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Wei, L.

Publications and source records attributed to Wei, L..

At least 91 records · Page 5Linked to original sources
bioRxiv2024

Comparison of urine proteome between obese people and normal weight people

ObjectiveTo explore whether urine proteome can reflect the difference between obese and normal weight people. MethodsUrine samples from obese and normal weight people were collected and identified by non-label quantitative proteomics using high performance liquid chromatography tandem mass spectrometry (LC-MS/MS). The difference proteins of urine proteome between obese and normal weight people were screened for protein function and biological pathway analysis. The urine proteome of obese individuals was compared with that of normal weight people, and the common differential proteins were counted to analyze the protein function and biological pathways. Reported biomarkers of obesity were searched in the urine proteome of obese individuals. Results38 different proteins can be identified in the urine proteome of obese people compared with normal weight people, some of which have been reported to be related to metabolism and obesity, and the biological processes of differential proteins are also related to metabolism and other processes. 8 common differential proteins in the urine proteome of obese individuals and normal weight people, among which some proteins have been reported to be related to metabolism and obesity, and the biological processes of differential proteins are also related to metabolism and other processes. Among the differential proteins in the urine proteome of obese individuals compared with the normal weight people, the reported obesity biomarkers can be matched. ConclusionsThe urine proteome can distinguish the obese people from the normal weight people, and the differential proteins in the urine proteome have key proteins that are known to be related to obesity and metabolism, and the biological processes of differential proteins also related biological processes such as nutrition and metabolism. Urine proteome has the potential to explore the pathogenesis of obesity and provide personalized treatment.

biochemistry↗
bioRxiv2023

Deconer: A comprehensive and systematic evaluation toolkit for reference-based cell type deconvolution algorithms using gene expression data

In recent years, computational methods for quantifying cell type proportions from transcription data have gained significant attention, particularly those reference-based methods which have demonstrated high accuracy. However, there is currently a lack of comprehensive evaluation and guidance for available reference-based deconvolution methods in cell proportion deconvolution analysis. In this study, we propose a comprehensive evaluation toolkit, called Deconer, specifically designed for reference-based deconvolution methods. Deconer provides various simulated and real gene expression datasets, including both bulk and single-cell sequencing data, and offers multiple visualization interfaces. By utilizing Deconer, we conducted systematic comparisons of 14 reference-based deconvolution methods from different perspectives, including method robustness, accuracy in deconvolving rare components, signature gene selection, and building external reference. We also performed an in-depth analysis of the application scenarios and challenges in cell proportion deconvolution methods. Finally, we provided constructive suggestions for users in selecting and developing cell proportion deconvolution algorithms. This work presents novel insights to researchers, assisting them in choosing appropriate toolkits, applying solutions in clinical contexts, and advancing the development of deconvolution tools tailored to gene expression data.

bioinformatics↗
bioRxiv2023

Multi-Level Contrastive Learning for Protein-Ligand Binding Residue Prediction

Protein-ligand interactions play a crucial role in various biological functions, with their accurate prediction being pivotal for drug discovery and design processes. Traditional methods for predicting protein-ligand interactions are limited. Some can only predict interactions with a specific molecule, restricting their applicability, while others aim for multiple types but fail to effectively utilize information across different interactions, leading to increased complexity and inefficiency. This study presents a novel deep learning model named MucLiPred and a dual contrastive learning mechanism aimed at improving the prediction of multiple ligand-protein interactions and the identification of potential ligand-binding residues. We proposed two novel contrastive learning paradigms at residue and type levels, training the discriminative representation of samples. The residue-level contrastive learning hones in on distinguishing binding from non-binding residues with precision, shedding light on nuanced local interactions. In contrast, the type-level contrastive learning delves into the overarching context of ligand types, ensuring that representations of identical ligand types gravitate closer in the representational space and bolstering the models proficiency in discerning interaction motifs, enhancing the models ability to recognize global interaction patterns. This approach culminates in nuanced multi-ligand predictions, unraveling relationships between various ligand types, and fortifying the potential for precise protein-ligand interaction predictions. Empirical findings underscore MucLiPreds dominance over existing models, highlighting its robustness and unparalleled prediction accuracy. The integration of dual contrastive learning techniques amplifies its capability to detect potential ligand-binding residues with precision. By optimizing the models structure, we discovered that separating representation and classification tasks, leads to improved performance. Consequently, MucLiPred stands out as a groundbreaking tool in protein-ligand interaction prediction, laying the groundwork for future endeavors in this complex arena.

