Biology subjects

Xie, L.

Publications and source records attributed to Xie, L..

At least 163 records · Page 9Linked to original sources

bioRxiv2021

Integrated analysis of single-cell and transcriptome based RNA-seq multilayer network and WGCNA for construction and validation of an immune cell-related prognostic model in clear cell renal cell carcinoma

Clear cell renal cell carcinoma (ccRCC) is the most common type of renal cancer (RCC). The increasing incidence and poor prognosis of ccRCC after tumour metastasis makes the study of its pathogenesis extremely important. Traditional studies mostly focus on the regulation of ccRCC by single gene, while ignoring the impact of tumour heterogeneity on disease progression. The purpose of this study is to construct a prognostic risk model for ccRCC by analysing the differential marker genes related to immune cells in the single-cell database for providing help in clinical diagnosis and targeted therapy. Single-cell data and ligand-receptor relationship pair data were downloaded from related publications, and ccRCC phenotype and expression profile data were downloaded from TCGA and CPTAC. The DEGs and marker genes of the immune cell were combined and then intersected with the ligand-receptor gene data, and the 981 ligand-receptor relationship pairs obtained were intersected with the target gene of the transcription factor afterwards; 7,987 immune cell multilayer network relationship pairs were finally observed. Then, the genes in the network and the genes in TCGA were intersected to obtain 966 genes for constructing a co-expression network. Subsequently, 53 genes in black and magenta modules related to prognosis were screened by WGCNA for subsequent analysis. Whereafter, using the data of TCGA, 28 genes with significant prognostic differences were screened out through univariate Cox regression analysis. After that, LASSO regression analysis of these genes was performed to obtain a prognostic risk scoring model containing 16 genes, and CPTAC data showed that the effectiveness of this model was good. The results of correlation analysis between the risk score and other clinical factors showed that age, grade, M, T, stage and risk score were all significantly different (p < 0.05), and the results of prognostic accuracy also reached the threshold of qualification. Combined with clinical information, univariate and multivariate Cox regression analyses verified that risk score was an independent prognostic factor (p < 0.05). A nomogram constructed based on a predictive model for predicting the overall survival was established, and internal validation performed well. Our findings suggest that the predictive model built based on the immune cells scRNA-seq will enable us to judge the prognosis of patients with ccRCC and provide more accurate directions for basic relevant research and clinical practice.

cancer biology↗

bioRxiv2021

DeepREAL: A Deep Learning Powered Multi-scale Modeling Framework Towards Predicting Out-of-distribution Receptor Activity of Ligand Binding

Drug discovery has witnessed intensive exploration of the problem of drug-target physical interactions over two decades, however, a strong drug binding affinity to a single target often fails to translate into desired clinical outcomes. A critical knowledge gap needs to be filled for correlating drug-target interactions with phenotypic responses: predicting the receptor activities or function selectivity upon the ligand binding (i.e., agonist vs. antagonist) on a genome-scale and for novel chemicals. Two major obstacles compound the difficulty on this direction: known data of receptor activity is far too scarce to train a robust model in light of genome-scale applications, and real-world applications need to deploy a model on data from various shifted distributions. To address these challenges, we have developed an end-to-end deep learning framework, DeepREAL, for multi-scale modeling of genome-wide receptor activities of ligand binding. DeepREAL utilizes self-supervised learning on tens of millions of protein sequences and pre-trained binary interaction classification to solve the data distribution shift and data scarcity problems. Extensive benchmark studies that simulate real-world scenarios demonstrate that DeepREAL achieves state-of-the-art performance in out-of-distribution settings.

bioinformatics↗

bioRxiv2021

Genetic Influence underlying Brain Connectivity Phenotype: A Study on Two Age-Specific Cohorts

