Discovering modulators of gene expression

HIF-1α Mediates Immunosuppression and Chemoresistance in Colorectal Cancer by Inhibiting CXCL9, -10 and -11

Uncertainty exists regarding the mechanisms by which hypoxia-inducible factors (HIFs) control CD8+T-cell migration into tumor microenvironments. Here, we found that HIF-1α knockdown or overexpression resulted in increased or decreased CXCL9, -10, and -11 expression in vitro, respectively. Gene Set Variation Analysis revealed that elevated HIF-1α levels correlated with a poor prognosis, severe pathological stage, and an absence of CD8+ T cells in the tumor microenvironment in colorectal cancer (CRC) patients. HIF-1α was inversely associated with pathways beneficial to anti-tumor immunotherapy and cytokine/chemokine function. In vivo, inhibiting HIF-1α or its upstream regulator BIRC2 significantly suppressed tumor growth and promoted CD8+ T-cell infiltration. CXCR3 neutralizing antibodies reversed these effects, implicating the involvement of CXCL9, -10, and -11/CXCR3 axis. The presence of HIF-1α weakened the upregulation of CXCL9, -10, and -11 by bleomycin and doxorubicin. Combining HIF-1α inhibition with bleomycin promoted CD8+ T-cell infiltration and tumor suppression in vivo. Moreover, doxorubicin could upregulate CXCL9, -10 and -11 by suppressing HIF-1α. Our findings highlight the potential of HIF-1α inhibition to improve CRC microenvironments and increase chemotherapy sensitivity.

Bioinformatics detection of modulators controlling splicing factor-dependent intron retention in the human brain

Alternative RNA splicing is an important means of genetic control and transcriptome diversity. However, when alternative splicing events are studied independently, coordinated splicing modulated by common factors is often not recognized. As a result, the molecular mechanisms of how splicing regulators promote or repress splice site recognition in a context-dependent manner are not well understood. The functional coupling between multiple gene regulatory layers suggests that splicing is modulated by additional genetic or epigenetic components. Here, we developed a bioinformatics approach to identify causal modulators of splicing activity based on the variation of gene expression in large RNA sequencing datasets. We applied this approach in a neurological context with hundreds of dorsolateral prefrontal cortex samples. Our model is strengthened with the incorporation of genetic variants to impute gene expression in a Mendelian randomization-based approach. We identified novel modulators of the splicing factor SRSF1, including UIMC1 and the long noncoding RNA CBR3-AS1, that function over dozens of SRSF1 intron retention splicing targets. This strategy can be widely used to identify modulators of RNA-binding proteins involved in tissue-specific alternative splicing.

Review: Precision Medicine Approaches for Genetic Cardiomyopathy: Targeting Phospholamban R14del

PURPOSE OF REVIEW

Heart failure is a syndrome with poor prognosis and no curative options for the majority of patients. The standard one-size-fits-all-treatment approach, targeting neurohormonal dysregulations, helps to modulate symptoms of heart failure, but fails to address the cause of the problem. Precision medicine aims to go beyond symptom modulation and targets pathophysiological mechanisms that underlie disease. In this review, an overview of how precision medicine can be approached as a treatment strategy for genetic heart disease will be discussed. PLN R14del, a genetic mutation known to cause cardiomyopathy, will be used as an example to describe the potential and pitfalls of precision medicine.

RECENT FINDINGS

PLN R14del is characterized by several disease hallmarks including calcium dysregulation, metabolic dysfunction, and protein aggregation. The identification of disease-related biological pathways and the effective targeting using several modalities, including gene silencing and signal transduction modulation, may eventually provide novel treatments for genetic heart disease. We propose a workflow on how to approach precision medicine in heart disease. This workflow focuses on deep phenotyping of patient derived material, including in vitro disease modeling. This will allow identification of therapeutic targets and disease modifiers, to be used for the identification of novel biomarkers and the development of precision medicine approaches for genetic cardiomyopathies.

