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1.  Childhood Obesity: A Role for Gut Microbiota? 
Obesity is a serious public health issue affecting both children and adults. Prevention and management of obesity is proposed to begin in childhood when environmental factors exert a long-term effect on the risk for obesity in adulthood. Thus, identifying modifiable factors may help to reduce this risk. Recent evidence suggests that gut microbiota is involved in the control of body weight, energy homeostasis and inflammation and thus, plays a role in the pathophysiology of obesity. Prebiotics and probiotics are of interest because they have been shown to alter the composition of gut microbiota and to affect food intake and appetite, body weight and composition and metabolic functions through gastrointestinal pathways and modulation of the gut bacterial community. As shown in this review, prebiotics and probiotics have physiologic functions that contribute to changes in the composition of gut microbiota, maintenance of a healthy body weight and control of factors associated with childhood obesity through their effects on mechanisms controlling food intake, fat storage and alterations in gut microbiota.
doi:10.3390/ijerph120100162
PMCID: PMC4306855  PMID: 25546278
childhood obesity; gut microbiota; prebiotics; probiotics; body weight; composition
2.  iRegulon: From a Gene List to a Gene Regulatory Network Using Large Motif and Track Collections 
PLoS Computational Biology  2014;10(7):e1003731.
Identifying master regulators of biological processes and mapping their downstream gene networks are key challenges in systems biology. We developed a computational method, called iRegulon, to reverse-engineer the transcriptional regulatory network underlying a co-expressed gene set using cis-regulatory sequence analysis. iRegulon implements a genome-wide ranking-and-recovery approach to detect enriched transcription factor motifs and their optimal sets of direct targets. We increase the accuracy of network inference by using very large motif collections of up to ten thousand position weight matrices collected from various species, and linking these to candidate human TFs via a motif2TF procedure. We validate iRegulon on gene sets derived from ENCODE ChIP-seq data with increasing levels of noise, and we compare iRegulon with existing motif discovery methods. Next, we use iRegulon on more challenging types of gene lists, including microRNA target sets, protein-protein interaction networks, and genetic perturbation data. In particular, we over-activate p53 in breast cancer cells, followed by RNA-seq and ChIP-seq, and could identify an extensive up-regulated network controlled directly by p53. Similarly we map a repressive network with no indication of direct p53 regulation but rather an indirect effect via E2F and NFY. Finally, we generalize our computational framework to include regulatory tracks such as ChIP-seq data and show how motif and track discovery can be combined to map functional regulatory interactions among co-expressed genes. iRegulon is available as a Cytoscape plugin from http://iregulon.aertslab.org.
Author Summary
Gene regulatory networks control developmental, homeostatic, and disease processes by governing precise levels and spatio-temporal patterns of gene expression. Determining their topology can provide mechanistic insight into these processes. Gene regulatory networks consist of interactions between transcription factors and their direct target genes. Each regulatory interaction represents the binding of the transcription factor to a specific DNA binding site near its target gene. Here we present a computational method, called iRegulon, to identify master regulators and direct target genes in a human gene signature, i.e. a set of co-expressed genes. iRegulon relies on the analysis of the regulatory sequences around each gene in the gene set to detect enriched TF motifs or ChIP-seq peaks, using databases of nearly 10.000 TF motifs and 1000 ChIP-seq data sets or “tracks”. Next, it associates enriched motifs and tracks with candidate transcription factors and determines the optimal subset of direct target genes. We validate iRegulon on ENCODE data, and use it in combination with RNA-seq and ChIP-seq data to map a p53 downstream network with new predicted co-factors and targets. iRegulon is available as a Cytoscape plugin, supporting human, mouse, and Drosophila genes, and provides access to hundreds of cancer-related TF-target subnetworks or “regulons”.
doi:10.1371/journal.pcbi.1003731
PMCID: PMC4109854  PMID: 25058159
3.  Erratum to 
Autophagy  2012;8(7):1163.
doi:10.4161/auto.21428
PMCID: PMC3429560
Lafora disease; autophagy; glycogen metabolism; laforin; malin; neurodegeneration
4.  Robust Target Gene Discovery through Transcriptome Perturbations and Genome-Wide Enhancer Predictions in Drosophila Uncovers a Regulatory Basis for Sensory Specification 
PLoS Biology  2010;8(7):e1000435.
CisTarget X is a novel computational method that accurately predicts Atonal governed regulatory networks in the retina of the fruit fly.
A comprehensive systems-level understanding of developmental programs requires the mapping of the underlying gene regulatory networks. While significant progress has been made in mapping a few such networks, almost all gene regulatory networks underlying cell-fate specification remain unknown and their discovery is significantly hampered by the paucity of generalized, in vivo validated tools of target gene and functional enhancer discovery. We combined genetic transcriptome perturbations and comprehensive computational analyses to identify a large cohort of target genes of the proneural and tumor suppressor factor Atonal, which specifies the switch from undifferentiated pluripotent cells to R8 photoreceptor neurons during larval development. Extensive in vivo validations of the predicted targets for the proneural factor Atonal demonstrate a 50% success rate of bona fide targets. Furthermore we show that these enhancers are functionally conserved by cloning orthologous enhancers from Drosophila ananassae and D. virilis in D. melanogaster. Finally, to investigate cis-regulatory cross-talk between Ato and other retinal differentiation transcription factors (TFs), we performed motif analyses and independent target predictions for Eyeless, Senseless, Suppressor of Hairless, Rough, and Glass. Our analyses show that cisTargetX identifies the correct motif from a set of coexpressed genes and accurately predicts target genes of individual TFs. The validated set of novel Ato targets exhibit functional enrichment of signaling molecules and a subset is predicted to be coregulated by other TFs within the retinal gene regulatory network.
Author Summary
Tens of thousands of regulatory elements determine the spatiotemporal expression pattern of protein-coding genes in the metazoan genome. Each regulatory element, when bound by the appropriate transcription factors, can affect the temporal transcription of a nearby target gene in a particular cell type. Annotating the genome for regulatory elements, as well as determining the input transcription factors for each element, is a key challenge in genome biology. In this study, we introduce a computational method, cisTargetX, that predicts transcription factor binding motifs and their target genes through the integration of gene expression data and comparative genomics. We first validate this method in silico using public gene expression data and, then, apply cisTargetX to the developmental program governing photoreceptor neuron specification in the retina of Drosophila melanogaster. Particularly, we perturbed predicted key transcription factors during the initial steps of neurogenesis; measure gene expression by microarrays; identify motifs and predict target genes; validate the predictions in vivo using transgenic animals; and study several functional and evolutionary aspects of the validated regulatory elements for the proneural factor Atonal. Overall, we show that cisTargetX efficiently predicts genetic regulatory interactions and provides mechanistic insight into gene regulatory networks of postembryonic developmental systems.
doi:10.1371/journal.pbio.1000435
PMCID: PMC2910651  PMID: 20668662

Results 1-4 (4)