Biology subjects

Zhu, Y.

Publications and source records attributed to Zhu, Y..

At least 109 records · Page 6Linked to original sources

bioRxiv2026

A NOVEL MITOCHONDRIAL PEPTIDE ESSENTIAL FOR RESPIRATORY CAPACITY PROMOTES GROWTH, YIELD AND ABIOTIC STRESS TOLERANCE IN PLANTS

Efforts to boost crop productivity have focused on improving photosynthesis. However, plant respiration consumes over 90% of fixed carbon, fueling growth, stress responses, and resource acquisition. Despite its metabolic centrality, respiratory regulation remains underexplored as a yield target. Here, we identify AtMLSP1 (At4g17085), a 57-amino-acid mitochondrial inner membrane peptide in Arabidopsis thaliana. Loss-of-function atmlsp1 mutants show impaired growth, while AtMLSP1 overexpression boosts biomass, seed yield, and high light tolerance. No induction of alternative oxidase protein abundance or activity was observed in atmlsp1 plants, a response normally triggered by respiratory perturbation. Mitochondria isolated from atmlsp1 lines had an approximately 90% reduction in the amount of the intermembrane space protein Cytochrome c and a 30% reduction in all subunits of complex III, while other respiratory complexes remain unaffected. As expected, loss-of-function plants atmlsp1 reveal a significant decrease in total respiratory capacity and membrane potential, but mitochondrial integrity, and abundance of complex I, II and complex IV were unchanged. Overexpression of the orthologues of this gene in Oryza sativa (rice) and Glycine max (soybean) results in a significant increases in yield (seed number and size) under field conditions across different locations.

plant biology↗

bioRxiv2026

iCNG99: a validated genome-scale metabolic model of Cryptococcus neoformans strain H99

Cryptococcus neoformans is a ubiquitous environmental fungus that can also cause life-threatening infections in immunocompromised individuals. As a competent pathogen, Cryptococcus needs to reprogram its metabolism to adapt to the drastic differences between environmental niches and host niches. A well-curated genome-scale metabolic model (GEM) is a powerful tool to facilitate the investigation of the metabolic resilience of an organism. Here we reconstructed and validated iCNG99, a GEM for C. neoformans reference strain H99, and evaluated its predictive performance across 43 growth conditions and gene essentiality benchmarks. The model achieved high confidence essential gene prediction (precision = 0.77) and recapitulated pathways targeted by clinically available antifungals. Integration with transcriptomic and metabolomic data enabled iCNG99 to capture condition-specific metabolic adaptations and to identify candidate vulnerabilities in drug tolerance, revealing metabolic adaptations associated with survival within host conditions and drug susceptibility. Together, iCNG99 provides a systems-level computational platform for investigating C. neoformans metabolism and for prioritizing antifungal vulnerabilities.

systems biology↗

bioRxiv2026

π-MSNet: A billion-scale, AI-ready living proteomics data portal

Artificial intelligence (AI) is reshaping proteomics workflows, delivering remarkable gains in both peptide identification sensitivity and quantitative performance. However, the potential of deep learning models in proteomics has not been fully exploited due to the scarcity of large-scale, high-quality and consistently labeled datasets. Here, we present {pi}-MSNet, a billion-scale, AI-ready living mass spectrometry (MS) data portal. Using a uniform identification and quality control workflow, it comprises over 1.66 billion MS/MS spectra, 501 million peptide-spectrum matches (PSMs), and 9 million precursors from 36,356 LC-MS/MS runs across ten instrument types and 55 diverse species. Through community collaboration, the data are shared via international, interactive, and living web resources. Enabled by the built-in MSNetLoader Python API for seamless and scalable data access--with native support for PyTorch and TensorFlow--{pi}-MSNet provides an AI-ready data framework for efficient training and systematic benchmarking of multiple models across three representative tasks (e.g., MS/MS spectrum prediction, retention time prediction, and de novo peptide sequencing). In particular, by retraining multiple models on {pi}-MSNet, we achieved consistent performance improvements over their original versions. These improved models were subsequently integrated into the {pi}-MSNet agent to enable interactive, deployment-free use. Through SDRF (Sample and Data Relationship Format) metadata, an open-source cloud analysis workflow, and a community-driven interactive data portal that supports continuous data submission, {pi}-MSNet serves as a living, AI-ready resource for reproducible benchmarking, robust model training, and accelerated AI innovation in proteomics.

bioinformatics↗

bioRxiv2026

A residual-ratio framework for auditing transcriptomic gene signatures against background expression structure

