REGGAE: a novel approach for the identification of key transcriptional regulators

GeneTrail: A Framework for the Analysis of High-Throughput Profiles

Experimental high-throughput techniques, like next-generation sequencing or microarrays, are nowadays routinely applied to create detailed molecular profiles of cells. In general, these platforms generate high-dimensional and noisy data sets. For their analysis, powerful bioinformatics tools are required to gain novel insights into the biological processes under investigation. Here, we present an overview of the GeneTrail tool suite that offers rich functionality for the analysis and visualization of (epi-)genomic, transcriptomic, miRNomic, and proteomic profiles. Our framework enables the analysis of standard bulk, time-series, and single-cell measurements and includes various state-of-the-art methods to identify potentially deregulated biological processes and to detect driving factors within those deregulated processes. We highlight the capabilities of our web service with an analysis of a single-cell COVID-19 data set that demonstrates its potential for uncovering complex molecular mechanisms. GeneTrail can be accessed freely and without login requirements at http://genetrail.bioinf.uni-sb.de.

Discovering key transcriptomic regulators in pancreatic ductal adenocarcinoma using Dirichlet process Gaussian mixture model

Pancreatic Ductal Adenocarcinoma (PDAC) is the most lethal type of pancreatic cancer, late detection leading to its therapeutic failure. This study aims to determine the key regulatory genes and their impacts on the disease’s progression, helping the disease’s etiology, which is still mostly unknown. We leverage the landmark advantages of time-series gene expression data of this disease and thereby identified the key regulators that capture the characteristics of gene activity patterns in the cancer progression. We have identified the key gene modules and predicted the functions of top genes from a reconstructed gene association network (GAN). A variation of the partial correlation method is utilized to analyze the GAN, followed by a gene function prediction task. Moreover, we have identified regulators for each target gene by gene regulatory network inference using the dynamical GENIE3 (dynGENIE3) algorithm. The Dirichlet process Gaussian process mixture model and cubic spline regression model (splineTimeR) are employed to identify the key gene modules and differentially expressed genes, respectively. Our analysis demonstrates a panel of key regulators and gene modules that are crucial for PDAC disease progression.

The Efficacy of Sunitinib Treatment of Renal Cancer Cells Is Associated with the Protein PHAX In Vitro

Anti-angiogenic agents, such as the multi-tyrosine kinase inhibitor sunitinib, are key first line therapies for metastatic clear cell renal cell carcinoma (ccRCC), but their mechanism of action is not fully understood. Here, we take steps towards validating a computational prediction based on differential transcriptome network analysis that phosphorylated adapter RNA export protein (PHAX) is associated with sunitinib drug treatment. The regulatory impact factor differential network algorithm run on patient tissue samples suggests PHAX is likely an important regulator through changes in genome-wide network connectivity. Immunofluorescence staining of patient tumours showed strong localisation of PHAX to the microvasculature consistent with the anti-angiogenic effect of sunitinib. In normal kidney tissue, PHAX protein abundance was low but increased with tumour grade (G1 vs. G3/4; p < 0.01), consistent with a possible role in cancer progression. In organ culture, ccRCC cells had higher levels of PHAX protein expression than normal kidney cells, and sunitinib increased PHAX protein expression in a dose dependent manner (untreated vs. 100 µM; p < 0.05). PHAX knockdown in a ccRCC organ culture model impacted the ability of sunitinib to cause cancer cell death (p < 0.0001 untreated vs. treated), suggesting a role for PHAX in mediating the efficacy of sunitinib.

Enhanced identification of significant regulators of gene expression

Background

Diseases like cancer will lead to changes in gene expression, and it is relevant to identify key regulatory genes that can be linked directly to these changes. This can be done by computing a Regulatory Impact Factor (RIF) score for relevant regulators. However, this computation is based on estimating correlated patterns of gene expression, often Pearson correlation, and an assumption about a set of specific regulators, normally transcription factors. This study explores alternative measures of correlation, using the Fisher and Sobolev metrics, and an extended set of regulators, including epigenetic regulators and long non-coding RNAs (lncRNAs). Data on prostate cancer have been used to explore the effect of these modifications.

