Biology subjects

Li, W. V.

Publications and source records attributed to Li, W. V..

17 recordsLinked to original sources

bioRxiv2026

RNASeek: A Cross-Phyla Generative Foundation Model for Multipurpose RNA Modeling and Reinforcement Learning-Based Design

RNA plays central roles in regulating information flow and provides a versatile substrate for engineering biological functions. While large language models (LLMs) have transformed natural language processing and protein design, a general framework connecting RNA foundation models to functional sequence design remains limited. Here, we present RNASeek, a 1.6-billion-parameter generative foundation model built on a DeepSeek architecture and trained on a cross-phyla transcriptomic corpus for RNA sequence representation and generation. Natural-language tokens enable flexible conditional prediction and sequence design using a unified backbone. RNASeek captures species-specific transcript features and intron-exon boundaries in a zero-shot setting. We then fine-tune RNASeek to predict ribozyme self-cleavage activity and viral mRNA stability, revealing interpretable sequence features associated with function, including ribozyme loop flexibility and stem stability, as well as AU-rich motifs associated with mRNA stability. We use these functional predictors as reward models and apply Group Relative Policy Optimization (GRPO) to update the generation policy of RNASeek toward sequences with desired properties. GRPO-guided generation produces faster-cleaving ribozymes and stability-enhancing 3' UTRs while satisfying user-specified IUPAC constraints. Experimentally validated RNASeek-generated ribozymes achieve wild-type levels of activity, while RNASeek-generated 3' UTR sequences exceed the performance of the training data and benchmarked AI-generated 3' UTRs. Together, RNASeek establishes a unified pretrain-predict-optimize framework that connects learned RNA function to controllable de novo sequence design and provides a general strategy for engineering regulatory RNAs with desired properties.

bioengineering↗

bioRxiv2025

A Machine Learning Approach to Predicting Dyspnea with Noninvasive Biomarkers

RationaleDyspnea is the subjective sensation of breathing discomfort. This symptom is highly prevalent in patients with chronic and critical illness, and its presence is associated with poor clinical outcomes and long-term psychological trauma. The multidimensional nature of the neurophysiological mechanisms underlying dyspnea, paired with individual variation in its presentation, makes identifying and monitoring this symptom difficult, particularly in non-communicative patients. ObjectiveUndetected and untreated dyspnea in critically ill patients is a significant problem contributing to patient suffering. Therefore, the objective of this study was to investigate the feasibility of machine learning methods for assessing and continuously monitoring dyspnea using easily obtained noninvasive biomarkers. MethodsWe recruited healthy participants (N = 60, 35 women) and stimulated dyspnea using a forced end-tidal semi-rebreathing circuit to modulate arterial oxygen and carbon dioxide levels while collecting non-invasive biomarker data and continuous self-reported dyspnea severity scores. This data was used to train machine-learning models to predict the presence or absence of significant dyspnea (Numeric Rating Scale [≥] 3). We then compared the performance of our final model to observational estimates by trained healthcare providers. Measurements and Main ResultsThe final model (Random Forest) performed well (PR-AUC=0.832) and exceeded the accuracy of observation estimates made on the same participants using the Respiratory Distress Observational Scale (RDOS) (accuracy=54%). ConclusionsThese results indicate that machine learning models can utilize non-invasive biomarker inputs to accurately predict carbon dioxide- and hypoxia-induced dyspnea in a healthy population during spontaneous breathing.

physiology↗

bioRxiv2025

Harmonizing Heterogeneous Single-Cell Gene Expression Data with Individual-Level Covariate Information

MotivationThe growing availability of single-cell RNA sequencing (scRNA-seq) data highlights the necessity for robust integration methods to uncover both shared and unique cellular features across samples. These datasets often exhibit technical variations and biological differences, complicating integrative analyses. While numerous integration methods have been proposed, many fail to account for individual-level covariates or are limited to discrete variables. ResultsTo address these limitations, we propose scINSIGHT2, a generalized linear latent variable model that accommodates both continuous covariates, such as age, and discrete factors, such as disease conditions. Through both simulation studies and real-data applications, we demonstrate that scINSIGHT2 accurately harmonizes scRNA-seq datasets, whether from single or multiple sources. These results highlight scINSIGHT2s utility in capturing meaningful biological insights from scRNA-seq data while accounting for individual-level variation. Availability and implementationThe scINSIGHT2 method has been implemented as an R package, which is available at https://github.com/yudimu/scINSIGHT2/.

