Gene-chip studies of adipogenesis-regulated microRNAs in mouse primary adipocytes and human obesity

<p>Abstract</p> <p>Background</p> <p>Adipose tissue abundance relies partly on the factors that regulate adipogenesis, i.e. proliferation and differentiation of adipocytes. While components of the transcriptional program that initiates adipogenesis is well-known, the importance of microRNAs in adipogenesis is less well studied. We thus set out to investigate whether miRNAs would be actively modulated during adipogenesis and obesity.</p> <p>Methods</p> <p>Several models exist to study adipogenesis <it>in vitro</it>, of which the cell line 3T3-L1 is the most well known, albeit not the most physiologically appropriate. Thus, as an alternative, we produced EXIQON microarray of brown and white <it>primary </it>murine adipocytes (prior to and following differentiation) to yield global profiles of miRNAs.</p> <p>Results</p> <p>We found 65 miRNAs regulated during <it>in vitro </it>adipogenesis in primary adipocytes. We evaluated the similarity of our responses to those found in non-primary cell models, through literature data-mining. When comparing primary adipocyte profiles, with those of cell lines reported in the literature, we found a high degree of difference in 'adipogenesis' regulated miRNAs suggesting that the model systems may not be accurately representing adipogenesis. The expression of 10 adipogenesis-regulated miRNAs were studied using real-time qPCR and then we selected 5 miRNAs, that showed robust expression, were profiled in subcutaneous adipose tissue obtained from 20 humans with a range of body mass indices (BMI, range = 21-48, and all samples have U133+2 Affymetrix profiles provided). Of the miRNAs tested, mir-21 was robustly expressed in human adipose tissue and positively correlated with BMI (R2 = 0.49, p < 0.001).</p> <p>Conclusion</p> <p>In conclusion, we provide a preliminary analysis of miRNAs associated with primary cell <it>in vitro </it>adipogenesis and demonstrate that the inflammation-associated miRNA, mir-21 is up-regulated in subcutaneous adipose tissue in human obesity. Further, we provide a novel transcriptomics database of EXIQON and Affymetrix adipocyte profiles to facilitate data mining.</p

Mining tissue specificity, gene connectivity and disease association to reveal a set of genes that modify the action of disease causing genes

<p>Abstract</p> <p>Background</p> <p>The tissue specificity of gene expression has been linked to a number of significant outcomes including level of expression, and differential rates of polymorphism, evolution and disease association. Recent studies have also shown the importance of exploring differential gene connectivity and sequence conservation in the identification of disease-associated genes. However, no study relates gene interactions with tissue specificity and disease association.</p> <p>Methods</p> <p>We adopted an <it>a priori </it>approach making as few assumptions as possible to analyse the interplay among gene-gene interactions with tissue specificity and its subsequent likelihood of association with disease. We mined three large datasets comprising expression data drawn from massively parallel signature sequencing across 32 tissues, describing a set of 55,606 true positive interactions for 7,197 genes, and microarray expression results generated during the profiling of systemic inflammation, from which 126,543 interactions among 7,090 genes were reported.</p> <p>Results</p> <p>Amongst the myriad of complex relationships identified between expression, disease, connectivity and tissue specificity, some interesting patterns emerged. These include elevated rates of expression and network connectivity in housekeeping and disease-associated tissue-specific genes. We found that disease-associated genes are more likely to show tissue specific expression and most frequently interact with other disease genes. Using the thresholds defined in these observations, we develop a guilt-by-association algorithm and discover a group of 112 non-disease annotated genes that predominantly interact with disease-associated genes, impacting on disease outcomes.</p> <p>Conclusion</p> <p>We conclude that parameters such as tissue specificity and network connectivity can be used in combination to identify a group of genes, not previously confirmed as disease causing, that are involved in interactions with disease causing genes. Our guilt-by-association algorithm should be useful for the discovery of additional modifiers of genetic diseases, and more generally, for the ability to associate genes of unknown function to clusters of genes with defined functions allowing for novel biological inference that can be subsequently validated.</p

Development of advanced methods for large-scale transcriptomic profiling and application to screening of metabolism disrupting compounds

