Dissection of a complex transcriptional response using genome-wide transcriptional modelling

Kinetic modeling of stem cell transcriptome dynamics to identify regulatory modules of normal and disturbed neuroectodermal differentiation

Thousands of transcriptome data sets are available, but approaches for their use in dynamic cell response modelling are few, especially for processes affected simultaneously by two orthogonal influencing variables. We approached this problem for neuroepithelial development of human pluripotent stem cells (differentiation variable), in the presence or absence of valproic acid (signaling variable). Using few basic assumptions (sequential differentiation states of cells; discrete on/off states for individual genes in these states), and time-resolved transcriptome data, a comprehensive model of spontaneous and perturbed gene expression dynamics was developed. The model made reliable predictions (average correlation of 0.85 between predicted and subsequently tested expression values). Even regulations predicted to be non-monotonic were successfully validated by PCR in new sets of experiments. Transient patterns of gene regulation were identified from model predictions. They pointed towards activation of Wnt signaling as a candidate pathway leading to a redirection of differentiation away from neuroepithelial cells towards neural crest. Intervention experiments, using a Wnt/beta-catenin antagonist, led to a phenotypic rescue of this disturbed differentiation. Thus, our broadly applicable model allows the analysis of transcriptome changes in complex time/perturbation matrices.

Global Analysis of the Human RNA Degradome Reveals Widespread Decapped and Endonucleolytic Cleaved Transcripts

RNA decay is an important regulatory mechanism for gene expression at the posttranscriptional level. Although the main pathways and major enzymes that facilitate this process are well defined, global analysis of RNA turnover remains under-investigated. Recent advances in the application of next-generation sequencing technology enable its use in order to examine various RNA decay patterns at the genome-wide scale. In this study, we investigated human RNA decay patterns using parallel analysis of RNA end-sequencing (PARE-seq) data from XRN1-knockdown HeLa cell lines, followed by a comparison of steady state and degraded mRNA levels from RNA-seq and PARE-seq data, respectively. The results revealed 1103 and 1347 transcripts classified as stable and unstable candidates, respectively. Of the unstable candidates, we found that a subset of the replication-dependent histone transcripts was polyadenylated and rapidly degraded. Additionally, we identified 380 endonucleolytically cleaved candidates by analyzing the most abundant PARE sequence on a transcript. Of these, 41.4% of genes were classified as unstable genes, which implied that their endonucleolytic cleavage might affect their mRNA stability. Furthermore, we identified 1877 decapped candidates, including HSP90B1 and SWI5, having the most abundant PARE sequences at the 5′-end positions of the transcripts. These results provide a useful resource for further analysis of RNA decay patterns in human cells.

Gene Expression in Parp1 Deficient Mice Exposed to a Median Lethal Dose of Gamma Rays

There is a current interest in the development of biodosimetric methods for rapidly assessing radiation exposure in the wake of a large-scale radiological event. This work was initially focused on determining the exposure dose to an individual using biological indicators. Gene expression signatures show promise for biodosimetric application, but little is known about how these signatures might translate for the assessment of radiological injury in radiosensitive individuals, who comprise a significant proportion of the general population, and who would likely require treatment after exposure to lower doses. Using Parp1^-/- mice as a model radiation-sensitive genotype, we have investigated the effect of this DNA repair deficiency on the gene expression response to radiation. Although Parp1 is known to play general roles in regulating transcription, the pattern of gene expression changes observed in Parp1^-/- mice 24 h postirradiation to a LD_50/30 was remarkably similar to that in wild-type mice after exposure to LD_50/30. Similar levels of activation of both the p53 and NFκB radiation response pathways were indicated in both strains. In contrast, exposure of wild-type mice to a sublethal dose that was equal to the Parp1^-/- LD_50/30 resulted in a lower magnitude gene expression response. Thus, Parp1^-/- mice displayed a heightened gene expression response to radiation, which was more similar to the wild-type response to an equitoxic dose than to an equal absorbed dose. Gene expression classifiers trained on the wild-type data correctly identified all wild-type samples as unexposed, exposed to a sublethal dose or exposed to an LD_50/30. All unexposed samples from Parp1^-/- mice were also correctly classified with the same gene set, and 80% of irradiated Parp1^-/- samples were identified as exposed to an LD_50/30. The results of this study suggest that, at least for some pathways that may influence radiosensitivity in humans, specific gene expression signatures have the potential to accurately detect the extent of radiological injury, rather than serving only as a surrogate of physical radiation dose.

