reduce three-dimensional morphology to a single scalar. Treating normal aortic morphology as a stochastic dynamical system, we pose a continuous-time drift-diffusion process in a two-coordinate state space of normalized surface area (A) and normalized fluctuation in integrated Gaussian curvature ({delta} K), and fit closed-form solutions of the Fokker-Planck equation by maximum likelihood to a sex-balanced, age-uniform cohort spanning infancy to age 99. Inter-individual variability is treated as a fitted diffusion parameter rather than as residual scatter, which is distinct from prior normative studies that report variability as scatter around a regression line. The framework identifies two growth regimes for aortic size (childhood expansion followed by persistent adult growth, with adult males growing approximately 70% faster than adult females) and a single dynamical regime for aortic shape, with heteroscedastic variability accumulating at a rate comparable to the mean drift over the lifespan. Applied to independent cohorts of acute and chronic thoracic aortic dissections, the multivariate model identifies over 95% as statistical outliers via Mahalanobis distance, consistently outperforming either coordinate alone. The same probabilistic envelope that describes normal aging thus defines a baseline against which disease can be detected, supporting a shift toward dynamic, age- and sex-aware assessment of thoracic aortic pathology.

The recent availability of at-home menstrual cycle tracking technology has created opportunities for personalized assessment of reproductive health, alongside improved characterization of hormone patterns in women with and without reproductive disorders such as polyendocrine metabolic ovarian syndrome (PMOS), which affects approximately 10% of reproductive-age women. In this study, we leverage self-tracked urinary hormone data to develop an autoregressive Hidden Markov model (arHMM) that maps cycle days to physiologically meaningful phases based on hormone trajectories. By modeling day-to-day hormonal dynamics rather than absolute hormone levels, and allowing variable phase durations, this approach accommodates substantial variability in menstrual cycles, thereby enabling meaningful comparisons within and between individuals. Across more than 3800 cycles from over 1100 individuals, we find that arHMM-derived phases reproduce expected hormonal patterns within follicular, periovulatory, and luteal phases, and that phase-based timing for hormone testing outperforms conventional cycle day-based testing in capturing the luteinizing hormone surge and post-ovulatory progesterone rise, highlighting limitations of fixed-day clinical protocols. We identify phase-specific differences between healthy controls and individuals with self-reported PMOS, including lower luteinizing hormone in the periovulatory phase, and reduced luteal-phase progesterone levels in PMOS. Furthermore, features derived from arHMM phase assignments enable classification of PMOS status with ~78% accuracy, demonstrating the potential of this approach for non-invasive PMOS screening.

Severe preeclampsia (sPE) is a major cause of maternal and fetal morbidity worldwide, yet its placental molecular heterogeneity remains poorly defined by current clinical diagnosis. To resolve the molecular architecture of sPE, here we integrated DNA methylation and proteomic profiling from a multi-ethnical cohort of 444 placentas from the Hawaiian Biorepository (HiBR), including 169 sPE cases, matched preterm controls and full-term controls. To address cellular heterogeneity in bulk placental tissue, we developed HOMED (Hierarchically Optimized Methylation Deconvolution), a single-cell-guided hierarchical framework for inferring placental cell-type composition from DNA methylation data. HOMED-adjusted integrative analyses identified extensive subtype-specific alterations involving hypoxia, angiogenesis, immune activation, trophoblast differentiation and metabolic remodeling. Molecular stratification revealed two reproducible sPE subtypes with divergent placental aging trajectories. One subtype exhibited a pre-mature placental state marked by accelerated placental aging, whereas the other displayed slower accelerated placental aging but a substantially increased risk of small-for-gestational-age birth (P = 0.028). These subtypes were independently replicated across six external cohorts and further supported by proteomic signatures achieving a classification accuracy of 0.88. Integrative epigenomic and proteomic analyses linked the growth-restricted subtype to hypoxia-associated glycolytic remodeling, suggesting distinct pathogenic mechanisms underlying clinically diagnosed sPE. Together, our findings redefine severe preeclampsia as a biologically heterogeneous placental disorder composed of molecularly distinct subtypes with divergent aging trajectories and fetal growth outcomes, providing a framework for mechanism-based stratification and precision obstetric medicine.

