Biology subjects

You, R.

Publications and source records attributed to You, R..

At least 19 recordsLinked to original sources

bioRxiv2025

Flu Virus Infection in Juvenile Mice Leads to Lifelong Multi-Organ Damage and Parkinsonian Pathological Changes in Aging

The widespread prevalence of Long COVID underscores the potential for viral infections to exert prolonged effects on hosts long after initial recovery. Despite this recognition, the long-term and even lifelong consequences of viral infections remain poorly understood. In this study, we infected juvenile mice with influenza viruses and systematically examined the lifelong effects (from 4 to 450 days post-infection) of these infections. Pathological analysis revealed persistent lifelong damage in multiple organs, including chronic lung inflammation and fibrosis, along with significant cardiac and renal pathology. Viral infection led to substantial neuronal loss in key brain regions of mice (hippocampal CA1, CA3, and striatum) during middle and old age. Strikingly, we identified two hallmark pathological features of Parkinsons disease - dopaminergic neuron degeneration and Lewy body-like -synuclein inclusions - in middle and aged infected mice. Transcriptomic profiling demonstrated sustained upregulation of inflammation-related genes in both lung and blood tissues, correlating with observed pathological changes. Lung-derived secreted proteins, encoded by differentially expressed genes, may mediate cross-organ communication affecting cardiac, renal, and neurological function. Brain transcriptome analysis at three months post-infection revealed downregulation of neurodevelopmental genes, potentially contributing to subsequent neuronal loss and Parkinsonian pathology. These findings collectively suggest that pulmonary influenza infection can induce systemic multi-organ effects through both secretory pathways and chronic inflammatory responses. The study offers crucial insights for developing more effective strategies to prevent and manage infectious diseases and their long-term sequelae.

microbiology↗

bioRxiv2024

A universal single-cell transcriptomics atlas of human lung decodes multiple pulmonary diseases

Human lung is a complex organ susceptible to various diseases. Single-cell transcriptomic studies provide rich data to targeting specific research questions. Here, we present uniLUNG, the largest lung transcriptomic cell atlas, comprising over 10 million cells across 20 disease states and healthy controls. We ensembled a universal hierarchical annotation framework and conducted a full benchmarking of data integration to define a standardized nomenclature and marker genes for lung cell types. Using uniLUNG, we identified Lym-monocyte and T-like B cells, new cell types in specific lung diseases, confirming their existence by comparing with external single-cell atlases. Additionally, we discovered the NSCLC-like SCLC subpopulation, a transitional malignant cell population associated with the transition from NSCLC to SCLC, which was validated and further characterized in spatial dimensions, revealing its complex role in tumour progression. Overall, uniLUNG represents a comprehensive range of human lung cell diversity, providing valuable data resources and a reliable foundation for lung single-cell research. HIGHLIGHTSO_LIThe largest scRNA atlas for human lung covers 10 million cells from 20 lung states. C_LIO_LIA four-level universal cell annotation framework encompasses 120 lung cell types. C_LIO_LIComprehensive benchmarking on 18 strategies guides data integration. C_LIO_LISpecific distribution of Lym-monocytes and T-like B cells in specific lung diseases. C_LIO_LIThe NSCLC-like SCLC subpopulation in transitional events of malignant cells from NSCLC to SCLC. C_LI

bioinformatics↗

bioRxiv2024

Systematic identification and characterization of virus lncRNAs suggests extensive structural mimicry of host lncRNAs

Virus long non-coding RNAs (vlncRNAs) play crucial roles in viral infections, yet their identification and characterization remain limited. This study identified 5,053 novel vlncRNAs across 25 viral species using third-generation sequencing, with two from Influenza A virus and Vesicular stomatitis virus validated by RT-qPCR. Most vlncRNAs originated from dsDNA viruses. Only 1% having annotated RNA families, suggesting many novel RNA structures. Although only seven vlncRNAs shared sequence similarities with human lncRNAs (hlncRNAs), 772 vlncRNAs from 15 human viruses structurally mimicked hlncRNAs. These vlncRNA and hlncRNAs bound to similar miRNAs, potentially acting as miRNA sponges to promote essential life process. Splicing analysis showed vlncRNAs had a prevalence of alternative first exon. Finally, we developed vlncRNAbase (http://computationalbiology.cn/vlncRNAbase/#/) to store and organize the newly identified and known vlncRNAs. Overall, the study provides a valuable resource for further investigation into vlncRNAs and deepens our understanding of the diversity, structure and function of the molecule.