TNF-α modulates genome-wide redistribution of ΔNp63α/TAp73 and NF-κB cREL interactive binding on TP53 and AP-1 motifs to promote an oncogenic gene program in squamous cancer

The Cancer Genome Atlas (TCGA) network study of 12 cancer types (PanCancer 12) revealed frequent mutation of TP53, and amplification and expression of related TP63 isoform ΔNp63 in squamous cancers. Further, aberrant expression of inflammatory genes and TP53/p63/p73 targets were detected in the PanCancer 12 project, reminiscent of gene programs comodulated by cREL/ΔNp63/TAp73 transcription factors we uncovered in head and neck squamous cell carcinomas (HNSCCs). However, how inflammatory gene signatures and cREL/p63/p73 targets are comodulated genome wide is unclear. Here, we examined how the inflammatory factor tumor necrosis factor-α (TNF-α) broadly modulates redistribution of cREL with ΔNp63α/TAp73 complexes and signatures genome wide in the HNSCC model UM-SCC46 using chromatin immunoprecipitation sequencing (ChIP-seq). TNF-α enhanced genome-wide co-occupancy of cREL with ΔNp63α on TP53/p63 sites, while unexpectedly promoting redistribution of TAp73 from TP53 to activator protein-1 (AP-1) sites. cREL, ΔNp63α and TAp73 binding and oligomerization on NF-κB-, TP53- or AP-1-specific sequences were independently validated by ChIP-qPCR (quantitative PCR), oligonucleotide-binding assays and analytical ultracentrifugation. Function of the binding activity was confirmed using TP53-, AP-1- and NF-κB-specific REs or p21, SERPINE1 and IL-6 promoter luciferase reporter activities. Concurrently, TNF-α regulated a broad gene network with cobinding activities for cREL, ΔNp63α and TAp73 observed upon array profiling and reverse transcription-PCR. Overlapping target gene signatures were observed in squamous cancer subsets and in inflamed skin of transgenic mice overexpressing ΔNp63α. Furthermore, multiple target genes identified in this study were linked to TP63 and TP73 activity and increased gene expression in large squamous cancer samples from PanCancer 12 TCGA by CircleMap. PARADIGM inferred pathway analysis revealed the network connection of TP63 and NF-κB complexes through an AP-1 hub, further supporting our findings. Thus, inflammatory cytokine TNF-α mediates genome-wide redistribution of the cREL/p63/p73, and AP-1 interactome, to diminish TAp73 tumor suppressor function and reciprocally activate NF-κB and AP-1 gene programs implicated in malignancy.

A genome wide transcriptional model of the complex response to pre-TCR signalling during thymocyte differentiation

Developing thymocytes require pre-TCR signalling to differentiate from CD4-CD8- double negative to CD4+CD8+ double positive cell. Here we followed the transcriptional response to pre-TCR signalling in a synchronised population of differentiating double negative thymocytes. This time series analysis revealed a complex transcriptional response, in which thousands of genes were up and down-regulated before changes in cell surface phenotype were detected. Genome-wide measurement of RNA degradation of individual genes showed great heterogeneity in the rate of degradation between different genes. We therefore used time course expression and degradation data and a genome wide transcriptional modelling (GWTM) strategy to model the transcriptional response of genes up-regulated on pre-TCR signal transduction. This analysis revealed five major temporally distinct transcriptional activities that up regulate transcription through time, whereas down-regulation of expression occurred in three waves. Our model thus placed known regulators in a temporal perspective, and in addition identified novel candidate regulators of thymocyte differentiation.

