Identification of molecular candidates which regulate calcium-dependent CD8+ T-cell cytotoxicity.

Cytotoxic CD8+ T lymphocytes (CTL) eliminate infected cells or transformed tumor cells by releasing perforin-containing cytotoxic granules at the immunological synapse. The secretion of such granules depends on Ca2+-influx through store operated Ca2+ channels, formed by STIM (stromal interaction molecule)-activated Orai proteins. Whereas molecular mechanisms of the secretion machinery are well understood, much less is known about the molecular machinery that regulates the efficiency of Ca2+-dependent target cell killing. CTL killing efficiency is of high interest considering the number of studies on CD8+ T lymphocytes modified for clinical use. Here, we isolated total RNA from primary human cells: natural killer (NK) cells, non-stimulated CD8+ T-cells, and from Staphylococcus aureus enterotoxin A (SEA) stimulated CD8+ T-cells (SEA-CTL) and conducted whole genome expression profiling by microarray experiments. Based on differential expression analysis of the transcriptome data and analysis of master regulator genes, we identified 31 candidates which potentially regulate Ca2+-homeostasis in CTL. To investigate a putative function of these candidates in CTL cytotoxicity, we transfected either SEA-stimulated CTL (SEA-CTL) or antigen specific CD8+ T-cell clones (CTL-MART-1) with siRNAs specific against the identified candidates and analyzed the killing capacity using a real-time killing assay. In addition, we complemented the analysis by studying the effect of inhibitory substances acting on the candidate proteins if available. Finally, to unmask their involvement in Ca2+ dependent cytotoxicity, candidates were also analyzed under Ca2+-limiting conditions. Overall, we identified four hits, CCR5 (C-C chemokine receptor type five), KCNN4 (potassium calcium-activated channel subfamily N), RCAN3 (regulator of calcineurin) and BCL (B-cell lymphoma) 2 which clearly affect the efficiency of Ca2+ dependent cytotoxicity in CTL-MART-1 cells, CCR5, BCL2, and KCNN4 in a positive manner, and RCAN3 in a negative way.

Identification of Biomarkers Controlling Cell Fate In Blood Cell Development.

A blood cell lineage consists of several consecutive developmental stages starting from the pluri- or multipotent stem cell to a state of terminal differentiation. Despite their importance for human biology, the regulatory pathways and gene networks that govern these differentiation processes are not yet fully understood. This is in part due to challenges associated with delineating the interactions between transcription factors (TFs) and their corresponding target genes. A possible step forward in this case is provided by the increasing amount of expression data, as a basis for linking differentiation stages and gene activities. Here, we present a novel hierarchical approach to identify characteristic expression peak patterns that global regulators excert along the differentiation path of cell lineages. Based on such simple patterns, we identified cell state-specific marker genes and extracted TFs that likely drive their differentiation. Integration of the mean expression values of stage-specific "key player" genes yielded a distinct peaking pattern for each lineage that was used to identify further genes in the dataset which behave similarly. Incorporating the set of TFs that regulate these genes led to a set of stage-specific regulators that control the biological process of cell fate. As proof of concept, we considered two expression datasets covering key differentiation events in blood cell formation of mice.

TFmiR2: constructing and analyzing disease-, tissue- and process-specific transcription factor and microRNA co-regulatory networks.

SUMMARY: TFmiR2 is a freely available web server for constructing and analyzing integrated transcription factor (TF) and microRNA (miRNA) co-regulatory networks for human and mouse. TFmiR2 generates tissue- and biological process-specific networks for the set of deregulated genes and miRNAs provided by the user. Furthermore, the service can now identify key driver genes and miRNAs in the constructed networks by utilizing the graph theoretical concept of a minimum connected dominating set. These putative key players as well as the newly implemented four-node TF-miRNA motifs yield novel insights that may assist in developing new therapeutic approaches. AVAILABILITY AND IMPLEMENTATION: The TFmiR2 web server is available at http://service.bioinformatik.uni-saarland.de/tfmir2. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

TopControl: A Tool to Prioritize Candidate Disease-associated Genes based on Topological Network Features.

