Biology subjects

Ling, Y.

Publications and source records attributed to Ling, Y..

At least 19 recordsLinked to original sources

bioRxiv2026

A multi-tissue epigenomic atlas links the sheep non-coding genome to domestication and complex traits

Non-coding regulatory variation drives complex traits, domestication, and evolutionary adaptation, yet the sheep genome lacks high-resolution functional annotation. Here we present SheepEpimap, a multi-tissue regulatory atlas harmonizing 516 CUT&Tag histone modifications, ATAC-seq, and RNA-seq datasets across 43 adult tissues in sheep. We annotated 2.93 million cis-regulatory elements, yielding 557,441 enhancer-gene pairs and 145,407 variants with allele-specific effects. By training a sequence-to-function deep-learning model, we decoded the base-pair syntax of chromatin accessibility, annotated transcription factor motif instances genome-wide, and constructed 12,210 tissue-specific gene regulatory networks (GRNs). Integrating this resource with multi-tissue expression quantitative trait loci, selection sweeps, and genome-wide association studies prioritized non-coding variants driving domestication and complex traits. Finally, cross-species analysis revealed that sequence-conserved, tissue-matched enhancers were significantly enriched in the heritability of complex traits and diseases in humans. In summary, SheepEpimap (https://genome.ucsc.edu/s/mengzhu/SheepEpimap) provides an open-access foundational ecosystem for sheep functional genomics, precision breeding, and comparative biology.

genetics↗

bioRxiv2026

OmniAge: a compendium of aging omic biomarkers links mitotic clocks to clonal hematopoiesis and causality

Interest in aging omic biomarkers has grown due to their ability to quantify biological age. Most of these biomarkers have been derived in blood and fall into many diverse categories, yet relatively little is known about their correlative patterns, especially between biomarkers from different categories. Here we present the OmniAge R and Python package, a collection of 413 aging omic biomarkers representing 12 different categories, including traditional epigenetic clocks, epigenetic mitotic clocks, DNA methylation-based proxies for clonal hematopoiesis and inflammaging, causal clocks, cell-type specific epigenetic clocks and single-cell transcriptomic clocks. By studying their inter-class correlations across large blood datasets, we reveal associations of mitotic age with clonal hematopoiesis subtypes and causal clocks, which are predictive of cancer risk. Using proxies of serum protein levels, we further dissect associations with mitotic clocks, clonal hematopoiesis and causal clocks into distinct biological processes mapping to key aging pathways. Applying OmniAge to multi-modal data of sorted immune cell-types reveals that age-acceleration derived from transcriptomic and epigenetic clocks correlate, but that this is driven by underlying cell-type heterogeneity. In summary, the OmniAge package is an exploratory tool for evaluating large numbers of aging omic biomarkers, and to aid discovery and generate new hypotheses.

bioinformatics↗

bioRxiv2026

Spatio-temporal mapping of immune cell dynamics during human sequential lymph node metastasis

Regional lymph node (LN) metastasis critically influences distant metastatic progression, anti-tumour immunity, and patient prognosis. While tumour-induced immune modulation in tumour-draining LNs (TDLNs) has been extensively studied using murine models, the systematic reconstruction of the immune system from primary tumours through TDLNs and subsequent lymph nodes in human cancer progression remains understudied. Here, we utilised integrated multi-omics approaches, including imaging mass cytometry, single-cell RNA sequencing, Visium and Xenium spatial transcriptomics, and multi-colour immunofluorescence to systematically characterise immune cell dynamics across 147 paired primary tumours, sentinel TDLNs (S-TDLNs), and secondary axillary LNs (ALNs) obtained from 50 treatment-naive triple-negative breast cancer patients with different progression statuses. Our comprehensive profiling revealed critical immune alterations, such as decreased type-2 conventional dendritic cells (cDC2), naive T cells, and B cells, along with an increase in immunosuppressive macrophages. Developing a novel single-cell transformer model, we identified substantial alterations in various immune cell populations, notably MARCO+ macrophages, which strongly correlated with breast cancer patient survival outcomes. Spatial analysis combined with our newly integrated cell-cell interaction platform revealed diminished immune cell communication and impaired priming interactions among dendritic cells, B cells, and T cells within metastatic lymph nodes and primary tumour sites. In an independent neoadjuvant immunotherapy cohort of 36 TNBC patients with 52 lymph node samples, we found preservation of CD1c cDC2 in lymph nodes predicted pathological complete response and longer event-free survival, highlighting cDC2 as a potential biomarker and therapeutic target. Collectively, this systematic mapping of immune landscape alterations during human sequential LN metastasis provides essential insights for understanding cancer metastasis mechanisms and paves the way for innovative immunotherapeutic strategies.

