Biology subjects

Qian, X.

Publications and source records attributed to Qian, X..

At least 19 recordsLinked to original sources

bioRxiv2026

Agent K: An Agent-Orchestrated Pipeline for Mapping Genes to Biosynthetic Pathway towards Metabolite Discovery

The identification of biosynthetic gene clusters (BGCs) and their associated metabolic path- ways is central to genome-guided natural product discovery. However, existing computational tools depend on fragmented workflows for data processing, annotation, and analysis, which re- quire substantial manual intervention and lack seamless integration across heterogeneous plat- forms and tools. Here, we present, Agent K, an agent-orchestrated framework that automates the end-to-end process from bacterial whole genome sequence analysis to biosynthetic pathway mapping and metabolite discovery as candidate gene products for functional insights. The sys- tem employs a large-language-model (LLM) agent to coordinate multiple bioinformatics tools, enabling dynamic execution, data integration, result interpretation, and flexible and intuitive visualization. By integrating genomic sequences to functional annotations and pathway-level reconstructions, the framework facilitates the identification of candidate gene products, their biosynthetic pathways associated with individual BGCs, and the putative metabolites produced by those pathways. The developed novel agentic framework provides a user-friendly scalable and extensible solution for automated high-throughput analysis and mapping of bacterial secondary metabolism pathways for interactive exploration of candidate potential.

bioinformatics↗

bioRxiv2026

Microbial Reductive Dechlorination of Hexachlorobutadiene by Dehalococcoides

Hexachlorobutadiene (HCBD), a persistent organic pollutant (POP), poses a significant threat to ecological environments and human health. While anaerobic microbial transformation is a promising remediation strategy, the specific microorganisms and metabolic mechanisms involved have remained poorly understood. This study demonstrates the successful enrichment of an anaerobic microbial consortium from river sediment, with lactate as an electron donor, capable of transforming HCBD. Within 240 days, 62.5 mol of HCBD was sequentially dechlorinated through pentachlorobutadiene (PCBD), tetrachlorobutadiene (TeCBD), trichlorobutadiene (TCBD), and dichlorobutadiene (DCBD) to chlorobutadiene (CBD). Co-amendment with HCBD and trichloroethene (TCE) resulted in complete removal of both compounds. 16S rRNA gene sequencing and quantitative PCR showed enrichment of Dehalococcoides, linking its population growth to HCBD and TCE dechlorination. This study demonstrates that Dehalococcoides growth was associated with HCBD transformation in the enrichment cultures, broadens our understanding of the metabolic capabilities of Dehalococcoides, and identifies a candidate gene for this important environmental process, offering valuable insights for the bioremediation of sites contaminated with HCBD.

microbiology↗

bioRxiv2026

UPLiFT: Uncertainty Parameterized Activation for Lightweight Fine-Tuning

Learned protein representations contribute to the broad utility of pretrained protein language models (PLMs), often enhanced by task-specific fine-tuning where parameter-efficient techniques are imperative due to large network size. Our work takes an orthogonal approach to such fine-tuning: rather than optimizing the network weights, we formulate adaptation as an optimization task over the parameterized activation space. Motivated by the prior work on curvature-tuning, we propose UPLiFT, Uncertainty Parameterized activation for Lightweight Fine-Tuning, with enhanced adaptation capacity when the underlying model--whether pretrained or randomly initialized--remains fixed, for significantly improved parameter efficiency. Furthermore, its learnable non-linearity naturally allows to model uncertainty over the activation space through fine-tuning in a variational inference framework. Evaluation across multiple protein datasets shows that UPLiFT performs competitively while requiring 3-11x fewer trainable parameters than weight-only adaptations. Bayesian UPLiFT further improves calibration while largely preserving the predictive performance of deterministically fine-tuned models. The narrow predictive performance margin with the low-rank adaptation baseline in residue-level tasks highlights the potential of our proposed approach as a parameter-efficient fine-tuning technique for PLM-based predictors.

bioinformatics↗

bioRxiv2026

Structural basis of coiled-coil domain-mediated inhibition of A. baumannii AbTir NAD+ nucleosidase activity