BackgroundTranscriptomic gene signatures are widely used to infer pathway activity and biological mechanism from bulk cancer expression data, yet current evaluation strategies primarily emphasize internal coherence, predictive performance, or scoring robustness. A quantitative framework for assessing how much signature variation remains independent of background expression structure has been lacking. ResultsUnlike existing single-number signature-quality metrics such as Berglund uniqueness, residual-ratio auditing reports a trajectory across null-model richness: for each signature we compute the residual ratio [Formula] at progressively enriched expression-PC subspaces, together with an inverse-participation-ratio (IPR) concentration diagnostic that reports the effective number of axes absorbing each signature. Applied to a curated 17-entry benchmark, all 50 MSigDB Hallmark gene sets, and 1,181 Reactome pathways across 8 TCGA cancer types (4,462 samples), with external validation in METABRIC, the framework produces two complementary readouts. First, the curated panel is absorbed into the ExprPC50 subspace at residual ratios 18-43% below size-matched random 30-gene baselines in every cancer (curated mean r{perp} range 0.109-0.177 vs. random mean 0.182-0.288), providing the frameworks central quantitative discrimination between biologically coherent signatures and arbitrary gene combinations. Second, within the curated panel the ExprPC50 residual ratio is negatively correlated with the top-5 absorption concentration in every cancer (Spearman{rho} from -0.59 in PRAD to -0.89 in SKCM, median -0.71; all 8 significant at p < 0.05, most at p < 10-3); we report this correlation as a descriptive geometric property of the null-model coordinate system rather than as a biological law, because 1,000 random 30-gene draws projected through the same top-50 expression-PC basis reproduce the same pan-cancer median{rho} (-0.73; Supplementary Table S16), and it is robust to compositional nuisance: after rebuilding the null basis as immune-PC1 {oplus} stromal-PC1 {oplus} proliferation-PC1 plus 47 residual PCs, the per-cancer{rho} becomes more negative rather than shallower (median -0.86; Supplementary Table S17), ruling out tumor purity, immune infiltrate, and stromal fraction as drivers of the pattern. Because absorption at ExprPC50 is a geometric property of how any signature direction sits in expression-PC space, tier-level distributional structure at this operating point is not separable beyond the low-vs-upper band split: a Kruskal-Wallis omnibus is significant (p = 4.9 x 10-13), but pairwise Dunns post-hoc tests show that Tiers 1, 4, and 5 are not separable (pBH > 0.2). The trajectory shape itself is empirically bootstrap-invariant: across 200 sample-level fixed-basis bootstrap resamples of the 17 curated entries in BRCA, the mean pairwise Pearson correlation of trajectory-shape vectors is 0.999, and individual cell-level 95% bootstrap CI half-widths at B= 1,000 resamples are in the range 0.002-0.053. External replication in the METABRIC breast cancer cohort (nsamples = 1,980, microarray) showed moderate-to-strong rank-ordering concordance with TCGA-BRCA across the 17 curated entries (Spearman{rho} = 0.72 on the 17-signature ordering, 95% Fisher-z CI 0.37-0.89, p = 0.001). Under an upper-bound sensitivity analysis, 45 of 50 Hallmark gene sets and 992 of 1,181 Reactome pathways had ExprPC200 residual ratios below the mean of their size-matched random baselines--a descriptive statistic reflecting axis alignment under rich null models, not a failure rate. In causal DAG simulations (nrep = 100 replicates), a signature driven entirely by a latent confounder retained r{perp} = 0.233 at ExprPC50, numerically comparable to Tier 1 validated drivers, so a single-point residual ratio cannot adjudicate confounder-independence. The frameworks load-bearing signals are therefore the trajectory shape (statistically invariant under sample-level resampling) and the magnitude gap between the curated panel and its random 30-gene baseline (the curated-vs-random discrimination), read jointly--not the value of r{perp} at any single null-model dimensionality. O_TBL View this table: org.highwire.dtl.DTLVardef@1b56848org.highwire.dtl.DTLVardef@d18636org.highwire.dtl.DTLVardef@1c26db4org.highwire.dtl.DTLVardef@1b0620corg.highwire.dtl.DTLVardef@f507d2_HPS_FORMAT_FIGEXP M_TBL O_FLOATNOTable S16:C_FLOATNO O_TABLECAPTIONRandom-gene-set null for the ExprPC50 r{perp}-vs-c(5) correlation, and curated-panel absolute gap vs random baselines. For each of the 8 primary-analysis TCGA cancer cohorts, we drew B = 1,000 random 30-gene sets from the gene universe of the preprocessed expression matrix and computed, for each draw, the residual ratio r{perp}(k = 50) and the top-5 absorption concentration c(5) under the same top-50 