Biology subjects

Yin, J.

Publications and source records attributed to Yin, J..

At least 19 recordsLinked to original sources

bioRxiv2026

HapSolo2: GPU-accelerated optimization for removing secondary haplotigs from diploid genome assemblies

BackgroundSecondary haplotigs retained in a primary assembly inflate genome size and duplicate gene content, and their removal remains a necessary curation step for heterozygous diploid genomes. HapSolo (Solares et al. 2021) treats this step as an optimization problem: it searches three pairwise alignment thresholds (percent identity, query coverage, and the ratio of aligned query to aligned reference length) to minimize a cost defined over single-copy, duplicated, and missing conserved orthologs. The original implementation depended on a separate BUSCO v3 run for every contig, on Python 2.7 and pandas in its inner loop, on a single randomized forward-walk optimizer, and on OrthoDB v9 lineage datasets that are no longer distributed. ResultsHere we report HapSolo2, a reimplementation that replaces the per-contig BUSCO step with miniprot-based ortholog classification against OrthoDB v9, v10, or v12.2 lineage datasets, adds steepest descent and simulated annealing to the original random forward walk, and evaluates all walkers in batched CuPy array operations on a single GPU. On an avocado assembly of 5,122 contigs scored against 1,440 orthologs, 100,000 iterations of random walk and steepest descent ran 5.6- and 7.6-fold faster on one NVIDIA RTX A6000 than on 10 CPU processes. Across 45 runs on four diploid assemblies (Anopheles funestus, Persea americana, Vitis vinifera, and Amblyraja radi-ata; 212 Mb to 3.2 Gb) and three OrthoDB releases, the three optimizers reached best costs within 1.8% of one another. Independent assessment with BUSCO v6.1 showed that HapSolo2 reduced duplicated orthologs from 52.0% to 5.6% in P. americana, from 17.9% to 10.4% in V. vinifera, and from 7.8% to 2.8% in A. funestus, with completeness decreasing by at most 1.3 percentage points. On the avocado assembly, HapSolo2 reduced the cost reached by the original HapSolo from 0.2200 to 0.1036 and raised contig N50 from 3,366 kb to 4,608 kb. ConclusionsHapSolo2 makes ortholog-guided haplotig removal practical in minutes on a single workstation, independent of a BUSCO installation, and reproducible across OrthoDB releases. It is distributed as an installable Python package with a unified command-line pipeline, an assembly statistics and plotting tool, an Apptainer container, and archived copies of the legacy ortholog datasets.

bioinformatics↗

bioRxiv2026

Cyclin D-CDK4/6 couple proliferation to membrane and mitochondrial protein supply through a DeSI1 phospho-switch

Cyclin D-CDK4/6 complexes drive cell-cycle entry through an RB-E2F-dependent transcriptional program, but how they coordinate proliferation with the membrane and organelle protein supply required for growth is unclear. We identify DeSI1 as a cyclin D-CDK4/6 substrate whose phosphorylation at S25 converts a latent homodimer into an active monomeric deubiquitylase that recognizes hydrophobic proteins such as those bearing transmembrane domains and mitochondrial targeting sequences. Phosphorylated DeSI1 extends the lifetime of newly synthesized hydrophobic proteins that support membrane and mitochondrial capacity. Constitutive DeSI1 activation uncouples this proteostatic program from metabolic supply, creating a cytidine-nucleotide supply-demand imbalance associated with impaired CTP-dependent phospholipid homeostasis, cardiolipin depletion, and mitochondrial decompensation - defects that cytidine reverses. In mice, constitutive DeSI1 activation causes progressive cerebellar degeneration with membrane-protein accumulation, respiratory-chain loss, and phospholipid depletion. These findings define a post-translational mechanism coupling cell-cycle entry to membrane and mitochondrial capacity, and reveal the cost of uncoupling this program from its metabolic support.

