Biology subjects

Liu, Z.

Publications and source records attributed to Liu, Z..

At least 343 records · Page 19Linked to original sources

bioRxiv2025

Identification of chemical features that influence mycomembrane permeation and antitubercular activity

Tuberculosis (TB), caused by Mycobacterium tuberculosis (Mtb), is the deadliest single-agent infection worldwide. Current antibiotic treatment for TB takes a minimum of four months, underscoring the need for better therapeutics. The unique mycobacterial cell envelope, particularly the outermost mycomembrane, has long been thought to promote intrinsic antibiotic resistance by limiting compound entry into Mtb. Understanding chemical features that influence permeation across the mycomembrane may enable more accurate predictions of whole cell anti-Mtb activity, leading to development of more effective antibacterials. Here we query the mycomembrane permeation of over 1500 azide-tagged compounds in live Mtb with the bioorthogonal click chemistry-based assay PAC-MAN. We use cheminformatics and machine learning to identify chemical features associated with mycomembrane permeation and show that they have predictive value via systematic modification of two small molecule series. Additionally, we find that chemical features that influence mycomembrane permeation correlate with anti-Mtb activity in large compound libraries. These findings suggest that the mycomembrane is indeed a significant barrier to whole cell activity in Mtb and provide a rational framework for designing or modifying compounds to overcome this barrier.

microbiology↗

bioRxiv2025

Investigate Channel Rectifications and Neural Dynamics by An Electrodiffusive Gauss-Nernst-Planck Approach

Electrodiffusion plays a crucial role in modulating ion channel conductivity and neural firing dynamics within the nervous system. However, its influence on neural function remains underexplored. In this work, we introduce a novel Gauss-Nernst-Planck (GNP) approach to investigate how electrodiffusive dynamics influence ion channel rectification and neural activity. For the first time, we demonstrate that membrane conductivity is determined by both ion permeability and the harmonic mean of intramembrane ion concentration, bridging the gap between the permeability-based Goldman-Hodgkin-Katz (GHK) model and conductance-based models. We characterize the rectification properties of GABAA, AMPA, and leaky channels by estimating their single-channel permeabilities and conductance. By integrating these rectifying channels into neurodynamic models, our GNP neurodynamic model reveals how electrodiffusive dynamics fundamentally shape neural firing by modulating membrane conductance and the interplay between passive and active ion transport-mechanisms that are often overlooked in conventional conductance-based neurodynamic models. This study provides a fundamental mechanistic understanding of electrodiffusion regulation in neural activity and establishes a robust framework for future research in neurophysiology.

biophysics↗

bioRxiv2025

A foundation model for protein-ligand affinity prediction through jointly optimizing virtual screening and hit-to-lead optimization

The structure of the protein binding pocket governs the ligand binding affinity by providing crucial intermolecular interactions and spatial compatibility. While existing methods have leveraged these structural insights to advance affinity prediction, they often treat virtual screening and hit-to-lead optimization separately, mainly due to incompatible speed-accuracy requirements. However, these two tasks complement each other, and their integration enables broader chemical exploration while preserving focus on affinity-determining substructures. Here, we present LigUnity, a foundation model for affinity prediction that jointly embeds ligands and pockets into a shared space. In particular, LigUnity learns coarse-grained active/inactive distinction through scaffold discrimination and fine-grained pocket-specific ligand preference through pharmacophore ranking. We demonstrate the effectiveness and versatility of LigUnity on eight benchmarks across six settings. In virtual screening, LigUnity outperforms 24 methods with >50% improvement and demonstrates robust generalization to novel targets. In hit-to-lead optimization, it achieves state-of-the-art performance across split-by-time, split-by-scaffold, and split-by-unit settings, emerging as a cost-efficient alternative to free energy perturbation. We further showcase how LigUnity can be employed in an active learning framework for TYK2 to efficiently find optimal ligands. Collectively, these results establish LigUnity as a versatile foundation model for affinity prediction, offering broad applicability across the drug discovery pipeline.

bioinformatics↗

bioRxiv2025

China Autism Brain Imaging Consortium: Charting Brain Growth in Chinese Children with Autism

