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Background: Classification of biological samples of gene expression data is a basic building block in solving several problems in the field of bioinformatics like cancer and other disease diagnosis and making a proper treatment plan. One big challenge in sample classification is handling large dimensional and redundant gene expression data. To reduce the complexity of handling this high dimensional data, gene/feature selection plays a major role., Results: The current paper explores the use of biological knowledge acquired from Gene Ontology database in selecting the proper subset of genes which can further participate in clustering of samples. The proposed feature selection technique is unsupervised in nature as it does not utilize any class label information in the process of gene selection. At the end, a multi-objective clustering approach is deployed to cluster the available set of samples in the reduced gene space., Conclusions: Reported results show that consideration of biological knowledge in gene selection technique not only reduces the feature space dimensionality in great extent but also improves the accuracy of sample classification. The obtained reduced gene space is validated using strong biological significance tests. In order to prove the supremacy of our proposed gene selection based sample clustering technique, a thorough comparative analysis has also been performed with state-of-the-art techniques.

Objective: Bioinformatics is widely used in the field of cancer research, but in the research of pathogenesis of congenital malformations the situation is different. The aim of this study was to explore the underlying mechanism using bioinformatics approach., Methods: The data were available from Mouse Genome Informatics and Pubmed. Protein-protein interaction (PPI) network of pathogenic genes was conducted using STRING. Gene ontology and pathway enrichment analyses were also performed to pathogenic genes., Results: Total 63 genes were identified as pathogenic genes in the study. The PPI networks for pathogenic genes were constructed, which contained 62 nodes and 228 edges with PAX6, FGFR1 and CTNNB1 as the hub genes. All the genes were linked to 921 pathways in biological processes, 31 pathways in cell component, 41 pathways in molecular function, and 76 pathways in the KEGG. These genes were discovered significantly enriched in embryonic organ development, ear morphogenesis, ear development, and regulation of RNA synthesis and processing., Conclusions: bioinformatics methods were utilized to analysis pathogenic genes involved in microtia development, including pathogenic genes identifying, PPI network construction and functional analysis. And we also predicted that several potential mechanisms might contribute to occurrence of microtia by disturbing GO terms and pathways. This approach could be useful for the study of the etiology and pathogenesis of microtia., (Copyright © 2017 Elsevier B.V. All rights reserved.)

Background: High-density marker panels and/or whole-genome sequencing, coupled with advanced phenotyping pipelines and sophisticated statistical methods, have dramatically increased our ability to generate lists of candidate genes or regions that are putatively associated with phenotypes or processes of interest. However, the speed with which we can validate genes, or even make reasonable biological interpretations about the principles underlying them, has not kept pace. A promising approach that runs parallel to explicitly validating individual genes is analyzing a set of genes together and assessing the biological similarities among them. This is often achieved via gene ontology analysis, a powerful tool that involves evaluating publicly available gene annotations. However, additional resources such as Medical Subject Headings (MeSH) can also be used to evaluate sets of genes to make biological interpretations., Results: In this manuscript, we describe utilizing MeSH terms to make biological interpretations in maize. MeSH terms are assigned to PubMed-indexed manuscripts by the National Library of Medicine, and can be directly mapped to genes to develop gene annotations. Once mapped, these terms can be evaluated for enrichment in sets of genes or similarity between gene sets to provide biological insights. Here, we implement MeSH analyses in five maize datasets to demonstrate how MeSH can be leveraged by the maize and broader crop-genomics community., Conclusions: We demonstrate that MeSH terms can be effectively leveraged to generate hypotheses and make biological interpretations in maize, and we provide a pipeline that enables the use of MeSH terms in other plant species.