bioinformatics↗
bioRxiv2023

Benchmarking multi-omics integration algorithms across single-cell RNA and ATAC data

Recent advancements in single-cell sequencing technologies have generated extensive omics data in various modalities and revolutionized cell research, especially in the single cell RNA and ATAC data. The joint analysis across scRNA-seq data and scATAC-seq data has paved the way to comprehending the cellular heterogeneity and complex cellular regulatory networks. Multi-omics integration is gaining attention as an important step in joint analysis, and the number of computational tools in this field is growing rapidly. In this paper, we benchmarked 12 multi-omics integration methods on three integration tasks via qualitative visualization and quantitative metrics, considering six main aspects that matter in multi-omics data analysis. Overall, we found that different methods have their own advantages on different aspects, while some methods outperformed other methods in most aspects. We therefore provided guidelines for selecting appropriate methods for specific scenarios and tasks to help obtain meaningful insights from multi-omics data integration.

bioinformatics↗
bioRxiv2023

An electromyography-based multi-muscle fatigue model to investigate operational task performance

We developed a multi-muscle fatigue model (MMFM) by incorporating electromyography (EMG)-based amplitude and frequency parameters, the fast-to-slow twitch muscle fiber ratio, a time multiplier to linearize the cumulative effect of time, and a muscle multiplier to standardize the combined effect of the number of muscles being considered. We validated the model by investigating fatigue development patterns of ten male subjects performing one sustained-till-exhaustion static and two repetitive dynamic tasks (low and high task difficulty levels) using 0.91 Kg and 2.72 Kg dumbbells. The results indicated that the MMFM was sensitive to fatigue-related neuromuscular changes and predicted shoulder joint fatigue accurately.

bioengineering↗
bioRxiv2023

Paternal genomic resources from the YanHuang cohort suggested a Weakly-Differentiated Multi-source Admixture model for the formation of Hans founding ancestral lineages

The large-scale human genome revolution and rapidly advanced statistical innovation have updated our understanding of the fine-scale and complex genetic structure, the entire landscape of genetic diversity and the evolutionary trajectories of spatiotemporally different ancients and ethnolinguistically diverse modern populations. Recent ancient DNA research provided a detailed and complex admixture picture of ancient Europeans but limited insights into East Asians as the few available genomes. Y-chromosome variations in the male-specific regions, served as molecular archaeological tool, have unique evolutionary features that can be utilized to reconstruct the origin and subsequent interaction of ancient East Asian paternal lineages. We launched the YanHuang cohort using our designed highest-resolution capture sequencing panel to explore the detailed evolutionary trajectory of the Han Chinese, one of the largest ethnic groups in the world. We reported one of the largest uniparental genomic resources and observed multiple founding paternal lineages dominant in ancient western Eurasian, Siberian and East Asian participating in the formation of the gene pool of the Han Chinese. We identified fine-scale paternal genetic structure correlated with different patterns of ancient population interaction and geographical mountain barriers (Qinling-Huaihe line and Nanling Mountains), suggesting isolation-enhanced and admixture-introduced genetic differentiation enhanced the complexity of the Han Chinese genomic diversity. We observed a strong direct correlation between the frequency of multiple founding lineages of the Han Chinese and the proportion of subsistence-related ancestry sources related to western pastoralists, Holocene Mongolian Plateau people and ancient East Asians, reflecting the ancient migration events contributed to our identified patterns of Chinese paternal genomic diversity. We finally provided one novel and the most plausible admixture-by-admixture model, the Weakly-Differentiated Multi-Source Admixture model, as the major genetic mechanism to illuminate our observed pattern of complex interactions of multiple ancestral sources and landscape of the Han Chinese paternal genetic diversity. Generally, we presented one large-scale uniparental genomic resource from the YanHuang cohort, portrayed one novel admixture formation model and presented the entire genomic landscape with multiple ancestral sources related to ancient herders, hunter-gatherers and farmers who participated in the ancestral formation of the Han Chinese.

