A sparse conditional Gaussian graphical model for analysis of genetical genomics data

Genetical genomics experiments have now been routinely conducted to measure both the genetic markers and gene expression data on the same subjects. The gene expression levels are often treated as quantitative traits and are subject to standard genetic analysis in order to identify the gene expression quantitative loci (eQTL). However, the genetic architecture for many gene expressions may be complex, and poorly estimated genetic architecture may compromise the inferences of the dependency structures of the genes at the transcriptional level. In this paper we introduce a sparse conditional Gaussian graphical model for studying the conditional independent relationships among a set of gene expressions adjusting for possible genetic effects where the gene expressions are modeled with seemingly unrelated regressions. We present an efficient coordinate descent algorithm to obtain the penalized estimation of both the regression coefficients and the sparse concentration matrix. The corresponding graph can be used to determine the conditional independence among a group of genes while adjusting for shared genetic effects. Simulation experiments and asymptotic convergence rates and sparsistency are used to justify our proposed methods. By sparsistency, we mean the property that all parameters that are zero are actually estimated as zero with probability tending to one. We apply our methods to the analysis of a yeast eQTL data set and demonstrate that the conditional Gaussian graphical model leads to a more interpretable gene network than a standard Gaussian graphical model based on gene expression data alone.Comment: Published in at http://dx.doi.org/10.1214/11-AOAS494 the Annals of Applied Statistics (http://www.imstat.org/aoas/) by the Institute of Mathematical Statistics (http://www.imstat.org

ROCK signalling induced gene expression changes in mouse pancreatic ductal adenocarcinoma cells

The RhoA and RhoC GTPases act via the ROCK1 and ROCK2 kinases to promote actomyosin contraction, resulting in directly induced changes in cytoskeleton structures and altered gene transcription via several possible indirect routes. Elevated activation of the Rho/ROCK pathway has been reported in several diseases and pathological conditions, including disorders of the central nervous system, cardiovascular dysfunctions and cancer. To determine how increased ROCK signalling affected gene expression in pancreatic ductal adenocarcinoma (PDAC) cells, we transduced mouse PDAC cell lines with retroviral constructs encoding fusion proteins that enable conditional activation of ROCK1 or ROCK2, and subsequently performed RNA sequencing (RNA-Seq) using the Illumina NextSeq 500 platform. We describe how gene expression datasets were generated and validated by comparing data obtained by RNA-Seq with RT-qPCR results. Activation of ROCK1 or ROCK2 signalling induced significant changes in gene expression that could be used to determine how actomyosin contractility influences gene transcription in pancreatic cancer

EBF1-deficient bone marrow stroma elicits persistent changes in HSC potential

Crosstalk between mesenchymal stromal cells (MSCs) and hematopoietic stem cells (HSCs) is essential for hematopoietic homeostasis and lineage output. Here, we investigate how transcriptional changes in bone marrow (BM) MSCs result in long-lasting effects on HSCs. Single-cell analysis of Cxcl12-abundant reticular (CAR) cells and PDGFRα+Sca1+ (PαS) cells revealed an extensive cellular heterogeneity but uniform expression of the transcription factor gene Ebf1. Conditional deletion of Ebf1 in these MSCs altered their cellular composition, chromatin structure and gene expression profiles, including the reduced expression of adhesion-related genes. Functionally, the stromal-specific Ebf1 inactivation results in impaired adhesion of HSCs, leading to reduced quiescence and diminished myeloid output. Most notably, HSCs residing in the Ebf1-deficient niche underwent changes in their cellular composition and chromatin structure that persist in serial transplantations. Thus, genetic alterations in the BM niche lead to long-term functional changes of HSCs

Gene expression in large pedigrees: analytic approaches.