It has been reported that tumor growth and proliferation correspond to mitochondrial dysfunction and that the tumor cellular microenvironment plays a key role in tumor progression, representing an area that might be manipulated to confer therapeutic anti-tumor benefits. In this article, we have identified mitochondrial genes, largely nuclear-encoded genes, which are differentially expressed in breast cancer epithelial and stromal cells compared to cells from normal breast tissues. We determined that gene expression of the mitochondrial membrane respiratory chain complex I and IV and ATP synthesis were reduced in both in epithelial and stromal cancer cells compared to normal breast cells. We also found transport-related genes were significantly more highly expressed in breast cancer epithelial cells. Our data also suggest that mitochondria are likely to proliferate in breast cancer stromal cells, which is supported by the observation that MRPL12, POLG, and RNASEH1 are all up-regulated in cancerous stromal cells. In addition, we present an improved simulated annealing algorithm, SANetWalker, which can be used to detect the functional module. At the same time, this method has a minimal effect on network topology and can be used to identify the highest confidence functional module. Using SANetWalker, we obtained the highest confidence (90%) functional module with a fumarate hydratase (FH)-centered network with 40 nodes and 107 edges. Functional analysis revealed that glutamine metabolism genes were significantly up-regulated in both epithelial and stromal cells from breast cancer tissues, which implicates glutamine metabolism in breast cancer growth and metastasis., (Copyright © 2016 Elsevier B.V. All rights reserved.)

MicroRNA (miRNA) is now attracting attention as a powerful negative regulator of messenger RNA(mRNA) levels, and is implicated in the modulation of important mRNA networks involved in toxicity. In this study, we assessed the effects of particulate matter 2.5 (PM 2.5 ), one of the most significant air pollutants, on miRNA and target gene expression. We exposed human alveolar epithelial cell (A549) to two types of PM 2.5 [water (W-PM 2.5 ) and organic (O-PM 2.5 ) soluble extracts] and performed miRNA microarray analysis. A total of 37 miRNAs and 62 miRNAs were altered 1.3-fold in W-PM 2.5 and O-PM 2.5 , respectively. Integrated analyses of miRNA and mRNA expression profiles identified negative correlations between miRNA and mRNA in both W-PM 2.5 and O-PM 2.5 exposure groups. Gene ontology and Kyoto encyclopedia of genes and genomes (KEGG) pathway analyses showed that the 35 W-PM 2.5 target genes are involved in responses to nutrients, positive regulation of biosynthetic processes, positive regulation of nucleobase, nucleoside, and nucleotide, and nucleic acid metabolic processes; while the 69 O-PM 2.5 target genes are involved in DNA replication, cell cycle processes, the M phase, and the cell cycle check point. We suggest that these target genes may play important roles in PM 2.5 -induced respiratory toxicity by miRNA regulation. These results demonstrate an integrated miRNA-mRNA approach for identifying molecular events induced by environmental pollutants in an in vitro human model. © 2016 Wiley Periodicals, Inc. Environ Toxicol 32: 302-310, 2017., (© 2016 Wiley Periodicals, Inc.)

Microbes play important roles in almost every aspect of life, including human health and diseases. Facilitated by the rapid development of sequencing technologies, metagenomics research has accelerated the accumulation of genomic sequences of microbial species that had been inaccessible before. Analysis of the metagenomic sequencing data can reveal not only the species but also the functional composition of microbial communities. Here, we report a pipeline for functional annotation of metagenomic datasets. The pipeline is built from several programs that we have developed for metagenomic sequence analysis including a protein-coding gene predictor for short reads (or contigs) and a fast similarity search tool. Given a metagenomic dataset, the pipeline reports putative protein-coding genes (or gene fragments) and functional annotations of the genes in Gene Ontology (GO) terms and Enzyme Commission (EC) numbers, and potential metabolic pathways that are likely encoded by the metagenome. Fun4Me is available for download at https://sourceforge.net/projects/fun4me .