TIR (Toll/interleukin-1 receptor) domains are central components of innate immunity and cell-death pathways across diverse forms of life. In bacteria, TIR domain-containing proteins participate in diverse antiphage defence systems, several of which rely on NADase activity carried out by TIR domains. This nucleosidase activity is often regulated by additional domains that modulate activation and specificity in response to infection. AbTir, a TIR domain-containing protein from Acinetobacter baumannii, contains an N-terminal CC domain that negatively regulates its NADase activity, but the structural basis of this regulation remains unknown. Here, we used X-ray crystallography to investigate the molecular basis of CC domain-mediated regulation in AbTir. We determined the crystal structure of full-length AbTir in complex with a nanobody, revealing a symmetric AbTir TIR dimer in which only one of the two symmetry-related CC helices is accommodated at the dimer surface, breaking the overall symmetry of the full-length protein. Structural comparisons with other bacterial TIR domains suggest that such TIR dimers likely represent an inactive state, in which the NAD+-binding site has not yet formed. Phylogenetic analysis places AbTir within a family of bacterial TIR proteins associated with defence-related genomic loci, although phage-spot assays performed in the surrogate host Escherichia coli did not detect measurable antiphage activity. Together, these findings reveal an unusual structural mechanism of CC domain-mediated inhibition of a bacterial TIR NADase and expand our understanding of the mechanistic diversity of bacterial TIR proteins.

biochemistry↗

bioRxiv2026

Intraindividual longitudinal changes of the proteome reveal dynamic markers of biological aging

Proteomic aging clocks have emerged as biomarkers of aging, but most rely on cross-sectional data. Here, we present a proteomic aging clock based on longitudinal measurements of liquid chromatography-mass spectrometry plasma proteomics at up to three time points in the population-based KORA cohort, comprising 9,105 samples from 4,100 individuals in the final dataset aged 25-88 years. The model predicts chronological age and yields a measure of proteomic age acceleration associated with lifestyle and clinical risk factors. Risk factor-stratified analyses reveal shared proteomic signatures among cardiometabolic exposures and distinct signatures for smoking and physical activity. Within individuals, dyslipidemia and impaired kidney function track transitions to increases in age acceleration, while higher age acceleration is associated with increased all-cause mortality. Mediation analysis suggests that risk factors are linked to mortality indirectly through age acceleration. Affected proteins and pathways highlight extracellular matrix remodeling, complement and coagulation pathways, and liver-derived plasma proteins as key contributors to aging. Mendelian randomization identifies proteins with evidence consistent with a causal role in aging-related traits. Together, these findings are consistent with biological aging being dynamically responsive to changes in modifiable risk factors.

bioinformatics↗

bioRxiv2026

Conserved microglial programmes, non-transferable classifiers: boundaries of the single-cell TBI molecular clock

Background: Traumatic brain injury (TBI) pathology evolves over hours to months, and molecular staging could complement clinical and imaging-based assessment. Single-cell transcriptomics captures cell-type-resolved temporal programs, but whether a transcriptional "molecular clock" trained on one injury model can be transferred to others remains unknown. Methods: We compiled four public mouse TBI single-cell/single-nucleus datasets spanning two injury models - controlled cortical impact (GSE277487; CEREBRI) and fluid percussion injury (GSE160763) - plus an independent mild-FPI cohort reserved for validation (GSE247339), totalling 307,991 cells across four pathological stages (control, acute, subacute, chronic). Classifiers were evaluated at cell and sample levels (held-out libraries) against a permutation null, with per-cell-type, strict leave-one-dataset-out (LODO) generalization, glial-only transfer and gene-level SHAP analyses. Results: Within GSE277487, sample-level balanced accuracy reached 0.67 (permutation null 0.32 +/- 0.09; p = 0.001), and the signal was reproduced in the independent mild-FPI cohort (0.40 vs null 0.19 +/- 0.05; p = 0.0010). Cell-level cross-validation overestimated performance by ~0.16, quantifying within-sample leakage. Cross-dataset generalization was poor (strict LODO balanced accuracy 0.20-0.31); the acute immediate-early response was untransferable (recall 0.002) and 80% of chronic (6-month) cells were assigned to the subacute class. Glial-only models raised subacute recall across models (0.91 and 0.75), but precision was indistinguishable from a subacute-only classifier (0.51 vs 0.50; 0.58 vs 0.58) and one-vs-rest AUROC was at or below chance (0.38-0.54) across four classifier families - classifier collapse, not transferable discrimination. The reproducible cross-model component was instead a gene-expression programme: a microglial complement/lysosomal damage programme (Trem2-Apoe axis) was up-regulated across the severe CCI and FPI models (13/13 concordant genes), whereas the immediate-early module was model-specific. Conclusions: A single-cell molecular clock for TBI staging is feasible within datasets once within-sample leakage is controlled, but it does not transfer across datasets as a classifier: apparent cross-model transfer reflected classifier collapse onto the majority class, and the reproducible cross-model component was a microglial complement/lysosomal gene-expression programme. These findings define the boundaries of cross-dataset time inference in TBI and provide evaluation criteria for claims of transferable single-cell classifiers.

