Biology subjects

Yang, H.

Publications and source records attributed to Yang, H..

At least 505 records · Page 28Linked to original sources

bioRxiv2021

Novel inhibition mechanism of SARS-CoV-2 main protease by ebselen and its derivatives

The global emergence of SARS-CoV-2 has triggered numerous efforts to develop therapeutic options for COVID-19 pandemic. The main protease of SARS-CoV-2 (Mpro), which is a critical enzyme for transcription and replication of SARS-CoV-2, is a key target for therapeutic development against COVID-19. An organoselenium drug called ebselen has recently been demonstrated to have strong inhibition against Mpro and antiviral activity but its molecular mode of action is unknown preventing further development. We have examined the binding modes of ebselen and its derivative in Mpro via high resolution co-crystallography and investigated their chemical reactivity via mass spectrometry. Stronger Mpro inhibition than ebselen and potent ability to rescue infected cells were observed for a number of ebselen derivatives. A free selenium atom bound with cysteine 145 of Mpro catalytic dyad has been revealed by crystallographic studies of Mpro with ebselen and MR6-31-2 suggesting hydrolysis of the enzyme bound organoselenium covalent adduct, formation of a phenolic by-product is confirmed by mass spectrometry. The target engagement of these compounds with an unprecedented mechanism of SARS-CoV-2 Mpro inhibition suggests wider therapeutic applications of organo-selenium compounds in SARS-CoV-2 and other zoonotic beta-corona viruses.

biophysics↗

bioRxiv2021

ChexMix: A Literature Content Extraction Tool for Bioentities

Biomedical databases grow by more than a thousand new publications every day. The large volume of biomedical literature that is being published at an unprecedented rate hinders the discovery of relevant knowledge from keywords of interest to gather new insights and form hypotheses. A text-mining tool, PubTator, helps to automatically annotate bioentities, such as species, chemicals, genes, and diseases, from PubMed abstracts and full-text articles. However, the manual re-organization and analysis of bioentities is a non-trivial and highly time-consuming task. ChexMix was designed to extract the unique identifiers of bioentities from query results. Herein, ChexMix was used to construct a taxonomic tree with allied species among Korean native plants and to extract the medical subject headings unique identifier of the bioentities, which co-occurred with the keywords in the same literature. ChexMix discovered the allied species related to a keyword of interest and experimentally proved its usefulness for multi-species analysis.

bioinformatics↗

bioRxiv2021

Expansion-Assisted Iterative-FISH defines lateral hypothalamus spatio-molecular organization

Determining the spatial organization and morphological characteristics of molecularly defined cell types is a major bottleneck for characterizing the architecture underpinning brain function. We developed Expansion-Assisted Iterative Fluorescence In Situ Hybridization (EASI-FISH) to survey gene expression in brain tissue, as well as a turnkey computational pipeline to rapidly process large EASI-FISH image datasets. EASI-FISH was optimized for thick brain sections (300 {micro}m) to facilitate reconstruction of spatio-molecular domains that generalize across brains. Using the EASI-FISH pipeline, we investigated the spatial distribution of dozens of molecularly defined cell types in the lateral hypothalamic area (LHA), a brain region with poorly defined anatomical organization. Mapping cell types in the LHA revealed nine novel spatially and molecularly defined subregions. EASI-FISH also facilitates iterative re-analysis of scRNA-Seq datasets to determine marker-genes that further dissociated spatial and morphological heterogeneity. The EASI-FISH pipeline democratizes mapping molecularly defined cell types, enabling discoveries about brain organization. Highlights- EASI-FISH enables robust gene expression profiling in thick brain slices - A turnkey analysis pipeline for facile analysis of large EASI-FISH image datasets - EASI-FISH reveals novel subregions of the lateral hypothalamus - Identification of rare cell types based on morphological and spatial heterogeneity

neuroscience↗

bioRxiv2021

Present and Future Ecological Niche Modeling of Rift Valley fever in East Africa in Response to Climate Change