Gamabufotalin (CS-6) is a major bufadienolide of Chansu, which shows desirable metabolic stability and less adverse effect in cancer therapy. CS-6 treatment inhibited the proliferation of NSCLC in a nanomolar range. And CS-6 could induce G2/M cell cycle arrest and apoptosis in A549 cells. However, its molecular mechanism in antitumor activity remains poorly understood. We employed a quantitative proteomics approach to identify the potential cellular targets of CS-6, and found 38 possible target-related proteins. Among them, 31 proteins were closely related in the protein-protein interaction network. One of the regulatory nodes in key pathways was occupied by Hsp90. Molecular docking revealed that CS-6 interacted with the ATP-binding sites of Hsp90. In addition, CS-6 inhibited the chaperone function of Hsp90 and reduced expression of Hsp90-dependent client proteins. Moreover, CS-6 markedly down-regulated the protein level of Hsp90 in tumor tissues of the xenograft mice. Taken together, our results suggest that CS-6 might be a novel inhibitor of Hsp90, and the possible network associated with CS-6 target-related proteins was constructed, which provided experimental evidence for the preclinical value of using CS-6 as an effective antitumor agent in treatment of NSCLC.

Analysis of repetitive DNA sequence content and divergence among the repetitive functional classes is a well-accepted approach for estimation of inter- and intra-generic differences in plant genomes. Among these elements, microsatellites, or Simple Sequence Repeats (SSRs), have been widely demonstrated as powerful genetic markers for species and varieties discrimination. We present PlantFuncSSRs platform having more than 364 plant species with more than 2 million functional SSRs. They are provided with detailed annotations for easy functional browsing of SSRs and with information on primer pairs and associated functional domains. PlantFuncSSRs can be leveraged to identify functional-based genic variability among the species of interest, which might be of particular interest in developing functional markers in plants. This comprehensive on-line portal unifies mining of SSRs from first and next generation sequencing datasets, corresponding primer pairs and associated in-depth functional annotation such as gene ontology annotation, gene interactions and its identification from reference protein databases. PlantFuncSSRs is freely accessible at: http://www.bioinfocabd.upo.es/plantssr.

The vast majority of human multiexon genes undergo alternative splicing and produce a variety of splice variant transcripts and proteins, which can perform different functions. These protein-coding splice variants (PCSVs) greatly increase the functional diversity of proteins. Most functional annotation algorithms have been developed at the gene level; the lack of isoform-level gold standards is an important intellectual limitation for currently available machine learning algorithms. The accumulation of a large amount of RNA-seq data in the public domain greatly increases our ability to examine the functional annotation of genes at isoform level. In the present study, we used a multiple instance learning (MIL)-based approach for predicting the function of PCSVs. We used transcript-level expression values and gene-level functional associations from the Gene Ontology database. A support vector machine (SVM)-based 5-fold cross-validation technique was applied. Comparatively, genes with multiple PCSVs performed better than single PCSV genes, and performance also improved when more examples were available to train the models. We demonstrated our predictions using literature evidence of ADAM15, LMNA/C, and DMXL2 genes. All predictions have been implemented in a web resource called "IsoFunc", which is freely available for the global scientific community through http://guanlab.ccmb.med.umich.edu/isofunc .