cell biology↗

bioRxiv2026

TEDDY: An integrative workflow for TE-chimeric isoform reconstruction and systematic characterization of TE-dependent transcriptional regulation

Transposable elements (TEs) substantially expand transcriptional regulation and complexity. Despite advances in sequencing technologies, the systematic interrogation of TE-dependent transcripts across diverse samples and platforms remains limited. Here we introduce TEDDY, a computational workflow for large-scale reconstruction, quantification and regulatory inference of TE-dependent isoforms. Using TEDDY, we resolved the landscape of TE-dependent isoforms in mammalian preimplantation development, revealing a conserved pattern of stage-specific isoform generation via species-specific TE exonization. We reconstruct a TE-dependent regulatory network underlying pluripotent-to-totipotent transition wherein key transcription factors are driven by TE-derived promoters and validated experimentally. Within this network, Arid3a, a novel regulator nominated by TEDDY, was functionally validated as essential for totipotency establishment and early development. Further application of TEDDY to hepatocellular carcinoma identified recurrence-associated prognostic isoforms, underscoring their clinical relevance. Benchmarking establishes TEDDY's accuracy and efficiency with unique capabilities in full-length isoform recovery, cross-sample and -platform analysis, visualization, and TF-TE-gene network reconstruction, making TEDDY applicable across diverse biological contexts.

genomics↗

bioRxiv2026

A Generative Virtual Tissue Model Enables Computational Design of Therapeutic Perturbation Strategies

Computational design has transformed many fields of engineering, where simulators can explore millions of candidate design configurations before experimental development and testing. Therapeutic design in biomedicine has resisted computational design approaches because disease progression and therapeutic response emerge from interactions among many cell types within human tissue, governed by biochemical parameters that are largely unknown and potentially unknowable. Here, we introduce the Cell Interaction Foundation Model (CIFM), a virtual tissue model that forward-simulates the transcriptional dynamics of cells in human tissue under arbitrary therapeutic conditions based upon a spatial transcriptomic seed. CIFM is a geometric graph neural network trained by self-supervised masked-transcriptome prediction on millions of cellular microenvironments spanning human tissue types and disease states; generative, auto-regressive, monte-carlo play-out, then, simulates transcriptional dynamics under combinatorial perturbations from a spatial transcriptomic seed. We validate CIFM by showing accuracy gains in gene expression prediction and imputation, disease classification, recapitulation of perturbation responses in prostate cancer models, and recovery of T cell-tumor signaling measured in cell-cell sequencing experiments. Beyond such conventional tasks, CIFM enables target identification and therapeutic design through generative tissue simulation play-outs. Analyzing over 106 single and combinatorial perturbations, CIFM designs immunotherapy strategies for cancer and autoimmune disease that exploit combinatorial manipulation of signaling pathways to induce or suppress immune activation. Broadly, CIFM shows how generative artificial intelligence methods can be applied to model emergent behavior in highly interacting biological systems, yielding new approaches to fundamental understanding of tissue behavior as well as large-scale therapeutic design.

bioinformatics↗

bioRxiv2026

DuplexFM: Transferable small-RNA target representations link miRNA interactions to siRNA efficacy prediction