Educational attainment-related polygenic scores have been implicated in autism spectrum disorder (ASD), but how parental polygenic scores shape offspring phenotypes remains unclear. Using genotyping and exome-sequencing data from 142,357 individuals (55,252 ASD cases) in a large ASD cohort, we dissected the direct and indirect genetic effects of educational attainment-related polygenic scores on ASD phenotypes. Trio-model analyses showed that parental polygenic scores for educational attainment (PGSEA ) were associated with milder core ASD symptoms, including social deficits and repetitive behaviors, predominantly through indirect genetic effects, whereas their associations with comorbidities were driven predominantly by direct genetic effects. PGSEA was also significantly negatively associated with rare variant burden and prenatal factors, although these factors contributed largely independently to most phenotypes. Adjustment for full-scale intelligence quotient (FSIQ) and socioeconomic status (SES) partially attenuated the indirect effects of PGSEA on offspring phenotypes. Finally, higher parental PGSEA was associated with later age at diagnosis in offspring, partly through its protective effects on ASD phenotypes. These findings indicate that indirect genetic effects of parentalPGSEA contribute substantially to phenotypic variation in ASD and highlight family-mediated pathways as an important component of ASD heterogeneity.

Postoperative resilience varies widely among older adults, yet the biological drivers of recovery remain unclear. We evaluated whether preoperative immune profiles, measured in plasma and through ex vivo whole blood stimulation, predict resilience to the acute stress of total knee arthroplasty. A total of 152 adults (greater or equal to 60 years) in the PRIME KNEE cohort underwent elective total knee arthroplasty and had available blood samples for measurement of 45 immune biomarkers, quantified in plasma and in whole blood stimulated ex vivo for 24 hours with lipopolysaccharide (LPS) or influenza antigen (FLU). Resilience was assessed using Expected Recovery Differential (ERD) and Resilience Trajectory (RT) across pain severity, pain interference, lower extremity physical activities of daily living (LE PADLs), and step counts. An exploratory stability selection framework using LASSO identified biomarker predictors of postoperative outcomes. Plasma and stimulated biomarkers showed broadly similar predictive performance. A shared set of biomarkers, including LBP, leptin, TNFR1, CD30, and LIF, was consistently selected across models. Immune predictors explained ~12-24% of the variance in resilience outcomes. Distinct immune signatures emerged for pain versus functional recovery: pain related predictors mapped to local inflammatory and neuroimmune pathways, whereas function related predictors reflected systemic inflammatory load and cytokine signaling. Preoperative immune biomarkers, whether measured in plasma or after ex vivo stimulation, capture meaningful variance in postoperative resilience. The divergence between pain related and function related immune signatures highlights biologically distinct pathways underlying different dimensions of recovery and supports further development of immune based perioperative risk assessment.

Context: Although knee extensor and flexor strength deficits are well-documented after anterior cruciate ligament reconstruction, limited data exist characterizing how strength recovery evolves over time. Understanding the temporal patterns of recovery, and how they differ by autograft type, is critical for optimizing rehabilitation and return-to-sport decision-making. Objective: To characterize temporal trends in knee extensor and flexor strength recovery during the first year post-ACLR and evaluate differences between patellar tendon and hamstring tendon autografts. Design: Case series. Setting: Sports physical therapy clinics within a large health system. Participants: Five hundred three patients (17.8 {+/-} 3.0 y) who underwent primary reconstruction with either patellar tendon or hamstring tendon autografts and completed a combined 730 return-to-sport tests within 12 months postoperatively. Main Outcome Measures: Normalized peak isokinetic concentric knee extension and flexion torques for involved and uninvolved limbs, and normalized symmetry indices for knee extension and flexion strength. Results: Knee extension strength on both limbs and extension strength symmetry improved over time. Patients with hamstring autografts demonstrated superior involved leg knee extension strength and better extension strength symmetry compared with those receiving patellar tendon autografts, although uninvolved leg strength was similar between autografts. Knee flexion strength on both limbs and flexion strength symmetry also improved over time. Patellar tendon autograft patients exhibited greater strength symmetry, despite no between autografts for flexion strength for the involved or uninvolved limb. Conclusions: Autograft significantly influences muscle strength recovery following anterior cruciate ligament reconstruction. Hamstring tendon autografts are associated with superior recovery of knee extension strength and strength symmetry compared to patellar tendon autografts. These findings underscore the need for graft-specific rehabilitation strategies and earlier identification of patients at risk for delayed recovery.

