Biology subjects

Huang, T.

Publications and source records attributed to Huang, T..

151 records · Page 9Linked to original sources

bioRxiv2019

High-throughput single-particle tracking reveals nested membrane nanodomain organization that dictates Ras diffusion and trafficking

Membrane nanodomains have been implicated in Ras signaling, but what these domains are and how they interact with Ras remain obscure. Using high throughput single particle tracking with photoactivated localization microscopy and detailed trajectory analysis, here we show that distinct membrane domains dictate KRas diffusion and trafficking in U2OS cells. KRas exhibits an immobile state in domains [~]70 nm in size, each embedded in a larger domain ([~]200 nm) that confers intermediate mobility, while the rest of the membrane supports fast diffusion. Moreover, KRas is continuously removed from the membrane via the immobile state and replenished to the fast state, likely coupled to internalization and recycling. Importantly, both the diffusion and trafficking properties of KRas remain invariant over a broad range of protein expression levels. Our results reveal how membrane organization dictates KRas diffusion and trafficking and offer insight into how Ras signaling may be regulated through spatial mechanisms.

biophysics↗

bioRxiv2018

The self-organization model reveals systematic characteristics of aging

Aging is a fundamental biological process, where key bio-markers interact with each other and synergistically regulate the aging process. Thus aging dysfunction will induce many disorders. Finding aging markers and re-constructing networks based on multi-omics data (i.e. methylation, transcriptional and so on) are informative to study the aging process. However, optimizing the model to predict aging have not been performed systemically, although it is critical to identify potential molecular mechanism of aging relative diseases.\n\nThis paper aims to model the aging self-organization system using a serious of supervised learning methods, and study complex molecular mechanism of aging at system level: i.e. optimizing the aging network; summarizing interactions between aging markers; accumulating patterns of aging markers within module; finding order-parameters of the aging self-organization system.\n\nIn this work, the normal aging process is modeled based on multi-omics profiles across tissues. In addition, the computational pipeline aims to model aging self-organizing systems and study the relationship between aging and related diseases (i.e. cancers), thus provide useful indexes of aging related diseases and improve diagnostic effects for both pre- and pro- gnosis.

systems biology↗

bioRxiv2018

gencore: an efficient tool to generate consensus reads for error suppressing and duplicate removing of NGS data

BackgroundRemoving duplicates might be considered as a well-resolved problem in next-generation sequencing (NGS) data processing domain. However, as NGS technology gains more recognition in clinical applications (i.e. cancer testing), researchers start to pay more attention to its sequencing errors, and prefer to remove these errors while performing deduplication operations. Recently, a new technology called unique molecular identifier (UMI) has been developed to better identify sequencing reads derived from different DNA fragments. Most existing duplicate removing tools cannot handle the UMI-integrated data. Some modern tools can work with UMIs, but are usually slow and use too much memory, making them not suitable for cloud-based deployment. Furthermore, existing tools rarely report rich statistical results, which are very important for quality control and downstream analysis. These unmet requirements drove us to develop an ultra-fast, simple, little-weighted but powerful tool for duplicate removing and sequence error suppressing, with features of handling UMIs and reporting informative results.\n\nResultsThis paper presents an efficient tool gencore for duplicate removing and sequence error suppressing of NGS data. This tool clusters the mapped sequencing reads and merges reads in each cluster to generate one single consensus read. While the consensus read is generated, the random errors introduced by library construction and sequencing can be removed. This error-suppressing feature makes gencore very suitable for the application of detecting ultra-low frequency mutations from deep sequencing data. When unique molecular identifier (UMI) technology is applied, gencore can use them to identify the reads derived from same original DNA fragment. gencore reports statistical results in both HTML and JSON formats. The HTML format report contains many interactive figures plotting statistical coverage and duplication information. The JSON format report contains all the statistical results, and is interpretable for downstream programs.\n\nConclusionsComparing to the conventional tools like Picard and SAMtools, gencore greatly reduces the output datas mapping mismatches, which are mostly caused by errors. Comparing to some new tools like UMI-Reducer and UMI-tools, gencore runs much faster, uses less memory, generates better consensus reads and provides simpler interfaces. To our best knowledge, gencore is the only duplicate removing tool that generates both informative HTML and JSON reports. This tool is available at: https://github.com/OpenGene/gencore

bioinformatics↗

bioRxiv2018

Transcriptome analysis of fasting caecotrophy on hepatic lipid metabolism in New Zealand rabbits

