Biology subjects

Li, S.

Publications and source records attributed to Li, S..

At least 37 records · Page 2Linked to original sources

bioRxiv2026

AmbiDose: per-cell removal budgets for ambient RNA contamination

Ambient RNA contamination confounds expression analysis in droplet-based single-cell and single-nucleus RNA sequencing. Here we present AmbiDose, which combines an ambient profile estimated from empty droplets with cell-specific nominal removal budgets and expression-based protection of lineage-enriched genes. Its ambient dose guides count removal rather than estimating a calibrated contamination fraction. In a held-out human-mouse GEM-X mixture, AmbiDose removed 92.0% of cross-species UMIs while retaining 99.6% of same-species UMIs. It reduced off-target marker expression in kidney-tumor and fetal-liver cohorts and yielded the lowest proximal-tubule marker leakage among the tested methods across five strain-mixed kidney libraries. Simulations supported the contribution of cell-specific dose estimates but identified reduced endogenous retention or incomplete removal when ambient and endogenous expression overlapped. AmbiDose is available as a Python package compatible with Scanpy.

bioinformatics↗

bioRxiv2026

Asymmetric assembly underlies activation mechanism of neuronal GIRK1/2 channels

G protein-gated inwardly rectifying potassium (GIRK; Kir3) channels are principal effectors of inhibitory GPCR signaling. Here we combine cryo-electron microscopy, single-molecule imaging, electrophysiology and molecular dynamics to define the architecture and activation of neuronal GIRK1/2 channels. GIRK1/2 preferentially assembles as an asymmetric heterotetramer containing one GIRK1 and three GIRK2 subunits. Three PIP2; molecules bound to GIRK2 promote partial cytoplasmic-domain docking, whereas engagement of a fourth PIP2 by GIRK1 completes docking and opens the helix bundle crossing gate. GIRK1 also remodels the electrostatic environment beneath the selectivity filter, where its E141 and D173 residues create a permeation module that enhances K+ conduction. The GIRK1-selective activator ML297 binds at the GIRK1-GIRK2 interface and controls both permeation and gating. These findings establish how asymmetric assembly couples sequential lipid-dependent gating to efficient K+ permeation and GIRK1-dependent pharmacological activation.

biophysics↗

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

Deep learning models detect neuromuscular disease and quantify functional impairment from video-derived biomechanics data

Measurements of human movement are critical for evaluating patients with neuromuscular diseases. However, current clinical tools are limited in sensitivity and can fail to detect the full spectrum of movement changes patients may exhibit. These limitations can obscure early signs of decline, delaying interventions that could preserve function and independence. OpenCap, a smartphone application that performs video-based markerless motion capture, can help address this gap by enabling scalable, out-of-lab measurements of human movement. Here, we present a method for both detecting disease and quantifying the severity of impaired movement from smartphone videos of patient motion. We used OpenCap to measure kinematics from 337 individuals with diagnoses spanning 12 rare, genetically-defined neuromuscular diseases and 78 age-matched healthy controls. Our dataset is the largest existing publicly available resource of body segment kinematics for this class of diseases. These data were collected in neuromuscular clinics during regular clinic visits as well as at conferences and community events across the United States. We trained deep learning models on these kinematic time series to differentiate between participants with neuromuscular disease and controls (AUROC = 0.97 [0.93-1.00]). We also developed a movement impairment severity score that correlates with both participant ratings of their own functional abilities ({rho} = -0.81, p < 0.001) and existing clinical tests of movement capabilities such as run speed ({rho} = -0.83, p < 0.001). Our methods for detecting disease and deriving quantitative biomarkers of movement impairment from smartphone videos have the potential to complement clinical assessments, inform screening protocols, and improve clinical trial endpoints.

bioengineering↗

bioRxiv2026

Genome-wide mapping of endogenous topoisomerase cleavage complexes reveals TOP1 at the interface of DNA topological regulation and genome instability