bioinformatics↗

bioRxiv2024

Role of AMPK in Atrial Metabolic Homeostasis and Substrate Preference

Atrial fibrillation is the most common clinical arrhythmia and may be due in part to metabolic stress. Atrial specific deletion of the master metabolic sensor, AMP-activated protein kinase (AMPK), induces atrial remodeling culminating in atrial fibrillation in mice, implicating AMPK signaling in the maintenance of atrial electrical and structural homeostasis. However, atrial substrate preference for mitochondrial oxidation and the role of AMPK in regulating atrial metabolism are unknown. Here, using LC-MS/MS methodology combined with infusions of [13C6]glucose and [13C4]{beta}-hydroxybutyrate in conscious mice, we demonstrate that conditional deletion of atrial AMPK catalytic subunits shifts mitochondrial atrial metabolism away from fatty acid oxidation and towards pyruvate oxidation. LC-MS/MS-based quantification of acyl-CoAs demonstrated decreased atrial tissue content of long-chain fatty acyl-CoAs. Proteomic analysis revealed a broad downregulation of proteins responsible for fatty acid uptake (LPL, CD36, FABP3), acylation and oxidation. Atrial AMPK deletion reduced expression of atrial PGC1- and downstream PGC1-/PPAR/RXR regulated gene transcripts. In contrast, atrial [14C]2-deoxyglucose uptake and GLUT1 expression increased with fasting in mice with AMPK deletion, while the expression of glycolytic enzymes exhibited heterogenous changes. Thus, these results highlight the crucial homeostatic role of AMPK in the atrium, with loss of atrial AMPK leading to downregulation of the PGC1-/PPAR pathway and broad metabolic reprogramming with a loss of fatty acid oxidation, which may contribute to atrial remodeling and arrhythmia.

physiology↗

bioRxiv2024

Chk2 sustains PLK1 activity in mitosis to ensure proper chromosome segregation

Polo-like kinase 1 (PLK1) protects against genome instability by ensuring timely and accurate mitotic cell division. PLK1 activity is tightly regulated throughout the cell cycle. Although the pathways that initially activate PLK1 in G2 are well-characterized, the factors that directly regulate PLK1 in mitosis remain poorly understood. Here, we identify that human PLK1 activity is sustained by the DNA damage response kinase Checkpoint kinase 2 (Chk2) in mitosis. Chk2 directly phosphorylates PLK1 T210, a residue on its T-loop whose phosphorylation is essential for full PLK1 kinase activity. Loss of Chk2-dependent PLK1 activity causes increased mitotic errors, including chromosome misalignment, chromosome missegregation, and cytokinetic defects. Moreover, Chk2 deficiency increases sensitivity to PLK1 inhibitors, suggesting that Chk2 status may be an informative biomarker for PLK1 inhibitor efficacy. This work demonstrates that Chk2 sustains mitotic PLK1 activity and protects genome stability through discrete functions in interphase DNA damage repair and mitotic chromosome segregation.

cell biology↗

bioRxiv2024

A proteome-wide quantitative guide for nanoscale spatially resolved extraction of membrane proteins into native nanodiscs

The intricate molecular environment of the native membrane profoundly influences every aspect of membrane protein (MP) biology. Despite this, the most prevalent method of studying MPs uses detergent-like molecules that disrupt and remove this vital local membrane context. This severely impedes our ability to quantitatively decipher the local molecular context and comprehend its regulatory role in the structure, function, and biogenesis of MPs. Using a library of membrane-active polymers we have developed a platform for the high-throughput analysis of the membrane proteome. The platform enables near-complete spatially resolved extraction of target MPs directly from their endogenous membranes into native nanodiscs that maintain the local membrane context. We accompany this advancement with an open-access database that quantifies the polymer-specific extraction variability for 2065 unique mammalian MPs and provides the most optimized condition for each of them. Our method enables rapid and near-complete extraction and purification of target MPs directly from their endogenous organellar membranes at physiological expression levels while maintaining the nanoscale local membrane environment. Going beyond the plasma membrane proteome, our platform enables extraction from any target organellar membrane including the endoplasmic reticulum, mitochondria, lysosome, Golgi, and even transient organelles such as the autophagosome. To further validate this platform, we took several independent MPs and demonstrated how our resource can enable rapid extraction and purification of target MPs from different organellar membranes with high efficiency and purity. Further, taking two synaptic vesicle MPs, we show how the database can be extended to capture multiprotein complexes between overexpressed MPs. We expect these publicly available resources to empower researchers across disciplines to efficiently capture membrane nano-scoops containing a target MP and interface with structural, functional, and other bioanalytical approaches. We demonstrate an example of this by combining our extraction platform with single-molecule TIRF imaging to demonstrate how it can enable rapid determination of homo-oligomeric states of target MPs in native cell membranes.