immunology↗

bioRxiv2026

Benchmarking zero-shot single-cell foundation model embeddings for cellular dynamics reconstruction

Reconstructing cellular trajectories from time-resolved single-cell transcriptomics is fundamental to understanding processes from embryonic development to cancer progression. While single-cell foundation models (scFMs) promise universal biological representations through large-scale pretraining, their capacity to capture the non-linear dynamics governing cell-fate decisions remains uncharacterized. Here we systematically benchmark multiple scFMs across challenging biomedical scenarios involving branching lineages and continuous state transitions. By coupling zero-shot scFM embeddings with dynamic optimal transport, we evaluated their performance against a traditional highly variable gene (HVG) baseline in backtracking progenitor states, interpolating transition intermediates, and extrapolating future fates. We find that zero-shot scFM embeddings underperform the HVG baseline across diverse biological systems, particularly in recovering the distributional complexity of unobserved cells. Mechanistic analysis reveals that current scFM architectures tend to over-compress subtle temporal signals, causing an artificial "linearization" of branched biological structures that may obscure critical divergence points in disease progression. Our findings suggest that while scFMs provide unified cell-state views, the HVG baseline remains more robust for trajectory inference, identifying a fundamental "temporal-compression" bottleneck that must be addressed to develop next-generation, dynamics-aware foundation models.

bioinformatics↗

bioRxiv2025

Mass spectrometry-based proteomic and glycoproteomic drafts of fifteen human body fluids

The study of proteins and N-glycosylation in human body fluids (HBFs) is crucial for understanding both physiological and pathological states, offering valuable insights for disease diagnostics and monitoring. In this study, we present a comprehensive proteomics and glycoproteomics dataset featuring 15 types of precious HBFs (plasma (P), urine (U), cerebrospinal fluid (CSF), pleural fluid (PF), ascitic fluid (AF), synovial fluid (SF), pericardial fluid (PCF), peritoneal dialysis effluent (PDE), bile (B), gastric juice (GJ), seminal plasma (SP), saliva (SA), pancreatic juice (PJ), sweat (SW), and tear (T)). We also provide detailed protocols for protein processing specific to each type of HBF. The resulting peptides and enriched intact N-glycopeptides were analyzed via liquid chromatography-tandem mass spectrometry (LC-MS/MS) in data independent acquisition (DIA) and combined electron transfer/higher-energy collisional dissociation and stepped collision energy/higher-energy collisional dissociation (EThcD-sceHCD) mode, respectively. The resulting spectra and searched files have been uploaded to the online repository iProX (www.iprox.cn) and made easily accessible (PXD068799) to facilitate further research into HBF proteomics and glycoproteomics.

systems biology↗

bioRxiv2025

Real-Time Viability Assessment of Ex Vivo Mouse Kidneys for Transplant Applications Using Dynamic Optical Coherence Tomography

Kidney transplantation remains the preferred treatment for patients with end-stage kidney disease. However, the ongoing shortage of donor organs continues to limit the availability of transplant treatments. Existing evaluation methods, such as the kidney donor profile index (KDPI) and pre-transplant donor biopsy (PTDB), have various limitations, including low discriminative power, invasiveness, and sampling errors, which reduce their effectiveness in organ quality assessment and contribute to the risk of unnecessary organ discard. In this study, we explored the dynamic optical coherence tomography (DOCT) as a real-time, non-invasive approach to monitor the viability of ex vivo mouse kidneys during static cold storage over 48 hours. The dynamic metrics logarithmic intensity variance (LIV), early OCT correlation decay speed (OCDSe), and late OCT correlation decay speed (OCDSl) were extracted from OCT signal fluctuations to quantify temporal and spatial tissue activity and deterioration. Our results demonstrate that DOCT provides complementary functional information to morphological assessment offered by conventional OCT imaging, showing potential to improve pre-transplant organ evaluation and clinic decision-making.