Autism Spectrum Disorder (ASD) is a lifelong neurodevelopmental condition characterized by atypical brain growth. While advances in neuroimaging and openly sharing large-sample datasets such as the Autism Brain Imaging Data Exchange (ABIDE) have improved understanding of ASD, most studies focus on adolescents and adults, with early brain development-critical for diagnosis and intervention-remaining underexplored. Existing research predominantly involves Western samples, offering limited insight and generalizability into non-Caucasian populations. We introduce the China Autism Brain Imaging Consortium (CABIC) (https://php.bdnilab.com/resources/), a grassroots effort by researchers across the country to aggregate previously collected multi-site structural MRI datasets and phenotypic information from 1,451 autistic children and 1,119 typically developing children, covering an age range from early childhood to school age (1.0 - 12.92 years). Here, we present this resource and depict brain growth charts to push forward a more comprehensive understanding of the brain development in Chinese autism children. We constructed brain growth charts that reveal a developmental shift in autistic children, transitioning from early overgrowth to delayed maturation. Regional analyses identified distinct atypical trajectories across specific brain regions. Individual deviation scores quantified inter-subject variability, characterizing the heterogeneity of brain development in ASD. Comparative analyses between CABIC and ABIDE highlighted differences potentially attributable to ethnicity and culture, advancing our understanding of cross-population neurodevelopmental diversity. CABIC MRI datasets will be shared publicly to foster investigation of the potential neural mechanisms underlying ASD in non-Western populations and support efforts toward precision medicine for autistic individuals across diverse backgrounds.

neuroscience↗

bioRxiv2025

Comparative molecular landscapes of immature neurons in the mammalian dentate gyrus across species reveal special features in humans

Immature dentate granule cells (imGCs) arising from adult hippocampal neurogenesis contribute to plasticity, learning and memory, but their evolutionary changes across species and specialized features in humans remain poorly understood. Here we performed machine learning-augmented analysis of published single-cell RNA-sequencing datasets and identified macaque imGCs with transcriptome-wide immature neuronal characteristics. Our cross-species comparisons among humans, monkeys, pigs, and mice showed few shared (such as DPYSL5), but mostly species-specific gene expression in imGCs that converged onto common biological processes regulating neuronal development. We further identified human-specific transcriptomic features of imGCs and demonstrated functional roles of human imGC-enriched expression of a family of proton-transporting vacuolar-type ATPase subtypes in development of imGCs derived from human pluripotent stem cells. Our study reveals divergent gene expression patterns but convergent biological processes in the molecular characteristics of imGCs across species, highlighting the importance of conducting independent molecular and functional analyses for adult neurogenesis in different species.

neuroscience↗

bioRxiv2025

Targeting the Protein-Protein Interaction Between the CDC37 Co-Chaperone and Client Kinases by an Allosteric RAF Dimer Breaker

Braftide, originally designed as a potent allosteric RAF kinase dimer disruptor, was intended to inhibit RAF dimerization by targeting the conserved RAF dimer interface. Intriguingly, Braftide has also been observed to trigger proteasome-mediated protein degradation with an unclear mechanism of action. This study elucidates the mechanism underlying Braftides dual functionality and assesses its potential as a chemical probe to target kinase-chaperone interaction. CDC37, a selectivity co-chaperone in the HSP90 chaperone machinery, plays a crucial role in facilitating the recognition of client kinase. The RAF dimer interface overlaps with the CDC37-kinase client recognition motif, known as the C helix-{beta}4 loop. Using co-immunoprecipitation and NanoBiT assays, we confirmed Braftides ability to selectively disrupt the CDC37-client kinase interaction while sparing HSP90. Through deuterium exchange mass spectrometry, molecular dynamic simulations, and in vitro crosslinking analyses, we mapped Braftides binding region within the BRAF kinase domain, as well as the CDC37 region implicated in the association of CDC37-client kinase complex. Consequently, this disruption destabilizes RAF kinase clients, resulting in proteasomal degradation, reduced cellular proliferation, and increased apoptosis in cancer cell lines. Furthermore, Braftide exhibits synergy with HSP90 inhibitors, jointly destabilizing both the CDC37-RAF complex and HSP90. Our work demonstrates the feasibility of disrupting the CDC37-client kinase interaction as an innovative therapeutic strategy and identifies the C helix-{beta}4 loop as a novel allosteric site with significant potential for the development of next-generation therapeutics.

