Deep sequencing reveals important roles of microRNAs in response to drought and salinity stress in cotton

Drought and salinity are two major environmental factors adversely affecting plant growth and productivity. However, the regulatory mechanism is unknown. In this study, the potential roles of small regulatory microRNAs (miRNAs) in cotton response to those stresses were investigated. Using next-generation deep sequencing, a total of 337 miRNAs with precursors were identified, comprising 289 known miRNAs and 48 novel miRNAs. Of these miRNAs, 155 miRNAs were expressed differentially. Target prediction, Gene Ontology (GO)-based functional classification, and Kyoto Encyclopedia of Genes and Genomes (KEGG)-based functional enrichment show that these miRNAs might play roles in response to salinity and drought stresses through targeting a series of stress-related genes. Degradome sequencing analysis showed that at least 55 predicted target genes were further validated to be regulated by 60 miRNAs. CitationRank-based literature mining was employed to determinhe the importance of genes related to drought and salinity stress. The NAC, MYB, and MAPK families were ranked top under the context of drought and salinity, indicating their important roles for the plant to combat drought and salinity stress. According to target prediction, a series of cotton miRNAs are associated with these top-ranked genes, including miR164, miR172, miR396, miR1520, miR6158, ghr-n24, ghr-n56, and ghr-n59. Interestingly, 163 cotton miRNAs were also identified to target 210 genes that are important in fibre development. These results will contribute to cotton stress-resistant breeding as well as understanding fibre development

DRAR-CPI: a server for identifying drug repositioning potential and adverse drug reactions via the chemical–protein interactome

Identifying new indications for existing drugs (drug repositioning) is an efficient way of maximizing their potential. Adverse drug reaction (ADR) is one of the leading causes of death among hospitalized patients. As both new indications and ADRs are caused by unexpected chemical–protein interactions on off-targets, it is reasonable to predict these interactions by mining the chemical–protein interactome (CPI). Making such predictions has recently been facilitated by a web server named DRAR-CPI. This server has a representative collection of drug molecules and targetable human proteins built up from our work in drug repositioning and ADR. When a user submits a molecule, the server will give the positive or negative association scores between the user’s molecule and our library drugs based on their interaction profiles towards the targets. Users can thus predict the indications or ADRs of their molecule based on the association scores towards our library drugs. We have matched our predictions of drug–drug associations with those predicted via gene-expression profiles, achieving a matching rate as high as 74%. We have also successfully predicted the connections between anti-psychotics and anti-infectives, indicating the underlying relevance of anti-psychotics in the potential treatment of infections, vice versa. This server is freely available at http://cpi.bio-x.cn/drar/

ReCGiP, a database of reproduction candidate genes in pigs based on bibliomics

<p>Abstract</p> <p>Background</p> <p>Reproduction in pigs is one of the most economically important traits. To improve the reproductive performances, numerous studies have focused on the identification of candidate genes. However, it is hard for one to read all literatures thoroughly to get information. So we have developed a database providing candidate genes for reproductive researches in pig by mining and processing existing biological literatures in human and pigs, named as ReCGiP.</p> <p>Description</p> <p>Based on text-mining and comparative genomics, ReCGiP presents diverse information of reproduction-relevant genes in human and pig. The genes were sorted by the degree of relevance with the reproduction topics and were visualized in a gene's co-occurrence network where two genes were connected if they were co-cited in a PubMed abstract. The 'hub' genes which had more 'neighbors' were thought to be have more important functions and could be identified by the user in their web browser. In addition, ReCGiP provided integrated GO annotation, OMIM and biological pathway information collected from the Internet. Both pig and human gene information can be found in the database, which is now available.</p> <p>Conclusions</p> <p>ReCGiP is a unique database providing information on reproduction related genes for pig. It can be used in the area of the molecular genetics, the genetic linkage map, and the breeding of the pig and other livestock. Moreover, it can be used as a reference for human reproduction research.</p