Topoisomerase cleavage complexes (TOPcc) are covalent protein-DNA intermediates formed transiently during topoisomerase catalysis and constitute a major class of DNA-protein crosslinks in human cells, yet their endogenous genomic distributions remain difficult to resolve. Here, we developed protein-DNA adduct immunoprecipitation followed by sequencing (pDAIP-seq), a highly sensitive method for detecting endogenous TOPcc that enables their genome-wide profiling under normal physiological conditions without stabilization by topoisomerase poisons. Using pDAIP-seq, we found that camptothecin treatment, required by existing TOP1cc profiling methods, not only increased TOP1cc abundance but also substantially altered its genomic distribution. Endogenous TOP1cc were globally reduced upon transcriptional suppression and increased upon TOP2A depletion, supporting TOP1cc regulation by the maintenance of genome-wide DNA topological homeostasis. Interestingly, both the regional distribution and nucleotide-scale positioning of TOP1cc, including the positions of TOP1 catalytic cleavage sites, were largely specified by local DNA sequence context. By extending pDAIP-seq to capture transient covalent protein-DNA intermediates involved in DNA repair, including TDP1-, PARP1-, and KU70-DNA adducts, we further identified endogenous TOP1cc as a potential major source of spontaneous DNA breaks in human cells. Finally, pDAIP-seq also enabled genome-wide mapping of TOP2cc, TOP3cc, and SPO11-DNA covalent intermediates in mouse testis undergoing programmed meiotic DNA cleavage. Together, these findings establish pDAIP-seq as a broadly applicable method for the mapping of covalent protein-DNA intermediates and reveal endogenous TOP1cc as a key intermediate between DNA topological regulation and human genome instability.

genomics↗

bioRxiv2026

Topoisomerase-mediated resolution of negative DNA supercoiling sustains human transcription and gene activation

Transcription generates DNA supercoiling that can influence gene expression, but how transcription-associated supercoiling shapes RNA polymerase II (RNAPII) dynamics in human cells remain poorly understood. Here, we show that topoisomerase-mediated resolution of negative DNA supercoiling is required for productive transcription. Acute topoisomerase perturbation causes negative supercoiling to accumulate around transcription start sites (TSS), with greater negative supercoiling accumulation associated with stronger transcription reduction. Elevated negative supercoiling behind the elongating RNAPII, but not positive supercoiling ahead, is associated with reduced RNAPII elongation rates. Using NOTE-seq2 to simultaneously profile newly synthesized and total RNA in single cells, we further demonstrate that acute TOP1 depletion increases gene expression noise by reducing both transcriptional burst size and frequency. Mechanistically, negative supercoiling accumulation at TSS increases RNAPII promoter occupancy and promoter-proximal pausing, providing a potential basis for reduced burst size. Micro-C analysis shows that TOP1 depletion largely preserves chromatin compartments and TADs but weakens short-range chromatin loops, including enhancer-promoter interactions, likely contributing to reduced burst frequency. Finally, during T-cell activation, impaired resolution of negative supercoiling compromises rapid gene upregulation and attenuates the transcriptional activation program. Together, these findings establish topoisomerase-mediated resolution of negative DNA supercoiling as a key mechanism linking DNA topological homeostasis to RNAPII dynamics, chromatin looping, and rapid gene activation.

genomics↗

bioRxiv2026

SEEKER: A genome-scale library-on-library screening platform for deciphering T cell recognition of antigen

Decoding which antigens activate a given T cell receptor (TCR) is a central challenge in immunology. We present SEEKER, a functional genome-scale library-on-library screening platform built on three elements: a Jurkat-derived T-APC cell (TAPCell) that combinatorially expresses one TCR and one peptide-MHC drawn from separate libraries together with an NFAT activation reporter; encapsulation of single TAPCells in a thermos-reversible hydrogel for isolated clonal expansion, so recognition is read out as intraclonal activation; and dual-asymmetric PCR that links TCR- and pMHC-encoding sequences into a heritable unit, enabling progressive hit enrichment over screening rounds. SEEKER interrogates ~10^8 TCR-pMHC combinations per run without prior knowledge. Screening joint-infiltrating CD8+ T cells from HLA-B*27+ ankylosing spondylitis patients against proteome-wide libraries validated 53 TCR-peptide pairs, uncovered extensive cross-recognition of self and common viral epitopes, and revealed highly polyreactive T cells recognizing up to 80 autoantigens, implicating virus-triggered poly-autoreactivity in multi-organ autoimmune diseases.