Background Endometrial cancer exhibits marked molecular and immune heterogeneity that is only partially explained by established genomic biomarkers. We investigated whether T cell receptor (TCR) repertoire architecture captures complementary dimensions of antitumor immunity beyond conventional molecular classification. Methods Paired tumor and peripheral blood samples from eight patients with molecularly characterized endometrial cancer underwent TCR repertoire profiling. Diversity, clonality, and tumor blood overlap metrics were integrated with genomic variables, including tumor mutational burden (TMB), genomic instability metric (GIM), and POLE status. Principal component analysis and correlation analyses were used to identify major dimensions of repertoire organization. Composite Immune Focusing and Immune Sharing Scores were derived to summarize dominant repertoire patterns. Results The first two principal components explained 70.1% of total repertoire variance and revealed substantial heterogeneity independent of histological subtype. TMB was strongly associated with reduced repertoire diversity and increased clonal dominance, resulting in a robust association with the Immune Focusing Score ({rho} = 0.88, p = 0.004). POLE mutated tumors occupied the extreme end of this focusing continuum. In contrast, genomic instability was associated with increased tumor blood repertoire overlap and preserved diversity, reflected by a strong correlation between GIM and the Immune Sharing Score ({rho} = 0.76, p = 0.027). The two immune scores showed minimal correlation with each other ({rho} = -0.24, p = 0.57), indicating that they capture largely independent aspects of immune organization. Conclusion Integrative analysis of TCR repertoire architecture and tumor genomics identifies distinct immunogenomic states in endometrial cancer that are not fully captured by conventional molecular classification. If validated in larger cohorts, immune focusing and immune sharing metrics may provide complementary biomarkers for patient stratification and immunotherapy-oriented precision oncology

Background: While genetic factors strongly influence brain aging trajectories, variants conferring cognitive resilience remain poorly characterized. The neurokinin-3 receptor (NK3-R), encoded by Tachykinin Receptor 3 (TACR3), modulates cholinergic signaling in memory circuits vulnerable to aging. Previous studies linked the non-WT expression of the TACR3 variant rs2765 with cognitive decline and reduced volume of the hippocampus and basal forebrain, but systematic replication and mechanistic validation were lacking. Methods: We investigated rs2765 in the preregistered AgeGain cohort of cognitively healthy older adults (n=188) with independent validation in the ADNI cohort (n=809) which includes persons with and without Alzheimers Disease (AD) that show healthy cognition, mild cognitive impairment or dementia. Analyses integrated structural neuroimaging, longitudinal cognitive assessments, epigenetic aging (PhenoAge), genome-wide methylation profiling, and mechanistic validation through luciferase assays and cross-species protein expression studies. Results: The infrequent protective rs2765 WT variant, found in 12.8% of Europeans, conferred 49% slower cognitive decline (p = 0.002) for amyloid-positive individuals of the ADNI cohort and 3.7 years younger epigenetic age (p = 0.013, 95% CI: 0.79-6.67 years) in the cognitively healthy AgeGain cohort. WT carriers showed larger hippocampal and basal forebrain volumes across cohorts, with Allen Brain Atlas integration revealing these outcomes to occur exclusively in regions where TACR3 expression positively correlated with gray matter volume. Mechanistically, the non-WT variant ameliorated RBMX-mediated post-transcriptional regulation, reducing NK3-R protein expression by 25-40% in vitro and ex vivo murine brain slice models. Senescence-accelerated mice exhibited reduced endogenous NK3-R expression, phenocopying the predicted functional consequences of the variant. In AgeGain participants, genome-wide methylation profiling identified 2,313 differentially methylated CpGs affecting 228 pathways spanning glutamatergic signaling, acetylcholine receptor pathways, chromatin remodeling, and angiogenesis, suggesting coordinated molecular reprogramming from synaptic function to systemic aging. Conclusions: rs2765 WT confers resilience to age- and AD-related cognitive decline through RBMX-dependent regulation of NK3-R expression, with effects of remarkable size cascading from memory to systemic aging. rs2765 genotyping could stratify individuals for NK3-R modulator therapy (e.g., fezolinetant or senktides) and identify those maintaining function despite pathological burden, complementing APOE-based risk assessment in precision geromedicine.

