Topological comparison of methods for predicting transcriptional cooperativity in yeast

<p>Abstract</p> <p>Background</p> <p>The cooperative interaction between transcription factors has a decisive role in the control of the fate of the eukaryotic cell. Computational approaches for characterizing cooperative transcription factors in yeast, however, are based on different rationales and provide a low overlap between their results. Because the wealth of information contained in protein interaction networks and regulatory networks has proven highly effective in elucidating functional relationships between proteins, we compared different sets of cooperative transcription factor pairs (predicted by four different computational methods) within the frame of those networks.</p> <p>Results</p> <p>Our results show that the overlap between the sets of cooperative transcription factors predicted by the different methods is low yet significant. Cooperative transcription factors predicted by all methods are closer and more clustered in the protein interaction network than expected by chance. On the other hand, members of a cooperative transcription factor pair neither seemed to regulate each other nor shared similar regulatory inputs, although they do regulate similar groups of target genes.</p> <p>Conclusion</p> <p>Despite the different definitions of transcriptional cooperativity and the different computational approaches used to characterize cooperativity between transcription factors, the analysis of their roles in the framework of the protein interaction network and the regulatory network indicates a common denominator for the predictions under study. The knowledge of the shared topological properties of cooperative transcription factor pairs in both networks can be useful not only for designing better prediction methods but also for better understanding the complexities of transcriptional control in eukaryotes.</p

Motifs, themes and thematic maps of an integrated Saccharomyces cerevisiae interaction network

BACKGROUND: Large-scale studies have revealed networks of various biological interaction types, such as protein-protein interaction, genetic interaction, transcriptional regulation, sequence homology, and expression correlation. Recurring patterns of interconnection, or 'network motifs', have revealed biological insights for networks containing either one or two types of interaction. RESULTS: To study more complex relationships involving multiple biological interaction types, we assembled an integrated Saccharomyces cerevisiae network in which nodes represent genes (or their protein products) and differently colored links represent the aforementioned five biological interaction types. We examined three- and four-node interconnection patterns containing multiple interaction types and found many enriched multi-color network motifs. Furthermore, we showed that most of the motifs form 'network themes' – classes of higher-order recurring interconnection patterns that encompass multiple occurrences of network motifs. Network themes can be tied to specific biological phenomena and may represent more fundamental network design principles. Examples of network themes include a pair of protein complexes with many inter-complex genetic interactions – the 'compensatory complexes' theme. Thematic maps – networks rendered in terms of such themes – can simplify an otherwise confusing tangle of biological relationships. We show this by mapping the S. cerevisiae network in terms of two specific network themes. CONCLUSION: Significantly enriched motifs in an integrated S. cerevisiae interaction network are often signatures of network themes, higher-order network structures that correspond to biological phenomena. Representing networks in terms of network themes provides a useful simplification of complex biological relationships

Identifying causal models between genetically regulated methylation patterns and gene expression in healthy colon tissue

Background: DNA methylation is involved in the regulation of gene expression and phenotypic variation, but the inter-relationship between genetic variation, DNA methylation and gene expression remains poorly understood. Here we combine the analysis of genetic variants related to methylation markers (methylation quantitative trait loci: mQTLs) and gene expression (expression quantitative trait loci: eQTLs) with methylation markers related to gene expression (expression quantitative trait methylation: eQTMs), to provide novel insights into the genetic/epigenetic architecture of colocalizing molecular markers. Results: Normal mucosa from 100 patients with colon cancer and 50 healthy donors included in the Colonomics project have been analyzed. Linear models have been used to find mQTLs and eQTMs within 1 Mb of the target gene. From 32,446 eQTLs previously detected, we found a total of 6850 SNPs, 114 CpGs and 52 genes interrelated, generating 13,987 significant combinations of co-occurring associations (meQTLs) after Bonferromi correction. Non-redundant meQTLs were 54, enriched in genes involved in metabolism of glucose and xenobiotics and immune system. SNPs in meQTLs were enriched in regulatory elements (enhancers and promoters) compared to random SNPs within 1 Mb of genes. Three colorectal cancer GWAS SNPs were related to methylation changes, and four SNPs were related to chemerin levels. Bayesian networks have been used to identify putative causal relationships among associated SNPs, CpG and gene expression triads. We identified that most of these combinations showed the canonical pathway of methylation markers causes gene expression variation (60.1%) or non-causal relationship between methylation and gene expression (33.9%); however, in up to 6% of these combinations, gene expression was causing variation in methylation markers. Conclusions: In this study we provided a characterization of the regulation between genetic variants and inter-dependent methylation markers and gene expression in a set of 150 healthy colon tissue samples. This is an important finding for the understanding of molecular susceptibility on colon-related complex diseases