evolutionary biology↗
bioRxiv2023

Host kinase regulation of Plasmodium vivax dormant and replicating liver stages

Upon transmission to the liver, Plasmodium vivax parasites form replicating schizonts, which continue to initiate blood-stage infection, or dormant hypnozoites that reactivate weeks to months after initial infection. P. vivax phenotypes in the field vary significantly, including the ratio of schizonts to hypnozoites formed and the frequency and timing of relapse. Evidence suggests that both parasite genetics and environmental factors underly this heterogeneity. We previously demonstrated that data on the effect of a panel of kinase inhibitors with overlapping targets on Plasmodium liver stage infection, in combination with a computational approach called kinase regression (KiR), can be used to uncover novel host regulators of infection. Here, we applied KiR to evaluate the extent to which P. vivax liver-stage parasites are susceptible to changes in host kinase activity. We identified a role for a subset of host kinases in regulating schizont and hypnozoite infection and schizont size and characterized overlap as well as variability in host phosphosignaling dependencies between parasite forms and across multiple patient isolates. Striking, our data point to variability in host dependencies across P. vivax isolates, suggesting one possible origin of the heterogeneity observed across P. vivax in the field.

microbiology↗
bioRxiv2023

Cell context-specific Synthetic lethality Prediction and Mechanism Analysis

Synthetic lethality (SL) holds significant promise as a targeted cancer therapy by selectively eliminating tumor cells while sparing normal cells. However, the discovery of SL gene pairs has encountered tremendous challenges, including high costs and low efficiency of experimental methods. Current computational approaches only provide limited insights because of overlooking the crucial aspects of cellular context dependency and mechanistic understanding of SL pairs. To overcome these challenges, we have developed SLWise, a deep-learning model capable of predicting SL interactions in diverse cellular backgrounds. We evaluated SLWise using a real world ground truth standard. The evaluation demonstrated that SLWise outperformed benchmark models in SL prediction. Additionally, we proposed a novel analysis scheme called SLAD-CE (Synthetic Lethal Associated Gene Detection and Cell Damage Evaluation) for the identification of abnormal essential genes induced by SL gene pairs and tracking the extent of cell damage. Leveraging the cell-line-specific input feature L1000 and employing Gene Set Enrichment Analysis (GSEA), SLAD-CE provides valuable insights into the underlying mechanisms of SLWise-predicted gene pairs. The combined utilization of SLWise and SLAD-CE offers an approach for predicting and analyzing SL interactions in specific cellular contexts. Our findings highlight the potential of SLWise and SLAD-CE in advancing SL-based therapies by improving prediction accuracy and enhancing mechanistic understanding, ultimately contributing to the development of effective precision treatments for cancer.

cancer biology↗
bioRxiv2023

Antibody Humanization via Protein Language Model and Neighbor Retrieval

Antibody (Ab), also known as immunoglobulin (Ig), is an essential macromolecule involved in human immune response and plays an increasingly vital role in drug discovery. However, the development of antibody drugs heavily relies on humanization of murine antibodies, which often necessitates multiple rounds of sequence optimizations through laborious experimental processes. In recent years, the remarkable capabilities of machine learning have revolutionized the field of natural sciences and have also demonstrated promising applications in the field of antibody humanization. Here, we present Protein-LAnguage-model-knN (PLAN), a machine learning model leveraging protein language model and information retrieval for improving humanization of antibodies. Further, we propose DE, a computed value shows a positive correlation with antigen-binding affinity. Our in silico experimental results demonstrate that 1) the PLAN-humanized sequences average humanness score reaches 0.592, improving over the best existing method by 44.7%; 2) a 63% overlap between the PLAN-proposed mutations and the mutations validated through wet lab experiments, which is 16.7% higher than the best existing result; 3) comparable antigen-binding affinity after DE guided back mutation.

