Biology subjects

Ge, S.

Publications and source records attributed to Ge, S..

40 records · Page 3Linked to original sources

bioRxiv2019

Recent origin of an XX/XY sex-determination system in the ancient plant lineage Ginkgo biloba

Sexual dimorphism like dioecy (separate male and female individuals) have evolved in diverse multicellular eukaryotes while the molecular mechanisms underlying the development of such a key biological trait remains elusive (1). The living fossil Ginkgo biloba represents an early diverged lineage of land plants with dioecy. However, its sex-determination system and molecular basis have long been controversial or unknown. In the present research, we assembled the first and largest to date chromosome-level genome of a non-model tree species using Hi-C data. With this reference genome, we addressed both questions using genome resequencing data gathered from 97 male and 265 female trees of ginkgo, as well as transcriptome data from three developmental stages for both sexes. Our results support vertebrate-like XY chromosomes for ginkgo and five potential sex-determination genes, which may originate ~14 million years ago. This is the earliest diverged sex determination region in all reported plants as yet. The present research resolved a long-term controversy, lay a foundation for future studies on the origin and evolution of plant sexes, and provide genetic markers for sex identification of ginkgo which will be valuable for both nurseries and field ecology of ginkgo.

evolutionary biology↗

bioRxiv2018

A computational strategy for finding novel targets and therapeutic compounds for opioid dependence

Opioids are widely used for treating different types of pains, but overuse and abuse of prescription opioids have led to opioid epidemic in the United States. Besides analgesic effects, chronic use of opioid can also cause tolerance, dependence, and even addiction. Effective treatment of opioid addiction remains a big challenge today. Studies on addictive effects of opioids focus on striatum, a main component in the brain responsible for drug dependence and addiction. Some transcription regulators have been associated with opioid addiction, but relationship between analgesic effects of opioids and dependence behaviors mediated by them at the molecular level has not been thoroughly investigated. In this paper, we developed a new computational strategy that identifies novel targets and potential therapeutic molecular compounds for opioid dependence and addiction. We employed several statistical and machine learning techniques and identified differentially expressed genes over time which were associated with dependence-related behaviors after exposure to either morphine or heroin, as well as potential transcription regulators that regulate these genes, using time course gene expression data from mouse striatum. Moreover, our findings revealed that some of these dependence-associated genes and transcription regulators are known to play key roles in opioid-mediated analgesia and tolerance, suggesting that an intricate relationship between opioid-induce pain-related pathways and dependence may develop at an early stage during opioid exposure. Finally, we determined small compounds that can potentially target the dependence-associated genes and transcription regulators. These compounds may facilitate development of effective therapy for opioid dependence and addiction. We also built a database (http://daportals.org) for all opioid-induced dependence-associated genes and transcription regulators that we discovered, as well as the small compounds that target those genes and transcription regulators.

systems biology↗

bioRxiv2018

ShinyGO: a graphical enrichment tool for animals and plants

MotivationGene lists are routinely produced from various genome-wide studies. Enrichment analysis can link these gene lists with underlying molecular pathways by using functional categories such as gene ontology (GO).\n\nResultsTo complement existing tools, we developed ShinyGO with several features: (1) large annotation database from GO and many other sources for over 200 plant and animal species, (2) graphical visualization of enrichment results and gene characteristics, and (3) application program interface (API) access to KEGG and STRING for the retrieval of pathway diagrams and protein-protein interaction networks. ShinyGO is an intuitive, graphical web application that can help researchers gain actionable insights from gene lists.\n\nAvailabilityhttp://ge-lab.org/go/\n\nContactgexijin@gmail.com\n\nSupplementary informationSupplementary data are available at Bioinformatics online.

bioinformatics↗

bioRxiv2016

GSKB: A gene set database for pathway analysis in mouse

Interpretation of high-throughput genomics data based on biological pathways constitutes a constant challenge, partly because of the lack of supporting pathway database. In this study, we created a functional genomics knowledgebase in mouse, which includes 33,261 pathways and gene sets compiled from 40 sources such as Gene Ontology, KEGG, GeneSetDB, PANTHER, microRNA and transcription factor target genes, etc. In addition, we also manually collected and curated 8,747 lists of differentially expressed genes from 2,526 published gene expression studies to enable the detection of similarity to previously reported gene expression signatures. These two types of data constitute a Gene Set Knowledgebase (GSKB), which can be readily used by various pathway analysis software such as gene set enrichment analysis (GSEA). Using our knowledgebase, we were able to detect the correct microRNA (miR-29) pathway that was suppressed using antisense oligonucleotides and confirmed its role in inhibiting fibrogenesis, which might involve upregulation of transcription factor SMAD3. The knowledgebase can be queried as a source of published gene lists for further meta-analysis. Through meta-analysis of 56 published gene lists related to retina cells, we revealed two fundamentally different types of gene expression changes. One is related to stress and inflammatory response blamed for causing blindness in many diseases; the other associated with visual perception by normal retina cells. GSKB is available online at http://ge-lab.org/gs/, and also as a Bioconductor package (gskb, https://bioconductor.org/packages/gskb/). This database enables in-depth interpretation of mouse genomics data both in terms of known pathways and the context of thousands of published expression signatures.

bioinformatics↗