Identifying cooperative transcriptional regulations using protein–protein interactions

Cooperative transcriptional activations among multiple transcription factors (TFs) are important to understand the mechanisms of complex transcriptional regulations in eukaryotes. Previous studies have attempted to find cooperative TFs based on gene expression data with gene expression profiles as a measure of similarity of gene regulations. In this paper, we use protein–protein interaction data to infer synergistic binding of cooperative TFs. Our fundamental idea is based on the assumption that genes contributing to a similar biological process are regulated under the same control mechanism. First, the protein–protein interaction networks are used to calculate the similarity of biological processes among genes. Second, we integrate this similarity and the chromatin immuno-precipitation data to identify cooperative TFs. Our computational experiments in yeast show that predictions made by our method have successfully identified eight pairs of cooperative TFs that have literature evidences but could not be identified by the previous method. Further, 12 new possible pairs have been inferred and we have examined the biological relevances for them. However, since a typical problem using protein–protein interaction data is that many false-positive data are contained, we propose a method combining various biological data to increase the prediction accuracy

Genetic Determinants of Facial Clefting: Analysis of 357 Candidate Genes Using Two National Cleft Studies from Scandinavia

Facial clefts are common birth defects with a strong genetic component. To identify fetal genetic risk factors for clefting, 1536 SNPs in 357 candidate genes were genotyped in two population-based samples from Scandinavia (Norway: 562 case-parent and 592 control-parent triads; Denmark: 235 case-parent triads).We used two complementary statistical methods, TRIMM and HAPLIN, to look for associations across these two national samples. TRIMM tests for association in each gene by using multi-SNP genotypes from case-parent triads directly without the need to infer haplotypes. HAPLIN on the other hand estimates the full haplotype distribution over a set of SNPs and estimates relative risks associated with each haplotype. For isolated cleft lip with or without cleft palate (I-CL/P), TRIMM and HAPLIN both identified significant associations with IRF6 and ADH1C in both populations, but only HAPLIN found an association with FGF12. For isolated cleft palate (I-CP), TRIMM found associations with ALX3, MKX, and PDGFC in both populations, but only the association with PDGFC was identified by HAPLIN. In addition, HAPLIN identified an association with ETV5 that was not detected by TRIMM.Strong associations with seven genes were replicated in the Scandinavian samples and our approach effectively replicated the strongest previously known association in clefting--with IRF6. Based on two national cleft cohorts of similar ancestry, two robust statistical methods and a large panel of SNPs in the most promising cleft candidate genes to date, this study identified a previously unknown association with clefting for ADH1C and provides additional candidates and analytic approaches to advance the field

The X-factor in ART: does the use of assisted reproductive technologies influence DNA methylation on the X chromosome?

Background Assisted reproductive technologies (ART) may perturb DNA methylation (DNAm) in early embryonic development. Although a handful of epigenome-wide association studies of ART have been published, none have investigated CpGs on the X chromosome. To bridge this knowledge gap, we leveraged one of the largest collections of mother–father–newborn trios of ART and non-ART (natural) conceptions to date to investigate sex-specific DNAm differences on the X chromosome. The discovery cohort consisted of 982 ART and 963 non-ART trios from the Norwegian Mother, Father, and Child Cohort Study (MoBa). To verify our results from the MoBa cohort, we used an external cohort of 149 ART and 58 non-ART neonates from the Australian ‘Clinical review of the Health of adults conceived following Assisted Reproductive Technologies’ (CHART) study. The Illumina EPIC array was used to measure DNAm in both datasets. In the MoBa cohort, we performed a set of X-chromosome-wide association studies (‘XWASs’ hereafter) to search for sex-specific DNAm differences between ART and non-ART newborns. We tested several models to investigate the influence of various confounders, including parental DNAm. We also searched for differentially methylated regions (DMRs) and regions of co-methylation flanking the most significant CpGs. Additionally, we ran an analogous model to our main model on the external CHART dataset. Results In the MoBa cohort, we found more differentially methylated CpGs and DMRs in girls than boys. Most of the associations persisted after controlling for parental DNAm and other confounders. Many of the significant CpGs and DMRs were in gene-promoter regions, and several of the genes linked to these CpGs are expressed in tissues relevant for both ART and sex (testis, placenta, and fallopian tube). We found no support for parental DNAm-dependent features as an explanation for the observed associations in the newborns. The most significant CpG in the boys-only analysis was in UBE2DNL, which is expressed in testes but with unknown function. The most significant CpGs in the girls-only analysis were in EIF2S3 and AMOT. These three loci also displayed differential DNAm in the CHART cohort. Conclusions Genes that co-localized with the significant CpGs and DMRs associated with ART are implicated in several key biological processes (e.g., neurodevelopment) and disorders (e.g., intellectual disability and autism). These connections are particularly compelling in light of previous findings indicating that neurodevelopmental outcomes differ in ART-conceived children compared to those naturally conceived.publishedVersio

