EMBO Molecular Medicine

Despite suppressive antiretroviral therapy, many people with HIV (PWH) retain chronic interferon-associated immune dysregulation. Observational data from the Swiss HIV Cohort Study linked asymptomatic mycobacterial exposure to lower viral set points, reduced interferon-associated activity, and attenuated HIV-specific antibody responses, a pattern sharing features with HIV elite controllers and natural hosts of primate lentiviruses. We therefore examined whether Bacillus Calmette-Guerin (BCG) vaccination could induce a related immune configuration in ART-treated PWH. Using longitudinal systems-level profiling within the BELIEVE trial, we found that BCG reduced constitutive NK cell IFN-{gamma} production and PBMC-mediated direct cytotoxicity without impairing inducible cytokine responses or antibody-dependent cellular cytotoxicity. Multiomic and proteomic analyses showed reduced interferon- and activation-associated programs, while adaptive immune parameters remained largely stable and follow-up revealed no obvious adverse clinical pattern. This configuration, reduced baseline interferon activity coexisting with preserved Fc-dependent effector function, shares selected features with immune states described in natural lentiviral control and provides a rationale for testing BCG in combination with antibody-based HIV interventions.

Nonarteritic anterior ischemic optic neuropathy (NAION) is the leading cause of acute optic nerve related vision loss in older adults, yet no disease modifying therapy exists. Although ischemia is a defining feature of NAION, prior therapeutic efforts targeting vascular insufficiency or nonspecific oxidative stress have failed to prevent irreversible retinal ganglion cell (RGC) degeneration, underscoring an unresolved mechanistic gap between ischemic insult and permanent axonal failure. In this endeavour, we identify lipid peroxidation as an important driver of neurodegeneration in NAION. Analyses of human NAION retina, together with a rigorously validated mouse model, demonstrated a remarkable activation of phospholipid peroxidation within the retina following ischemic injury. RGC-specific overexpression of glutathione peroxidase 4 (GPX4), the only known enzyme capable of directly detoxifying phospholipid hydroperoxides within biological membranes, confers striking protection of RGC survival, axonal integrity, and visual function. We further demonstrate that mitochondrial-targeted GPX4 provides superior protection, suggesting mitochondria as a critical locus of lipid peroxidation-driven vulnerability in NAION. Leveraging real-time multiparametric in vivo imaging to directly interrogate axonal metabolism and function, we demonstrate that RGC-specific GPX4 overexpression robustly restores axonal and retinal mitochondrial abundance, improves ATP bioenergetics, and suppresses superoxide stress following optic nerve ischemia. Mitochondria-targeted GPX4 expression further restores axonal transport and retinofugal projections to central visual targets, thereby stabilizing visual pathway connectivity. Notably, these neuroprotective effects are recapitulated by Ebselen, a clinically tested GPX mimetic, identifying lipid peroxide detoxification as a translatable and imaging-validated therapeutic strategy. Collectively, this work establishes ischemia-induced lipid peroxidation as an essential driver of neurodegeneration in NAION and identifies GPX4 as a key molecular determinant of retinal ganglion cell resilience.

