Clustered gene expression changes flank targeted gene loci in knockout mice

Background

Gene expression profiling using microarrays is a powerful technology widely used to study regulatory networks. Profiling of mRNA levels in mutant organisms has the potential to identify genes regulated by the mutated protein.

Methodology/Principle Findings

Using tissues from multiple lines of knockout mice we have examined genome-wide changes in gene expression. We report that a significant proportion of changed genes were found near the targeted gene.

Conclusions/Significance

The apparent clustering of these genes was explained by the presence of flanking DNA from the parental ES cell. We provide recommendations for the analysis and reporting of microarray data from knockout mice     

Inferring the perturbed microRNA regulatory networks from gene expression data using a network propagation based method

Background

MicroRNAs (miRNAs) are a class of endogenous small regulatory RNAs. Identifications of the dys-regulated or perturbed miRNAs and their key target genes are important for understanding the regulatory networks associated with the studied cellular processes. Several computational methods have been developed to infer the perturbed miRNA regulatory networks by integrating genome-wide gene expression data and sequence-based miRNA-target predictions. However, most of them only use the expression information of the miRNA direct targets, rarely considering the secondary effects of miRNA perturbation on the global gene regulatory networks.

Results

We proposed a network propagation based method to infer the perturbed miRNAs and their key target genes by integrating gene expressions and global gene regulatory network information. The method used random walk with restart in gene regulatory networks to model the network effects of the miRNA perturbation. Then, it evaluated the significance of the correlation between the network effects of the miRNA perturbation and the gene differential expression levels with a forward searching strategy. Results show that our method outperformed several compared methods in rediscovering the experimentally perturbed miRNAs in cancer cell lines. Then, we applied it on a gene expression dataset of colorectal cancer clinical patient samples and inferred the perturbed miRNA regulatory networks of colorectal cancer, including several known oncogenic or tumor-suppressive miRNAs, such as miR-17, miR-26 and miR-145.

Conclusions

Our network propagation based method takes advantage of the network effect of the miRNA perturbation on its target genes. It is a useful approach to infer the perturbed miRNAs and their key target genes associated with the studied biological processes using gene expression data.

Electronic supplementary material

The online version of this article (doi:10.1186/1471-2105-15-255) contains supplementary material, which is available to authorized users.     

Gene network inference using continuous time Bayesian networks: a comparative study and application to Th17 cell differentiation

Background

Dynamic aspects of gene regulatory networks are typically investigated by measuring system variables at multiple time points. Current state-of-the-art computational approaches for reconstructing gene networks directly build on such data, making a strong assumption that the system evolves in a synchronous fashion at fixed points in time. However, nowadays omics data are being generated with increasing time course granularity. Thus, modellers now have the possibility to represent the system as evolving in continuous time and to improve the models’ expressiveness.

Results

Continuous time Bayesian networks are proposed as a new approach for gene network reconstruction from time course expression data. Their performance was compared to two state-of-the-art methods: dynamic Bayesian networks and Granger causality analysis. On simulated data, the methods comparison was carried out for networks of increasing size, for measurements taken at different time granularity densities and for measurements unevenly spaced over time. Continuous time Bayesian networks outperformed the other methods in terms of the accuracy of regulatory interactions learnt from data for all network sizes. Furthermore, their performance degraded smoothly as the size of the network increased. Continuous time Bayesian networks were significantly better than dynamic Bayesian networks for all time granularities tested and better than Granger causality for dense time series. Both continuous time Bayesian networks and Granger causality performed robustly for unevenly spaced time series, with no significant loss of performance compared to the evenly spaced case, while the same did not hold true for dynamic Bayesian networks. The comparison included the IRMA experimental datasets which confirmed the effectiveness of the proposed method. Continuous time Bayesian networks were then applied to elucidate the regulatory mechanisms controlling murine T helper 17 (Th17) cell differentiation and were found to be effective in discovering well-known regulatory mechanisms, as well as new plausible biological insights.

Conclusions

Continuous time Bayesian networks were effective on networks of both small and large size and were particularly feasible when the measurements were not evenly distributed over time. Reconstruction of the murine Th17 cell differentiation network using continuous time Bayesian networks revealed several autocrine loops, suggesting that Th17 cells may be auto regulating their own differentiation process.     

From knockouts to networks: establishing direct cause-effect relationships through graph analysis

Background

Reverse-engineering gene networks from expression profiles is a difficult problem for which a multitude of techniques have been developed over the last decade. The yearly organized DREAM challenges allow for a fair evaluation and unbiased comparison of these methods.

Results

We propose an inference algorithm that combines confidence matrices, computed as the standard scores from single-gene knockout data, with the down-ranking of feed-forward edges. Substantial improvements on the predictions can be obtained after the execution of this second step.

Conclusions

Our algorithm was awarded the best overall performance at the DREAM4 In Silico 100-gene network sub-challenge, proving to be effective in inferring medium-size gene regulatory networks. This success demonstrates once again the decisive importance of gene expression data obtained after systematic gene perturbations and highlights the usefulness of graph analysis to increase the reliability of inference.     

Leucine-rich repeat kinase 2 modulates retinoic acid-induced neuronal differentiation of murine embryonic stem cells

Background

Dominant mutations in the leucine-rich repeat kinase 2 (LRRK2) gene are the most prevalent cause of Parkinson''s disease, however, little is known about the biological function of LRRK2 protein. LRRK2 is expressed in neural precursor cells suggesting a role in neurodevelopment.