bioinformatics↗

bioRxiv2024

Structure-aware protein sequence alignment using contrastive learning

Protein alignment is indispensable for biological discovery, supporting structure comparison, functional annotation, and evolutionary inference. While structure-based methods are highly effective at detecting structural similarity, their applicability is constrained by the limited availability of experimentally resolved protein structures and high computational cost. Sequence-based approaches using pretrained protein language models (pLMs) provide scalable alternatives, yet supervised methods based on differentiable dynamic programming have not consistently outperformed simpler unsupervised strategies. Here, we present CLAlign, a structure-aware protein alignment framework based on contrastive learning. CLAlign fine-tunes a pretrained pLM to generate structure-aware residue-level embeddings enriched with structural context, without relying on differentiable dynamic programming. It represents the first supervised approach to consistently outperform unsupervised pLM-based methods, and it naturally extends to both sequence- and structure-based alignment by flexibly adopting different protein language model encoders. CLAlign achieves state-of-the-art accuracy on the MALIDUP and MALISAM benchmarks, outperforming existing sequence-based methods by large margins while remaining highly efficient. Moreover, its alignment scores show clear biological interpretability: in remote homology detection on SCOPe, CLAlign performs comparably to structure-based methods such as TM-align while far exceeding all sequence-based baselines. Together, these results establish CLAlign as a simple, extensible, and biologically meaningful framework for protein alignment.

bioinformatics↗

bioRxiv2023

uniLIVER: a Human Liver Cell Atlas for Data-Driven Cellular State Mapping

The liver performs several vital functions such as metabolism, toxin removal and glucose storage through the coordination of various cell types. The cell type compositions and cellular states undergo significant changes in abnormal conditions such as fatty liver, cirrhosis and liver cancer. As the recent breakthrough of the single-cell/single-nucleus RNA-seq (sc/snRNA-seq) techniques, there is a great opportunity to establish a reference cell map of liver at single cell resolution with transcriptome-wise features. In this study, we build a unified liver cell atlas uniLIVER by integrative analyzing a large-scale sc/snRNA-seq data collection of normal human liver with 331,125 cells and 79 samples from 6 datasets. Besides the hierarchical cell type annotations, uniLIVER also proposed a novel data-driven strategy to map any query dataset to the normal reference map by developing a machine learning based framework named LiverCT. Applying LiverCT on the datasets from multiple abnormal conditions (1,867,641 cells and 439 samples from 12 datasets), the alterations of cell type compositions and cellular states were systematically investigated in liver cancer.