Gene therapy is rapidly emerging as a transformative treatment for monogenic neurological disorders, including pediatric movement disorders such as aromatic L-amino acid decarboxylase (AADC) deficiency. However, its success critically depends on defining target cells and windows for therapeutic intervention. Here, we present an open-access single-nucleus transcriptomic atlas of the human basal ganglia spanning a therapy-relevant window from second/third trimester to the perinatal period and adulthood. Across 35,755 nuclei, we identify major (non-)neuronal cell types, retrace developmental trajectories, and characterize gene-regulatory networks. We identify so far unrecognized human-specific expression of key neuronal signaling genes, including GNAO1 and ADCY5, and discuss the implications for targeted gene replacement therapies. Unexpectedly, we found that the Huntingtin gene (HTT) is already expressed during prenatal stages of human brain development, supporting a previously proposed neurodevelopmental component of Huntington's disease, which should be considered in diagnostic and therapeutic strategies. Moreover, FOXG1 expression and regulon activity are predominantly located in a prenatal time window, suggesting constraints on the effectiveness of postnatal interventions. Our findings highlight the importance of datasets capturing human brain development in real time and provide a publicly available resource to guide precision gene therapy strategies in the future.

Diabetic peripheral neuropathy (DPN) is a common and disabling condition for which no disease-modifying therapies are available. Glycemic and metabolic drivers do not fully explain why only a subset of individuals with diabetes develop DPN, and genetic contributors remain poorly defined. We aimed to perform a multi-population genome-wide association study (GWAS) of DPN to highlight potential new etiological pathways and therapeutic targets. Methods We performed a multi-population GWAS of neuropathy in people with and without diabetes using the VA Million Veteran Program and UK Biobank, followed by replication in the All of Us Research Program (AoU), and gene-based and gene-set analyses to identify implicated pathways. Causal relationships between circulating serine levels and DPN were further tested using two sample Mendelian randomization. To further evaluate pathogenic potential, we analyzed rare, high impact variants in GWAS implicated genes among individuals with unresolved inherited neuropathies using the GENESIS platform. Findings Among individuals with type 2 diabetes, we identified seven genome wide significant loci (p<5x10-): PHGDH and PSPH (key serine synthesis genes), TEAD1, CYP4F11, LARGE1, FTO, and COBLL1. No loci were significant in individuals without diabetes or with type 1 diabetes. Four loci (PHGDH, TEAD1, FTO and CYP4F11) replicated in AoU (p <0.05). Mendelian randomization demonstrated that higher genetically predicted serine levels were associated with lower DPN risk, consistent with a causal role of serine metabolism in disease pathogenesis. Rare-variant burden analyses revealed associations of predicted deleterious variants with inherited neuropathy case status in PHGDH (odds ratio [OR] 12.7 [95% CI 7.9, 20.4]), PSPH (OR 8.5 [7.2, 10.2]), PHKG1 (OR 4.8 [3.7, 6.3]), and LARGE1 (OR 0.007 [0.0004, 0.1]). Interpretation Convergent genetic evidence across common and rare variation implicates serine synthesis as a key pathway in DPN. These findings link diabetic and inherited neuropathies through a shared metabolic mechanism, identifying serine metabolism as a potential therapeutic target.

