RASOnD-a comprehensive resource and search tool for RAS superfamily oncogenes from various species

Identification of breast cancer risk modules via an integrated strategy

Breast cancer is one of the most common malignant cancers among females worldwide. This complex disease is not caused by a single gene, but resulted from multi-gene interactions, which could be represented by biological networks. Network modules are composed of genes with significant similarities in terms of expression, function and disease association. Therefore, the identification of disease risk modules could contribute to understanding the molecular mechanisms underlying breast cancer. In this paper, an integrated disease risk module identification strategy was proposed according to a multi-objective programming model for two similarity criteria as well as significance of permutation tests in Markov random field module score, function consistency score and Pearson correlation coefficient difference score. Three breast cancer risk modules were identified from a breast cancer-related interaction network. Genes in these risk modules were confirmed to play critical roles in breast cancer by literature review. These risk modules were enriched in breast cancer-related pathways or functions and could distinguish between breast tumor and normal samples with high accuracy for not only the microarray dataset used for breast cancer risk module identification, but also another two independent datasets. Our integrated strategy could be extended to other complex diseases to identify their risk modules and reveal their pathogenesis.

Microarray analysis of differentially expressed genes in L929 mouse fibroblast cells exposed to leptin and hypoxia

Leptin and hypoxia are pro-fibrotic factors involved in fibrogenesis, however, the gene expression profiles remain to be fully elucidated. The aim of the present study was to investigate the regulatory roles of leptin and hypoxia on the L929 mouse fibroblast cell line. The cells were assigned to a normoxia, normoxia with leptin, hypoxia, and hypoxia with leptin group. The cDNA expression was detected using an Agilent mRNA array platform. The differentially expressed genes (DEGs) in response to leptin and hypoxia were identified using reverse transcription-quantitative polymerase chain reaction analysis, followed by clustering analysis, Gene Ontology analysis and pathway analysis. As a result, 54, 1,507 and 1,502 DEGs were found in response to leptin, hypoxia and the two combined, respectively, among which 52 (96.30%), 467 (30.99%) and 495 (32.96%) of the DEGs were downregulated. The most significant functional terms in response to leptin were meiosis I for biological process (P=0.0041) and synaptonemal complex for cell component (P=0.0013). Only one significant pathway responded to leptin, which was axon guidance (P=0.029). Flow cytometry confirmed that leptin promoted L929 cell proliferation. The most significant functional terms in response to hypoxia were ion binding for molecular function (P=7.8621E-05), glucose metabolic process for biological process (P=0.0008) and cell projection part for cell component (P=0.003). There were 12 pathways, which significantly responded to hypoxia (P<0.05) and the pathway with the highest significance was the chemokine signaling pathway (P=0.0001), which comprised 28 genes, including C-C motif ligand (CCL)1, C-X-C motif ligand (CXCL)9, CXCL10, son of sevenless homolog 1, AKT serine/threonine kinase 2, Rho-associated protein kinase 1, vav guanine nucleotide exchange factor 1, CCL17, arrestin β1 and C-C motif chemokine receptor 2. In conclusion, the present study showed that leptin and hypoxia altered the profiles of gene expression in L929 cells. These findings not only extend the cell spectrum of leptin on cell proliferation, but also improve current understanding of hypoxia in fibroblast cells.

Small GTPases and Stress Responses of vvran1 in the Straw Mushroom Volvariella volvacea