BackgroundWe currently have the ability to quantify transcript abundance of messenger RNA (mRNA), genome-wide, using microarray technologies. Analyzing genotype, phenotype and expression data from 20 pedigrees, the members of our Genetic Analysis Workshop (GAW) 19 gene expression group published 9 papers, tackling some timely and important problems and questions. To study the complexity and interrelationships of genetics and gene expression, we used established statistical tools, developed newer statistical tools, and developed and applied extensions to these tools.MethodsTo study gene expression correlations in the pedigree members (without incorporating genotype or trait data into the analysis), 2 papers used principal components analysis, weighted gene coexpression network analysis, meta-analyses, gene enrichment analyses, and linear mixed models. To explore the relationship between genetics and gene expression, 2 papers studied expression quantitative trait locus allelic heterogeneity through conditional association analyses, and epistasis through interaction analyses. A third paper assessed the feasibility of applying allele-specific binding to filter potential regulatory single-nucleotide polymorphisms (SNPs). Analytic approaches included linear mixed models based on measured genotypes in pedigrees, permutation tests, and covariance kernels. To incorporate both genotype and phenotype data with gene expression, 4 groups employed linear mixed models, nonparametric weighted U statistics, structural equation modeling, Bayesian unified frameworks, and multiple regression.Results and discussionRegarding the analysis of pedigree data, we found that gene expression is familial, indicating that at least 1 factor for pedigree membership or multiple factors for the degree of relationship should be included in analyses, and we developed a method to adjust for familiality prior to conducting weighted co-expression gene network analysis. For SNP association and conditional analyses, we found FaST-LMM (Factored Spectrally Transformed Linear Mixed Model) and SOLAR-MGA (Sequential Oligogenic Linkage Analysis Routines -Major Gene Analysis) have similar type 1 and type 2 errors and can be used almost interchangeably. To improve the power and precision of association tests, prior knowledge of DNase-I hypersensitivity sites or other relevant biological annotations can be incorporated into the analyses. On a biological level, eQTL (expression quantitative trait loci) are genetically complex, exhibiting both allelic heterogeneity and epistasis. Including both genotype and phenotype data together with measurements of gene expression was found to be generally advantageous in terms of generating improved levels of significance and in providing more interpretable biological models.ConclusionsPedigrees can be used to conduct analyses of and enhance gene expression studies

Mutant loxP vectors for selectable marker recycle and conditional knock-outs

BACKGROUND: Gene disruption by targeted integration of transfected constructs becomes increasingly popular for studies of gene function. The chicken B cell line DT40 has been widely used as a model for gene knock-outs due to its high targeted integration activity. Disruption of multiple genes and complementation of the phenotypes is, however, restricted by the number of available selectable marker genes. It is therefore highly desirable to recycle the selectable markers using a site-specific recombination system like Cre/loxP. RESULTS: We constructed three plasmid vectors (neoR, puroR and bsr), which carry selectable marker genes flanked by two different mutant loxP sites. After stable transfection, the marker genes can be excised from the genome by transient induction of Cre recombinase expression. This excision converts the two mutant loxP sites to an inactive double-mutant loxP. Furthermore we constructed a versatile expression vector to clone cDNA expression cassettes between mutant loxP sites. This vector can also be used to design knock-out constructs in which the floxed marker gene is combined with a cDNA expression cassette. This construct enables gene knock-out and complementation in a single step. Gene expression can subsequently be terminated by the Cre mediated deletion of the cDNA expression cassette. This strategy is powerful for analyzing essential genes, whose disruption brings lethality to the mutant cell. CONCLUSIONS: Mutant loxP vectors have been developed for the recycle of selectable markers and conditional gene knock-out approaches. As the marker and the cDNA expression cassettes are driven by the universally active and evolutionary conserved β-actin promoter, they can be used for the selection of stable transfectants in a wide range of cell lines