BackgroundHuman brain structural connectivity is an important imaging quantitative trait for brain development and aging. Mapping the network connectivity to the phenotypic variation provides fundamental insights in understanding the relationship between detailed brain topological architecture, function, and dysfunction. However, the underlying neurobiological mechanism from gene to brain connectome, and to phenotypic outcomes, and whether this mechanism changes over time, remain unclear. MethodsThis study analyzes diffusion weighted imaging data from two age-specific neuroimaging cohorts, extracts structural connectome topological network measures, performs genome-wide association studies (GWAS) of the measures, and examines the causality of genetic influences on phenotypic outcomes mediated via connectivity measures. ResultsOur empirical study has yielded several significant findings: 1) It identified genetic makeup underlying structural connectivity changes in the human brain connectome for both age groups. Specifically, it revealed a novel association between the minor allele (G) of rs7937515 and the decreased network segregation measures of the left middle temporal gyrus across young and elderly adults, indicating a consistent genetic effect on brain connectivity across the lifespan. 2) It revealed rs7937515 as a genetic marker for body mass index (BMI) in young adults but not in elderly adults. 3) It discovered brain network segregation alterations as a potential neuroimaging biomarker for obesity. 4) It demonstrated the hemispheric asymmetry of structural network organization in genetic association analyses and outcome-relevant studies. DiscussionThese imaging genetic findings underlying brain connectome warrant further investigation for exploring their potential influences on brain-related diseases, given the significant involvement of altered connectivity in neurological, psychiatric and physical disorders. Impact StatementThe genetic architecture underlying brain connectivity, and whether this mechanism changes over time, remain largely unknown. To understand the inter-individual variability at different life stages, this study performed genome-wide association studies of brain network connectivity measures from two age-specific neuroimaging cohorts, and identified a common association between the minor allele (G) of rs7937515 and decreased network segregation measures of the left middle temporal gyrus. The mediation analysis further elucidated neurobiological pathway of brain connectivity mediators linking the genes FAM86C1/FOLR3 with body mass index. This study provided new insights into the genetic mechanism of inter-regional connectivity alteration in the brain.

genetics↗

bioRxiv2021

Predictive Modeling of Multiplex Chemical Phenomics for Novel Cells and Patients: Applied to Personalized Alzheimer's Disease Drug Repurposing

Chemical phenomics which measures multiplex chemical-induced phenotypic response of cells or patients, particularly dose-dependent transcriptomics and drug-response curves, provides new opportunities for in silico mechanism-driven phenotype-based drug discovery. However, state-of-the-art computational methods only focus on predicting a single phenotypic readout and are less successful in screening compounds for novel cells or individual patients. We designed a new deep learning model, MultiDCP, to enable high-throughput compound screening based on multiplex chemical phenomics for the first time, and further expand the scope of chemical phenomics to unexplored cells and patients. The novelties of MultiDCP lie in a multi-task learning framework with a novel knowledge-driven autoencoder to integrate incoherent labeled and unlabeled omics data, and a teacher-student training strategy to exploit unreliable data. MultiDCP significantly outperforms the state-of-the-art for novel cell lines. The predicted chemical transcriptomics demonstrate a stronger predictive power than noisy experimental data for downstream tasks. We applied MultiDCP to repurpose individualized drugs for Alzheimers disease, suggesting that MultiDCP is a potentially powerful tool for personalized medicine.

bioinformatics↗

bioRxiv2021

A new single-cell level R-index for EGFR-TKI resistance and survival prediction in LUAD

EGFR-TKIs achieved excellent efficacy in EGFR-mutated patients. Unfortunately, most patients would inevitably develop progressive disease within a median of 10 to 14 months. Predicting the resistance probability remains a challenge. Therefore, we created an R-index model trained by single-cell RNA data with the OCLR algorithm. This model can be applied to estimate the level of EGFR-TKI resistance in cell line and xenograft mice models and predict prognosis in multiple cohorts. Comparing the high and the low R-index group, we found that the glycolysis pathway and KRAS up-regulation pathway were related to resistance, and MDSC was the leading cause of immunosuppression in the tumor microenvironment. These results are consistent with previous studies indicating that the R-index provides an insight into resistance status and a new way to explore resistance mechanisms and clinical treatment by the combination of Glucose metabolism-targeted or MDSC-targeted therapies. This is the first quantification method of EGFR-TKI resistance based on single-cell sequencing data solving the problem of the mixed resistance state of tumor cells and helping explore transcriptome characteristics of drug-resistant cell populations.

bioinformatics↗

bioRxiv2021

A phylotranscriptome study using silica gel-dried leaf tissues produces an updated robust phylogeny of Ranunculaceae

