Andreas J. Gruber

ISMARA: Automated modeling of genomic signals as a democracy of regulatory motifs

Accurate reconstruction of the regulatory networks that control gene expression is one of the key current challenges in molecular biology. Although gene expression and chromatin state dynamics are ultimately encoded by constellations of binding sites recognized by regulators such as transcriptions factors (TFs) and microRNAs (miRNAs), our understanding of this regulatory code and its context-dependent read-out remains very limited. Given that there are thousands of potential regulators in mammals, it is not practical to use direct experimentation to identify which of these play a key role for a particular system of interest. We developed a methodology that models gene expression or chromatin modifications in terms of genome-wide predictions of regulatory sites and completely automated it into a web-based tool called ISMARA (Integrated System for Motif Activity Response Analysis). Given only gene expression or chromatin state data across a set of samples as input, ISMARA identifies the key TFs and miRNAs driving expression/chromatin changes and makes detailed predictions regarding their regulatory roles. These include predicted activities of the regulators across the samples, their genome-wide targets, enriched gene categories among the targets, and direct interactions between the regulators. Applying ISMARA to data sets from well-studied systems, we show that it consistently identifies known key regulators ab initio. We also present a number of novel predictions including regulatory interactions in innate immunity, a master regulator of mucociliary differentiation, TFs consistently disregulated in cancer, and TFs that mediate specific chromatin modifications.

Comparative assessment of methods for the computational inference of transcript isoform abundance from RNA-seq data

BackgroundUnderstanding the regulation of gene expression, including transcription start site usage, alternative splicing, and polyadenylation, requires accurate quantification of expression levels down to the level of individual transcript isoforms. To comparatively evaluate the accuracy of the many methods that have been proposed for estimating transcript isoform abundance from RNA sequencing data, we have used both synthetic data as well as an independent experimental method for quantifying the abundance of transcript ends at the genome-wide level.ResultsWe found that many tools have good accuracy and yield better estimates of gene-level expression compared to commonly used count-based approaches, but they vary widely in memory and runtime requirements. Nucleotide composition and intron/exon structure have comparatively little influence on the accuracy of expression estimates, which correlates most strongly with transcript/gene expression levels. To facilitate the reproduction and further extension of our study, we provide datasets, source code, and an online analysis tool on a companion website, where developers can upload expression estimates obtained with their own tool to compare them to those inferred by the methods assessed here.ConclusionsAs many methods for quantifying isoform abundance with comparable accuracy are available, a user’s choice will likely be determined by factors such as the memory and runtime requirements, as well as the availability of methods for downstream analyses. Sequencing-based methods to quantify the abundance of specific transcript regions could complement validation schemes based on synthetic data and quantitative PCR in future or ongoing assessments of RNA-seq analysis methods.

Pegylated IFN-α regulates hepatic gene expression through transient Jak/STAT activation

The use of pegylated interferon-α (pegIFN-α) has replaced unmodified recombinant IFN-α for the treatment of chronic viral hepatitis. While the superior antiviral efficacy of pegIFN-α is generally attributed to improved pharmacokinetic properties, the pharmacodynamic effects of pegIFN-α in the liver have not been studied. Here, we analyzed pegIFN-α-induced signaling and gene regulation in paired liver biopsies obtained prior to treatment and during the first week following pegIFN-α injection in 18 patients with chronic hepatitis C. Despite sustained high concentrations of pegIFN-α in serum, the Jak/STAT pathway was activated in hepatocytes only on the first day after pegIFN-α administration. Evaluation of liver biopsies revealed that pegIFN-α induces hundreds of genes that can be classified into four clusters based on different temporal expression profiles. In all clusters, gene transcription was mainly driven by IFN-stimulated gene factor 3 (ISGF3). Compared with conventional IFN-α therapy, pegIFN-α induced a broader spectrum of gene expression, including many genes involved in cellular immunity. IFN-induced secondary transcription factors did not result in additional waves of gene expression. Our data indicate that the superior antiviral efficacy of pegIFN-α is not the result of prolonged Jak/STAT pathway activation in hepatocytes, but rather is due to induction of additional genes that are involved in cellular immune responses.