biochemistry↗

bioRxiv2025

SUGP1 loss is the sole driver of SF3B1 hotspot mutant missplicing in cancer

SF3B1 is the most frequently mutated splicing factor in cancer. Mechanistically, such mutations cause missplicing by promoting aberrant 3 splice site usage; however, how this occurs remains controversial. To address this issue, we employed a computational screen of 600 splicing-related proteins to identify those whose reduced expression recapitulated mutant SF3B1 splicing dysregulation. Strikingly, our analysis revealed only two proteins whose loss reproduced this effect. Extending our previous findings, loss of the G-patch protein SUGP1 recapitulated almost all splicing defects induced by SF3B1 hotspot mutations. Unexpectedly, loss of the RNA helicase Aquarius (AQR) reproduced [~]40% of these defects. However, we found that AQR knockdown caused significant SUGP1 missplicing and reduced protein levels, suggesting that AQR loss reproduced mutant SF3B1 splicing defects only indirectly. This study advances our understanding of missplicing caused by oncogenic SF3B1 mutations, and highlights the fundamental role of SUGP1 in this process.

molecular biology↗

bioRxiv2025

A perturbation proteomics-based foundation model for virtual cell construction

Building a virtual cell requires comprehensive understanding of protein network dynamics of a cell which necessitates large-scale perturbation proteome data and intelligent computational models learned from the proteome data corpus. Here, we generate a large-scale dataset of over 38 million perturbed protein measurements in breast cancer cell lines and develop a neural ordinary differential equation-based foundation model, namely ProteinTalks. During pretraining, ProteinTalks gains a fundamental understanding of cellular protein network dynamics. Our model encodes protein networks and exhibits consistently improved predictive accuracy across various downstream tasks, highlighting its generalization capabilities and adaptability. In cancer cells, ProteinTalks robustly predicts drug efficacy and synergy, identifies novel drug combinations, and, through its interpretability, uncovers resistance-associated proteins. When applied to more complex system, patient-derived tumor xenografts, ProteinTalks predicts potential responses to drugs. Its integration with clinical patient data enhances the prognosis prediction of breast cancer patients. Collectively, we present a foundational model based on proteome dynamics, offering potential for various downstream applications, including drug discovery, and providing a basis for developing virtual cells.

systems biology↗

bioRxiv2025

A lateralized pathway for associating nutrients with flavors

Animals learn about the external world, in part, via interoceptive signals1,2. For example, the nutrient content of food is first estimated in the mouth, in the form of flavor, and then measured again via slower signals from the gut. How these signals from the mouth and gut are integrated to drive learning is unknown. Here we identify a lateralized dopamine pathway that is specialized for learning about the nutrient content of food. We show that dopamine neurons in the ventral tegmental area (VTADA) are necessary for associating nutrients with flavors, and that post-ingestive nutrients trigger DA release selectively in a small region of the anterior basolateral amygdala (BLA) but not canonical DA targets in striatum. Remarkably, this nutrient-triggered DA release occurs preferentially on the left side of the brain in both mice and humans, revealing that the DA system is functionally lateralized. We identify the gut sensors that are responsible for nutrient-triggered DA release; show that they activate BLA-projecting DA neurons defined by expression of cholecystokinin (CCK); and demonstrate that stimulation of DA axon terminals in the anterior BLA drives flavor-nutrient learning but not other aspects of feeding behavior. Two-photon imaging of neurons in the left anterior BLA reveals that they integrate gustatory and post-ingestive cues, and silencing these neurons prevents flavor-nutrient learning. These findings establish a neural basis for how animals learn about the nutrient content of their food. They also reveal unexpectedly that post-ingestive nutrients are differentially represented on the right and left sides of the brain.

neuroscience↗

bioRxiv2025

The extensive diversity of nairoviruses reveals the complexity and high risk of tick-borne diseases in northeastern China