CellBox: Interpretable Machine Learning for Perturbation Biology with Application to the Design of Cancer Combination Therapy

Systematic perturbation of cells followed by comprehensive measurements of molecular and phenotypic responses provides informative data resources for constructing computational models of cell biology. Models that generalize well beyond training data can be used to identify combinatorial perturbations of potential therapeutic interest. Major challenges for machine learning on large biological datasets are to find global optima in a complex multidimensional space and mechanistically interpret the solutions. To address these challenges, we introduce a hybrid approach that combines explicit mathematical models of cell dynamics with a machine-learning framework, implemented in TensorFlow. We tested the modeling framework on a perturbation-response dataset of a melanoma cell line after drug treatments. The models can be efficiently trained to describe cellular behavior accurately. Even though completely data driven and independent of prior knowledge, the resulting de novo network models recapitulate some known interactions. The approach is readily applicable to various kinetic models of cell biology. A record of this paper's Transparent Peer Review process is included in the Supplemental Information.

Differential Co-Expression Analyses Allow the Identification of Critical Signalling Pathways Altered during Tumour Transformation and Progression

Biological systems respond to perturbations through the rewiring of molecular interactions, organised in gene regulatory networks (GRNs). Among these, the increasingly high availability of transcriptomic data makes gene co-expression networks the most exploited ones. Differential co-expression networks are useful tools to identify changes in response to an external perturbation, such as mutations predisposing to cancer development, and leading to changes in the activity of gene expression regulators or signalling. They can help explain the robustness of cancer cells to perturbations and identify promising candidates for targeted therapy, moreover providing higher specificity with respect to standard co-expression methods. Here, we comprehensively review the literature about the methods developed to assess differential co-expression and their applications to cancer biology. Via the comparison of normal and diseased conditions and of different tumour stages, studies based on these methods led to the definition of pathways involved in gene network reorganisation upon oncogenes' mutations and tumour progression, often converging on immune system signalling. A relevant implementation still lagging behind is the integration of different data types, which would greatly improve network interpretability. Most importantly, performance and predictivity evaluation of the large variety of mathematical models proposed would urgently require experimental validations and systematic comparisons. We believe that future work on differential gene co-expression networks, complemented with additional omics data and experimentally tested, will considerably improve our insights into the biology of tumours.

Modulator-Dependent RBPs Changes Alternative Splicing Outcomes in Kidney Cancer

Alternative splicing alterations can contribute to human disease. The ability of an RNA-binding protein to regulate alternative splicing outcomes can be modulated by a variety of genetic and epigenetic mechanisms. In this study, we use a computational framework to investigate the roles of certain genes, termed modulators, on changing RBPs' effect on splicing regulation. A total of 1,040,254 modulator-mediated RBP-splicing interactions were identified, including 137 RBPs, 4,309 splicing events and 2,905 modulator candidates from TCGA-KIRC RNA sequencing data. Modulators function categories were defined according to the correlation changes between RBPs expression and their targets splicing outcomes. QKI, as one of the RBPs influencing the most splicing events, attracted our attention in this study: 2,014 changing triplets were identified, including 1,101 modulators and 187 splicing events. Pathway enrichment analysis showed that QKI splicing targets were enriched in tight junction pathway, endocytosis and MAPK signaling pathways, all of which are highly associated with cancer development and progression. This is the first instance of a comprehensive study on how alternative splicing outcomes changes are associated with different expression level of certain proteins, even though they were regulated by the same RBP. Our work may provide a novel view on understanding alternative splicing mechanisms in kidney cancer.

A Novel Small Peptide Inhibitor of NFκB, RH10, Blocks Oxidative Stress-Dependent Phenotypes in Cancer

Background

The RH domain of GRK5 is an effective modulator of cancer growth through the inhibition of NFκB activity. The aim of this study was to identify the minimum effective sequence of RH that is still able to inhibit tumor growth and could be used as a peptide-based drug for therapy.

Methods

Starting from the RH sequence, small peptides were cloned and tested in KAT-4 cells. The effects on NFκB signaling and its dependent phenotypes were evaluated by Western blot, TUNEL assay, proliferation assay, and angiogenesis in vitro. In vivo experiments were performed in KAT-4 xenografts in Balb/c nude mice.

Results

A minimum RH ten amino acids long sequence (RH10) was able to interact with IκB, to increase IκB levels, to induce apoptosis, to inhibit KAT4-cell proliferation, NFκB activation, ROS production, and angiogenesis in vitro. In vivo, the peptide inhibited tumor growth in a dose-dependent manner. We also tested its effects in combination with chemotherapeutic drugs and radiotherapy. RH10 ameliorated the antitumor responses to cisplatin, doxorubicin, and ionizing radiation.

