Biology subjects

Liu, Z.

Publications and source records attributed to Liu, Z..

At least 271 records · Page 15Linked to original sources

bioRxiv2025

Prehistoric Global Migration of Vanishing Gut Microbes With Humans

The gut microbiome is crucial for health and greatly affected by lifestyle. Many microbes common in non-industrialized populations are disappearing or extinct in industrialized populations. Understanding which microbes have been long-term residents of the human gut, and may have co-evolved with humans, has implications for the importance of microbial biodiversity loss for health. However, the genetic complexities of microbial evolution and the plasticity of gut microbiome composition have made it challenging to define these long-term associations. Here, we performed deep metagenomic sequencing of the Tsimane horticulturalists of Bolivia and compared their gut microbiomes with the Hadza hunter-gatherers of Tanzania. These two populations, whose ancestors were separated for tens of thousands of years, share 1,231 microbial species, most of which are absent in industrialized populations. Population genetic analyses in 636 of these shared species revealed patterns of microbial divergence and gene flow consistent with prehistoric human co-migration, with estimated split times that approximately align with human migration out of Africa and into the Americas. Our findings indicate that a diverse gut microbiome co-migrated with humans around the globe, persisting over millennia. However, many of these species are now vanishing in industrialized populations, and the consequences for human health remain uncertain.

microbiology↗

bioRxiv2025

CLM-access: A Specialized Foundation Model for High-dimensional Single-cell ATAC-seq analysis

Inspired by the success of large language models (LLMs) in natural language processing, cell language models (CLMs) have emerged as a promising paradigm for learning cell representations from high-dimensional single-cell data--particularly transcriptomic profiles from scRNA-seq. These foundation models have shown remarkable potential across a variety of downstream applications. However, there remains a lack of foundation models for scATAC-seq data, which measures chromatin accessibility at single-cell level and is critical for decoding epigenetic regulation. Developing such models is considerably more challenging due to the unique characteristics of scATAC-seq data, including the vast number of chromatin regions, lack of standardized annotations, extreme sparsity, and near-binary distributions. To address these challenges, we systematically explore various strategies and propose CLM-access, a specialized foundation model for scATAC-seq data. CLM-access incorporates three main innovations: (1) an unified data processing pipeline that maps 2.8 million cells onto an unified reference of over 1 million chromatin regions; (2) a specialized patching and embedding strategy to effectively manage high-dimensional inputs; and (3) a tailored masking and loss function design that preserves fine-grained regional information while enhancing training efficiency and representation quality. With comprehensive benchmarks, we show that CLM-access significantly outperforms existing methods in key downstream tasks, including batch effect correction, cell type annotation, RNA expression prediction, and multi-modal integration. This work establishes a scalable and interpretable foundation model for single-cell epigenomic analysis and expands the application of CLMs in single-cell research. Code is available at https://github.com/HIM-AIM/CLM-access

bioinformatics↗

bioRxiv2025

DNA Sequence Trace Reconstruction Using Deep Learning

Deciphering DNA sequences is fundamental to unlocking the mysteries of life, but the high dimensionality and complexity of biological sequence data significantly hinder knowledge discovery. In particular, the challenges of sequence length, repetitive regions, and structural complexity make it difficult to directly reconstruct complete DNA sequences from raw data. Therefore, this paper proposes a DNA sequence trace reconstruction model, DNARetrace, which performs preprocessing and dataset construction, and then employs a Bidirectional Fourier-Kolmogorov-Arnold Network (Bi-FKGAT), using an extremely unbalanced loss function for link prediction, so as to reconstruct the original DNA sequence. In multi-angle experiments using both simulated and real data, DNARetrace successfully reconstructs DNA sequence traces across large-scale datasets derived from various DNA sequencing methods, overcoming the bias of current approaches toward specific sequencing platforms, and achieves competitive outcomes in DNA storage and genomics downstream tasks. We further validated the expandability of the proposed methods in DNA sequence classification and metagenomic binning tasks. In summary, DNARetrace is compatible with various sequencing scenarios; it reduces the difficulty of discovering novelty knowledge directly from high-complexity raw data, and it provides a reusable tool to accelerate DNA sequence processing and applications.

genomics↗

bioRxiv2025

Sequestration of the phagocyte metabolite itaconate by P. aeruginosa RpoN promotes successful pulmonary infection