MiRNAs as regulators of gene expression modulate development and energy metabolism of skeletal muscle

It is important to understand the molecular networks affecting biological properties of muscle in order to improve the efficiency of meat production and meat quality in domestic animals. The discovery of miRNA represents an important breakthrough in biology in recent years. MiRNA function identification has become one of the active research fields in muscle biology addressing muscle development, growth and metabolism. This thesis aims at the identification of miRNAs differentially expressed in skeletal muscle at various developmental stages and in pig breeds differing in muscularity. Moreover, links between miRNAs and mRNAs should be shown in order to address biofunctions affected by miRNAs in muscle. Finally, miRNAs impacted on muscle metabolism should be validated exemplarity by in vitro cell culture experimants. The first approach demonstrates the comprehensive miRNA expression profiles of longissimus dorsi (LD) during muscle development and growth. A comparative study on two distinct phenotypic pigs were performed using miRNA custom designed arrays. Two different key stages 63 and 91 days post-conception (dpc), and one adult stage (180 days post-natum) were analysed in German Landrace (DL) and Pietrain (Pi) breeds. Several potential candidate miRNAs are significantly up-regulated and associated with muscular developmental stages and breed types. The Affymetrix GeneChip porcine genome microarrays were also used to obtain the differential transcriptional profile of mRNA targets of the same animals. The combination of miRNA–mRNA expression data and Ingenuity Pathway Analysis established complex miRNA–dependent regulatory networks. A number of miRNA–mRNA interactions, that were associated to cellular growth and proliferation and lipid-metabolism functions, revealed insights into their role during skeletal muscle development and growth. The second approach involves in muscle growth in post mortem pig traits (crossbred [PI×(DL×DE)] population, n = 207). The experiment integrated miRNA and mRNA expression together with network analysis by using weighted gene co-expression network analysis (WGCNA). In this part, we identified the negative miRNA-mRNA co-expression networks which revealed several biological pathways underlying the difference of meat properties and muscle traits (i.e. glucose metabolic process, mitochondrial ribosome and oxidative phosphorylation). In the last approach, C2C12 in vitro model studies revealed that miRNAs are modulated in cellular ATP production and energy metabolism processes during myogenic differentiation. Correlation analyses were performed between ATP level, miRNA and mRNA microarray expression profiles during C2C12 differentiation. Among 14 significant miRNAs as representing cellular ATP regulators involved in mitochondrial energy metabolism, miR-423-3p is a novel regulator for cellular ATP/ energy metabolism via targeting the group of mitochondrial energy metabolism genes (Cox6a2, Ndufb7, and Ndufs5). In conclusion, the present study further adds a comprehensive knowledge on the systems perspective of the skeletal muscle miRNAs and their target genes regulation networks that influence on skeletal muscle starting from early muscle development to mature muscle growth.MiRNAs regulieren die Genexpression und modulieren die Entwicklung und den Energiestoffwechsel der Skelettmuskulatur Das Verständnis von molekularen Netzwerken mit Einfluss auf die biologischen Eigenschaften des Muskels ist notwendig, um die Effizienz der Fleischproduktion und die Fleischqualität in Nutztieren zu verbessern. Die Erforschung von miRNAs stellt einen entscheidenden Durchbruch in der Biologie in den letzten Jahren