sample-space PC basis used for the curated benchmark (Methods [§]Statistical analysis; reference implementation accompanies the project repository as script 35). Column "Empirical curated{rho} " repeats the 17-signature Spearman{rho} between r{perp} and c(5) reported in the main text and in Supplementary Table S10; column "Null-A{rho} (random 30-gene)" gives the Spearman{rho} across the 1,000 random-draw (r{perp}, c(5)) pairs per cancer; column "{Delta} (emp null-A)" reports the difference. For reference, column "Null-B{rho} " gives the corresponding Spearman{rho} across 1,000 iid Gaussian unit vectors h (0, IN) in sample space (a uniform random direction that does not inherit the expression covariance geometry). Rightmost columns compare the curated 17-entry panels mean r{perp} at ExprPC50 to the random 30-gene baseline mean, both in absolute units and as a percentage gap; this magnitude gap is the quantitative discrimination between curated biological signatures and arbitrary gene combinations on which the frameworks central claim rests. C_TABLECAPTION C_TBL O_TBL View this table: org.highwire.dtl.DTLVardef@d4a1d3org.highwire.dtl.DTLVardef@1cc3baaorg.highwire.dtl.DTLVardef@1611aecorg.highwire.dtl.DTLVardef@2e9c76org.highwire.dtl.DTLVardef@220711_HPS_FORMAT_FIGEXP M_TBL O_FLOATNOTable S17:C_FLOATNO O_TABLECAPTIONPurity-aware null for the curated-panel ExprPC50 r{perp}-vs-c(5) correlation. For each cancer we rebuild a rank-50 sample-space basis as follows. Columns 1-3 are the PC1 directions of: an immune-infiltrate proxy panel (CD3D, CD3E, CD4, CD8A, CD8B, CD19, CD68, PTPRC, FOXP3, IFNG); a stromal/fibroblast proxy panel (COL1A1, COL1A2, COL3A1, VIM, FN1, ACTA2, PDGFRA, PDGFRB); and the 50 proliferation markers already used in the null-model hierarchy ([§]Null model hierarchy). Columns 4-50 are the top-47 PCs of the residual expression matrix Y - QbioQ Y after QR-orthonormalization of the 3-column biological block; the full 50-column basis is re-orthonormalized by a final QR pass. The 17 curated benchmark entries are then re-scored under this purity-aware basis, and the per-cancer Spearman{rho} between r{perp} and c(5) is recomputed. Column "Standard{rho} " is the value reported in the main text (same as Supplementary Table S16, column "Empirical curated"); column "Purity-adjusted{rho} " is the value under the new basis; column "{Delta}" is the difference. This test was pre-specified as a check on whether the observed{rho} (r{perp}, c(5)) could be driven by tumor-purity, immune, or stromal composition artifacts that a standard top-50 PC basis would implicitly absorb. C_TABLECAPTION C_TBL ConclusionsResidual-ratio auditing provides an interpretable and practical framework for quantifying how much of a transcriptomic gene signatures variance remains orthogonal to a chosen background-expression model. The two statistically reliable quantities it reports are (i) the shape of the trajectory r{perp}(k) across null-model richness, which is bootstrap-invariant across sample-level resamples, and (ii) the magnitude gap between the curated panels residual ratio and size-matched random 30-gene baselines at a fixed operating point, which is 18-43% in all 8 TCGA cancers and survives a purity-aware null-model construction. The negative correlation between r{perp} and the top-5 absorption concentration c (curated-panel median{rho} = -0.71) is reproduced by random 30-gene sets under the same basis (random-draw median{rho} = -0.73) and is therefore best read as a descriptive geometric property of the null-model coordinate system rather than a biological discovery about curated signatures. Any single operating-point residual ratio carries materially wider cell-level uncertainty than the trajectory shape and cannot, on its own, adjudicate confounder-independence. The frameworks outputs describe a signatures geometric relationship to modeled background expression structure and do not evaluate clinical utility: a signature with a low residual ratio may still be clinically valuable when that low value reflects alignment with a strong prognostic or actionable program such as proliferation, immune infiltration, or cell cycle, and the framework is not a substitute for calibrated prognostic or predictive classifiers. All findings are based on bulk RNA-seq (TCGA PanCancer Atlas, 8 cancer types) and microarray (METABRIC) data; transfer to single-cell, single-nucleus, or spatial transcriptomics is out of scope and not claimed. Used within this scope--reading the trajectory shape and the magnitude-gap signal jointly, rather than the value of r{perp} at any one k--the framework adds a complementary audit layer to existing pathway-scoring and experimental-validation workflows, and supports more calibrated interpretation, comparison, and reporting of transcriptomic gene signatures in cancer studies.

