Genome-wide discovery of modulators of transcriptional interactions in human B lymphocytes

Transcriptional interactions in a cell are modulated by a variety of mechanisms that prevent their representation as pure pairwise interactions between a transcription factor and its target(s). These include, among others, transcription factor activation by phosphorylation and acetylation, formation of active complexes with one or more co-factors, and mRNA/protein degradation and stabilization processes. This paper presents a first step towards the systematic, genome-wide computational inference of genes that modulate the interactions of specific transcription factors at the post-transcriptional level. The method uses a statistical test based on changes in the mutual information between a transcription factor and each of its candidate targets, conditional on the expression of a third gene. The approach was first validated on a synthetic network model, and then tested in the context of a mammalian cellular system. By analyzing 254 microarray expression profiles of normal and tumor related human B lymphocytes, we investigated the post transcriptional modulators of the MYC proto-oncogene, an important transcription factor involved in tumorigenesis. Our method discovered a set of 100 putative modulator genes, responsible for modulating 205 regulatory relationships between MYC and its targets. The set is significantly enriched in molecules with function consistent with their activities as modulators of cellular interactions, recapitulates established MYC regulation pathways, and provides a notable repertoire of novel regulators of MYC function. The approach has broad applicability and can be used to discover modulators of any other transcription factor, provided that adequate expression profile data are available.Comment: 15 pages, 3 figures, 2 tables; minor changes following referees' comments; accepted to RECOMB0

Dissecting interferon-induced transcriptional programs in human peripheral blood cells

Interferons are key modulators of the immune system, and are central to the control of many diseases. The response of immune cells to stimuli in complex populations is the product of direct and indirect effects, and of homotypic and heterotypic cell interactions. Dissecting the global transcriptional profiles of immune cell populations may provide insights into this regulatory interplay. The host transcriptional response may also be useful in discriminating between disease states, and in understanding pathophysiology. The transcriptional programs of cell populations in health therefore provide a paradigm for deconvoluting disease-associated gene expression profiles.We used human cDNA microarrays to (1) compare the gene expression programs in human peripheral blood mononuclear cells (PBMCs) elicited by 6 major mediators of the immune response: interferons alpha, beta, omega and gamma, IL12 and TNFalpha; and (2) characterize the transcriptional responses of purified immune cell populations (CD4+ and CD8+ T cells, B cells, NK cells and monocytes) to IFNgamma stimulation. We defined a highly stereotyped response to type I interferons, while responses to IFNgamma and IL12 were largely restricted to a subset of type I interferon-inducible genes. TNFalpha stimulation resulted in a distinct pattern of gene expression. Cell type-specific transcriptional programs were identified, highlighting the pronounced response of monocytes to IFNgamma, and emergent properties associated with IFN-mediated activation of mixed cell populations. This information provides a detailed view of cellular activation by immune mediators, and contributes an interpretive framework for the definition of host immune responses in a variety of disease settings

Reconstruction of metabolic networks from high-throughput metabolite profiling data: in silico analysis of red blood cell metabolism

We investigate the ability of algorithms developed for reverse engineering of transcriptional regulatory networks to reconstruct metabolic networks from high-throughput metabolite profiling data. For this, we generate synthetic metabolic profiles for benchmarking purposes based on a well-established model for red blood cell metabolism. A variety of data sets is generated, accounting for different properties of real metabolic networks, such as experimental noise, metabolite correlations, and temporal dynamics. These data sets are made available online. We apply ARACNE, a mainstream transcriptional networks reverse engineering algorithm, to these data sets and observe performance comparable to that obtained in the transcriptional domain, for which the algorithm was originally designed.Comment: 14 pages, 3 figures. Presented at the DIMACS Workshop on Dialogue on Reverse Engineering Assessment and Methods (DREAM), Sep 200

Nuclear pore complex-mediated modulation of TCR signaling is required for naïve CD4+ T cell homeostasis.

Nuclear pore complexes (NPCs) are channels connecting the nucleus with the cytoplasm. We report that loss of the tissue-specific NPC component Nup210 causes a severe deficit of naïve CD4+ T cells. Nup210-deficient CD4+ T lymphocytes develop normally but fail to survive in the periphery. The decreased survival results from both an impaired ability to transmit tonic T cell receptor (TCR) signals and increased levels of Fas, which sensitize Nup210-/- naïve CD4+ T cells to Fas-mediated cell death. Mechanistically, Nup210 regulates these processes by modulating the expression of Cav2 (encoding Caveolin-2) and Jun at the nuclear periphery. Whereas the TCR-dependent and CD4+ T cell-specific upregulation of Cav2 is critical for proximal TCR signaling, cJun expression is required for STAT3-dependent repression of Fas. Our results uncover an unexpected role for Nup210 as a cell-intrinsic regulator of TCR signaling and T cell homeostasis and expose NPCs as key players in the adaptive immune system

The increasing complexity of glucocorticoid receptor signaling and regulation

Glucocorticoids, although being one of the eldest drugs in the clinic and despite their widespread usage for the treatment of inflammatory and immune disorders and cancer, have not yet come of age when it comes to a full understanding of how they work. The majority of the biological actions of glucocorticoid hormones are explained by a wide diversity in the cellular action mechanism of the hormone-activated Glucocorticoid Receptor (GR). All molecular mechanisms described in the current overview are not only complex, exhibiting an astonishing degree of gene- and tissue-specificity, but on top of this they are also non-exclusive. This layering of mechanisms makes it extremely difficult for researchers to extract the crucial pieces of information that would assist in a rational design of drugs with an improved therapeutic profile, i.e. a satisfying and maintained therapeutic response in the absence of the many incapacitating glucocorticoid-associated side effects, such as diabetes, osteoporosis, muscle wasting, depression etc. In direct correlation with increased glucocorticoid usage as observed in the clinic, the impetus and desire to reveal all of these mechanisms -and most importantly, to try to integrate them in a sensible manner for the sake of finding better alternatives- has never been stronger

1F. Retinoic acid-related orphans in GtoPdb v.2023.1

Retinoic acid receptor-related orphan receptors (ROR, nomenclature as agreed by the NC-IUPHAR Subcommittee on Nuclear Hormone Receptors [11, 3]) have yet to be assigned a definitive endogenous ligand, although RORα may be synthesized with a ‘captured’ agonist such as cholesterol [68, 67]

Aberrantly Expressed CeRNAs Account for Missing Genomic Variability of Cancer Genes via MicroRNA-Mediated Interactions

There is growing evidence that RNAs compete for binding and regulation by a finite pool of microRNAs (miRs), thus regulating each other through a competing endogenous RNA (ceRNA) mechanism. My dissertation work focused on systematically studying ceRNA interactions in cancer by reverse-engineering context-specific miR-RNA interactions and ceRNA regulatory interactions across multiple tumor types and study the effects of these interactions in cancer. I attempted to use ceRNA interactions to explain how genetic and epigenetic alterations are propagated to target established drivers of tumorigenesis. Using bioinformatics analysis of primary tumor samples and experimental validation in cell lines, I have investigated the roles that mRNAs and noncoding RNAs can play in tumorigenesis via ceRNA interactions. Specifically, I studied how RNAs target tumor-suppressors and oncogenes as ceRNAs, and attempted to accounting for some of the missing genomic variability in tumors