Bi-directional gene set enrichment and canonical correlation analysis identify key diet-sensitive pathways and biomarkers of metabolic syndrome

Broad Transcriptomic Impact of Sorafenib and Its Relation to the Antitumoral Properties in Liver Cancer Cells

Simple Summary

Hepatocellular carcinoma (HCC) is the fourth most frequent cause of cancer-related mortality worldwide. While ablation, resection and orthotopic liver transplantation are indicated at an early stage of the disease, Sorafenib (Sfb) is the current most administrated first-line treatment for advanced HCC, even though its therapeutic benefit is limited due to the appearance of resistance. Deep knowledge on the molecular consequences of Sfb-treatment is essentially required for optimizing novel therapeutic strategies to improve the outcomes for patients with advanced HCC. In this study, we analyzed differential gene expression changes in two well characterized liver cancer cell lines upon a Sfb-treatment, demonstrating that both lines responded similarly to the treatment. Our results provide valuable information on the molecular action of Sfb on diverse cellular fundamental processes such as DNA repair, translation and proteostasis and identify rationalization issues that could provide a different therapeutic perspective to Sfb.

Abstract

Hepatocellular carcinoma (HCC) is one of the most frequent and essentially incurable cancers in its advanced stages. The tyrosine kinase inhibitor Sorafenib (Sfb) remains the globally accepted treatment for advanced HCC. However, the extent of its therapeutic benefit is limited. Sfb exerts antitumor activity through its cytotoxic, anti-proliferative and pro-apoptotic roles in HCC cells. To better understand the molecular mechanisms underlying these effects, we used RNA sequencing to generate comprehensive transcriptome profiles of HepG2 and SNU423, hepatoblastoma- (HB) and HCC-derived cell lines, respectively, following a Sfb treatment at a pharmacological dose. This resulted in similar alterations of gene expression in both cell lines. Genes functionally related to membrane trafficking, stress-responsible and unfolded protein responses, circadian clock and activation of apoptosis were predominantly upregulated, while genes involved in cell growth and cycle, DNA replication and repair, ribosome biogenesis, translation initiation and proteostasis were downregulated. Our results suggest that Sfb causes primary effects on cellular stress that lead to upregulation of selective responses to compensate for its negative effect and restore homeostasis. No significant differences were found specifically affecting each cell line, indicating the robustness of the Sfb mechanism of action despite the heterogeneity of liver cancer. We discuss our results on terms of providing rationalization for possible strategies to improve Sfb clinical outcomes.

Food intake regulation by leptin: Mechanisms mediating gluconeogenesis and energy expenditure

Regulation of blood glucose levels and body fat is critical for survival. Leptin circulates freely in blood and controls body weight and food intake mainly through hypothalamic receptors and regulates glucose metabolism in the liver both directly through leptin receptors and indirectly via the hypothalamic receptors of central nervous system. Leptin affects food intake regulation and eventually glucose metabolism, lipometabolism, endocrine and immune functions, reproductive function, adipose tissue metabolism and energy expenditure. Leptin also exerts peripheral effects directly on glucose metabolism and gluconeogenesis. Most of obese human subjects have elevated plasma levels of leptin associated to the size of their total adipose tissue mass. Hence gluconeogenic function may be an essential factor in the regulation of nutritional intake and weight gain. The aim of this review is therefore to identify and module the possible effects of leptin with special application in gluconeogenesis. In addition, this review includes the study of fat consumption and energy expenditure in the body. Specific modulation of leptin receptors and adipose tissues functioning could have important inference on therapeutic strategies.