High-throughput transcriptomic profiling has become a ubiquitous tool to assay an organism transcriptome and to characterize gene expression patterns in different cellular states or disease conditions, as well as in response to molecular and pharmacologic perturbations. Refinements to data preparation techniques have enabled integration of transcriptomic profiling into large-scale biomedical studies, generally devised to elucidate phenotypic factors contributing to transcriptional differences across a cohort of interest. Understanding these factors and the mechanisms through which they contribute to disease is a principal objective of numerous projects, such as The Cancer Genome Atlas and the Cancer Cell Line Encyclopedia. Additionally, transcriptomic profiling has been applied in toxicogenomic screening studies, which profile molecular responses of chemical perturbations in order to identify environmental toxicants and characterize their mechanisms-of-action. Further adoption of high-throughput transcriptomic profiling requires continued effort to improve and lower the costs of implementation. Accordingly, my dissertation work encompasses both the development and assessment of cost-effective RNA sequencing platforms, and of novel machine learning techniques applicable to the analyses of large-scale transcriptomic data sets. The utility of these techniques is evaluated through their application to a toxicogenomic screen in which our lab profiled exposures of adipocytes to metabolic disrupting chemicals. Such exposures have been implicated in metabolic dyshomeostasis, the predominant cause of obesity pathogenesis. Considering that an estimated 10% of the global population is obese, understanding the role these exposures play in disrupting metabolic balance has the potential to help combating this pervasive health threat. This dissertation consists of three sections. In the first section, I assess data generated by a highly-multiplexed RNA sequencing platform developed by our section, and report on its significantly better quality relative to similar platforms, and on its comparable quality to more expensive platforms. Next, I present the analysis of a toxicogenomic screen of metabolic disrupting compounds. This analysis crucially relied on novel supervised and unsupervised machine learning techniques which I specifically developed to take advantage of the experimental design we adopted for data generation. Lastly, I describe the further development, evaluation, and optimization of one of these methods, K2Taxonomer, into a computational tool for unsupervised molecular subgrouping of bulk and single-cell gene expression data, and for the comprehensive in-silico annotation of the discovered subgroups

Quantifying differential gene connectivity between disease states for objective identification of disease-relevant genes

<p>Abstract</p> <p>Background</p> <p>Network modeling of whole transcriptome expression data enables characterization of complex epistatic (gene-gene) interactions that underlie cellular functions. Though numerous methods have been proposed and successfully implemented to develop these networks, there are no formal methods for comparing differences in network connectivity patterns as a function of phenotypic trait.</p> <p>Results</p> <p>Here we describe a novel approach for quantifying the differences in gene-gene connectivity patterns across disease states based on Graphical Gaussian Models (GGMs). We compare the posterior probabilities of connectivity for each gene pair across two disease states, expressed as a posterior odds-ratio (postOR) for each pair, which can be used to identify network components most relevant to disease status. The method can also be generalized to model differential gene connectivity patterns within previously defined gene sets, gene networks and pathways. We demonstrate that the GGM method reliably detects differences in network connectivity patterns in datasets of varying sample size. Applying this method to two independent breast cancer expression data sets, we identified numerous reproducible differences in network connectivity across histological grades of breast cancer, including several published gene sets and pathways. Most notably, our model identified two gene hubs (MMP12 and CXCL13) that each exhibited differential connectivity to more than 30 transcripts in both datasets. Both genes have been previously implicated in breast cancer pathobiology, but themselves are not differentially expressed by histologic grade in either dataset, and would thus have not been identified using traditional differential gene expression testing approaches. In addition, 16 curated gene sets demonstrated significant differential connectivity in both data sets, including the matrix metalloproteinases, PPAR alpha sequence targets, and the PUFA synthesis pathway.</p> <p>Conclusions</p> <p>Our results suggest that GGM can be used to formally evaluate differences in global interactome connectivity across disease states, and can serve as a powerful tool for exploring the molecular events that contribute to disease at a systems level.</p