dar. Die Identifizierung von miRNA-Funktionen wurde seit dem eines der aufstrebenden Forschungsschwerpunkte in der Muskelbiologie mit Bezug auf Muskelentwicklung, -wachstum und -stoffwechsel. Das Ziel dieser Dissertation ist die Identifizierung von miRNAs mit differenzieller Expression in der Skelettmuskulatur im Hinblick auf verschiedene Entwicklungsstadien und Schweinerassen mit unterschiedlichem Muskelansatz. Im Weiteren soll die Verknüpfung von miRNA- und mRNA-Datensätzen helfen, durch miRNA beeinflusste Biofunktionen im Muskel zu benennen. Abschließend sollen exemplarisch einige miRNAs mit Einfluss auf den Muskelmetabolismus durch in vitro Zellkulturstudien validiert werden. Der erste Forschungsansatz lieferte umfassende miRNA-Expressionsprofile des longissimus dorsi (LD) während der Muskelentwicklung und des Wachstums. Dazu wurden Schweine mit unterschiedlicher phänotypischer Ausprägung unter der Verwendung von spezifisch gefertigten miRNA-Arrays vergleichend analysiert. Tiere der Deutschen Landrasse (DL) und der Rasse Pietrain (Pi) wurden zu zwei wesentlichen pränatalen Entwicklungszeitpunkten (am 63 und 91 Tag nach Empfängnis) sowie im adulten Stadium (180 Tage nach Geburt) untersucht. Für zahlreiche potentielle Kandidaten-miRNAs konnte gezeigt werden, dass diese signifikant hochreguliert sind und Assoziationen zu muskulären Entwicklungsstadien und der Rasse aufzeigten. Zusätzlich wurden porcine Genommikroarrays (Affymetrix GeneChip) verwendet um Profile der differentiell exprimierten mRNA-targets im gleichen Tier zu untersuchen. Durch die Kombination von miRNA- und mRNA-Expressionsdaten gekoppelt mit Ergebnissen aus der Analyse von regulierten Signalwegen (Ingenuity pathway analysis) konnte ein Komplex aus miRNA-abhängigen regulatorischen Netzwerken etabliert werden. Zahlreiche miRNA-mRNA-Interaktionen im Zusammenhang mit Funktionen des zellulären Wachstums, der Proliferation und des Fettstoffwechsels, ermöglichten Einblicke in die Funktion dieser Wechselwirkungen während der Entwicklung und des Wachstums der Skelettmuskulatur. Der zweite Forschungsansatz berücksichtigt das Muskelwachstum in relevanten post mortem Merkmalen (Kreuzungsrasse [Pi x (DLxDE), n=207). Für diesen Ansatz wurden die Expressionsdaten der miRNA- und mRNA-Analysen in einem Ko-Expressionsnetzwerk integriert. Dabei wurden die Wechselwirkungen zwischen verschiedenen Komponenten berücksichtigt und gewichtet. Negative miRNA-mRNA-Ko-Expressionsnetzwerke konnten identifiziert werden. Diese deuten auf biologisch relevante Signalwegen hin, welche mit unterschiedlichen Ausprägungen der Fleischeigenschaften und Merkmalen der Muskulatur in Zusammenhang stehen (z.B. Prozesse des Glucosemetabolismus, mitochondriale Ribosomen und oxidative Phosphorylierung). Im abschließenden Forschungsansatz konnte durch Analysen des C2C12-Muskelzellmodells gezeigt werden, dass miRNAs im Zusammenhang mit der zellulären ATP-Produktion und mit Prozessen des Energiemetabolismus im Rahmen der myogenen Differenzierung reguliert werden. Dazu wurden zum Zeitpunkt der C2C12-Zelldifferenzierung ermittelte ATP-Gehalte und miRNA- und mRNA-Mikroarray-Expressionsprofile miteinander verknüpft. Unter den 14 miRNAs, die als zelluläre ATP-Regulatoren am mitochondrialen Energiemetabolismus beteiligt sind, konnte miR-423-3p, durch den Einfluss auf Gene aus der Gruppe des mitochondrialen Energiemetabolismus (Cox6a2, Ndufb7 und Ndufs5), als neuer Regulator für zelluläres ATP bzw. den Energiemetabolismus bestätigt werden. Zusammenfassend liefern die vorliegenden Studien wesentliche Erkenntnisse zu systemischen Funktionen der miRNAs in der Skelettmuskulatur und verdeutlichen ihren Einfluss auf Gennetzwerke, welche die Prozesse von der frühen Muskelentwicklung bis hin zum Muskelwachstum beeinflussen