AbstractThe phagocyte immunometabolite itaconate, normally toxic to bacteria, functions as a signal to stimulate the adaptation of the pulmonary pathogen Pseudomonas aeruginosa to the lung. Itaconate is actively transported into P. aeruginosa where it induces {sigma}54 rpoN expression and co-valently binds cysteine residues on RpoN. RpoN not only functions as a sink to limit itaconate toxicity but S- itaconated RpoN promotes increased utilization of the Entner Doudoroff pathway, optimizing bacterial metabolism in the setting of inflammation. S-itaconation of RpoN directs a global metabolic response that fuels pulmonary infection.

microbiology↗

bioRxiv2025

Structural and dynamic insights into phosphate uptake by PHT1 transporter in rice

Phosphorus is an essential macronutrient for plants, primarily absorbed from the soil as inorganic phosphate (Pi) through root-located Pi transporters. Despite decades of research into these transporters as targets for developing Pi-efficient crops, their mechanisms for Pi import remain poorly understood. Here, we present the cryo-EM structures of the rice Pi importer OsPHT1;11 in both Pi-bound and unbound forms, characterize its conformational dynamics, and reveal how these dynamics contribute to its transport function. Pi is recognized through conserved residues found in plants, with its translocation facilitated by a typical alternating-access mechanism. SmFRET analyses reveal that this transporter undergoes dynamic conformational changes, which are differentially linked to its Pi transport capability, with a predominance of extracellular open conformations favoring Pi transport, while more populated intracellular open conformations hinder it. These findings provide insights into Pi uptake in plants and offer a foundation for designing genetically modified crops with improved phosphate efficiency.

biochemistry↗

bioRxiv2025

Structures of Naked Mole-Rat, Tuco-Tuco, and Guinea Pig Ribosomes: Is rRNA Fragmentation Linked to Translational Fidelity?

Ribosomes are central to protein synthesis in all organisms. Among mammals, the ribosome functional core is highly conserved. Remarkably, two rodent species, the naked mole-rat (NMR) and tuco-tuco display fragmented 28S rRNA, coupled with high translational fidelity and long lifespan. The unusual ribosomal architecture in the NMR and tuco-tuco has been speculated to be linked to high translational fidelity. Here we show, by single-particle cryo-electron microscopy (cryo-EM), that despite the fragmentation of their rRNA, NMR and tuco-tuco ribosomes retain their core functional architecture. Compared to ribosomes of the guinea pig, a phylogenetically related rodent without 28S rRNA fragmentation, ribosomes of NMR and tuco-tuco exhibit poorly resolved, certain expansion segments. In contrast, the structure of the guinea pig ribosome shows high similarity to human ribosome. Enhanced translational fidelity in the NMR and tuco-tuco may stem from subtle, allosteric effects in dynamics, linked to rRNA fragmentation.

biochemistry↗

bioRxiv2025

scDIAGRAM: Detecting Chromatin Compartments from Individual Single-Cell Hi-C Matrix without Imputation or Reference Features

Single-cell Hi-C (scHi-C) provides unprecedented insight into 3D genome organization, but its sparse and noisy data pose challenges in accurately detecting A/B compartments, which are crucial for understanding chromatin structure and gene regulation. We presented scDIAGRAM, a data-driven method for annotating A/B compartments in single cells using direct statistical modeling and graph community detection. Unlike existing approaches, scDIAGRAM operates without relying on external information, such as the CpG density or imputation techniques, and preserves cell-to-cell heterogeneity. Accuracy and robustness of scDIAGRAM were illustrated through simulated scHi-C datasets and a human cell line. We applied scDIAGRAM to real scHi-C datasets from the mouse brain cortex, mouse embryonic development, and human acute myeloid leukemia (AML), demonstrating its ability to capture compartmental shifts associated with transcriptional variation. This robust framework offers new insights into the functional roles of chromatin compartments at single-cell resolution across various biological contexts.

bioinformatics↗

bioRxiv2025

Secretion and transfer of adipose lipoprotein lipase utilizes neutral sphingomyelinase 2 generated exosomes