neuroscience↗

bioRxiv2026

From Geometry to Hierarchy: Charting the Dominant Modes of Human Cortical Function Through Development

Cortical function in the human cortex is spatially patterned along a macroscale information processing hierarchy. This hierarchy tracks a sensory-association axis anchored at one end by unimodal sensory regions and at the other by transmodal association areas. Biophysical models of brain dynamics suggest that this axis should align with the dominant, resonant modes of cortical anatomy and geometry, but they do not. Instead, the dominant anatomical modes correspond to simple spatial gradients extending along rostro-caudal, medio-lateral, and dorso-ventral axes. How does this divergence between anatomical and functional modes of the cortex arise? Here, using four independent functional magnetic resonance imaging datasets spanning infancy to young adulthood (n=1,512, aged 0-21 years), we show that this spatial divergence is attributable to the maturation of long-range cortico-cortical inter-regional functional coupling. Specifically, we find that the dominant functional mode of the human cortex during the first 3 to 4 years of life closely resembles the rostro-caudal mode of cortical geometry, then shifts to a rudimentary sensory-association-like mode between 4 and 6 years of age and ultimately matures into a prototypical sensory-association mode by age 13. In infancy and early childhood, a linear model predicting the topography of the dominant functional mode from modes of cortical geometry substantially outperforms a model relying on the adult sensory-association mode, whereas the reverse is true in later stages of development. Furthermore, targeted removal of long-range connections causes the adult functional mode to regress from a sensory-association topography to an infant-like rostro-caudal mode. Our findings indicate that the maturation of long-range cortico-cortical coupling supports the gradual emergence of hierarchical modes of cortical function from an organization initially dominated by geometric constraints.

neuroscience↗

bioRxiv2026

AnnoAudit: a marker-based protocol for auditing single-cell atlas annotations reveals annotation-driven artifacts in a widely used traumatic brain injury resource

Single-cell atlas annotations are routinely treated as ground truth but rarely validated before use. We present AnnoAudit, a marker-based audit protocol that combines four convergent checks - marker scoring, unsupervised clustering, margin-gated module scoring, and applicability-gated pretrained models - into a composite Annotation Contamination Score (ACS), plus an independent-gene discrimination step that distinguishes genuine mis-assignment from ambient-RNA detection artifacts, and a trajectory-correlation fingerprint tracing suspicious signals to their cell type of origin. Applied to CEREBRI (GSE269748), a widely used single-cell TBI atlas (73 citations; 65 citing works; six re-analysis studies, none of which validated the annotations), the audit shows that contamination is systematic: the official "glutamatergic neuron" label is 97.8% non-excitatory by markers, with at least 67.4% of its cells independently confirmed as non-neuronal (microglia-dominated); the GABAergic (46.6%), OPC (66.1%), and astrocyte (37.7%) labels are likewise contaminated by marker-confirmed microglia, whereas the microglial, oligodendrocyte, endothelial, and pericyte labels are largely clean (85-96% self-confirmed). The contamination is state-dependent: astrocyte and OPC labels are largely correct in uninjured controls (76.0% OPC self-marker; 18.5% discrimination-confirmed contamination for the astrocyte label) but collapse in the acute 24-h window (14-16% self-marker; 57-71% microglia), recovering by 6 months - so the annotation failure concentrates precisely in the window of maximal injury response. This temporal structure generates coherent false signals - a biphasic trajectory for 40 of 307 ion-channel genes, a KCNC3-specific OXPHOS signature, and an inverted KCNC3 trajectory at 7 days - whereas the corrected response is a sustained acute KCNC3 up-regulation conserved across three independent datasets and three injury models. Simulation-calibrated ACS is 82.6% for CEREBRI. In a human ALS atlas (GSE330130), an ambient-aware re-analysis shows that the official neuron labels are largely correct (1.8-6.1% contamination after discrimination), confirming that the audit does not flag well-annotated resources, and that marker-only estimates on snRNA data with high ambient RNA must be interpreted with the discrimination step. AnnoAudit needs only the deposited count matrix and a canonical panel; we propose it as a routine quality step for atlas-based re-analysis.