Exploring Off-Targets and Off-Systems for Adverse Drug Reactions via Chemical-Protein Interactome — Clozapine-Induced Agranulocytosis as a Case Study

In the era of personalized medical practice, understanding the genetic basis of patient-specific adverse drug reaction (ADR) is a major challenge. Clozapine provides effective treatments for schizophrenia but its usage is limited because of life-threatening agranulocytosis. A recent high impact study showed the necessity of moving clozapine to a first line drug, thus identifying the biomarkers for drug-induced agranulocytosis has become important. Here we report a methodology termed as antithesis chemical-protein interactome (CPI), which utilizes the docking method to mimic the differences in the drug-protein interactions across a panel of human proteins. Using this method, we identified HSPA1A, a known susceptibility gene for CIA, to be the off-target of clozapine. Furthermore, the mRNA expression of HSPA1A-related genes (off-target associated systems) was also found to be differentially expressed in clozapine treated leukemia cell line. Apart from identifying the CIA causal genes we identified several novel candidate genes which could be responsible for agranulocytosis. Proteins related to reactive oxygen clearance system, such as oxidoreductases and glutathione metabolite enzymes, were significantly enriched in the antithesis CPI. This methodology conducted a multi-dimensional analysis of drugs' perturbation to the biological system, investigating both the off-targets and the associated off-systems to explore the molecular basis of an adverse event or the new uses for old drugs

Identification of Hub Genes Related to the Recovery Phase of Irradiation Injury by Microarray and Integrated Gene Network Analysis

BACKGROUND: Irradiation commonly causes long-term bone marrow injury charactertized by defective HSC self-renewal and a decrease in HSC reserve. However, the effect of high-dose IR on global gene expression during bone marrow recovery remains unknown. METHODOLOGY: Microarray analysis was used to identify differentially expressed genes that are likely to be critical for bone marrow recovery. Multiple bioinformatics analyses were conducted to identify key hub genes, pathways and biological processes. PRINCIPAL FINDINGS: 1) We identified 1302 differentially expressed genes in murine bone marrow at 3, 7, 11 and 21 days after irradiation. Eleven of these genes are known to be HSC self-renewal associated genes, including Adipoq, Ccl3, Ccnd1, Ccnd2, Cdkn1a, Cxcl12, Junb, Pten, Tal1, Thy1 and Tnf; 2) These 1302 differentially expressed genes function in multiple biological processes of immunity, including hematopoiesis and response to stimuli, and cellular processes including cell proliferation, differentiation, adhesion and signaling; 3) Dynamic Gene Network analysis identified a subgroup of 25 core genes that participate in immune response, regulation of transcription and nucleosome assembly; 4) A comparison of our data with known irradiation-related genes extracted from literature showed 42 genes that matched the results of our microarray analysis, thus demonstrated consistency between studies; 5) Protein-protein interaction network and pathway analyses indicated several essential protein-protein interactions and signaling pathways, including focal adhesion and several immune-related signaling pathways. CONCLUSIONS: Comparisons to other gene array datasets indicate that global gene expression profiles of irradiation damaged bone marrow show significant differences between injury and recovery phases. Our data suggest that immune response (including hematopoiesis) can be considered as a critical biological process in bone marrow recovery. Several critical hub genes that are key members of significant pathways or gene networks were identified by our comprehensive analysis

Targets of drugs are generally, and targets of drugs having side effects are specifically good spreaders of human interactome perturbations

