Identifying new targets in leukemogenesis using computational approaches

There is a need to identify novel targets in Acute Lymphoblastic Leukemia (ALL), a hematopoietic cancer affecting children, to improve our understanding of disease biology and that can be used for developing new therapeutics. Hence, the aim of our study was to find new genes as targets using in silico studies; for this we retrieved the top 10% overexpressed genes from Oncomine public domain microarray expression database; 530 overexpressed genes were short-listed from Oncomine database. Then, using prioritization tools such as ENDEAVOUR, DIR and TOPPGene online tools, we found fifty-four genes common to the three prioritization tools which formed our candidate leukemogenic genes for this study. As per the protocol we selected thirty training genes from PubMed. The prioritized and training genes were then used to construct STRING functional association network, which was further analyzed using cytoHubba hub analysis tool to investigate new genes which could form drug targets in leukemia. Analysis of the STRING protein network built from these prioritized and training genes led to identification of two hub genes, SMAD2 and CDK9, which were not implicated in leukemogenesis earlier. Filtering out from several hundred genes in the network we also found MEN1, HDAC1 and LCK genes, which re-emphasized the important role of these genes in leukemogenesis. This is the first report on these five additional signature genes in leukemogenesis. We propose these as new targets for developing novel therapeutics and also as biomarkers in leukemogenesis, which could be important for prognosis and diagnosis.     

Differential gene expression in the developing human macula: microarray analysis using rare tissue samples

The macula is a unique and important region in the primate retina that achieves high resolution and color vision in the central visual field. We recently reported data obtained from microarray analysis of gene expression in the macula of the human fetal retina (Kozulin et al., Mol Vis 15:45–59, 1). In this paper, we describe the preliminary analyses undertaken to visualize differences and verify comparability of the replicates used in that study, report the differential expression of other gene families obtained from the analysis, and show the reproducibility of our findings in several gene families by quantitative real-time PCR.     

Gene network inference using continuous time Bayesian networks: a comparative study and application to Th17 cell differentiation

Background

Dynamic aspects of gene regulatory networks are typically investigated by measuring system variables at multiple time points. Current state-of-the-art computational approaches for reconstructing gene networks directly build on such data, making a strong assumption that the system evolves in a synchronous fashion at fixed points in time. However, nowadays omics data are being generated with increasing time course granularity. Thus, modellers now have the possibility to represent the system as evolving in continuous time and to improve the models’ expressiveness.

Results

Continuous time Bayesian networks are proposed as a new approach for gene network reconstruction from time course expression data. Their performance was compared to two state-of-the-art methods: dynamic Bayesian networks and Granger causality analysis. On simulated data, the methods comparison was carried out for networks of increasing size, for measurements taken at different time granularity densities and for measurements unevenly spaced over time. Continuous time Bayesian networks outperformed the other methods in terms of the accuracy of regulatory interactions learnt from data for all network sizes. Furthermore, their performance degraded smoothly as the size of the network increased. Continuous time Bayesian networks were significantly better than dynamic Bayesian networks for all time granularities tested and better than Granger causality for dense time series. Both continuous time Bayesian networks and Granger causality performed robustly for unevenly spaced time series, with no significant loss of performance compared to the evenly spaced case, while the same did not hold true for dynamic Bayesian networks. The comparison included the IRMA experimental datasets which confirmed the effectiveness of the proposed method. Continuous time Bayesian networks were then applied to elucidate the regulatory mechanisms controlling murine T helper 17 (Th17) cell differentiation and were found to be effective in discovering well-known regulatory mechanisms, as well as new plausible biological insights.

Conclusions

Continuous time Bayesian networks were effective on networks of both small and large size and were particularly feasible when the measurements were not evenly distributed over time. Reconstruction of the murine Th17 cell differentiation network using continuous time Bayesian networks revealed several autocrine loops, suggesting that Th17 cells may be auto regulating their own differentiation process.     

Differential regulation of host cellular genes by HIV-1 viral protein R (Vpr): cDNA microarray analysis using isogenic virus

HIV-1 Vpr is a protein with multiple functions. It has been suggested that such pleiotropic effects by a viral protein may be mediated by its association with viral and cellular proteins or through modulation of expression of specific cellular genes. To address this, we used cDNA microarray techniques to analyze the regulation of a panel of host cellular genes by HIV-1 Vpr using isogenic HIV-1 either with or without Vpr expression. Results indicate that Vpr downregulated the expression of genes involved in cell cycle/proliferation regulation, DNA repair, tumor antigens, and immune activation factors, and upregulated many ribosomal and structural proteins. These results for the first time reveal the involvement of several potential cellular genes, which may be useful, both for understanding Vpr functions and for the development of therapeutics targeting the Vpr molecule.     

Characterizing the role of mechanical signals in gene regulatory networks using Long SAGE

A systems understanding of mechanical regulation is critical for determining how cells proliferate and differentiate. To better understand the biological process in which mechanical signals regulate cells, we globally investigated the gene expression profiling via long serial analysis of gene expression (Long SAGE) in osteoblasts after exposure to mechanical stretching. The analysis showed that the differentially expressed genes were related with many physiological processes, including signal transduction, cell proliferation and apoptosis. Several genes that were seldom or never studied in osteoblasts have been found in this study. We further analyzed the signal pathways and provided gene regulatory networks activated by mechanical signals. Many changed genes in our data were contributed to ECM-integrin-FAK mediated pathway and mainly influenced actin-cytoskeleton dynamic remodeling, cell proliferation and differentiation. We also provided evidence supporting the hypothesis that endoplasmic reticulum and mitochondrion were combined to dedicate to calcium regulation. Taken together, our experiments provided a systemic view on biological processes and mechanotransduction network in osteoblasts, suggesting that mechanical signals regulate osteoblast through a greater diversity of interactions and pathways than previously appreciated.     

Exploring the multi-drug resistance in Escherichia coli O157:H7 by gene interaction network: A systems biology approach

In the present study, we have constructed an interaction network of 29 antibiotic resistant genes along with 777 interactions in E. coli O157:H7. Gene ontology analysis reveals that 94, 89 and 67 genes have roles in the cellular process, biological process and molecular function respectively. Gene complexes related to tripartite efflux pumps mdtEF-tolC and ABC family efflux pump macAB-tolC play key roles in multidrug efflux systems. It is noteworthy to mention that, 19 genes are involved in multi-efflux pumps and they play a significant role in multidrug resistance (MDR); while 18 genes are vital for fatty acid synthesis. Interestingly, we found that the four genes arnABCD are involved in both MDR and in fatty acid synthesis. Hence these genes could be targeted for new drug discovery. On the whole, our results provide a detailed understanding of the mode of MDR mechanisms in E.coli O157:H7.