Adipose cell metabolism modulation by red wine procyanidins

Flavonoids, and more specifically, red wine procyanidins, have many beneficial effectsagainst pathologies such as cardiovascular heart disease and related illnesses. Althoughadipose tissue has a central role in some of these pathologies, including obesity and diabetes,there is a lack of information about the effects of procyanidins on this tissue. This thesisaddresses this question. The effects of a grape seed procyanidin extract (GSPE) on the lipidand glucose metabolism of adipocytes were evaluated by taking the 3T3-L1 cell line as amodel of study. Results show that the GSPE has insulinomimetic effects, stimulating glucoseuptake, glycogen synthesis and trigliceride synthesis. To achieve this, the GSPE shares someof the mechanisms and intracellular mediators of the insulin-signalling pathways (such asGLUT-4 translocation, PI3K and p38 MAPK) but it must also use other, complementary,mechanisms. These results suggest that procyanidins have beneficial effects on diabetesand/or insulin resistance. This is partially proven by in vivo studies that show that GSPE hasantihyperglycemic properties on streptozotozin-induced diabetic rats. Also analyzed in thisthesis are the molecular mechanisms used by GSPE to explain the already described lipolyticeffects. Protein kinase A and PPARã are shown to be involved in these effects. Some ofthese results opened up another line of study into the effects of GSPE on the differentiationprocess of the 3T3-L1. These studies showed that procyanidins alter the differentiation ofpreadipocytes when added at the induction of differentiation. Since an increase in thenumber of adipocytes has a negative effect on obesity, this is a promising characteristic ofGSPE that should be taken into account when its possible antiobesity properties are studied.Als flavonoides, i més concretament a les procianidines del vi negre, se'ls han atribuït moltespropietats beneficioses contra diverses patologies, com les malalties cardiovasculars i altrespatologies relacionades. Tot i que el teixit adipós juga un paper important en algunesd'aquestes patologies, com la obesitat i la diabetis, la informació referent l'acció de lesprocianidines en aquest teixit és escassa. Aquesta tesis estudia els efectes de les procianidinesderivades de pinyol de raïm (GSPE) en l'adipòcit, i per a dur-ho a terme es pren com amodel d'estudi la línia cel.lular 3T3-L1. Per una banda es descriuen els efectes del GSPE enel metabolisme de lípids i glúcids de la cèl.lula adiposa. El GSPE fa un paperinsulinomimètic: estimula la captació de glucosa, la síntesi de glicògen i la síntesi de triacilglicerols. L'anàlisi dels mecanismes moleculars per exercir aquests efectes mostra que GSPEen part comparteix mecanismes i vies de senyalització propis de la insulina (translocació deGLUT-4, PI3K, p38 MAPK); tanmateix, s'observa que GSPE ha d'usar també altresmecanismes complementaris. Aquests resultats suggereixen que GSPE pot tenir efectespositius en situacions de diabetis i/o resistència a insulina, donat que a més a més, els estudisin vivo mostren que GSPE és antihiperglicèmic en condicions de diabetis induïda perestreptozotocina. En aquesta tesis també s'analitzen els mecanismes moleculars queexplicarien els efectes lipolítics de les procianidines descrits en estudis previs, i s'ha trobatque la proteina kinasa A i PPARã hi estan involucrats. Part d'aquests resultats han obert unaaltra via d'estudi sobre els efectes de la GSPE en el procés de diferenciació de la cèl.lulaadiposa on s'ha observat que el tractament amb procianidines a l'inici de la diferenciaciódificulta aquesta transformació. Donat que l'augment del nombre d'adipòcits afectanegativament la obesitat, aquest efecte de les procianidines és una característicaprometedora que caldrà tenir en compte en l'estudi del seu possible paper antiobesitat

Skeletal muscle specific genes networks in cattle

While physiological differences across skeletal muscles have been described, the differential gene expression underlying them and the discovery of how they interact to perform specific biological processes are largely to be elucidated. The purpose of the present study was, firstly, to profile by cDNA microarrays the differential gene expression between two skeletal muscle types, Psoas major (PM) and Flexor digitorum (FD), in beef cattle and then to interpret the results in the context of a bovine gene coexpression network, detecting possible changes in connectivity across the skeletal muscle system. Eighty four genes were differentially expressed (DE) between muscles. Approximately 54% encoded metabolic enzymes and structural-contractile proteins. DE genes were involved in similar processes and functions, but the proportion of genes in each category varied within each muscle. A correlation matrix was obtained for 61 out of the 84 DE genes from a gene coexpression network. Different groups of coexpression were observed, the largest one having 28 metabolic and contractile genes, up-regulated in PM, and mainly encoding fast-glycolytic fibre structural components and glycolytic enzymes. In FD, genes related to cell support seemed to constitute its identity feature and did not positively correlate to the rest of DE genes in FD. Moreover, changes in connectivity for some DE genes were observed in the different gene ontologies. Our results confirm the existence of a muscle dependent transcription and coexpression pattern and suggest the necessity of integrating different muscle types to perform comprehensive networks for the transcriptional landscape of bovine skeletal muscle

Link-based quantitative methods to identify differentially coexpressed genes and gene Pairs

<p>Abstract</p> <p>Background</p> <p>Differential coexpression analysis (DCEA) is increasingly used for investigating the global transcriptional mechanisms underlying phenotypic changes. Current DCEA methods mostly adopt a gene connectivity-based strategy to estimate differential coexpression, which is characterized by comparing the numbers of gene neighbors in different coexpression networks. Although it simplifies the calculation, this strategy mixes up the identities of different coexpression neighbors of a gene, and fails to differentiate significant differential coexpression changes from those trivial ones. Especially, the correlation-reversal is easily missed although it probably indicates remarkable biological significance.</p> <p>Results</p> <p>We developed two link-based quantitative methods, DCp and DCe, to identify differentially coexpressed genes and gene pairs (links). Bearing the uniqueness of exploiting the quantitative coexpression change of each gene pair in the coexpression networks, both methods proved to be superior to currently popular methods in simulation studies. Re-mining of a publicly available type 2 diabetes (T2D) expression dataset from the perspective of differential coexpression analysis led to additional discoveries than those from differential expression analysis.</p> <p>Conclusions</p> <p>This work pointed out the critical weakness of current popular DCEA methods, and proposed two link-based DCEA algorithms that will make contribution to the development of DCEA and help extend it to a broader spectrum.</p