Biology subjects

Feng, Y.

Publications and source records attributed to Feng, Y..

At least 37 records · Page 2Linked to original sources

bioRxiv2026

Deconvolving the structural heterogeneity of alpha-Synuclein in vitro and in situ

The structural states of proteins in cells and tissues provide important insight into their functional states, but studying protein structures in situ remains challenging. Furthermore, a single protein can adopt multiple conformations in cells, which typically cannot be assessed by most structural approaches. Here we developed a novel approach, based on structural proteomics fingerprints, for the quantitative analysis of the distribution of structural states of a protein in vitro and in situ. We applied it to the Parkinsons disease hallmark protein alpha-synuclein (aSyn), for which various structural states (disordered, helical, oligomeric and amyloid fibrillar, among others) have been characterized in vitro, but for which the in vivo structural states remain hotly debated. We measured structure-specific proteolytic fingerprints from well-characterized aSyn in vitro conformations and used them to quantitatively determine the aSyn conformational composition in samples of interest. We first benchmarked our approach using ground truth datasets of known composition and showed that, during in vitro amyloid fibril formation, we could simultaneously detect a time-dependent decrease in disordered monomeric aSyn, an increase in {beta}-sheet-rich oligomers, and a delayed rise in amyloid fibrils. We then applied the method to complex, biologically relevant samples. In a S. cerevisiae aSyn overex-pression model, aSyn was predominantly helical, with an increased helical fraction accompanying its relocalization from the plasma membrane to cytosolic lipid droplets. This shift was linked to proteome-wide changes in lipid droplet homeostasis and fatty acid and ergosterol metabolism, underscoring the role of lipid metabolism and droplet formation in aSyn biology. Importantly, we also detected helical aSyn in human iPSC-derived cortical neurons, supporting the physiological relevance of this conformation. Finally, neurons differentiated from PD patient-derived iPSCs showed elevated levels of {beta}-sheet-rich aSyn compared to wild-type cells. Our approach allowed the in situ identification and quantification of different structural states of aSyn directly in cell lysates. Since several proteins can adopt multiple, functionally-relevant conformations in cells, our approach should be broadly applicable to in situ, quantitative structural and functional studies of proteins.

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

SPECTRA: Spatial Inference for Tractometry Toward Precision Mapping of White Matter Microstructure

Diffusion MRI tractometry characterizes white matter microstructure along fiber bundles, but standard along-tract profiling collapses measurements across the bundle cross-section, obscuring radial heterogeneity and producing spatially inconsistent units of inference. We present SPECTRA (Spatial Inference for Tractometry), a framework designed to address these limitations through a unified design of parameterization and statistical inference. First, we propose a 2D bundle parameterization that extends along-tract profiling to include a radial dimension defined on the atlas bundle. Second, we develop a two-stage hierarchical false discovery rate (hFDR) procedure for multi-bundle inference, which aggregates evidence at a coarser spatial scale before proceeding to finer-grained inference, with spatial scales derived from a Matern kernel. Across extensive simulation conditions, we found that hFDR improves statistical power and reduces the sample size required to detect effects compared to global FDR correction, while maintaining appropriate error control. We further characterized how sensitivity-specificity tradeoffs depend on sample size, the magnitude, spatial extent, and configurations of effects, thereby providing practical guidance for tractometry study design. In an empirical analysis of mild cognitive impairment and dementia in more than 4,000 subjects across 63 bundles, SPECTRA revealed spatially localized patterns that were absent in 1D profiles. Together, these results demonstrate that spatially resolved parameterization and adaptive error control jointly enable precise mapping of white matter microstructure in large-scale tractometry studies. SPECTRA is openly available as a Python package.

neuroscience↗

bioRxiv2026

α-Synuclein Facilitates Spontaneous Dopamine Release in a Calcium- and Phosphorylation-Dependent Manner