The utility of transcriptome data in plant phylogenetics has gained popularity in recent years. However, because RNA degrades much more easily than DNA, the logistics of obtaining fresh tissues has become a major limiting factor for widely applying this method. Here, we used Ranunculaceae to test whether silica-dried plant tissues could be used for RNA extraction and subsequent phylogenomic studies. We sequenced 27 transcriptomes, 21 from silica gel-dried (SD-samples) and six from liquid nitrogen-preserved (LN-samples) leaf tissues, and downloaded 27 additional transcriptomes from GenBank. Our results showed that although the LN-samples produced slightly better reads than the SD-samples, there were no significant differences in RNA quality and quantity, assembled contig lengths and numbers, and BUSCO comparisons between two treatments. Using this data, we conducted phylogenomic analyses, including concatenated- and coalescent-based phylogenetic reconstruction, molecular dating, coalescent simulation, phylogenetic network estimation, and whole genome duplication (WGD) inference. The resulting phylogeny was consistent with previous studies with higher resolution and statistical support. The 11 core Ranunculaceae tribes grouped into two chromosome type clades (T- and R-types), with high support. Discordance among gene trees is likely due to hybridization and introgression, ancient genetic polymorphism and incomplete lineage sorting. Our results strongly support one ancient hybridization event within the R-type clade and three WGD events in Ranunculales. Evolution of the three Ranunculaceae chromosome types is likely not directly related to WGD events. By clearly resolving the Ranunculaceae phylogeny, we demonstrated that SD-samples can be used for RNA-seq and phylotranscriptomic studies of angiosperms.

evolutionary biology↗

bioRxiv2021

Tmem138, a photoreceptor connecting cilium (CC) protein, is required for rhodopsin transport across the cilium and outer segment (OS) biogenesis

Photoreceptor connecting cilium (CC) is structurally analogous to the transition zone (TZ) of primary cilia and gates the molecular trafficking between the inner and the outer segment (OS). Retinal dystrophies with underlying CC defects are manifested in a broad array of syndromic conditions known as ciliopathies as well as non-syndromic retinal degenerations. Despite extensive studies, protein trafficking across the photoreceptor CC is largely unknown. Here we genetically inactivated mouse Tmem138, a gene encoding a ciliary membrane protein localized to the ciliary TZ and linked to Joubert syndrome (JBTS). Germline deletion of Tmem138 abolished OS morphogenesis followed by rapid photoreceptor degeneration. Tmem138 was found localized to the photoreceptor CC and, accordingly, the molecular compartments of the CC and axoneme of the mutant photoreceptors were altered despite ciliogenesis proceeding normally at the early stage of photoreceptor development. To gain further insights into Tmem138 function in OS biogenesis, we focused on trafficking of rhodopsin, the most abundant protein of the OS. Mislocalization of rhodopsin was readily observed as early as P5 in the mutant photoreceptors prior to growth of the OS. Ablation of Tmem138 in mature rods recapitulated the molecular changes in the germline mutants, causing well-formed outer segment discs to disintegrate accompanied by mislocalization of rhodopsin in the cell body. Furthermore, Tmem138 interacted with rhodopsin, and two additional CC compartment proteins Ahi1 and Tmem231, which were both altered in the mutant photoreceptors. Taken together, these results suggest that Tmem138 has a distinct role in gating the transport of rhodopsin and likely other OS bound proteins through formation of CC transport complex(es).

cell biology↗

bioRxiv2021

Robust Prediction of Patient-Specific Clinical Response to Unseen Drugs From in vitro Screens Using Context-aware Deconfounding Autoencoder

Accurate and robust prediction of patient-specific responses to drug treatments is critical for drug development and personalized medicine. However, patient data are often too scarce to train a generalized machine learning model. Although many methods have been developed to utilize cell line data, few of them can reliably predict individual patient clinical responses to new drugs due to data distribution shift and confounding factors. We develop a novel Context-aware Deconfounding Autoencoder (CODE-AE) that can extract common biological signals masked by context-specific patterns and confounding factors. Extensive studies demonstrate that CODE-AE effectively alleviates the out-of-distribution problem for the model generalization, significantly improves accuracy and robustness over state-of-the-art methods in both predicting patient-specific ex vivo and in vivo drug responses purely from in vitro screens and disentangling intrinsic biological signals from confounding factors. Using CODE-AE, we screened 50 drugs for 9,808 cancer patients and discovered novel personalized anti-cancer therapies and drug-response biomarkers. Contact: lxie@iscb.org