Characterization of Sirt2 using conditional RNAi in mice

Within the past eight years, RNA interference (RNAi) has emerged as a powerful experimental tool for gene function analysis in mice. Reversible control of shRNA mediated RNAi has been achieved by using a tetracycline (tet)-inducible promoter. In the presence of the inductor doxycycline (dox), shRNA mediated gene silencing is initiated, whereas RNAi mechanism is blocked in the absence of dox. To achieve spatially and temporally regulated RNAi, the tet inducible system was combined with a Cre/loxP based strategy for tissue specific activation of shRNA constructs. To this end, a loxP-flanked "promoter inhibitory element" (PIE) was placed between the proximal (PSE) and distal sequence element (DSE) of a dox inducible promoter such that promoter function is completely blocked. Re-activation can be achieved through Cre mediated excision of PIE. To allow for gene silencing in a selected tissue, Cre expression can be regulated by a tissue-specific promoter. In mouse ES cells, the system mediated tight regulation of shRNA expression upon Cre mediated activation and dox administration, reaching knockdown efficiencies of >80%. Unexpectedly, the system showed a limited activity in transgenic mice when applied for conditional silencing of two different targets, LacZ and Sirt2. Sirt2 is a member of the sirtuin family which has considerably gained attention in vitro for its possible role in many physiological processes, including adipogenesis and neurodegenerative diseases. To investigate the function of Sirt2 in vivo, the unmodified dox-responsive and tet-inducible promoter was further used for conditional RNAi in transgenic mice. Inducible shRNA expression resulted in efficient silencing of Sirt2 (>90%) in all tissues which have been analyzed. Suppression of Sirt2 during embryogenesis resulted in offspring consisting of equal ratios of wild type and transgenic pups, indicating that Sirt2 is not indispensable for development. In adult animals, glucose metabolism, insulin sensitivity and energy balance appeared to be unaffected by Sirt2 deficiency. Likewise, expression of PPARγ, a downstream target of Sirt2, was not found to be altered upon Sirt2 inhibition. Finally, Sirt2 silencing was induced in an experimental model of Parkinson disease (PD). Data from Rotarod performances to study motor behaviour did not provide any evidence for a role of Sirt2 in PD pathogenesis as suggested by previous in vitro studies. Taken together, conditional Sirt2 silencing in vivo does not support speculation concerning a central role of Sirt2 in physiological processes, embryogenesis and in a mouse model of Parkinson disease

Silencing of the Pink1 gene expression by conditional RNAi does not induce dopaminergic neuron death in mice.

Transgenic RNAi, an alternative to the gene knockout approach, can induce hypomorphic phenotypes that resemble those of the gene knockout in mice. Conditional transgenic RNAi is an attractive choice of method for reverse genetics in vivo because it can achieve temporal and spatial silencing of targeted genes. Pol III promoters such as U6 are widely used to drive the expression of RNAi transgenes in animals. Tested in transgenic mice, a Cre-loxP inducible U6 promoter drove the broad expression of an shRNA against the Pink1 gene whose loss-of-functional mutations cause one form of familial Parkinson\u27s disease. The expression of the shRNA was tightly regulated and, when induced, silenced the Pink1 gene product by more than 95% in mouse brain. However, these mice did not develop dopaminergic neurodegeneration, suggesting that silencing of the Pink1 gene expression from embryo in mice is insufficient to cause similar biochemical or morphological changes that are observed in Parkinson\u27s disease. The results demonstrate that silencing of the PINK1 gene does not induce a reliable mouse model for Parkinson\u27s disease, but that technically the inducible U6 promoter is useful for conditional RNAi in vivo

Differential expression analysis with global network adjustment

<p>Background: Large-scale chromosomal deletions or other non-specific perturbations of the transcriptome can alter the expression of hundreds or thousands of genes, and it is of biological interest to understand which genes are most profoundly affected. We present a method for predicting a gene’s expression as a function of other genes thereby accounting for the effect of transcriptional regulation that confounds the identification of genes differentially expressed relative to a regulatory network. The challenge in constructing such models is that the number of possible regulator transcripts within a global network is on the order of thousands, and the number of biological samples is typically on the order of 10. Nevertheless, there are large gene expression databases that can be used to construct networks that could be helpful in modeling transcriptional regulation in smaller experiments.</p> <p>Results: We demonstrate a type of penalized regression model that can be estimated from large gene expression databases, and then applied to smaller experiments. The ridge parameter is selected by minimizing the cross-validation error of the predictions in the independent out-sample. This tends to increase the model stability and leads to a much greater degree of parameter shrinkage, but the resulting biased estimation is mitigated by a second round of regression. Nevertheless, the proposed computationally efficient “over-shrinkage” method outperforms previously used LASSO-based techniques. In two independent datasets, we find that the median proportion of explained variability in expression is approximately 25%, and this results in a substantial increase in the signal-to-noise ratio allowing more powerful inferences on differential gene expression leading to biologically intuitive findings. We also show that a large proportion of gene dependencies are conditional on the biological state, which would be impossible with standard differential expression methods.</p> <p>Conclusions: By adjusting for the effects of the global network on individual genes, both the sensitivity and reliability of differential expression measures are greatly improved.</p&gt