Biology subjects

Ma, L.

Publications and source records attributed to Ma, L..

352 records · Page 20Linked to original sources

bioRxiv2018

DensityPath: a level-set algorithm to visualize and reconstruct cell developmental trajectories for large-scale single-cell RNAseq data

Cell fates are determined by transition-states which occur during complex biological pro-cesses such as proliferation and differentiation. The advance in single-cell RNA sequencing (scRNAseq) provides the snapshots of single cell transcriptomes, thus offering an essential opportunity to study such complex biological processes. Here, we introduce a novel algorithm, DensityPath, which visualizes and reconstructs the underlying cell developmental trajectories for large-scale scRNAseq data. DensityPath has three merits. Firstly, by adopting the nonlinear dimension reduction algorithm elastic embedding, DensityPath reveals the intrinsic structures of the data. Secondly, by applying the powerful level set clustering method, DensityPath extracts the separate high density clusters of representative cell states (RCSs) from the single cell multimodal density landscape of gene expression space, enabling it to handle the heterogeneous scRNAseq data elegantly and accurately. Thirdly, DensityPath constructs cell state-transition path by finding the geodesic minimum spanning tree of the RCSs on the surface of the density landscape, making it more computationally efficient and accurate for large-scale dataset. The cell state-transition path constructed by DensityPath has the physical interpretation as the minimum-transition-energy (least-cost) path. We demonstrate that DensityPath is capable of identifying complex cell development trajectories with bifurcating and trifurcating branches on the human preimplantation embryos. We demonstrate that DensityPath is robust and has high accuracy of pseudotime calculation and branch assignment on the real scRNAseq as well as simulated datasets.

systems biology↗

bioRxiv2018

Generating genomic platforms to study Candida albicans pathogenesis

The advent of the genomic era has made elucidating gene function at large scale a pressing challenge. ORFeome collections, whereby almost all ORFs of a given species are cloned and can be subsequently leveraged in multiple functional genomic approaches, represent valuable resources towards this endeavor. Here we provide novel, genome-scale tools for the study of Candida albicans, a commensal yeast that is also responsible for frequent superficial and disseminated infections in humans. We have generated an ORFeome collection composed of 5,102 ORFs cloned in a Gateway donor vector, representing 83% of the currently annotated coding sequences of C. albicans. Sequencing data of the cloned ORFs are available in the CandidaOrfDB database at http://candidaorfeome.eu. We also engineered 49 expression vectors with a choice of promoters, tags, and selection markers and demonstrated their applicability to the study of target ORFs transferred from the C. albicans ORFeome. In addition, the use of the ORFeome in the detection of protein-protein interaction was demonstrated. Mating-compatible strains as well as Gateway-compatible two-hybrid vectors were engineered, validated and used in a proof of concept experiment. These unique and valuable resources should greatly facilitate future functional studies in C. albicans and the elucidation of mechanisms that underlie its pathogenicity.

genomics↗

bioRxiv2018

Genome-wide identification of CDC34 that stabilizes EGFR and promotes lung carcinogenesis

To systematically identify ubiquitin pathway genes that are critical to lung carcinogenesis, we used a genome-wide silencing method in this study to knockdown 696 genes in non-small cell lung cancer (NSCLC) cells. We identified 31 candidates that were required for cell proliferation in two NSCLC lines, among which the E2 ubiquitin conjugase CDC34 represented the most significant one. CDC34 was elevated in tumor tissues in 67 of 102 (65.7%) NSCLCs, and smokers had higher CDC34 than nonsmokers. The expression of CDC34 was inversely associated with overall survival of the patients. Forced expression of CDC34 promoted, whereas knockdown of CDC34 inhibited lung cancer in vitro and in vivo. CDC34 bound EGFR and competed with E3 ligase c-Cbl to inhibit the polyubiquitination and subsequent degradation of EGFR. In EGFR-L858R and EGFR-T790M/Del(exon 19)-driven lung cancer in mice, knockdown of CDC34 by lentivirus mediated transfection of short hairpin RNA significantly inhibited tumor formation. These results demonstrate that an E2 enzyme is capable of competing with E3 ligase to inhibit ubiquitination and subsequent degradation of oncoprotein substrate, and CDC34 represents an attractive therapeutic target for NSCLCs with or without drug-resistant EGFR mutations.

cancer biology↗

bioRxiv2017

Diverse laboratory colonies of Aedes aegypti harbor the same adult midgut bacterial microbiome