immunology↗

bioRxiv2026

DisenTE: Sparse Pattern-Context Modeling for Interpretable Translation-Efficiency Matrix Completion

Partially observed object-by-context matrices arise across data-rich science, where dominant object effects can obscure smaller but informative context-dependent variation. We study this problem in a translation-efficiency atlas of 9,494 5' UTRs across 78 cellular and tissue contexts. We present DisenTE, a sequence-conditioned neural model that combines separate sequence and context branches with a sparse low-rank pattern-context channel. Each module pairs a sequence-derived activation with context-specific deployment weights, forming a dictionary whose sequence and context components can be examined separately. Under five-fold within-panel entry masking, DisenTE achieves a UTR-centered residual Spearman correlation of 0.641 +/- 0.005, compared with 0.304 +/- 0.003 for the strongest reference model. The learned dictionary retains 11 of 20 candidate modules. CTM 6 has the largest overlap with an external TOP set and a cap-proximal pyrimidine pattern; CTMs 5 and 7 also overlap the set but have purine-containing consensuses. The evidence supports CTM 6 as a TOP sequence anchor and CTMs 5 and 7 as TOP-set-associated factors. On this dataset, DisenTE improves completion over the evaluated references and provides module-level summaries of its fitted context-dependent variation.

bioinformatics↗

bioRxiv2026

Deciphering the gene-centric spatial dynamics across tissue and time with GeneSPOT

Spatial omics technologies provide unprecedented opportunities to investigate how molecular programs are organized within tissues. However, existing analytical frameworks primarily focus on identifying cellular states and characterizing differentially expressed molecular features across these states. The relational spatial organization of molecular features and its dynamics across biological contexts therefore remain largely unexplored. Here, we present GeneSPOT, a gene-centric computational framework for characterizing the spatial relationships among genes and quantifying their reorganization across biological conditions. Although gene-centric in design, GeneSPOT generalizes to other molecular features. Across diverse applications, GeneSPOT resolved tissue structures, integrated paired and unpaired spatial multi-omics data, unveiled the spatial convergence of gene programs during embryonic development, and identified aging-associated spatial reorganization among genes whose expression did not change significantly with age. GeneSPOT thus complements conventional cell-centric analysis, providing a framework for deciphering how spatial relationships among molecular features are reorganized across biological conditions and over time.

bioinformatics↗

bioRxiv2026

Platform-specific count-matrix preprocessing workflows for spatial transcriptomics data analysis

Preprocessing of spatial transcriptomics (ST) count matrices is critical for removing technical variation while preserving biological signals, but optimal strategies remain unclear across diverse platforms. We systematically benchmarked 371 preprocessing workflows across 45 data spanning 11 mainstream ST platforms, evaluating their performance using specifically designed complementary metrics. To further assess preprocessing factors upstream of count-matrix generation, we additionally evaluated image preprocessing, including cell segmentation and post-segmentation transcript processing, across representative imaging-based ST platforms. We found that no universal count-matrix preprocessing workflow performed optimally across all platforms, while data from the same platforms exhibited striking consistency in optimal count-matrix preprocessing workflows. We identified the key data structure characteristics and count-matrix preprocessing steps that critically influence preprocessing performance. We developed platform-specific and data structure-guided recommendations to assist users in selecting the optimal count-matrix preprocessing workflow, validated across a broader range of downstream tasks. We demonstrated that our data-driven recommendations uncover subtle biologically meaningful spatial patterns in new data that were obscured by suboptimal count-matrix preprocessing. Our findings suggest best practices for ST data count-matrix preprocessing.