bioinformatics↗
bioRxiv2023

Ancient farmer and steppe pastoralist-related founding lineages contributed to the complex landscape of episodes in the diversification of Chinese paternal lineages

Ancient DNA advances have reported the complex genetic history of Eurasians, but how the knowledge of ancient subsistence strategy shifts and population movements influenced the fine-scale paternal genetic structure in East Asia has not been assessed. Here, we reported one integrated Y-chromosome genomic database of 15,530 people, including 1753 ancient people and newly-reported 919 individuals genotyped using our recently-developed targeted sequencing YHSeqY3000 panel, to explore Chinese genomic diversity, population evolutionary tracts and their genetic formation mechanism. We identified four major ancient technological innovations and population movements that shaped the landscape of Chinese paternal lineages. First, the expansion of millet farmers and early East Asians from the Yellow River Basin carrying the major O2/D subclades promoted the formation of the Sino-Tibetan peoples major composition and accelerated the Tibetan Plateaus permanent occupation. Second, rice farmers dispersal from the Yangtze River Valley carrying O1 and some sublineages of O2 contributed significantly to Tai-Kadai, Austronesian, Hmong-Mien, Austroasiatic people and southern Han Chinese. Third, Siberian-related paternal lineages of Q and C originated and boomed from Neolithic hunter-gatherers from the Mongolian Plateau and the Amur River Basin and significantly influenced the gene pools of northern Chinese. Fourth, western Eurasian-derived J, G and R lineages initially spread with Yamnaya steppe pastoralists and other proto-Indo-European people and further widely dispersed via the trans-Eurasian cultural communication along the Eurasian Steppe and the ancient Silk Road, remaining genetic trajectories in northwestern Chinese. Our work provided comprehensive modern and ancient genetic evidence to illuminate the impact of population interaction from the ancient farmer or herder-based societies on the genetic diversity patterns of modern people, revised our understandings of ancestral sources of Chinese paternal lineages, underscored the scientific imperative of the large-scale genomic resources of dense spatiotemporal underrepresented sampling populations to understand human evolutionary history.

genetics↗
bioRxiv2023

Patient-Specific Analysis of Co-expression Networks for Predicting Clinical Outcomes in Breast Cancer

To effectively understand the underlying mechanisms of disease and inform the development of personalized therapies, it is critical to harness the power of differential co-expression network analysis. Despite the promise of differential co-expression network analysis in precision medicine, current approaches have a major limitation: they measure an average differential network across multiple samples, which means the specific etiology of individual patients is often overlooked. To address this, we present Cosinet, a differential co-expression-based single-sample network rewiring degree quantification tool. By analyzing two breast cancer datasets, we demonstrate that Cosinet can identify important differences in gene co-expression patterns between individual patients and generate scores for each individual that are significantly associated with overall survival, recurrence-free interval, and other clinical outcomes, even after adjusting for risk factors such as age, tumor size, HER2 status and PAM50 subtypes. Cosinet represents a remarkable development toward unlocking the potential of differential co-expression analysis in the context of precision medicine.

bioinformatics↗
bioRxiv2023

Efficient computation by molecular competition networks

Most biomolecular systems exhibit computation abilities, which are often achieved through complex networks such as signal transduction networks. Particularly, molecular competition in these networks can introduce crosstalk and serve as a hidden layer for cellular information processing. Despite the increasing evidence of competition contributing to efficient cellular computation, how this occurs and the extent of computational capacity it confers remain elusive. In this study, we introduced a mathematical model for Molecular Competition Networks (MCNs) and employed a machine learning-based optimization method to explore their computational capacity. Our findings revealed that MCNs, when compared to their non-competitive counterparts, demonstrate superior performance in both discrete decision-making and analog computation tasks. Furthermore, we examined how real biological constraints influence the computational capacity of MCNs, and highlighted the nonnegligible role of weak interactions. The study suggested the potential of MCNs as efficient computational structures in both in vivo and in silico scenarios, providing new insights into the understanding and application of cellular information processing.