bioengineering↗

bioRxiv2025

Comparative Temporal Transcriptomic Analysis of SOD1 Mutations in iPSC-Motor Neurons

Mutations in the SOD1 gene are among the most significant genetic contributors to amyotrophic lateral sclerosis (ALS), with different variants linked to varying disease severity. To investigate the molecular mechanisms driving this variability, we conducted RNA sequencing on spinal motor neurons (MNs) differentiated from isogenic human induced pluripotent stem cell (iPSC) lines engineered via CRISPR/Cas9. These lines carried two representative SOD1 heterogenous mutations, D91A and G94A, and were analyzed at Days 10 and 20 of neuronal maturation stage to capture the temporal changes of gene expression. We aim to explore how these mutations affect MN function, identify distinct molecular pathways that may explain the variable severity of ALS, and investigate the role of translation and metabolic dysregulation in disease progression.

bioinformatics↗

bioRxiv2025

Identification of immune-mitophagy related gene in PD based on single-cell sequencing and Mendelian randomization

Parkinsons disease (PD) involves dysregulated mitophagy and immuneresponses, though the underlying mechanisms remain unclear. To identify key genes linking these pathways, we integrated single-cell RNA sequencing (GSE157783) with Mendelian randomization(MR) analysis, screening mitophagy-related genes (MRGs) and immune-related genes (IRGs). Differential expression analysis across PD cell subpopulations, combined with MR, revealed four causal genes: SLC11A1and DDX17(protective) and MRAS and PDIA3(risk). These genes were enriched in antigen presentation and calcium signaling pathways and exhibited dynamic expression in astrocytes and microglia during differentiation. Subsequent protein-protein interaction(PPI) network, regulatory (SCENIC), and drug-target analyses further characterized their roles. Validation in MPTP-induced PD mice confirmed behavioral deficits and altered expression of these genes, supporting their functional relevance. Our findings highlight SLC11A1, DDX17, MRAS, and PDIA3 as critical mitophagy-immune hubs in PD, offering mechanistic insights and therapeutic targets. Author SummaryPD is a neurodegenerative disorder linked to mitochondrial dysfunction (mitophagy) and immune dysregulation, yet the interplay between these mechanisms remains poorly understood. Using integrative single-cell sequencing and MR analyses, we identified four key genes-SLC11A1, DDX17, MRAS, and PDIA3-that bridge immune and mitophagy pathways in PD pathogenesis. These genes exhibit causal relationships with PD risk, offering novel insights into diagnostic and therapeutic strategies.

genetics↗

bioRxiv2025

Meta-AlignNN: A meta-learning framework for stable brain-computer interface performance across subjects, time, and tasks

Practical application of brain-computer interfaces (BCIs) requires stable mapping between neuronal activity and behavior through various behavioral contexts and for different individuals. Due to neural activity instability, BCIs require frequent recalibration to maintain robust performance. Early approaches to addressing BCI stability issues mainly focused on tackling the challenge of neural activity changes over time. However, future BCI applications involve diversified scenarios and subjects, requiring solutions that address neural variability across time, subjects, and tasks. This study proposes a meta-learning-based algorithm to achieve BCI stability, named "Meta-AlignNN." By capitalizing on the consistency of neural population dynamics, it provides a unified solution for maintaining BCI stability, robustness, and scalability across subjects, time, and tasks. Tested over two years on four tasks with three monkeys, as well as on public datasets, the approach has achieved significantly excellent performance in both offline decoding and real-time brain-control, outperforming existing methods. Our findings provides a foundation for meeting the clinical demands of longterm, efficient, and stable usability across patients and tasks, offering a compelling solution for practical BCI applications.