bioinformatics↗

bioRxiv2026

Engineering age-adaptive mRNA lipid nanoparticle cancer vaccines via reprogramming systemic gene expression

Lipid nanoparticle (LNP)-based mRNA vaccines have transformed cancer immunotherapy, yet their efficacy in older individuals, who represent the majority of cancer patients, remains poorly understood. Here, we uncover a critical and previously underappreciated barrier to mRNA LNP cancer vaccine performance in aged hosts: impaired systemic transgene expression. Using the SM-102 mRNA LNPs as a benchmark formulation, we show that while local immune activation and antigen presentation at the injection site and draining lymph nodes remain largely intact with age, transgene expression in peripheral organs, including the liver, lungs, and spleen, is markedly reduced. This deficit limits the magnitude and durability of CD8 and CD4 T cell responses and substantially compromises tumour control. Transcriptomic profiling further reveals that attenuated transgene expression parallels broad attenuation of antigen processing, presentation, and activation pathways in immune cells from aged animals, implicating impaired systemic mRNA translation as a central driver of the downstream immune defects and antitumour efficacy loss. Building on these insights, we identify a rationally selected LNP formulation that reestablishes distal antigen expression across age groups as an engineering strategy to revive T cell immunity, achieving full rescue of therapeutic efficacy in aged mice without additional intervention. Together, these findings establish systemic mRNA translation as a tuneable lever of vaccine performance and highlight that optimizing LNP formulations to sustain systemic transgene expression across age groups may enable next-generation, age-adaptive mRNA vaccines for cancer and other diseases of aging.

bioengineering↗

bioRxiv2026

Spectral Graph Features for Reference-free RNA 3D Quality Assessment

MotivationExisting RNA 3D structure quality assessment (QA) methods rely on local geometric descriptors or statistical potentials that evaluate atomic-level contacts but are blind to global topological coherence. This creates a critical failure mode--structures that are "locally correct but globally wrong"--where well-formed local helices mask misplaced domains and incorrect overall packing. ResultsWe introduce SpecRNA-QA, a lightweight RNA QA method based on multi-scale graph-Laplacian features of inter-nucleotide contact networks. In CASP16 leave-one-out cross-validation, it achieves median per-target Spearman{rho} = 0.69 (target-clustered bootstrap 95% CI [0.64, 0.73]) versus 0.47 for an internal geometry baseline--a +0.22 gap that is significant at p = 1.2 x 10-10 (one-sided Wilcoxon signed-rank) and reflects a per-target win rate of 93%. The gain is concentrated on large, multi-domain RNAs, where global coherence is poorly captured by local descriptors. In a contextual comparison with established statistical potentials, local energy-based scores remain strongest on compact RNAs, while SpecRNA-QA yields the strongest signal we observed on targets longer than 200 nt; within the single-threaded runtime budget used here, the strongest local-energy comparator, rsRNASP, timed out on 22 of 26 large targets, and we report an explicit paired head-to-head on the four commonly scored targets in Section 4.2. A training-free heuristic variant further shows that the spectral prior carries intrinsic quality information even in the absence of labeled QA data. AvailabilitySpecRNA-QA is available as a Python package at https://github.com/yudabitrends/specrnaq. Contactybi3@gsu.edu Supplementary informationSupplementary data are available online. Key PointsO_LISpecRNA-QA uses multi-scale graph-Laplacian spectra to score global RNA fold coherence that local geometric descriptors and local statistical potentials can miss. C_LIO_LIThe method uncovers a size-dependent division of labor: on compact RNAs that can be scored exhaustively, atom-level statistical potentials such as rsRNASP remain strongest, whereas on >200 nt RNAs--where the strongest local comparator times out on most targets under the single-threaded runtime budget used here--SpecRNA-QA provides the strongest signal we observed. C_LIO_LIHeat-kernel traces at intermediate diffusion times emerge as the most discriminative spectral features and form an interpretable bridge between local packing and long-range tertiary organization. C_LIO_LIA training-free heuristic variant of SpecRNA-QA retains informative spectral signal without any labeled QA data, supporting the interpretation of the learned model as amplifying a real structural signal rather than overfitting one. C_LI