High-throughput data analysis and data integration for vaccine trials

Rational vaccine development can benefit from biomarker studies, which help to predict, optimize and evaluate the immunogenicity of vaccines and ultimately provide surrogate endpoints for vaccine trials. Systems biology approaches facilitate acquisition of both simple biomarkers and complex biosignatures. Yet, evaluation of high-throughput (HT) data requires a plethora of tools for data integration and analysis. In this review, we present an overview of methods for evaluation and integration of large amounts of data collected in vaccine trials from similar and divergent molecular HT techniques, such as transcriptomic, proteomic and metabolic profiling. We will describe a selection of relevant statistical and bioinformatic approaches that are frequently associated with systems biology. We will present data dimension reduction techniques, functional analysis approaches and methods of integrating heterogeneous HT data. Finally, we will provide a few examples of applications of these techniques in vaccine research and development.

Effect of the absolute statistic on gene-sampling gene-set analysis methods

Gene-set enrichment analysis and its modified versions have commonly been used for identifying altered functions or pathways in disease from microarray data. In particular, the simple gene-sampling gene-set analysis methods have been heavily used for datasets with only a few sample replicates. The biggest problem with this approach is the highly inflated false-positive rate. In this paper, the effect of absolute gene statistic on gene-sampling gene-set analysis methods is systematically investigated. Thus far, the absolute gene statistic has merely been regarded as a supplementary method for capturing the bidirectional changes in each gene set. Here, it is shown that incorporating the absolute gene statistic in gene-sampling gene-set analysis substantially reduces the false-positive rate and improves the overall discriminatory ability. Its effect was investigated by power, false-positive rate, and receiver operating curve for a number of simulated and real datasets. The performances of gene-set analysis methods in one-tailed (genome-wide association study) and two-tailed (gene expression data) tests were also compared and discussed.

Genetic associations with micronutrient levels identified in immune and gastrointestinal networks

The discovery of vitamins and clarification of their role in preventing frank essential nutrient deficiencies occurred in the early 1900s. Much vitamin research has understandably focused on public health and the effects of single nutrients to alleviate acute conditions. The physiological processes for maintaining health, however, are complex systems that depend upon interactions between multiple nutrients, environmental factors, and genetic makeup. To analyze the relationship between these factors and nutritional health, data were obtained from an observational, community-based participatory research program of children and teens (age 6–14) enrolled in a summer day camp in the Delta region of Arkansas. Assessments of erythrocyte S-adenosylmethionine (SAM) and S-adenosylhomocysteine (SAH), plasma homocysteine (Hcy) and 6 organic micronutrients (retinol, 25-hydroxy vitamin D3, pyridoxal, thiamin, riboflavin, and vitamin E), and 1,129 plasma proteins were performed at 3 time points in each of 2 years. Genetic makeup was analyzed with 1 M SNP genotyping arrays, and nutrient status was assessed with 24-h dietary intake questionnaires. A pattern of metabolites (met_PC1) that included the ratio of erythrocyte SAM/SAH, Hcy, and 5 vitamins were identified by principal component analysis. Met_PC1 levels were significantly associated with (1) single-nucleotide polymorphisms, (2) levels of plasma proteins, and (3) multilocus genotypes coding for gastrointestinal and immune functions, as identified in a global network of metabolic/protein–protein interactions. Subsequent mining of data from curated pathway, network, and genome-wide association studies identified genetic and functional relationships that may be explained by gene–nutrient interactions. The systems nutrition strategy described here has thus associated a multivariate metabolite pattern in blood with genes involved in immune and gastrointestinal functions.

Consensus statement understanding health and malnutrition through a systems approach: the ENOUGH program for early life

Nutrition research, like most biomedical disciplines, adopted and often uses experimental approaches based on Beadle and Tatum's one gene-one polypeptide hypothesis, thereby reducing biological processes to single reactions or pathways. Systems thinking is needed to understand the complexity of health and disease processes requiring measurements of physiological processes, as well as environmental and social factors, which may alter the expression of genetic information. Analysis of physiological processes with omics technologies to assess systems' responses has only become available over the past decade and remains costly. Studies of environmental and social conditions known to alter health are often not connected to biomedical research. While these facts are widely accepted, developing and conducting comprehensive research programs for health are often beyond financial and human resources of single research groups. We propose a new research program on essential nutrients for optimal underpinning of growth and health (ENOUGH) that will use systems approaches with more comprehensive measurements and biostatistical analysis of the many biological and environmental factors that influence undernutrition. Creating a knowledge base for nutrition and health is a necessary first step toward developing solutions targeted to different populations in diverse social and physical environments for the two billion undernourished people in developed and developing economies.