bioengineering↗

bioRxiv2025

An explainable artificial intelligence framework reveals mutations associated with drug resistance in Mycobacterium tuberculosis

Understanding the mechanisms of drug resistance in Mycobacterium tuberculosis (MTB) is essential for the rapid detection of resistance and for guiding effective treatment, ultimately contributing to reducing the global burden of tuberculosis (TB). Under anti-TB drugs pressure, MTB continues to accumulate resistance loci. The current repertoire of known resistance-associated mutations requires further refinement, necessitating efficient methods for the timely identification of potential resistance sites. Here, we introduce xAI-MTBDR, an explainable artificial intelligence framework designed to identify new resistance-associated mutations and predict drug resistance in MTB. It outperforms state-of-the-art methods in predicting drug resistance for all first-line drugs, and scoring each mutations contribution to resistance. By leveraging public whole-genome sequencing data from nearly 40,000 MTB isolates, the framework identified 788 candidate resistance-related mutations and revealed 30 potential resistance markers, several of which are structurally located closer to their respective drugs compared to known resistance mutations. Furthermore, these scores enabled the framework to efficiently subgroup isolates with different resistance mechanisms and reflect varying levels of resistance. The framework serves as a valuable tool for accurate detection of drug-resistant MTB and offers new insights into its underlying mechanisms.

microbiology↗

bioRxiv2024

PLM_Sol: predicting protein solubility by benchmarking multiple protein language models with the updated Escherichia coli protein solubility dataset

Protein solubility plays a crucial role in various biotechnological, industrial and biomedical applications. With the reduction in sequencing and gene synthesis costs, the adoption of high-throughput experimental screening coupled with tailored bioinformatic prediction has witnessed a rapidly growing trend for the development of novel functional enzymes of interest (EOI). High protein solubility rates are essential in this process and accurate prediction of solubility is a challenging task. As deep learning technology continues to evolve, attention-based protein language models (PLMs) can extract intrinsic information from protein sequences to a greater extent. Leveraging these models along with the increasing availability of protein solubility data inferred from structural database like the Protein Data Bank (PDB), holds great potential to enhance the prediction of protein solubility. In this study, we curated an Updated Escherichia coli (E.coli) protein Solubility DataSet (UESolDS) and employed a combination of multiple PLMs and classification layers to predict protein solubility. The resulting best-performing model, named Protein Language Model-based protein Solubility prediction model (PLM_Sol), demonstrated significant improvements over previous reported models, achieving a notable 5.7% increase in accuracy, 9% increase in F1_score, and 10.4% increase in MCC score on the independent test set. Moreover, additional evaluation utilizing our in-house synthesized protein resource as test data, encompassing diverse types of enzymes, also showcased the superior performance of PLM_Sol. Overall, PLM_Sol exhibited consistent and promising performance across both independent test set and experimental set, thereby making it well-suited for facilitating large-scale EOI studies. PLM_Sol is available as a standalone program and as an easy-to-use model at https://zenodo.org/doi/10.5281/zenodo.10675340.

bioinformatics↗

bioRxiv2024

Deficiency in ribosome biogenesis causes streptomycin resistance and impairs motility in Salmonella

The ribosome is the central hub for protein synthesis and the target of many antibiotics. Whereas the majority of ribosome-targeting antibiotics inhibit protein synthesis and are bacteriostatic, aminoglycosides promote protein mistranslation and are bactericidal. Understanding the resistance mechanisms of bacteria against aminoglycosides is not only vital for improving the efficacy of this critically important group of antibiotics but also crucial for studying the molecular basis of translational fidelity. In this work, we analyzed Salmonella mutants evolved in the presence of the aminoglycoside streptomycin (Str) and identified a novel gene rimP to be involved in Str resistance. RimP is a ribosome assembly factor critical for the maturation of the 30S small subunit that binds Str. Deficiency in RimP increases resistance against Str and facilitates the development of even higher resistance. Deleting rimP decreases mistranslation and cellular uptake of Str, and further impairs flagellar motility. Our work thus highlights a previously unknown mechanism of aminoglycoside resistance via defective ribosome assembly.