Contributions of transcription and mRNA decay to gene expression dynamics of fission yeast in response to oxidative stress

The cooperation of transcriptional and post-transcriptional levels of control to shape gene regulation is only partially understood. Here we show that a combination of two simple and non-invasive genomic techniques, coupled with kinetic mathematical modeling, afford insight into the intricate dynamics of RNA regulation in response to oxidative stress in the fission yeast Schizosaccharomyces pombe. This study reveals a dominant role of transcriptional regulation in response to stress, but also points to the first minutes after stress induction as a critical time when the coordinated control of mRNA turnover can support the control of transcription for rapid gene regulation. In addition, we uncover specialized gene expression strategies associated with distinct functional gene groups, such as simultaneous transcriptional repression and mRNA destabilization for genes encoding ribosomal proteins, delayed mRNA destabilization with varying contribution of transcription for ribosome biogenesis genes, dominant roles of mRNA stabilization for genes functioning in protein degradation, and adjustment of both transcription and mRNA turnover during the adaptation to stress. We also show that genes regulated independently of the bZIP transcription factor Atf1p are predominantly controlled by mRNA turnover, and identify putative cis-regulatory sequences that are associated with different gene expression strategies during the stress response. This study highlights the intricate and multi-faceted interplay between transcription and RNA turnover during the dynamic regulatory response to stress.

Hierarchical modularity in ERα transcriptional network is associated with distinct functions and implicates clinical outcomes

Recent genome-wide profiling reveals highly complex regulation networks among ERα and its targets. We integrated estrogen (E2)-stimulated time-series ERα ChIP-seq and gene expression data to identify the ERα-centered transcription factor (TF) hubs and their target genes, and inferred the time-variant hierarchical network structures using a Bayesian multivariate modeling approach. With its recurrent motif patterns, we determined three embedded regulatory modules from the ERα core transcriptional network. The GO analyses revealed the distinct biological function associated with each of three embedded modules. The survival analysis showed the genes in each module were able to render a significant survival correlation in breast cancer patient cohorts. In summary, our Bayesian statistical modeling and modularity analysis not only reveals the dynamic properties of the ERα-centered regulatory network and associated distinct biological functions, but also provides a reliable and effective genomic analytical approach for the analysis of dynamic regulatory network for any given TF.

Genome-wide technology for determining RNA stability in mammalian cells: historical perspective and recent advantages based on modified nucleotide labeling

Changing the abundance of transcripts by regulated RNA degradation is a critical step in the control of various biological pathways. Recently, genome-wide inhibitor-free technologies for determining RNA stabilities in mammalian cells have been developed. In these methods, endogenous RNAs are pulse labeled by uridine analogs [e.g., 4-thiouridine (4sU), 5-etyniluridine (EU) and 5'-bromo-uridine (BrU)], followed by purification of labeled de novo RNAs. These technologies have revealed that the specific half-life of each mRNA is closely related to its physiological function. Genes with short-lived mRNAs are significantly enriched among regulatory genes, while genes with long-lived mRNAs are enriched among housekeeping genes. This review describes the recent progress of experimental procedures for measuring RNA stability.

Computational inference of mRNA stability from histone modification and transcriptome profiles

Histone modifications play important roles in regulating eukaryotic gene expression and have been used to model expression levels. Here, we present a regression model to systematically infer mRNA stability by comparing transcriptome profiles with ChIP-seq of H3K4me3, H3K27me3 and H3K36me3. The results from multiple human and mouse cell lines show that the inferred unstable mRNAs have significantly longer 3′Untranslated Regions (UTRs) and more microRNA binding sites within 3′UTR than the inferred stable mRNAs. Regression residuals derived from RNA-seq, but not from GRO-seq, are highly correlated with the half-lives measured by pulse-labeling experiments, supporting the rationale of our inference. Whereas, the functions enriched in the inferred stable and unstable mRNAs are consistent with those from pulse-labeling experiments, we found the unstable mRNAs have higher cell-type specificity under functional constraint. We conclude that the systematical use of histone modifications can differentiate non-expressed mRNAs from unstable mRNAs, and distinguish stable mRNAs from highly expressed ones. In summary, we represent the first computational model of mRNA stability inference that compares transcriptome and epigenome profiles, and provides an alternative strategy for directing experimental measurements.

Regulation of lymphocyte development and function by RNA-binding proteins

Lymphocyte development requires cells to progress through a series of stages, each associated with changes in gene expression. Intense effort has been invested into characterising the dynamic networks of transcription factors underlying these regulated changes. Whilst transcription factors determine the tempo at which mRNA is produced, recent results highlight the importance of the selective regulation of mRNA decay and translation in regulating gene expression. These processes are regulated by sequence-specific RNA-binding proteins (RBP) as well as noncoding RNA such as microRNAs. RNA-binding proteins are emerging as important regulators of cell fate and function in both developing and mature lymphocytes. At the molecular level the function of RNA-binding proteins is integrated with signal transduction pathways that also govern gene transcription.