Rationale: Pulmonary hypertension is defined by pulmonary hemodynamics, but diagnostic and prognostic biomarkers remain limited. Nuclear magnetic resonance (NMR) spectroscopy provides detailed insights, particularly in the lipid metabolism. Objectives: To explore circulating NMR-derived metabolites and lipoprotein-related parameters for their association with pulmonary hemodynamics and to analyse their prognostic properties in pulmonary arterial hypertension (PAH). Methods: Retrospective analysis of a PAH cohort with complete diagnostic workup including right heart catheterization and baseline serum samples, from the prospective GRaz Pulmonary Hypertension-Metabolism (GRAPH-M) registry. Measurements: NMR-derived metabolites and lipoprotein-related parameters were analyzed for their association with clinically relevant parameters of PAH. We defined PHIHDL, a score derived from high-density lipoprotein (HDL) related measures based on their strong association with pulmonary hemodynamics, and evaluated its prognostic value. Results: We included 100 patients with PAH treated at the PH clinic of LKH University Hospital, Medical University of Graz, between 2011 and 2021. Age was 61{+/-}15 years, female/male ratio 2.5, BMI 26 {+/-}7 kg/m2, mPAP 41{+/-}16 mmHg, PAWP 8.8{+/-}3.2 mmHg, PVR 8.0{+/-}4.9 WU, and median survival was 8.0 years. During follow-up, 46 patients died. We identified a cluster of 12 HDL-related measures that showed significant inverse association to pulmonary hemodynamics and derived PHIHDL from the reversed scaled average of these particles. PHIHDL was associated with all-cause mortality after adjustment for age and sex (HR 2.96, 95% CI 1.52-5.70), independent of the clinical risk scores COMPERA 2.0 and REVEAL Lite2. Conclusion: PHIHDL, a pulmonary hemodynamics-based metabolomic score, provides independent prognostic information beyond established risk scores in PAH.

Transmission-blocking vaccines (TBVs) are a promising strategy to reduce malaria transmission by targeting parasite stages within the mosquito. However, parasite genetic diversity may limit vaccine efficacy. We used next-generation amplicon deep sequencing to identify non-synonymous single nucleotide polymorphisms (SNPs) in Pfs25 from 184 Plasmodium falciparum isolates from Senegal, Tanzania, Ghana, and Burkina Faso. Prioritized SNPs were introduced into P. falciparum via CRISPR-Cas9. For the G116C variant, gametocyte development was evaluated by microscopy and qPCR, and mosquito infectivity was assessed by SMFAs. We identified 26 SNPs, including 24 novel variants. Functional assays showed that the Pfs25 G116C mutation did not affect gametocyte development or exflagellation. SMFA showed no significant differences in oocyst prevalence or intensity between mutant and WT parasites. These findings highlight the importance of integrating genetic surveillance with functional validation to guide the development of effective transmission blocking interventions

Prognostic models in oncology are developed one cancer at a time, from that cancer's own labelled outcomes, and fail where prognostic information is scarcest. Rare cancers account for roughly a fifth of diagnoses and most paediatric malignancies, yet seldom supply enough events for a reliable time-to-event model. We therefore asked whether a representation learned without outcome labels can supply what those cohorts cannot. A Transformer encoder was pretrained by masked field-value modelling on 9425135 tumour records from the SEER 17 registries, diagnosed in 2000 to 2023. Only diagnosis-time fields passing a fail-closed coding-verification gate were admitted, and each record was emitted as an era-specific and a harmonised view, keeping two decades of recoding auditable. The encoder was then frozen and read by a linear Cox head for overall survival. Nine rare cancers were removed from the pretraining corpus entirely, each requiring an independent pretraining run. On a sealed test partition, all nine exceeded an architecture-identical random frozen encoder in Harrell concordance by +0.0034 to +0.0368, every lower confidence limit above zero. At 256 labelled patients, all 67 cancers favoured the pretrained representation over budget-matched Cox regression, median difference +0.0283. The advantage was bounded: given the entire training set, Cox regression was favoured in seven of nine rare cancers. The encoder did not outperform a field-frequency baseline on its own objective, so upstream reconstruction did not predict downstream transfer. Outcome-agnostic registry pretraining carries prognostic signal into cancers it has never seen, and is most useful where labels are fewest, without establishing clinical utility.