MicroRNAs (miRNAs) and small interfering RNAs (siRNAs) share Argonaute-mediated guide-target recognition, yet quantitative siRNA efficacy measurements are substantially scarcer and more costly to generate than miRNA-target interaction data. We therefore asked whether miRNA interaction data could provide transferable supervision for siRNA efficacy prediction. Here we present DuplexFM, a biologically grounded framework that uses sample-specific gates to integrate five evidence sources: pairing and sequence-context priors, duplex energetics, experimentally supervised mRNA accessibility, target-to-guide cross-attention, and contextual token-pair compatibility. The accessibility expert, trained on nucleotide-resolution icSHAPE measurements, achieved a held-out nucleotide-level Pearson correlation of 0.627 and evaluated accessibility at seed match and energy-supported candidate sites. On miRBench v7, three independently trained DuplexFM models achieved a macro APS of 0.873 (SD = 0.002), soft-voting increased this to 0.876 and yielded the highest APS on all four test sets. We then froze the miRNA-trained representation and trained only a lightweight residual head with 24 siRNA-specific descriptors. Transfer improved Pearson and Spearman correlations, AUPRC, and F1 over the descriptor-only baseline in all six evaluation settings. The ensemble achieved the highest Pearson and Spearman correlations in four settings, whereas OligoFormer remained stronger on Huesken and Takayuki. These findings show that experimentally grounded accessibility and miRNA-derived interaction representations provide complementary, transferable information, supporting a parameter-efficient route towards unified modeling of Argonaute-guided RNA regulation. Code and data are available at https://github.com/cbaiming/DuplexFM.

bioinformatics↗

bioRxiv2026

Genomic Profiling of Estrogen-Related Receptor Identifies Adult Adipocyte-Specific Targets in Drosophila

Adipose tissue is a key metabolic organ for carbohydrate and lipid metabolism. Multiple nutrient sensing pathways and metabolic enzymes operate in adipocytes to control energy production and lipid mobilization in response to physiological conditions. Important regulators of glycolytic and lipid metabolism in many species are members of the estrogen-related receptor (ERR) family of nuclear receptors. We previously showed that ERR is required for transcriptional regulation of glycolytic and pentose phosphate pathway enzymes in adult Drosophila females, consistent with what is observed in ERR mutant males and larvae. However, the cell-type specific targets of ERR in adipose and other tissues have not been fully elucidated. Here, using a modified targeted DamID approach (NanoDam), we identified ERR occupancy specifically in adult adipocytes at the loci that encode genes involved in glycolysis, the pentose phosphate pathway, and fatty acid metabolism. Overall, our results predict that ERR centrally functions in adipose tissue to regulate multiple metabolic pathways.

physiology↗

bioRxiv2026

Interpretable gene networks from single-cell foundation models reveal conserved neurogenic dysfunction in Parkinson's disease

Interpreting large-scale single-cell transcriptomic data remains a major challenge for understanding disease mechanisms. Recent single-cell foundation models learn rich representations of gene relationships across millions of cells, yet methods for translating these embeddings into biologically interpretable gene networks remain limited. Here we present scGENet, a computational framework that constructs context-specific gene interaction networks from foundation model-derived gene embeddings. By fine-tuning pretrained models on transcriptomic data from human midbrain organoids, scGENet generates transcriptome-scale gene modules that capture biologically meaningful cellular programs. Benchmarking across multiple foundation models demonstrates that networks derived from a fine-tuned scGPT brain model show the highest concordance with curated neuronal pathways, Parkinsons disease (PD) genetic risk loci, and independent patient-derived transcriptional signatures. Applying this framework to human iPSC-derived PD midbrain organoids reveals transcriptional modules associated with neuronal differentiation, synaptic signaling, and cell-cycle regulation. Single-nucleus RNA sequencing further links these programs to altered cellular composition, including reduced dopaminergic neurons, expansion of radial glia-like progenitors, and a dopaminergic neuron subtype expressing SNCA and VGLUT2. Integration with independent human substantia nigra datasets identifies a conserved neurogenic program disrupted across genetic and idiopathic PD. Together, these results establish a generalizable strategy for extracting interpretable gene networks from single-cell foundation models, enabling systematic discovery of disease-relevant molecular programs across diverse tissues and datasets.

neuroscience↗

bioRxiv2026

A rapid sporozoite viability assay identifies anti-Cryptosporidium parvum leads and targetable enzymatic activities in the invasive stage