Rift Valley fever (RVF) has been linked with recurrent outbreaks among humans and livestock in several parts of the globe. Predicting RVFs habitat suitability under different climate scenarios offers vital information for developing informed management schemes. The present study evaluated the probable impacts of climate change on the distribution of RVF disease in East Africa (E. A.), using the maximum entropy (MaxEnt) model and the disease outbreak cases. Considering the potential of the spread of the disease in the East Africa region, we utilized two representative concentration pathways (RCP 4.5 and RCP 8.5) climate scenarios in the 2050s and 2070s (average for 2041-2060, and 2061-2080), respectively. All models had satisfactory AUC values of more than 0.809, which are considered excellent. Jackknife tests revealed that Bio4 (temperature seasonality), land use, and population density were the main factors influencing RVF distribution in the region. From the risk maps generated, we infer that, without regulations, this disease might establish itself across more extensive areas in the region, including most of Rwanda and Burundi. The ongoing trade between East African countries and changing climates could intensify RVF spread into new geographic extents with suitable habitats for the important zoonosis. The predicted suitable areas for RVF in eastern Kenya, southern Tanzania, and Somalia overlaps to a large extent where cattle keeping and pastoralism are highly practiced, thereby signifying the urgency to manage and control the disease. This work validates RVF outbreak cases effectiveness to map the diseases distribution, thus contributing to enhanced ecological modeling and improved disease tracking and control efforts in East Africa.

ecology↗

bioRxiv2021

The methylation of SDC2 and TFPI2 defined three methylator phenotypes of colorectal cancer

Methylation-based noninvasive molecular diagnostics are easy and feasible tools for the early detection of colorectal cancer (CRC). However, many of them have the limitation of low sensitivity with some CRCs detection failed in clinical practice. In this study, the clinical and pathological characteristics, as well as molecular features of three methylator-groups, defined by the promoter methylation status of SDC2 and TFPI2, were investigated in order to improve the performance of CRC detection. The Illumina Infinium 450k Human DNA methylation data and clinical information of CRCs were collected from The Cancer Genome Atlas (TCGA) project and Gene Expression Omnibus (GEO) database. CRC samples were divided into three groups, HH (dual-positive), HL (single positive) and LL (dual-negative) according to the methylation status of SDC2 and TFPI2 promoters. Differences in age, tumor location, microsatellite instable status and differentially expressed genes (DEGs) were evaluated among the three groups and these findings were then confirmed in our inner CRC dataset. The combination of methylated SDC2 and TFPI2 showed a superior performance of distinguishing CRCs from normal controls than each alone. Samples of HL group were more often originated from left-side CRCs whereas very few of them were from right-side (P < 0.05). HH grouped CRCs showed a higher level of microsatellite instability and mutation load than other two groups (mean nonsynonymous mutations for HH/HL/LL: 10.55/3.91/7.02, P = 0.0055). All mutations of BRAF, one of the five typical CpG island methylator phenotype (CIMP) related genes, were found in HH group (HH/HL/LL: 51/0/0, P = 0.018). Also there was a significantly older patient age at the diagnosis in HH group. Gene expression analysis identified 37, 84 and 22 group-specific DEGs for HH, HL and LL, respectively. Functional enrichment analysis suggested that HH specific DEGs were mainly related to the regulation of transcription and other processes, while LL specific DEGs were enriched in the biological processes of extracellular matrix interaction and cell migration. The three defined mathylator groups showed great difference in tumor location, patient age, MSI and ECM biological process, which could facilitate the development of more effective biomarkers for CRC detection.

cancer biology↗

bioRxiv2021

Metatranscriptomic comparison of endophytic and pathogenic Fusarium-Arabidopsis interactions reveals plant transcriptional plasticity