Lipoprotein lipase (LPL) is critical for clearance of circulating triglycerides and for tissue fatty acid supply. LPL is primarily synthesized and secreted by adipocytes into the interstitium and must traffic from there to the abluminal/basolateral side of capillary endothelial cells. There, LPL binds glycosylphosphatidylinositol-anchored protein 1, GPIHBP1, which stabilizes the protein and facilitates its movement across the endothelial cells to the luminal side where it functions in hydrolysis of lipoprotein triglycerides. Importance of LPL traffic is supported by findings that rare mutations in GPIHBP1 cause hypertriglyceridemia. However our understanding of how LPL is secreted by adipocytes and traffics to endothelial cells is incomplete. Here we examined the possibility that secretion and traffic of adipocyte LPL might involve generation of small extracellular vesicles (sEVs/exosomes) which often mediate cell-cell communication. Proteomic analysis of sEVs secreted by adipocytes showed them enriched in LPL. To study LPL secretion and transfer we generated human derived pre-adipocytes (HPA) that stably express tagged LPL (FLAG and His epitopes). LPL pulldown and sEV isolation from HPA conditioned media documented that greater than 70% of secreted LPL is present in sEVs. The mechanism for LPL secretion in sEVs was found to involve the ESCRT-independent neutral sphingomyelinase 2 (nSMase2) pathway, as treatment with the nSMase2 inhibitor GW4869 reduced secretion by 80%. The above observations were reproduced using highly sensitive nanoparticle flow cytometry. The sEV associated LPL has lipolytic activity and it is released by heparin addition indicating it is on the sEV surface. In addition, using human derived microvascular endothelial cells with stable lentiviral expression of GPIHBP1 we show that LPL positive sEVs transfer LPL to these cells, but not to control cells without GPIHBP1. Our findings suggest that sEV formation by nSMase2 controls adipocyte LPL secretion and traffic, that sEVs protect LPL activity and facilitate LPL transfer to GPIHBP1 on endothelial cells.

cell biology↗

bioRxiv2025

Revealing the nervous system requirements of Alzheimer disease risk genes in Drosophila

Most Alzheimers disease (AD) susceptibility genes have poorly understood roles in the central nervous system (CNS). To address this gap, we systematically characterized 100 conserved candidate AD risk genes using a cross-species strategy in the fruit fly, Drosophila melanogaster. Genes were prioritized based primarily on human functional genomic evidence. We generated custom, loss-of-function alleles for each of the conserved fly orthologs. Most of the genes (80%) are expressed in the adult brain, including 24 neuron- and 13 glia-specific expression patterns. Overall, we identify 50 candidate AD risk gene homologs with requirements for CNS structure or function, including 18 whose loss of function causes neurodegeneration (e.g., Snx6/SNX32 and ClC-a/CLCN1), 35 required for neurophysiology (e.g., Arr1/ARRB2, stai/STMN4), and 8 with diminished CNS resilience following a thermal or mechanical stress (e.g., cindr/CD2AP, Amph/BIN1). In a parallel screen, we found 28 AD risk gene homologs (e.g, Ets98B/SPI1, Yod1/YOD1) that modify the neurotoxicity of either amyloid-{beta} peptide or tau protein, which aggregate to form AD pathology. To translate our findings back to human AD, we developed and deployed oligogenic risk scores based on gene clusters with shared nervous system phenotypes in flies, pinpointing functional pathways that differentially drive AD risk. Our results--available online via the Alzheimers Locus Integrative Cross-species Explorer (alice.nrihub.org)--reveal novel nervous system requirements for dozens of AD risk genes and may enable dissection of causal heterogeneity in AD.

genetics↗

bioRxiv2025

Overcoming breeding constraints in polyploid oat from evolutionary insights

Polyploidy provides adaptive advantages in plants by buffering deleterious mutations1,2. While polyploidization can enhance agronomic traits such as increased biomass, known as the gigas effect1-4, increasing genetic gain in polyploid crops remains a critical but difficult goal due to the difficulty in dissecting complex trait inheritance5,6. Here we present chromosome-scale genome assemblies for 26 Avena taxa, spanning diploid, tetraploid, and hexaploid lineages. We traced four independent polyploidization events across the genus, including the formation of A. agadiriana as an allotetraploid (AgAgAg'Ag'), and revealed a reticulate evolutionary history shaped by gene flow involving four subgenomes (A, B, C, D), for example, the hybrid speciation of A. hirtula. Transcriptomic analysis of 286 samples across 11 tissues, combined with deleterious mutation analysis from a cultivated population of 112 accessions, showed that polyploidization led to widespread functional redundancy among homoeologs, supporting a genome-wide buffering effect. However, derived allele frequency analysis revealed that, while disrupting functional genes may yield desirable traits, the buffering effect impedes the fixation of beneficial loss-of-function mutations and thereby limits breeding efficiency. Based on these integrative analyses, we propose breeding strategies to circumvent these limitations by targeting beneficial loss-of-function alleles within the complex polyploid background of oat. Our study highlights broader challenges in the improvement of polyploid crops and provides a foundation for future breeding strategies.

evolutionary biology↗

bioRxiv2025

From Dataset Curation to Unified Evaluation: Revisiting Structure Prediction Benchmarks with PXMeter