Pathogenic variants in ATP13A2, which encodes an endolysosomal polyamine exporter, cause Kufor-Rakeb syndrome and are associated with early-onset parkinsonism and related neurodegenerative disorders, however, the mechanisms by which ATP13A2 dysfunction drives disease remain incompletely defined. In Atp13a2 knockout mice, we identified an early, transient reduction in brain polyamines that precedes overt gliosis and behavioural abnormalities. Pharmacological polyamine depletion exacerbates phenotypes, whereas oral supplementation of spermidine, but not spermine, rescues parkinsonian symptoms establishing metabolic polyamine deficiency as a pathogenic driver. Mechanistically, spermidine counteracts microglia lysosomal dysfunction in the brain and exerts mitochondrial antioxidant and anti-inflammatory effects in primary mouse microglia, thereby improving neuronal integrity. In the absence of Atp13a2, microglial spermidine import relies on the related polyamine transporter Atp13a3. Importantly, these findings translate to human systems, whereby spermidine attenuates inflammation in ATP13A2-deficient human differentiated microglia, while postmortem ATP13A2-deficient brain analysis confirms increased microglia reactivity. Spermidine also rescues motor deficits and dopaminergic neuron loss in ATP13A2-deficient Drosophila and other fly parkinsonism models. Together, these findings identify early polyamine dysregulation as a mechanistic contributor to ATP13A2-associated parkinsonism and nominate spermidine supplementation as a potential therapeutic strategy for ATP13A2-driven pathology and possibly a broader range of parkinsonian sub-types.

Background. Plasma and serum metabolomic studies of myalgic encephalomyelitis / chronic fatigue syndrome (ME/CFS) have repeatedly implicated hypometabolic, lipid, mitochondrial, redox and tryptophan-kynurenine pathways, but prior cohorts have been modest in size and have used heterogeneous case definitions. Whether similar pathway-level signals are detectable at scale in dried blood spots (DBS), across questionnaire-derived fatigue constructs and across orthogonal LC gradients in the same individuals remains unresolved. Methods. We profiled DBS extracts from 1,784 community-cohort adults by reverse-phase LC-MS using paired 5 min and 15 min gradients. Six questionnaire-derived endpoints captured a pragmatic self-reported PEM-like phenotype, a DSQ-derived PEM-like construct, high or review clinical status, temporal fatigue state, comorbid fatigue and self-reported chronic fatigue. The locked primary endpoint for Phase 1 was pragmatic_fatigue_pem with 226 cases and 914 controls after excluding major metabolic comorbidity. We tested a biology-first panel comprising 22 literature-curated metabolites represented by four participant-level descriptors each, and evaluated three discovery extensions: a targeted m/z search of additional literature candidates, a hypothesis-free univariate screen across 4,553 5 min and 5,625 15 min consensus features, and pairwise z-difference ratios. Endpoint-specific Ridge classifiers were evaluated by five-fold out-of-fold AUC with bootstrap stability filtering. Cross-gradient agreement was assessed by per-metabolite AUC concordance between paired 5 min and 15 min profiles. Severity was modelled as an ordinal grade derived from the number of fatigue criteria met and chronic-fatigue-form status. Results. The biology-first DBS panel achieved out-of-fold AUC 0.81 for the pragmatic self-reported PEM-like endpoint (226 cases / 914 controls). The DSQ-derived PEM-like construct reached AUC 0.60 (57 cases / 201 controls) on the un-filtered set and AUC 0.778 (SD 0.013, twenty seeds) in a post-hoc signature-decomposition follow-up restricted to participants without a self-declared major-metabolic-history tag (29 cases / 230 controls); both are treated as construct-validity anchors rather than as provoked or clinically adjudicated PEM. An optimised operationalisation of the same construct (panel-self normalisation, restriction to non-comorbid participants and demographic covariates) reached AUC 0.71 (95 % CI 0.55 to 0.76), and an exploratory age-stratified signature decomposition suggested age-dependent pathway composition that requires confirmation given small per-stratum case counts. Stable contributors mapped to carnitine-shuttle, TCA-cycle, redox-thiol and tryptophan-kynurenine pathways. Cross-gradient analysis of 22 matched metabolites yielded Pearson r = 0.62 for signed univariate effects (p = 0.002; 68 % directional agreement). The metabolomic score increased with severity grade (Spearman rho = 0.45, p = 4 x 10^-91; median scores 0.24, 0.51 and 0.75 across grades 0, 1 and 2). Sensitivity analyses on the covariate-complete subset (n = 565; 138 cases / 427 controls) showed that the DBS signal was robust to adjustment for age, sex, BMI and medication burden (DBS-only AUC 0.76, DBS plus covariates 0.78, covariates only 0.64), and produced a metabolomic-specific lift of approximately 0.13 AUC over the strongest anti-leak declarative cross-form questionnaire baseline (AUC 0.63). DBS-only AUC was stable across sex, age and BMI subgroups, and a 1:4 nearest-neighbour matched analysis on age, sex and BMI yielded AUC 0.72 (95 % CI 0.67 to 0.77). The observed pattern supported pathway-level convergence with prior ME/CFS metabolomics literature, including carnitine shuttle, fatty-acid beta-oxidation, TCA cycle, redox-thiol, urea cycle, glycerophospholipid and tryptophan-kynurenine axes. In contrast, the hypothesis-free 15 min screen produced high-AUC features that mapped predominantly to environmental or technical signals, including pesticide, industrial-amine and mobile-phase artifact annotations; only one of eight top leads, a truncated oxidised phospholipid, was biologically plausible, and none had tandem-MS support. Conclusions. In this large community cohort, a literature-curated DBS metabolomic panel captured pathway-level biology associated with a questionnaire-derived PEM-like fatigue phenotype, showed directional concordance across LC gradients, scaled with symptom severity and remained robust to key demographic, anthropometric and anti-leak questionnaire baselines. The findings converge with several metabolic axes previously reported in ME/CFS plasma and serum studies, including carnitine-shuttle, TCA-cycle, redox-thiol, urea-cycle, glycerophospholipid and tryptophan-kynurenine pathways. They should not be interpreted as clinical validation of a diagnostic test, screening tool or objective provoked-PEM biomarker. Rather, they support at-home-compatible DBS metabolomics as a biologically grounded platform for future clinically adjudicated validation, decision-support development and longitudinal monitoring in fatigue and PEM-like syndromes. Because DBS contains cellular and plasma-derived components, matrix effects must be considered when comparing individual metabolites with venous plasma or serum studies, and hypothesis-free screening at this scale can preferentially surface exposome or technical variance unless molecular identification is enforced before biological interpretation.