-Synuclein (aSyn) is central to Parkinsons disease pathogenesis, yet its native physiological role at the presynapse remain poorly defined. Here, super-resolution imaging in dopaminergic neurons reveals that endogenous aSyn localises within nanometres of L-type voltage-gated calcium channels (LTCC), with closer proximity under both spontaneous neuronal activity and stimulated conditions compared to when extracellular calcium is chelated. Blocking Ca2+/calmodulin-dependent kinase II (CaMKII) reduces aSyn clustering at LTCC under spontaneous activity, suggesting that calcium entry and downstream calcium-dependent kinase activity contribute to aSyn localisation. Moreover, quantitative single-molecule analyses indicate that calcium increases the abundance of both total and serine129 phosphorylated (pS129) aSyn in synaptosomes under spontaneous conditions, and NMR analysis reveals that both calcium and S129 phosphorylation increase the binding affinity of aSyn to synaptic vesicles. Functional assays further demonstrate that LTCC blockade elevates intracellular DA levels exclusively in the presence of aSyn under spontaneous but not stimulated conditions. Finally, biochemical fractionation and multi-colour single-molecule imaging reveal that aSyn preferentially associates with small vesicles that are not obligately coupled to full-fusion associated recycling pools. These results suggest that aSyn acts as a calcium- and phosphorylation-regulated modulator of spontaneous DA release through pathways that are largely independent of full-fusion recycling mechanisms, and that pS129 aSyn is not solely a pathological marker but may also reflects physiological regulation. Together, these insights provide a framework for understanding how therapeutic strategies targeting aSyn may impact its normal synaptic functions.

neuroscience↗

bioRxiv2026

VeloTrace Reconciles Divergent Velocity and Trajectory in Single-cell Transcriptomics with Deep Neural ODE

Cellular identity and fate transitions are governed by continuous molecular processes that form dynamic trajectories within a high-dimensional transcriptomic landscape. Existing methods attempt to model these dynamics from two complementary perspectives: trajectory inference and velocity modeling. Ideally, velocity and trajectory are dual aspects of transcriptomic dynamics where velocity is tangent to trajectory everywhere. This inherent connection between velocity and trajectory is currently absent in transcriptomic analysis. Splicing velocity are precision-limited to inadequately-sequenced genes, while trajectory inference prioritizes the modeling of global trends while omitting local dynamics. This divergence breaks the geometric continuity between local velocities and global trajectories, hindering the reliable interpretation of developmental dynamics. To reconcile trajectory inference and RNA velocity, we introduce VeloTrace, a framework that unifies them through Neural Ordinary Differential Equations (NeuralODEs). VeloTrace learns a continuous-time velocity field whose integral curves constitute the trajectory itself, while ensuring that velocities are tangent to integral paths everywhere. Leveraging a splicing quality score, VeloTrace incorporates high-quality splicing velocity as partial supervision for velocity orientation and grounding. During optimization, VeloTrace incorporates a Monte Carlo multi-time-frame supervision strategy to ensure coherence between local and global trajectorys and suppress sequencing-induced stochastic diffusion. Through refining the velocity field and cell-specific parameters for pseudo-time, expression, and velocity, VeloTrace reconstructs a smooth, local-and-global-coherent velocity-vector-guided flow in the transcriptomic latent space. This strategy ensures a complementary integration of velocity and trajectory, imputing the transcriptional kinetics for genes of insufficient strength, whose kinetics cannot be accurately portrayed by splicing velocity. In simulation benchmarks, VeloTrace captured the transcriptional dynamics of all expressed genes, even those with inadequate sequencing coverage, producing velocity directions that were most consistent with the true direction and every-where tangential across the entire process, outperforming state-of-the-art methods, including scVelo, UniTVelo, VeloVI and scTour. VeloTrace uniquely reconciles RNA velocity and trajectory inference, creating a velocity field where each cell can infer past and future transitions from its current state. Moreover, VeloTrace extends reliable velocity estimation to a broader set of genes. When applied to mouse neural stem cell differentiation data, it successfully recovers dynamics of driver genes for two developmental lineages, including those with low expression, shedding light on their regulatory roles during differentiation. This unified framework lays the foundation for more accurate modeling of gene regulation and cell fate decisions in complex biological systems.

