Biology subjects

Zhou, X.

Publications and source records attributed to Zhou, X..

At least 19 recordsLinked to original sources

bioRxiv2026

NK-RCPP: a receptor-conditioned computational workflow for prioritizing neurokinin-receptor peptide candidates

Prioritizing peptides for closely related G protein-coupled receptors requires comparing target-directed scores with evidence from other structural contexts. We applied the Neurokinin Receptor-Conditioned Peptide Prioritization workflow (NK-RCPP) to 96 generated candidates for neurokinin receptors NK1R, NK2R and NK3R. The retrospective analysis combined receptor-matched on-target REF15 scores with 4,518 paired structural-neighbour comparisons covering 82 candidates. Lower on-target scores than the matched controls were observed for 14/32 NK1R, 23/32 NK2R and 6/32 NK3R candidates. Adding the neighbour-score criterion retained 16 of the 33 evaluable on-target-favoured candidates, reducing that pool by 51.5%. At matched receptor-specific shortlist sizes, half of the candidate identities differed from on-target-only selection. NK2R-04 and NK2R-19 remained selected at the higher neighbour-fraction threshold of 0.75. Fourteen NK2R candidates lacked neighbour summaries, leaving their joint-rule status unresolved. Sequence-frequency and embedding analyses described the candidates within a 1,048-peptide comparison space, and an exploratory 10-peptide NK2R comparison showed differences in standardized scores with uncertain score-potency agreement. These findings show how paired structural-neighbour scoring and threshold choice shape candidate selection within a receptor family. The resulting shortlist provides a defined set for testing whether joint prioritization enriches for active, receptor-preferred peptides.

bioinformatics↗

bioRxiv2026

Reinforcement learning with evolutionary priors unlocks multi-site enzyme optimization from sparse experimental feedback

Industrial enzymes underpin sustainable manufacturing, yet natural proteins rarely combine the activity, robustness and application compatibility required for practical use. Engineering such properties requires identifying beneficial combinations of interacting mutations within vast sequence spaces that are too large to search exhaustively and too sparsely sampled to model reliably. Here we introduce AlphaVariant, a reinforcement-learning (RL) framework that couples a generative evolutionary prior with iteratively updated low-N fitness models to enable feedback-driven search of large combinatorial protein sequence spaces. Across six experimentally measured protein fitness landscapes, AlphaVariant ranked first on the three large, non-enumerable multi-site landscapes. In two successive rounds of savinase activity optimization, AlphaVariant-designed libraries yielded 75.4% and 83.8% activity-improved variants among experimentally characterized candidates even as the accessible mutation space expanded from 25 to 67 positions. The search identified beneficial mutation combinations involving up to 17 structurally dispersed sites and produced a variant with 7.1-fold higher activity than wild type after only two design-test cycles. We then extended AlphaVariant to jointly optimize activity, thermostability and a patent-informed constraint for intellectual-property freedom. This multi-objective search yielded variants that outperformed the commercial protease Uno 100 L in both biochemical assays and application-relevant detergent washing tests. These results demonstrate that generative RL can efficiently navigate vast protein fitness landscapes, progressively incorporate multiple engineering objectives and convert sparse experimental feedback into enzyme variants with industrially relevant performance, effectively resolving the limits of biocatalyst engineering.

bioinformatics↗

bioRxiv2026

Exposure to p,p'-dichlorodiphenyldichloroethylene (p,p'-DDE) Perturbs Cell State and Stem Cell Markers and Induces an Epidermal Phenotype in Human Breast Cells