Conclusion

Our data propose RH10 as a potential peptide-based drug to use for cancer treatment both alone or in combination with anticancer therapies.

Modulation of transcriptional activity in brain lower grade glioma by alternative splicing

Proteins that modify the activity of transcription factors (TFs) are often called modulators and play a vital role in gene transcriptional regulation. Alternative splicing is a critical step of gene processing, and differentially spliced isoforms may have different functions. Alternative splicing can modulate gene function by adding or removing certain protein domains and thereby influence the activity of a protein. The objective of this study is to investigate the role of alternative splicing in modulating the transcriptional regulation in brain lower grade glioma (LGG), especially transcription factor ELK1, which is closely related to various disorders, including Alzheimer’s disease and Down syndrome. The results showed that changes in the exon inclusion ratio of proteins APP and STK16 are associated with changes in the expression correlation between ELK1 and its targets. In addition, the structural features of the two modulators are strongly associated with the pathological impact of exon inclusion. The results of our analysis suggest that alternatively spliced proteins have different functions in modifying transcription factors and can thereby induce the dysregulation of multiple genes.

Identification and characterization of lncRNA mediated transcriptional dysregulation dictates lncRNA roles in glioblastoma

Long non-coding RNAs (lncRNAs) modulate gene expression, and lncRNA misregulation is associated with cancer. However, precise functional roles in biological and disease processes have been described for only a few lncRNAs. Identification of genome-wide lncRNA-mediated transcriptional dysregulations may improve cancer treatments. In the present study, we used a computational framework that combined lncRNA and gene expression profiles with transcription factor (TF)-target relationships to comprehensively identify dysregulatory lncRNA-TF-gene triplets. In glioblastoma (GBM), we found that most lncRNAs affect multiple targets and primarily affect TF activity in trans. Six different classes of lncRNA-mediated transcriptional dysregulations were identified, with most lncRNAs either enhancing or attenuating target gene expression. Functional analysis of lncRNAs via their dysregulated targets implicated lncRNA modulators in some hallmarks of cancer, providing a new way to predict lncRNA function. Finally, we identified several lncRNA-TF-gene triplets (including HOTAIR-MXI1-CD58/PRKCE and HOTAIR-ATF5-NCAM1) that are associated with glioblastoma prognosis. The integration of lncRNA modulators into transcriptional regulatory networks will further enhance our understanding of lncRNA functions in cancer.

Large-scale automated machine reading discovers new cancer-driving mechanisms

PubMed, a repository and search engine for biomedical literature, now indexes >1 million articles each year. This exceeds the processing capacity of human domain experts, limiting our ability to truly understand many diseases. We present Reach, a system for automated, large-scale machine reading of biomedical papers that can extract mechanistic descriptions of biological processes with relatively high precision at high throughput. We demonstrate that combining the extracted pathway fragments with existing biological data analysis algorithms that rely on curated models helps identify and explain a large number of previously unidentified mutually exclusive altered signaling pathways in seven different cancer types. This work shows that combining human-curated ‘big mechanisms’ with extracted ‘big data’ can lead to a causal, predictive understanding of cellular processes and unlock important downstream applications.

Roles of alternative splicing in modulating transcriptional regulation

Background

The ability of a transcription factor to regulate its targets is modulated by a variety of genetic and epigenetic mechanisms. Alternative splicing can modulate gene function by adding or removing certain protein domains, and therefore affect the activity of protein. Reverse engineering of gene regulatory networks using gene expression profiles has proven valuable in dissecting the logical relationships among multiple proteins during the transcriptional regulation. However, it is unclear whether alternative splicing of certain proteins affects the activity of other transcription factors.

Results

In order to investigate the roles of alternative splicing during transcriptional regulation, we constructed a statistical model to infer whether the alternative splicing events of modulator proteins can affect the ability of key transcription factors in regulating the expression levels of their transcriptional targets. We tested our strategy in KIRC (Kidney Renal Clear Cell Carcinoma) using the RNA-seq data downloaded from TCGA (the Cancer Genomic Atlas). We identified 828of modulation relationships between the splicing levels of modulator proteins and activity levels of transcription factors. For instance, we found that the activity levels of GR (glucocorticoid receptor) protein, a key transcription factor in kidney, can be influenced by the splicing status of multiple proteins, including TP53, MDM2 (mouse double minute 2 homolog), RBM14 (RNA-binding protein 14) and SLK (STE20 like kinase). The influenced GR-targets are enriched by key cancer-related pathways, including p53 signaling pathway, TR/RXR activation, CAR/RXR activation, G1/S checkpoint regulation pathway, and G2/M DNA damage checkpoint regulation pathway.