BackgroundRabbit produce two kinds of feces: hard and soft feces, and they have a preference for consuming the latter. Although this habit of rabbits has been reported for many years, little is known on whether this behavior will impact growth performance and metabolism. The RNA-Seq technology is an effective means of analyzing transcript groups to clarify molecular mechanisms. The aim of the present study was to investigate the effects of fasting caecotrophy on growth performance and lipid metabolism in rabbits.\n\nResultsOur results indicated that, compared with the control group, the final body weight, weight gain, liver weight, specific growth rate and feed conversion ratio were all decreased in the experimental group (P<0.05). Oil red staining of the liver tissue indicated that fasting caecotrophy resulted in decrease of lipid droplet accumulation. RNA sequencing (RNA-seq) analysis revealed a total of 301.2 million raw reads approximately 45.06 Gb of high-quality clean data. The data were mapped to the rabbit genome (http://www.ensembl.org/Oryctolagus_cuniculus). After a five step filtering process, 14964 genes were identified, including 444 differentially expressed genes (P<0.05, foldchange[≥]1). Especially for remarkable changes of genes related to lipid metabolism, RT-PCR further validated the remarkable decrease of these genes in fasting caecotrophy group, including CYP7A1, PPARG, ABCA1, ABCB1, ABCG1, GPAM, SREBP, etc. KEGG annotation of the differentially expressed genes indicated that the main pathways affected were retinol metabolism, pentose and glucuronide interactions, starch and sucrose metabolism, fatty acid degradation, steroid hormone biosynthesis.\n\nConclusionIn conclusion, the present study revealed that banning caecotrophy reduced growth rate and altered lipid metabolism, our results laid instructive basis for rabbit feeding and production. These data also provides a reference for studying the effects of soft feces on other small herbivores.

animal behavior and cognition↗

bioRxiv2018

Designer Sinorhizobium meliloti strains and multi-functional vectors for direct inter-kingdom transfer of high G+C content DNA

Storage and manipulation of large DNA fragments is crucial for synthetic biology applications, yet DNA with high G+C content can be unstable in many host organisms. Here, we report the development of Sinorhizobium meliloti as a new universal host that can store DNA, including high G+C content, and mobilize DNA to Escherichia coli, Saccharomyces cerevisiae, and the eukaryotic microalgae Phaeodactylum tricornutum. We deleted the S. meliloti hsdR restriction-system to enable DNA transformation with up to 1.4 x 105 efficiency. Multi-host and multi-functional shuttle vectors (MHS) were constructed and shown to stably replicate in S. meliloti, E. coli, S. cerevisiae, and P. tricornutum, with a copy-number inducible E. coli origin for isolating plasmid DNA. Crucially, we demonstrated that S. meliloti can act as a universal conjugative donor for MHS plasmids with a cargo of at least 62 kb of G+C rich DNA derived from Deinococcus radiodurans.

synthetic biology↗

bioRxiv2018

Widespread and polymorphous noncoding amino acid residues in human sperm proteome

Proteins are usually deciphered by translation of the coding genome; however, their amino acid residues are seldom determined directly across the proteome. Herein, we describe a systematic workflow for identifying all possible protein residues that differ from the coding genome, termed noncoded amino acids (ncAAs). By measuring the mass differences between the coding amino acids and the actual protein residues in human spermatozoa, over a million nonzero delta masses were detected, fallen into 424 high-quality Gaussian clusters and 571 high-confidence ncAAs spanning 29,053 protein sites. Most ncAAs are novel with unresolved side-chains and discriminative between healthy individuals and patients with oligoasthenospermia. For validation, 40 out of 98 ncAAs that matched with amino acid substitutions were confirmed by exon sequencing. This workflow revealed the widespread existence of previously unreported ncAAs in the sperm proteome, which represents a new dimension on the understanding of amino acid polymorphisms at the proteomic level.\n\nHighlightsO_LI571 ncAAs spanning 108,000 protein sites were identified in human sperm proteome.\nC_LIO_LIMost ncAAs are novel with unresolved sidechains and found at unreported protein sites.\nC_LIO_LIExon sequencing confirmed 40 of 98 ncAAs that matched with amino acid substitutions.\nC_LIO_LIMany ncAAs are linked with disease and have potential for diagnosis and targeting.\nC_LI\n\neTOC BlurbWe describe a systematic identification of all possible protein residues that were not encoded by their genomic sequences. A total of 571 high-confidence most novel noncoded amino acids were identified in human sperm proteome, corresponding to over 108,000 ncAA-containing protein sites. For validation, 40 out of 98 ncAAs that matched to amino acid substitutions were confirmed by exon sequencing. These ncAAs are discriminative between individuals and expand our understanding of amino acid polymorphisms in human proteomes and diseases.

molecular biology↗

bioRxiv2017

ActiveDriverDB: human disease mutations and genome variation in post-translational modification sites of proteins

Interpretation of genetic variation is required for understanding genotype-phenotype associations, mechanisms of inherited disease, and drivers of cancer. Millions of single nucleotide variants (SNVs) in human genomes are known and thousands are associated with disease. An estimated 20% of disease-associated missense SNVs are located in protein sites of post-translational modifications (PTMs), chemical modifications of amino acids that extend protein function. ActiveDriverDB is a comprehensive human proteo-genomics database that annotates disease mutations and population variants using PTMs. We integrated >385,000 published PTM sites with [~]3.8 million missense SNVs from The Cancer Genome Atlas (TCGA), the ClinVar database of disease genes, and inter-individual variation from human genome sequencing projects. The database includes interaction networks of proteins, upstream enzymes such as kinases, and drugs targeting these enzymes. We also predicted network-rewiring impact of mutations by analyzing gains and losses of kinase-bound sequence motifs. ActiveDriverDB provides detailed visualization, filtering, browsing and searching options for studying PTM-associated SNVs. Users can upload mutation datasets interactively and use our application programming interface for pipelines. Integrative analysis of SNVs and PTMs helps decipher molecular mechanisms of phenotypes and disease, as exemplified by case studies of disease genes TP53, BRCA2 and VHL. The open-source database is available at https://www.ActiveDriverDB.org.

bioinformatics↗