bioinformatics↗

bioRxiv2023

WWP2 MEDIATES THE METABOLIC REPROGRAMMING OF RENAL MYOFIBROBLASTS TO PROMOTE KIDNEY FIBROSIS

Renal fibrosis is a common pathological endpoint in chronic kidney disease (CKD) that is challenging to reverse. Although myofibroblasts are mainly responsible for the accumulation of a fibrillar collagen-rich extracellular matrix (ECM) in fibrotic kidney, recent studies have unveiled their diversity in terms of proliferative and fibrotic characteristics. This diversity could be linked with the existence of different metabolic states, and myofibroblast metabolic reprogramming may contribute to the pathogenesis and progression of renal fibrosis. Here, we reveal an unexpected role of the E3 ubiquitin-protein ligase WWP2 in the metabolic reprogramming of myofibroblasts during renal fibrosis. The tubulointerstitial expression of WWP2 contributes to the progression of fibrosis in CKD patients, and in pre-clinical murine models of CKD. WWP2 deficiency increases fatty acid oxidation and activates the pentose phosphate pathway, boosting mitochondrial respiration at the expense of glycolysis. This concurrently promotes myofibroblast proliferation and halts pro-fibrotic activation, reducing the severity of kidney fibrosis. Mechanistically, WWP2 suppresses the transcription of PGC-1, a metabolic mediator shaping myofibroblast fibrotic response. Pharmacological interventions targeting PGC-1 reverse the effects of WWP2 on fibrotic myofibroblasts. These findings demonstrate the influence of WWP2 on essential metabolic pathways involved in fibrogenesis, uncovering the WWP2-PGC-1 axis that orchestrates the metabolic reprogramming of myofibroblasts during renal fibrosis. Our study presents a potential novel target for therapeutic intervention in the treatment of chronic kidney disease. Graphical abstract O_FIG O_LINKSMALLFIG WIDTH=177 HEIGHT=200 SRC="FIGDIR/small/554242v1_ufig1.gif" ALT="Figure 1"> View larger version (52K): org.highwire.dtl.DTLVardef@1847ffeorg.highwire.dtl.DTLVardef@1ef029aorg.highwire.dtl.DTLVardef@93e0f1org.highwire.dtl.DTLVardef@950d49_HPS_FORMAT_FIGEXP M_FIG C_FIG HighlightsO_LIWWP2 expression is elevated in the tubulointerstitium of fibrotic kidneys and contributes to CKD pathogenesis and progression. C_LIO_LIWWP2 uncouples the pro-fibrotic activation and cell proliferation in renal myofibroblasts. C_LIO_LIWWP2 controls mitochondrial respiration in renal myofibroblasts through the metabolic regulator PGC-1 C_LIO_LIMyofibroblast metabolic reprogramming mediates the effect of WWP2 on fibrotic myofibroblasts. C_LI

cell biology↗

bioRxiv2023

HCCDB v2.0: Decompose the Expression Variations by Single-cell RNA-seq and Spatial Transcriptomics in HCC

Large-scale transcriptomic data are crucial for understanding the molecular features of hepatocellular carcinoma (HCC). By integrating 15 transcriptomic datasets of HCC clinical samples, the first version of HCCDB was released in 2018. The meta-analysis of differentially expressed genes and prognosis-related genes across multiple datasets provides a systematic view of the altered biological processes and the inter-patient heterogeneities of HCC with high reproducibility and robustness. After four years, the database needs to integrate recently published datasets. Furthermore, the latest single-cell and spatial transcriptomics provided a great opportunity to decipher the complex gene expression variations at the cellular level with spatial architecture. Here, we present HCCDB v2.0, an updated version that combines bulk, single-cell, and spatial transcriptomic data of HCC clinical samples. It dramatically expands the bulk sample size, adding 1656 new samples of 11 datasets to the existing 3917 samples, thereby enhancing the reliability of transcriptomic meta-analysis. A total of 182,832 cells and 69,352 spatial spots are added to the single-cell and spatial transcriptomics sections, respectively. A novel single-cell level and 2-dimension (sc-2D) metric was proposed to summarize the cell type-specific and dysregulated gene expression patterns. Results are all graphically visualized in our online portal, allowing users to easily retrieve data through a user-friendly interface and navigate between different views. With extensive clinical phenotypes and transcriptomic data in the database, we show two applications for identifying prognosis-associated cells and tumor microenvironment. HCCDB v2.0 is available at http://lifeome.net/database/hccdb2.

cancer biology↗

bioRxiv2023

Protein language model powers accurate and fast sequence search for remote homology

Homologous protein search is one of the most commonly used methods for protein annotation and analysis. Compared to structure search, detecting distant evolutionary relationships from sequences alone remains challenging. Here we propose PLMSearch (Protein Language Model), a homologous protein search method with only sequences as input. With deep representations from a pre-trained protein language model to predict similarity, PLMSearch can capture the remote homology information hidden behind the sequences. Extensive experimental results show that PLMSearch can search millions of query-target protein pairs in seconds like MMseqs2 while increasing the sensitivity by more than threefold, and is comparable to state-of-the-art structure search methods. In particular, unlike traditional sequence search methods, PLMSearch can recall most remote homology pairs with low sequence similarity but sharing similar structures. PLMSearch is freely available at https://dmiip.sjtu.edu.cn/PLMSearch.