Conclusions

Our analysis suggests, for the first time, that exon inclusion levels of certain regulatory proteins can affect the activities of many transcription factors. Such analysis can potentially unravel a novel mechanism of how splicing variation influences the cellular function and provide important insights for how dysregulation of splicing outcome can lead to various diseases.

Electronic supplementary material

The online version of this article (doi:10.1186/s12918-017-0465-6) contains supplementary material, which is available to authorized users.

Organ-specific gene modulation: Principles and applications in cancer research

Microarray and next generation sequencing has led to the exploration of correlated gene patterns and their shared functions. Gene modulators are proteins that alter the activity of transcription factors and influence the expression of their target genes. It is assumed that modulators are dependent on transcription factors. Several algorithms have been developed for the detection of gene modulators. On the other hand, it is becoming increasingly evident that modulators play a crucial role in carcinogenesis by interfering with fundamental biologic processes. Therapeutic gene modulation that is based on artificial modification of endogenous gene functions by designer molecules is an exciting new field of investigation.

Inferring genome-wide functional modulatory network: a case study on NF-κB/RelA transcription factor

How different pathways lead to the activation of a specific transcription factor (TF) with specific effects is not fully understood. We model context-specific transcriptional regulation as a modulatory network: triplets composed of a TF, target gene, and modulator. Modulators usually affect the activity of a specific TF at the posttranscriptional level in a target gene-specific action mode. This action may be classified as enhancement, attenuation, or inversion of either activation or inhibition. As a case study, we inferred, from a large collection of expression profiles, all potential modulations of NF-κB/RelA. The predicted modulators include many proteins previously not reported as physically binding to RelA but with relevant functions, such as RNA processing, cell cycle, mitochondrion, ubiquitin-dependent proteolysis, and chromatin modification. Modulators from different processes exert specific prevalent action modes on distinct pathways. Modulators from noncoding RNA, RNA-binding proteins, TFs, and kinases modulate the NF-κB/RelA activity with specific action modes consistent with their molecular functions and modulation level. The modulatory networks of NF-κB/RelA in the context epithelial-mesenchymal transition (EMT) and burn injury have different modulators, including those involved in extracellular matrix (FBN1), cytoskeletal regulation (ACTN1), and metastasis-associated lung adenocarcinoma transcript 1 (MALAT1), a long intergenic nonprotein coding RNA, and tumor suppression (FOXP1) for EMT, and TXNIP, GAPDH, PKM2, IFIT5, LDHA, NID1, and TPP1 for burn injury.

Kinetically-defined component actions in gene repression

Gene repression by transcription factors, and glucocorticoid receptors (GR) in particular, is a critical, but poorly understood, physiological response. Among the many unresolved questions is the difference between GR regulated induction and repression, and whether transcription cofactor action is the same in both. Because activity classifications based on changes in gene product level are mechanistically uninformative, we present a theory for gene repression in which the mechanisms of factor action are defined kinetically and are consistent for both gene repression and induction. The theory is generally applicable and amenable to predictions if the dose-response curve for gene repression is non-cooperative with a unit Hill coefficient, which is observed for GR-regulated repression of AP1LUC reporter induction by phorbol myristate acetate. The theory predicts the mechanism of GR and cofactors, and where they act with respect to each other, based on how each cofactor alters the plots of various kinetic parameters vs. cofactor. We show that the kinetically-defined mechanism of action of each of four factors (reporter gene, p160 coactivator TIF2, and two pharmaceuticals [NU6027 and phenanthroline]) is the same in GR-regulated repression and induction. What differs is the position of GR action. This insight should simplify clinical efforts to differentially modulate factor actions in gene induction vs. gene repression.