We aimed to elucidate the potential mechanisms of long non-coding RNAs (lncRNAs) in the progression of non-small cell lung cancer (NSCLC). The microarray datasets of GSE37764, including 3 primary NSCLC tumors and 3 matched normal tissues isolated from 6 Korean female never-smokers, were downloaded from Gene Expression Omnibus database. The differentially expressed lncRNAs and mRNA in NSCLC samples were identified using NOISeq package. Co-expression network of differentially expressed lncRNAs and mRNA was established. Gene Ontology (GO) and pathway enrichment analysis were respectively performed. Finally, lncRNAs related to NSCLC were predicted by blasting the differentially expressed lncRNAs with all predicted lncRNAs related to NSCLC. A total of 182 and 539 differentially expressed lncRNAs and mRNA (109 up- and 73 down-regulated lncRNAs; 307 up- and 232 down-regulated mRNA) were respectively identified. Among them, 4 up-regulated lncRNAs, like lnc-geranylgeranyl diphosphate synthase 1 (GGPS1), lnc-zinc finger protein 793 (ZNF793) and lnc-serine/threonine kinase 4 (STK4), and 4 down-regulated lncRNAs including lnc-LOC284440 and lnc-peptidylprolyl isomerase E-like pseudogene (PPIEL), and lnc-zinc finger protein 461 (ZNF461) were predicted related to NSCLC. lncSSPS1, lnc-ZNF793 and lnc-STK4 were co-expressed with linker for activation of T cells (LAT) and Lck interacting transmembrane adaptor 1 (LIME1). Lnc-LOC284440, lnc-PPIEL and lnc-ZNF461 were co-expressed with Src-like-adaptor 2 (SLA2) and defensin beta 4A (DEFB4A). Our study indicates that immune response may be a crucial mechanism involved in NSCLC progression. Lnc-GGPS1, lnc-ZNF793, lnc-STK4, lnc-LOC284440, lnc-PPIEL, and lnc-ZNF461 may be involved in immune response for promoting NSCLC progression via co-expressing with LAT, LIME1, SLA2 and DEFB4A., (Copyright © 2016 Elsevier GmbH. All rights reserved.)

Mass spectrometry (MS) has become the method of choice for the large-scale analysis of protein ubiquitylation. There exist a number of proposed methods for mapping ubiquitin sites, each with different pros and cons. We present here a protocol for the MS analysis of the ubiquitin-proteome captured by TUBEs and subsequent data analysis. Using dedicated software and algorithms, specific information on the presence of ubiquitylated peptides can be obtained from the MS search results. In addition, a quantitative and functional analysis of the ubiquitylated proteins and their interacting partners helps to unravel the biological and molecular processes they are involved in.

In this protocol, we explain how to run ab initio motif discovery in order to gather putative transcription factor binding motifs (TFBMs) from sets of genomic regions returned by ChIP-seq experiments. The protocol starts from a set of peak coordinates (genomic regions) which can be either downloaded from ChIP-seq databases, or produced by a peak-calling software tool. We provide a concise description of the successive steps to discover motifs, cluster the motifs returned by different motif discovery algorithms, and compare them with reference motif databases. The protocol is documented with detailed notes explaining the rationale underlying the choice of options. The interpretation of the results is illustrated with an example from the model plant Arabidopsis thaliana.

Background & Aims: The alimentary tract contains a diffuse endocrine system comprising enteroendocrine cells that secrete peptides or biogenic amines to regulate digestion, insulin secretion, food intake, and energy homeostasis. Lineage analysis in the stomach revealed that a significant fraction of endocrine cells in the gastric corpus did not arise from Neurogenin3 (Neurog3)-expressing cells, unlike enteroendocrine cells elsewhere in the digestive tract. We aimed to isolate enriched serotonin-secreting and enterochromaffin-like (ECL) cells from the stomach and to clarify their cellular origin. Methods: We used Neurogenic differentiation 1 (NeuroD1) and Neurog3 lineage analysis and examined the differentiation of serotonin-producing and ECL cells in stomach tissues of NeuroD1-cre;ROSAtdTom, tryptophan hydroxylase 1 (Tph1)–cyan fluorescent protein (CFP), c-Kitwsh/wsh, and Neurog3Cre;ROSAtdTom mice by immunohistochemistry. We used fluorescence-activated cell sorting to isolate each cell type for gene expression analysis. We also performed RNA sequencing analysis of ECL cells. Results: Neither serotonin-secreting nor ECL cells of the corpus arose from cells expressing NeuroD1. Serotonin-secreting cells expressed a number of mast cell genes but not genes associated with endocrine differentiation; they did not develop in c-Kitwsh/wsh mice and were labeled with transplanted bone marrow cells. RNA sequencing analysis of ECL cells revealed high expression levels of many genes common to endocrine cells, including transcription factors, hormones, ion channels, and solute transporters but not markers of bone marrow cells. Conclusions: Serotonin-expressing cells of the gastric corpus of mice appear to be bone marrow–derived mucosal mast cells. Gene expression analysis of ECL cells indicated that they are endocrine cells of epithelial origin that do not express the same transcription factors as their intestinal enteroendocrine cell counterparts. [Copyright &y& Elsevier]