Results

A tool for computation of RIF scores with alternative correlation measures and extended sets of regulators was developed and tested on gene expression data for prostate cancer. The study showed that the Fisher and Sobolev metrics lead to improved identification of well-documented regulators of gene expression in prostate cancer, and the sets of identified key regulators showed improved overlap with previously defined gene sets of relevance to cancer. The extended set of regulators lead to identification of several interesting candidates for further studies, including lncRNAs. Several key processes were identified as important, including spindle assembly and the epithelial-mesenchymal transition (EMT).

Conclusions

The study has shown that using alternative metrics of correlation can improve the performance of tools based on correlation of gene expression in genomic data. The Fisher and Sobolev metrics should be considered also in other correlation-based applications.

Improved linking of motifs to their TFs using domain information

Motivation

A central aim of molecular biology is to identify mechanisms of transcriptional regulation. Transcription factors (TFs), which are DNA-binding proteins, are highly involved in these processes, thus a crucial information is to know where TFs interact with DNA and to be aware of the TFs’ DNA-binding motifs. For that reason, computational tools exist that link DNA-binding motifs to TFs either without sequence information or based on TF-associated sequences, e.g. identified via a chromatin immunoprecipitation followed by sequencing (ChIP-seq) experiment.

In this paper, we present MASSIF, a novel method to improve the performance of existing tools that link motifs to TFs relying on TF-associated sequences. MASSIF is based on the idea that a DNA-binding motif, which is correctly linked to a TF, should be assigned to a DNA-binding domain (DBD) similar to that of the mapped TF. Because DNA-binding motifs are in general not linked to DBDs, it is not possible to compare the DBD of a TF and the motif directly. Instead we created a DBD collection, which consist of TFs with a known DBD and an associated motif. This collection enables us to evaluate how likely it is that a linked motif and a TF of interest are associated to the same DBD. We named this similarity measure domain score, and represent it as a P-value. We developed two different ways to improve the performance of existing tools that link motifs to TFs based on TF-associated sequences: (i) using meta-analysis to combine P-values from one or several of these tools with the P-value of the domain score and (ii) filter unlikely motifs based on the domain score.

Results

We demonstrate the functionality of MASSIF on several human ChIP-seq datasets, using either motifs from the HOCOMOCO database or de novo identified ones as input motifs. In addition, we show that both variants of our method improve the performance of tools that link motifs to TFs based on TF-associated sequences significantly independent of the considered DBD type.

Availability and implementation

MASSIF is freely available online at https://github.com/SchulzLab/MASSIF.

Supplementary information

Supplementary data are available at Bioinformatics online.

The role of TCF3 as potential master regulator in blastemal Wilms tumors

Wilms tumors are the most common type of pediatric kidney tumors. While the overall prognosis for patients is favorable, especially tumors that exhibit a blastemal subtype after preoperative chemotherapy have a poor prognosis. For an improved risk assessment and therapy stratification, it is essential to identify the driving factors that are distinctive for this aggressive subtype. In our study, we compared gene expression profiles of 33 tumor biopsies (17 blastemal and 16 other tumors) after neoadjuvant chemotherapy. The analysis of this dataset using the Regulator Gene Association Enrichment algorithm successfully identified several biomarkers and associated molecular mechanisms that distinguish between blastemal and nonblastemal Wilms tumors. Specifically, regulators involved in embryonic development and epigenetic processes like chromatin remodeling and histone modification play an essential role in blastemal tumors. In this context, we especially identified TCF3 as the central regulatory element. Furthermore, the comparison of ChIP-Seq data of Wilms tumor cell cultures from a blastemal mouse xenograft and a stromal tumor provided further evidence that the chromatin states of blastemal cells share characteristics with embryonic stem cells that are not present in the stromal tumor cell line. These stem-cell like characteristics could potentially add to the increased malignancy and chemoresistance of the blastemal subtype. Along with TCF3, we detected several additional biomarkers that are distinctive for blastemal Wilms tumors after neoadjuvant chemotherapy and that may provide leads for new therapeutic regimens.