BACKGROUND: Cerebral ischemia following subarachnoid hemorrhage (SAH) has traditionally been considered transient because functional alterations of the cerebral microcirculation are thought to be self-limiting. However, we identified a previously unrecognized vasculopathy, perivascular fibrosis of the cerebral microcirculation (PFCM), characterized by excessive type I collagen deposition after SAH. This study investigated the mechanisms underlying PFCM and its subsequent effects on cerebral hemodynamics. METHODS: In vivo SAH was modeled in mice by autologous blood injection, whereas oxygenated hemoglobin (OxyHb) exposure was used to mimic SAH in vitro. Pericyte-deficient mice (Pdgfr{beta}+/-) and pericyte-specific vestigial-like family member 3 (VGLL3) conditional knockout mice (Vgll3{Delta}PC) were generated. Pericyte contractility was measured by nanoindentation and traction force microscopy. Molecular mechanisms were examined using Western blotting, immunofluorescence, CUT&Tag, RNA-seq, transmission electron microscopy, and molecular docking. PFCM, impaired dilation of the cerebral microcirculation, and cerebral autoregulation were assessed by two-photon imaging, transcranial Doppler with continuous blood pressure monitoring, super-resolution ultrasound imaging, and photoacoustic imaging. RESULTS: After SAH, mice developed long-term cerebral autoregulation dysfunction marked by impaired dilation of the cerebral microcirculation, with the abnormality being most evident within the relatively lower blood pressure range. The marked reduction in PFCM in Pdgfr{beta}+/- mice indicated that pericytes were the principal cellular contributors. Mechanistically, OxyHb-induced cytoskeletal remodeling in vitro increased pericyte contractility and promoted nuclear translocation of SAH-upregulated VGLL3. This was followed by increased genomic occupancy, Col1a1 transcriptional activation, and type I collagen deposition. Pericyte-specific VGLL3 knockout abolished PFCM and, consequently, significantly alleviated long-term cerebral autoregulation dysfunction. CONCLUSIONS: Our findings identify PFCM mediated by pericytic VGLL3 as a novel vasculopathy leading to long-term cerebral autoregulation dysfunction after SAH.

Accurate determination of kinase inhibitor binding modes could provide essential information for understanding resistance mechanisms and accelerating drug discovery. While conventional structural methods such as X-ray crystallography, cryo-EM and NMR provide high-resolution information but are low-throughput and capture largely static snapshots of dynamic protein-ligand interactions Here, we introduce a single-molecule nanopore tweezer platform that functionally subtypes ATP-competitive Abl kinase inhibitors by resolving distinct ionic current signatures of Abl-inhibitor complexes. This approach distinguishes Type I, Type IIA, and Type IIB inhibitors without structural determination. We further show how clinically relevant Abl variants (T315I and E255V) reshape inhibitor engagement and binding modes. By combining baseline probability features with wavelet-based time-frequency descriptors, ensemble machine-learning models achieved 97.5% classification accuracy across seven kinase inhibitor binding modes at sub-angstrom resolution and enabled deconvolution of mixed-inhibitor samples at nanomolar concentrations. These results establish nanopore tweezers as a label-free, super-resolution platform for profiling kinase conformational states and inhibitor binding modes, complementing structural approaches and supporting precision oncology.

Antimicrobial resistance is a public health challenge, driving the need for rapid, cost-effective diagnostic support tools. Artificial intelligence (AI) may enable prediction of susceptibility to untested antibiotics from known susceptibility results, but prospective clinical validation is required before routine use. We evaluated an AI-based decision support method, trained on invasive isolates from the European Surveillance System (TESSy), for prediction of antibiotic susceptibility in clinical Escherichia coli urine isolates. The evaluation included 99 E. coli isolates from urine samples with diversity in age, sex, and antibiotic susceptibility. Predictions were evaluated for 14 antibiotics using patient metadata and susceptibility results for 4-8 antibiotics as input. Prediction uncertainty was handled using conformal prediction, allowing abstention when confidence was insufficient. EUCAST disk diffusion test results were used as reference and genomic sequence data was used to explore mechanisms of the AI performance. Without conformal prediction, 84% of predictions were correct when susceptibility results of six antibiotics were used to predict susceptibility to eight additional antibiotics. Across all predictions generated using susceptibility results for six antibiotics as input, the major and very major error rates were 19% and 12%, respectively. Prediction errors varied between antibiotics and were associated with certain phenotypic and genotypic resistance patterns. Conformal prediction reduced errors but increased abstentions; at confidence levels of 90%, 95%, and 97.5%, the model abstained in 9.6%, 14%, and 22% of instances. The method showed promising performance, but its clinical use remains limited and may require diagnostic data beyond susceptibility test results and demographic variables.