bioinformatics↗

bioRxiv2026

TxCyto: A machine learning framework for estimating cytokine activity from whole transcriptome

Cytokines are critical mediators of intercellular communication, and a comprehensive characterization of their activity is essential for understanding health and disease. Existing tools to infer cytokine activity rely on experimental measurements. However, such measurements are available only for a small minority (43) of cytokines, and moreover, cytokine activity and response are highly context-specific, making a comprehensive experimental profiling across tissues, disease states, and biological contexts impractical. To address this gap, we developed TxCyto - a deep learning-based framework that infers the activity of cytokines, and more broadly of the tumor secretome, directly from the whole transcriptome profile of a sample. Trained on pan-cancer TCGA tumor transcriptomes, TxCyto was extensively validated in multiple independent datasets, including cytokine perturbation experiments. Across multiple cancer immunotherapy cohorts, TxCyto identified cytokines whose predicted activity was associated with therapeutic response. Furthermore, in spatial transcriptomic data for Liver cancer, TxCyto discovered spatial niches associated with response to immunotherapy. Overall, we develop a machine learning tool -TxCyto, for predicting the activity of 645 cytokines and tumor secretome from readily available whole transcriptomes. The TxCyto framework is generally applicable to other classes of regulatory molecules and TxCyto code base, and the tools are provided at https://github.com/Rahulncbs/TxCyto.

systems biology↗

bioRxiv2026

BRCA2 loss drives a PKM1-dominant glycolytic state that is selectively lethal to pyruvate kinase activation

Homologous recombination deficiency (HRD), most commonly arising from BRCA1/2 loss, is accompanied by metabolic remodeling whose contours and therapeutic implications remain poorly defined. Through pan-cancer analysis of 10,619 tumors from The Cancer Genome Atlas (TCGA) and isogenic BRCA1/2-deficient models, we show that HRD tumors display broad metabolic rewiring, with consistent alterations in glucose metabolism. Unexpectedly, BRCA1/2 loss is associated with selective upregulation of the constitutively active glycolytic enzyme Pyruvate Kinase M1 (PKM1) isoform, contrasting with the PKM2-dominant state of most cancers, and produces an elevated pyruvate kinase activity state. Overactivating PKM, with small molecules activators, including the FDA-approved drug mitapivat, triggers superoxide accumulation, disrupts PINK1-dependent mitochondrial quality control, induces DNA damage, and selectively kills BRCA2-deficient tumors in vivo, while sparing BRCA2-proficient counterparts. These findings recast HRD as a metabolic state and identify PKM activation as a candidate therapeutic strategy for biomarker-selected HR-deficient cancers.

cancer biology↗

bioRxiv2026

Plasmid architecture determines the stability of inverted terminal repeats in adeno-associated virus vectors

Recombinant vectors derived from adeno-associated viruses (rAAVs) are a mainstay of human gene therapy. rAAVs are produced from plasmids containing transgene cassettes flanked by inverted terminal repeats (ITRs), which form structured DNA elements that stabilize the ends of the single-stranded viral genome and are the only viral sequences required in cis for genome packaging. For decades, it has been recognized that propagation of ITR-containing plasmids can result in deletions and other mutations, prompting the use of specialized bacterial strains, modified growth conditions, and truncated or altered ITRs. Despite these practices, ITR instability remains a persistent source of plasmid heterogeneity. To identify determinants of ITR stability, we evaluated ITR integrity in one of the original cloned AAV2 genome isolates, a reconstructed AAV2 plasmid, and a synthetic rAAV vector containing full-length native AAV2 ITRs. We established a quantitative bioinformatic workflow for analyzing ITR-containing plasmids and virus preparations from raw Oxford Nanopore sequencing data. These experiments showed that ITRs were highly stable during short-term culture, whereas prolonged culture revealed strong positional effects, with preferential loss or mutation of the ITR nearest the plasmid origin of replication. Consistent with this model, a survey of 7,041 sequence-verifiable AAV plasmids from the Addgene repository identified a widely disseminated 11-bp ITR deletion in 4,773 plasmids; among analyzable two-ITR plasmids, this deletion was located in the origin-proximal ITR in 95.3% of cases. Guided by these findings, we constructed a novel rAAV entry vector with stable full-length native AAV2 ITRs that enabled efficient packaging of a 4,750-bp all-in-one CRISPR-Cas9 cassette. Finally, we developed a cell-based strategy to compare the effects of ITR mutations on rAAV genome integration, providing preliminary evidence that ITR sequence variation can influence integration outcomes. Together, these findings show that ITR instability is a preventable, position-dependent property of plasmid architecture and identify ITR integrity as an important variable in rAAV vector design and quality control.

