A property-based analysis of human transcription factors

Cancer-driving mutations are enriched in genic regions intolerant to germline variation

Large reference datasets of protein-coding variation in human populations have allowed us to determine which genes and genic subregions are intolerant to germline genetic variation. There is also a growing number of genes implicated in severe Mendelian diseases that overlap with genes implicated in cancer. We hypothesized that cancer-driving mutations might be enriched in genic subregions that are depleted of germline variation relative to somatic variation. We introduce a new metric, OncMTR (oncology missense tolerance ratio), which uses 125,748 exomes in the Genome Aggregation Database (gnomAD) to identify these genic subregions. We demonstrate that OncMTR can significantly predict driver mutations implicated in hematologic malignancies. Divergent OncMTR regions were enriched for cancer-relevant protein domains, and overlaying OncMTR scores on protein structures identified functionally important protein residues. Last, we performed a rare variant, gene-based collapsing analysis on an independent set of 394,694 exomes from the UK Biobank and find that OncMTR markedly improves genetic signals for hematologic malignancies.

Extended transcriptome analysis reveals genome-wide lncRNA-mediated epigenetic dysregulation in colorectal cancer

It is estimated that the rate of epigenetic changes may be orders of magnitude higher than that of genetic changes and that purely epigenetic mechanisms may explain why cancers arise with few or no recurrent mutations. However, supporting evidence remains limited, partly due to the cost of experimentally studying genome-wide epigenetic dysregulation. Since genome modification enzymes are recruited by long noncoding RNAs (lncRNAs) to specific genomic sites, analyzing differentially expressed genes and differentially methylated regions (DMRs) at the DNA binding sites of differentially expressed lncRNAs is important for uncovering epigenetic dysregulation. We performed RNA-seq and MeDIP-seq on a set of colorectal cancer (CRC) and normal colon samples and developed an analysis pipeline for combined analyses of gene expression, DNA methylation, and lncRNA/DNA binding. The genes identified in our data and important for CRC agree with widely reported findings. We found that aberrantly transcribed noncoding transcripts may epigenetically dysregulate genes, that correlated gene expression is significantly determined by epigenetic dysregulation, that differentially expressed noncoding transcripts and their epigenetic targets form distinct modules in different cancer cells, and that many hub lncRNAs in these modules are primate-specific. These results suggest that lncRNA-mediated epigenetic dysregulation greatly determines aberrant gene expression and that epigenetic dysregulation is highly species-specific. The analysis pipeline can effectively unveil cancer- and cell-specific modules of epigenetic dysregulation, and such modules may provide novel clues for identifying diagnostic, therapeutic, and prognostic targets for epigenetic dysregulation.

Predicting transcription factor binding motifs from DNA-binding domains, chromatin accessibility and gene expression data

Transcription factors (TFs) play crucial roles in regulating gene expression through interactions with specific DNA sequences. Recently, the sequence motif of almost 400 human TFs have been identified using high-throughput SELEX sequencing. However, there remain a large number of TFs (∼800) with no high-throughput-derived binding motifs. Computational methods capable of associating known motifs to such TFs will avoid tremendous experimental efforts and enable deeper understanding of transcriptional regulatory functions. We present a method to associate known motifs to TFs (MATLAB code is available in Supplementary Materials). Our method is based on a probabilistic framework that not only exploits DNA-binding domains and specificities, but also integrates open chromatin, gene expression and genomic data to accurately infer monomeric and homodimeric binding motifs. Our analysis resulted in the assignment of motifs to 200 TFs with no SELEX-derived motifs, roughly a 50% increase compared to the existing coverage.

Feature-based classification of human transcription factors into hypothetical sub-classes related to regulatory function

Background

Transcription factors are key proteins in the regulation of gene transcription. An important step in this process is the opening of chromatin in order to make genomic regions available for transcription. Data on DNase I hypersensitivity has previously been used to label a subset of transcription factors as Pioneers, Settlers and Migrants to describe their potential role in this process. These labels represent an interesting hypothesis on gene regulation and possibly a useful approach for data analysis, and therefore we wanted to expand the set of labeled transcription factors to include as many known factors as possible. We have used a well-annotated dataset of 1175 transcription factors as input to supervised machine learning methods, using the subset with previously assigned labels as training set. We then used the final classifier to label the additional transcription factors according to their potential role as Pioneers, Settlers and Migrants. The full set of labeled transcription factors was used to investigate associated properties and functions of each class, including an analysis of interaction data for transcription factors based on DNA co-binding and protein-protein interactions. We also used the assigned labels to analyze a previously published set of gene lists associated with a time course experiment on cell differentiation.

Results

The analysis showed that the classification of transcription factors with respect to their potential role in chromatin opening largely was determined by how they bind to DNA. Each subclass of transcription factors was enriched for properties that seemed to characterize the subclass relative to its role in gene regulation, with very general functions for Pioneers, whereas Migrants to a larger extent were associated with specific processes. Further analysis showed that the expanded classification is a useful resource for analyzing other datasets on transcription factors with respect to their potential role in gene regulation. The analysis of transcription factor interaction data showed complementary differences between the subclasses, where transcription factors labeled as Pioneers often interact with other transcription factors through DNA co-binding, whereas Migrants to a larger extent use protein-protein interactions. The analysis of time course data on cell differentiation indicated a shift in the regulatory program associated with Pioneer-like transcription factors during differentiation.

Conclusions

The expanded classification is an interesting resource for analyzing data on gene regulation, as illustrated here on transcription factor interaction data and data from a time course experiment. The potential regulatory function of transcription factors seems largely to be determined by how they bind DNA, but is also influenced by how they interact with each other through cooperativity and protein-protein interactions.

Electronic supplementary material

The online version of this article (doi:10.1186/s12859-016-1349-2) contains supplementary material, which is available to authorized users.

Gene regulation in the immediate-early response process

Immediate-early genes (IEGs) can be activated and transcribed within minutes after stimulation, without the need for de novo protein synthesis, and they are stimulated in response to both cell-extrinsic and cell-intrinsic signals. Extracellular signals are transduced from the cell surface, through receptors activating a chain of proteins in the cell, in particular extracellular-signal-regulated kinases (ERKs), mitogen-activated protein kinases (MAPKs) and members of the RhoA-actin pathway. These communicate through a signaling cascade by adding phosphate groups to neighboring proteins, and this will eventually activate and translocate TFs to the nucleus and thereby induce gene expression. The gene activation also involves proximal and distal enhancers that interact with promoters to simulate gene expression. The immediate-early genes have essential biological roles, in particular in stress response, like the immune system, and in differentiation. Therefore they also have important roles in various diseases, including cancer development. In this paper we summarize some recent advances on key aspects of the activation and regulation of immediate-early genes.