Immune checkpoint inhibitors targeting the PD-1/PD-L1 axis have shown great promise in treating bladder cancer and are now part of the standard treatment for advanced disease. However, many patients still fail to respond to treatment and at present many biomarkers are assessed but have yet shown only limited results. Therefore, with the advent of combination treatments and the increase of immune related adverse event, the search for reliable predictive biomarkers is paramount. Using a multiplexed enhanced FRET-FLIM based technique (QF-Pro) we quantified the interaction of PD-1/PD-L1, CTLA-4/CD80 and TIGIT/CD155 immune checkpoints in a pre-treatment TMA of 46 patients treated with atezolizumab. The association between higher PD-1/PD-L1 ICP interaction state and treatment efficacy was demonstrated in the male sample cohort, where it identified patients with better PFS. Conversely, patients exhibiting higher CTLA-4/CD80 engagement had a worse response to atezolizumab. Remarkably, the dual assessment of patients with high PD-1/PD-L1 and low CTLA-4/CD80 allowed to identify the best responders. These results indicate that the monitoring of patients immune profile in urothelial carcinoma might be critical in identifying patients who may benefit from combination therapy.

High-grade serous ovarian carcinoma (HGSC) responds poorly to immune checkpoint blockade, partly due to a macrophage-dominated immunosuppressive microenvironment. We integrated single-cell spatial proteomics and spatial transcriptomics across 50 HGSC tumors and applied SPACEstat to resolve higher-order immune communities and their transcriptional programs. We identified six immune community types, with macrophage-dominated Myelonets representing the predominant spatial pattern of immune organisation. In chemotherapy-exposed tumors, Myelonets showed coordinated lipid metabolism-immunosuppression and inflammation-MHC-II macrophage transcriptional programs, with SPP1, C1Q, VEGF, MMPs, and CCL18 linked to immunosuppressive states and fibroblasts emerging as key mediators of macrophage communication. Chemotherapy contracted large Myelonets while increasing CD8+ T-cell organization into Lymphonets. Persistent macrophage dominance within Myelonets was associated with adverse outcomes among patients who achieved a complete response to treatment. Together, we identify Myelonets as clinically relevant, multicellular immunoregulatory niches sustained by spatiotemporally coordinated macrophage programs and stromal crosstalk.