bioinformatics↗

bioRxiv2026

sPRR signaling in macrophages via the AT1R/Yap/Taz axis to induce renal fibrosis

BackgroundWithin the kidney, (pro) renin receptor (PRR) is abundantly expressed in the collecting duct (CD) and modulate physiological and pathophysiological processes. We have recently reported that activation of CD PRR mediates obstructive renal fibrosis in a mouse model of unilateral ureteral obstruction (UUO). The current study addresses the underlying mechanisms by examining the profibrotic pathway mediated by soluble PRR (sPRR)-dependent alternative macrophage activation. MethodsWe performed UUO or sham surgery on mice with CD-specific deletion of PRR (CD PRR KO) and floxed controls. After 7 days, we assessed fibrosis-related parameters, inflammatory cytokines, M1/M2 macrophage markers, other gene expression markers of kidney injury, and the concentration of plasma sPRR. We also administered vehicle or site-1 protease (S1P) inhibitor PF-429242 (PF) to C57BL/6 mice with UUO to determine the role of sPRR. Experiments were performed in vitro to examine the mechanism of sPRR-His-mediated macrophage M2 polarization and activation of potential target genes in bone-marrow-derived macrophages (BMDMs). ResultsCompared with the floxed control, CD PRR KO decreased macrophage accumulation, M2 polarization, and Yap/Taz expression while improving renal fibrosis and suppressing plasma sPRR levels following UUO. In BMDMs, sPRR-His treatment promoted macrophage M2 polarization, fibrosis, and Yap/Taz expression, which was dependent on angiotensin type 1 receptor (AT1R). ConclusionCD PRR-derived sPRR acts via ATR to promote macrophage M2 polarization and stimulates the AT1R/Yap/Taz axis, which leads to renal fibrosis during UUO.

pathology↗

bioRxiv2026

Along-Tract Microstructural Alterations Associated with Stimulant Misuse Localized using Diffusion MRI Tractometry

Diffusion brain MRI (dMRI) studies of substance use disorders have reported widespread but modest white matter (WM) microstructural alterations with limited anatomical specificity. Here, we applied segment-wise along-tract 3D tractometry to brain dMRI scans to localize fine-scale WM alterations associated with stimulant misuse using two complementary analytical frameworks: Bundle Analytics (BUAN) and Medial Tractography Analysis (MeTA). We analyzed 3D profiles of widely-used diffusion metrics across 33 major WM bundles in independent cohorts of cocaine (74 cases;58 controls) and amphetamine (22 cases;18 controls) users, testing the statistical associations with brain microstructure of pooled stimulant effects, substance-specific effects, and direct comparisons between stimulant classes. Segment-wise analyses revealed focal differences localized to specific tract segments rather than uniform differences along entire bundles. In pooled stimulant misuse, convergent findings across analysis pipelines were localized to hippocampal pathways and were consistent with altered microstructural organization. Amphetamines misuse showed a broader pattern of segment-wise differences across commissural, projection, and association pathways, involving altered axonal organization. No robust segment-wise differences were detected for cocaine misuse or between stimulant classes. These results show that WM alterations are spatially localized and reproducible across tractometry frameworks, highlighting the value of along-tract 3D mapping for improving anatomical specificity in addiction neuroimaging.

neuroscience↗

bioRxiv2026

SMAD4 loss drives chromosomal instability during tumourigenesis via translational reprogramming

