Time course of gene expression profiling in the liver of experimental mice infected with Echinococcus multilocularis

Background

Alveolar echinococcosis (AE) is a severe chronic parasitic disease which behaves like a slow-growing liver cancer. Clinical observations suggest that the parasite, Echinococcus multilocularis (E. multilocularis) influences liver homeostasis and hepatic cell metabolism. However, this has never been analyzed during the time course of infection in the common model of secondary echinococcosis in experimental mice.

Methodology/Principal Findings

Gene expression profiles were assessed using DNA microarray analysis, 1, 2, 3 and 6 months after injection of E. multilocularis metacestode in the liver of susceptible mice. Data were collected at different time points to monitor the dynamic behavior of gene expression. 557 differentially expressed genes were identified at one or more time points, including 351 up-regulated and 228 down-regulated genes. Time-course analysis indicated, at the initial stage of E. multilocularis infection (month 1–2), that most of up-regulated pathways were related to immune processes and cell trafficking such as chemokine-, mitogen-activated protein kinase (MAPK) signaling, and down-regulated pathways were related to xenobiotic metabolism; at the middle stage (month 3), MAPK signaling pathway was maintained and peroxisome proliferator-activated receptor (PPAR) signaling pathway emerged; at the late stage (month 6), most of up-regulated pathways were related to PPAR signaling pathway, complement and coagulation cascades, while down-regulated pathways were related to metabolism of xenobiotics by cytochrome P450. Quantitative RT-PCR analysis of a random selection of 19 genes confirmed the reliability of the microarray data. Immunohistochemistry analysis showed that proliferating cell nuclear antigen (PCNA) was increased in the liver of E. multilocularis infected mice from 2 months to 6 months.

Conclusions

E. multilocularis metacestode definitely exerts a deep influence on liver homeostasis, by modifying a number of gene expression and metabolic pathways. It especially promotes hepatic cell proliferation, as evidenced by the increased PCNA constantly found in all the experimental time-points we studied and by an increased gene expression of key metabolic pathways.     

Integrated Analysis of Mutation Data from Various Sources Identifies Key Genes and Signaling Pathways in Hepatocellular Carcinoma

Background

Recently, a number of studies have performed genome or exome sequencing of hepatocellular carcinoma (HCC) and identified hundreds or even thousands of mutations in protein-coding genes. However, these studies have only focused on a limited number of candidate genes, and many important mutation resources remain to be explored.

Principal Findings

In this study, we integrated mutation data obtained from various sources and performed pathway and network analysis. We identified 113 pathways that were significantly mutated in HCC samples and found that the mutated genes included in these pathways contained high percentages of known cancer genes, and damaging genes and also demonstrated high conservation scores, indicating their important roles in liver tumorigenesis. Five classes of pathways that were mutated most frequently included (a) proliferation and apoptosis related pathways, (b) tumor microenvironment related pathways, (c) neural signaling related pathways, (d) metabolic related pathways, and (e) circadian related pathways. Network analysis further revealed that the mutated genes with the highest betweenness coefficients, such as the well-known cancer genes TP53, CTNNB1 and recently identified novel mutated genes GNAL and the ADCY family, may play key roles in these significantly mutated pathways. Finally, we highlight several key genes (e.g., RPS6KA3 and PCLO) and pathways (e.g., axon guidance) in which the mutations were associated with clinical features.

Conclusions

Our workflow illustrates the increased statistical power of integrating multiple studies of the same subject, which can provide biological insights that would otherwise be masked under individual sample sets. This type of bioinformatics approach is consistent with the necessity of making the best use of the ever increasing data provided in valuable databases, such as TCGA, to enhance the speed of deciphering human cancers.     

Gradient Descent Optimization in Gene Regulatory Pathways

Background

Gene Regulatory Networks (GRNs) have become a major focus of interest in recent years. Elucidating the architecture and dynamics of large scale gene regulatory networks is an important goal in systems biology. The knowledge of the gene regulatory networks further gives insights about gene regulatory pathways. This information leads to many potential applications in medicine and molecular biology, examples of which are identification of metabolic pathways, complex genetic diseases, drug discovery and toxicology analysis. High-throughput technologies allow studying various aspects of gene regulatory networks on a genome-wide scale and we will discuss recent advances as well as limitations and future challenges for gene network modeling. Novel approaches are needed to both infer the causal genes and generate hypothesis on the underlying regulatory mechanisms.