The increasing burden of tick-borne virus infections is becoming a global public health concern. In the current study, we identified nine tick-borne nairoviral species in northeastern China through metagenomic sequencing, which are phylogenetic classified into four groups: I) Wetland virus (WELV), II;) Songling virus (SGLV), Jian nairovirus (JANV), Yanbian nairo tick virus 1 (YBNTV-1), Shanxi tick virus 2 (SXTV-2), III;) Yezo virus (YEZV), and IV;) Beiji nairovirus (BJNV), Yichun nairovirus (YCNV), and Hunchun nairovirus (HCNV). These nairoviruses are influenced by both the species of ticks and their geographical distribution. Apart from the previously isolated pathogenic WELV, SGLV, YEZV, and BJNV, we successfully isolated JANV and YBNTV-1, which can replicate in Vero and HEK-293T cells. Two routes of nairovirus spread into northeastern China were evaluated. Group I and II viruses, vectored by Haemaphysalis sp. ticks, likely originated in Central or East China and spread via migratory birds along the Chinese coastline. Group III; and IV viruses, vectored by Ixodes persulcatus ticks, are believed to have originated in Europe and spread via rodents across the Eurasian continent. Our study reveals the extensive diversity and wide distribution of tick-borne nairoviruses in northeastern China, highlights the potential pathogenic risk of the newly discovered nairoviruses, and clarifies their origins and potential migration routes. The results will be of crucial significance for the tick-borne diseases control and prevention in China.

microbiology↗

bioRxiv2025

Lemur: A Single-Cell Foundation Model with Fine-Tuning-Free Hierarchical Cell-Type Generation for Drosophila melanogaster

Single-cell genomics has revolutionized our understanding of cellular heterogeneity, but automating its analysis remains an open challenge. Cell-type annotation represents a critical bottleneck, particularly as datasets grow in size and complexity. While foundation models have shown promise in addressing this challenge, existing approaches require extensive fine-tuning for effective cell-type annotation. Here, we present Lemur (Large Expression Model for Understanding scRNA-seq), a single-cell foundation model specifically designed for Drosophila melanogaster. Lemur achieves fine-tuning-free cell-type annotation through comprehensive pre-training on an integrated whole-organism atlas with a unified cell-type annotation schema. To leverage this unified schema, we developed a dedicated hierarchical cell-type decoder architecture. This approach enables Lemur to generate consistent cell-type predictions across multiple levels of granularity without requiring additional training on new datasets. The model demonstrates strong performance across diverse tissue types, experimental conditions, and sequencing technologies. It also achieves batch-effect correction without explicitly training for this task. This automated analysis capability positions Lemur as an effective tool for the fly research community. Beyond its immediate applications, Lemur establishes a framework for accelerating biological discovery. It enables rapid iteration between computational predictions and experimental validation in the highly controlled Drosophila melanogaster system, with potential implications for translational research in human biology, particularly in aging and neurodegenerative disease studies.

bioinformatics↗

bioRxiv2025

Fatty Acid Transport Protein-2 (FATP2) Inhibition Enhances Glucose Tolerance through α-Cell-mediated GLP-1 Secretion

Type 2 diabetes affects more than 30 million people in the US, and a major complication is kidney disease. During the analysis of lipotoxicity in diabetic kidney disease, global fatty acid transport protein-2 (FATP2) gene deletion was noted to markedly reduce plasma glucose in db/db mice due to sustained insulin secretion. To identify the mechanism, we observed that islet FATP2 expression was restricted to -cells, and -cell FATP2 was functional. Direct evidence of FATP2KO-induced -cell-mediated GLP-1 secretion included increased GLP-1-positive -cell mass in FATP2KO db/db mice, small molecule FATP2 inhibitor enhancement of GLP-1 secretion in TC1-6 cells and human islets, and exendin[9-39]-inhibitable insulin secretion in FATP2 inhibitor-treated human islets. FATP2-dependent enteroendocrine GLP-1 secretion was excluded by demonstration of similar glucose tolerance and plasma GLP-1 concentrations in db/db FATP2KO mice following oral versus intraperitoneal glucose loading, non-overlapping FATP2 and preproglucagon mRNA expression, and lack of FATP2/GLP-1 co-immunolocalization in intestine. We conclude that FATP2 deletion or inhibition exerts glucose-lowering effects through -cell-mediated GLP-1 secretion and paracrine {beta}-cell insulin release. Graphical abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=136 SRC="FIGDIR/small/635976v1_ufig1.gif" ALT="Figure 1"> View larger version (28K): org.highwire.dtl.DTLVardef@190c799org.highwire.dtl.DTLVardef@203076org.highwire.dtl.DTLVardef@14dbf26org.highwire.dtl.DTLVardef@140869e_HPS_FORMAT_FIGEXP M_FIG C_FIG

physiology↗

bioRxiv2025

Identification of High-Risk Cells in Single-Cell Spatially Resolved Transcriptomics Data Using Deep Transfer Learning