systems biology↗
bioRxiv2023

Multiple Receptor Tyrosine Kinases Regulate Dengue Infection of Hepatocytes

Dengue is an arboviral disease causing severe illness in over 500,000 people each year. Currently, there is no way to constrain dengue in the clinic. Host kinase regulators of dengue virus (DENV) infection have the potential to be disrupted by existing therapeutics to prevent infection and/or disease progression. To evaluate kinase regulation of DENV infection, we performed kinase regression (KiR), a machine learning approach that predicts kinase regulators of infection using existing drug-target information and a small drug screen. We infected hepatocytes with DENV in vitro in the presence of a panel of 38 kinase inhibitors then quantified the effect of each inhibitor on infection rate. We employed elastic net regularization on these data to obtain predictions of which of 300 kinases are regulating DENV infection. Thirty-six kinases were predicted to have a functional role. Intriguingly, seven of the predicted kinases - EPH receptor A4 (EPHA4), EPH receptor B3 (EPHB3), EPH receptor B4 (EPHB4), erb-b2 receptor tyrosine kinase 2 (ERBB2), fibroblast growth factor receptor 2 (FGFR2), Insulin like growth factor 1 receptor (IGF1R), and ret proto-oncogene (RET) - belong to the receptor tyrosine kinase (RTK) family, which are already therapeutic targets in the clinic. We demonstrate that predicted RTKs are expressed at higher levels in DENV infected cells. Knockdown of ERBB2, FGFR2 and IGF1R reduces DENV infection in hepatocytes. Finally, we observe differential temporal induction of ERBB2 and IGF1R following DENV infection, highlighting their unique roles in regulating DENV. Collectively, our findings underscore the significance of multiple RTKs in DENV infection and advocate further exploration of RTK-oriented interventions against dengue.

systems biology↗
bioRxiv2023

Using graph-based model to identify cell specific synthetic lethal effects

Synthetic lethal (SL) pairs are pairs of genes whose simultaneous loss-of-function results in cell death, while a damaging mutation of either gene alone does not affect the cells survival. This makes SL pairs attractive targets for precision cancer therapies, as targeting the unimpaired gene of the SL pair can selectively kill cancer cells that already harbor the impaired gene. Limited by the difficulty of finding true SL pairs, especially on specific cell types, the identification of SL targets still relies on expensive, time-consuming experimental approaches. In this work, we utilized various cell-line specific omics data to design a deep learning model for predicting SL pairs on particular cell-lines. By incorporating multiple types of cell-specific omics data with a self-attention module, we represent gene relationships as graphs. Our approach demonstrates the potential to facilitate the discovery of cell-specific SL targets for cancer therapeutics, providing a tool to unearth mechanisms underlying the origin of SL in cancer biology. Our approach allows for prediction of SL pairs in a cell-specific manner and enhances cancer precision medicine. The code and data of our approach can be found at https://github.com/promethiume/SLwise HighlightsO_LIFew computational methods can systematically predict SL pairs at a cell-specific level, and their performance may not generalize well to clinical scenarios due to the heterogeneity of cancer types. C_LIO_LIThe SLWise utilizes various cell-line specific omics data to design a deep learning model with a graph-based representation and self-attention mechanism. C_LIO_LIThis approach allows for the prediction of SL pairs in a cell-specific manner, providing valuable insights on effectively identifying the cell-type specific SL targets for personalized treatment strategies. C_LI

bioinformatics↗
bioRxiv2023

Modeling Human Spine-Spinal Cord Organogenesis by hPSC-Derived Neuromesodermal Progenitors

