Computational studies reveal co-occurrence of two mutations in IL7R gene of high-grade serous carcinoma patients

Computational investigations into structure and function impact of novel mutations identified in targeted exons from ovarian cancer cell lines

The lack of sensitive and specific biomarkers for ovarian cancer leads to late stage diagnosis of the disease in a majority of the cases. Mutation accumulation is the basis for cancer progression, thus identifying mutations is an important step in the disease diagnosis. In the present study, a comprehensive analysis of fifteen Next Generation Sequencing samples from thirteen ovarian cancer cell lines was carried out for the identification of new mutations. The study revealed eight clinically significant novel mutations in six ovarian cancer oncogenes, viz. SMARCA4, ARID1A, PPP2R1A, CTNNB1, DICER1 and PIK3CA. In-depth computational analysis revealed that the mutations affected the structure of the proteins in terms of stability, solvent accessible surface area and molecular dynamics. Moreover, the mutations were present in functionally significant domains of the proteins, thereby adversely affecting the protein functionality. PPI network for SMARCA4, CTNNB1, DICER1, PIK3CA, PPP2R1A and ARID1A showed that these genes were involved in certain significant pathways affecting various hallmarks of cancer. For further validation, in vitro studies were performed that revealed hypermutability of the CTNNB1 gene. Through this study we have identified some key mutations and have analysed their structural and functional impact. The study establishes some key mutations, which can be potentially explored as biomarker and drug target.Communicated by Ramaswamy H. Sarma.

RNA-Seq Analysis of Clinical Samples from TCGA Reveal Molecular Signatures for Ovarian Cancer

Identifying differentially expressed genes and co-expression modules lead to novel biomarkers. GO, pathway enrichment, network, and tumor stage analysis of 318 ovarian cancer samples from TCGA, categorised into primary and recurrent, pre-menopause and post-menopause, and early and late stage tumors was performed. Upregulated and downregulated genes in primary vs recurrent, early stage vs late-stage and pre-menopause vs post-menopause tumors were 84 and 62, 84 and 35, and 88 and 14, respectively. IRAK2 and CXCL8 had higher expression in recurrent tumors while REG1A had higher expression in post-menopause samples. In late stage tumors constant expression of IRAK2 and REG1A was observed, while that of CXCL8 and EGF decreased. These genes may be potential biomarkers for the diagnosis of the disease.

Applications of Omics Technology for Livestock Selection and Improvement

Conventional animal selection and breeding methods were based on the phenotypic performance of the animals. These methods have limitations, particularly for sex-limited traits and traits expressed later in the life cycle (e.g., carcass traits). Consequently, the genetic gain has been slow with high generation intervals. With the advent of high-throughput omics techniques and the availability of multi-omics technologies and sophisticated analytic packages, several promising tools and methods have been developed to estimate the actual genetic potential of the animals. It has now become possible to collect and access large and complex datasets comprising different genomics, transcriptomics, proteomics, metabolomics, and phonemics data as well as animal-level data (such as longevity, behavior, adaptation, etc.,), which provides new opportunities to better understand the mechanisms regulating animals’ actual performance. The cost of omics technology and expertise of several fields like biology, bioinformatics, statistics, and computational biology make these technology impediments to its use in some cases. The population size and accurate phenotypic data recordings are other significant constraints for appropriate selection and breeding strategies. Nevertheless, omics technologies can estimate more accurate breeding values (BVs) and increase the genetic gain by assisting the section of genetically superior, disease-free animals at an early stage of life for enhancing animal productivity and profitability. This manuscript provides an overview of various omics technologies and their limitations for animal genetic selection and breeding decisions.