BackgroundHost-associated microbes, collectively known as the microbiota, play an important role in the biology of multicellular organisms. In mosquito vectors of human pathogens, the gut bacterial microbiota influences vectorial capacity and has become the subject of intense study. In laboratory studies of vector biology, genetic effects are often inferred from differences between geographically and genetically diverse colonies of mosquitoes that are reared in the same insectary. It is unclear, however, to what extent genetic effects can be confounded by uncontrolled differences in the microbiota composition among mosquito colonies. To address this question, we used 16S metagenomics to compare the midgut bacterial microbiome of six recent laboratory colonies of Aedes aegypti representing the geographical range and genetic diversity of the species.\n\nResultsWe found that the diversity, abundance, and community structure of the midgut bacterial microbiome was remarkably similar among the six different colonies of Ae. aegypti, regardless of their geographic origin. We also confirmed the relatively low complexity of bacterial communities inhabiting the mosquito midgut.\n\nConclusionsOur finding that geographically diverse colonies of Ae. aegypti reared in the same insectary harbor a similar gut bacterial microbiome supports the conclusion that the gut microbiota of adult mosquitoes is environmentally determined regardless of the host genotype. Thus, uncontrolled differences in microbiota composition are unlikely to represent a significant confounding factor in genetic studies of vector biology.

genetics↗

bioRxiv2017

An epigenetic mechanism for cavefish eye degeneration

Coding and non-coding mutations in DNA contribute significantly to phenotypic variability during evolution. However, less is known about the role of epigenetics in this process. Although previous studies have identified eye development genes associated with the loss of eyes phenotype in the Pachon blind cave morph of the Mexican tetra Astyanax mexicanus1-6, no inactivating mutations have been found in any of these genes2,3,7-10. Here we show that excess DNA methylation-based epigenetic silencing promotes eye degeneration in blind cave Astyanax mexicanus. By performing parallel analyses in Astyanax mexicanus cave and surface morphs and in the zebrafish Danio rerio, we have discovered that DNA methylation mediates eye-specific gene repression and globally regulates early eye development. The most significantly hypermethylated and down-regulated genes in the cave morph are also linked to human eye disorders, suggesting the function of these genes is conserved across the vertebrates. Our results show that changes in DNA methylation-based gene repression can serve as an important molecular mechanism generating phenotypic diversity during development and evolution.

evolutionary biology↗

bioRxiv2017

Single-molecule force spectroscopy of protein-membrane interactions

Many biological processes rely on protein-membrane interactions in the presence of mechanical forces, yet high resolution methods to quantify such interactions are lacking. Here, we describe a single-molecule force spectroscopy approach to quantify membrane binding of C2 domains in Synaptotagmin-1 (Syt1) and Extended Synaptotagmin-2 (E-Syt2). Syts and E-Syts bind the plasma membrane via multiple C2 domains, bridging the plasma membrane with synaptic vesicles or endoplasmic reticulum to regulate membrane fusion or lipid exchange, respectively. In our approach single proteins attached to membranes supported on silica beads are pulled by optical tweezers, allowing membrane binding and unbinding transitions to be measured with unprecedented spatiotemporal resolution. C2 domains from either protein resisted unbinding forces of 2-7 pN and had binding energies of 4-14 kBT per C2 domain. Regulation by bilayer composition or Ca2+ recapitulated known properties of both proteins. The method can be widely applied to study protein-membrane interactions.

biophysics↗

bioRxiv2017

5-Hydroxymethylcytosine signatures in cell-free DNA provide information about tumor types and stages

5-Hydroxymethylcytosine (5hmC) is an important mammalian DNA epigenetic modification that has been linked to gene regulation and cancer pathogenesis. Here we explored the diagnostic potential of 5hmC in circulating cell-free DNA (cfDNA) using a sensitive chemical labeling-based low-input shotgun sequencing approach. We sequenced cell-free 5hmC from 49 patients of seven different cancer types and found distinct features that could be used to predict cancer types and stages with high accuracy. Specifically, we discovered that lung cancer leads to a progressive global loss of 5hmC in cfDNA, whereas hepatocellular carcinoma and pancreatic cancer lead to disease-specific changes in the cell-free hydroxymethylome. Our proof of principle results suggest that cell-free 5hmC signatures may potentially be used not only to identify cancer types but also to track tumor stage in some cancers.\n\nOne Sentence SummaryAnalyzing the epigenetic modification 5-hydroxymethylcysoine in circulating cell-free DNA reveals tumor tissue of origin and stages for cancer diagnostics.

genomics↗

bioRxiv2017

Systems Biology Analysis Using A Genome-Scale Metabolic Model Shows That Phosphine Triggers Global Metabolic Suppression In A Resistant Strain Of C. elegans