microbiology↗

bioRxiv2022

Spatial transcriptome profiling of in vitro 3D tumouroids to study tumour-stroma interactions

Bioengineering facets of the tumour microenvironment (TME) are essential in 3D tissue models to accurately recapitulate tumour progression. Stromal cells are key components of the TME and their incorporation into 3D biomimetic bioengineered tumour-stroma models is essential to be able to mimic the TME. By engineering tumouroids with distinct tumour and stromal compartments, it has been possible to identify how gene expression is altered by the presence of different stromal cells using spatial transcriptomics. Ameloblastoma is a benign epithelial tumour of the jawbone and in engineered multi-compartment tumouroids increased expression of oncogenes was found where osteoblasts (bone stroma) were present. Engineering a gingival fibroblast stroma resulted in increased matrix remodelling genes in the ameloblastoma tumour. This study provides evidence to show the stromal specific effect on tumour behaviour and illustrates the importance of engineering biologically relevant stroma for engineered tumour models. Our novel results show that an engineered fibroblast stroma causes the upregulation of matrix remodelling genes in ameloblastoma which directly correlates to measured invasion in the model. In contrast the presence of an osteoblast/bone stroma increases the expression of oncogenes by ameloblastoma cells.

bioengineering↗

bioRxiv2022

Control of Leaf Width by the APC/CTAD1-WL1-NAL1 Pathway in Rice

Leaf morphology is one of the most important features of the ideal plant architecture. However, the genetic and molecular mechanisms controlling leaf morphology in crops remain largely unknown, despite their central importance. Here we demonstrate that the APC/CTAD1-WL1-NAL1 pathway regulates leaf width in rice, and mutation of WL1 leads to width leaf variation. WL1 interacts with TAD1 and is degraded by APC/CTAD1, with the loss of TAD1 function resulting in narrow leaves. The WL1 protein directly binds to the regulatory region of NAL1 and recruits the corepressor TOPLESS-RELATED PROTEIN to inhibit NAL1 expression by down-regulating the level of histone acetylation of chromatin. Furthermore, biochemical and genetic analyses revealed that TAD1, WL1, and NAL1 function in a common pathway to control leaf width. Our study establishes an important framework for the APC/CTAD1-WL1-NAL1 pathway-mediated control of leaf width in rice and introduces novel perspectives for using this regulatory pathway for improving crop plant architecture.

plant biology↗

bioRxiv2021

Machine learning enables accurate and rapid prediction of active molecules against breast cancer cells

SummaryBreast cancer (BC) has surpassed lung cancer as the most frequently occurring cancer, and it is the leading cause of cancer-related death in women. Therefore, there is an urgent need to discover or design new drug candidates for BC treatment. In this study, we first collected a series of structurally diverse datasets consisting of 33,757 active and 21,152 inactive compounds for 13 breast cancer cell lines and one normal breast cell line commonly used in in vitro antiproliferative assays. Predictive models were then developed using five conventional machine learning algorithms, including naive Bayesian, support vector machine, k-Nearest Neighbors, random forest, and extreme gradient boosting, as well as five deep learning algorithms, including deep neural networks, graph convolutional networks, graph attention network, message passing neural networks, and Attentive FP. A total of 476 single models and 112 fusion models were constructed based on three types of molecular representations including molecular descriptors, fingerprints, and graphs. The evaluation results demonstrate that the best model for each BC cell subtype can achieve high predictive accuracy for the test sets with AUC values of 0.689-0.993. Moreover, important structural fragments related to BC cell inhibition were identified and interpreted. To facilitate the use of the model, an online webserver called ChemBC and its local version software were developed to predict potential anti-BC agents. AvailabilityChemBC webserver is available at http://chembc.idruglab.cn/ and its local version Python software is maintained at a GitHub repository (https://github.com/idruglab/ChemBC). Contactzjqgmc@163.com or lingwang@scut.edu.cn Supplementary informationSupplementary data are available at Bioinformatics online.