Recent advances in deep learning have significantly improved the accuracy of structure prediction for biomolecular complexes; however, robust evaluation of these models remains a major challenge. We introduce PXMeter, an open-source toolkit that support consistent and reproducible evaluation of diverse predictive models across a broad spectrum of biological complex structures. PXMeter provides a unified and reproducible benchmarking framework, offering valuable insights to support the ongoing improvement of structure prediction methods. We also present a high-quality benchmark dataset curated from recently deposited structures in the Protein Data Bank (PDB). These entries are manually reviewed to exclude non-biological interactions, ensuring reliable evaluation. Using these resources, we conducted a comprehensive benchmark of several structure prediction models, namely Chai-1, Boltz-1, and Protenix. Our benchmarking results demonstrate the advancements achieved by deep learning models, while also identifying ongoing challenges--especially in modeling protein-protein and protein-RNA interactions. Project Pagehttps://github.com/bytedance/PXMeter

bioinformatics↗

bioRxiv2025

A Photo-regulatable Intein Based Trans-Splicing tool for Protein and Organelle Relocalization to Different Subcellular Compartments

The dynamics of protein subcellular localization are intricately regulated, requiring new tools for controlling protein translocation to uncover the biological significance of post-translational regulation of protein localization. Here, we developed a new method, Protein Rerouting via INtein-mediated Trans-Splicing (PRINTS), which enables precise control of protein translocation across diverse subcellular compartments. By reconstituting functional signaling peptides, PRINTS can efficiently relocalize fluorescent proteins to the 26S proteasome, nucleus, mitochondria, plasma membrane, endomembrane organelles, and liquid-liquid phase separation (LLPS) condensates. Furthermore, we incorporated the optically regulated dimerization domain CRY2clust into the PRINTS system, achieving light-mediated control of protein entry into the cell membrane and LLPS membraneless compartments. Strikingly, we observed that HNRNPA1 promiscuously recruit CRY2- or intein-containing proteins into LLPS condensate, whereas FXR1, another LLPS protein showed minimal non-specific activity. PRINTS also allow organelle relocalization, such as LLPS condensates or mitochondria to cell membrane in a light-controllable manner. Overall, PRINTS provides a versatile and robust platform for manipulating protein and organelle subcellular translocations, offering a powerful tool to investigate the regulatory coordination and crosstalk between membranous and membraneless compartments in response to physiological needs.

cell biology↗

bioRxiv2025

Novel polymer fixed-target microfluidic platforms with an ultra-thin moisture barrier for serial macromolecular crystallography

The advent of ultrabright fourth generation X-ray light sources, including X-ray free-electron lasers (XFELs) and diffraction limited synchrotrons, has significantly advanced the field of serial macromolecular protein crystallography (SX). SX experiments demand a continuous supply of fresh microcrystalline sample, ideally while minimizing overall sample consumption. Here, we introduce a novel, robust, and user-friendly polymer film technology that can be assembled in various configurations to encapsulate protein microcrystals and provide sample support for SX. This system provides an efficient hydration barrier over extended durations while maintaining an exceptionally low X-ray background. We have validated this technology by assessing hydration retention under both ambient and ultra-high vacuum conditions, and by evaluating its mechanical stability under XFEL pulses. Furthermore, we have demonstrated the effectiveness of this approach in two room-temperature serial crystallography studies to determine the structure of a 24 kDa Rapid Encystment Phenotype (REP24) protein from Franciscella tularensis.

biophysics↗

bioRxiv2025

TabSyM: A Generative Pipeline for Small Multi-Cohort Omics Tabular Data

Machine learning applications in biomedicine such as omics data analysis are frequently hindered by datasets that are small, high-dimensional, and affected by batch effects across different patient cohorts. To address these challenges, we introduce TabSyM, a modular generative pipeline that synthesizes high-quality, task-relevant data to improve predictive modeling. TabSyM integrates three key stages: it extends a diffusion-based model (TabDDPM) to generate new omics data, employs a novel task-aware sampling mechanism guided by Bayesian optimization to select the most informative synthetic samples, and uses a Multi-Domain Adversarial Network (MDAN) to align data distributions for cross-cohort generalization. We validated our pipeline on a challenging, real-world task of predicting 3-year survival in gastric cancer patients from high-dimensional scRNA-seq data across five cohorts. The full TabSyM pipeline achieved a 30.2% AUROC improvement over the best tree-based models and an 11.5% AUROC gain over leading automated machine learning frameworks. Furthermore, the generative and sampling components are model-agnostic and can substantially boost the performance of classical models like XGBoost independently. These results establish that combining generative modeling with task-aware sampling and domain adaptation provides a robust and effective strategy for overcoming critical data limitations in biomedical tabular data analysis.