molecular biology↗

bioRxiv2026

CAR T cell targeting of inflammatory myeloid progenitors in the bone marrow remodels border-associated macrophages and reverses cognitive aging

Aging is associated with progressive neuroinflammation and cognitive decline, yet the cellular sources that sustain this process and whether they can be targeted peripherally remain unclear. Here, we identify inflammatory border-associated macrophages (BAMs) as key drivers of neuroinflammation in aging and show that their therapeutic and prophylactic elimination through intrathecal CAR T cell therapy restores cognitive performance in mouse models of aging and Alzheimer's disease. Furthermore, targeting of aged inflammatory bone marrow myeloid progenitors through either intravenous CAR T cells, which do not infiltrate the brain, or through transplantation of CAR T-treated progenitors is sufficient to reduce neuroinflammation and cognitive impairment. These findings reveal that bone marrow myeloid progenitors harbor a heritable inflammatory transcriptional state that is transmitted to their BAM progeny, driving neuroinflammation and cognitive deterioration, and conserved in human aging. Critically, this proinflammatory state is marked by the upregulation of surface proteins, enabling precise peripheral CAR T cell targeting of these progenitors for long-lasting therapeutic effects in cognitive aging.

cell biology↗

bioRxiv2026

CycDelta: Multimodal Residue-Graph Learning of Permeability Changes in Cyclic Peptides

Membrane permeability remains a major challenge in cyclic-peptide drug discovery, where optimization often depends on estimating changes relative to measured reference compounds. Existing predictors mainly model absolute permeability, while heterogeneity across literature sources complicates pooled training and comparative evaluation. We present CycDelta, a multimodal residue-graph model trained directly on within-source permeability differences. CycDelta combines directed message passing with pretrained Uni-Mol monomer embeddings, physicochemical descriptors and assay information in a shared encoder for reference and query peptides. To evaluate this comparative formulation under controlled conditions, we establish CycDeltaBench, comprising in-distribution, scaffold-based out-of-distribution, external and zero-shot unseen-assay tests. On the out-of-distribution task, CycDelta achieved Pearson 0.69 versus 0.46 for the strongest baseline. It also led all evaluated methods on two external data sets and achieved zero-shot Spearman 0.70 versus 0.32 for the strongest baseline on an unseen assay. Residue-level attribution relates predictions to monomer contributions, while gradient-guided screening supports prioritization of permeability-enhancing analogue hypotheses. These results position CycDelta as a transferable and interpretable framework for comparative permeability prediction and cyclic-peptide optimization.

bioinformatics↗

bioRxiv2026

A unified intermediate-resolution framework for modeling biomolecular condensates at near-cellular complexity

Biomolecular condensates, formed through phase separation of multivalent biomolecules, are central to cellular organization and function. Here, we present a unified, physics-based intermediate-resolution framework for efficient and quantitative simulation of biomolecular phase separation at near-cellular complexity. The framework integrates the HyRes protein and iConNA nucleic acid models to explicitly describe major driving forces and capture transient local and global structural features underlying phase transitions. With well-balanced hydrogen bonding, electrostatic, cation-{pi}, and hydrophobic interactions, as well as nucleic acid base stacking, base pairing, and ion-mediated effects, the framework accurately predicts saturation concentrations of over 60 disordered proteins (within tenfold) and phase diagrams of homotypic RNA and heterotypic protein-nucleic acid condensates. We further introduce iConMetabolome, a machine learning-enabled library of cellular metabolites that captures metabolite-driven phase separation and reproduces experimental partitioning trends across condensates. This unified platform enables the dissection of the mechanisms and regulation of condensation in biology and disease.