People with HIV exhibit elevated inflammation and cardiovascular risk despite antiretroviral therapy. To define the genetic architecture of inflammasome-associated inflammation, we performed whole-genome sequencing and quantified plasma IL-6, IL-1{beta}, and IL-18 in 1,000 ART-suppressed PWH from the U.S. Military HIV Natural History Study. Genome-wide analyses identified 14 loci implicating antiviral defense (DDX17, DDX41, EEA1, BCL11A), lipid metabolism (ABCA1, ABCA12, ABCC1, AGMO), and vascular remodeling (KLHL29, RNF213, ETV1). Transcriptome-wide analyses across cardiovascular and immune tissues identified regulatory programs linking interferon signaling, immune activation, and vascular biology to circulating cytokine levels. Mendelian randomization analyses supported causal relationships between inflammasome-associated cytokines and vascular events. Functional integration with genome-wide CRISPR perturbation datasets in primary CD4 T cells linked cytokine-associated loci to HIV antiviral pathways and cytokine regulatory networks. External validation in cohorts without HIV demonstrated pathway-level convergence despite limited variant-level overlap. These findings define genetic mechanisms linking inflammasome signaling, antiviral defense, and cardiovascular risk.

Functional ex vivo assays using live tumor tissues have demonstrated strong predictive accuracy for response to immune checkpoint inhibitors (ICIs) but are not scalable, requiring manual processing of large resections collected at academic centers. Here, an ex vivo live tumor fragment (LTF) platform was developed using standard-of-care biopsies from 228 patients with suspected malignancy collected across prospective, multicenter observational trials and biobanks. Hierarchical clustering of ICI-mediated changes in cytokine production identified two groups: responders and nonresponders. A binary classifier (elive index) using 8 cytokines achieved an AUC of 0.99 for cluster prediction. elive index correctly predicted clinical benefit in 93% (26/28) of patients (P = 3.2x10-5) and accurately identified 83% (10/12) of objective responders. Critically, elive responders were identified among biomarker-negative patients, highlighting the platform as a scalable approach that complements existing companion diagnostics and expands the population of patients identified to benefit from ICI therapy.

