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.

Rare ABCA7 variants in 2 German families with Alzheimer disease

Objective

The aim of this study was to identify variants associated with familial late-onset Alzheimer disease (AD) using whole-genome sequencing.

Methods

Several families with an autosomal dominant inheritance pattern of AD were analyzed by whole-genome sequencing. Variants were prioritized for rare, likely pathogenic variants in genes already known to be associated with AD and confirmed by Sanger sequencing using standard protocols.

Results

We identified 2 rare ABCA7 variants (rs143718918 and rs538591288) with varying penetrance in 2 independent German AD families, respectively. The single nucleotide variant (SNV) rs143718918 causes a missense mutation, and the deletion rs538591288 causes a frameshift mutation of ABCA7. Both variants have previously been reported in larger cohorts but with incomplete segregation information. ABCA7 is one of more than 20 AD risk loci that have so far been identified by genome-wide association studies, and both common and rare variants of ABCA7 have previously been described in different populations with higher frequencies in AD cases than in controls and varying penetrance. Furthermore, ABCA7 is known to be involved in several AD-relevant pathways.

Conclusions

We conclude that both SNVs might contribute to the development of AD in the examined family members. Together with previous findings, our data confirm ABCA7 as one of the most relevant AD risk 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.

Linking Hematopoietic Differentiation to Co-Expressed Sets of Pluripotency-Associated and Imprinted Genes and to Regulatory microRNA-Transcription Factor Motifs

Maintenance of cell pluripotency, differentiation, and reprogramming are regulated by complex gene regulatory networks (GRNs) including monoallelically-expressed imprinted genes. Besides transcriptional control, epigenetic modifications and microRNAs contribute to cellular differentiation. As a model system for studying the capacity of cells to preserve their pluripotency state and the onset of differentiation and subsequent specialization, murine hematopoiesis was used and compared to embryonic stem cells (ESCs) as a control. Using published microarray data, the expression profiles of two sets of genes, pluripotent and imprinted, were compared to a third set of known hematopoietic genes. We found that more than half of the pluripotent and imprinted genes are clearly upregulated in ESCs but subsequently repressed during hematopoiesis. The remaining genes were either upregulated in hematopoietic progenitors or in differentiated blood cells. The three gene sets each consist of three similarly behaving gene groups with similar expression profiles in various lineages of the hematopoietic system as well as in ESCs. To explain this co-regulation behavior, we explored the transcriptional and post-transcriptional mechanisms of pluripotent and imprinted genes and their regulator/target miRNAs in six different hematopoietic lineages. Therewith, lineage-specific transcription factor (TF)-miRNA regulatory networks were generated and their topologies and functional impacts during hematopoiesis were analyzed. This led to the identification of TF-miRNA co-regulatory motifs, for which we validated the contribution to the cellular development of the corresponding lineage in terms of statistical significance and relevance to biological evidence. This analysis also identified key miRNAs and TFs/genes that might play important roles in the derived lineage networks. These molecular associations suggest new aspects of the cellular regulation of the onset of cellular differentiation and during hematopoiesis involving, on one hand, pluripotent genes that were previously not discussed in the context of hematopoiesis and, on the other hand, involve genes that are related to genomic imprinting. These are new links between hematopoiesis and cellular differentiation and the important field of epigenetic modifications.

Integrative network-based approach identifies key genetic elements in breast invasive carcinoma

BACKGROUND

Breast cancer is a genetically heterogeneous type of cancer that belongs to the most prevalent types with a high mortality rate. Treatment and prognosis of breast cancer would profit largely from a correct classification and identification of genetic key drivers and major determinants driving the tumorigenesis process. In the light of the availability of tumor genomic and epigenomic data from different sources and experiments, new integrative approaches are needed to boost the probability of identifying such genetic key drivers. We present here an integrative network-based approach that is able to associate regulatory network interactions with the development of breast carcinoma by integrating information from gene expression, DNA methylation, miRNA expression, and somatic mutation datasets.

RESULTS

Our results showed strong association between regulatory elements from different data sources in terms of the mutual regulatory influence and genomic proximity. By analyzing different types of regulatory interactions, TF-gene, miRNA-mRNA, and proximity analysis of somatic variants, we identified 106 genes, 68 miRNAs, and 9 mutations that are candidate drivers of oncogenic processes in breast cancer. Moreover, we unraveled regulatory interactions among these key drivers and the other elements in the breast cancer network. Intriguingly, about one third of the identified driver genes are targeted by known anti-cancer drugs and the majority of the identified key miRNAs are implicated in cancerogenesis of multiple organs. Also, the identified driver mutations likely cause damaging effects on protein functions. The constructed gene network and the identified key drivers were compared to well-established network-based methods.

CONCLUSION

The integrated molecular analysis enabled by the presented network-based approach substantially expands our knowledge base of prospective genomic drivers of genes, miRNAs, and mutations. For a good part of the identified key drivers there exists solid evidence for involvement in the development of breast carcinomas. Our approach also unraveled the complex regulatory interactions comprising the identified key drivers. These genomic drivers could be further investigated in the wet lab as potential candidates for new drug targets. This integrative approach can be applied in a similar fashion to other cancer types, complex diseases, or for studying cellular differentiation processes.

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.