Current phenotypic screens for anti-Cryptosporidium compounds typically quantify intracellular parasite growth in host cell cultures after two days of infection. Here, we developed a rapid, host cell-free phenotypic assay that directly measures compound-induced loss of viability in excysted Cryptosporidium parvum sporozoites, the invasive stage that initiates infection. We first compared qRT-PCR, luminescence ATP, and resazurin fluorescence readouts for detecting viable sporozoites. The luminescence ATP assay provided the best balance of linear dynamic range, assay time, parasite input, and cost, and was therefore adapted for high-throughput screening. Screening 5,000 bioactive compounds at 40 M identified 28 primary hits with [≥]50% inhibition of sporozoite viability, including 14 with >60% inhibition. Secondary screening of these 14 compounds at 4 M identified five hits retaining >50% inhibition: ZL0420, sulbactam, abexinostat, kojic acid, and SIB-1757. All five showed submicromolar activity against free sporozoites, with EC50 values of 0.073-0.311 M. Four compounds, abexinostat, ZL0420, SIB-1757, and sulbactam, also inhibited intracellular parasite growth in vitro, with EC50 values of 0.316-11.87 M and selectivity indices from >17 to >107. In an IFN-{gamma}-knockout mouse model, these four compounds reduced oocyst shedding over the 35-day experiment by 53.7-79.0% based on area-under-the-curve analysis and improved body-weight trajectories and ileal histopathology. Biochemical assays further showed that abexinostat inhibited native parasite HDAC activity at low nanomolar concentrations, while sulbactam inhibited a {beta}-lactamase-like activity in sporozoite lysates. These findings establish sporozoite viability as a rapid screening endpoint and identify anti-Cryptosporidium leads associated with targetable enzymatic activities in the invasive stage. Author summaryCryptosporidium parvum is a major cause of diarrheal disease in humans and young animals, but treatment options remain limited. Most laboratory screens for new drugs against this parasite require infection of host cells and measurement of parasite growth after one or more days. We developed a faster approach that tests whether compounds can directly damage freshly excysted sporozoites -- the parasite stage that first invades intestinal cells -- or reduce their viability. This assay can be completed within a few hours and does not require host cells. Using this approach, we screened 5,000 bioactive compounds and identified several molecules that rapidly reduced sporozoite viability. Four of these compounds also inhibited parasite growth in cell culture and reduced infection severity in a mouse model, as measured by parasite shedding, body-weight changes, and intestinal pathology. We further showed that one compound inhibits parasite histone deacetylase activity, while another inhibits a {beta}-lactamase-like activity present in sporozoites. Our study provides a rapid screening strategy and highlights vulnerable biological activities in the invasive stage of C. parvum.

microbiology↗

bioRxiv2026

Complement receptor-mediated uptake into renal mononuclear phagocytes promotes intracellular UPEC persistence and limits β-lactam efficacy in pyelonephritis

IntroductionAcute pyelonephritis remains a major clinical problem. Relapses occur despite apparently appropriate antibiotic therapy, suggesting that uropathogenic Escherichia coli (UPEC) persist in intrarenal niches. Intracellular bacterial reservoirs are a plausible explanation, but the relevant host cells, entry mechanisms and therapeutic implications in the kidney remain undefined. In principle, such reservoirs should favor choosing intracellularly active antibiotics, but increasing resistance to many of these agents leaves {beta}-lactams widely used in clinical practice, despite their predominantly extracellular activity. MethodsWe analyzed murine pyelonephritis to identify the cellular reservoir of persistent UPEC. We generated mice genetically deficient for complement receptors CR3 and CR4 and tested their role in bacterial entry and persistence in vivo. Pharmacological complement receptor inhibition was applied to assess whether blocking bacterial re-entry into host cells improves antibiotic efficacy. ResultsRenal MNP were identified as the major intracellular reservoir for UPEC in mice. Complement opsonization enabled bacterial entry into these cells through CR3 and CR4, allowing UPEC to evade neutrophil-mediated killing and extracellularly active antibiotics. Genetic deletion of CR3 and CR4 abolished intracellular bacterial persistence and reduced renal bacterial burden. Because MNP undergo physiological turnover, intracellular UPEC must periodically exit host cells and infect new ones. Pharmacological inhibition of complement receptors prevented such bacterial re-entry and enhanced the efficacy of {beta}-lactam antibiotics which cannot penetrate cell membranes. ConclusionsComplement receptor-mediated entry into renal MNP establishes an intracellular UPEC reservoir that promotes persistence during experimental pyelonephritis. Blocking these receptors prevents renewal of the intracellular niche and improves {beta}-lactam efficacy in mice.