Service members with traumatic brain injury are at approximately two- to four-fold higher risk of Alzheimer's disease or related dementias than those without such an injury, with risk increasing with injury severity. The amyloid/tau/neurodegeneration biomarker framework treats amyloid, tau, and neurodegeneration as independent axes but omits astroglial injury, despite evidence that reactive astrogliosis (indexed by glial fibrillary acidic protein, GFAP) must be elevated for cognitive decline to occur in amyloid-positive individuals. Total GFAP immunoassays aggregate intact protein with multiple calpain- and caspase-cleaved proteoforms, blurring the biological signal. We compared a calpain-cleaved GFAP neoepitope, the glial fibrillary acidic protein neoepitope (neoGFAP), against total GFAP across the full traumatic brain injury--mild cognitive impairment--Alzheimer's disease continuum in Veterans using a two-stage plasma-to-cerebrospinal-fluid biomarker approach. A plasma triage gate combining phosphorylated tau 217 and amyloid beta 42 was applied to 367 unique subjects; a cerebrospinal-fluid benchmarking cohort of 57 subjects (controls, chronic blast traumatic brain injury, mild cognitive impairment, and Alzheimer's disease) received head-to-head neoGFAP and total GFAP measurement. In the whole benchmarking cohort, neoGFAP discriminated mild cognitive impairment plus Alzheimer's disease from non-Alzheimer subjects with an area under the receiver-operating-characteristic curve of 0.81 versus 0.73 for total GFAP, a trend-level advantage that did not reach nominal significance. Within the gate-positive, amyloid-committed subset of 23 subjects, neoGFAP dominance became significant by McNemar's exact test (six discordant subjects favored neoGFAP, none the reverse). Across diagnostic contrasts, neoGFAP outperformed total GFAP for Alzheimer's disease versus control and, importantly for Veterans, for mild cognitive impairment versus chronic blast-exposed Veterans without cognitive impairment. In chronic blast injury, neoGFAP was paradoxically depleted relative to controls, consistent with tissue sequestration of aggregated proteoform fragments. Unbiased proteomic profiling confirmed coordinated elevation across astrocytic, neuronal, mitochondrial, and microglial compartments. An exploratory subject-level reclassification improved accuracy from 71.1 percent using plasma alone to 79.5 percent with added cerebrospinal-fluid markers and age. In a same-cohort ProQuantum replication (n=57), CSF neoGFAP preserved its discrimination advantage over total GFAP for MCI+AD versus non-AD (AUROC 0.76 vs 0.72; cross-platform Spearman {rho}=0.84), while plasma neoGFAP achieved AUROC 0.90, comparable to pTau217 (0.92) and exceeding A{beta}42/40 (0.84). In this small sample, neoGFAP is a superior proteoform-resolved diagnostic and prognostic biomarker across the continuum and supports adding an astroglial-proteoform axis to amyloid/tau/neurodegeneration biomarker frameworks in high-risk populations.

Background Fluid biomarkers enable the demonstration of the biological effects of novel therapies in Alzheimers disease (AD). However, longitudinal biomarker data are sparse and sample size calculations for fluid biomarkers are often lacking. Here, we provided longitudinal CSF and plasma AD biomarkers measured in samples collected in a placebo arm in a 1.5-year phase 2b trial, allowing us to study natural trajectories, required sample sizes and heterogeneity in early AD clinical trials. Methods We studied individuals from the placebo group (MCI due to AD (n=65) and AD dementia (n=41)) of the T-817MA trial (NCT04191486) with positive CSF AD biomarkers (mean age=69(7) years, Female=63%). Longitudinal biomarker changes in CSF (A{beta}42, A{beta}40, A{beta}42/40, pTau181, pTau217, NFL, tTau, YKL40, NRGN, ABL1, CHIT1, CLEC5A, ITGB2, MMP10, SDC4, SPON2, THBD) and plasma biomarkers (A{beta}42, A{beta}40, A{beta}42/40, pTau181, pTau217, NFL, GFAP) were analyzed with linear mixed-effect models. Required sample size estimates for predefined treatment effects were generated. Lastly, we investigated the influence of between person variability in biomarker change by simulating a randomized clinical trial (1:1) 10000 times, and assessed the group differences at 1.5 years. Findings Fourteen biomarkers changed over time, with the largest annual changes observed for plasma pTau217 (+9.8%), CSF MMP10 (+7.1%), and CSF NFL (+6.9%), and CSF A{beta}40 by (-4.0%), CSF pTau217 (-3.0%), and CSF NRGN (-2.5%). To show a 30% change, similar to biomarker effects of approved AD drugs, almost all markers required less than 45 patients per trial arm. To reach normalized levels, established CSF markers required lower sample sizes than plasma markers. The effects of heterogeneity over time were approximately twice as large in plasma compared to CSF. Interpretation These findings offer insights into the biomarker trajectories and power in early AD, supporting more informed endpoint selection and forming a frame of reference for the interpretation of treatment effects in clinical trials.