bioinformatics↗

bioRxiv2023

Fast and accurate DNASeq Variant Calling workflow composed of LUSH toolkit

BackgroundWhole genome sequencing (WGS) is becoming increasingly prevalent for molecular diagnosis, staging and prognosis because of its declining costs and the ability to detect nearly all genes associated with a patients disease. The currently widely accepted variant calling pipeline, GATK, is limited in terms of its computational speed and efficiency, which cannot meet the growing analysis needs. MethodsIn this study, we propose a fast and accurate DNASeq variant calling workflow that is purely composed of tools from LUSH toolkit. The LUSH pipeline is highly optimized for the WGS pipeline based on SOAPnuke, BWA and GATK which can be deployed on any general-purpose CPU-based computing system. We validated the accuracy, speed and scalability of the LUSH pipeline on several standard WGS datasets. ResultsOur test results show that the LUSH pipeline and the GATK pipeline are highly consistent in terms of accuracy, achieving over 99% precision and recall on NA12878. For speed, the LUSH pipeline completes 30x WGS data in 1.6 hours, which is about 17x faster than the GATK pipeline. From BAM to VCF, LUSH_HC even takes only 12 minutes, about 76x faster than GATK. Moreover, the LUSH pipeline shows favorable scalability in terms of thread and sequencing depth. ConclusionThe LUSH pipeline provides far superior computational speed to GATK while maintaining a high level of accuracy comparable to that of GATK, which greatly facilitates bedside analysis of acute patients, large-scale cohort data analysis, and variant calling in crop breeding programs.

bioinformatics↗

bioRxiv2022

NetGO 3.0: Protein Language Model Improves Large-scale Functional Annotations

As one of the state-of-the-art automated function prediction (AFP) methods, NetGO 2.0 integrates multi-source information to improve the performance. However, it mainly utilizes the proteins with experimentally supported functional annotations without leveraging valuable information from a vast number of unannotated proteins. Recently, protein language models have been proposed to learn informative representations (e.g., Evolutionary Scale Modelling (ESM)-1b embedding) from protein sequences based on self-supervision. We represent each protein by ESM-1b and use logistic regression (LR) to train a new model, LR-ESM, for AFP. The experimental results show that LR-ESM achieves comparable performance with the best-performing component of NetGO 2.0. Therefore, by incorporating LR-ESM into NetGO 2.0, we develop NetGO 3.0 to improve the performance of AFP extensively. NetGO 3.0 is freely accessible at https://dmiip.sjtu.edu.cn/ng3.0.

bioinformatics↗

bioRxiv2022

Uptake of tumor-derived microparticles induces metabolic reprogramming of macrophages in the early metastatic lung.

The formation of a pre-metastatic niche is a critical step during the metastatic spread of cancer. One way by which primary tumors prime host cells at future metastatic sites is through local shedding of tumor-derived microparticles as a consequence of vascular sheer flow. However, it remains unclear how the uptake of such particles by resident immune cells affects their phenotype and function. Here we show that ingestion of tumor-derived microparticles by macrophages induces a rapid metabolic and phenotypic switch that is characterized by enhanced mitochondrial mass and function, increased oxidative phosphorylation and upregulation of cellular adhesion molecules resulting in reduced motility in the early metastatic lung. We show that this reprogramming event is dependent on signaling through the mTORC1, but not mTORC2 pathway, and is unique to uptake of tumor-derived microparticles. Together, these data support a mechanism by which uptake of tumor-derived microparticles induces reprogramming of macrophages to shape their fate and function in the early metastatic lung.