bioinformatics↗

bioRxiv2026

Cell-nanoplastics association impacts cell proliferationand motility

Detection of micro- and nanoplastics (MNPs) in human tissues has raised growing concern about their biological effects on tissue and cell function. While previous studies have examined MNP-cell interaction, most focused on limited cell and plastic types. Here, we present a comprehensive, quantitative investigation into how different types of nanoplastics (NPs) associate with and affect diverse cell types under physiologically relevant conditions. Using microfluidic-calibrated fluorescence microscopy, we quantify NP accumulation in cells in vitro and match cellular NP concentrations to levels reported in human tissues. While cell-associated NPs could be gradually released in vitro, they persist in vivo for over one month without detectable reduction in a mouse model. We discover that NP exposure at these levels broadly impairs cell proliferation across epithelial, endothelial, fibroblast, and immune cells, with cell type-dependent sensitivity. NP exposure also reduces motility in T cells and fibroblasts, with more complex effects observed in macrophages. Mechanistically, NP-cell association and trans-epithelial transport involved not only classical endocytic regulators but also pathways related to ion and water transport. Notably, NP association and release were highly sensitive to the extracellular fluid environment within the physiological range. By testing inhibitors of these pathways, we identified molecules that reduce NP-cell association and promote release. We further compared common NPs found in human samples and widely used in research: polystyrene (PS), polyethylene (PE), and polypropylene (PP). Although these NPs similarly impaired proliferation and motility, they showed markedly different cellular association and release dynamics. These findings reveal the impact of NPs on tissue cell functions and uncover novel regulatory pathways, establishing a quantitative framework for studying NP-cell interactions in biologically relevant conditions.

cell biology↗

bioRxiv2026

Integrative Multi-cohort Transcriptomics and Network Pharmacology Analysis Reveals Key Network Nodes and Potential Drug Clues in PCOS Granulosa Cells

BackgroundPolycystic ovary syndrome (PCOS) is a prevalent endocrine disorder with complex pathophysiology and limited therapeutic options. Identifying key molecular drivers and potential drug candidates is critical for improving clinical outcomes. MethodsWe integrated multi-cohort transcriptomics (GSE155489, GSE138518, GSE226146) with weighted gene co-expression network analysis (WGCNA), protein-protein interaction (PPI) network analysis, and drug repurposing. Differential expression analysis identified 1,039 DEGs, and WGCNA identified 10 PCOS-associated modules. Intersection of DEGs with module genes yielded 498 core candidate genes, which were subjected to functional enrichment, PPI network analysis, and connectivity map-based drug repurposing (CLUE/LINCS). Candidate drugs were further evaluated by molecular docking and ADMET prediction using a triple intersection strategy (hub genes, high differential expression, drug-target evidence). ResultsFunctional enrichment revealed significant enrichment in cell adhesion and TGF-beta signaling. PPI network analysis identified CD44 as the top hub gene (degree=42). Drug repurposing identified 106 candidate drugs, including troglitazone and enzalutamide. Using the triple intersection strategy, five genes (ID2, NR4A1, GJA5, ID1, MYH11) were prioritized for molecular docking. GJA5 showed strong predicted binding affinity with flufenamic acid (-7.88 kcal/mol), and cytosporone B exhibited favorable druglikeness (0 Lipinski violations). ConclusionThis study systematically characterizes PCOS-associated gene networks and provides a prioritized set of candidate targets and drugs through a purely computational framework. CD44 emerges as a key network node with potential relevance in PCOS pathophysiology. These findings offer testable hypotheses for future mechanistic studies and drug discovery efforts in PCOS.

systems biology↗

bioRxiv2026

Celastrol alleviates SGLT2 inhibitor-induced diabetic hyperketonemia by inhibiting hepatic ketogenesis

SGLT2 inhibitor (SGLT2i)-induced diabetic hyperketonemia is a life-threatening acute complication of diabetes. While Celastrol has been reported to exert beneficial effects on obesity; its potential role in ketogenesis remains unclear. In this study, Celastrol administration significantly attenuates the fasting-induced elevation of blood {beta}-hydroxybutyrate. Moreover, a 7-day course of Celastrol (1 mg/kg/day) leads to reductions in body weight and fat mass. Mechanistically, Celastrol specifically downregulates HMGCS2 expression and suppressess hepatic ketogenesis through inhibiting PPAR expression in the short term ([≤] 2 days). However, after prolonged treatment for 7 days, Celastrol modulates both PPARand serum free fatty acids (FFAs) levels. Furthermore, anti-ketogenic effect of Celastrol is abolished in Ppar{square} /{square} mice. Importantly, Celastrol effectively ameliorates SGLT2i-induced hyperketonemia. In summary, Celastrol curbs hepatic ketone overproduction in a PPAR-dependent manner, indicating its protective potential against SGLT2i-induced hyperketonemia.

molecular biology↗

bioRxiv2026

nELAVL phosphorylation by CDKL5 regulates inter-condensates composition and communication to promote experience-dependent maturation of the visual cortex