bioinformatics↗

bioRxiv2021

Fitness effects of CRISPR endonucleases in Drosophila melanogaster populations

CRISPR/Cas9 systems provide a highly efficient and flexible genome editing technology with numerous potential applications in areas ranging from gene therapy to population control. Some proposed applications involve CRISPR/Cas9 endonucleases integrated into an organisms genome, which raises questions about potentially harmful effects to the transgenic individuals. One application where this is particularly relevant are CRISPR-based gene drives, which promise a mechanism for rapid genetic alteration of entire populations. The performance of such drives can strongly depend on fitness costs experienced by drive carriers, yet relatively little is known about the magnitude and causes of these costs. Here, we assess the fitness effects of genomic CRISPR/Cas9 expression in Drosophila melanogaster cage populations by tracking allele frequencies of four different transgenic constructs, designed to disentangle direct fitness costs due to the integration, expression, and target-site activity of Cas9 from costs due to potential off-target cleavage. Using a maximum likelihood framework, we find a moderate level of fitness costs due to off-target effects but do not detect significant direct costs. Costs of off-target effects are minimized for a construct with Cas9HF1, a high-fidelity version of Cas9. We further demonstrate that using Cas9HF1 instead of standard Cas9 in a homing drive achieves similar drive conversion efficiency. Our results suggest that gene drives should be designed with high-fidelity endonucleases and may have implications for other applications that involve genomic integration of CRISPR endonucleases.

evolutionary biology↗

bioRxiv2021

Phytophthora capsici sterol reductase PcDHCR7 has a role in mycelium development and pathogenicity

The de novo biosynthesis of sterols is critical for eukaryotes, however, some organisms lack this pathway including most oomycetes. Phytophthora spp. are sterol auxotroph but remarkably, have retained a few genes encoding enzymes in the sterol biosynthesis pathway. Here we investigated the function of PcDHCR7, a gene in Phytophthora capsici predicted to encode the {bigtriangleup}7-sterol reductase. When expressed in Saccharomyces cerevisiae, PcDHCR7 showed a {bigtriangleup}7-sterol reductase activity. Knocking out PcDHCR7 in P. capsici resulted in loss of the capacity to transform ergosterol into brassicasterol, which means PcDHCR7 has a {bigtriangleup}7-sterol reductase activity in P. capsici itself. This enables P. capsici to transform sterols recruited from the environment for better use. Biological characteristics were compared between wild-type isolate and PcDHCR7 knock-out transformants. The results indicated that PcDHCR7 plays a key role in mycelium development and pathogenicity of zoospores in P. capsici.

microbiology↗

bioRxiv2021

Auditory attention detection with EEG channel attention

Auditory attention detection (AAD) seeks to detect the attended speech from EEG signals in a multi-talker scenario, i.e. cocktail party. As the EEG channels reflect the activities of different brain areas, a task-oriented channel selection technique improves the performance of brain-computer interface applications. In this study, we propose a soft channel attention mechanism, instead of hard channel selection, that derives an EEG channel mask by optimizing the auditory attention detection task. The neural AAD system consists of a neural channel attention mechanism and a convolutional neural network (CNN) classifier. We evaluate the proposed framework on a publicly available database. We achieve 88.3% and 77.2% for 2-second and 0.1-second decision windows with 64-channel EEG; and 86.1% and 83.9% for 2-second decision windows with 32-channel and 16-channel EEG, respectively. The proposed framework outperforms other competitive models by a large margin in all test cases.

neuroscience↗

bioRxiv2021

Multi-scale predictive modeling discovers Ibudilast as a polypharmacological agent to improve hippocampal dependent spatial learning and memory and mitigate plaque and tangle pathology in a transgenic rat model of Alzheimer's disease