immunology↗

bioRxiv2022

Immediate myeloid depot for SARS-CoV-2 in the human lung

In the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) pandemic, considerable focus has been placed on a model of viral entry into host epithelial populations, with a separate focus upon the responding immune system dysfunction that exacerbates or causes disease. We developed a precision-cut lung slice model to investigate very early host-viral pathogenesis and found that SARS-CoV-2 had a rapid and specific tropism for myeloid populations in the human lung. Infection of alveolar macrophages was partially dependent upon their expression of ACE2, and the infections were productive for amplifying virus, both findings which were in contrast with their neutralization of another pandemic virus, Influenza A virus (IAV). Compared to IAV, SARS-CoV-2 was extremely poor at inducing interferon-stimulated genes in infected myeloid cells, providing a window of opportunity for modest titers to amplify within these cells. Endotracheal aspirate samples from humans with the acute respiratory distress syndrome (ARDS) from COVID-19 confirmed the lung slice findings, revealing a persistent myeloid depot. In the early phase of SARS-CoV-2 infection, myeloid cells may provide a safe harbor for the virus with minimal immune stimulatory cues being generated, resulting in effective viral colonization and quenching of the immune system.

immunology↗

bioRxiv2021

DeepMHCII: A Novel Binding Core-Aware Deep InteractionModel for Accurate MHC II-peptide Binding Affinity Prediction

Computationally predicting MHC-peptide binding affinity is an important problem in immunological bioinformatics. Recent cutting-edge deep learning-based methods for this problem are unable to achieve satisfactory performance for MHC class II molecules. This is because such methods generate the input by simply concatenating the two given sequences: (the estimated binding core of) a peptide and (the pseudo sequence of) an MHC class II molecule, ignoring the biological knowledge behind the interactions of the two molecules. We thus propose a binding core-aware deep learning-based model, DeepMHCII, with binding interaction convolution layer (BICL), which allows integrating all potential binding cores (in a given peptide) and the MHC pseudo (binding) sequence, through modeling the interaction with multiple convolutional kernels. Extensive empirical experiments with four large-scale datasets demonstrate that DeepMHCII significantly outperformed four state-of-the-art methods under numerous settings, such as five-fold cross-validation, leave one molecule out, validation with independent testing sets, and binding core prediction. All these results with visualization of the predicted binding cores indicate the effectiveness and importance of properly modeling biological facts in deep learning for high performance and knowledge discovery. DeepMHCII is publicly available at https://weilab.sjtu.edu.cn/DeepMHCII/.

bioinformatics↗

bioRxiv2021

MetaBinner: a high-performance and stand-alone ensemble binning method to recover individual genomes from complex microbial communities

Binning is an essential procedure during metagenomic data analysis. However, the available individual binning methods usually do not simultaneously fully use different features or biological information. Furthermore, it is challenging to integrate multiple binning results efficiently and effectively. Therefore, we developed an ensemble binner, MetaBinner, which generates component results with multiple types of features and utilizes single-copy gene (SCG) information for k-means initialization. It then utilizes a two-step ensemble strategy based on SCGs to integrate the component results. Extensive experimental results over three large-scale simulated datasets and one real-world dataset demonstrate that MetaBinner outperforms other state-of-the-art individual binners and ensemble binners. MetaBinner is freely available at https://github.com/ziyewang/MetaBinner.

bioinformatics↗

bioRxiv2021

A human ensemble cell atlas (hECA) enables in data cell sorting

Single-cell omics data can characterize multifaceted features of massive cells and bring significant insights to biomedical researches. The accumulation of single-cell data provides growing resources for constructing atlases for all cells of a human organ or the whole body. The true assembly of a cell atlas should be cell-centric rather than file-centric. We proposed a unified information framework enabling seamless cell-centric data assembly and developed a human Ensemble Cell Atlas (hECA) as an instance. hECA version 1.0 assembled scRNA-seq data across multiple studies into one orchestrated data repository. It contains 1,093,299 labeled cells and metadata from 116 published human single-cell studies, covering 38 human organs and 11 systems. We invented three methods of applications based on the cell-centric assembly: "In data" cell sorting enables targeted data retrieval in the full atlas with customizable logic expressions; The "quantitative portraiture" system provides a multi-view presentation of biological entities (organs, cell types, and genes) of multiple granularities; The customizable reference creation allows users to use the cell-centric assembly to generate references for their own cell type annotations. Case studies on agile construction of user-defined sub-atlases and "in data" investigation of CAR-T off-targets in multiple organs showed the great potential of cell-centric atlas assembly.