Modulation of epigenetic regulators and cell fate decisions by miRNAs

Mammalian gene expression is controlled at multiple levels by a variety of regulators, including chromatin modifiers, transcription factors and miRNAs. The latter are small, ncRNAs that inhibit the expression of target mRNAs by reducing both their stability and translation rate. In this review, we summarize the recent work towards characterizing miRNA targets that are themselves involved in the regulation of gene expression at the epigenetic level. Epigenetic regulators are strongly enriched among the predicted targets of miRNAs, which may contribute to the documented importance of miRNAs for pluripotency, organism development and somatic cell reprogramming.

Embryonic stem cell-specific microRNAs contribute to pluripotency by inhibiting regulators of multiple differentiation pathways

The findings that microRNAs (miRNAs) are essential for early development in many species and that embryonic miRNAs can reprogram somatic cells into induced pluripotent stem cells suggest that these miRNAs act directly on transcriptional and chromatin regulators of pluripotency. To elucidate the transcription regulatory networks immediately downstream of embryonic miRNAs, we extended the motif activity response analysis approach that infers the regulatory impact of both transcription factors (TFs) and miRNAs from genome-wide expression states. Applying this approach to multiple experimental data sets generated from mouse embryonic stem cells (ESCs) that did or did not express miRNAs of the ESC-specific miR-290-295 cluster, we identified multiple TFs that are direct miRNA targets, some of which are known to be active during cell differentiation. Our results provide new insights into the transcription regulatory network downstream of ESC-specific miRNAs, indicating that these miRNAs act on cell cycle and chromatin regulators at several levels and downregulate TFs that are involved in the innate immune response.

Exploiting the multiplexing capabilities of tandem mass tags for high-throughput estimation of cellular protein abundances by mass spectrometry

The generation of dynamic models of biological processes critically depends on the determination of precise cellular concentrations of biomolecules. Measurements of system-wide absolute protein levels are particularly valuable information in systems biology. Recently, mass spectrometry based proteomics approaches have been developed to estimate protein concentrations on a proteome-wide scale. However, for very complex proteomes, fractionation steps are required, increasing samples number and instrument analysis time. As a result, the number of full proteomes that can be routinely analyzed is limited. Here we combined absolute quantification strategies with the multiplexing capabilities of isobaric tandem mass tags to determine cellular protein abundances in a high throughput and proteome-wide scale even for highly complex biological systems, such as a whole human cell line. We generated two independent data sets to demonstrate the power of the approach regarding sample throughput, dynamic range, quantitative precision and accuracy as well as proteome coverage in comparison to existing mass spectrometry based strategies.

TFAP2A is a component of the ZEB1/2 network that regulates TGFB1-induced epithelial to mesenchymal transition

BackgroundThe transition between epithelial and mesenchymal phenotypes (EMT) occurs in a variety of contexts. It is critical for mammalian development and it is also involved in tumor initiation and progression. Master transcription factor (TF) regulators of this process are conserved between mouse and human.MethodsFrom a computational analysis of a variety of high-throughput sequencing data sets we initially inferred that TFAP2A is connected to the core EMT network in both species. We then analysed publicly available human breast cancer data for TFAP2A expression and also studied the expression (by mRNA sequencing), activity (by monitoring the expression of its predicted targets), and binding (by electrophoretic mobility shift assay and chromatin immunoprecipitation) of this factor in a mouse mammary gland EMT model system (NMuMG) cell line.ResultsWe found that upon induction of EMT, the activity of TFAP2A, reflected in the expression level of its predicted targets, is up-regulated in a variety of systems, both murine and human, while TFAP2A’s expression is increased in more “stem-like” cancers. We provide strong evidence for the direct interaction between the TFAP2A TF and the ZEB2 promoter and we demonstrate that this interaction affects ZEB2 expression. Overexpression of TFAP2A from an exogenous construct perturbs EMT, however, in a manner similar to the downregulation of endogenous TFAP2A that takes place during EMT.ConclusionsOur study reveals that TFAP2A is a conserved component of the core network that regulates EMT, acting as a repressor of many genes, including ZEB2.ReviewersThis article has been reviewed by Dr. Martijn Huynen and Dr. Nicola Aceto.