In large data sets discovery is often limited to pre-conceived hypotheses and data fishing. Here we tested whether systematic exploration of AI generated hypotheses could uncover clinically meaningful signals in extensively studied data. We deployed AutoDiscovery, a newly launched large language model (LLM) framework designed to search for hypotheses based on surprisal and systematically interrogate complex datasets, on The Cancer Genome Atlas breast cancer cohort. The system did not identify clinically meaningful novel findings without human input. However, a seeded warm-start run with minimal text input from an oncologist revealed multiple interesting and surprising hypotheses. Among these was that a robust immune signature was present across all subtypes of invasive lobular carcinoma (ILC) that exceeded invasive ductal carcinoma (IDC). This observation was independently validated in independent cohorts and confirmed by high-sensitivity multi-immunofluorescence tumor tissue analyses. These results suggest immunotherapy approaches should be tested in ILC including early stage ER+HER2- ILC; these patients are currently excluded from large neoadjuvant immunotherapy trials. They further demonstrate that surprisal-based hypothesis generation frameworks can extract previously unappreciated patterns from deeply interrogated cancer datasets and imply that disease domain experts working with LLMs can derive more meaningful insights from complex data than either could achieve alone.

Pneumococcal vaccination of older adults is primarily guided by age and clinical eligibility, despite substantial variation in individual risk of severe pneumonia. Here, we used longitudinal electronic health records from 787,538 adults aged [≥]65 years to evaluate the real-world effectiveness of the 20-valent pneumococcal conjugate vaccine (PCV20) and quantify clinical benefit according to baseline risk of pneumonia hospitalization. We developed and validated a machine-learning model using pre-PCV20 data to estimate individual 12-month hospitalization risk and integrated these predictions into a propensity score matching framework. Overall vaccine effectiveness against pneumonia hospitalization was 16.5% (95% CI, 10.6-22.1), but this population-level estimate masked substantial heterogeneity in clinical benefit. The 60% at lowest predicted risk, characterized by younger age and fewer pulmonary and other chronic conditions, showed no measurable reduction in hospitalization (VE, 3.1%; 95% CI, -14.4 to 18.0) and had an estimated 1-year number needed to vaccinate (NNV) of 7,423, compared with 184 and 115 in the intermediate- and high-risk groups, respectively. These findings suggest that incorporating baseline risk into adult pneumococcal vaccination strategies could enable more targeted and potentially better-timed vaccination.

Next-generation sequencing (NGS) for the detection of somatic variants has become the method of choice in a variety of molecular oncology fields and in the clinic. Its use ranges from sequencing entire tumor genomes and transcriptomes to targeted clinical diagnostic gene panels. The NYU Langone Genome PACT (Profiling of Actionable Cancer Targets, LG-PACT) assay is a qualitative in vitro diagnostic test that uses targeted next generation sequencing (NGS) of formalin-fixed paraffin-embedded (FFPE) tumor tissue matched with normal specimens from patients to detect gene alterations in a targeted panel covering 606 genes and the TERT promoter. Indications for testing are cancer (solid tumors and hematological malignancies) where a mutational profile from multiple genes would be informative for disease stratification, prognosis, or treatment options including targeted therapies and eligibility for clinical trials. The test is intended to provide information on somatic mutations including point mutations, small insertions/deletions (indels), and copy number aberrations for diagnostic and treatment decisions. LG-PACT is a United States Food and Drug Administration (FDA) cleared diagnostic test (510K: K202304). The clinical interpretation of sequencing data of molecular tumor markers from NGS encompasses automated variant calling tools with human interpretation. This final mostly manual review of data step is intensive, involving highly trained scientists, encompassing literature review, interpretation and clinical tier classification by pathologists, who then provide a complete molecular diagnostic report to the treating oncologists. We provide analysis of 1339 clinical genomic profiles from 31 different cancers and their subtypes, comprising of central nervous system (CNS) 792 (59%) cases (incl. meningioma, glioma and glioblastoma), with 267 (20%) cases predominantly of lung, pancreatic and colorectal and 280 of others (21%). Here, we present the technical challenges of validating an NGS oncological diagnostic targeted assay for clinical grade accuracy and sensitivity for patient care. We show how copy number alterations provide a more comprehensive description of the tumors genomic profile. We then outline the utility of targeted panel sequencing based on certified pathologist selection of reportable variants for our current patient cohort. Where analysis of variant detection has led to 49.4% (661/1339) of our clinical tumor samples containing mutations in known therapy targeted genes, 35.6% (477/1339) with mutation detected in other genes, and 15% (201/1339) cases being negative.