Perspective: a systems approach to diabetes research

We review here the status of human type 2 diabetes studies from a genetic, epidemiological, and clinical (intervention) perspective. Most studies limit analyses to one or a few omic technologies providing data of components of physiological processes. Since all chronic diseases are multifactorial and arise from complex interactions between genetic makeup and environment, type 2 diabetes mellitus (T2DM) is a collection of sub-phenotypes resulting in high fasting glucose. The underlying gene–environment interactions that produce these classes of T2DM are imperfectly characterized. Based on assessments of the complexity of T2DM, we propose a systems biology approach to advance the understanding of origin, onset, development, prevention, and treatment of this complex disease. This systems-based strategy is based on new study design principles and the integrated application of omics technologies: we pursue longitudinal studies in which each subject is analyzed at both homeostasis and after (healthy and safe) challenges. Each enrolled subject functions thereby as their own case and control and this design avoids assigning the subjects a priori to case and control groups based on limited phenotyping. Analyses at different time points along this longitudinal investigation are performed with a comprehensive set of omics platforms. These data sets are generated in a biological context, rather than biochemical compound class-driven manner, which we term “systems omics.”

Gene selection heuristic algorithm for nutrigenomics studies

Large datasets from -omics studies need to be deeply investigated. The aim of this paper is to provide a new method (LEM method) for the search of transcriptome and metabolome connections. The heuristic algorithm here described extends the classical canonical correlation analysis (CCA) to a high number of variables (without regularization) and combines well-conditioning and fast-computing in "R." Reduced CCA models are summarized in PageRank matrices, the product of which gives a stochastic matrix that resumes the self-avoiding walk covered by the algorithm. Then, a homogeneous Markov process applied to this stochastic matrix converges the probabilities of interconnection between genes, providing a selection of disjointed subsets of genes. This is an alternative to regularized generalized CCA for the determination of blocks within the structure matrix. Each gene subset is thus linked to the whole metabolic or clinical dataset that represents the biological phenotype of interest. Moreover, this selection process reaches the aim of biologists who often need small sets of genes for further validation or extended phenotyping. The algorithm is shown to work efficiently on three published datasets, resulting in meaningfully broadened gene networks.

Visualising associations between paired 'omics' data sets

BACKGROUND

Each omics platform is now able to generate a large amount of data. Genomics, proteomics, metabolomics, interactomics are compiled at an ever increasing pace and now form a core part of the fundamental systems biology framework. Recently, several integrative approaches have been proposed to extract meaningful information. However, these approaches lack of visualisation outputs to fully unravel the complex associations between different biological entities.

RESULTS

The multivariate statistical approaches 'regularized Canonical Correlation Analysis' and 'sparse Partial Least Squares regression' were recently developed to integrate two types of highly dimensional 'omics' data and to select relevant information. Using the results of these methods, we propose to revisit few graphical outputs to better understand the relationships between two 'omics' data and to better visualise the correlation structure between the different biological entities. These graphical outputs include Correlation Circle plots, Relevance Networks and Clustered Image Maps. We demonstrate the usefulness of such graphical outputs on several biological data sets and further assess their biological relevance using gene ontology analysis.

CONCLUSIONS

Such graphical outputs are undoubtedly useful to aid the interpretation of these promising integrative analysis tools and will certainly help in addressing fundamental biological questions and understanding systems as a whole.

AVAILABILITY

The graphical tools described in this paper are implemented in the freely available R package mixOmics and in its associated web application.