Breast cancer is the second leading cause of cancer deaths in U.S. women. Endocrine disruptors, such as the DDT metabolite DDE, are environmental factors linked to breast cancer risk. Emerging evidence shows that during carcinogenesis, cells reacquire stem cell traits and exhibit phenotypic plasticity. In these phenotypically plastic states, breast cells transition between phenotypes, for example, from epithelial to mesenchymal (EMT) phenotypes, or from luminal to basal. Discovering how environmental factors alter cell phenotype may be a critical step in preventing and treating breast cancer. We applied a novel high-content imaging method for the evaluation of expression of stemness and cell state markers (ALDH1A3, CD44, CD24, KRT8, and KRT14) and evaluated these markers after introducing environmentally relevant concentrations of DDE (25 nM to 25 M) to non-tumorigenic MCF10A breast epithelial cells grown in 3D anchorage independent conditions. After identifying surprisingly profound cell state alterations, including a loss of KRT8 and KRT14 expression, by 25 M DDE exposure, we used transcriptomic profiling along with pharmaceutical and genetic knockdown approaches to characterize the molecular mechanisms by which DDE exposure modulates breast epithelial cell state. Keratinization and formation of the cornified envelope were biological pathways upregulated with a maximum fold change of 360.47. We identify DLX3 as a transcription factor which is inducible by DDE exposure and is also highly upregulated in human breast cancers, reflecting a novel mechanism by which environmental exposures may promote cellular plasticity and breast carcinogenesis.

pharmacology and toxicology↗

bioRxiv2026

Quantitative fluorescence lifetime imaging of protein microenvironments by genetically encoded compact benzodiazole probes

Quantitative fluorescence lifetime imaging microscopy (FLIM) is limited by a lack of probes that combine single-residue specificity, minimal steric perturbation, visible-light excitation, and strong environment-dependent lifetime contrast. We introduce benzodiazole-based non-canonical amino acids (BD-ncAAs) as compact, genetically encoded sensors for site-specific FLIM. Using an engineered pyrrolysyl-tRNA synthetase pair, we incorporate three different BD-ncAAs into proteins in living cells. Site-specific incorporation of BD-ncAAs into a nanobody at different positions preserved native structure and antigen binding. The lead probe, cyano-substituted CBDS, exhibits visible-light excitation, large Stokes shifts, and pronounced environment-dependent fluorescence lifetimes spanning 1 to 24 ns. Under wash-free, live-cell FLIM conditions, CBDS quantitatively resolves localized structural microenvironments and transmembrane hydration gradients, while also distinguishing distinct states during protein secretion in eukaryotes. BD-ncAAs present genetically encoded, minimally perturbing reporters for tracking target proteins, detecting interactions with binding partners, and quantifying site-specific solvent accessibility in living cells by quantitative FLIM.

biophysics↗

bioRxiv2026

Receptor-like kinase RLK15 binds a fungal RNase effector to enhance autophosphorylation and activate host defense

Fungal pathogens secrete diverse effectors to suppress host immunity, yet the plant receptors that perceive these virulence factors often remain elusive. Here, we identify Ss1624, a secreted RNase from the sugarcane smut pathogen Sporisorium scitamineum, and show that it is recognized by the plasma-membrane-localized CrRLK1L-family receptor kinase RLK15. This recognition triggers canonical immune outputs, including reactive oxygen species burst, callose deposition, defense gene activation, and induces hypersensitive cell death. Mechanistically, Ss1624 physically interacts with the receptor-like kinase RLK15, which is required for Ss1624-triggered cell death. Functional assays indicate that this interaction enhances RLK15 autophosphorylation in a kinase activity-dependent manner, suggesting that Ss1624 can act as an elicitor. Transcript profiling further reveals coordinated expression patterns of Ss1624 and RLK15 during infection, and perturbation of Ss1624 expression alters RLK15 transcript levels and pathogenic outcomes. Extending these findings to rice, the ortholog OsRLK15 contributes positively to resistance to bacterial and fungal diseases without yield penalty in rice. Our results support a model in which a secreted RNase effector is directly sensed by a receptor kinase to initiate broad-spectrum immune responses, providing insights into host-pathogen interactions and identifying RLK15 as a candidate for crop improvement.

microbiology↗

bioRxiv2026

SVC: A Vision Transformer-based Spatially and Virtually Embedded Cell Model for Deciphering Subcellular Spatial Transcriptomic Heterogeneity