Background. Type 2 diabetes mellitus (T2D) is defined by progressive pancreatic {beta}-cell dysfunction whose molecular underpinnings remain incompletely understood. Single-cohort transcriptomic analyses of donor islets have yielded heterogeneous gene lists of limited cross-study reproducibility, constraining both mechanistic interpretation and biomarker development. Methods. We combined two complementary analytical strategies applied to four public human islet transcriptomic cohorts (GSE25724, GSE20966, GSE38642, and GSE164416; n = 7-57 donors per contrast). For the integrative arm, three microarray datasets and one bulk RNA-seq dataset were processed independently and unified through gene-level random-effects meta-analysis, hallmark pathway scoring (GSVA/MSigDB), and iterative module refinement, yielding a two-axis disease framework. For the diagnostic arm, a consensus multi-method machine learning pipeline, combining LASSO penalized logistic regression, Support Vector Machine Recursive Feature Elimination (SVM-RFE), and Random Forest importance scoring, was applied to 184 differentially expressed genes from the RNA-seq cohort, with all normalization steps performed within leave-one-out cross-validation (LOOCV) folds to prevent data leakage. Machine learning classification of the RNA-seq cohort was additionally subjected to external transportability testing in the independent bulk human islet RNA-seq cohort GSE50244 using an overlap-restricted reduced score and a threshold fixed in the discovery cohort. Results. Meta-analysis across all four cohorts identified 337 high-confidence T2D-associated genes (96.1% directional concordance in beta-cell-enriched tissue). These were distilled into two refined 14-gene modules: ImmuneStress (MICB, HLA-DRA, HLA-DPA1, IL1R2, and others) and BetaCellIdentitySecretion (RASGRP1, PPP1R1A, SLC2A2, and others), whose composite IsletDysfunctionScore provided the most stable cross-platform separation of non-diabetic from T2D islets (Hedges' g = 1.80, p = 9.83 x $10^-17$, $\text{I}^2$= 0%). Consistent with progressive disease, IsletDysfunctionScore increased monotonically from non-diabetic to impaired glucose tolerance to T2D. Separately, the machine learning pipeline derived a 10-gene diagnostic panel: GABRA2, SLC2A2, ARG2, DKK3, PRIMA1, TAFA4, HHATL, PARVG, RNU1-70P, and the novel lncRNA ENSG00000284653, that achieved perfect discrimination in LOOCV (AUC = 1.000, sensitivity = 1.000, specificity = 1.000, zero misclassifications across all 57 donors). A leakage-verification experiment confirmed that this performance reflected genuine biological signal: global quantile normalization prior to cross-validation collapsed AUC to 0.380. External testing showed that 8 of the 10 panel genes were measurable in GSE50244. The frozen 8-gene reduced score retained strong discrimination (external AUC = 0.907), with 6 of 8 genes preserving directional concordance, but the discovery-derived threshold did not transfer because the external score distribution was shifted upward and compressed, yielding complete sensitivity but zero specificity at the frozen cutoff Conclusions. Integrating pathway-level meta-analysis with machine learning classification, we present a coherent two-axis model: immune/stress activation and loss of beta-cell identity/secretory competence, together with a compact, biologically interpretable 10-gene diagnostic signature. Panel genes converge on GABA signaling, glucose transport, arginine metabolism, WNT pathway inhibition, and a novel lncRNA, providing both mechanistic hypotheses and high-priority targets for external validation. These findings offer a reproducible transcriptomic scaffold for future mechanistic, biomarker, and clinical translation studies of human islet dysfunction. They also support external transportability of the core biological signal, while indicating that absolute operating thresholds are cohort-dependent and would require recalibration before deployment in independent datasets.