Correcting positional correlations in Affymetrix® genome chips

We report and model a previously undescribed systematic error causing spurious excess correlations that depend on the distance between probes on Affymetrix® microarrays. The phenomenon affects pairs of features with large chip separations, up to over 100 probes apart. The effect may have a significant impact on analysis of correlations in large collections of expression data, where the systematic experimental errors are repeated in many data sets. Examples of such studies include analysis of functions and interactions in groups of genes, as well as global properties of genomes. We find that the average correlations between probes on Affymetrix microarrays are larger for smaller chip distances, which points out to a previously undescribed positional artifact. The magnitude of the artifact depends on the design of the chip, and we find it to be especially high for the yeast S98 microarray, where spurious excess correlations reach 0.1 at a distance of 50 probes. We have designed an algorithm to correct this bias and provide new data sets with the corrected expression values. This algorithm was successfully implemented to remove the positional artifact from the S98 chip data while preserving the integrity of the data.

Modulation of gene expression regulated by the transcription factor NF-κB/RelA

Modulators (Ms) are proteins that modify the activity of transcription factors (TFs) and influence expression of their target genes (TGs). To discover modulators of NF-κB/RelA, we first identified 365 NF-κB/RelA-binding proteins using liquid chromatography-tandem mass spectrometry (LC-MS/MS). We used a probabilistic model to infer 8349 (M, NF-κB/RelA, TG) triplets and their modes of modulatory action from our combined LC-MS/MS and ChIP-Seq (ChIP followed by next generation sequencing) data, published RelA modulators and TGs, and a compendium of gene expression profiles. Hierarchical clustering of the derived modulatory network revealed functional subnetworks and suggested new pathways modulating RelA transcriptional activity. The modulators with the highest number of TGs and most non-random distribution of action modes (measured by Shannon entropy) are consistent with published reports. Our results provide a repertoire of testable hypotheses for experimental validation. One of the NF-κB/RelA modulators we identified is STAT1. The inferred (STAT1, NF-κB/RelA, TG) triplets were validated by LC-selected reaction monitoring-MS and the results of STAT1 deletion in human fibrosarcoma cells. Overall, we have identified 562 NF-κB/RelA modulators, which are potential drug targets, and clarified mechanisms of achieving NF-κB/RelA multiple functions through modulators. Our approach can be readily applied to other TFs.

Inferring transcription factor collaborations in gene regulatory networks

Background

Living cells are realized by complex gene expression programs that are moderated by regulatory proteins called transcription factors (TFs). The TFs control the differential expression of target genes in the context of transcriptional regulatory networks (TRNs), either individually or in groups. Deciphering the mechanisms of how the TFs control the expression of target genes is a challenging task, especially when multiple TFs collaboratively participate in the transcriptional regulation.

Results

We model the underlying regulatory interactions in terms of the directions (activation or repression) and their logical roles (necessary and/or sufficient) with a modified association rule mining approach, called mTRIM. The experiment on Yeast discovered 670 regulatory interactions, in which multiple TFs express their functions on common target genes collaboratively. The evaluation on yeast genetic interactions, TF knockouts and a synthetic dataset shows that our algorithm is significantly better than the existing ones.

Conclusions

mTRIM is a novel method to infer TF collaborations in transcriptional regulation networks. mTRIM is available at http://www.msu.edu/~jinchen/mTRIM.

Pathway-based drug repositioning using causal inference

Background

Recent in vivo studies showed new hopes of drug repositioning through causality inference from drugs to disease. Inspired by their success, here we present an in silico method for building a causal network (CauseNet) between drugs and diseases, in an attempt to systematically identify new therapeutic uses of existing drugs.

Methods

Unlike the traditional 'one drug-one target-one disease' causal model, we simultaneously consider all possible causal chains connecting drugs to diseases via target- and gene-involved pathways based on rich information in several expert-curated knowledge-bases. With statistical learning, our method estimates transition likelihood of each causal chain in the network based on known drug-disease treatment associations (e.g. bexarotene treats skin cancer).

Results

To demonstrate its validity, our method showed high performance (AUC = 0.859) in cross validation. Moreover, our top scored prediction results are highly enriched in literature and clinical trials. As a showcase of its utility, we show several drugs for potential re-use in Crohn's Disease.

Conclusions

We successfully developed a computational method for discovering new uses of existing drugs based on casual inference in a layered drug-target-pathway-gene- disease network. The results showed that our proposed method enables hypothesis generation from public accessible biological data for drug repositioning.