BackgroundPest insects are increasingly resistant to phosphine gas, which is used globally to protect grain reserves. The enzyme dihydrolipoamide dehydrogenase (DLD) is a phosphine resistance factor and participates in four key steps of core metabolism, making it a potential central metabolic regulator.\n\nResultsHere we used microarray data and NMR-based metabolomics to characterize the phosphine response of wild-type C. elegans and the phosphine-resistant strain dld-1(wr4) which has a partial loss-of-function mutation in the gene for DLD. In addition, we have constructed CeCon, a C. elegans genome-scale metabolic model to facilitate integration of gene expression and metabolomics data.\n\nConclusionsThe resulting systems biology analysis is consistent with the hypothesis that adaptation to a hypometabolic state is the most prominent mechanism of phosphine resistance in this nematode strain. The involvement of DLD in regulating and creating hypometabolic adaptation has implications for other biological phenomena involving hypometabolism, such as reperfusion injury and metabolic resistance.

systems biology↗

bioRxiv2017

BGDMdocker: an workflow base on Docker for analysis and visualization pan-genome and biosynthetic gene clusters of Bacterial

MotivationAt present Docker technology has received increasing level of attention throughout the bioinformatics community. However, its implementation details have not yet been mastered by most biologists and applied widely in biological researches. In order to popularizing this technology in the bioinformatics and sufficiently use plenty of public resources of bioinformatics tools (Dockerfile and image of scommunity, officially and privately) in Docker Hub Registry and other Docker sources based on Docker, we introduced full and accurate instance of a bioinformatics workflow based on Docker to analyse and visualize pan-genome and biosynthetic gene clusters of a bacteria in this article, provided the solutions for mining bioinformatics big data from various public biology databases. You could be guided step-by-step through the workflow process from docker file to build up your own images and run an container fast creating an workflow.\n\nResultsWe presented a BGDMdocker (bacterial genome data mining docker-based) workflow based on docker. The workflow consists of three integrated toolkits, Prokka v1.11, panX, and antiSMASH3.0. The dependencies were all written in Dockerfile, to build docker image and run container for analysing pan-genome of total 44 Bacillus amyloliquefaciens strains, which were retrieved from public? database. The pan-genome totally includes 172,432 gene, 2,306 Core gene cluster. The visualized pan-genomic data such as alignment, phylogenetic trees, maps mutations within that cluster to the branches of the tree, infers loss and gain of genes on the core-genome phylogeny for each gene cluster were presented. Besides, 997 known (MIBiG database) and 553 unknown (antiSMASH-predicted clusters and Pfam database) genes of biosynthesis gene clusters types and orthologous groups were mined in all strains. This workflow could also be used for other species pan-genome analysis and visualization. The display of visual data can completely duplicated as well as done in this paper. All result data and relevant tools and files can be downloaded from our website with no need to register. The pan-genome and biosynthetic gene clusters analysis and visualization can be fully reusable immediately in different computing platforms (Linux, Windows, Mac and deployed in the cloud), achieved cross platform deployment flexibility, rapid development integrated software package.\n\nAvailability and implementationBGDMdocker is available at http://42.96.173.25/bapgd/ and the source code under GPL license is available at https://github.com/cgwyx/debian_prokka_panx_antismash_biodocker.\n\nContactchenggongwyx@foxmail.com\n\nSupplementary informationSupplementary data are available at biorxiv online.

bioinformatics↗

bioRxiv2016

Statistical testing and power analysis for brain-wide association study

The identification of connexel-wise associations, which involves examining functional connectivities between pairwise voxels across the whole brain, is both statistically and computationally challenging. Although such a connexel-wise methodology has recently been adopted by brain-wide association studies (BWAS) to identify connectivity changes in several mental disorders, such as schizophrenia, autism and depression [Cheng et al., 2015a,b, 2016], the multiple correction and power analysis methods designed specifically for connexel-wise analysis are still lacking. Therefore, we herein report the development of a rigorous statistical framework for connexel-wise significance testing based on the Gaussian random field theory. It includes controlling the family-wise error rate (FWER) of multiple hypothesis testings using topological inference methods, and calculating power and sample size for a connexel-wise study. Our theoretical framework can control the false-positive rate accurately, as validated empirically using two resting-state fMRI datasets. Compared with Bonferroni correction and false discovery rate (FDR), it can reduce false-positive rate and increase statistical power by appropriately utilizing the spatial information of fMRI data. Importantly, our method considerably reduces the computational complexity of a permutation-or simulation-based approach, thus, it can efficiently tackle large datasets with ultra-high resolution images. The utility of our method is shown in a case-control study. Our approach can identify altered functional connectivities in a major depression disorder dataset, whereas existing methods failed. A software package is available at https://github.com/weikanggong/BWAS.

neuroscience↗