Plants are continuously exposed to beneficial and pathogenic microbes, but how plants recognize and respond to friends versus foes remains poorly understood. Here, we compared the molecular response of Arabidopsis thaliana independently challenged with a Fusarium oxysporum endophyte Fo47 versus a pathogen Fo5176. These two Fusarium oxysporum strains share a core genome of about 46 Mb, in addition to unique 1,229 and 5,415 accessory genes. Metatranscriptomic data reveal a shared pattern of expression for most plant genes ([~]80%) in responding to both fungal inoculums at all time points from 12 to 96 h post inoculation (HPI). However, the distinct responding genes depict transcriptional plasticity, as the pathogenic interaction activates plant stress responses and suppresses plant growth/development related functions, while the endophytic interaction attenuates host immunity but activates plant nitrogen assimilation. The differences in reprogramming of the plant transcriptome are most obvious in 12 HPI, the earliest time point sampled and are linked to accessory genes in both fungal genomes. Collectively, our results indicate that the A. thaliana and F. oxysporum interaction displays both transcriptome conservation and plasticity in the early stages of infection, providing insights into the fine-tuning of gene regulation underlying plant differential responses to fungal endophytes and pathogens. One-sentence summaryMultiomics analysis reveals the regulatory plasticity of plants in response to beneficial and antagonistic microbes, resulting in distinct phenotypes and rewired transcriptional networks.

plant biology↗

bioRxiv2021

Cytoplasmic domain and enzymatic activity of ACE2 is not required for PI4KB dependent endocytosis entry of SARS-CoV-2 into host cells

The recent COVID-19 pandemic poses a global health emergency. Cellular entry of the causative agent SARS-CoV-2 is mediated by its spike protein interacting with cellular receptor- human angiotensin converting enzyme 2 (ACE2). Here, we used lentivirus based pseudotypes bearing spike protein to demonstrate that entry of SARS-CoV-2 into host cells is dependent on clathrin-mediated endocytosis, and phosphoinositides play essential role during this process. In addition, we showed that the intracellular domain and the catalytic activity of ACE2 is not required for efficient virus entry. These results provide new insights into SARS-CoV-2 cellular entry and present potential targets for drug development.

microbiology↗

bioRxiv2021

Combined Metabolic Activators decrease liver steatosis by activating mitochondrial metabolism in a Golden Syrian hamster study

The prevalence of non-alcohol fatty liver disease (NAFLD), defined as the livers excessive fat accumulation, continues to increase dramatically. We have recently revealed the molecular mechanism underlying NAFLD using in-depth multi-omics profiling and identified that combined metabolic activators (CMA) could be administered to decrease the amount of hepatic steatosis (HS) in mouse model and NAFLD patients based on systems analysis. Here, we investigated the effects of a CMA including L-carnitine, N-acetyl-l-cysteine, nicotinamide riboside and betaine on a Golden Syrian hamster NAFLD model fed with HFD, and found that HS was decreased with the administration of CMA. To explore the mechanisms involved in the clearance HS, we generated liver transcriptomics data before and after CMA administration, and integrated these data using liver-specific genome-scale metabolic model of liver tissue. We systemically determined the molecular changes after the supplementation of CMAs and found that it activates mitochondria in the liver tissue by modulating the global fatty acid, amino acids, antioxidant and folate metabolism.

systems biology↗

bioRxiv2021

A phosphorylation of RIPK3 kinase initiates an intracellular apoptotic pathway that promotes corpus luteum regression

Receptor-interacting serine/threonine-protein kinase 3 (RIPK3) normally signals to necroptosis by phosphorylating MLKL. We report here that when the cellular RIPK3 chaperone Hsp90/CDC37 level is low, RIPK3 also signals to apoptosis. The apoptotic function of RIPK3 requires phosphorylation of the serine 165/threonine 166 sites on its kinase activation loop, resulting in inactivation of RIPK3 kinase activity while gaining the ability to recruit RIPK1, FADD, and caspase-8 to form a cytosolic caspase-activating complex, thereby triggering apoptosis. We found that PGF2 induces RIPK3 expression in luteal granulosa cells in the ovary to cause luteal regression through this RIPK3-mediated apoptosis pathway. Mice carrying homozygous phosphorylation-resistant RIPK3 S165A/T166A knockin mutations failed to respond to PGF2 but retained pro-necroptotic function, whereas mice with phospho-mimicking S165D/T166E homozygous knockin mutation underwent spontaneous apoptosis in multiple RIPK3-expressing tissues and died shortly after birth. Thus, RIPK3 signals to either necroptosis or apoptosis depending on its serine 165/threonine 166 phosphorylation status.