Using biological pathway data with paxtools

A rapidly growing corpus of formal, computable pathway information can be used to answer important biological questions including finding non-trivial connections between cellular processes, identifying significantly altered portions of the cellular network in a disease state and building predictive models that can be used for precision medicine. Due to its complexity and fragmented nature, however, working with pathway data is still difficult. We present Paxtools, a Java library that contains algorithms, software components and converters for biological pathways represented in the standard BioPAX language. Paxtools allows scientists to focus on their scientific problem by removing technical barriers to access and analyse pathway information. Paxtools can run on any platform that has a Java Runtime Environment and was tested on most modern operating systems. Paxtools is open source and is available under the Lesser GNU public license (LGPL), which allows users to freely use the code in their software systems with a requirement for attribution. Source code for the current release (4.2.0) can be found in Software S1. A detailed manual for obtaining and using Paxtools can be found in Protocol S1. The latest sources and release bundles can be obtained from biopax.org/paxtools.

Gene regulation, modulation, and their applications in gene expression data analysis

Common microarray and next-generation sequencing data analysis concentrate on tumor subtype classification, marker detection, and transcriptional regulation discovery during biological processes by exploring the correlated gene expression patterns and their shared functions. Genetic regulatory network (GRN) based approaches have been employed in many large studies in order to scrutinize for dysregulation and potential treatment controls. In addition to gene regulation and network construction, the concept of the network modulator that has significant systemic impact has been proposed, and detection algorithms have been developed in past years. Here we provide a unified mathematic description of these methods, followed with a brief survey of these modulator identification algorithms. As an early attempt to extend the concept to new RNA regulation mechanism, competitive endogenous RNA (ceRNA), into a modulator framework, we provide two applications to illustrate the network construction, modulation effect, and the preliminary finding from these networks. Those methods we surveyed and developed are used to dissect the regulated network under different modulators. Not limit to these, the concept of “modulation” can adapt to various biological mechanisms to discover the novel gene regulation mechanisms.

Using answer set programming to integrate RNA expression with signalling pathway information to infer how mutations affect ageing

A challenge of systems biology is to integrate incomplete knowledge on pathways with existing experimental data sets and relate these to measured phenotypes. Research on ageing often generates such incomplete data, creating difficulties in integrating RNA expression with information about biological processes and the phenotypes of ageing, including longevity. Here, we develop a logic-based method that employs Answer Set Programming, and use it to infer signalling effects of genetic perturbations, based on a model of the insulin signalling pathway. We apply our method to RNA expression data from Drosophila mutants in the insulin pathway that alter lifespan, in a foxo dependent fashion. We use this information to deduce how the pathway influences lifespan in the mutant animals. We also develop a method for inferring the largest common sub-paths within each of our signalling predictions. Our comparisons reveal consistent homeostatic mechanisms across both long- and short-lived mutants. The transcriptional changes observed in each mutation usually provide negative feedback to signalling predicted for that mutation. We also identify an S6K-mediated feedback in two long-lived mutants that suggests a crosstalk between these pathways in mutants of the insulin pathway, in vivo. By formulating the problem as a logic-based theory in a qualitative fashion, we are able to use the efficient search facilities of Answer Set Programming, allowing us to explore larger pathways, combine molecular changes with pathways and phenotype and infer effects on signalling in in vivo, whole-organism, mutants, where direct signalling stimulation assays are difficult to perform. Our methods are available in the web-service NetEffects: http://www.ebi.ac.uk/thornton-srv/software/NetEffects.

Identification of location and kinetically defined mechanism of cofactors and reporter genes in the cascade of steroid-regulated transactivation

A currently obscure area of steroid hormone action is where the component factors, including receptor and reporter gene, act. The DNA binding of factors can be precisely defined, but the location and timing of factor binding and action are usually not equivalent. These questions are addressed for several factors (e.g. glucocorticoid receptor (GR), reporter, TIF2, NCoR, NELF-A, sSMRT, and STAMP) using our recently developed competition assay. This assay reveals both the kinetically defined mechanism of factor action and where the above factors act relative to both each other and the equilibrium equivalent to the rate-limiting step, which we call the concentration limiting step (CLS). The utility of this competition assay would be greatly increased if the position of the CLS is invariant and if the factor acting at the CLS is known. Here we report that the exogenous GREtkLUC reporter acts at the CLS as an accelerator for gene induction by GRs in U2OS cells. This mechanism of reporter function at the CLS persists with different reporters, factors, receptors, and cell types. We, therefore, propose that the reporter gene always acts at the CLS during gene induction and constitutes a landmark around which one can order the actions of all other factors. Current data suggest that how and where GR and the short form of SMRT act is also constant. These results validate a novel and rational methodology for identifying distally acting factors that would be attractive targets for pharmaceutical intervention in the treatment of diseases involving GR-regulated genes.