bioinformatics↗

bioRxiv2021

Evolutionary insights into a non-coding deletion of SARS-CoV-2 B.1.1.7

Three prevalent SARS-CoV-2 Variants of Concern (VOCs) were emerged and caused epidemic waves. It is essential to uncover the key genetic changes that cause the high transmissibility of VOCs. However, different viral mutations are generally tightly linked so traditional population genetic methods may not reliably detect beneficial mutation. In this study, we proposed a new pandemic-scale phylogenomic approach to detect mutations crucial to transmissibility. We analyzed 3,646,973 high-quality SARS-CoV-2 genomic sequences and the epidemiology metadata. Based on the sequential occurrence order of mutations and the instantaneously accelerated furcation rate, the analysis revealed that two non-coding mutations at the position of 28271 (g.a28271-/t) might be crucial for the high transmissibility of Alpha, Delta and Omicron VOCs. Both two mutations cause an A-to-T change at the core Kozak site of the N gene. The analysis also revealed that the non-coding mutations (g.a28271-/t) alone are unlikely to cause high viral transmissibility, indicating epistasis or multilocus interaction in viral transmissibility. A convergent evolutionary analysis revealed that g.a28271-/t, S:P681H/R and N:R203K/M occur independently in the three-VOC lineages, suggesting a potential interaction among these mutations. Therefore, this study unveils that non-synonymous and non-coding mutations could affect the transmissibility synergistically.

evolutionary biology↗

bioRxiv2021

Unbalanced dietary patterns contribute to the pathogenesis of precocious puberty by affecting gut microbiota and host metabolites

Precocious puberty (PP) mostly stems from endocrine disorders. However, its triggering factors, especially for the early onset of partial PP, and the associated pathogenic mechanisms remain ambiguous. In this study, a systematic analysis in the form of a questionnaire of lifestyles, gut microbiome, and serum metabolome data was carried out to examine the pathogenesis of PP in a cohort comprised of 200 girls, with or without PP. The analysis revealed substantial alterations in gut microbiota, serum metabolites, as well as lifestyle patterns in the PP group, which were characterized by an elevated abundance of {beta}-glucuronidase-producing and butyrate-producing bacteria, and excessive lipid concentration with decreased levels of organic nitrogen compounds in the serum of the participants. These differential microbes and metabolites tend to be reliable non-invasive diagnostic biomarkers aiding the early diagnosis of PP and exhibit a strong discriminative power (AUC = 0.93 and AUC = 0.97, respectively). Furthermore, the microbial biomarkers were confirmed in an independent validation cohort (n = 83, AUC = 0.85). Moreover, structural equation modeling revealed that unhealthy dietary habits were the primary contributors for the alteration of gut microbiota and serum metabolites, triggering the imbalance in the host hormones that leads to premature physical development. Our study determines a causal relationship among the gut microbiota, host metabolites, diet, and clinical characteristics of preadolescent girls who experienced early onset of PP, and formulates non-invasive diagnostic tools demonstrating excellent performance for the early detection of PP.

microbiology↗

bioRxiv2020

An interactive viral genome evolution network analysis system enabling rapid large-scale molecular tracing of SARS-CoV-2

Comprehensive analyses of viral genomes can provide a global picture on SARS-CoV-2 transmission and help to predict the oncoming trends of pandemic. This molecular tracing is mainly conducted through extensive phylogenetic network analyses. However, the rapid accumulation of SARS-CoV-2 genomes presents an unprecedented data size and complexity that has exceeded the capacity of existing methods in constructing evolution network through virus genotyping. Here we report a Viral genome Evolution Network Analysis System (VENAS), which uses Hamming distances adjusted by the minor allele frequency to construct viral genome evolution network. The resulting network was topologically clustered and divided using community detection algorithm, and potential evolution paths were further inferred with a network disassortativity trimming algorithm. We also employed parallel computing technology to achieve rapid processing and interactive visualization of >10,000 viral genomes, enabling accurate detection and subtyping of the viral mutations through different stages of Covid-19 pandemic. In particular, several core viral mutations can be independently identified and linked to early transmission events in Covid-19 pandemic. As a general platform for comprehensive viral genome analysis, VENAS serves as a useful computational tool in the current and future pandemics.

evolutionary biology↗