Chromosomal instability (CIN), arising from errors in chromosome segregation during cell division, is a hallmark of cancer. Whilst CIN can result from several mitotic defects, the mechanisms that initiate CIN to drive tumourigenesis remain incompletely understood. Here, we show that loss of SMAD4 reprograms translation to induce CIN, resulting in tumour formation. Multi-omics analysis of tumourigenesis models driven by loss of SMAD4 complemented by functional studies demonstrate that loss of SMAD4 in pre-neoplastic cells leads to dysfunctional mitosis and an altered global translation landscape. We show that CDK11B is translationally downregulated in SMAD4-\- cells, and re-expression of the mitosis-specific isoform of this protein (CDK11B-p58) rescues the mitotic defects. Analysis of patient tumours reveals a strong correlation between markers of CIN and SMAD4 status, indicating the clinical relevance of this phenotype. Collectively, we reveal a previously unrecognised role for SMAD4 as a gatekeeper for CIN-mediated tumourigenesis via regulation of translation.

cancer biology↗

bioRxiv2026

Computational Fluid Particle Dynamics-Informed Machine Learning Prototype for a User-Centered Smart Inhaler Enabling Uniform Drug Delivery to Small Airways

Small airways are the primary sites of airflow obstruction in chronic obstructive pulmonary disease. Effective delivery of aerosolized drug particles to these regions is crucial to maximize treatment efficacy while minimizing side effects. However, conventional inhalation therapy approaches (i.e., full-mouth particle release and inhalation (FMD)) typically result in insufficient drug deposition in the small airways and an uneven distribution across the five lung lobes. To address such deficiencies, the goals of this study are triple folds: (1) to develop a fast and accurate framework to secure target drug delivery (TDD) nozzle diameter and location based on the conventional computational fluid particle dynamics (CFPD)-FMD simulations, (2) to develop a CFPD-informed machine learning (ML) inverse-design framework that predicts optimal inhaler nozzle parameters based on patient-specific breathing patterns and drug properties, and (3) to demonstrate the feasibility of embedding this framework into a user-centered smart inhaler prototype to improve uniform TTD to the small airways across all five lung lobes. Specifically, a subject-specific mouth-to-generation-10 human respiratory system was employed, and 108 high-fidelity CFPD-FMD simulations were performed under varied physiological and design parameters, including tidal volume, particle diameter, release location, and release timing. Particle release maps generated from those CFPD-FMD simulations via backtracking identified optimal nozzle diameters and locations that promote uniform multi-lobe drug delivery while limiting off-target deposition. Accordingly, a dataset was compiled with inputs (i.e., flow rate, particle size, release z-coordinate, release time) and targets (i.e., nozzle center x- and y-coordinates, nozzle diameter). These inputs and targets form the CFPD-TDD dataset, on which 16 ML models were trained to learn inverse mapping from patient- and drug-specific inputs to optimal nozzle design parameters. Performance was evaluated using mean squared error (MSE) and mean absolute error (MAE) overall and per target feature. Parametric analysis using CFPD-FMD simulations was conducted to determine how patient-specific and drug-specific factors affect pulmonary air-particle transport dynamics and to explain why achieving CFPD-TDD in small airways with CFPD-FMD strategies remains challenging. Furthermore, the ML evaluation in this feasibility study demonstrated robust learning of the inverse mapping from patient-specific inputs to optimal nozzle parameters. Four top-performing models showed consistently low MSE/MAE across cases, and an ensemble (i.e., mixed model (MixModel)) combining their strengths was formulated. Independent CFPD-TDD simulations beyond the training and testing datasets were used as the ground truth to validate ML-predicted nozzle configurations. Compared with conventional CFPD-FMD strategies, ML-guided nozzle designs significantly improved inter-lobar deposition uniformity and reduced off-target deposition in the upper airways, demonstrating the feasibility of ML-enabled TDD to the small airways. Overall, this study establishes a CFPD-informed ML inverse-design framework as a viable algorithmic foundation for user-centered smart inhalers, enabling adaptive, patient-specific TDD to the small airways with improved deposition uniformity across all five lung lobes. By integrating first-principle-based CFPD with ML, this work provides a methodological pathway toward next-generation smart inhalers for more effective treatment of small airway diseases.

