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

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.

Weighted gene co-expression identification of CDKN1A as a hub inflammation gene following cardiopulmonary bypass in children with congenital heart disease

Background

Congenital heart disease (CHD) is the most common type of birth defect. Most patients with CHD require surgery, and cardiopulmonary bypass (CPB) is the most common surgery performed.

Methods

The present study utilized weighted gene co-expression network analysis (WGCNA) to identify key inflammation genes after CPB for CHD. The GSE132176 dataset was downloaded from the Gene Expression Omnibus(GEO) database for WGCNA to identify the modules closely related to clinical traits. Disease enrichment, functional annotation and pathway enrichment were performed on genes in the module closely related to clinical traits using Enrichr and Metascape. Immune infiltration analysis was also performed on the training dataset using CIBERSORT. Finally, we identified hub genes using high gene significance (GS), high module members (MMs) and Cytoscape, and we verified the hub genes using an independent dataset and Western blot analysis.

Results

WGCNA showed that the brown module with 461 genes had the highest correlation to CHD after CPB. Functional annotation and pathway enrichment analysis were performed using Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses, which showed that genes in the brown module were enriched in inflammation-related pathways. In the disease enrichment analysis, genes in the brown module were enriched for inflammatory diseases. After the 30 most highly associated brown intramodular genes were screened, a protein-protein interaction network was constructed using the STRING online analysis website. The protein-protein interaction results were then calculated using 12 algorithms in the cytoHubba plugin of Cytoscape software. The final result showed that CDKN1A was the fundamental gene of post-CPB for CHD. Using another independent validation dataset (GSE12486), we confirmed that CDKN1A was significantly differentially expressed between preoperative and postoperative CPB (Wilcoxon, P = 0.0079; T-test, P = 0.006). In addition, CDKN1A expression was elevated in eosinophils, neutrophils, memory CD4 T cells and activated mast cells. Western blot analysis showed that the expression of CDKN1A protein was significantly higher postoperative CPB than preoperative CPB. Moreover, CDKN1A was mainly related to inflammation.

Conclusion

In summary, we found a relationship between CDKN1A and inflammation after CPB for congenital heart disease by WGCNA, experiments and various bioinformatics methods. Thus, CDKN1A maybe serve as a biomarker or therapeutic target for accurate diagnosis and treatment of inflammation after CPB in the future.

Emerging landscape of molecular interaction networks: Opportunities, challenges and prospects

Network biology finds application in interpreting molecular interaction networks and providing insightful inferences using graph theoretical analysis of biological systems. The integration of computational bio-modelling approaches with different hybrid network-based techniques provides additional information about the behaviour of complex systems. With increasing advances in high-throughput technologies in biological research, attempts have been made to incorporate this information into network structures, which has led to a continuous update of network biology approaches over time. The newly minted centrality measures accommodate the details of omics data and regulatory network structure information. The unification of graph network properties with classical mathematical and computational modelling approaches and technologically advanced approaches like machine-learning- and artificial intelligence-based algorithms leverages the potential application of these techniques. These computational advances prove beneficial and serve various applications such as essential gene prediction, identification of drug–disease interaction and gene prioritization. Hence, in this review, we have provided a comprehensive overview of the emerging landscape of molecular interaction networks using graph theoretical approaches. With the aim to provide information on the wide range of applications of network biology approaches in understanding the interaction and regulation of genes, proteins, enzymes and metabolites at different molecular levels, we have reviewed the methods that utilize network topological properties, emerging hybrid network-based approaches and applications that integrate machine learning techniques to analyse molecular interaction networks. Further, we have discussed the applications of these approaches in biomedical research with a note on future prospects.

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.