bioinformatics↗

bioRxiv2025

Diet context gates AgRP neuron involvement in Semaglutide-induced weight loss

Semaglutide, a GLP-1R agonist, is widely used for obesity and type 2 diabetes, but its neural mechanisms remain unclear. AgRP neurons regulate energy balance, yet their role in Semaglutides effects is unknown. We show that sustained treatment of female mice with Semaglutide leads to activation rather than inhibition of AgRP neurons. Ablation or hypofunction of AgRP neurons through cell-specific knockout of Sirt1 reduces Semaglutide-induced weight loss and impairs its hypoglycemic effects in female mice under Standard Diet. However, acute or chronic exposure to High-Fat Diet makes AgRP neurons dispensable for weight loss, suggesting that neural substrates for the actions of Semaglutide depends on dietary composition. Re-exposure to Standard Diet recovers the necessity for AgRP neurons, underscoring the influence of nutritional status on GLP-1R pathways. Our findings show the necessity for AgRP neurons in sustaining Semaglutide-induced weight loss in female mice on standard diet in vivo.

neuroscience↗

bioRxiv2025

Inferring Progressive Disconnection in Alzheimer's Disease with Probabilistic Boolean Networks

The modern understanding of Alzheimers disease as a disconnection syndrome presents the challenge of quantifying the directed influence between brain regions. To address this, we apply probabilistic Boolean networks to model effective brain connectivity for the first time, introducing a novel framework for analyzing functional magnetic resonance imaging data from a cohort comprising normal controls, individuals with mild cognitive impairment (MCI), and Alzheimers patients. Our robust statistical analysis identified five significant connections, each exhibiting a linear decline in influence throughout the disease spectrum. We observed a progressive disruption of pathways from the Default Mode Network to the Medial Temporal Lobe, capturing a key psychophysiological mechanism underlying Alzheimers disease. These findings demonstrate the potential of our framework as a powerful tool for modeling network-level dynamics in neurodegeneration.

bioinformatics↗

bioRxiv2025

Structure-activity Relationship for Diarylpyrazoles as Inhibitors of the Fungal Kinase Yck2

Candida albicans is a growing global health threat, causing 1.5 million invasive infections and 1 million deaths annually. Yeast casein kinase 2 (Yck2) in C. albicans has emerged as an antifungal target of the kinase inhibitor LY364947 (LY). Herein, we report Yck2 structure-activity relationships for 3,4- and 3,4,5-substituted pyrazole analogs of LY. X-ray crystallography and in vitro profiling revealed the importance of the hinge-binding heterocycle for Yck2 inhibition and fungal kinome selectivity. A hydrogen-bond network between the inhibitor, a bound water molecule, and catalytic residues within the ATP pocket was identified as a key determinant of selectivity over other fungal and human kinases. Phenol analog 11 showed remarkable selectivity for Yck2 and Yck22 over all other C. albicans protein kinases. Several of the LY analogs, including 11, demonstrated improved antifungal activity. These findings provide a framework for translating human kinase inhibitors into highly selective antifungal Yck2 inhibitors. Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=108 SRC="FIGDIR/small/664496v1_ufig1.gif" ALT="Figure 1"> View larger version (18K): org.highwire.dtl.DTLVardef@ef6a04org.highwire.dtl.DTLVardef@193b73org.highwire.dtl.DTLVardef@8e109corg.highwire.dtl.DTLVardef@e4a243_HPS_FORMAT_FIGEXP M_FIG C_FIG

microbiology↗

bioRxiv2025

f-BGM enables fungi-specific genome mining in high accuracy and interpretability

Emerging artificial intelligence (AI)-based genome mining methods have revolutionized the paradigm of bacterial secondary metabolite (SM) discovery. Fortunately, recent data accumulation of fungal biosynthetic gene clusters (BGC) fairly offers opportunities for systematic development and evaluation of fungi-specific pipelines. In this work, we proposed a deep learning framework termed as f-BGM specifically for fungal genome mining. By designing a novel self-attention-based architecture to augment inter-domain associations in local genomic contexts, f-BGM exhibits superior performance over existing AI-based methods in both in-distribution and out-of-distribution benchmark tests for BGC detection. Further analyses demonstrate that f-BGM is of decent interpretability on deciphering single-domain and -protein importance, as well as inter-domain partnership. By establishing additional binary classification models, f-BGM also achieves high-quality identification of core enzymes within given BGCs. Finally, case studies of f-BGM-driven genome mining in marine fungi uncovers biosynthetic potential underestimated by the rule-based method antiSMASH, as supported by experimental and computational validation.

bioinformatics↗