bioengineering↗

bioRxiv2026

The Stochastic Pacemaker: Cumulative Behavioral Noise Drives Morphological Plasticity in Pea Aphids

Phenotypic variation within a single genotype under the same environment (intragenetic variation), the biologically meaningful part of Verror, is frequently treated as a statistical nuisance rather than a biological reality, yet it represents an evolutionary driver of fitness that remains poorly integrated into evolutionary theory. The mechanism that translates such stochasticity into deterministic developmental phenotypic outcomes is not well understood. Here, we test a cumulative stochasticity model using wing polyphenism of the pea aphid (Acyrthosiphon pisum), where asexual mothers produce winged instead of wingless offspring in response to tactile cues. The model predicts that stochastic variation in maternal locomotor behavior alters the rate of tactile cue accumulation and thereby influences the probability of producing winged offspring. We demonstrate that maternal locomotor activity acts as a "stochastic pacemaker", where an individuals movement determines the rate at which it actively constructs its environment and accumulates environmental cues. Our results reveal that genotypes differ significantly in both mobility and the temporal pattern of wing induction, with behavioral variation explaining approximately 20% of the total phenotypic variance across genotypes. Crucially, we show that maternal mobility increases progressively during crowding, accompanied by significant temporal heteroscedasticity. This expanding variance and increase in mean suggest that initial, trivial stochasticity is magnified into systematic behavioral divergence through the integration of environmental signals. By demonstrating that total accumulated locomotor activity predicts offspring phenotype, we provide a mechanistic bridge between transient behavioral noise and stable morphological shifts. More broadly, our work reveals that Verror is a dynamic product of behavioral history, suggesting a fundamental role for individual-level niche construction in generating macro-phenotypic diversity.

evolutionary biology↗

bioRxiv2026

Telomere-to-telomere assembly and haplotype analysis of tetraploid Dendrobium officinale illuminate Orchidaceae polyploid evolution and mycorrhizal symbiosis genes

Dendrobium officinale is a typical epiphytic orchid. We report the telomere-to-telomere (T2T) genome assembly for D. officinale, representing the first T2T reference genome within the Orchidaceae family. The assembly is anchored to 19 chromosomes and contains 38 complete telomeres and 15 characterized centromeres. We further generated haplotype-resolved assemblies of the autotetraploid genome, identifying 12,761 sets of tetra-allelic genes. Based on synonymous substitution analysis, we inferred that the autotetraploidization event occurred approximately 0.86 million years ago. A systematic analysis of the SWEET gene family across the genus Dendrobium revealed that the gene family size is shaped primarily by epiphytic types and environmental factors. In D. officinale from Langshan, eight SWEET genes were specifically expressed in roots, suggesting they may play specialized roles in the root mycorrhizal system, potentially contributing to the D. officinales ability to recruit and maintain fungal partners. Together, these resources provide valuable foundations for studies of orchid evolution, functional genomics, and molecular breeding.

bioinformatics↗

bioRxiv2026

Rewiring of 3D Enhancer-Promoter Interactome Underlies Diabetic Endothelial Dysfunction