bioinformatics↗

bioRxiv2021

Critical Assessment of Metagenome Interpretation - the second round of challenges

Evaluating metagenomic software is key for optimizing metagenome interpretation and focus of the community-driven initiative for the Critical Assessment of Metagenome Interpretation (CAMI). In its second challenge, CAMI engaged the community to assess their methods on realistic and complex metagenomic datasets with long and short reads, created from [~]1,700 novel and known microbial genomes, as well as [~]600 novel plasmids and viruses. Altogether 5,002 results by 76 program versions were analyzed, representing a 22x increase in results. Substantial improvements were seen in metagenome assembly, some due to using long-read data. The presence of related strains still was challenging for assembly and genome binning, as was assembly quality for the latter. Taxon profilers demonstrated a marked maturation, with taxon profilers and binners excelling at higher bacterial taxonomic ranks, but underperforming for viruses and archaea. Assessment of clinical pathogen detection techniques revealed a need to improve reproducibility. Analysis of program runtimes and memory usage identified highly efficient programs, including some top performers with other metrics. The CAMI II results identify current challenges, but also guide researchers in selecting methods for specific analyses.

bioinformatics↗

bioRxiv2021

Active Surveillance Characterizes Human Intratumoral T Cell Exhaustion

Intratumoral T cells that might otherwise control tumors are often identified in an exhausted state, defined by specific epigenetic modifications as well as upregulation of genes such as CD38, CTLA-4 and PD-1. While the term might imply inactivity, there has been little study of this state at the phenotypic level in tumors to understand the extent of their incapacitation. Starting with the observation that T cells move more quickly through mouse tumors as residence time increases and they progress towards exhaustion, we elaborated a non-stimulatory live-biopsy method for real-time study of T cell behaviors within individual patient tumors. Using two-photon microscopy, we studied native CD8 T cells interacting with APCs and with cancer cells in different micro-niches of human tumors, finding that T cell speed was variable by region and by patient, was independent of T cell density and was inversely correlated with local tumor density. Across a range of tumor types, we found a strong relationship between CD8 T cell motility and exhausted T cell state that corresponds to observations made in mouse models where exhausted T cells move faster. While this is a small study, it demonstrates at least two types of T cell dynamic states in individual human tumors and supports the existence of an active program in exhausted T cells that extends beyond incapacitating them.

immunology↗

bioRxiv2020

Global Absence and Targeting of Protective Immune States in Severe COVID-19.

While SARS-CoV-2 infection has pleiotropic and systemic effects in some patients, many others experience milder symptoms. We sought a holistic understanding of the severe/mild distinction in COVID-19 pathology, and its origins. We performed a whole-blood preserving single-cell analysis protocol to integrate contributions from all major cell types including neutrophils, monocytes, platelets, lymphocytes and the contents of serum. Patients with mild COVID-19 disease display a coordinated pattern of interferon-stimulated gene (ISG) expression across every cell population and these cells are systemically absent in patients with severe disease. Severe COVID-19 patients also paradoxically produce very high anti-SARS-CoV-2 antibody titers and have lower viral load as compared to mild disease. Examination of the serum from severe patients demonstrates that they uniquely produce antibodies with multiple patterns of specificity against interferon-stimulated cells and that those antibodies functionally block the production of the mild disease-associated ISG-expressing cells. Overzealous and auto-directed antibody responses pit the immune system against itself in many COVID-19 patients and this defines targets for immunotherapies to allow immune systems to provide viral defense. One Sentence SummaryIn severe COVID-19 patients, the immune system fails to generate cells that define mild disease; antibodies in their serum actively prevents the successful production of those cells.

immunology↗