An emerging role of RNA-binding proteins as multifunctional regulators of lymphocyte development and function

Sequence-specific RNA-binding proteins (RBP) and the regulation of RNA decay have long been recognized as important regulators of the inflammatory response. RBP influence gene expression throughout the lifespan of the mRNA by regulating splicing, polyadenylation, cellular localization, translation, and decay. Increasing evidence now indicates that these proteins, together with the RNA decay machinery that they recruit, also regulate the development and activation of lymphocytes. The activity of RBP is regulated by the same signal transduction pathways that govern lymphocyte development and differentiation in response to antigen and cytokine receptor engagement. Roles for these proteins in regulating the diverse functions of lymphocytes are becoming increasingly apparent.

Weighted correlation network analysis (WGCNA) applied to the tomato fruit metabolome

BACKGROUND

Advances in "omics" technologies have revolutionized the collection of biological data. A matching revolution in our understanding of biological systems, however, will only be realized when similar advances are made in informatic analysis of the resulting "big data." Here, we compare the capabilities of three conventional and novel statistical approaches to summarize and decipher the tomato metabolome.

METHODOLOGY

Principal component analysis (PCA), batch learning self-organizing maps (BL-SOM) and weighted gene co-expression network analysis (WGCNA) were applied to a multivariate NMR dataset collected from developmentally staged tomato fruits belonging to several genotypes. While PCA and BL-SOM are appropriate and commonly used methods, WGCNA holds several advantages in the analysis of highly multivariate, complex data.

CONCLUSIONS

PCA separated the two major genetic backgrounds (AC and NC), but provided little further information. Both BL-SOM and WGCNA clustered metabolites by expression, but WGCNA additionally defined "modules" of co-expressed metabolites explicitly and provided additional network statistics that described the systems properties of the tomato metabolic network. Our first application of WGCNA to tomato metabolomics data identified three major modules of metabolites that were associated with ripening-related traits and genetic background.

Coupled pre-mRNA and mRNA dynamics unveil operational strategies underlying transcriptional responses to stimuli

Genome-wide simultaneous measurements of pre-mRNA and mRNA expression reveal unexpected time-dependent transcript production and degradation profiles in response to external stimulus, as well as a striking lack of concordance between mRNA abundance and transcript production profiles.

By analyzing the signals from intronic probes of exon arrays, we performed, for the first time, genome-wide measurement of pre-mRNA expression dynamics.

We discovered a striking lack of correspondence between mRNA and pre-mRNA temporal expression profiles following stimulus, demonstrating that measurement of mRNA dynamics does not suffice to infer transcript production profiles.

By combining simultaneous measurement of pre-mRNA and mRNA profiles with a simple new quantitative theoretical description of transcription, we are able to infer complex time dependence of both transcript production and mRNA degradation.

The production profiles of many transcripts reveal an operational strategy we termed Production Overshoot, which is used to accelerate mRNA response. The biological relevance of our findings was substantiated by observing similar results when studying the response of three different mammalian cell types to different stimuli.

Transcriptional responses to extracellular stimuli involve tuning the rates of transcript production and degradation. Here, we show that the time-dependent profiles of these rates can be inferred from simultaneous measurements of precursor mRNA (pre-mRNA) and mature mRNA profiles. Transcriptome-wide measurements demonstrate that genes with similar mRNA profiles often exhibit marked differences in the amplitude and onset of their production rate. The latter is characterized by a large dynamic range, with a group of genes exhibiting an unexpectedly strong transient production overshoot, thereby accelerating their induction and, when combined with time-dependent degradation, shaping transient responses with precise timing and amplitude.