biophysics↗

bioRxiv2026

Transcriptome analysis reveals CCND2 promotes myogenic differentiation of pig skeletal muscle

ABSTRACT Objective: Skeletal muscle growth rate differs substantially among diverse pig breeds. In the present study, RNA-seq was performed to identify differentially expressed genes (DEGs) across pig breeds with distinct growth rates. CCND2 showed obvious differential expression among pig breeds. Although CCND2 is essential for cell cycle control, its biological function in skeletal muscle differentiation has rarely been reported. This work aims to identify DEGs in pig breeds with different growth rates and explore CCND2 role in porcine skeletal muscle development. Methods: RNA-seq was performed to identify differentially expressed genes (DEGs) across pig breeds with distinct growth rates. Weighted gene co-expression network analysis (WGCNA) was used to identify gene modules potentially associated with skeletal muscle growth and development. Quantitative reverse transcription polymerase chain reaction (qRT-PCR) analysis was performed to validate the role of CCND2 in skeletal muscle differentiation. Results We identified 5,416, 4,846, and 2,777 DEGs in the Duroc pig (DU) vs. Jiangquan black pig (JQ), DU vs. Laiwu pigs (LW), and JQ vs. LW comparison groups, respectively. WGCNA identified two gene modules (lightgreen and darkmagenta) potentially associated with skeletal muscle growth and development. CCND2 exhibited differential expression in comparisons between DU vs JQ and DU vs LW, with higher expression observed in Duroc pigs. qRT-PCR analysis revealed that overexpression of CCND2 at day 6 (D6) of cell differentiation significantly increased the expression levels of CCND2 gene and markers of myogenic differentiation (p < 0.05). Conversely, knockdown of CCND2 significantly decreased the expression levels of CCND2 gene and myogenic differentiation markers (p < 0.05). Conclusions This study identified breed-specific DEGs and key gene modules in porcine skeletal muscle and verified that CCND2 promotes myogenic differentiation. These findings provide a genetic theoretical basis for further investigations into skeletal muscle growth and development. Keywords pig, skeletal muscle, transcriptome, WGCNA, CCND2

genomics↗

bioRxiv2026

Maleic acid adjuvates BCG to induce CD4+CD8+ T cells and sterilize tuberculosis

Anti-tuberculosis (TB) immunity typically emphasizes CD4+ T and CD8+ T cells that only provide limited protection against Mycobacterium tuberculosis (Mtb) infection. The anti-TB function of CD4+CD8+ T cells remains unexplored, mostly due to the dogma that ThPOK and Runx3 antagonize each other to block their development and their scarcity. Here, we show that immunization of BCG (Bacillus Calmette-Gu&eacuterin) with Complete Freund's Adjuvant (CFA) markedly induced CD4+CD8+ T cells through the gut microbiota Clostridium butyricum or its metabolite maleic acid. Mtb-specific CD4+CD8+ T cells exhibited a broad T-cell receptor repertoire and sterilized Mtb infection by inducing SLAMF1 and neutrophil responses. Mechanistically, maleic acid interacted with PAK2 to promote STAT5 phosphorylation at Ser127, thus upregulating Ets1 expression. Direct binding of Ets1 to the Runx3 and ThPOK promoters orchestrated their expression to redirect the CD8+ T cell differentiation towards the CD4+CD8+ lineage. Moreover, immunization of BCG plus Clostridium butyricum or maleic acid sterilized Mtb infection in mouse models. These findings establish CD4+CD8+ T cells as superior immune effectors against Mtb, a concept that could fundamentally redirect the current paradigm for anti-TB vaccine development.

microbiology↗