Subcellular transcript localization shapes gene regulation and cellular function, yet profiling it at scale remains difficult, limiting our ability to study subcellular heterogeneity across tissues and disease. Here, we present SVC, a Vision Transformer-based spatially and virtually embedded cell model that learns subcellular transcript organization within native tissue context directly from subcellular-resolution spatial transcriptomics. SVC represents each gene's subcellular localization as an image patch and uses other genes within the same cell as context, enabling subcellular contextual learning of spatial dependencies that govern intracellular organization. By integrating gene-function priors, cell morphology, spatial context, and, when available, cell identity, SVC captures unified, spatially grounded representations across genes and cells that connect subcellular organization to cellular state and tissue microenvironment. Across diverse subcellular-resolution spatial transcriptomics datasets, SVC accurately predicts cell-specific subcellular transcript localization and co-organization, generalizes across tissues and technical platforms, and supports subcellular imputation of unmeasured genes and in silico perturbation. Leveraging these capabilities, SVC highlights disease-associated patterns of intracellular transcript reorganization and spatially heterogeneous responses to gene perturbations. Beyond prediction, SVC reveals systematic variation in subcellular transcript organization associated with gene function, cellular states, and tissue microenvironments, uncovering de novo spatial biology that is not accessible by cell-level analysis. SVC's learned representations further transfer to cell- and tissue-level tasks, including cell-level expression imputation, cell clustering, and spatial domain detection. Together, SVC provides a general framework for modeling and interrogating subcellular transcript organization in situ, enabling scalable discovery of subcellular spatial biology.

bioinformatics↗

bioRxiv2026

Deep learning-based prediction of wall shear stress throughout the cardiac cycle in middle cerebral artery bifurcation aneurysms

Background and Objective: Cardiac-cycle wall shear stress (WSS) is relevant to intracranial aneurysm hemodynamic assessment, but transient computational fluid dynamics (CFD) is costly. Existing surrogates use steady-flow priors or joint steady/transient training; here we evaluate the transfer of separately pretrained spatial parameters. This study develops the Steady-to-Transient Estimation of Pulsatile Wall Shear Stress (STEP) framework, a two-stage geometric learning approach for full-cycle WSS prediction. Methods: Using the synthetic AneuG-Flow dataset, Stage 0 pretrained a geometric tokenizer, LaB-GATr backbone, and WSS decoder on 11,656 steady CFD cases. Stage 1 initialized these components from the pretrained model and fine-tuned them end to end using 449 transient training cases, with 72 validation and 57 primary test cases. Waveform embeddings multiplicatively conditioned geometric features across blocks at 80 cardiac-cycle time points. Gaussian curvature was included as an auxiliary geometry feature. Ablations evaluated steady initialization, temporal conditioning, and curvature, with rank-biserial effect sizes and Holm-adjusted p values. A fixed model was also evaluated on 231 additional cases, with registered-geometry overlap audited across stages. Results: On the primary test set, STEP achieved a whole-cycle WSS vector mean absolute error (MAE) of 1.688 Pa and root-mean-square error (RMSE) of 2.877 Pa. Time-averaged WSS, oscillatory shear index, and relative residence time MAEs were 1.040 Pa, 0.00625, and 0.06734 Pa-1, respectively. Steady initialization reduced vector MAE by 27.93% relative to training the same transient architecture from scratch; the complete blockwise conditioning configuration reduced MAE by 8.04% relative to input-only additive conditioning (rank-biserial correlations 0.995 and 0.982, respectively; Holm-adjusted p < 0.001). Curvature yielded a 3.46% reduction without a significant paired effect (adjusted p = 0.0521). The 80-time-point WSS prediction required a mean of 4.169 s (SD 0.063 s). Additional-cohort MAE was 1.712 Pa, but 185/231 geometries matched Stage 0 training, versus 1/57 in the primary test set. Conclusions: STEP demonstrated effective reuse of steady-pretrained spatial parameters for cardiac-cycle WSS prediction under the evaluated synthetic CFD protocol. The strongest controlled gains were associated with steady initialization and the complete temporal-conditioning configuration. The common inlet waveform and cross-stage geometry overlap limit generalization claims.

bioengineering↗

bioRxiv2026

Contrasting gene gain and loss shape metabolic diversification in the Ralstonia solanacearum species complex