A gene-set, an important concept in microarray expression analysis and systems biology, is a collection of genes and/or their products (i.e. proteins) that have some features in common. There are many different ways to construct gene-sets, but a systematic organization of these ways is lacking. Gene-sets are mainly organized ad hoc in current public-domain databases, with group header names often determined by practical reasons (such as the types of technology in obtaining the gene-sets or a balanced number of gene-sets under a header). Here we aim at providing a gene-set organization principle according to the level at which genes are connected: homology, physical map proximity, chemical interaction, biological, and phenotypic-medical levels. We also distinguish two types of connections between genes: actual connection versus sharing of a label. Actual connections denote direct biological interactions, whereas shared label connection denotes shared membership in a group. Some extensions of the framework are also addressed such as overlapping of gene-sets, modules, and the incorporation of other non-protein-coding entities such as microRNAs., (Copyright © 2015 Elsevier Ltd. All rights reserved.)

We have shown that aldehydes impact the bronchial airway gene expression associated with inflammatory responses. In this study, we sought to determine whether microRNA (miRNA) plays a role in regulating the airway gene expression response to aldehyde exposure. We analyzed the whole genome miRNA and mRNA expression profiles of human alveolar epithelial cells exposed to 3 aldehydes (propanal, butanal, and pentanal) to identify aldehyde-sensitive miRNAs and to characterize the relationships between miRNAs and the expression of candidate cytokine-related genes, which are activated in response to inflammatory signals. Microarray analysis identified 15 miRNAs for propanal, 25 miRNAs for butanal, and 10 miRNAs for pentanal, which were differentially expressed in A549 human alveolar epithelial cells compared with vehicle control samples. Integrated analyses of miRNA and mRNA expression profiles identified significant miRNA-mRNA correlations. Gene ontology (GO) analysis of putative target genes (443 genes for propanal, 2166 genes for butanal, and 364 genes for pentanal) showed that the biological category "cytokine-cytokine receptor interaction" was prominently annotated. Moreover, we detected increased levels of interleukin (IL)-6 and IL-8 released in the 3 aldehyde exposure groups. Through an integrated analysis of the miRNA and mRNA expression profiles of aldehydes, we provide evidence that aldehyde can affect cytokine-induced toxicity signaling. Therefore, this study demonstrates the added value of an integrated miRNA-mRNA approach for identifying molecular events altered by environmental pollutants in an in vitro human model., (Copyright © 2015 Elsevier Ireland Ltd. All rights reserved.)

In the context of climate change, the water-use efficiency (WUE) of highly productive tree varieties, such as eucalypts, has become a major issue for breeding programmes. This study set out to dissect the genetic architecture of carbon isotope composition (δ(13) C), a proxy of WUE, across several environments. A family of Eucalyptus urophylla × E. grandis was planted in three trials and phenotyped for δ(13) C and growth traits. High-resolution genetic maps enabled us to target genomic regions underlying δ(13) C quantitative trait loci (QTLs) on the E. grandis genome. Of the 15 QTLs identified for δ(13) C, nine were stable across the environments and three displayed significant QTL-by-environment interaction, suggesting medium to high genetic determinism for this trait. Only one colocalization was found between growth and δ(13) C. Gene ontology (GO) term enrichment analysis suggested candidate genes related to foliar δ(13) C, including two involved in the regulation of stomatal movements. This study provides the first report of the genetic architecture of δ(13) C and its relation to growth in Eucalyptus. The low correlations found between the two traits at phenotypic and genetic levels suggest the possibility of improving the WUE of Eucalyptus varieties without having an impact on breeding for growth., (© 2015 CIRAD. New Phytologist © 2015 New Phytologist Trust.)