Methodology

In the present article, we introduce a new method for identifying a set of optimal gene regulatory pathways by using structural equations as a tool for modeling gene regulatory networks. The method, first of all, generates data on reaction flows in a pathway. A set of constraints is formulated incorporating weighting coefficients. Finally the gene regulatory pathways are obtained through optimization of an objective function with respect to these weighting coefficients. The effectiveness of the present method is successfully tested on ten gene regulatory networks existing in the literature. A comparative study with the existing extreme pathway analysis also forms a part of this investigation. The results compare favorably with earlier experimental results. The validated pathways point to a combination of previously documented and novel findings.

Conclusions

We show that our method can correctly identify the causal genes and effectively output experimentally verified pathways. The present method has been successful in deriving the optimal regulatory pathways for all the regulatory networks considered. The biological significance and applicability of the optimal pathways has also been discussed. Finally the usefulness of the present method on genetic engineering is depicted with an example.     

Mapping genetically compensatory pathways from synthetic lethal interactions in yeast

Background

Synthetic lethal genetic interaction analysis has been successfully applied to predicting the functions of genes and their pathway identities. In the context of synthetic lethal interaction data alone, the global similarity of synthetic lethal interaction patterns between two genes is used to predict gene function. With physical interaction data, such as protein-protein interactions, the enrichment of physical interactions within subsets of genes and the enrichment of synthetic lethal interactions between those subsets of genes are used as an indication of compensatory pathways.

Result

In this paper, we propose a method of mapping genetically compensatory pathways from synthetic lethal interactions. Our method is designed to discover pairs of gene-sets in which synthetic lethal interactions are depleted among the genes in an individual set and where such gene-set pairs are connected by many synthetic lethal interactions. By its nature, our method could select compensatory pathway pairs that buffer the deleterious effect of the failure of either one, without the need of physical interaction data. By focusing on compensatory pathway pairs where genes in each individual pathway have a highly homogenous cellular function, we show that many cellular functions have genetically compensatory properties.

Conclusion

We conclude that synthetic lethal interaction data are a powerful source to map genetically compensatory pathways, especially in systems lacking physical interaction information, and that the cellular function network contains abundant compensatory properties.     

Integrating biological pathways and genomic profiles with ChiBE 2

Background

Dynamic visual exploration of detailed pathway information can help researchers digest and interpret complex mechanisms and genomic datasets.

Results

ChiBE is a free, open-source software tool for visualizing, querying, and analyzing human biological pathways in BioPAX format. The recently released version 2 can search for neighborhoods, paths between molecules, and common regulators/targets of molecules, on large integrated cellular networks in the Pathway Commons database as well as in local BioPAX models. Resulting networks can be automatically laid out for visualization using a graphically rich, process-centric notation. Profiling data from the cBioPortal for Cancer Genomics and expression data from the Gene Expression Omnibus can be overlaid on these networks.

Conclusions

ChiBE’s new capabilities are organized around a genomics-oriented workflow and offer a unique comprehensive pathway analysis solution for genomics researchers. The software is freely available at http://code.google.com/p/chibe.     

In Vivo Detection of Extrapancreatic Insulin Gene Expression in Diabetic Mice by Bioluminescence Imaging

Background

Extrapancreatic tissues such as liver may serve as potential sources of tissue for generating insulin-producing cells. The dynamics of insulin gene promoter activity in extrapancreatic tissues may be monitored in vivo by bioluminescence-imaging (BLI) of transgenic mice Tg(RIP-luc) expressing the firefly luciferase (luc) under a rat-insulin gene promoter (RIP).

Methods

The Tg(RIP-luc) mice were made diabetic by a single injection of the pancreatic β-cell toxin streptozotocin. Control mice were treated with saline. Mice were subject to serum glucose measurement and bioluminescence imaging daily. On day eight of the treatment, mice were sacrificed and tissues harvested for quantitative luciferase activity measurement, luciferase protein cellular localization, and insulin gene expression analysis.