BackgroundDiabetic vascular complications are driven by endothelial dysfunction, yet the role of 3D genome organization in this process is unknown. We sought to define the alterations in chromatin architecture in diabetic endothelium and identify the key regulators involved. MethodsWe generated a high-resolution 3D epigenomic atlas of diabetic endothelial cells from mouse models and human subjects using H3K27ac HiChIP, complemented by ChIP-seq, ATAC-seq, and RNA-seq. A human cohort was used to assess protein expression in diabetic versus non-diabetic endothelial cells. To identify JUNB-interacting proteins, we performed rapid immunoprecipitation mass spectrometry of endogenous proteins (RIME), with protein-protein interaction validated by co-immunoprecipitation. Functional validation was performed using in vitro, ex vivo, and in vivo approaches, including endothelial-specific knockdown in a diabetic hindlimb ischemia model. ResultsMulti-omics profiling revealed extensive enhancer reprogramming in diabetic endothelium, with AP-1 binding motifs being consistently and selectively enriched in downregulated enhancers across three distinct diabetic models. Analysis of a human cohort confirmed significantly reduced JUNB protein levels in diabetic endothelial cells. We identified widespread disruption of JUNB-anchored enhancer-promoter interactions, which underlies transcriptional repression of key endothelial genes. RIME and co-immunoprecipitation established the E3 ubiquitin ligase RBBP6 as a direct JUNB interactor that promotes its polyubiquitination and proteasomal degradation in response to hyperglycemia. Human cohort analysis further showed reciprocal elevation of RBBP6 in diabetic endothelial cells. Either JUNB overexpression or RBBP6 knockdown restored enhancer-promoter connectivity, reactivated vasoprotective transcriptional programs, and rescued endothelial function. Critically, endothelial-specific knockdown of Rbbp6 in diabetic mice restored endothelium-dependent vasorelaxation and improved perfusion recovery after hindlimb ischemia, independent of systemic glucose levels. ConclusionsOur study unveils a novel mechanism whereby hyperglycemia induces enhancer reprogramming and disrupts endothelial 3D genome architecture through RBBP6-mediated degradation of JUNB. The RBBP6-JUNB axis is established as a crucial link between metabolic stress and epigenomic reprogramming in vascular disease, presenting a promising therapeutic target for diabetic vasculopathy.

genomics↗

bioRxiv2026

MORPHE: Bridging Image Generation and Spatial Omics for Tissue Synthesis

Spatially resolved omics technologies reveal tissue organization at single-cell resolution but remain limited by the cost of the assays, incomplete spatial coverage, 2D-only imaging, and experimental artifacts. These factors motivate the need for in silico methods that can reconstruct or extend tissue context beyond what current spatial measurements provide. We present MORPHE (MOdeling of stRuctured sPatial High-dimensional Embeddings), an AI framework that learns to synthesize biologically faithful tissue architecture directly from spatial-omics data. MORPHE introduces a graph-informed probabilistic embedding that maps discrete cell identities and their spatial relationships into a continuous RGB-like latent space compatible with diffusion modeling. This representational bridge enables spatial cellular maps to leverage large pre-trained image-generative models while preserving biological interpretability upon decoding. By modeling cells as the fundamental units of generation and learning how their identities and spatial relationships collectively give rise to large-scale tissue structure, MORPHE enables generation and reconstruction of tissue architecture at single-cell resolution. We applied the method across large-scale single-cell proteomic datasets from the intestine and single-cell transcriptomic datasets from the brain, showing computational scalability acrosss millions of cells. We used MORPHE on these datasets to outpaint beyond experimentally restricted fields of view, inpaint missing or experimentally damaged tissue regions, and perform cross-tissue imputation, connecting separated tissue regions into a single contiguous sample in both 2D and 3D. MORPHE represents a new class of tissue generation algorithms that will help solve current limitations and challenges with single-cell spatial-omics datasets.

systems biology↗

bioRxiv2026

Tabular foundation model predicts alternative lengthening of telomeres (ALT) and identifies SMARCAL1 as a target in ALT-driven cancers

Alternative lengthening of telomeres (ALT) is a telomerase-independent pathway used by aggressive cancers to maintain their replicative immortality. Because ALT is absent from normal human cells, it is an appealing target for cancer therapy, but the lack of ability to determine ALT status at scale has hindered discovery. Here, we developed ALTitude, a machine learning method from a tabular foundation model that infers ALT from cell line whole genome sequencing data, without need for paired germline analysis. We deployed ALTitude across the DepMap, doubling the number of known ALT+ cancer models. Systematic integration of ALTitude with CRISPR-Cas9 screens yielded the selective dependency on SMARCAL1 in ALT+ cell lines, where we show it stabilizes the ALT phenotype. Acute depletion of SMARCAL1 leads to G2/M arrest, mitotic catastrophe, and cell death. These data provide a valuable resource for studying ALT-related genomic features and present SMARCAL1 as a therapeutic target for ALT+ malignancies.