RNA-seq is being rapidly adopted for the profiling of the transcriptomes in different areas of biology, especially in the studies related to gene regulation. The discovery of differentially expressed genes (DEGs) between adult animals of Jeju Native Pig (JNP) and Berkshire breeds of Sus scrofa, is of particular interest for the current study. For the better understanding of the gene expression profiles of the liver and longissimus dorsi muscle, DEGs were identified via RNA-seq. Sequence reads were obtained from Illumina HiSeq2000 and mapped to the pig reference genome (Sscrofa10.2) using Tophat2. We identified 169 and 39 DEGs in the liver and muscle of JNP respectively, by comparison with Berkshire breed. Out of all identified genes, 41 genes in the liver and 9 genes in the muscle have given significant expression. Gene ontology (GO) terms of developmental process and KEGG pathway analysis showed that metabolic, immune response and protein binding were commonly enriched pathways in the two tissues. Further the heat map analysis by ArrayStar has shown the different levels of expression in JNP with respect to the Berkshire breed. The validation through real time PCR and western blotting also confirmed the differential expression of genes in both breeds. Genes pertaining to metabolic process and inflammatory and immune system are more enriched in Berkshire breed. This comparative transcriptome analysis of two tissues suggests a subset of novel marker genes which expressed differently between the JNP and Berkshire., (Copyright © 2014 Elsevier B.V. All rights reserved.)

Inhalation is an important route of aldehyde exposure, and lung is one of the main targets of aldehyde toxicity. Octanal is distributed ubiquitously in the environment and is a component of indoor air pollutants. We investigated whether octanal exposure enhances the inflammatory response in the human respiratory system by increasing the expression and release of cytokines and chemokines. The effect of octanal in transcriptomic modulation was assessed in the human alveolar epithelial cell line A549 using oligonucleotide arrays. We identified a set of genes differentially expressed upon octanal exposure that may be useful for monitoring octanal pulmonary toxicity. These genes were classified according to the Gene Ontology functional category and Kyoto Encyclopedia of Genes and Genomes analysis to explore the biological processes related to octanal-induced pulmonary toxicity. The results show that octanal affects the expression of several chemokines and inflammatory cytokines and increases the levels of interleukin 6 (IL-6) and IL-8 released. In conclusion, octanal exposure modulates the expression of cytokines and chemokines important in the development of lung injury and disease. This suggests that inflammation contributes to octanal-induced lung damage and that the inflammatory genes expressed should be studied in detail, thereby laying the groundwork for future biomonitoring studies., (© The Author(s) 2014.)

Nitrogen (N) is an essential macronutrient for plant growth and development. Plants adapt to changes in N availability partly by changes in global gene expression. We integrated publicly available root microarray data under contrasting nitrate conditions to identify new genes and functions important for adaptive nitrate responses in Arabidopsis thaliana roots. Overall, more than 2000 genes exhibited changes in expression in response to nitrate treatments in Arabidopsis thaliana root organs. Global regulation of gene expression by nitrate depends largely on the experimental context. However, despite significant differences from experiment to experiment in the identity of regulated genes, there is a robust nitrate response of specific biological functions. Integrative gene network analysis uncovered relationships between nitrate-responsive genes and 11 highly co-expressed gene clusters (modules). Four of these gene network modules have robust nitrate responsive functions such as transport, signaling, and metabolism. Network analysis hypothesized G2-like transcription factors are key regulatory factors controlling transport and signaling functions. Our meta-analysis highlights the role of biological processes not studied before in the context of the nitrate response such as root hair development and provides testable hypothesis to advance our understanding of nitrate responses in plants.