Network-based methods are playing an increasingly important role in drug design. Our main question in this paper was whether the efficiency of drug target proteins to spread perturbations in the human interactome is larger if the binding drugs have side effects, as compared to those which have no reported side effects. Our results showed that in general, drug targets were better spreaders of perturbations than non-target proteins, and in particular, targets of drugs with side effects were also better spreaders of perturbations than targets of drugs having no reported side effects in human protein-protein interaction networks. Colorectal cancer-related proteins were good spreaders and had a high centrality, while type 2 diabetes-related proteins showed an average spreading efficiency and had an average centrality in the human interactome. Moreover, the interactome-distance between drug targets and disease-related proteins was higher in diabetes than in colorectal cancer. Our results may help a better understanding of the network position and dynamics of drug targets and disease-related proteins, and may contribute to develop additional, network-based tests to increase the potential safety of drug candidates.Comment: 49 pages, 2 figures, 2 tables, 10 supplementary figures, 13 supplementary table

Development of Computational Tools and Resources for Cotton microRNA Analysis

MicroRNAs (miRNAs) are an extensive class of small regulatory RNAs which regulate gene expression at the posttranscriptional levels. miRNAs target genes for mRNA cleavage or translation inhibition based on the complementary between the mRNAs and its corresponding miRNAs, and these miRNA target genes control development timing, organ development and response to environmental stress; thus miRNAs have been shown to play important roles in almost all biological and metabolic processes. Upland cotton (Gossypium hirsutum L.), one of the most important fiber producing crops, is widely planted in the world. Upland cotton originated from the reunion of two ancestral cotton genomes (A and D genomes) approximately 1-2 Myr ago, owning a complicated genome of allotetraploid (AADD, 2n=4x=52), with a haploid genome size estimated to be around 2.5 Gb. To date, about 80 miRNAs have been subsequentially identified in cotton by computational prediction or small RNA sequencing, many of which were also shown to be expressed differentially during fiber development. However, although miRNA-related research has become one of the hottest research in biology in the past decade and thousands of miRNAs have been identified, miRNA-related research in cotton is far beyond other plant species. One of the major reason is because of limited computational tools and resources for cotton. In this dissertation project, we first developed a comprehensive computational tool named miRDeepFinder, which can be used for miRNA identification, target prediction and GO-/KEGG-based functional analysis for both model and non-model plant species. A case study with a small RNA sequencing data of Arabidopsis showed miRDeepFinder is an accurate and robust tool for plant miRNA analysis in deep sequencing, since 12 of 13 novel miRNAs in Arabidopsis identified by miRDeepFinder were further confirmed by qRT-PCR. miRDeepFinder also incorporated the popularly-used Cleaveland software package for analysis of degradome sequencing data. Although cotton genome is still not available, huge cotton ESTs could be a good data resource for identification of cotton miRNAs and their targets. To better utilize cotton ESTs for miRNA identification, we globally re-assembled all the cotton ESTs and developed it to a cotton EST database, in which cotton coding genes and miRNAs were deeply annotated using BLASTx, BLASTn, Gene Ontology (GO), and Kyoto Encyclopedia of Genes and Genomes (KEGG) resources. A total of 28,432 unique contigs were assembled from all 268,786 cotton ESTs currently available, belonging into 5,461 groups with a maximum cluster size of 196 members. Using these contigs, we also performed EST-based investigations of comparative transcriptome similarity between cotton and other plant species, sequence polymorphisms, expressed miRNAs and their targets, and SSR analysis. A total of 27,956 indel mutants and 149,616 single nucleotide polymorphisms (SNPs) were identified from consensus contigs. In a comparison with six model plant species, cotton ESTs show the highest overall similarity to grape. We also identified 151 and 4,214 EST-simple sequence repeats (SSRs) from contigs and raw ESTs respectively. Finally, all results were integrated to a comprehensive web-based cotton EST database (www.leonxie.com), in order to make these data widely available, and to facilitate access to EST-related genetic information. Subsequently, 3 cotton small RNA sequencing libraries treated by control, drought and salinity were sequenced. Based on miRDeepFinder, annotated cotton EST database, and cotton D genome of Gossypium raimondii, we identified 337 miRNAs with precursors in total, including 289 known miRNAs and 48 novel miRNAs. 