Metabolic repertoires are continually remodeled by gene acquisition and loss, but how these contrasting processes jointly generate phenotypic diversity within bacterial species complexes remains poorly understood. Here, we elucidated the genetic and evolutionary basis of the classical biovar phenotypes of the Ralstonia solanacearum species complex (RSSC), which are defined by differential utilization of three hexitols (sorbitol, mannitol, and dulcitol) and three disaccharides (lactose, maltose, and cellobiose). Although this classification has been used for over half a century, the genetic determinants and evolutionary origins of these phenotypes have not been systematically resolved. Machine-learning analysis of genomic data from 250 RSSC strains with documented biovar phenotypes, combined with genome-wide association analysis and gene deletion, identified the smoEFGK gene cluster and flavin adenine dinucleotide (FAD)-dependent glucose dehydrogenase (FADGDH) as the major determinants of hexitol and disaccharide utilization, respectively. Comparative phylogenomics revealed fundamentally different evolutionary histories of these loci. An ancestral smoEFGK system was lost early in RSSC evolution and subsequently reacquired by the ancestor of phylotype I through horizontal transfer from a Burkholderia-related lineage, whereas vertically inherited FADGDH underwent recurrent, independent losses across multiple RSSC lineages. Extending the analysis to 1,082 RSSC strains further showed that disaccharide-utilization capacity was significantly depleted among monocot-associated strains after accounting for phylogenetic relatedness and geographic origin. Together, these findings reveal how contrasting trajectories of gene gain and loss can reshape bacterial metabolic repertoires, generate lineage-associated phenotypic diversity, and potentially contribute to ecological differentiation.

genomics↗

bioRxiv2026

DEPICT enables sample-centric therapeutic prioritization across evolving and spatially heterogeneous tumor states

Accurate treatment selection remains a major challenge in precision oncology because tumors with similar genomic alterations can respond differently to therapy. Most drug-response models evaluate individual drugs or drug-sample pairs, whereas clinical decision-making requires therapies to be prioritized within each tumor context. Here we present DEPICT (Drug Efficacy Prediction via Integrated ConText), a sample-centric deep learning framework that reframes drug-response prediction as the relative prioritization of therapies within an individual tumor context. DEPICT integrates transcriptomic and mutational features with prior alteration-therapy relationship and was trained on 231,880 cell line-drug pairs comprising 941 cancer cell lines and 289 compounds. Without retraining, DEPICT retained predictive performance in independently profiled cell lines, experimentally induced drug-resistant states and matched primary lung tumors and patient-derived organoids. Representations learned exclusively from bulk pharmacogenomic data further captured progressive drug-response states at single-cell resolution and outperformed existing single-cell drug-response inference methods. Applied to spatial transcriptomic data, DEPICT resolved clone-associated therapeutic heterogeneity within individual lung tumors, and predicted regional differences were recapitulated in matched multiregional organoid drug-response assays. These findings establish sample-centric therapeutic prioritization as a transferable strategy for modeling dynamic and spatially heterogeneous drug vulnerabilities across biological scales.

bioinformatics↗

bioRxiv2026

Neutrophils orchestrate pre-metastatic immunosuppressive bone marrow remodeling during prostate cancer metastasis

The dynamic reprogramming of the bone marrow (BM) microenvironment during metastatic progression is poorly defined. Through single-cell profiling of BM samples spanning benign conditions, high-risk bone metastatic potential localized prostate cancer (HMP) and established bone metastatic prostate cancer (PCa), we uncovered a primary tumor-to-BM signaling axis that orchestrates niche formation through neutrophil-mediated remodeling. Tumor-associated neutrophils (TANs) emerge during the HMP stage, persist throughout metastatic progression, correlate with poor patient prognosis, and exhibit direct immunosuppressive activity that contributes to a BM environment permissive to metastatic colonization. Functional and genetic studies showed that tumor-associated systemic IGF1 reprogrammed BM neutrophils through IGF1R-MAPK/ERK-KLF4 signaling, with IL-1{beta}, reactive oxygen species and nitric oxide contributing to TAN-mediated T cell suppression. Tumor-cell Igf1 perturbation, IGF1R blockade and neutrophil-specific Igf1r deletion attenuated TAN-associated BM remodeling, and reduced subsequent bone metastatic progression. In bone-metastasis models, combined IGF1R and PD-1 blockade suppressed bone-metastatic progression. Translationally, our investigator-initiated clinical trial (NCT06866548) of teprotumumab plus tislelizumab in patients with established metastatic castration-resistant prostate cancer (mCRPC) provided initial safety data and exploratory clinical observations. Together, these findings identify IGF1R-dependent neutrophil reprogramming as a functionally important component of the evolving PCa bone metastatic microenvironment and provide a rationale for therapeutic targeting of IGF1R signaling together with immune checkpoint blockade.