Virtual mutagenesis of the yeast cyclins genetic network reveals complex dynamics of transcriptional control networks

Study of genetic networks has moved from qualitative description of interactions between regulators and regulated genes to the analysis of the interaction dynamics. This paper focuses on the analysis of dynamics of one particular network – the yeast cyclins network. Using a dedicated mathematical model of gene expression and a procedure for computation of the parameters of the model from experimental data, a complete numerical model of the dynamics of the cyclins genetic network was attained. The model allowed for performing virtual experiments on the network and observing their influence on the expression dynamics of the genes downstream in the regulatory cascade. Results show that when the network structure is more complicated, and the regulatory interactions are indirect, results of gene deletion are highly unpredictable. As a consequence of quantitative behavior of the genes and their connections within the network, causal relationship between a regulator and target gene may not be discovered by gene deletion. Without including the dynamics of the system into the network, its functional properties cannot be studied and interpreted correctly.

Metabolic labeling of RNA uncovers principles of RNA production and degradation dynamics in mammalian cells

Cellular RNA levels are determined by the interplay of RNA production, processing and degradation. However, because most studies of RNA regulation do not distinguish the separate contributions of these processes, little is known about how they are temporally integrated. Here we combine metabolic labeling of RNA at high temporal resolution with advanced RNA quantification and computational modeling to estimate RNA transcription and degradation rates during the response of mouse dendritic cells to lipopolysaccharide. We find that changes in transcription rates determine the majority of temporal changes in RNA levels, but that changes in degradation rates are important for shaping sharp 'peaked' responses. We used sequencing of the newly transcribed RNA population to estimate temporally constant RNA processing and degradation rates genome wide. Degradation rates vary significantly between genes and contribute to the observed differences in the dynamic response. Certain transcripts, including those encoding cytokines and transcription factors, mature faster. Our study provides a quantitative approach to study the integrative process of RNA regulation.

Crosstalk between c-Jun and TAp73alpha/beta contributes to the apoptosis-survival balance

The p53-family member p73 plays a role in various cellular signaling pathways during development and growth control and it can have tumor suppressor properties. Several isoforms of p73 exist with considerable differences in their function. Whereas the functions of the N-terminal isoforms (TA and ΔNp73) and their opposing pro- and antiapoptotic roles have become evident, the functional differences of the distinct C-terminal splice forms of TAp73 have remained unclear. Here, we characterized the global genomic binding sites for TAp73α and TAp73β by chromatin immunoprecipitation sequencing as well as the transcriptional responses by performing RNA sequencing. We identified a specific p73 consensus binding motif and found a strong enrichment of AP1 motifs in close proximity to binding sites for TAp73α. These AP1 motif-containing target genes are selectively upregulated by TAp73α, while their mRNA expression is repressed upon TAp73β induction. We show that their expression is dependent on endogenous c-Jun and that recruitment of c-Jun to the respective AP1 sites was impaired upon TAp73β expression, in part due to downregulation of c-Jun. Several of these AP1-site containing TAp73α-induced genes impinge on apoptosis induction, suggesting an underlying molecular mechanism for the observed functional differences between TAp73α and TAp73β.

Global coordination of transcriptional control and mRNA decay during cellular differentiation

We have systematically identified the targets of the Schizosaccharomyces pombe RNA-binding protein Meu5p, which is transiently induced during cellular differentiation. Meu5p-bound transcripts (>80) are expressed at low levels and have shorter half-lives in meu5 mutants, suggesting that Meu5p binding stabilizes its RNA targets.

Most Meu5p targets are induced during differentiation by the activity of the Mei4p transcription factor. However, although most Mei4p targets display a sharp peak of expression, Meu5p targets are expressed for a longer period. In the absence of Meu5p, all Mei4p targets are expressed with similar kinetics (similar to non-Meu5p targets). Therefore, Meu5p determines the temporal profile of its targets.

As the meu5 gene is itself a target of the transcription factor Mei4p, the RNA-binding protein Meu5p and their shared targets form a feed-forward loop (FFL), a network motif that is common in transcriptional networks.

Our data highlight the importance of considering both transcriptional and posttranscriptional controls to understand dynamic changes in RNA levels, and provide insight into the structure of the regulatory networks that integrate transcription and RNA decay.

RNA levels are determined by the balance between RNA production (transcription) and degradation (decay or turnover). Therefore, cells can alter transcript levels by modulating either or both processes. Regulation of transcriptional initiation is one of the most common ways to regulate RNA levels. This function is frequently performed by transcription factors (TFs), which recognize specific sequence motifs on the promoters of their target genes and activate or repress their transcription. At the posttranscriptional level, RNA-binding proteins (RBPs) can bind to specific sequences on their target RNAs and regulate their rates of turnover.