biophysics↗

bioRxiv2021

scINSIGHT for interpreting single-cell gene expression from biologically heterogeneous data

The increasing number of scRNA-seq data emphasizes the need for integrative analysis to interpret similarities and differences between single-cell samples. Even though different batch effect removal methods have been developed, none of the existing methods is suitable for het-erogeneous single-cell samples coming from multiple biological conditions. To address this challenge, we propose a method named scINSIGHT to learn coordinated gene expression patterns that are common among or specific to different biological conditions, offering a unique chance to identify cellular identities and key biological processes across single-cell samples. We have evaluated scINSIGHT in comparison with state-of-the-art methods using simulated and real data, which consistently demonstrate its improved performance. In addition, our results show the applicability of scINSIGHT in diverse biomedical and clinical problems.

bioinformatics↗

bioRxiv2021

MAAPER: model-based analysis of alternative polyadenylation using 3' end-linked reads

Most eukaryotic genes harbor multiple cleavage and polyadenylation sites (PASs), leading to expression of alternative polyadenylation (APA) isoforms. APA regulation has been implicated in a diverse array of physiological and pathological conditions. While RNA sequencing tools that generate reads containing the PAS, named onSite reads, have been instrumental in identifying PASs, they have not been widely used. By contrast, a growing number of methods generate reads that are close to the PAS, named nearSite reads, including the 3 end counting strategy commonly used in single cell analysis. How these nearSite reads can be used for APA analysis, however, is poorly studied. Here, we present a computational method, named model-based analysis of alternative polyadenylation using 3 end-linked reads (MAAPER), to examine APA using nearSite reads. MAAPER uses a probabilistic model to predict PASs for nearSite reads with high accuracy and sensitivity, and examines different types of APA events, including those in 3UTRs and introns, with robust statistics. We show MAAPERs accuracy with data from both bulk and single cell RNA samples and its applicability in unpaired or paired experimental designs. Our result also highlights the importance of using well annotated PASs for nearSite read analysis.

bioinformatics↗

bioRxiv2020

scDesign2: an interpretable simulator that generates high-fidelity single-cell gene expression count data with gene correlations captured

In the burgeoning field of single-cell transcriptomics, a pressing challenge is to benchmark various experimental protocols and numerous computational methods in an unbiased manner. Although dozens of simulators have been developed for single-cell RNA-seq (scRNA-seq) data, they lack the capacity to simultaneously achieve all the three goals: preserving genes, capturing gene correlations, and generating any number of cells with varying sequencing depths. To fill in this gap, here we propose scDesign2, a transparent simulator that achieves all the three goals and generates high-fidelity synthetic data for multiple scRNA-seq protocols and other single-cell gene expression count-based technologies. Compared with existing simulators, scDesign2 is advantageous in its transparent use of probabilistic models and is unique in its ability to capture gene correlations via copula. We verify that scDesign2 generates more realistic synthetic data for four scRNA-seq protocols (10x Genomics, CEL-Seq2, Fluidigm C1, and Smart-Seq2) and two single-cell spatial transcriptomics protocols (MERFISH and pciSeq) than existing simulators do. Under two typical computational tasks, cell clustering and rare cell type detection, we demonstrate that scDesign2 provides informative guidance on deciding the optimal sequencing depth and cell number in single-cell RNA-seq experimental design, and that scDesign2 can effectively benchmark computational methods under varying sequencing depths and cell numbers. With these advantages, scDesign2 is a powerful tool for single-cell researchers to design experiments, develop computational methods, and choose appropriate methods for specific data analysis needs.

bioinformatics↗

bioRxiv2020

scLink: Inferring Sparse Gene Co-expression Networks from Single-cell Expression Data

A system-level understanding of the regulation and coordination mechanisms of gene expression is essential to understanding the complexity of biological processes in health and disease. With the rapid development of single-cell RNA sequencing technologies, it is now possible to investigate gene interactions in a cell-type-specific manner. Here we propose the scLink method, which uses statistical network modeling to understand the co-expression relationships among genes and to construct sparse gene co-expression networks from single-cell gene expression data. We use both simulation and real data studies to demonstrate the advantages of scLink and its ability to improve single-cell gene network analysis. The source code used in this article is available at https://github.com/Vivianstats/scLink.