cell biology↗

bioRxiv2021

The C. elegans DAF-19M module: a shift from general ciliogenesis to ciliary and behavioral specialization

In animals, cilia are important for the interaction with environments and the proper function of tissues and organs. Understanding the distinctive identities of each type of ciliated cell is essential for therapeutic solutions for ciliopathies, complex disorders with impairments of various organs caused by defective cilia development and function. Here, we report a regulatory module consisting of a cascade of transcription factors and their target genes that confer the cell type-specific ciliary identities on the IL2 ciliated neurons in C. elegans. We found that DAF-19M, isoform of the sole C. elegans RFX transcription factor DAF-19, through X-box promoter motif variants, heads a regulatory module in IL2 neurons, comprising the core target genes klp-6 (kinesin), osm-9 (TRP channel), and cwp-4 (novel); under the overall control of terminal selector proteins UNC-86 and CFI-1. Considering the conservation of this DAF-19M module in IL2 neurons for nictation, a dauer larva-specific behavior, and in male-specific neurons for mating behavior, we propose the existence of an evolutionarily adaptable, hard-wired genetic module for distinct behaviors that share the feature "recognizing the environment."

molecular biology↗

bioRxiv2021

Network-based modeling of herb combinations in Traditional Chinese Medicine

Traditional Chinese Medicine (TCM) has been practiced for thousands of years for treating human diseases. In comparison to modern medicine, one of the advantages of TCM is the principle of herb compatibility, known as TCM formulae. A TCM formula usually consists of multiple herbs to achieve the maximum treatment effects, where their interactions are believed to elicit the therapeutic effects. Despite being a fundamental component of TCM, the rationale of combining specific herb combinations remains unclear. In this study, we proposed a network-based method to quantify the interactions in herb pairs. We constructed a protein-protein interaction network for a given herb pair by retrieving the associated ingredients and protein targets, and determined multiple network-based distances including the closest, shortest, center, kernel, and separation, both at the ingredient and at the target levels. We found that the frequently used herb pairs tend to have shorter distances compared to random herb pairs, suggesting that a therapeutic herb pair is more likely to affect neighboring proteins in the human interactome. Furthermore, we found that the center distance determined at the ingredient level improves the discrimination of top-frequent herb pairs from random herb pairs, suggesting the rationale of considering the topologically important ingredients for inferring the mechanisms of action of TCM. Taken together, we have provided a network pharmacology framework to quantify the degree of herb interactions, which shall help explore the space of herb combinations more effectively to identify the synergistic compound interactions based on network topology.

systems biology↗

bioRxiv2021

Large field of view-spatially resolved transcriptomics at nanoscale resolution

Spatially resolved transcriptomic technologies are promising tools to study cell fate decisions in a physical microenvironment, which is fundamental for enhancing our knowledge of mammalian development. However, the imbalance between resolution, transcript capture and field of view of current methodologies precludes their systematic application to analyze relatively large and three-dimensional mid- and late-gestation mammalian embryos. Here, we combined DNA nanoball (DNB) patterned arrays and tissue RNA capture to create SpaTial Enhanced REsolution Omics-sequencing (Stereo-seq). This approach allows transcriptomic profiling of large histological sections with high resolution and sensitivity. We have applied Stereo-seq to study the kinetics and directionality of transcriptional variation in a time course of mouse organogenesis. We used this information to gain insight into the molecular basis of regional specification, neuronal migration and differentiation in the developing brain. Furthermore, we mapped the expression of a panel of developmental disease-related loci on our global transcriptomic maps to define the spatiotemporal windows of tissue vulnerability. Our panoramic atlas constitutes an essential resource to investigate longstanding questions concerning normal and abnormal mammalian development.