Mutations in Cyclin-Dependent Kinase-Like 5 (CDKL5) cause CDKL5 deficiency disorder (CDD), an X-linked neurodevelopmental condition. Through a phosphoproteomic screen, we identified the neuron-specific nELAVL family of RNA-binding proteins as direct activity-dependent substrates of CDKL5. In support of this regulatory axis, single-nuclei transcriptomics of Cdkl5 knockout (KO) cortices revealed an enriched reduction in activity-dependent mRNAs. Mechanistically, we show that nELAVL proteins undergo phase separation to form biomolecular condensates, the size of which is gated by CDKL5 phosphorylation. Loss of CDKL5 leads to enlarged nELAVL condensates, which exhibit reduced binding affinity for the target mRNA Fos, resulting in its accelerated degradation. This disruption extends to inter-condensate communication: phosphodeficient nELAVL show diminished interaction with P-bodies, which themselves become enlarged in CDD mutant iNeurons. Functionally, the absence of nELAVL phosphorylation recapitulates the deficits in experience-dependent visual function observed in Cdkl5 KO mice. Our findings establish a critical molecular mechanism by which CDKL5-mediated phosphorylation governs mRNA metabolism by tuning the properties of nELAVL condensates and their communication with other biomolecular condensates, ultimately promoting experience-dependent maturation of the visual cortex.

neuroscience↗

bioRxiv2026

TRaP: An Open-source, Reproducible Framework for Raman Spectral Preprocessing across Heterogeneous Systems

Raman spectroscopy offers a uniquely rich window into molecular structure and composition, making it a powerful tool across fields ranging from materials science to biology. However, the reproducibility of Raman data analysis remains a fundamental bottleneck. In practice, transforming raw spectra into meaningful results is far from standardized: workflows are often complex, fragmented, and implemented through highly customized, case-specific code. This challenge is compounded by the lack of unified open-source pipelines and the diversity of acquisition systems, each introducing its own file formats, calibration schemes, and correction requirements. Consequently, researchers must frequently rely on manual, ad hoc reconciliation of processing steps. To address this gap, we introduce TRaP (Toolbox for Reproducible Raman Processing), an open-source, GUI-based Python toolkit designed to bring reproducibility, transparency, and portability to Raman spectral analysis. TRaP unifies the entire preprocessing-to-analysis pipeline within a single, coherent framework that operates consistently across heterogeneous instrument platforms (e.g., Clinical Fiber-optic Raman System, Commercial Portable System and Commercial Raman Microscope). Central to its design is the concept of fully shareable, declarative workflows: users can encode complete processing pipelines into a single configuration file (e.g., JSON), enabling others to reproduce results instantly without reimplementing code or reverse-engineering undocumented steps. Beyond convenience, TRaP integrates configuration management, X-axis calibration, spectral response correction, interactive processing, and batch execution into a workflow-driven architecture that enforces deterministic, repeatable operations. Every transformation is explicitly recorded, making the full processing history transparent, inspectable, and reproducible. This eliminates ambiguity in how results are generated and ensures that identical protocols can be applied consistently across datasets and experimental contexts. Through representative use cases, we show that TRaP enables seamless, reproducible preprocessing of Raman spectra acquired from diverse platforms within a unified environment. We hope TRaP can empower Raman data processing as a reproducible, shareable, and systematized scientific practice, aligning it with modern standards for computational research. TRaP is released as an open-source software at https://github.com/hrlblab/TRaP

bioengineering↗

bioRxiv2026

IL-1β and TNF drive endothelial dysfunction and coagulopathy in acute COVID-19.

Vascular dysfunction and coagulopathy are hallmarks of severe COVID-19. How SARS-CoV-2 infection drives endothelial dysfunction, despite the virus not infecting or replicating in endothelial cells, remains controversial. Here, we used an in vitro co-culture model of the human pulmonary epithelial-endothelial cell barrier to investigate which inflammatory mediators drive endothelial dysfunction during SARS-CoV-2 infection. SARS-CoV-2 infection of primary human bronchial epithelial cells increased adjacent endothelial cell expression of the leukocyte adhesion marker ICAM-1, disrupted endothelial VE-cadherin junctions, promoted endothelial cell death, and promoted platelet adherence to gaps in the endothelial monolayers. Dexamethasone treatment rescued these dysregulated endothelial phenotypes in infected co-cultures, confirming that inflammatory signalling was the primary driver of SARS-CoV-2-induced endothelial dysfunction. Specifically, epithelial-derived TNF and IL-1{beta} promoted endothelial dysfunction, as inhibition of TNF or IL-1R signalling blocked SARS-CoV-2-induced endothelial dysfunction in co-cultures. SARS-CoV-2-infected wild-type mice, but not TNF, IL-1{beta}, or TNF/IL-1{beta}- deficient mice, displayed increased endothelial ICAM-1 expression, while an anti-IL-1{beta} monoclonal antibody prevented SARS-CoV-2-induced ICAM-1 expression and fibrin deposition in aged K18-ACE2 mice. Our data indicate that TNF and IL-1{beta} are the specific cytokines that drive multiple aspects of endothelial dysfunction during acute SARS-CoV-2 infection, and that inhibiting their signalling pathways may provide therapeutic benefit in preventing vascular complications of COVID-19.