Cardiovascular diseases (CVDs) are highly heritable, but pathogenesis at the organ and physiological level is still poorly defined. Polygenic risk scores (PRSs), which estimate individual genetic susceptibility to a disease, may allow for the identification of associated abnormal organ structures. Ultimately, identifying where cardiovascular polygenic risk manifests can guide early interventions, shape mechanistic hypotheses, and motivate prevention trials for cardiac remodelling. This study investigated the association between PRSs for five common CVDs [heart failure (HF), coronary artery disease (CAD), atrial fibrillation (AF), abdominal aortic aneurysm (AAA) and ischaemic stroke (IS)] and 28 imaging-derived phenotypes (IDPs) from cardiac magnetic resonance imaging of ~62,000 participants in UK Biobank. To investigate the cardiac features associated with elevated polygenic risk of CVDs, we tested CVD PRSs against cardiac IDPs and identified 97 significant associations (FDR [&le;] 0.05). We further identified 32 significant putative mediators between CVD PRSs and incident disease events, revealing that across CVDs, polygenic risk manifested as distinct patterns in cardiac structures. HF implicated all cardiac chambers, including left ventricular and left atrial dysfunction alongside enlarged aorta. AF was characterised by biatrial enlargement and reduced ejection fractions, most prominently in the left atrium but also involving left ventricular wall thickness. IS exhibited left ventricular hypertrophy and left atrial dysfunction, while CAD predominantly involved left ventricular hypertrophy. AAA was primarily characterised by enlarged descending aorta. Overall, cardiac IDPs mediated a substantial proportion of polygenic risk for CVDs, in particular for HF. Taken together, our results show that cardiac structure and function lie on the pathway between polygenic risk and cardiovascular events.

Mood and anxiety disorders emerge predominantly in adolescence, yet they are usually identified only once symptoms have consolidated, when intervention can only be reactive. A marker that registers the loss of healthy brain function before symptoms crystallise would allow earlier and more targeted treatment, much as caged canaries once warned miners of danger before it became apparent. Here we report such a marker using a single baseline resting-state functional MRI scan in 150 adolescents in the Human Connectome Project Boston Adolescent Neuroimaging of Depression and Anxiety (HCP BANDA) cohort, allowing us to prospectively predict depression and anxiety symptoms one year later in held-out participants at r = 0.60, substantially above the effect-size ceiling reported for functional connectivity in the same data. The marker is not computed from raw functional connectivity but read out from a whole-brain generative model fitted to each individual's dynamics, which gives access to interference structure that covariance-based features cannot represent. The regions driving the prediction, including precuneus, ventromedial prefrontal and anterior cingulate cortices, are among those previously implicated in internalising disorders, and the same signature tracks cognitive variation in healthy participants and is mechanistically linked to the efficiency of task-related computation. These findings establish a mechanistically interpretable and prospectively predictive marker of adolescent mental health and define a clear path towards external validation and clinical use.

Human metapneumovirus (HMPV) causes acute lower respiratory infections, primarily affecting young children and older adults, with seasonal outbreaks peaking annually in March or April in the United States and other temperate regions in the Northern hemisphere. However, the factors driving HMPV seasonality in the United States remain poorly understood. We analyzed laboratory-confirmed HMPV cases and age-specific emergency department visits across 10 US regions, fitting an age-stratified dynamic transmission model to assess spatiotemporal patterns and investigate the influence of environmental variables and viral interference from RSV on HMPV transmission rates. We found that models incorporating climate variables into the transmission rate, including vapor pressure, precipitation, potential evapotranspiration, and minimum temperature, could not capture the timing of HMPV activity across all regions. Instead, HMPV timing was associated with RSV activity, with the HMPV transmission rate reduced in the presence of RSV. We showed that, unlike RSV, only models incorporating viral interference could reproduce the biennial pattern of HMPV observed in some regions, characterized by alternating late-small and early-large epidemics. Furthermore, our model successfully reproduced post-COVID-19 HMPV and RSV epidemics and predicted that RSV interventions are not likely to lead to a substantial increase in HMPV activity despite decreasing competition from RSV. Our work unravels the spatiotemporal dynamics of HMPV and its interaction with RSV, informing future seasonal forecasting and intervention strategies for HMPV.