Inferring the regulatory interaction models of transcription factors in transcriptional regulatory networks

Living cells are realized by complex gene expression programs that are moderated by regulatory proteins called transcription factors (TFs). The TFs control the differential expression of target genes in the context of transcriptional regulatory networks (TRNs), either individually or in groups. Deciphering the mechanisms of how the TFs control the differential expression of a target gene in a TRN is challenging, especially when multiple TFs collaboratively participate in the transcriptional regulation. To unravel the roles of the TFs in the regulatory networks, we model the underlying regulatory interactions in terms of the TF-target interactions' directions (activation or repression) and their corresponding logical roles (necessary and/or sufficient). We design a set of constraints that relate gene expression patterns to regulatory interaction models, and develop TRIM (Transcriptional Regulatory Interaction Model Inference), a new hidden Markov model, to infer the models of TF-target interactions in large-scale TRNs of complex organisms. Besides, by training TRIM with wild-type time-series gene expression data, the activation timepoints of each regulatory module can be obtained. To demonstrate the advantages of TRIM, we applied it on yeast TRN to infer the TF-target interaction models for individual TFs as well as pairs of TFs in collaborative regulatory modules. By comparing with TF knockout and other gene expression data, we were able to show that the performance of TRIM is clearly higher than DREM (the best existing algorithm). In addition, on an individual Arabidopsis binding network, we showed that the target genes' expression correlations can be significantly improved by incorporating the TF-target regulatory interaction models inferred by TRIM into the expression data analysis, which may introduce new knowledge in transcriptional dynamics and bioactivation.

Deducing the temporal order of cofactor function in ligand-regulated gene transcription: theory and experimental verification

Cofactors are intimately involved in steroid-regulated gene expression. Two critical questions are (1) the steps at which cofactors exert their biological activities and (2) the nature of that activity. Here we show that a new mathematical theory of steroid hormone action can be used to deduce the kinetic properties and reaction sequence position for the functioning of any two cofactors relative to a concentration limiting step (CLS) and to each other. The predictions of the theory, which can be applied using graphical methods similar to those of enzyme kinetics, are validated by obtaining internally consistent data for pair-wise analyses of three cofactors (TIF2, sSMRT, and NCoR) in U2OS cells. The analysis of TIF2 and sSMRT actions on GR-induction of an endogenous gene gave results identical to those with an exogenous reporter. Thus new tools to determine previously unobtainable information about the nature and position of cofactor action in any process displaying first-order Hill plot kinetics are now available.

BiC: a web server for calculating bimodality of coexpression between gene and protein networks

Bimodal patterns of expression have recently been shown to be useful not only in prioritizing genes that distinguish phenotypes, but also in prioritizing network models that correlate with proteomic evidence. In particular, subgroups of strongly coexpressed gene pairs result in an increased variance of the correlation distribution. This variance, a measure of association between sets of genes (or proteins), can be summarized as the bimodality of coexpression (BiC). We developed an online tool to calculate the BiC for user-defined gene lists and associated mRNA expression data. BiC is a comprehensive application that provides researchers with the ability to analyze both publicly available and user-collected array data.

AVAILABILITY

The freely available web service and the documentation can be accessed at http://gurkan.case.edu/software.

CONTACT

gurkan@case.edu.

Transcriptional regulation via TF-modifying enzymes: an integrative model-based analysis

Transcription factor activity is largely regulated through post-translational modification. Here, we report the first integrative model of transcription that includes both interactions between transcription factors and promoters, and between transcription factors and modifying enzymes. Simulations indicate that our method is robust against noise. We validated our tool on a well-studied stress response network in yeast and on a STAT1-mediated regulatory network in human B cells. Our work represents a significant step toward a comprehensive model of gene transcription.