In-silico characterization of a hypothetical protein of Sulfobacillus sp. hq2 for degradation of phthalate diesters.

Phthalate plasticizers are hazardous compounds capable of causing endocrine disruption, cancers, and developmental disorders. Phthalate diesters are commonly used plasticizers in plastic products (PVC pipes) that leach out into the environment due to changes in temperature, pressure, and pH, posing harmful effects on different life forms. Bioremediation of phthalate diesters utilizing bacterial esterase has been recognized as an efficient approach but few effective esterases capable of degrading a wide range of phthalate diesters have been identified. Further, the thermostability of these esterases is a highly desirable property for their applications in diverse in-situ conditions. In this present in-silico study a hypothetical protein (POB10642.1) as a high-potential esterase from a thermostable strain of Sulfobacillus sp. hq2 has been characterized. Analysis revealed a significant sequence identity of 42.67 % and structural similarity (RMSD 0.557) with known phthalate diester degrading EstS1 esterase and a high Tm range of 55-66 °C. Structural analysis revealed the presence of two cavities on the surface mediating toward the catalytic site forming a catalytic tunnel. The enzyme POB10642.1 has significant molecular docking binding energies in the range of -5.4 to -7.5 kcal/mol with several phthalate diesters, including Diethyl phthalate, Dipropyl phthalate, Dibutyl phthalate, Dipentyl phthalate, Dihexyl phthalate, Benzyl butyl phthalate, Dicyclohexyl phthalate, and Bis(2-ethylhexyl) phthalate. High stability of binding during 100 ns molecular dynamics simulations revealed efficient and stable binding of the enzyme with a wide range of phthalate diesters at its active site, demonstrating the ability of the identified esterase to interact with and degrade diverse phthalate diesters. Therefore, POB10642.1 esterase can be an efficient candidate to be utilized in the development of enzyme-based bioremediation technologies to reduce the toxic levels of phthalate diesters.

Understanding the functional impact of copy number alterations in breast cancer using a network modeling approach.

UNLABELLED: Copy number alterations (CNAs) are thought to account for 85% of the variation in gene expression observed among breast tumours. The expression of cis-associated genes is impacted by CNAs occurring at proximal loci of these genes, whereas the expression of trans-associated genes is impacted by CNAs occurring at distal loci. While a majority of these CNA-driven genes responsible for breast tumourigenesis are cis-associated, trans-associated genes are thought to further abet the development of cancer and influence disease outcomes in patients. Here we present a network-based approach that integrates copy-number and expression profiles to identify putative cis- and trans-associated genes in breast cancer pathogenesis. We validate these cis- and trans-associated genes by employing them to subtype a large cohort of breast tumours obtained from the METABRIC consortium, and demonstrate that these genes accurately reconstruct the ten subtypes of breast cancer. We observe that individual breast cancer subtypes are driven by distinct sets of cis- and trans-associated genes. Among the cis-associated genes, we recover several known drivers of breast cancer (e.g. CCND1, ERRB2, MDM2 and ZNF703) and some novel putative drivers (e.g. BRF2 and SF3B3). siRNA-mediated knockdown of BRF2 across a panel of breast cancer cell lines showed significant reduction in cell viability for ER-/HER2+ (MDA-MB-453) cells, but not in normal (MCF10A) cells thereby indicating that BRF2 could be a viable therapeutic target for estrogen receptor-negative/HER2-enriched (ER-/HER2+) cancers. Among the trans-associated genes, we identify modules of immune response (CD2, CD19, CD38 and CD79B), mitotic/cell-cycle kinases (e.g. AURKB, MELK, PLK1 and TTK), and DNA-damage response genes (e.g. RFC4 and FEN1). siRNA-mediated knockdown of RFC4 significantly reduced cell proliferation in ER-negative normal breast and cancer lines, thereby indicating that RFC4 is essential for both normal and cancer cell survival but could be a useful biomarker for aggressive (ER-negative) breast tumours. AVAILABILITY: under NetStrat.

Inferring synthetic lethal interactions from mutual exclusivity of genetic events in cancer.

BACKGROUND: Synthetic lethality (SL) refers to the genetic interaction between two or more genes where only their co-alteration (e.g. by mutations, amplifications or deletions) results in cell death. In recent years, SL has emerged as an attractive therapeutic strategy against cancer: by targeting the SL partners of altered genes in cancer cells, these cells can be selectively killed while sparing the normal cells. Consequently, a number of studies have attempted prediction of SL interactions in human, a majority by extrapolating SL interactions inferred through large-scale screens in model organisms. However, these predicted SL interactions either do not hold in human cells or do not include genes that are (frequently) altered in human cancers, and are therefore not attractive in the context of cancer therapy. RESULTS: Here, we develop a computational approach to infer SL interactions directly from frequently altered genes in human cancers. It is based on the observation that pairs of genes that are altered in a (significantly) mutually exclusive manner in cancers are likely to constitute lethal combinations. Using genomic copy-number and gene-expression data from four cancers, breast, prostate, ovarian and uterine (total 3980 samples) from The Cancer Genome Atlas, we identify 718 genes that are frequently amplified or upregulated, and are likely to be synthetic lethal with six key DNA-damage response (DDR) genes in these cancers. By comparing with published data on gene essentiality (~16000 genes) from ten DDR-deficient cancer cell lines, we show that our identified genes are enriched among the top quartile of essential genes in these cell lines, implying that our inferred genes are highly likely to be (synthetic) lethal upon knockdown in these cell lines. Among the inferred targets are tousled-like kinase 2 (TLK2) and the deubiquitinating enzyme ubiquitin-specific-processing protease 7 (USP7) whose overexpression correlates with poor survival in cancers. CONCLUSION: Mutual exclusivity between frequently occurring genetic events identifies synthetic lethal combinations in cancers. These identified genes are essential in cell lines, and are potential candidates for targeted cancer therapy. Availability: http://bioinformatics.org.au/tools-data/underMutExSL