microbiology↗

bioRxiv2026

CatESO: Differentiable Enzyme Sequence Optimization Guided by Substrate-Aware kcat Prediction

Enzymes drive biological chemistry and offer greener routes to chemicals, materials and medicines, yet their broader use as biocatalysts is often limited by insufficient catalytic turnover. Improving turnover is hard: measured rate constants are scarce and protein sequence space is vast. Deep-learning models now predict the turnover number, kcat, with growing accuracy, but they are typically applied after sequence generation to score or filter candidates, which separates the kinetic objective from the design itself. To bridge the gap between sequence generation and kinetic evaluation, we introduce CatESO, a differentiable sequence optimizer that enables direct, gradient-guided design of substrate-specific catalytic turnover. By backpropagating through a cross-modal kcat predictor under continuous sequence relaxation, CatESO co-optimizes predicted catalytic activity, evolutionary plausibility and structural integrity in one end-to-end framework, using ESM-2 and ESMFold to keep designs evolutionarily plausible and foldable. Across seven stringent out-of-distribution enzymes spanning EC classes 1-7, CatESO raised model-predicted kcat for the vast majority of designs, with a median predicted fold change of 1.52 while every variant retained a pLDDT above 70. Against RFdiffusion3-LigandMPNN pipeline and ZymCtrl, CatESO struck a better balance between predicted activity and structural confidence. By making substrate-conditioned kinetic objectives differentiable, CatESO carries differentiable protein design beyond structure- and binding-centred goals to enzyme catalytic function, giving a general route to function-oriented enzyme engineering.

biochemistry↗

bioRxiv2026

Practical Use of Advanced AI Frameworks on Real-Life Scientific Problems: Three Case Studies

Agentic artificial intelligence (AI) systems increasingly claim to automate scientific research, yet independent evaluations report persistent gaps between those claims and demonstrated capability. We tested frontier agentic AI systems on three practical problems: prediction of treatment non-response in immune-mediated inflammatory diseases, optical chemical structure recognition for literature mining, and prediction of drug-design-related properties from small datasets. Each problem was first assigned to autonomous frameworks and then reattempted as human-led, AI-assisted work. Autonomous runs failed in most cases, while human-led work produced reusable resources and modest but defensible performance, including new evidence for possible mechanisms of treatment resistance and a more practical benchmark for mining chemical structures from scientific papers. Property prediction was the single task on which one autonomous AI framework matched the human expert. We conclude that current frameworks can carry out engineering and analysis once a human expert leads the project, but cannot yet engineer a novel solution without oversight. The use of AI on real-life scientific problems remains an art rather than a routine technology.

bioinformatics↗

bioRxiv2026

Seeing the world in a grain of sand: Fine global and local representations in foveal and parafoveal vision