bioinformatics↗

bioRxiv2026

A Synthetic Microbial Therapy Rewires Antitumor Immunity Across Multiple Cancer Types

Microbial immunotherapies show promise against cancer, yet broad efficacy and mechanistic insight remain elusive. Here, we introduce SPIKE 1.0 (S1.0), a metabolically engineered bacterium that converts tryptophan into immunomodulatory hydroxyindoles via tryptophan monooxygenase to remodel the tumor microenvironment (TME). A single systemic dose of S1.0 elicited potent, durable antitumor responses across multiple murine models, including humanized mice, with minimal toxicity. S1.0 enhanced inflammatory signaling, activated innate and adaptive immunity, and promoted T cell persistence, memory, and resistance to exhaustion. It outperformed checkpoint inhibitors and synergized with chemotherapy. Multi-omics profiling revealed that S1.0 rewired amino acid metabolism in tumor-infiltrating immune cells and disrupted immunosuppressive networks. These results establish S1.0 as a scalable, cost-effective microbial immunotherapy with broad translational potential for solid tumors.

immunology↗

bioRxiv2026

Widespread use of invalid statistical tests in biomedical machine learning

Cross-validation is routinely used to compare performance in biomedical artificial intelligence. Standard tests ignore correlation across cross-validation folds, inflating false-positive rates. In a PRISMA-guided meta-analysis of 184 studies (impact factor [≥] 15) across 30 biomedical fields, 97% use invalid tests. Among studies with abstract-level claims supported by invalid tests, 59% rely on a spurious comparison: one that loses significance after correcting for this correlation. On average, regaining significance requires a 43% larger effect. Extending these findings to the broader literature, we estimate that spurious comparisons occur in nearly two in three studies and support abstract-level claims in nearly one in three studies. Simulations confirm false-positive rates paradoxically approach 100% when cross-validation is repeated to improve stability. We introduce SHARP, a redesign of cross-validation, which best balances false-positive control and power among 13 benchmarked tests. These results reveal widespread fragility in biomedical artificial intelligence and offer a practical route to valid comparisons.

bioinformatics↗

bioRxiv2026

Connectome-based spatial statistics enabling large-scale population analyses of human connectome across cohorts

Large-scale population analyses of structural connectome organization remain challenging because of cross-subject alignment, pathway interpretability and computational burden. No widely adopted standard exists for systematic evaluation across processing methods. We developed connectome-based spatial statistics (CBSS), a scalable framework for anatomically aligned and functionally informed quantification of white-matter microstructure that yields atlas-defined pathways organized into 13 functional networks. Using data from 56,510 UK Biobank participants together with five independent lifespan cohorts, we evaluated the streamline-, voxel- and network-level measures in the aspects of reliability, heritability, structure-function coupling, cognitive and behavioral prediction, brain aging patterns and lifespan trajectories across cohorts. The systematic evaluation workflow compares population-level white-matter representations across methods, spatial scales, tasks and datasets. The results support CBSS as a common connectome reference for large-scale, cross-cohort diffusion MRI studies.

