Key genes for modulating information flow play a temporal role as breast tumor coexpression networks are dynamically rewired by letrozole

Identification of miRNAs and related hub genes associated with the triple negative breast cancer using integrated bioinformatics analysis and in vitro approach

Triple negative breast cancer (TNBC) is an aggressive breast cancer subtype generally associated with younger women. Due to the lack of suitable drugable targets in TNBC, the microRNAs are considered as a better hope as therapeutic agents for the management of the disease. In this study, we identified differentially expressed miRNAs (DEMs) and associated hub genes in TNBC microarray data (GSE38167, GSE60714, and GSE10833) using bioinformatics tools. The identified miRNAs and genes were validated in the TNBC cell line model (MDA-MB-231) compared with the normal breast cells (MCF-10A) using the qRT-PCR technique. False-positive DEMs were avoided by comparing the DEMs profile of TNBC and triple positive breast cancer (TPBC) cell line model (BT474) compared with the MCF-10A cells data. In addition, we studied the effect of anticancer phytochemicals on the differential expression of miRNAs and genes in MDA-MB-231 cells. Furthermore, target predictions, functional enrichment and KEGG pathway analysis, mutation and copy number alterations, and overall survival analysis of DEMs in TNBC sample was investigated using standard computational tools. The study identifies first time the association of hsa-miR-1250, has-miR-1273, and has-miR-635 with the TNBC. DEMs showed significant association with the Wnt, ErbB, PI3-Akt and cAMP signaling pathways having clinical implications in TNBC tumorigenesis. The DEMs and hub genes (HOXC6 and ACVR2B) showed survival disadvantages in TNBC patients. In summary, the identified miRNAs and hub genes show important implications in TNBC tumorigenesis and patient survival. We recommend further experimental studies on pathophysiological mechanism of the identified miRNAs and hub genes in TNBC.Communicated by Ramaswamy H. Sarma.

Characterizing gene-gene interactions in a statistical epistasis network of twelve candidate genes for obesity

BACKGROUND

Recent findings have reemphasized the importance of epistasis, or gene-gene interactions, as a contributing factor to the unexplained heritability of obesity. Network-based methods such as statistical epistasis networks (SEN), present an intuitive framework to address the computational challenge of studying pairwise interactions between thousands of genetic variants. In this study, we aimed to analyze pairwise interactions that are associated with Body Mass Index (BMI) between SNPs from twelve genes robustly associated with obesity (BDNF, ETV5, FAIM2, FTO, GNPDA2, KCTD15, MC4R, MTCH2, NEGR1, SEC16B, SH2B1, and TMEM18).

METHODS

We used information gain measures to identify all SNP-SNP interactions among and between these genes that were related to obesity (BMI > 30 kg/m(2)) within the Framingham Heart Study Cohort; interactions exceeding a certain threshold were used to build an SEN. We also quantified whether interactions tend to occur more between SNPs from the same gene (dyadicity) or between SNPs from different genes (heterophilicity).

RESULTS

We identified a highly connected SEN of 709 SNPs and 1241 SNP-SNP interactions. Combining the SEN framework with dyadicity and heterophilicity analyses, we found 1 dyadic gene (TMEM18, P-value = 0.047) and 3 heterophilic genes (KCTD15, P-value = 0.045; SH2B1, P-value = 0.003; and TMEM18, P-value = 0.001). We also identified a lncRNA SNP (rs4358154) as a key node within the SEN using multiple network measures.

CONCLUSION

This study presents an analytical framework to characterize the global landscape of genetic interactions from genome-wide arrays and also to discover nodes of potential biological significance within the identified network.

Predicting functional and regulatory divergence of a drug resistance transporter gene in the human malaria parasite

Background

The paradigm of resistance evolution to chemotherapeutic agents is that a key coding mutation in a specific gene drives resistance to a particular drug. In the case of resistance to the anti-malarial drug chloroquine (CQ), a specific mutation in the transporter pfcrt is associated with resistance. Here, we apply a series of analytical steps to gene expression data from our lab and leverage 3 independent datasets to identify pfcrt-interacting genes. Resulting networks provide insights into pfcrt’s biological functions and regulation, as well as the divergent phenotypic effects of its allelic variants in different genetic backgrounds.