Exploiting variability of single cells to uncover the in vivo hierarchy of miRNA targets

MiRNAs are post-transcriptional repressors of gene expression that may additionally reduce the cell-to-cell variability in protein expression, induce correlations between target expression levels and provide a layer through which targets can influence each other’s expression as ‘competing RNAs’ (ceRNAs). Here we combined single cell sequencing of human embryonic kidney cells in which the expression of two distinct miRNAs was induced over a wide range, with mathematical modeling, to estimate Michaelis-Menten (KM)-type constants for hundreds of evolutionarily conserved miRNA targets. These parameters, which we inferred here for the first time in the context of the entire network of endogenous miRNA targets, vary over ~2 orders of magnitude. They reveal an in vivo hierarchy of miRNA targets, defined by the concentration of miRNA-Argonaute complexes at which the targets are most sensitively down-regulated. The data further reveals miRNA-induced correlations in target expression at the single cell level, as well as the response of target noise to the miRNA concentration. The approach is generalizable to other miRNAs and post-transcriptional regulators and provides a deeper understanding of gene expression dynamics.

Single‐cell mRNA profiling reveals the hierarchical response of miRNA targets to miRNA induction

miRNAs are small RNAs that regulate gene expression post‐transcriptionally. By repressing the translation and promoting the degradation of target mRNAs, miRNAs may reduce the cell‐to‐cell variability in protein expression, induce correlations between target expression levels, and provide a layer through which targets can influence each others expression as “competing RNAs” (ceRNAs). However, experimental evidence for these behaviors is limited. Combining mathematical modeling with RNA sequencing of individual human embryonic kidney cells in which the expression of two distinct miRNAs was induced over a wide range, we have inferred parameters describing the response of hundreds of miRNA targets to miRNA induction. Individual targets have widely different response dynamics, and only a small proportion of predicted targets exhibit high sensitivity to miRNA induction. Our data reveal for the first time the response parameters of the entire network of endogenous miRNA targets to miRNA induction, demonstrating that miRNAs correlate target expression and at the same time increase the variability in expression of individual targets across cells. The approach is generalizable to other miRNAs and post‐transcriptional regulators to improve the understanding of gene expression dynamics in individual cell types.

Discovery of global regulators of 3′ untranslated region processing in cancers with KAPAC

Background The processing of 3’ untranslated regions (3’ UTRs) of messenger RNAs is coordinated in relation to the cellular state. Systematic changes in 3’ UTR length through corresponding changes in the use of alternative polyadenylation (poly(A)) sites have been reported in various systems, including human cancers, yet the key regulators remain largely unknown. Results To uncover sequence elements that drive the use of poly(A) sites in specific conditions, we have developed PAQR, a method for quantifying poly(A) site use from RNA sequencing (RNA-seq) data and KAPAC, an approach that infers activities of oligomeric sequence motifs on poly(A) site choice. We demonstrate that these tools enable the discovery of sequence specificity and the binding site position-dependent activity of RNA-binding proteins (RBPs) on pre-mRNA cleavage and polyadenylation (CPA), from RNA-seq data obtained upon perturbing RBP expression. Furthermore, application of PAQR and KAPAC to RNA sequencing data from normal and tumor tissue samples uncovered sequence motifs that can explain changes in CPA within specific cancer types. In particular, our analysis points to the polypyrimidine tract binding protein 1 as key regulator of poly(A) site choice in glioblastoma. Conclusions The PAQR and KAPAC methods that we introduced here enable the identification of regulatory factors that shape 3’ UTR processing and the characterization of their binding position-dependent activity in physiological and pathological cell states, including human malignancies.3′ UTR length is regulated in relation to cellular state. To uncover key regulators of poly(A) site (PAS) use in specific conditions, we have developed PAQR, a method for quantifying PAS use from RNA sequencing data and KAPAC, an approach that infers activities of oligomeric sequence motifs on PAS choice. Application of PAQR and KAPAC to RNA sequencing data from normal and tumor tissue samples uncovered sequence motifs that can explain changes in cleavage and polyadenylation in specific cancers. In particular, our analysis points to Polypyrimidine tract binding protein 1 as a regulator of PAS choice in glioblastoma.