cell biology↗

bioRxiv2021

MolRep: A Deep Representation Learning Library for Molecular Property Prediction

SummaryRecently, novel representation learning algorithms have shown potential for predicting molecular properties. However, unified frameworks have not yet emerged for fairly measuring algorithmic progress, and experimental procedures of different representation models often lack rigorousness and are hardly reproducible. Herein, we have developed MolRep by unifying 16 state-of-the-art models across 4 popular molecular representations for application and comparison. Furthermore, we ran more than 12.5 million experiments to optimize hyperparameters for each method on 12 common benchmark data sets. As a result, CMPNN achieves the best results ranked the 1st in 5 out of 12 tasks with an average rank of 1.75. Relatively, ECC has good performance in classification tasks and MAT good for regression (both ranked 1st for 3 tasks) with an average rank of 2.71 and 2.6, respectively. AvailabilityThe source code is available at: https://github.com/biomed-AI/MolRep Supplementary informationSupplementary data are available online.

bioinformatics↗

bioRxiv2021

Fatty acid metabolic reprogramming promotes C. elegans development

Acetylcholine signaling has been reported to play essential roles in animal metabolic regulation and disease affected by diets. However, the underlying mechanisms that how diets regulate animal physiology and health are not well understood. Here we found that the acetylcholine receptor gene eat-2 was expressed in most of the pharyngeal muscles, which is in accordance to our previous report that EAT-2 received synaptic signals not only from pharyngeal MC neurons. The expression of fatty acid synthesis genes was significantly increased in both eat-2 and tmc-1 fast-growth mutants on CeMM food environment, compared to the wild-type. Excitingly, dietary fatty acids such as 15-methyl-hexadecanoic acid (C17ISO), palmitic acid (PA, C16:0) and stearic acid (SA, C18:0) supplementation, significantly accelerated wild-type worm development on CeMM, indicating that the fatty acid synthesis reprogramming is an essential strategy for C. elegans to regulate its development and growth on CeMM diet. Furthermore, we found that fatty acid elongase gene elo-6 knock-out significantly attenuated eat-2 mutant fast growth, while overexpression of elo-6 could rescue the eat-2; elo-6 double mutant slow development, which suggested that elo-6 played a major role in the above metabolic remodeling. Taken together, our report indicates that diets regulate neuromuscular circuit and modulate C. elegans development via fatty acid metabolic reprogramming. As most of the key genes and metabolites found in this study are conserved in both invertebrate and vertebrate animals, we believed that our results might provide essential clues to the molecular mechanisms underlying interactions among animal nutrition sensation, metabolism reprogramming and developmental regulation. Significance StatementDiets and nutritional composition affect animal development and human health, however the underlying mechanisms remain elusive. We demonstrate that the acetylcholine receptor gene eat-2 is expressed in most of pharyngeal muscles, and the expression of fatty acid synthesis genes is significantly increased in both eat-2 and tmc-1 fast-growth mutants on the synthetic chemical defined CeMM food environment. Dietary supplementation of several fatty acids significantly speed up animal development. Furthermore, we demonstrate that the fatty acid elongase gene elo-6 knock-out attenuates eat-2 mutant fast growth, and overexpression of wild-type elo-6 promotes the eat-2; elo-6 double mutant slow development. Our findings describe that acetylcholine signaling coordinate nutrition sensation and developmental regulation through fatty acid metabolic remodeling.

genetics↗

bioRxiv2020

Sequencing of clinical samples reveals that adaptation keeps establishing during H7N9 virus infection in humans