Foveal vision enables primates to perceive small objects with remarkable precision by resolving both fine-global form and fine-local detail. Yet current coarse-to-fine and local-to-global frameworks, derived largely from parafoveal recordings and large-object representations, posit that V1 encodes local detail whereas IT represents global form, leaving the neuronal basis of foveal object representation unresolved. Here we show that fine-global form and fine-local detail are encoded parallelly as early as V1 and preserved across V2, V4 and IT at both foveal (1{degrees}-2{degrees}) and parafoveal (2{degrees}-6{degrees}) retinal eccentricities. Coarse-global features dominate parafoveal vision and higher cortical areas. Response latencies revealed a rapid processing sequence, with 2-6 ms separating coarse-global, fine-local, and fine-global processing within parafoveal V1, V2 and V4. These findings establish a cortical neuronal framework to resolve fine-global form of small objects in foveal vision along primate ventral visual stream, supporting fine-scale perception during reading and fine object recognition. Highlights O_LIFoveal two-photon imaging across V1, V2, V4, and IT C_LIO_LIGlobal and local processing examined across eccentricity C_LIO_LIFine and coarse global representations emerge as early as V1 C_LIO_LIFine global and local representations are preserved from V1 up to IT C_LIO_LITightly timed coarse-to-fine-to-fine processing within each cortex C_LIO_LIFast coarse-global dominates parafoveal and higher cortical areas C_LI

neuroscience↗

bioRxiv2026

Decoding Immune Reconstitution Failure in People Living with HIV through Single-cell Genomics

Immune non-responders (INRs), a subset of people living with HIV (PLWH), fail to achieve full immune reconstitution and remain at increased risk of morbidity, mortality, and non-AIDS-related illnesses. As the mechanisms underlying this impaired immune recovery remain poorly understood, we performed single-cell multi-omics profiling on peripheral blood mononuclear cells from 43 INRs, 47 immune responders and 53 healthy donors. Our dataset comprises 2,744,009 transcriptomes and 1,226,658 chromatin-accessibility profiles across 58 identified immune cell types. INRs exhibited markedly elevated inflammatory signaling, increased apoptotic activity, and dysregulation of immune-activating ligand-receptor interactions. Additionally, we identified 2,996 interaction cis-eGenes and 5,938 cis-caPeaks, and validated key cell type-specific cis-xQTL effects associated with immune reconstitution failure. Furthermore, we identified 3,551 sc-eQTLs and 872 cell-state interaction eQTLs (ieQTLs) in CD4+ T cells. A genotype-specific upregulation of OAS3 in INRs, likely regulated by STAT1, was identified based on these results. We developed scPRISM (single cell Predictive Reconstitution Immune Status Model), a multi-modal framework that integrates scRNA-seq and scATAC-seq data to perform the dual tasks of differentiating disease states and predicting gene expression from chromatin accessibility, thereby deciphering the cell type-specific cis-regulatory basis of the disease. Our study delineates the transcriptional and epigenomic landscape of immune dysregulation in INRs and provides new genetic insights into HIV-associated immune reconstitution failure. These findings offer a foundation for developing biomarkers and precision therapies to restore immune function in PLWH.

genetics↗

bioRxiv2026

Recovering biological structure in sparse single-cell proteomics with GIRAFI

Single-cell proteomics (SCP) based on liquid-chromatography mass-spectrometry resolves protein-level cellular heterogeneity, but interpretation remains limited by detection-linked sparsity. SCP profiles continuous, peptide-derived intensities and has lower throughput than single-cell RNA sequencing, making denoising methods for large-scale, count-based transcriptomics difficult to apply. Here we present GIRAFI, a graph-informed statistical learning framework that imputes missing values and reveals reproducible cell states by constraining inference to dataset-aware, prior-knowledge-informed protein neighborhoods. We evaluated GIRAFI across SCP datasets spanning diverse biological/technical contexts. In masking-based recovery experiments and cell-type-specific protein-protein interaction inference, GIRAFI outperformed existing methods, and matched bulk proteomics comparisons corroborated recovery accuracy and ablations supported the graph-informed design. Beyond reduced replicate- and source-associated technical structure, GIRAFI recovered ground-truth cell-type annotations, improved cell state-resolved pathway analysis, and enabled trajectory inference consistent with known time courses. These results establish graph-constrained imputation as an effective strategy for improving SCP robustness, biological structure, interpretation, and cross-dataset comparability.