Small GTPases play important roles in the growth, development and environmental responses of eukaryotes. Based on the genomic sequence of the straw mushroom Volvariella volvacea, 44 small GTPases were identified. A clustering analysis using human small GTPases as the references revealed that V. volvacea small GTPases can be grouped into five families: nine are in the Ras family, 10 are in the Rho family, 15 are in the Rab family, one is in the Ran family and nine are in the Arf family. The transcription of vvran1 was up-regulated upon hydrogen peroxide (H₂O₂) stress, and could be repressed by diphenyleneiodonium chloride (DPI), a NADPH oxidase-specific inhibitor. The number of vvran1 transcripts also increased upon cold stress. Diphenyleneiodonium chloride, but not the superoxide dismutase (SOD) inhibitor diethy dithiocarbamate (DDC), could suppress the up-regulation of vvran1 gene expression to cold stress. These results combined with the high correlations between gene expression and superoxide anion (O₂⁻) generation indicated that vvran1 could be one of the candidate genes in the downstream of O₂⁻ mediated pathways that are generated by NADPH oxidase under low temperature and oxidative stresses.

Prevalent Accumulation of Non-Optimal Codons through Somatic Mutations in Human Cancers

Cancer is characterized by uncontrolled cell growth, and the cause of different cancers is generally attributed to checkpoint dysregulation of cell proliferation and apoptosis. Recent studies have shown that non-optimal codons were preferentially adopted by genes to generate cell cycle-dependent oscillations in protein levels. This raises the intriguing question of how dynamic changes of codon usage modulate the cancer genome to cope with a non-controlled proliferative cell cycle. In this study, we comprehensively analyzed the somatic mutations of codons in human cancers, and found that non-optimal codons tended to be accumulated through both synonymous and non-synonymous mutations compared with other types of genomic substitution. We further demonstrated that non-optimal codons were prevalently accumulated across different types of cancers, amino acids, and chromosomes, and genes with accumulation of non-optimal codons tended to be involved in protein interaction/signaling networks and encoded important enzymes in metabolic networks that played roles in cancer-related pathways. This study provides insights into the dynamics of codons in the cancer genome and demonstrates that accumulation of non-optimal codons may be an adaptive strategy for cancerous cells to win the competition with normal cells. This deeper interpretation of the patterns and the functional characterization of somatic mutations of codons will help to broaden the current understanding of the molecular basis of cancers.

Human cancer databases (review)

Cancer is one of the four major non‑communicable diseases (NCD), responsible for ~14.6% of all human deaths. Currently, there are >100 different known types of cancer and >500 genes involved in cancer. Ongoing research efforts have been focused on cancer etiology and therapy. As a result, there is an exponential growth of cancer‑associated data from diverse resources, such as scientific publications, genome‑wide association studies, gene expression experiments, gene‑gene or protein‑protein interaction data, enzymatic assays, epigenomics, immunomics and cytogenetics, stored in relevant repositories. These data are complex and heterogeneous, ranging from unprocessed, unstructured data in the form of raw sequences and polymorphisms to well‑annotated, structured data. Consequently, the storage, mining, retrieval and analysis of these data in an efficient and meaningful manner pose a major challenge to biomedical investigators. In the current review, we present the central, publicly accessible databases that contain data pertinent to cancer, the resources available for delivering and analyzing information from these databases, as well as databases dedicated to specific types of cancer. Examples for this wealth of cancer‑related information and bioinformatic tools have also been provided.

Bio/chemoinformatics in India: an outlook

With the advent of significant establishment and development of Internet facilities and computational infrastructure, an overview on bio/chemoinformatics is presented along with its multidisciplinary facts, promises and challenges. The Government of India has paved the way for more profound research in biological field with the use of computational facilities and schemes/projects to collaborate with scientists from different disciplines. Simultaneously, the growth of available biomedical data has provided fresh insight into the nature of redundant and compensatory data. Today, bioinformatics research in India is characterized by a powerful grid computing systems, great variety of biological questions addressed and the close collaborations between scientists and clinicians, with a full spectrum of focuses ranging from database building and methods development to biological discoveries. In fact, this outlook provides a resourceful platform highlighting the funding agencies, institutes and industries working in this direction, which would certainly be of great help to students seeking their career in bioinformatics. Thus, in short, this review highlights the current bio/chemoinformatics trend, educations, status, diverse applicability and demands for further development.