Putative disease-associated genes are often identified among those genes that are differentially expressed in disease and in normal conditions. This strategy typically yields thousands of genes. Gene prioritizing schemes boost the power of identifying the most promising disease-associated genes among such a set of candidates. We introduce here a novel system for prioritizing genes where a TF-miRNA co-regulatory network is constructed for the set of genes, while the ranks of the candidates are determined by topological and biological factors. For datasets on breast invasive carcinoma and liver hepatocellular carcinoma this novel prioritization technique identified a significant portion of known disease-associated genes and suggested new candidates which can be investigated later as putative disease-associated genes.

Topology Consistency of Disease-specific Differential Co-regulatory Networks.

BACKGROUND: Sets of differentially expressed genes often contain driver genes that induce disease processes. However, various methods for identifying differentially expressed genes yield quite different results. Thus, we investigated whether this affects the identification of key players in regulatory networks derived by downstream analysis from lists of differentially expressed genes. RESULTS: While the overlap between the sets of significant differentially expressed genes determined by DESeq, edgeR, voom and VST was only 26% in liver hepatocellular carcinoma and 28% in breast invasive carcinoma, the topologies of the regulatory networks constructed using the TFmiR webserver for the different sets of differentially expressed genes were found to be highly consistent with respect to hub-degree nodes, minimum dominating set and minimum connected dominating set. CONCLUSIONS: The findings suggest that key genes identified in regulatory networks derived by systematic analysis of differentially expressed genes may be a more robust basis for understanding diseases processes than simply inspecting the lists of differentially expressed genes.

Randomization Strategies Affect Motif Significance Analysis in TF-miRNA-Gene Regulatory Networks.

Gene-regulatory networks are an abstract way of capturing the regulatory connectivity between transcription factors, microRNAs, and target genes in biological cells. Here, we address the problem of identifying enriched co-regulatory three-node motifs that are found significantly more often in real network than in randomized networks. First, we compare two randomization strategies, that either only conserve the degree distribution of the nodes' in- and out-links, or that also conserve the degree distributions of different regulatory edge types. Then, we address the issue how convergence of randomization can be measured. We show that after at most 10 × |E| edge swappings, converged motif counts are obtained and the memory of initial edge identities is lost.

Identification of key player genes in gene regulatory networks.

BACKGROUND: Identifying the gene regulatory networks governing the workings and identity of cells is one of the main challenges in understanding processes such as cellular differentiation, reprogramming or cancerogenesis. One particular challenge is to identify the main drivers and master regulatory genes that control such cell fate transitions. In this work, we reformulate this problem as the optimization problems of computing a Minimum Dominating Set and a Minimum Connected Dominating Set for directed graphs. RESULTS: Both MDS and MCDS are applied to the well-studied gene regulatory networks of the model organisms E. coli and S. cerevisiae and to a pluripotency network for mouse embryonic stem cells. The results show that MCDS can capture most of the known key player genes identified so far in the model organisms. Moreover, this method suggests an additional small set of transcription factors as novel key players for governing the cell-specific gene regulatory network which can also be investigated with regard to diseases. To this aim, we investigated the ability of MCDS to define key drivers in breast cancer. The method identified many known drug targets as members of the MDS and MCDS. CONCLUSIONS: This paper proposes a new method to identify key player genes in gene regulatory networks. The Java implementation of the heuristic algorithm explained in this paper is available as a Cytoscape plugin at http://apps.cytoscape.org/apps/mcds . The SageMath programs for solving integer linear programming formulations used in the paper are available at https://github.com/maryamNazarieh/KeyRegulatoryGenes and as supplementary material.

TFmiR: a web server for constructing and analyzing disease-specific transcription factor and miRNA co-regulatory networks.

TFmiR is a freely available web server for deep and integrative analysis of combinatorial regulatory interactions between transcription factors, microRNAs and target genes that are involved in disease pathogenesis. Since the inner workings of cells rely on the correct functioning of an enormously complex system of activating and repressing interactions that can be perturbed in many ways, TFmiR helps to better elucidate cellular mechanisms at the molecular level from a network perspective. The provided topological and functional analyses promote TFmiR as a reliable systems biology tool for researchers across the life science communities. TFmiR web server is accessible through the following URL: http://service.bioinformatik.uni-saarland.de/tfmir.