Human trunk development, including spine and spinal cord organogenesis, is a coordinated, orderly, and interdependent process with spatiotemporal tissue patterning. However, the underlying cellular and molecular mechanisms remain largely unclear due to the lack of an effective model that can simulate the early development of human body axis. Here, we reported the long-term patterning and dynamic morphogenesis of human trunk through the formation of spine-spinal cord organoids (SSCOs) self-organized from three-dimensional culture of human PSC-derived neuromesodermal progenitors (NMPs). The SSCOs resembled the morphogenetic features of spine and spinal cord along the anterior-posterior axis, and showed the chondro-osteogenic and neural trajectories consistent with developmental dynamics of spine and spinal cord in gestational embryo through single-cell RNA sequencing (scRNA-seq). In addition, we identified a new HMMR+ bipotent cell population with self-renewal ability and neural/mesodermal competence but distinct from NMPs, which may be involved in trunk development and represent an invaluable tool for disease modeling of spine- and spinal cord-related disorders. Graphic Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=200 SRC="FIGDIR/small/549829v1_ufig1.gif" ALT="Figure 1"> View larger version (80K): org.highwire.dtl.DTLVardef@14bfd30org.highwire.dtl.DTLVardef@1db7fe8org.highwire.dtl.DTLVardef@1aa66a1org.highwire.dtl.DTLVardef@380932_HPS_FORMAT_FIGEXP M_FIG C_FIG

developmental biology↗
bioRxiv2023

A framework for Frizzled-G protein coupling and implications to the Wnt-PCP signaling pathways

The ten Frizzled receptors (FZDs) are essential in Wnt signaling and play important roles in embryonic development and tumorigenesis. Among these, FZD6 is closely associated with lens development. Understanding FZD activation mechanism is key to unlock these emerging targets. Here we present the cryo-EM structures of FZD6 and FZD3 which are known to relay non-canonical Wnt-PCP (planar cell polarity) signaling pathways as well as FZD1 in their G protein-coupled (active) and G protein-free (inactive) states, respectively. Comparison of the three inactive/active pairs unveiled a shared activation framework among all ten FZDs. Mutagenesis along with imaging and functional analysis on the human lens epithelial tissues suggested potential crosstalk between G-protein binding and Wnt-PCP signaling pathways. Together, this study provides an integrated understanding of FZD structure and function, and lays the foundation for developing therapeutic modulators to activate or inhibit FZD signaling for a range of disorders including cancers and cataracts.

biochemistry↗
bioRxiv2023

Personalized connectivity-based network targeting model of TMS for treatment of psychiatric disorders: computational feasibility and reproducibility

Consider the limited clinical efficacy of transcranial magnetic stimulation (TMS) due to heterogeneity in treatment outcomes, the utilization of individual functional connectivity (FC) can enhance the prediction accuracy in the network targeting model. However, the low signal-to-noise ratio (SNR) of FC poses a challenge when utilizing individual resting-state FC (rsFC). To overcome this challenge, proposed solutions include increasing the scan duration and employing clustering approaches to enhance the stability of FC. In this study, we aimed to evaluate the stability of a personalized functional-based network targeting model in individuals with major depressive disorder (MDD) and schizophrenia with auditory verbal hallucinations (AVH). Using resting-state functional magnetic resonance imaging (rsfMRI) data from the Human Connectome Project (HCP), we assessed the models stability and employed longer scan durations (7 minutes, 14 minutes, 21 minutes, 28 minutes) and clustering methodologies to improve the precision of identifying optimal individual sites. Our findings demonstrate that a scan duration of 28 minutes and the utilization of the clustering approach lead to stable identification of individual sites, as evidenced by the intraindividual distance falling below the [~]1cm spatial resolution of TMS. These findings contribute to the understanding of individualized TMS targeting and have implications for improving treatment outcomes in psychiatric disorders.

neuroscience↗