Alzheimers disease (AD) is a multifactorial disease that exhibits cognitive deficits, neuronal loss, amyloid plaques, neurofibrillary tangles and neuroinflammation in the brain. We developed a multi-scale predictive modeling strategy that integrates machine learning with biophysics and systems pharmacology to model drug actions from molecular interactions to phenotypic responses. We predicted that ibudilast (IBU), a phosphodiesterase inhibitor and toll-like receptor 4 (TLR4) antagonist, inhibited multiple kinases (e.g., IRAK1 and GSG2) as off-targets, modulated multiple AD-associated pathways, and reversed AD molecular phenotypes. We address for the first time the efficacy of ibudilast (IBU) in a transgenic rat model of AD. IBU-treated transgenic rats showed improved cognition and reduced hallmarks of AD pathology. RNA sequencing analyses in the hippocampus showed that IBU affected the expression of pro-inflammatory genes in the TLR signaling pathway. Our results identify IBU as a potential therapeutic to be repurposed for reducing neuroinflammation in AD by targeting TLR signaling.

pharmacology and toxicology↗

bioRxiv2021

Recovery of high-qualitied Genomes from a deep-inland Salt Lake Using BASALT

Metagenomic binning enables the in-depth characterization of microorganisms. To improve the resolution and efficiency of metagenomic binning, BASALT (Binning Across a Series of AssembLies Toolkit), a novel binning toolkit was present in this study, which recovers, compares and optimizes metagenomic assembled genomes (MAGs) across a series of assemblies from short-read, long-read or hybrid strategies. BASALT incorporates self-designed algorithms which automates the separation of redundant bins, elongate and refine best bins and improve contiguity. Evaluation using mock communities revealed that BASALT auto-binning obtained up to 51% more number of MAGs with up to 10 times better MAG quality from microbial community at low (132 genomes) and medium (596 genomes) complexity, compared to other binners such as DASTool, VAMB and metaWRAP. Using BASALT, a case-study analysis of a Salt Lake sediment microbial community from northwest arid region of China was performed, resulting in 426 non-redundant MAGs, including 352 and 69 bacterial and archaeal MAGs which could not be assigned to any known species from GTDB (ANI < 95%), respectively. In addition, two Lokiarchaeotal MAGs that belong to superphylum Asgardarchaeota were observed from Salt Lake sediment samples. This is the first time that candidate species from phylum Lokiarchaeota was found in the arid and deep-inland environment, filling the current knowledge gap of earth microbiome. Overall, BASALT is proven to be a robust toolkit for metagenomic binning, and more importantly, expand the Tree of Life.

bioinformatics↗

bioRxiv2021

Small molecule SWELL1-LRRC8 complex induction improves glycemic control and nonalcoholic fatty liver disease in murine Type 2 diabetes

Type 2 diabetes (T2D) is associated with insulin resistance, impaired insulin secretion from the pancreatic {beta}-cell, and nonalcoholic fatty liver disease (NAFLD). SWELL1 (LRRC8a) ablation impairs adipose and skeletal muscle insulin-pAKT2 signaling, {beta}-cell insulin secretion and glycemic control - suggesting that SWELL1-LRRC8 complex dysfunction contributes to T2D pathogenesis. Here, we show that ICl,SWELL and SWELL1 protein are reduced in adipose and {beta}-cells in murine and human T2D. Combining cryo-electron microscopy, molecular docking, medicinal chemistry, and functional studies, we define a structure activity relationship to rationally-designed active derivatives (SN-40X) of a SWELL1 channel inhibitor (DCPIB/SN-401), that bind the SWELL1-LRRC8 hexameric complex, restore SWELL1-LRRC8 protein, plasma membrane trafficking, signaling and islet insulin secretion via SWELL1-dependent mechanisms. In vivo, SN-401 and active SN-40X compounds restore glycemic control and prevents NAFLD by improving insulin-sensitivity and insulin secretion in murine T2D. These findings demonstrate that small molecule SWELL1 modulators restore SWELL1-dependent insulin-sensitivity and insulin secretion in T2D and may represent a first-in-class therapeutic approach for T2D and NAFLD.

physiology↗

bioRxiv2021

3D Mapping of Neurofibrillary Tangle Burden in the Human Medial Temporal Lobe

Tau protein neurofibrillary tangles (NFT) are closely linked to neuronal/synaptic loss and cognitive decline in Alzheimers disease (AD) and related dementias. Our knowledge of the pattern of NFT progression in the human brain, critical to the development of imaging biomarkers and interpretation of in vivo imaging studies in AD, is based on conventional 2D histology studies that only sample the brain sparsely. To address this limitation, ex vivo MRI and dense serial histological imaging in 18 human medial temporal lobe (MTL) specimens were used to construct 3D quantitative maps of NFT burden in the MTL at individual and group levels. These maps reveal significant variation in NFT burden along the anterior-posterior axis. While early NFT pathology is thought to be confined to the transentorhinal region, we find similar levels of NFT burden in this region and other MTL subregions, including amygdala, temporopolar cortex, and subiculum/CA1.