Background: The Centiloid (CL) scale standardizes global amyloid PET quantification and is widely used to define amyloid positivity. As a global summary measure, however, CL may not fully reflect the regional distribution of amyloid deposition, which can carry additional prognostic information about the rate of cognitive decline. Objective: To develop and externally validate a fixed, regional amyloid PET composite score that complements CL for predicting cognitive decline in Alzheimer's disease. Methods: The Regional Amyloid PET Score (RAPS) was derived from 82 FreeSurfer regions using machine learning with bootstrap stability selection to predict the rate of change in CDR-Sum of Boxes (CDR-SB) in 433 amyloid-positive ADNI [18F]florbetapir participants. The fixed nine-region weights were applied without retraining in a cross-tracer ADNI [18F]florbetaben subset (N = 71; largely overlapping the discovery participants) and two external validation cohorts, NACC SCAN (N = 1531; four tracers) and OASIS-3 (N = 428). Results: RAPS comprised nine regions. In ADNI, RAPS correlated more strongly with CDR-SB slope than CL and showed higher discrimination of rapid decliners (AUC 0.813 vs 0.713). Performance was directionally consistent across validation cohorts; in NACC SCAN, RAPS and CL independently predicted clinical progression. Cross-cohort meta-analysis of the three independent cohorts supported incremental discrimination beyond CL (pooled {Delta}AUC +0.066; I2 = 0%). Conclusions: RAPS, a fixed regional amyloid PET-derived score, may complement CL for prognostic stratification in Alzheimer's disease research.

Single-cell technologies enable linking disease-risk variants to gene regulatory effects in specific cell-state contexts. However, most so called "single-cell eQTL" studies use a "pseudobulking" strategy to identify expression Quantitative Trait Loci (eQTLs), obscuring subtle dynamic regulatory effects of disease alleles. Here, we propose Dynema (Dynamic eQTL mapping in single cells) for fast and accurate genome-wide mapping of context-dependent and independent eQTL effects at true single-cell resolution. To identify eQTLs, Dynema uses a Poisson model with cluster robust variance estimators (CRVEs) to account for correlation of single-cell profiles from the same individual. In contrast to other common methods, Dynema achieves statistical calibration and scales to genome-wide analysis in large single-cell datasets in realistic timeframes. We applied Dynema to two independent T cell datasets and identified reproducible cell-state-dependent eQTL effects. Some cell-state-dependent eQTLs are missed by pseudobulking approaches, and many others are conditionally independent from lead eQTL effects. We show that TSPAN32 and other autoimmune loci colocalize with cell-state-dependent eQTLs. Mapping context-dependent eQTLs at single-cell resolution enables the definition of the molecular effects of complex disease alleles.

Post-COVID-19 syndrome (PCS) is characterized by fatigue, neurological impairment and systemic symptoms. This heterogeneity of symptoms hinders biomarker development. Here, we profiled extracellular-vesicle (EV) surface markers in plasma and CSF from 61 participants with PCS (COVIDpost), 80 recovered controls (COVIDreco), and 10 participants with non-SARS-CoV-2 post-viral syndromes. EVs were analysed by bead-based multiplex flow cytometry using tetraspanin-directed (TSPN) and phosphatidylserine-directed lactadherin (PS) detection. Amongst 37 targets covering tetraspanins and vasculature-, immunity- and stemness-associated markers, none met a 1% false-discovery-rate threshold. However, L1-regularized logistic regression under fully nested 5x5 cross-validation identified a distributed plasma EV profile, with mean out-of-fold areas under the receiver operating characteristic curve (AUCs) of 0.788 (95% CI 0.715 - 0.852) for TSPN and 0.716 (95% CI 0.636 - 0.792) for PS detection. Across the pooled COVIDpost and COVIDreco population, EV classification scores covaried with clinical group differences, but did not track clinical severity within either cohort. These PCS-EV classification scores decreased at one-year follow-up in COVIDpost participants. Our findings identify an internally cross-validated multivariable EV surface profile associated with COVIDpost versus COVIDreco status and support independent validation and exploration of EV-based biomarkers in post-viral fatigue syndromes.