Mutual Zonated Interactions of Wnt and Hh Signaling Are Orchestrating the Metabolism of the Adult Liver in Mice and Human

The Hedgehog (Hh) and Wnt/β-Catenin (Wnt) cascades are morphogen pathways whose pronounced influence on adult liver metabolism has been identified in recent years. How both pathways communicate and control liver metabolic functions are largely unknown. Detecting core components of Wnt and Hh signaling and mathematical modeling showed that both pathways in healthy liver act largely complementary to each other in the pericentral (Wnt) and the periportal zone (Hh) and communicate mainly by mutual repression. The Wnt/Hh module inversely controls the spatiotemporal operation of various liver metabolic pathways, as revealed by transcriptome, proteome, and metabolome analyses. Shifting the balance to Wnt (activation) or Hh (inhibition) causes pericentralization and periportalization of liver functions, respectively. Thus, homeostasis of the Wnt/Hh module is essential for maintaining proper liver metabolism and to avoid the development of certain metabolic diseases. With caution due to minor species-specific differences, these conclusions may hold for human liver as well

The role of microRNAs in X-linked myotubular myopathy

Tese de mestrado, Biologia (Bioplogia Humana e Ambiente), 2009, Universidade de Lisboa, Faculdade de CiênciasX-linked myotubular myopathy (XLMTM) is a congenital neuromuscular disorder characterized by profound hypotonia and severe skeletal muscle weakness in the affected newborn males. The pathology is associated with mutations in the MTM1 gene leading to loss of function of the resulting encoded protein, myotubularin. Myotubularin is a phosphoinositol lipid phosphases known to be involved in endosome trafficking and membrane remodeling, however, the molecular mechanisms underlying myotubular myopathy are not yet clear. MicroRNAs (miRNAs) are post transcriptional modulators of gene expression and play an important role in many developmental processes and diseases. To identify functional miRNA-protein networks that may be dysregulated in myotubular myopathy, we performed miRNA as well as mRNA expression profiling of skeletal muscle of Mtm1 knockout mice. Bioinformatic analysis and real-time RTPCR validation resulted in identification of 12 miRNAs that showed significantly differential expression in Mtm1 mice. The functional targets of these miRNAs in myotubular myopathy were identified by a combinatorial approach in which computationally predicted targets genes of these 12 miRNAs were matched with statistically altered genes obtained by mRNA profiling of skeletal muscle tissues from Mtm1 mice. Ontological classification of target genes revealed genes primarily belonging to skeletal muscle development and maintenance, regulation of cell cycle and differentiation of muscle fibers. Expression analyses of miRNA-target genes identified from this study were also performed during earlier developmental time points (2 and 4 weeks) in Mtm1 mice for a better comprehensive insight of miRNA-mRNAs in the progression of the disease. We observed that an increase in the severity of XLMTM is associated with an increase in the fold change of several miRNAs and their target genes, suggesting their crucial role in pathology of myotubular myopathy. We hope understanding the molecular pathways involving these miRNA-mRNA networks, which are disrupted in myotubular myopathy, will contribute to uncovering the mechanisms of muscle development and maintenance and the development of new therapies for myotubular myopathy.Resumo alargado em português disponível no document

Tackling complexity in biological systems: Multi-scale approaches to tuberculosis infection

Tuberculosis is an ancient disease responsible for more than a million deaths per year worldwide, whose complex infection cycle involves dynamical processes that take place at different spatial and temporal scales, from single pathogenic cells to entire hosts' populations. In this thesis we study TB disease at different levels of description from the perspective of complex systems sciences. On the one hand, we use complex networks theory for the analysis of cell interactomes of the causative agent of the disease: the bacillus Mycobacterium tuberculosis. Here, we analyze the gene regulatory network of the bacterium, as well as its network of protein interactions and the way in which it is transformed as a consequence of gene expression adaptation to disparate environments. On the other hand, at the level of human societies, we develop new models for the description of TB spreading on complex populations. First, we develop mathematical models aimed at addressing, from a conceptual perspective, the interplay between complexity of hosts' populations and certain dynamical traits characteristic of TB spreading, like long latency periods and syndemic associations with other diseases. On the other hand, we develop a novel data-driven model for TB spreading with the objective of providing faithful impact evaluations for novel TB vaccines of different types