Results

Streptozotocin-induced diabetic Tg(RIP-luc) mice demonstrated a dramatic decline in the BLI signal intensity in the pancreas and a concomitant progressive increase in the signal intensity in the liver. An average of 5.7 fold increase in the liver signal intensity was detected in the mice that were exposed to hyperglycemia for 8 days. Ex vivo quantitative assays demonstrated a 34-fold induction of the enzyme activity in the liver of streptozotocin-treated mice compared to that of the buffer-treated controls. Luciferase-positive cells with oval-cell-like morphology were detected by immunohistochemistry in the liver samples of diabetic mice, but not in that of non-treated control transgenic mice. Gene expression analyses of liver RNA confirmed an elevated expression of insulin genes in the liver tissue exposed to hyperglycemia.

Conclusions

BLI is a sensitive method for monitoring insulin gene expression in extrapancreatic tissues in vivo. The BLI system may be used for in vivo screening of biological events or pharmacologic activators that have the potential of stimulating the generation of extrapancreatic insulin-producing cells.     

Systematic Pharmacogenomics Analysis of a Malay Whole Genome: Proof of Concept for Personalized Medicine

Background

With a higher throughput and lower cost in sequencing, second generation sequencing technology has immense potential for translation into clinical practice and in the realization of pharmacogenomics based patient care. The systematic analysis of whole genome sequences to assess patient to patient variability in pharmacokinetics and pharmacodynamics responses towards drugs would be the next step in future medicine in line with the vision of personalizing medicine.

Methods

Genomic DNA obtained from a 55 years old, self-declared healthy, anonymous male of Malay descent was sequenced. The subject''s mother died of lung cancer and the father had a history of schizophrenia and deceased at the age of 65 years old. A systematic, intuitive computational workflow/pipeline integrating custom algorithm in tandem with large datasets of variant annotations and gene functions for genetic variations with pharmacogenomics impact was developed. A comprehensive pathway map of drug transport, metabolism and action was used as a template to map non-synonymous variations with potential functional consequences.

Principal Findings

Over 3 million known variations and 100,898 novel variations in the Malay genome were identified. Further in-depth pharmacogenetics analysis revealed a total of 607 unique variants in 563 proteins, with the eventual identification of 4 drug transport genes, 2 drug metabolizing enzyme genes and 33 target genes harboring deleterious SNVs involved in pharmacological pathways, which could have a potential role in clinical settings.

Conclusions

The current study successfully unravels the potential of personal genome sequencing in understanding the functionally relevant variations with potential influence on drug transport, metabolism and differential therapeutic outcomes. These will be essential for realizing personalized medicine through the use of comprehensive computational pipeline for systematic data mining and analysis.     

Systematic transcriptome analysis of the zebrafish model of diamond-blackfan anemia induced by RPS24 deficiency

Background

Diamond–Blackfan anemia (DBA) is a class of human diseases linked to defective ribosome biogenesis that results in clinical phenotypes. Genetic mutations in ribosome protein (RP) genes lead to DBA phenotypes, including hematopoietic defects and physical deformities. However, little is known about the global regulatory network as well as key miRNAs and gene pathways in the zebrafish model of DBA.

Results

In this study, we establish the DBA model in zebrafish using an RPS24 morpholino and found that RPS24 is required for both primitive hematopoiesis and definitive hematopoiesis processes that are partially mediated by the p53 pathway. Several deregulated genes and miRNAs were found to be related to hematopoiesis, vascular development and apoptosis in RPS24-deficient zebrafish via RNA-seq and miRNA-seq data analysis, and a comprehensive regulatory network was first constructed to identify the mechanisms of key miRNAs and gene pathways in the model. Interestingly, we found that the central node genes in the network were almost all targeted by significantly deregulated miRNAs. Furthermore, the enforced expression of miR-142-3p, a uniquely expressed miRNA, causes a significant decrease in primitive erythrocyte progenitor cells and HSCs.

Conclusions

The present analyses demonstrate that the comprehensive regulatory network we constructed is useful for the functional prediction of new and important miRNAs in DBA and will provide insights into the pathogenesis of mutant rps24-mediated human DBA disease.