cancer biology↗

bioRxiv2026

Engineered CTLA-4 targeting chimeras for targeted protein degradation and immune synapse reprogramming

Despite the success of immune checkpoint blockade in cancer therapy, overcoming the immunosuppressive tumor microenvironment remains a major barrier to durable responses. Here, we introduce cytotoxic T-lymphocyte-associated antigen-4 (CTLA-4)-targeting chimeras (cTACs), a class of synthetic molecules that harness CTLA-4-mediated trans-endocytosis (TE) for targeted protein degradation across cellular boundaries. The lead candidate, cTAC4.0, is composed of the extracellular domain of CD80 fused to an anti-programmed death-ligand 1 (PD-L1) fragment via a human immunoglobulin G4 (IgG4) heavy chain. Unlike existing protein degraders that function within cancer cells via house-keeping pathways, cTAC4.0 operates in trans between immune and tumor cells. It redirects the natural CD80-CTLA-4 internalization to capture and degrade PD-L1 from tumor cells into T cells, while simultaneously converting PD-L1-mediated suppression into CD28-driven co-stimulation, depending on the relative availability of CTLA-4 or CD28. Mechanistically, we demonstrate that cTAC4.0 promotes CTLA-4-dependent internalization and lysosomal degradation of both membrane-bound and soluble PD-L1, reduces surface PD-L1 levels on target cells, and activates T cells in a CD28-dependent manner. Functionally, cTAC4.0 enhances the cytotoxicity of both bispecific T cell engagers and T cell receptor (TCR)-engineered T cells in vitro, and when combined with these engineered T cells, it suppresses tumor growth in vivo in a glioblastoma xenograft model. By coupling immune checkpoint degradation with T cell activation in an immune synapse-enriched manner, cTACs establish a paradigm for intercellular protein degradation systems that simultaneously deplete suppressive ligands and enhance T cell function to overcome resistance in cancer immunotherapy.

immunology↗

bioRxiv2026

AC-Diff: Anatomy-Contrast Disentangled Diffusion for Multi-Vendor Liver MRI Harmonization

Magnetic resonance imaging (MRI) exhibits substantial scanner and protocol dependent appearance variations, making harmonization challenging without distorting patient specific anatomy, particularly in abdominal imaging. We propose AC-Diff, an anatomy--contrast disentangled diffusion framework that formulates MRI harmonization as a factor-specific generative intervention. Using aligned multi-contrast supervision and cross-patient factor swapping, AC-Diff learns to separate anatomical structure from acquisition-dependent contrast. Unlike conventional image or latent diffusion, AC-Diff restricts stochastic generation to the contrast subspace while the source anatomy bypasses diffusion and is directly reused during reconstruction. Experiments on an in-house multi-vendor cohort and the external Duke Liver MRI dataset demonstrate improved target-domain alignment with strong structural preservation. In downstream liver segmentation, AC-Diff improves Dice from 0.942 for the original inputs to 0.954 for the harmonized images. These results support contrast-specific latent generation as a promising approach to anatomy-preserving MRI harmonization.

bioengineering↗

bioRxiv2026

Loss of DNA Demethylase Enhances Arsenic Resistance Via Reducing of PIN2 Antisense Long Noncoding RNA