neuroscience↗

bioRxiv2021

DenovoProfiling: a webserver for de novo generated molecule library profiling

With the advances of deep learning techniques, various architectures for molecular generation have been proposed for de novo drug design. Successful cases from academia and industrial demonstrated that the deep learning based de novo molecular design could efficiently accelerate the drug discovery process. The flourish of the de novo molecular generation methods and applications created great demand for the visualization and functional profiling for the de novo generated molecules. The rising of publicly available chemogenomic databases lays good foundations and create good opportunities for comprehensive profiling of the de novo library. In this paper, we present DenovoProfiling, a web server dedicated for de novo library visualization and functional profiling. Currently, DenovoProfiling contains six modules: (1) identification & visualization, (2) chemical space, (3) scaffold analysis, (4) molecular alignment, (5) target & pathways, and (6) drugs mapping. DenovoProfiling could provide structural identification, chemical space exploration, drugs mapping, and targets & pathways. The comprehensive annotated information could give user a clear picture of their de novo library and could provide guidance in the further selection of candidates for synthesis and biological confirmation. DenovoProfiling is freely available at http://denovoprofiling.xielab.net.

bioinformatics↗

bioRxiv2020

Evolution of the SARS-CoV-2 proteome in three dimensions (3D) during the first six months of the COVID-19 pandemic

Three-dimensional structures of SARS-CoV-2 and other coronaviral proteins archived in the Protein Data Bank were used to analyze viral proteome evolution during the first six months of the COVID-19 pandemic. Analyses of spatial locations, chemical properties, and structural and energetic impacts of the observed amino acid changes in >48,000 viral proteome sequences showed how each one of the 29 viral study proteins have undergone amino acid changes. Structural models computed for every unique sequence variant revealed that most substitutions map to protein surfaces and boundary layers with a minority affecting hydrophobic cores. Conservative changes were observed more frequently in cores versus boundary layers/surfaces. Active sites and protein-protein interfaces showed modest numbers of substitutions. Energetics calculations showed that the impact of substitutions on the thermodynamic stability of the proteome follows a universal bi-Gaussian distribution. Detailed results are presented for six drug discovery targets and four structural proteins comprising the virion, highlighting substitutions with the potential to impact protein structure, enzyme activity, and functional interfaces. Characterizing the evolution of the virus in three dimensions provides testable insights into viral protein function and should aid in structure-based drug discovery efforts as well as the prospective identification of amino acid substitutions with potential for drug resistance.

molecular biology↗

bioRxiv2020

Double Lock of a Potent Human Monoclonal Antibody against SARS-CoV-2

Receptor recognition and subsequent membrane fusion are essential for the establishment of successful infection by SARS-CoV-2. Halting these steps can cure COVID-19. Here we have identified and characterized a potent human monoclonal antibody, HB27, that blocks SARS-CoV-2 attachment to its cellular receptor at sub-nM concentrations. Remarkably, HB27 can also prevent SARS-CoV-2 membrane fusion. Consequently, a single dose of HB27 conferred effective protection against SARS-CoV-2 in two established mouse models. Rhesus macaques showed no obvious adverse events when administrated with 10-fold of effective dose of HB27. Cryo-EM studies on complex of SARS-CoV-2 trimeric S with HB27 Fab reveal that three Fab fragments work synergistically to occlude SARS-CoV-2 from binding to ACE2 receptor. Binding of the antibody also restrains any further conformational changes of the RBD, possibly interfering with progression from the prefusion to the postfusion stage. These results suggest that HB27 is a promising candidate for immuno-therapies against COVID-19. HighlightsO_LISARS-CoV-2 specific antibody, HB27, blocks viral receptor binding and membrane fusion C_LIO_LIHB27 confers prophylactic and therapeutic protection against SARS-CoV-2 in mice models C_LIO_LIRhesus macaques showed no adverse side effects when administered with HB27 C_LIO_LICryo-EM studies suggest that HB27 sterically occludes SARS-CoV-2 from its receptor C_LI

biophysics↗