Electronic supplementary material

The online version of this article (doi:10.1186/1471-2164-15-759) contains supplementary material, which is available to authorized users.     

Intestinal Microbiota Regulate Xenobiotic Metabolism in the Liver

Background

The liver is the central organ for xenobiotic metabolism (XM) and is regulated by nuclear receptors such as CAR and PXR, which control the metabolism of drugs. Here we report that gut microbiota influences liver gene expression and alters xenobiotic metabolism in animals exposed to barbiturates.

Principal findings

By comparing hepatic gene expression on microarrays from germfree (GF) and conventionally-raised mice (SPF), we identified a cluster of 112 differentially expressed target genes predominantly connected to xenobiotic metabolism and pathways inhibiting RXR function. These findings were functionally validated by exposing GF and SPF mice to pentobarbital which confirmed that xenobiotic metabolism in GF mice is significantly more efficient (shorter time of anesthesia) when compared to the SPF group.

Conclusion

Our data demonstrate that gut microbiota modulates hepatic gene expression and function by altering its xenobiotic response to drugs without direct contact with the liver.     

Network medicine analysis of COPD multimorbidities

Background

Patients with chronic obstructive pulmonary disease (COPD) often suffer concomitant disorders that worsen significantly their health status and vital prognosis. The pathogenic mechanisms underlying COPD multimorbidities are not completely understood, thus the exploration of potential molecular and biological linkages between COPD and their associated diseases is of great interest.

Methods

We developed a novel, unbiased, integrative network medicine approach for the analysis of the diseasome, interactome, the biological pathways and tobacco smoke exposome, which has been applied to the study of 16 prevalent COPD multimorbidities identified by clinical experts.

Results

Our analyses indicate that all COPD multimorbidities studied here are related at the molecular and biological level, sharing genes, proteins and biological pathways. By inspecting the connections of COPD with their associated diseases in more detail, we identified known biological pathways involved in COPD, such as inflammation, endothelial dysfunction or apoptosis, serving as a proof of concept of the methodology. More interestingly, we found previously overlooked biological pathways that might contribute to explain COPD multimorbidities, such as hemostasis in COPD multimorbidities other than cardiovascular disorders, and cell cycle pathway in the association of COPD with depression. Moreover, we also observed similarities between COPD multimorbidities at the pathway level, suggesting common biological mechanisms for different COPD multimorbidities. Finally, chemicals contained in the tobacco smoke target an average of 69% of the identified proteins participating in COPD multimorbidities.

Conclusions

The network medicine approach presented here allowed the identification of plausible molecular links between COPD and comorbid diseases, and showed that many of them are targets of the tobacco exposome, proposing new areas of research for understanding the molecular underpinning of COPD multimorbidities.

Electronic supplementary material

The online version of this article (doi:10.1186/s12931-014-0111-4) contains supplementary material, which is available to authorized users.     

Expression Levels of Obesity-Related Genes Are Associated with Weight Change in Kidney Transplant Recipients

Background

The aim of this study was to investigate the association of gene expression profiles in subcutaneous adipose tissue with weight change in kidney transplant recipients and to gain insights into the underlying mechanisms of weight gain.

Methodology/Principal Findings

A secondary data analysis was done on a subgroup (n = 26) of existing clinical and gene expression data from a larger prospective longitudinal study examining factors contributing to weight gain in transplant recipients. Measurements taken included adipose tissue gene expression profiles at time of transplant, baseline and six-month weight, and demographic data. Using multivariate linear regression analysis controlled for race and gender, expression levels of 1553 genes were significantly (p<0.05) associated with weight change. Functional analysis using Gene Ontology and Kyoto Encyclopedia of Genes and Genomes classifications identified metabolic pathways that were enriched in this dataset. Furthermore, GeneIndexer literature mining analysis identified a subset of genes that are highly associated with obesity in the literature and Ingenuity pathway analysis revealed several significant gene networks associated with metabolism and endocrine function. Polymorphisms in several of these genes have previously been linked to obesity.

Conclusions/Significance

We have successfully identified a set of molecular pathways that taken together may provide insights into the mechanisms of weight gain in kidney transplant recipients. Future work will be done to determine how these pathways may contribute to weight gain.