genomics↗

bioRxiv2020

Transcriptomics, metabolomics and lipidomics of chronically injured alveolar epithelial cells reveals similar features of IPF lung epithelium

The current hypothesis suggests that Idiopathic pulmonary fibrosis (IPF) arises as a result of chronic injury to alveolar epithelial cells and aberrant activation of multiple signaling pathways. Dysfunctional IPF lung epithelium manifests many hallmarks of aging tissues, including cellular senescence, mitochondrial dysfunction, metabolic dysregulation, and loss of proteostasis. Unfortunately, this disease is often fatal within 3-5 years from diagnosis, and there is no effective treatment. One of the major limitations to the development of novel treatments in IPF is that current models of the disease fail to resemble several features seen in elderly IPF patients. In this study, we sought to develop an in vitro epithelial injury model using repeated low levels of bleomycin to mimic the phenotypic and functional characteristics of the IPF lung epithelium. Consistent with the hallmarks of the aging lung epithelium, we found that chronic-injured epithelial cells exhibited features of senescence cells, including an increase in {beta}-galactosidase staining, induction of p53 and p21, mitochondrial dysfunction, excessive ROS production, and proteostasis alteration. Next, combined RNA sequencing, untargeted metabolomics, and lipidomics were performed to investigate the dynamic transcriptional, metabolic, and lipidomic profiling of our in vitro model. We identified that a total of 8,484 genes with different expression variations between the exposed group and the control group. According to our GO enrichment analysis, the down-regulated genes are involved in multiple biosynthetic and metabolic processes. In contrast, the up-regulated genes in our treated cells are responsible for epithelial cell migration and regulation of epithelial proliferation. Furthermore, metabolomics and lipidomics data revealed that overrepresented pathways were amino acid, fatty acid, and glycosphingolipid metabolism. This result suggests that by using our in vitro model, we were able to mimic the transcriptomic and metabolic alterations of those seen in the lung epithelium of IPF patients. We believe this model will be ideally suited for use in uncovering novel insights into the gene expression and molecular pathways of the IPF lung epithelium and performing screening of pharmaceutical compounds.

cell biology↗

bioRxiv2020

mbImpute: an accurate and robust imputation method for microbiome data

Microbiome studies have gained increased attention since many discoveries revealed connections between human microbiome compositions and diseases. A critical challenge in microbiome research is that excess non-biological zeros distort taxon abundances, complicate data analysis, and jeopardize the reliability of scientific discoveries. To address this issue, we propose the first imputation method, mbImpute, to identify and recover likely non-biological zeros by borrowing information jointly from similar samples, similar taxa, and optional metadata including sample covariates and taxon phylogeny. Comprehensive simulations verified that mbImpute achieved better imputation accuracy under multiple measures than five state-of-the-art imputation methods designed for non-microbiome data. In real data applications, we demonstrate that mbImpute improved the power and reproducibility of identifying disease-related taxa from microbiome data of type 2 diabetes and colorectal cancer.

genomics↗

bioRxiv2019

EpiAlign: an alignment-based bioinformatic tool for comparing chromatin state sequences

The availability of genome-wide epigenomic datasets enables in-depth studies of epigenetic modifications and their relationships with chromatin structures and gene expression. Various alignment tools have been developed to align nucleotide or protein sequences in order to identify structurally similar regions. However, there are currently no alignment methods specifically designed for comparing multi-track epigenomic signals and detecting common patterns that may explain functional or evolutionary similarities. We propose a new local alignment algorithm, EpiAlign, designed to compare chromatin state sequences learned from multi-track epigenomic signals and to identify locally aligned chromatin regions. EpiAlign is a dynamic programming algorithm that novelly incorporates varying lengths and frequencies of chromatin states. We demonstrate the effcacy of EpiAlign through extensive simulations and studies on the real data from the NIH Roadmap Epigenomics project. EpiAlign is able to extract recurrent chromatin state patterns along a single epigenome, and many of these patterns carry cell-type-specific characteristics. EpiAlign can also detect common chromatin state patterns across multiple epigenomes, and it will serve as a useful tool to group and distinguish epigenomic samples based on genome-wide or local chromatin state patterns.