neuroscience↗

bioRxiv2026

Toward Computationally Complete Spatial Omics

Multimodal spatial omics has transformed biology by mapping molecular complexity within intact tissues, yet current technologies remain limited in the number of modalities measured simultaneously and often produce lower-quality data than single-modality assays. We present COSIE, a computational framework that generates high-resolution, multilayered molecular landscapes across tissue sections, individuals, and platforms. COSIE integrates histology, epigenome, transcriptome, proteome, and metabolome into a unified representation. Applied to 12 datasets spanning 10 spatial technologies, eight modalities, and nine tissue types, ranging from thousands of spots to millions of cells, COSIE outperforms existing methods. It resolves tissue structures, enhances noisy measurements, predicts unmeasured modalities, and captures dynamic processes. In human tumors, COSIE identifies invasive subregions linked to clinical outcomes and predicts spatial gene expression in TCGA samples using only histology images. By transforming fragmented data into comprehensive spatial maps, COSIE advances computationally complete spatial omics and the creation of digital tissue twins for biomedicine.

genomics↗

bioRxiv2026

An Integrated Computational Antigen Discovery Pipeline with Hierarchical Filtering for Emerging Viral Variants

Emerging and evolving viral diseases, such as SARS-CoV-2, continue to pose significant global health challenges, underscoring the urgent need for rapid and scalable antigen discovery pipelines. This work presents a computational pipeline that integrates diverse computational tools and machine learning models to accelerate the identification and optimization of antigen candidates. The pipeline employs efficient filtering and consensus-based strategies to highlight epitopes with high therapeutic potential. We demonstrate its utility by significantly narrowing the antigen search space for Rift Valley fever virus (RVFV) and Mayaro virus (MAYV), and by effectively identifying conserved neutralizing epitopes in SARS-CoV-2. Our proposed computational antigen pipeline offers a powerful framework for expediting the development of future vaccines and therapeutics in response to emerging pathogens.

bioinformatics↗

bioRxiv2026

Rapid sequence-based screening of structure-disrupting protein mutations

Recent advances in AI-based protein structure prediction have dramatically reduced the cost of obtaining three-dimensional protein models and have become integral to modern protein engineering workflows. However, full structure prediction remains computationally prohibitive in high-throughput settings for mutation-based protein engineering, where thousands of candidate variants may need to be evaluated. In many such scenarios, the primary objective is not to resolve the complete structure of a candidate mutant, but rather to identify whether the introduced mutations are likely to induce substantial structural changes for rapid down-selection of candidates that conserve the wildtype structure. Protein language models (PLMs), trained solely on unlabeled natural protein sequence data, are known to encode rich structural information within their hidden representations. Motivated by this observation, we investigate a range of sequence-based ranking metrics derived from PLMs as efficient surrogates for structural deformation prediction. Through systematic evaluation across multiple proteins, mutation regimes, and structure-prediction backbones, we show that embedding distance--particularly the L1 distance between representations--provides a robust and computationally efficient signal for identifying structure-disrupting mutations. Our results demonstrate that sequence-level screening can substantially reduce the need for expensive structure prediction while preserving sensitivity to large structural perturbations, thereby providing the means to significantly speed up mutation-based protein design.

bioinformatics↗

bioRxiv2026

Structure-Based TCR-pMHC Binding Prediction and Generalization to Unseen Peptides

The interaction between T-cell receptors (TCRs) with the peptide-bound major histocompatibility complex (MHC) intricately impacts the functional specificity of T-cell-mediated adaptive immune response. Consequently, implication in immunotherapy has contributed to the ever-growing computational methods for TCR recognition, which have recently attracted structure-based approaches due to advancements in protein structure modeling. Despite access to structural information of the predicted binding interface, graph neural network (GNN)-based TCR-pMHC binding specificity classifiers tend to show poor accuracy for samples with unseen peptides. In this work, we comprehensively assess the potential factors that critically impact the generalization performance of classifiers trained with computationally predicted structures. Specifically, our experiments focus on analyzing the sensitivity of such predictors to the interaction features in the TCR-pMHC interface and the structural uncertainty. Building on the analysis, we demonstrate how the design of classifier architecture with auxiliary training objectives can improve the generalization performance to novel peptides not yet seen during model training. Overall, our work highlights the challenges of unseen peptide generalization from different perspectives of the GNN-based classifier paradigm, showcasing the strengths and weaknesses of the current state-of-the-art approaches in the generalization landscape.

bioinformatics↗

bioRxiv2026

Interpretable transcriptome-to-phenotype modeling of cell-painting nuclear morphology features from RNA-seq under low-dose radiation exposure