SummaryThe examination of high-risk cells and regions in tissue samples from spatially resolved transcriptomics platforms offers meaningful insights into specific disease processes. For existing methods, while cell types or clusters can be identified and associated with disease attributes, individual cells are unable to be associated in the same manner. MethodDiag-nostic Evidence GAuge of Single-cells and spatial transcriptomics (DEGAS), solves the above problem by employing latent representations of gene expression data and domain adaptation to transfer disease attributes from patients to individual cells from single-cell RNA sequencing datasets. In this research, we present and evaluate DEGASs versatility in adapting to data arising from various single-cell spatially resolved transcriptomics (scSRT) platforms. DEGAS successfully identified high-risk cells and regions in liver hepatocellular carcinoma and skin cutaneous melanoma, which were validated through known markers. Additionally, DEGAS was applied to our newly generated Type II Diabetes Xenium dataset revealing high-risk cells within the tissue samples. Availability and ImplementationThe DEGAS software can be accessed at https://github.com/tsteelejohnson91/DEGAS. For the updated smoothing functions and associated codes, visit https://github.com/dchatter04/DEGAS-Spatial-Smoothing. Sources for the datasets reviewed are detailed in their respective sections. A description of some datasets, along with extra tables and figures, is provided in the Supplementary Materials file. Our newly generated Xenium data for Type II Diabetes can be found at https://doi.org/10.7303/syn68699752.

bioinformatics↗

bioRxiv2025

Leopard Cats Occupied Human Settlements in China for 3,500 years before the Arrival of Domestic cats in 600-900 CE around the Tang Dynasty

The earliest cats in human settlements in China were not domestic cats (Felis catus), but native leopard cats (Prionailurus bengalensis). To trace when and how domestic cats arrived in East Asia, we analyzed 22 feline bones from 14 sites across China spanning 5,000 years. Nuclear and mitochondrial genomes revealed that leopard cats began occupying anthropogenic scenes around 5,400 years ago and last appeared in 150 CE. Following several centuries gap of archeological feline remains, the first known domestic cat (706-883 CE) in China was identified in Shaanxi during the Tang Dynasty. Genomic analysis suggested a white or partially white coat and a link to a contemporaneous domestic cat from Kazakhstan, indicating a likely dispersal route via the Silk Road. The two felids once independently occupied ancient anthropogenic environments in China but followed divergent paths and reached different destinations in human-animal interactions.

evolutionary biology↗

bioRxiv2025

A novel deep optimal transport framework reveals prostate cancer risk heterogeneity, Alzheimers disease risk heterogeneity, and myeloma cells associated with a short-term sham bortezomib response and progressive disease

Leveraging single-cell gene expression profiles can significantly enhance our understanding of diseases by associating single cells with traits such as disease subtypes, prognosis, and drug response. Although previous efforts have linked single cell clusters and groups with these attributes, they have primarily focused on changes in cell proportions while overlooking transcriptional changes at the single cell level. To further unravel cell heterogeneity with clusters and reveal the nuanced behaviors of cellular subtypes, it is essential to assess the disease associations of individual cells. Previous methods often fail to capture complex patterns that are only discernible through summarizing non-linear relationships across multiple genes. The Diagnostic Evidence GAuge of Single-cells/Spatial-transcriptomics (DEGAS) framework advances these efforts by aligning single cells and/or spatial transcriptomics regions with patients through a unified latent space using non-linear transformations learned from deep neural networks (DNNs). DEGAS achieves superior performance in analyzing single cell and spatial transcriptomics datasets, including Alzheimers disease (AD), multiple myeloma (MM), and prostate cancer (PDAC). Here we present DEGAS version 2, which has been updated with optimial transport based transfer learning and improved time-to-event loss functions, more advanced model architecture, and improved model baseline evaluations. DEGASv2 outperformed other methods in both single cell and spatial transcriptomic baseline comparisons. On the multiple myeloma discovery dataset, DEGASv2 enabled us to discover cell types that exhibited distinct drug response patterns over various time frames and were validated with time series single cell multiomic data that we generated, demonstrating a dangerous subtype of cell and novel therapeutic target.