bioinformatics↗

bioRxiv2018

A statistical simulator scDesign for rational scRNA-seq experimental design

Single-cell RNA-sequencing (scRNA-seq) has revolutionized biological sciences by revealing genome-wide gene expression levels within an individual cell. However, a critical challenge faced by researchers is how to optimize the choices of sequencing platforms, sequencing depths, and cell numbers in designing scRNA-seq experiments, so as to balance the exploration of the depth and breadth of transcriptome information. Here we present a flexible and robust simulator, scDesign, the first statistical framework for researchers to quantitatively assess practical scRNA-seq experimental design in the context of differential gene expression analysis. In addition to experimental design, scDesign also assists computational method development by generating high-quality synthetic scRNA-seq datasets under customized experimental settings. In an evaluation based on 17 cell types and six different protocols, scDesign outperformed four state-of-the-art scRNA-seq simulation methods and led to rational experimental design.

bioinformatics↗

bioRxiv2018

AIDE: annotation-assisted isoform discovery and abundanceestimation from RNA-seq data

Genome-wide accurate identification and quantification of full-length mRNA isoforms is crucial for investigating transcriptional and post-transcriptional regulatory mechanisms of biological phenomena. Despite continuing efforts in developing effective computational tools to identify or assemble full-length mRNA isoforms from second-generation RNA-seq data, it remains a challenge to accurately identify mRNA isoforms from short sequence reads due to the substantial information loss in RNA-seq experiments. Here we introduce a novel statistical method, AIDE (Annotation-assisted Isoform DiscovEry), the first approach that directly controls false isoform discoveries by implementing the testing-based model selection principle. Solving the isoform discovery problem in a stepwise and conservative manner, AIDE prioritizes the annotated isoforms and precisely identifies novel isoforms whose addition significantly improves the explanation of observed RNA-seq reads. We evaluate the performance of AIDE based on multiple simulated and real RNA-seq datasets followed by a PCR-Sanger sequencing validation. Our results show that AIDE effectively leverages the annotation information to compensate the information loss due to short read lengths. AIDE achieves the highest precision in isoform discovery and the lowest error rates in isoform abundance estimation, compared with three state-of-the-art methods Cufflinks, SLIDE, and StringTie. As a robust bioinformatics tool for transcriptome analysis, AIDE will enable researchers to discover novel transcripts with high confidence.

bioinformatics↗

bioRxiv2018

Basal Forebrain Deep Brain Stimulation Impacts the Regulation of Extracellular Vesicle Related Proteins in the Rat Brain

Extracellular vesicle (EV) signaling has attracted considerable attention in recent years because EVs play a key role in long distance cellular communication functions. EV studies have begun to reveal aspects of physiological and physiopathological regulation in numerous applications, although many areas remain to date largely unexplored. Deep brain stimulation (DBS) has shown remarkable therapeutic benefits of patients with neuropsychiatric disorders, but despite of the long and successful history of use, the mechanisms of action on neural ensemble activity are not yet fully understood. Here we explore how DBS of the basal forebrain impacts EV signaling in the rat brain. We employed differential centrifugations to isolate the EVs prefrontal cortex (PFC), hippocampus and striatum. We then performed quantitative analysis of EV-associated proteins using an MS-based proteomics method. We identified a considerable number of EV-associated proteins are modulated by DBS in three brain regions, some of which have been previously linked with central nervous system disorders. Particularly, neurofilament proteins NFL and NFM were both significantly changed in EVs of PFC, hippocampus and striatum after DBS stimulation compared with controls. The SOD1 protein, associated previously with neurodegenerative diseases, was significantly increased only in PFC. Our study is the first, to our knowledge, to use EV protein analysis to examine DBS effects on brain physiological regulation. Our findings open an entirely new perspective on brain area specific DBS effects.

neuroscience↗

bioRxiv2017

scImpute: Accurate And Robust Imputation For Single Cell RNA-Seq Data

The emerging single cell RNA sequencing (scRNA-seq) technologies enable the investigation of transcriptomic landscapes at single-cell resolution. The analysis of scRNA-seq data is complicated by excess zero or near zero counts, the so-called dropouts due to the low amounts of mRNA sequenced within individual cells. Downstream analysis of scRNA-seq would be severely biased if the dropout events are not properly corrected. We introduce scImpute, a statistical method to accurately and robustly impute the dropout values in scRNA-seq data. ScImpute automatically identifies gene expression values affected by dropout events, and only perform imputation on these values without introducing new bias to the rest data. ScImpute also detects outlier or rare cells and excludes them from imputation. Evaluation based on both simulated and real scRNA-seq data on mouse embryos, mouse brain cells, human blood cells, and human embryonic stem cells suggests that scImpute is an effective tool to recover transcriptome dynamics masked by dropout events. scImpute is shown to correct false zero counts, enhance the clustering of cell populations and subpopulations, improve the accuracy of differential expression analysis, and aid the study of gene expression dynamics.

bioinformatics↗