The H7 subtype avian influenza viruses (AIV) have a much longer history and their adaptation through evolution pose continuous threat to humans 1. Since 2013 March, the novel reasserted H7N9 subtype have transmitted to humans through their repeated assertion in the poultry market. Through repeated transmission, H7N9 gradually became the second AIV subtype posing greater public health risk after H5N1 2,3. After infection, how the virus tunes its genome to adapt and evolve in humans remains unknown. Through direct amplification of H7N9 and high throughput (HT) sequencing of full genomes from the swabs and lower respiratory tract samples collected from infected patients in Shenzhen, China, we have analyzed the in vivo H7N9 mutations at the level of whole genomes and have compared with the genomes derived by in vitro cultures. These comparisons and frequency analysis against the H7N9 genomes in the public database, 40 amino acids were identified that play potential roles in virus adaptation during H7N9 infection in humans. Various synonymous mutations were also identified that might be crucial to H7N9 adaptation in humans. The mechanism of these mutations occurred in a single infection are discussed in this study.

microbiology↗

bioRxiv2020

An Extensive Sequence Dataset of Gold-Standard Samples for Benchmarking and Development

Accurate standards and extensive development datasets are the foundation of technical progress. To facilitate benchmarking and development, we sequence 9 samples, covering the Genome in a Bottle truth sets on multiple instruments (NovaSeq, HiSeqX, HiSeq4000, PacBio Sequel II System) and sample preparations (PCR-Free, PCR-Positive) for both whole genome and multiple exome kits. We benchmark pipelines, quantifying strengths and limitations for sequencing and analysis methods. We identify variability within and between instruments, preparation methods, and analytical pipelines, across various sequencing depths. We discuss the relevance of this variability to downstream analyses, and strategies to reduce variability.

genomics↗

bioRxiv2020

HLA-E restricted, HIV-1 suppressing, Gag specific CD8+ T cells offer universal vaccine opportunities

Human leukocyte antigen-E (HLA-E) normally presents a HLA class Ia signal peptide to the NKG2A/C-CD94 regulatory receptors on natural killer (NK) cells and T cell subsets. Rhesus macaques immunized with a cytomegalovirus vectored simian immunodeficiency virus (SIV) vaccine, generated Mamu-E (HLA-E homolog) restricted T cell responses that mediated post-challenge SIV replication arrest in >50% of animals. However, human immunodeficiency virus type 1 (HIV-1) specific HLA-E restricted T cells have not been observed in HIV-1-infected individuals. Here we primed HLA-E restricted HIV-1 specific CD8+ T cells in vitro. These T cell clones, and allogeneic CD8+ T cells transduced with their T cell receptors, suppressed HIV-1 replication in CD4+ T cells in vitro. Vaccine induction of efficacious HLA-E restricted HIV-1 specific T cells should therefore be possible. One Sentence SummaryCD8+ T cells that recognize a Gag peptide presented by HLA-E suppress HIV-1 replication in vitro.

immunology↗

bioRxiv2020

precisionFDA Truth Challenge V2: Calling variants from short- and long-reads in difficult-to-map regions

The precisionFDA Truth Challenge V2 aimed to assess the state-of-the-art of variant calling in difficult-to-map regions and the Major Histocompatibility Complex (MHC). Starting with FASTQ files, 20 challenge participants applied their variant calling pipelines and submitted 64 variant callsets for one or more sequencing technologies (~35X Illumina, ~35X PacBio HiFi, and ~50X Oxford Nanopore Technologies). Submissions were evaluated following best practices for benchmarking small variants with the new GIAB benchmark sets and genome stratifications. Challenge submissions included a number of innovative methods for all three technologies, with graph-based and machine-learning methods scoring best for short-read and long-read datasets, respectively. New methods out-performed the 2016 Truth Challenge winners, and new machine-learning approaches combining multiple sequencing technologies performed particularly well. Recent developments in sequencing and variant calling have enabled benchmarking variants in challenging genomic regions, paving the way for the identification of previously unknown clinically relevant variants.

bioinformatics↗