Methodology/Principal Findings

In the present study, differential gene expression profiling revealed a faster silencing of pluripotency-associated genes, like Nanog, Oct4, and Lin28, during retinoic acid-induced neuronal differentiation of LRRK2-deficient mouse embryonic stem cells compared to wildtype cultures. By contrast, expression of neurotransmitter receptors and neurotransmitter release was increased in LRRK2+/− cultures indicating that LRRK2 promotes neuronal differentiation. Consistently, the number of neural progenitor cells was higher in the hippocampal dentate gyrus of adult LRRK2-deficient mice. Alterations in phosphorylation of the putative LRRK2 substrates, translation initiation factor 4E binding protein 1 and moesin, do not appear to be involved in altered differentiation, rather there is indirect evidence that a regulatory signaling network comprising retinoic acid receptors, let-7 miRNA and downstream target genes/mRNAs may be affected in LRRK2-deficient stem cells in culture.

Conclusion/Significance

Parkinson''s disease-linked LRRK2 mutations that associated with enhanced kinase activity may affect retinoic acid receptor signaling during neurodevelopment and/or neuronal maintenance as has been shown in other mouse models of chronic neurodegenerative diseases.     

Mirna expression profiles identify drivers in colorectal and pancreatic cancers

Background and Aim

Altered expression of microRNAs (miRNAs) hallmarks many cancer types. The study of the associations of miRNA expression profile and cancer phenotype could help identify the links between deregulation of miRNA expression and oncogenic pathways.

Methods

Expression profiling of 866 human miRNAs in 19 colorectal and 17 pancreatic cancers and in matched adjacent normal tissues was investigated. Classical paired t-test and random forest analyses were applied to identify miRNAs associated with tissue-specific tumors. Network analysis based on a computational approach to mine associations between cancer types and miRNAs was performed.

Results

The merge between the two statistical methods used to intersect the miRNAs differentially expressed in colon and pancreatic cancers allowed the identification of cancer-specific miRNA alterations. By miRNA-network analysis, tissue-specific patterns of miRNA deregulation were traced: the driving miRNAs were miR-195, miR-1280, miR-140-3p and miR-1246 in colorectal tumors, and miR-103, miR-23a and miR-15b in pancreatic cancers.

Conclusion

MiRNA expression profiles may identify cancer-specific signatures and potentially useful biomarkers for the diagnosis of tissue specific cancers. miRNA-network analysis help identify altered miRNA regulatory networks that could play a role in tumor pathogenesis.     

Reverse-engineering of gene networks for regulating early blood development from single-cell measurements

Background

Recent advances in omics technologies have raised great opportunities to study large-scale regulatory networks inside the cell. In addition, single-cell experiments have measured the gene and protein activities in a large number of cells under the same experimental conditions. However, a significant challenge in computational biology and bioinformatics is how to derive quantitative information from the single-cell observations and how to develop sophisticated mathematical models to describe the dynamic properties of regulatory networks using the derived quantitative information.

Methods

This work designs an integrated approach to reverse-engineer gene networks for regulating early blood development based on singel-cell experimental observations. The wanderlust algorithm is initially used to develop the pseudo-trajectory for the activities of a number of genes. Since the gene expression data in the developed pseudo-trajectory show large fluctuations, we then use Gaussian process regression methods to smooth the gene express data in order to obtain pseudo-trajectories with much less fluctuations. The proposed integrated framework consists of both bioinformatics algorithms to reconstruct the regulatory network and mathematical models using differential equations to describe the dynamics of gene expression.

Results

The developed approach is applied to study the network regulating early blood cell development. A graphic model is constructed for a regulatory network with forty genes and a dynamic model using differential equations is developed for a network of nine genes. Numerical results suggests that the proposed model is able to match experimental data very well. We also examine the networks with more regulatory relations and numerical results show that more regulations may exist. We test the possibility of auto-regulation but numerical simulations do not support the positive auto-regulation. In addition, robustness is used as an importantly additional criterion to select candidate networks.

Conclusion

The research results in this work shows that the developed approach is an efficient and effective method to reverse-engineer gene networks using single-cell experimental observations.

     

Inflammatory expression profiles in monocyte-to-macrophage differentiation in patients with systemic lupus erythematosus and relationship with atherosclerosis

Introduction

Our objectives were to examine mononuclear cell gene expression profiles in patients with systemic lupus erythematosus (SLE) and healthy controls and to compare subsets with and without atherosclerosis to determine which genes’ expression is related to atherosclerosis in SLE.

Methods

Monocytes were obtained from 20 patients with SLE and 16 healthy controls and were in vitro-differentiated into macrophages. Subjects also underwent laboratory and imaging studies to evaluate for subclinical atherosclerosis. Whole-genome RNA expression microarray was performed, and gene expression was examined.

Results

Gene expression profiling was used to identify gene signatures that differentiated patients from controls and individuals with and without atherosclerosis. In monocytes, 9 out of 20 patients with SLE had an interferon-inducible signature compared with 2 out of 16 controls. By looking at gene expression during monocyte-to-macrophage differentiation, we identified pathways which were differentially regulated between SLE and controls and identified signatures based on relevant intracellular signaling molecules which could differentiate SLE patients with atherosclerosis from controls. Among patients with SLE, we used a previously defined 344-gene atherosclerosis signature in monocyte-to-macrophage differentiation to identify patient subgroups with and without atherosclerosis. Interestingly, this signature further classified patients on the basis of the presence of SLE disease activity and cardiovascular risk factors.

Conclusions

Many genes were differentially regulated during monocyte-to-macrophage differentiation in SLE patients compared with controls. The expression of these genes in mononuclear cells is important in the pathogenesis of SLE, and molecular profiling using gene expression can help stratify SLE patients who may be at risk for development of atherosclerosis.