Results

To identify pfcrt-interacting genes, we analyze pfcrt co-expression networks in 2 phenotypic states - CQ-resistant (CQR) and CQ-sensitive (CQS) recombinant progeny clones - using a computational approach that prioritizes gene interactions into functional and regulatory relationships. For both phenotypic states, pfcrt co-expressed gene sets are associated with hemoglobin metabolism, consistent with CQ’s expected mode of action. To predict the drivers of co-expression divergence, we integrate topological relationships in the co-expression networks with available high confidence protein-protein interaction data. This analysis identifies 3 transcriptional regulators from the ApiAP2 family and histone acetylation as potential mediators of these divergences. We validate the predicted divergences in DNA mismatch repair and histone acetylation by measuring the effects of small molecule inhibitors in recombinant progeny clones combined with quantitative trait locus (QTL) mapping.

Conclusions

This work demonstrates the utility of differential co-expression viewed in a network framework to uncover functional and regulatory divergence in phenotypically distinct parasites. pfcrt-associated co-expression in the CQ resistant progeny highlights CQR-specific gene relationships and possible targeted intervention strategies. The approaches outlined here can be readily generalized to other parasite populations and drug resistances.

Electronic supplementary material

The online version of this article (doi:10.1186/s12864-015-1261-6) contains supplementary material, which is available to authorized users.

Contribution of bioinformatics prediction in microRNA-based cancer therapeutics

Despite enormous efforts, cancer remains one of the most lethal diseases in the world. With the advancement of high throughput technologies massive amounts of cancer data can be accessed and analyzed. Bioinformatics provides a platform to assist biologists in developing minimally invasive biomarkers to detect cancer, and in designing effective personalized therapies to treat cancer patients. Still, the early diagnosis, prognosis, and treatment of cancer are an open challenge for the research community. MicroRNAs (miRNAs) are small non-coding RNAs that serve to regulate gene expression. The discovery of deregulated miRNAs in cancer cells and tissues has led many to investigate the use of miRNAs as potential biomarkers for early detection, and as a therapeutic agent to treat cancer. Here we describe advancements in computational approaches to predict miRNAs and their targets, and discuss the role of bioinformatics in studying miRNAs in the context of human cancer.

Influence networks based on coexpression improve drug target discovery for the development of novel cancer therapeutics

Background

The demand for novel molecularly targeted drugs will continue to rise as we move forward toward the goal of personalizing cancer treatment to the molecular signature of individual tumors. However, the identification of targets and combinations of targets that can be safely and effectively modulated is one of the greatest challenges facing the drug discovery process. A promising approach is to use biological networks to prioritize targets based on their relative positions to one another, a property that affects their ability to maintain network integrity and propagate information-flow. Here, we introduce influence networks and demonstrate how they can be used to generate influence scores as a network-based metric to rank genes as potential drug targets.

Results

We use this approach to prioritize genes as drug target candidates in a set of ER ⁺ breast tumor samples collected during the course of neoadjuvant treatment with the aromatase inhibitor letrozole. We show that influential genes, those with high influence scores, tend to be essential and include a higher proportion of essential genes than those prioritized based on their position (i.e. hubs or bottlenecks) within the same network. Additionally, we show that influential genes represent novel biologically relevant drug targets for the treatment of ER ⁺ breast cancers. Moreover, we demonstrate that gene influence differs between untreated tumors and residual tumors that have adapted to drug treatment. In this way, influence scores capture the context-dependent functions of genes and present the opportunity to design combination treatment strategies that take advantage of the tumor adaptation process.

Conclusions

Influence networks efficiently find essential genes as promising drug targets and combinations of targets to inform the development of molecularly targeted drugs and their use.