cancer biology↗

bioRxiv2026

Improved prediction of virus-human protein-protein interactions by incorporating network topology and viral molecular mimicry

The protein-protein interactions (PPIs) between viruses and human play crucial roles in viral infections. Although numerous computational approaches have been proposed for predicting virus-human PPIs, their performances remain suboptimal and may be overestimated due to the lack of benchmark dataset. To address these limitations, we first constructed a carefully curated benchmark dataset, ensuring non-overlapped PPIs and minimum sequences similarity of both human and viral proteins in the training and test sets. Based on this dataset, we developed vhPPIpred, a machine learning-based prediction method that not only incorporated sequence embedding and evolutionary information but also leveraged network topology and viral molecular mimicry of human PPIs. Comparative experiments demonstrated that vhPPIpred outperformed five state-of-the-art methods on both our benchmark dataset and three independent datasets. vhPPIpred also achieved high computational efficiency, requiring relatively low runtime and memory. Finally, vhPPIpred was demonstrated to have great potential in identifying human virus receptors, and in inferring virus phenotypes as the virus-human PPIs predicted by vhPPIpred can be used to effectively infer virus virulence. In summary, this study provides a valuable benchmark dataset and an effective tool for virus-human PPI prediction, with potential applications in antiviral drug discovery, host-pathogen interaction research and early warnings of emerging viruses.

bioinformatics↗

bioRxiv2026

Disruption of the SYNGAP1 PDZ ligand motif accelerates differentiation of human iPSC-derived GABAergic neurons

SYNGAP1 haploinsufficiency is a leading genetic cause of neurodevelopmental disorders (NDD), including intellectual disability and epileptic encephalopathy. While most studies on SYNGAP1 function have focused on glutamatergic neurons, its role in GABAergic neurons and during early neuronal development is unclear. Using human iPSC-derived GABAergic neurons, we demonstrate that SYNGAP1 haploinsufficiency accelerates neuronal maturation, characterized by increased dendritic length, synaptic density, and maturation of synaptic structures. Disruption of the isoform-specific SYNGAP1 PDZ binding motif reproduces these phenotypes, highlighting the critical role of PDZ-mediated interactions in regulating GABAergic neuronal differentiation. Proteomic and phosphoproteomic analyses reveal significant dysregulation of synaptic proteins, RNA processing, and transcriptional control, with a significant increase in postsynaptic density proteins content. RNA-seq analysis suggest that the acceleration in neuronal differentiation starts few hours after neuronal induction setting a path to a faster neuronal and synapse maturation. These findings establish that SYNGAP1 acts as a key regulator of neuronal differentiation across both excitatory and inhibitory neurons. Our work underscores the importance of the SYNGAP1 PDZ ligand motif for normal neuronal development and suggests translational strategies targeting SYNGAP1 alpha1 isoform levels to mitigate SYNGAP1-related NDD.

neuroscience↗

bioRxiv2026

CellAwareGNN: Single-Cell Enhanced Knowledge Graph Foundation Model for Drug Indication Prediction