bioinformatics↗

bioRxiv2026

A suture-specific oocyst wall protein COWP4 is essential for excystation and infectivity, while COWP6 links wall architecture to host interaction in Cryptosporidium parvum

The oocyst of Cryptosporidium is a highly resilient transmission stage that protects sporozoites in the environment and mediates infection through a specialized opening known as the suture. While recent studies have identified Cryptosporidium oocyst wall proteins (COWPs) as structural components of the oocyst wall, the functional roles of individual COWPs and the molecular basis of suture biology remain poorly understood. Here, we performed a detailed characterization of two COWP family members, COWP4 and COWP6, combining immunolocalization, ultrastructural analysis, protein interaction assays, and genetic manipulation. We show that COWP4 is strictly localized to the oocyst suture, whereas COWP6 is distributed throughout the oocyst wall inner layer and enriched at the suture. COWP6 is additionally present in sporozoites and is secreted during parasite motility and host cell invasion, where it exhibits high-affinity binding to host cells. Structural analyses indicate that both proteins are cysteine-rich and likely form disulfide-stabilized architectures consistent with roles in wall assembly. Functional analyses reveal that COWP4 is essential for proper suture formation, excystation, and parasite infectivity, establishing the suture as a genetically defined gatekeeper for parasite transmission. In contrast, COWP6 functions as a multifunctional protein linking oocyst wall architecture to host interaction. We further demonstrate that COWP4 and COWP6 interact, suggesting coordinated assembly within the oocyst wall. Together, these findings provide a functional dissection of the Cryptosporidium oocyst suture and reveal distinct, non-redundant roles for individual COWPs. This work advances our understanding of oocyst wall biology and identifies COWP4 as an essential determinant of parasite transmission that may be targeted to disrupt the infectious cycle. Author summaryCryptosporidium is a major cause of diarrheal disease worldwide and is transmitted through environmentally resistant oocysts that protect infectious stages of the parasite. A defining feature of the oocyst is a specialized seam-like structure, called the suture, through which parasites exit to initiate infection. Although oocyst wall proteins (COWPs) have been identified, how individual proteins contribute to the structure and function of the oocyst wall, particularly the suture, has remained unclear. In this study, we investigated two oocyst wall proteins, COWP4 and COWP6, and uncovered distinct roles for each. We found that COWP4 is specifically localized to the suture and is essential for its proper formation, enabling parasites to exit the oocyst and establish infection. In contrast, COWP6 is distributed more broadly in the oocyst wall and also functions beyond it, being secreted during parasite movement and interacting with host cells. These findings reveal that the oocyst wall is composed of specialized proteins with non-redundant functions and identify the suture as a critical control point for parasite transmission. Understanding these mechanisms may help guide the development of new strategies to block infection by disrupting oocyst integrity or parasite release. HighlightsO_LICOWP4 is a suture-specific protein essential for oocyst excystation and infectivity C_LIO_LIGenetic disruption of COWP4 produces non-infectious oocysts with impaired viability C_LIO_LIThe oocyst suture is a genetically defined gatekeeper for parasite transmission C_LIO_LICOWP6 localizes to the wall inner layer and is enriched at the suture C_LIO_LICOWP6 is secreted by sporozoites and binds host cells with nanomolar affinity C_LIO_LI{middle dot} COWP4 and COWP6 directly interact, linking wall architecture to host interaction C_LI

microbiology↗

bioRxiv2026

Gut microbiota-derived indole-3-propionic acid preserves dorsal hippocampal catecholamines to prevent post-stroke cognitive impairment