RNA decay can be studied at the genome-wide level using microarrays or next-generation sequencing. The contribution of RNA turnover to transcript levels can be assessed by directly measuring decay rates. This is usually achieved by using microarrays to follow the decrease of RNA levels after inactivation of RNA polymerase II, or by in vivo labelling of newly synthesized RNA with modified nucleosides. These approaches can be applied to mutants in genes encoding RBPs, allowing the dissection of their specific functions in RNA turnover. Moreover, direct RBP targets can be identified by purifying RBP–RNA complexes, which are then analysed using microarrays (RIp-chip, for RBP Immunoprecipitation followed by analysis with DNA chips).

Many biological processes involve the establishment of complex programs of gene expression, in which the levels of hundreds of mRNAs are dynamically regulated. Although the genome-wide function of TFs in these processes has been studied extensively, much less is known about the contribution of RBPs, and especially about how the activity of TFs and RBPs is coordinated. Sexual differentiation of the fission yeast Schizosaccharomyces pombe culminates in meiosis and sporulation and is driven by an extensive gene expression program during which ∼40% of the genome (∼2000 genes) is regulated in complex temporal patterns. Transcriptional control is essential for the implementation of this program, and TFs responsible for the induction of most groups of upregulated genes have been identified. In particular, a transcription factor called Mei4p, which is itself transiently expressed during the meiotic divisions, induces the temporary expression of over 500 genes.

Here, we use genome-wide approaches to investigate the function of the Meu5p RBP, which is transiently induced by the Mei4p TF during the meiotic divisions. RIp-chip experiments identified >80 transcripts bound to Meu5p during meiosis, most of which were also targets of the Mei4p transcription factor. In meu5 mutants, Meu5p targets are expressed at low levels and have shorter half-lives, indicating that Meu5p stabilizes the transcripts it binds to. This stabilization has biological importance, as cells without meu5 are defective in spore formation.

Although the majority of Mei4p TF targets reach their peak in expression levels with similar kinetics, we noticed that the timing of their downregulation was heterogeneous. We could identify two discrete groups among Mei4p targets: a set of mRNAs with short (∼1 h) and sharp gene expression profiles (early decrease), and a group that displayed a broader expression pattern, with high levels of expression for 2–3 h (late decrease).

Most Meu5p RBP targets belonged to the late-decrease group, suggesting a simple model in which Meu5p might stabilize its targets, thus extending the duration of their expression. To test this idea, we followed gene expression in synchronized cultures of wild-type and meu5Δ meiotic cells. Although the expression of early decrease genes was not affected by the absence of meu5, late-decrease genes switched their profile to a pattern similar to that of early decrease genes. As transcription of meu5 is under the control of Mei4p, the TF Mei4p, the RBP Meu5p, and their common targets form a so-called feed-forward loop, in which a protein regulates a target both directly and indirectly through a second protein. This arrangement is common in transcriptional and protein phosphorylation networks.

Our results serve as a paradigm of how the coordination of the action of TFs and RBPs determines how RNA levels are dynamically regulated.

The function of transcription in dynamic gene expression programs has been extensively studied, but little is known about how it is integrated with RNA turnover at the genome-wide level. We investigated these questions using the meiotic gene expression program of Schizosaccharomyces pombe. We identified over 80 transcripts that co-purify with the meiotic-specific Meu5p RNA-binding protein. Their levels and half-lives were reduced in meu5 mutants, demonstrating that Meu5p stabilizes its targets. Most Meu5p-bound RNAs were also targets of the Mei4p transcription factor, which induces the transient expression of ∼500 meiotic genes. Although many Mei4p targets showed sharp expression peaks, Meu5p targets had broad expression profiles. In the absence of meu5, all Mei4p targets were expressed with similar kinetics, indicating that Meu5p alters the global features of the gene expression program. As Mei4p activates meu5 transcription, Mei4p, Meu5p and their common targets form a feed-forward loop, a motif common in transcriptional networks but not studied in the context of mRNA decay. Our data provide insight into the topology of regulatory networks integrating transcriptional and posttranscriptional controls.