bioinformatics↗

bioRxiv2025

Histone Deacetylase Inhibitor Largazole Deactivates A Subset of Superenchancers and Causes Mitotic Chromosome Mis-alignment by Suppressing SP1 and BRD4

Histone deacetylase inhibitors have been investigated as potential therapeutic agents for cancer and other diseases. HDIs are known to promote histone acetylation, resulting in an open chromatin conformation and generally increased gene expression. In previous work, we reported that a subset of genes, particularly those regulated by superenhancers, can be suppressed by the HDAC inhibitor largazole. To elucidate the molecular mechanisms underlying gene repression by largazole, we conducted transposase-accessible chromatin sequencing, ChIP-seq, and RNA-seq studies. Our findings revealed that while largazole treatment generally enhances chromatin accessibility, it selectively decreases the accessibility of a subset of superenhancer regions. These genomic regions, showing the most significant changes in the presence of largazole, were enriched with transcription factor binding motifs for SP1, BRD4, CTCF, and YY1. ChIP-seq analysis confirmed reduced binding of BRD4 and SP1 at their respective sites on chromatin, particularly at superenhancers regulating genes such as ID1, c-Myc and MCMs. Largazole exerts its effects by inhibiting DNA replication, RNA processing, and cell cycle progression, partially through the suppression of SP1 expression. Depletion of SP1 by shRNA mimics several key biological effects of largazole and increases cellular sensitivity to the drug. Specific to cell cycle regulation, we demonstrated that largazole disrupts G/M transition by interfering with chromosome alignment during metaphase, a phenotype also observed with SP1 depletion. Our results suggest that largazole exerts its growth-inhibitory effect by suppressing BRD4 and SP1 at super-enhancers, leading to cytostatic responses and mitotic dysfunction.

cancer biology↗

bioRxiv2025

Theoretical and Empirical Performance of Pseudo-likelihood-based Bayesian Inference of Species Trees under the Multispecies Coalescent

Likelihood-based inference under the multispecies coalescent provides accurate estimates of species trees. However, maximum likelihood and Bayesian inference are both computationally very demanding. Pseudo-likelihood has been previously proposed as a computationally efficient alternative to full phylogenetic likelihood calculations in the context of maximum likelihood estimation. However, theoretical and practical aspects of pseudo-likelihood in the context of Bayesian inference have not been explored. In this work, we provide strong theoretical guarantees and an empirical evaluation of pseudo-likelihood-based Bayesian inference of species trees under the multispecies coalescent. Our contributions are threefold. First, we prove a bound on the convergence rate for species tree topology inference and a Bernstein-von Mises result for branch lengths under model misspecification. Second, we provide an empirical comparison of full- and pseudo-likelihood-based Bayesian inference on synthetic data. Finally, we demonstrate the practical scalability of pseudo-likelihood-based inference by analyzing two biological datasets.

bioinformatics↗

bioRxiv2025

Root Cap-Specific Transcriptomics Identify the NAC Transcription Factor SOMBRERO as a Versatile Regulator of Auxin Gradients

Auxin gradients in the root cap play a crucial role in the root cap development and root tropism. In the central root cap, meristematic cells in the columella initial differentiate into gravity-sensing columella cell, and mature into border-like cells that eventually separate from the root cap. Golgi stacks exhibit distinct ultrastructural features in each cell type across the central root cap, serving as effective cell markers. This cell type-specific Golgi remodeling was inhibited in smb-3 mutant root cap. When we carried out transcriptomic analyses of isolated root cap from Col-0 and smb-3, transcript levels of numerous auxin-related genes, including those involved in auxin synthesis, transport, and signaling, were significantly altered in smb-3 mutant root caps. The auxin gradient was suppressed within the central root cap of smb-3 mutant lines and they displayed decreased sensitivity to N-1-naphthylphthalamic acid (NPA) in comparison to Col-0. Furthermore, we demonstrated that the SMB binds to the promoter regions of auxin-related genes displaying large changes in smb-3 root cap cells. Our findings suggest that SMB functions as a versatile transcription factor that regulates the expression of genes critical for local auxin distribution and responses in the root cap, thereby coordinating root cap cell differentiation and controlling root growth directions in response to external stimuli.

plant biology↗