DNA methylation dynamics regulate diverse plant stress responses, but the mechanisms governing arsenic (As) tolerance through epigenetic pathways remain largely unknown. Here we report that Arabidopsis mutants lacking the DNA demethylase, REPRESSOR OF SILENCING (ROS1), show substantially enhanced As resistance. Integrating transcriptome and methylome data, we found that PIN-FORMED 2 (PIN2), encoding an auxin efflux carrier that also transports As out of cells, represents a key ROS1 target. In ros1, we observed increased methylation in the last exon of PIN2, a region functioning as the promoter for an antisense long non-coding RNA (lncRNA). Hypermethylation of the promoter suppresses the antisense lncRNA, which follows reduction of sense PIN2 transcription. As a result, PIN2 expression increases in ros1 mutants during As exposure, enhancing As tolerance. Analysis of ros1 pin2 double mutants confirmed that PIN2 acts downstream of ROS1 in As tolerance. Our study demonstrates an epigenetic pathway in which ROS1 regulates As tolerance by controlling PIN2 expression through antisense lncRNA-mediated transcriptional interference, offering new epigenetic strategies using plants in heavy metal contaminated environments.

plant biology↗

bioRxiv2026

Selective depletion of upper-layer somatostatin interneuron subtypes in schizophrenia

Schizophrenia (SCZ) is associated with cortical GABAergic dysfunction, but whether inhibitory interneurons are lost or persist in an altered molecular state remains unresolved. Here, we harmonized seven prefrontal post-mortem single-nucleus RNA-seq datasets onto a fine-grained taxonomy of cortical cell types and meta-analyzed their gene expression and abundance changes in SCZ (298 controls, 171 SCZ). First, we find a subclass-wide reduction of SST mRNA within somatostatin (Sst) neurons. Second, we find reduced abundance (depletion) of a subset of upper-layer Sst interneurons and increased abundance of L6b excitatory neurons, with both changes confirmed in spatial transcriptomics (12 controls, 12 SCZ). Notably, SCZ genetic risk is enriched in the most depleted Sst cells. Depleted Sst subtypes highly express HCN1, partially correspond to primate-specialized CALB1-expressing double-bouquet cells, and are among the cells lost earliest in Alzheimer's disease. These upper-layer Sst interneurons constitute an intrinsically vulnerable population and a promising target for neuroprotective and compensatory therapies.

bioinformatics↗

bioRxiv2026

Functional reorganization of large-scale brain systems underlies emotion regulation maturation

Emotion regulation via cognitive reappraisal is essential for socioemotional development and mental health, yet the organizational logic of its supporting brain systems across youth remains unclear. Using a large multisite cohort, we demonstrate that maturation of reappraisal capacity reflects reorganization of a distributed architecture rather than progressive strengthening of prefrontal control. Reappraisal robustly reduced negative affect, with non-linear gains concentrated between 6 and 10.5 years. Bayesian systems identification resolved this architecture into four functionally distinct systems-reappraisal-specific, common-appraisal, modifiable-emotion, and non-modifiable-emotion components-spanning prefrontal-control, salience, default-mode and dorsal-attention networks. Age-related variation in anterior temporal cortex, emerged as essential hub, statistically accounted for developmental gains in reappraisal success. Multivariate modelling together with intersubject analyses further identified a cross-site-generalizable whole-brain reappraisal signature and revealed an age-dependent shift in brain-behaviour coupling, with neurobehavioural convergence increasing with age. These findings establish distributed network reorganization as a core mechanism of emotion regulation development and provide mechanistic targets for stage-matched interventions.

neuroscience↗

bioRxiv2026

An Oxidative Stress-Associated Seven-Gene Prognostic Signature in Lung Adenocarcinoma: Integrative Transcriptomic Analysis Across Public Cohorts