Graph foundation models have emerged as powerful tools for drug repurposing by enabling the prediction of novel drug-disease indications from large biomedical knowledge graphs. A representative example is TxGNN, which was previously developed and trained on PrimeKG, a comprehensive biomedical knowledge graph covering over 17,000 diseases. While TxGNN demonstrates strong performance, existing biomedical knowledge graphs largely lack fine-grained, cell-type-specific expression context. This limits their ability to capture disease mechanisms driven by dysregulated cellular programs, such as immune cell-specific pathways in autoimmune diseases. Moreover, prior evaluations typically test only randomly selected subsets of diseases, leaving many diseases unexamined and limiting conclusions about model performance across the full disease spectrum. To address these limitations, we first update PrimeKG to PrimeKG-U by incorporating expanded and curated biomedical knowledge and then develop TxGNN-U as a stronger graph-based baseline. Building on this foundation, we introduce CellAwareGNN, a graph foundation model that integrates single-cell genomics into PrimeKG-U. We construct a single-cell-enhanced knowledge graph, scPrimeKG, by incorporating cell-type-specific gene expression signatures from the OneK1K dataset, expanding PrimeKG from approximately 8.1 million edges and 129k nodes to over 14 million edges and 147k nodes. CellAwareGNN is pre-trained on all relation types in scPrimeKG and evaluated on drug indication prediction with explicit coverage of all diseases in the knowledge graph. CellAwareGNN consistently outperforms TxGNN and TxGNN-U. For drug indication prediction, CellAwareGNN achieves an AUPRC of 0.826, representing a 1.2% improvement over TxGNN-U (0.816) and a 3.4% improvement over TxGNN (0.799). Notably, for autoimmune diseases, CellAwareGNN attains an AUPRC of 0.864, improving by 2.0% over TxGNN-U (0.847) and 6.0% over TxGNN (0.815). Importantly, CellAwareGNN prioritizes promising repurposing candidates, including Ocrelizumab for Pemphigus via CD20-expressing B cells, Methotrexate for Pemphigus through DHFR and ATIC activity in T and B cells, and Rosiglitazone for Rheumatoid Arthritis through PPAR-{gamma} activation. These results demonstrate the value of incorporating cell-type-specific expression context to improve both predictive performance and biological interpretability in graph-based drug repurposing. CCS ConceptsO_LIApplied computing [->] Health informatics; Bioinformatics; C_LIO_LIComputing methodologies [->] Knowledge representation and reasoning; Neural networks. C_LI ACM Reference FormatXinmeng Zhang, Eugene Jeong, Chao Yan, Yubo Feng, Linshuoshuo Lyu, Xingyi Guo, and You Chen. 2026. CellAwareGNN: Single-Cell Enhanced Knowledge Graph Foundation Model for Drug Indication Prediction. In Proceedings of the 32nd ACM SIGKDD Conference on Knowledge Discovery and Data Mining V.2 (KDD 2026), August 9-13, 2026, Jeju Island, Republic of Korea. ACM, New York, NY, USA, 12 pages. https://doi.org/10.1145/3770855.3819012

bioinformatics↗

bioRxiv2026

Geoclimatic oscillations and ancient reciprocal adaptive introgression shape the evolutionary trajectories of threatened Coilia

Geoclimatic oscillations have repeatedly reshaped biodiversity, yet how ancient lineages persist and diversify through profound environmental upheavals remains a central question in evolutionary genomics. By integrating chromosome-level genome assemblies, population genomics, and transcriptomic data across the Coilia species complex, we reconstruct deep evolutionary histories of Coilia along the dynamic East Asian margins. Our analyses reveal divergences dating to the Miocene, coinciding with major tectonic and climatic transitions, including Tibetan Plateau uplift-linked reorganization of the Yangtze drainage toward the East China Sea, and the Middle Miocene Climatic Optimum. Despite long-term separation, we uncover evidence for ancient, reciprocal introgression among Coilia lineages, with gene flow confined to discrete genomic regions rather than distributed genome-wide. These introgressed regions are enriched for loci associated with immune function, vascular development, and osmoregulatory processes, and exhibit population genetic signatures consistent with positive selection. Temporal modelling indicates that this window of adaptive exchange preceded Pleistocene glacial intensification, after which gene flow became effectively restricted. Together, our results suggest that transiently permeable species boundaries historically acted as reservoirs of adaptive variation, shaping evolutionary trajectories and facilitating niche differentiation under repeated geoclimatic oscillations. This study highlights ancient adaptive introgression as an important evolutionary process contributing to the long-term persistence of threatened coastal fishes.

evolutionary biology↗