155 of 337 miRNAs were found to be expressed differentially amongst the three treatments. Target prediction, GO-based functional classification, and KEGG-based functional enrichment uncovered many miRNAs and their stress-related targets might play roles in response to salinity and drought stresses. Using CitationRank-based literature mining, we sorted out the importance of genes related to stress of drought and salinity, respectively. It turned out NAC family, MYB family and MAPK family were ranked top under the context of drought and salinity, indicating their important roles for plant to combat stress of drought and salinity. To identify potential miRNAs and mRNA genes that significantly contribute to cotton fiber development, we constructed two libraries of 1-DPA (days post anthesis)-old leaf and ovule and sequenced them. A total of 128 pre-miRNAs, including 120 conserved and 8 novel pre-miRNAs were identified in cotton by miRDeepFinder. At least 40 miRNAs were either leaf or ovule-specific, whereas 62 miRNAs were shared in both leaf and ovule. Many transcription factors and other genes important for development of fiber were predicted to be miRNA targets. 22 predicted miRNA-target pairs were further validated by degradome sequencing analysis. In addition to miRNAs, we also identified 11 potential tasiRNAs-derived genes, many of which also might be involved in fiber development. miRNAs from cotton A and D genomes that reunioned together ~1-2 Myr ago might experience similar evolution pattern with coding genes. However, little is known about miRNA origin, expansion, loss, duplication, whether different derived miRNAs exchange with or affect each other, and how different genome-derived miRNAs and different genome-derived coding gene interact in cotton. To this, we systematically investigated miRNA expansion, expression pattern, miRNA targets amongst three cotton species Gossypium hirsutum (AADD), Gossypium arboreum (AA), Gossypium raimondii (DD). The origin of miRNAs and coding genes were the first to be categorized in upland cotton. Our results also showed that cotton-specific miRNAs might undergo remarkably expansion and some highly conserved miRNAs were likely to be lost despite most of conserved miRNAs were remained after genome polyploidization. The comparison of miRNA expression during seedling and fiber at 5 developmental stages revealed that different genome-derived miRNAs and miRNA*s displayed asymmetric expression pattern, implicating their diverse function in upland cotton phenotype. Upon all the identified miRNAs identified in upland cotton above, we also globally investigated miRNA modification features in cotton. Besides the observation of some similar modification features with other plant species in cotton, we also found many interesting modification forms, such as modification balance between 5' and 3' end miRNAs. Comparison of isomiR expression shows differential miRNA modification amongst the 6 developmental stages in terms of selective modification form, development-dependent modification, and differential expression abundance. In contrast to previous reports, cytodine is more frequently truncated and tailed from the two ends of isomiRs in cotton, implying existence of a complex cytodine balance in isomiRs. Together, we developed a comprehensive computational tool and data resource for cotton miRNA research, and used these tools to investigate miRNA roles in cotton fiber development and response to abiotic stress. Cotton miRNA evolution and modification were also studied. Thus, our tools, data resources and research findings would contribute us to deciphering miRNA regulatory function and evolution in cotton.Ph.D

Targets of drugs are generally, and targets of drugs having side effects are specifically good spreaders of human interactome perturbations

Network-based methods are playing an increasingly important role in drug design. Our main question in this paper was whether the efficiency of drug target proteins to spread perturbations in the human interactome is larger if the binding drugs have side effects, as compared to those which have no reported side effects. Our results showed that in general, drug targets were better spreaders of perturbations than non-target proteins, and in particular, targets of drugs with side effects were also better spreaders of perturbations than targets of drugs having no reported side effects in human protein-protein interaction networks. Colorectal cancer-related proteins were good spreaders and had a high centrality, while type 2 diabetes-related proteins showed an average spreading efficiency and had an average centrality in the human interactome. Moreover, the interactome-distance between drug targets and disease-related proteins was higher in diabetes than in colorectal cancer. Our results may help a better understanding of the network position and dynamics of drug targets and disease-related proteins, and may contribute to develop additional, network-based tests to increase the potential safety of drug candidates