The core mouse response to infection by neospora caninum defined by gene set enrichment analyses

In this study, the BALB/c and Qs mouse responses to infection by the parasite Neospora caninum were investigated in order to identify host response mechanisms. Investigation was done using gene set (enrichment) analyses of microarray data. GSEA, MANOVA, Romer, subGSE and SAM-GS were used to study the contrasts Neospora strain type, Mouse type (BALB/c and Qs) and time post infection (6 hours post infection and 10 days post infection). The analyses show that the major signal in the core mouse response to infection is from time post infection and can be defined by gene ontology terms Protein Kinase Activity, Cell Proliferation and Transcription Initiation. Several terms linked to signaling, morphogenesis, response and fat metabolism were also identified. At 10 days post infection, genes associated with fatty acid metabolism were identified as up regulated in expression. The value of gene set (enrichment) analyses in the analysis of microarray data is discussed.

Network analysis of adipose tissue gene expression highlights altered metabolic and regulatory transcriptomic activity in high-fat-diet-fed IL-1RI knockout mice

A subacute inflammatory phenotype is implicated in the pathology of insulin resistance (IR) and type 2 diabetes mellitus. Interleukin (IL)-1α and IL-1β are produced by innate immune cells, including macrophages, and mediate their inflammatory response through the IL-1 type I receptor (IL-IRI). This study sought to understand the transcriptomic signature of adipose tissue in obese IL-1RI(-/-) mice. Following dietary intervention, markers of insulin sensitivity and inflammation in adipose tissue were determined, and gene expression was assessed with microarrays. IL-1RI(-/-) mice fed a high-fat diet (HFD) had significantly lower plasma inflammatory cytokine concentrations than wild-type mice. Metabolic network analysis of transcriptomic effects identified up-regulation and co-expression of genes involved in lipolysis, lipogenesis and tricarboxylic acid (TCA) cycle. Further assessment of gene expression in a network of protein interactions related to innate immunity highlighted Stat3 as a potential transcriptional regulator of IL-1 signalling. The complex, downstream effects of IL-1 signalling through the IL-1RI receptor remain poorly defined. Using network-based analyses of transcriptomic signatures in IL-1RI(-/-) mice, we have identified expression changes in genes involved in lipid cycling and TCA cycle, which may be more broadly indicative of a restoration of mitochondrial function in the context of HFD. Our results also highlight a potential role for Stat3 in linking IL-1 signalling to adipogenesis and IR.

Elevated Tumor Expression of PAI-1 and SNAI2 in Obese Esophageal Adenocarcinoma Patients and Impact on Prognosis

OBJECTIVES:

Obesity is linked to increased mortality from many cancer types, and esophageal adenocarcinoma (EAC) displays one of the strongest epidemiological associations. The aims of this study are to dissect molecular pathways linking obesity with EAC and to determine if obesity is linked to increased aggressiveness of this disease.

METHODS:

Affymetrix microarrays identified altered signaling pathways in an EAC cell line following coculture with visceral adipose tissue or isolated adipocytes from viscerally obese EAC patients (n=6). Differentially expressed genes were subsequently investigated in patient tumor biopsies by quantitative reverse transcriptase PCR and examined with respect to obesity status, tumor biology, and patient survival.

RESULTS:

Visceral adipose tissue induced expression of genes involved in epithelial mesenchymal transition (EMT), plasminogen activator inhibitor (PAI)-1, and transcription factor SNAI2, in an EAC cell line. In EAC patient tumor biopsies from obese patients, we noted elevated expression of these genes, together with reduced expression of epithelial marker E-cadherin. SNAI2 was associated with EAC prognosis.

CONCLUSIONS:

Expression of EMT genes, PAI-1 and SNAI2, was elevated in tumors of obese EAC patients, and SNAI2 was associated with poor survival. Genes deregulated in obesity and associated with prognosis may represent potential targets for treatment stratification of obese EAC patients.