cell biology↗

bioRxiv2026

Structural Complementarity Maximizes Feasibility and Stability in Microbial Community Coalescence

Microbial communities frequently coalesce through dispersal, disturbance, or deliberate transplantation, yet the dynamical consequences of such coalescence remain poorly understood. Here, we show that coalescence can function as a structural design mechanism to enhance microbial community robustness. Using a mechanistic consumer-resource model in which the balance between competition and metabolic cooperation is explicitly tunable, we quantify how interaction structure shapes both feasibility, namely the environmental domain supporting coexistence, and dynamical stability. Cooperation-dominated communities exhibit greater but more variable feasibility and intrinsic stability than competition-dominated communities. Strikingly, coalescing communities with maximally distinct interaction structures consistently maximizes both feasibility and stability by reducing alignment among interaction vectors and strengthening effective self-regulation in the resulting assemblage. Heterogeneous coalescence balances reduced facilitation, moderated interspecific effects, and stronger self-regulation. These results identify structural complementarity as a general principle for assembling robust microbial ecosystems and provide a theoretical foundation for microbiome engineering strategies that enhance persistence and functional stability.

ecology↗

bioRxiv2026

POLQ-driven repair scars shape the immunogenic landscape of homologous recombination-deficient pancreatic cancer

Pancreatic cancer (PC) is broadly resistant to immune checkpoint blockade, although a subset of homologous recombination-deficient (HRD) tumors exhibits durable immune engagement. The genomic features that distinguish these immune-responsive tumors from immune-inert HRD tumors remain poorly understood. Here we identify a microhomology-mediated end joining (MMEJ) repair scar, the MMEJ Deletion Footprint (MDF), as a genomic readout of POLQ-associated error-prone repair that enriches for frameshift indels. Across the multi-omic discovery cohort integrating tumor genomics, single-nucleus transcriptomics and spatial immune profiling, MDF-high HRD PC exhibited increased frameshift-indel-derived neoantigens and interferon programs. MDF was further associated with remodeling of the myeloid compartment toward MHC II-high dendritic cell-like antigen-presenting macrophage states and the immune synapse architecture marked by increased spatial interaction between APC-like macrophages and cytotoxic CD8+ T cells. These tissue-level features aligned with a functional trajectory shift of CD8+ T cells, consistent with effective anti-tumor immunity and was associated with favorable clinical outcomes of patients. Together, our findings position MMEJ-linked repair scarring as actionable biology that connects an HRD genotype to immune organization and suggests rational immunotherapy combinations that may enhance antigen presentation and myeloid activation to extend durable benefit in HRD-lineage cancers.

cancer biology↗

bioRxiv2026

Identification of the Phytophthora PAMP Pep-13 Receptor Using Diploid Potato Inbred Lines

Plants employ cell surface receptors to recognize pathogen-associated molecular patterns (PAMPs) and activate pattern-triggered immunity, a crucial defense mechanism against invading pathogens. Pep-13 is a PAMP derived from a class of conserved cell wall transglutaminases present in Phytophthora species, and its receptor PERU (Pep-13 receptor unit), was reported recently. In our parallel study, we observed distinct responses to Pep-13 between two diploid potato inbred lines: E4-63 recognizes Pep-13, whereas A6-10 does not. Genetic analysis demonstrated that Pep-13 recognition in E4-63 is controlled by a single genetic locus, tentatively designated TGER (Transglutaminase elicitor response). Through bulked segregant analysis sequencing, followed by complementation assays, we found that the TGERa gene in E4-63 is essential for Pep-13 recognition. Sequence alignment revealed that TGERa shares 99.91% amino acid sequence identity with PERU, indicating that TGERa and PERU are allelic variants of the same gene (PERU/TGERa). TGERb, a highly homologous gene of TGERa, was identified in the E4-63 genome; notably, TGERa, but not TGERb, can recognize Pep-13. We further demonstrated that TGERb exhibits defects in ligand binding. Additionally, we found that the TGERa allele in A6-10 is a weak allele with reduced expression levels, presumably resulting from a 3 kb DNA fragment insertion in its first intron. Heterologous introduction of TGERa into Nicotiana benthamiana and tomato significantly enhanced their resistance to Phytophthora infestans. Collectively, our findings confirm that PERU/TGERa functions as the Pep-13 receptor in potato and provide a valuable molecular target for improving Phytophthora resistance in plants.

plant biology↗

bioRxiv2026

CAPZ, but not canonical autophagy, regulates the endosomal-exosomal trafficking of plasma membrane PD-L1