With rapid advancements in high-throughput multi-modal profiling techniques across molecular, cellular to tissue scales, translating such multi-modal data into knowledge discovery for foundational understanding of cellular mechanisms is central in modern biomedical sciences. In this study, we focus on understanding how low-dose radiation exposure perturbs cellular morphology by linking high-dimensional transcriptomic responses to quantitative cell-phenotype readouts over time. We present a time-resolved inverse modeling framework that associates gene expression changes via RNA-sequencing with nuclear morphology features obtained from cell-painting imaging. Morphology responses were defined as treated-control differences for multiple nuclear features including size, shape, intensity, and textures, indexed by radiation dose and week. To capture time-dependent associations while maintaining interpretability, both RNA-sequencing and cell-painting data were stratified into four temporal phases (weeks 1-2, 3-4, 5-6, 7-9) and phase-dependent effects are encoded via gene-phase interaction predictors. To reduce confounding by dose trends and to evaluate generalization across time, we used a two-stage leave-one-week-out procedure: (i) a dose-only baseline model produced out-of-week residuals for each morphology feature, and (ii) elastic-net regression on phase-aware predictors modeled residual variation not explained by dose. Hyperparameters were selected via an exhaustive grid search scored by the correlation between observed residuals and out-of-week residual predictions, with additional sparsity diagnostics based on nonzero coefficient counts per fold. Stable predictors were identified by selection frequency and sign consistency across folds, then pruned further for multicollinearity and parsimony. Final reduced models were fit using ordinary least squares with heteroskedasticity-consistent standard errors to report effect estimates robust to non-constant variance. This workflow yields a transparent, time-stratified set of transcriptomic predictors associated with longitudinal nuclear morphology changes and provides a reproducible foundation for downstream biological interpretation and validation.

bioinformatics↗

bioRxiv2026

Reconstructing multi-scale tissue spatial architecture from single-cell RNA-seq with REMAP

Understanding spatial organization of cells is critical for deciphering tissue function and disease. Single-cell RNA-sequencing (scRNA-seq) profiles transcriptomes at scale but loses spatial context, while spatial transcriptomics (ST) preserves spatial information but is constrained by cost and gene coverage. Here, we present REMAP, a deep learning framework that integrates gene expression with neighborhood-level gene-gene covariance to reconstruct multi-scale spatial organization of scRNA-seq data using one or multiple ST references. Across 2D and 3D mouse brain, human fetal cortex, and seven human cancer types, REMAP consistently outperformed existing approaches. Applied to a human multiple sclerosis atlas, REMAP resolved microglial neighborhood heterogeneity, and identified a rare pro-inflammatory microglia-astrocyte subpopulation. Across diverse cancers, REMAP recovered conserved spatially-defined cancer-associated fibroblast subtypes with known prognostic significance. By transforming cost-efficient single-cell datasets into spatially interpretable tissue maps, REMAP enables spatial hypothesis generation, microenvironment discovery, and population-scale inference of conserved and perturbed architectural principles in human disease.

genomics↗

bioRxiv2026

Pannexin-1 channel activity regulates neurogenesis and cell survival in the developing cortex

Mutations in genes encoding a range of ion-conducting proteins disrupt development of the cerebral cortex in humans, often causing polymicrogyria (PMG); yet how ion conduction guides the development of cortical architecture is not clear. Here, we describe three individuals with brain malformations including PMG and microcephaly in whom de novo, missense mutations were identified in PANX1--encoding an ATP and ion conducting channel. We show that these PMG-associated PANX1 mutations (p.D14H, p.M37R, and p.N338T) disrupt normal glycosylation and confer gain-of-function with respect to ATP release and channel conductance. In vivo modeling of mutant PANX1 in cortical progenitor cells demonstrated disrupted cell migration and excess cell death in both mice and ferret models. In vitro modeling of the p.N338T allele in human induced pluripotent stem cell (hiPSC)-derived neurons further revealed changes in neuronal morphology and increased excitability, and dysregulation of key transcription factors critical for neurogenesis and cell fate. In addition, p.N338T iPSC-derived neural progenitor cells displayed increased PANX1 activation. Our results show that normal PANX1 function contributes to cortical structure through regulation of ion conductance and ATP release and provides insight into how these processes influence corticogenesis and cytoarchitecture more broadly.

neuroscience↗