BackgroundGut dysbiosis has been increasingly implicated in post-stroke cognitive impairment (PSCI), yet the causal contribution and therapeutic potential of gut microbiota-derived metabolites remain unclear. This study aimed to identify key microbiota-derived metabolites involved in PSCI and to elucidate their underlying mechanisms. ResultsWe found that both PSCI patients and middle cerebral artery occlusion (MCAO) mice exhibited distinct gut microbial alterations, characterized by a marked reduction in tryptophan-metabolizing bacteria and indole-3-propionic acid (IPA), a gut microbiota-derived tryptophan metabolite. Exogenous IPA administration alleviated PSCI-like phenotypes in MCAO mice. Mechanistically, IPA preserved tyrosine hydroxylase-positive (Th) fibers and catecholamine levels in the dorsal hippocampus. Further analyses showed that IPA binds to the adaptor protein Ywhab, promotes ERK activation, and enhances neuronal survival, thereby counteracting neuronal apoptosis-associated inflammation and subsequent Th fiber degeneration. ConclusionThese findings identify IPA as a gut microbiota-derived neuromodulator that mitigates PSCI by preserving dorsal hippocampal catecholaminergic transmission. IPA may therefore serve as a promising predictive biomarker and therapeutic candidate for PSCI.

neuroscience↗

bioRxiv2026

Single-cell full-length transcriptome of human lung reveals genetic effects on isoform regulation beyond gene-level expression

Genetic regulation of splicing uniquely contributes to trait-associated genome-wide association studies (GWAS) signals. However, quantitative trait loci (QTL) analysis using short-read sequencing of bulk tissues fails to capture full-length and cell-type-specific isoforms. Here, we present an isoform-level lung cell atlas from 129 never-smoking Korean women using single-cell long-read RNA-sequencing, identifying abundant unannotated and cell-type-specific isoforms. Isoform-level signatures of 37 lung cell types display a larger difference and therefore improve cell-type classification compared to gene-level expression. Notably, isoform-QTLs (isoQTLs) detect unannotated and/or cell-type-specific isoforms with independent genetic regulation from expression-QTL (eQTL), supported by enriched splicing functional elements. IsoQTLs nominate susceptibility isoforms from previously unexplained lung function and cancer GWAS loci, via eQTL-independent signals. We highlight a potentially functional novel variant of PPIL6 in multiciliated cells underlying lung cancer risk through alternative splicing. This isoform-level resource advances our understanding of cell-type-specific isoform regulation and its contribution to lung traits and diseases.

genetics↗

bioRxiv2026

Single-cell lung eQTL dataset of Asian never-smokers highlights the roles of alveolar cells in lung cancer etiology

Single-cell expression quantitative trait loci (sc-eQTL) analyses are powerful in identifying context-specific susceptibility genes from genome-wide association studies (GWAS) loci. However, few studies have comprehensively investigated cells of lung cancer origin in non-European populations. Here, we built a lung sc-eQTL dataset from 129 Korean women never-smokers with epithelial cell enrichment. eQTL mapping identified 2,229 genes with an eQTL in 33 cell types, including East Asian-specific findings when compared to predominantly European datasets. Integration with single-cell chromatin accessibility data demonstrated an enrichment of cell-type specific eQTLs in cell-type matched candidate enhancers, while shared eQTLs were more frequently found near promoters. Colocalization and transcriptome-wide association study unveiled 36 susceptibility genes from 22 cell types in 22 lung cancer loci, including 10 loci not achieving genome-wide significance in prior GWAS. Around 47% of these genes were from cells of the alveoli, underscoring their importance, especially in lung adenocarcinoma (LUAD) susceptibility. Focusing on the trajectory of alveolar epithelial cell regeneration, we detected 785 cell-state-interacting QTLs, which overlapped with 28% (10) of the identified susceptibility genes. Finally, we experimentally validated East Asian-and alveolar type 2 cell-specific eQTL of TCF7L2 underlying East Asian LUAD locus, 10q25.2. Consistent with its role as a Wnt/{beta}-catenin effector, TCF7L2 displayed significant effect on lung adenocarcinoma cell growth. Our data highlighted context-specific susceptibility genes, especially from alveolar cells of lung, contributing to lung cancer etiology.

genetics↗