Programmed death-ligand 1 (PD-L1) undergoes continuous endocytosis and intracellular trafficking that regulate its degradation, recycling, and exosomal release. Although PD-L1 internalization is known to depend on RAB5-mediated endocytosis, whether canonical autophagy contributes to its subsequent endosomal trafficking remains unclear. Here we show that the endosomal and exosomal trafficking of cell surface PD-L1 occurs independently of the canonical autophagy machinery. Genetic disruption of core autophagy components, including LC3B, ATG4B, ATG5, and ATG7, did not impair trafficking of internalized PD-L1 to early endosomes, multivesicular bodies, late endosomes, or extracellular vesicles. Pharmacologic inhibition of autophagosome-lysosome fusion enhanced accumulation of PD-L1 within RAB5-and CD63-positive compartments, but this effect persisted in cells lacking LC3B, ATG5, or ATG7, indicating that PD-L1 trafficking through the endosomal-exosomal pathway does not require canonical autophagy. Instead, we identify the actin-capping protein CAPZ as a key regulator of an endosomal maturation checkpoint that controls PD-L1 sorting. Loss of CAPZ impaired progression of PD-L1 from early to late endosomal compartments, reduced PD-L1 incorporation into multivesicular bodies and extracellular vesicles, and redirected PD-L1 toward RAB11-dependent recycling, resulting in increased plasma membrane PD-L1 abundance. These findings establish that PD-L1 membrane fate is determined by CAPZ-dependent endosomal maturation rather than canonical autophagy and identify endosomal trafficking machinery as a critical regulator of immune checkpoint distribution.

cell biology↗

bioRxiv2026

A bacterial effector blocks SUMOylation by steric occlusion of UBC9 via arginine-GlcNAcylation

The post-translational modifier SUMO is crucial for host antimicrobial defense. Here, we discover that Salmonella employs a unique strategy to dismantle the host SUMOylation machinery. Salmonella infection robustly inhibits global SUMOylation, particularly SUMO2/3 conjugation, without altering the expression of SUMO-cycle enzymes. Genetic screening identified the T3SS-2 effector SseK1 as both necessary and sufficient for this suppression. SseK1, an arginine-GlcNAcyltransferase, directly binds and specifically modifies the SUMO E2-conjugating enzyme UBC9 at arginine 17 (R17). Structural and biophysical analyses revealed that GlcNAcylation at R17 sterically hinders UBC9s interaction with SUMO, thereby inactivating the entire SUMOylation cascade. A unique C-terminal lid domain (ARHVQ motif) in SseK1 confers substrate specificity for UBC9, representing an evolutionary innovation within Salmonella genus. Quantitative SUMOylome profiling demonstrated that SseK1-mediated UBC9 inactivation reprograms the host SUMOylation landscape, impairing the modification of key immune regulators such as MyD88, Hspa8 and destabilizing proteins like PDCD4. Consequently, this mechanism is critical for Salmonella intracellular survival and systemic virulence in mice. Our study unveils a unique bacterial strategy to dismantle the host SUMOylation machinery via precise enzymatic inactivation of its central hub.

biochemistry↗

bioRxiv2026

Early clonal dominance at priming sets the trajectory for broad HIV serum neutralization

Inducing broadly neutralizing antibodies (bnAbs) remains a central challenge in HIV vaccine development 1-3. Germline-targeting immunogens are designed to activate rare bnAb precursor B cell lineages 4-12, yet the relationships between priming efficiency, clonal dominance, and downstream serum neutralization remain poorly defined. We recently demonstrated that vaccination with an engineered V2-apex germline-targeting trimer Q23-APEX-GT2 successfully recruits and activates rare long-CDRH3 B cell precursors in outbred macaques 13. Here, we dissect the immunological mechanisms governing bnAb precursor priming and early B cell expansion and define clonal features that drive progression to serum neutralization breadth. Our antigen-specific B cell analyses showed that Q23-APEX-GT2 consistently engaged long-CDRH3 precursors, although priming efficiency varied across animals. Longitudinal deep lineage tracing across lymph node and blood compartments revealed that early recruitment of multiple diverse long-CDRH3 lineages, followed by preferential expansion and dominance of one or two clones, strongly predicted serum neutralization potency. Subsequent CAP256.SU SHIV infection efficiently recalled vaccine-seeded clones, accelerated affinity maturation, and drove broad heterologous neutralization in most animals. Notably, one macaque with diverse and expanded V2-apex lineages rapidly achieved [~]70% serum neutralization breadth. Importantly, longitudinal tracing revealed that bona fide bnAbs can emerge from vaccine-primed precursors, while also uncovering "born-wrong" bnAb-like lineages that expand yet remain non-neutralizing, despite structurally validated recognition of the V2-apex bnAb site. Together, these findings establish priming efficiency coupled with early clonal dominance as key determinants of serum bnAb induction and provide a mechanistic framework to guide rational HIV vaccine design.

immunology↗