Lung adenocarcinoma is molecularly heterogeneous, and oxidative-stress programs can support either tumor restraint or tumor adaptation depending on cellular context. This study integrated public lung adenocarcinoma transcriptomic cohorts to identify oxidative-stress-associated expression features and evaluate their prognostic relevance. Expression profiles from The Cancer Genome Atlas, Genotype-Tissue Expression project, and GEO series GSE31210, GSE40791, and GSE30219 were analyzed. Differential expression, weighted gene co-expression network analysis, functional enrichment, univariable Cox regression, and least absolute shrinkage and selection operator Cox modeling were combined to derive a risk signature. Immune-cell enrichment, gene set enrichment analysis, gene set variation analysis, and pan-cancer analyses were used for biological characterization. A total of 1,305 genes differed between tumor and control samples, including 498 upregulated and 807 downregulated genes. Intersection of differentially expressed genes, the oxidative-stress-associated co-expression module, and the oxidative-stress gene set yielded 44 genes enriched in responses to reactive oxygen species and hydrogen peroxide, antioxidant and peroxidase activities, focal adhesion, Rap1 signaling, and PI3K-Akt signaling. A seven-gene signature comprising FBLN5, HBB, FYN, HGF, TFAP2A, PLIN5, and F2RL1 stratified the 523-sample training cohort and the 207-sample internal validation cohort into groups with different overall survival. Time-dependent areas under the receiver operating characteristic curve at 1, 3, and 5 years were 0.677, 0.622, and 0.649 in training and 0.613, 0.691, and 0.706 in internal validation. In the 85-case GSE30219 external cohort, corresponding values were 0.588, 0.661, and 0.631; survival separation followed the expected direction but did not reach statistical significance (log-rank P = 0.100). Seventeen immune-cell signatures differed between risk groups, while high-risk tumors were enriched for cell-cycle, DNA-replication, mismatch-repair, glycolytic, E2F, G2M-checkpoint, MYC-target, and mTORC1-related programs. The signature therefore captures reproducible oxidative-stress-associated transcriptional variation with moderate prognostic discrimination. Its clinical utility requires prospective evaluation, complete clinical adjustment, and experimental validation.

cancer biology↗

bioRxiv2026

Genetic trans-effects link triglyceride-rich lipoproteins to blood pressure

Background: Genome-wide association studies (GWASs) have identified thousands of genomic regions in which common variants affect blood pressure, but have yielded limited mechanistic insight. This study used a novel method of genetic analysis based on identifying core genes on which the _trans_-effects of common variants coalesce to influence blood pressure. Methods: We undertook GWASs of mean arterial pressure (MAP) and body mass index (BMI) in 373,882 individuals aged less than 60 years in the Our Future Health study. We used summary statistics from GWASs of proteins on the SomaScan and Olink platforms to compute genome-wide aggregated trans-effects (GATE) scores for each protein, and tested for association of MAP with these scores. Results: The strongest GATE score association with MAP was for lipoprotein lipase (LpL, encoded by LPL_. Genetic up-regulation of circulating levels of LpL was associated with lower MAP but higher BMI. Associations of MAP and BMI with GATE scores for circulating levels of proteins encoded by three other genes involved in lipid handling -- CD300LG, ADIPOQ, TIMP4 -- were similar to those with LpL. GATE scores for all four of these proteins were inversely associated with measurements by NMR spectroscopy of plasma triglyceride in chylomicrons and extremely large VLDL particles. Conclusions: These results point to a key role in hypertension for proteins that regulate post-prandial clearance of triglyceride-rich lipoproteins, independently of adiposity. This is consistent with experimental studies showing that accumulation of lipids in endothelial cells impairs endothelial function.

genetics↗

bioRxiv2026

PromptBio: An Agentic Platform for End-to-End Computational Biomedical Research

Modern biomedical research increasingly depends on complex computational analyses, yet translating a scientific question into a reliable workflow still requires substantial technical expertise and manual coordination. PromptBio is a multi-agent AI platform available through a web portal at https://promptbio.ai that addresses this challenge. Through natural-language interaction, its agent harness, PromptGenie, translates a research objective into an inspectable plan, executes the required research and analysis, and adapts subsequent steps as evidence and results emerge. PromptBio integrates reasoning with managed execution while preserving human oversight and a traceable record of the research process. It can apply validated methods, construct custom analyses, and incorporate external workflows, enabling researchers to move beyond fixed pipelines while supporting reproducibility. We evaluate the platform through benchmarks of end-to-end bioinformatics analysis and biomedical deep research, validation of representative omics and machine-learning skills, and a hypothesis-driven regulatory-genomics case study. PromptGenie achieved higher analytical accuracy and stronger evidence retrieval and synthesis than the comparison agents, while the evaluated domain skills produced results consistent with established methods. The case study further demonstrates how PromptBio can integrate human-specific genomic and epigenomic features to investigate cortical development. Together, these findings show that PromptBio can coordinate complex biomedical analyses under researcher oversight, offering an accessible approach for accelerating scientific iteration and transforming research questions into transparent, reusable computational workflows.

bioinformatics↗