Viral infections have long been proposed as environmental contributors to neurodegenerative diseases, including Parkinson's disease (PD), yet the molecular mechanisms linking infection and neurodegeneration are not well defined. Neuroinflammation and disruption of central nervous system (CNS) homeostasis have emerged as potential mediators. In this study, we used severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), the causative agent of COVID-19, as a model pathogen to investigate convergent molecular pathways between viral infection and PD. Single-nucleus RNA sequencing (snRNA-seq) was performed on post-mortem striatal tissue from 14 individuals stratified into four groups: COVID-19 only (COVID-19), PD only (PD), comorbid PD with COVID-19 (PD/COVID-19), and controls (Control). The PD/COVID-19 group exhibited an expanded astrocytic population and a pronounced interferon-associated molecular signature characterized by increased expression of canonical interferon-stimulated genes, including IFI44L (average log2FC= 3.9; adjusted p=2.3 x 10-373), IFI44 (average log2FC=2.9; adjusted p=8.0 x 10-266), ISG15 (average log2FC=3.1; adjusted p=1.2 x 10-197), and RSAD2 (average log2FC= 3.5; adjusted p=8.6 x 10-111). Pathway analyses demonstrated activation of innate immune and antiviral signaling pathways, particularly within microglia and astrocytes, including interferon signaling, pattern-recognition receptor pathways, and complement-associated responses. In parallel, genes involved in lipid metabolism, cholesterol homeostasis, synaptic maintenance, and neuronal signaling were reduced across disease groups. Proteomic analyses independently confirmed enrichment of antiviral and interferon-associated pathways and identified convergent suppression of sterol, cholesterol, and lipid metabolic processes. Our findings identify a convergent molecular signature linking PD and COVID-19, pronounced in comorbid individuals and characterized by interferon-driven innate immune activation, glial inflammatory responses, and dysregulation of lipid metabolic homeostasis. Collectively, the data support a model in which severe viral infection amplifies biological pathways already implicated in PD pathogenesis.

Dissecting the complexity of antibody responses in orthopoxvirus (OPXV) infected individuals is essential for elucidating protective mechanisms and identifying candidate protective immunogens. Here, we profiled the acute humoral response in 51 mpox cases, showing distinct IgG trajectories among multiple antigens alongside the rise of plasma neutralizing activities to plateau within 6 weeks after symptom onset. Utilizing a single-cell transcriptomic and BCR sequencing based antigen-agnostic mAb isolation workflow, we further generated monoclonal antibodies (mAbs) from 254 expanded peripheral B cell clones of 3 patients. We discerned 97 specific mAbs recognizing at least 12 different OPXV proteins via integrated screening approaches, which comprised neutralizing antibodies binding unconventional viral targets and antibodies exhibiting extraordinary in vitro and in vivo anti-OPXV effects. The number of OPXV-specific mAbs recovered per donor reflected the percentage of expanded clones among circulating B cells. More interestingly, we demonstrated that the inferred unmutated common ancestors (UCAs) of neutralizing antibody clones did not necessarily react with OPXV, implying that OPXV neutralizing antibodies might frequently originate from B cells previously activated by unknown antigens. Our work establishes an efficient workflow for antigen-agnostic isolation of pathogen specific mAbs and reveals previously unclarified features of antibody responses induced by acute MPXV infection.