Novel aspects of PPARalpha-mediated regulation of lipid and xenobiotic metabolism revealed through a nutrigenomic study

Calorie Restriction Using High-Fat/Low-Carbohydrate Diet Suppresses Liver Fat Accumulation and Pancreatic Beta-Cell Dedifferentiation in Obese Diabetic Mice

In diabetes, pancreatic β-cells gradually lose their ability to secrete insulin with disease progression. β-cell dysfunction is a contributing factor to diabetes severity. Recently, islet cell heterogeneity, exemplified by β-cell dedifferentiation and identified in diabetic animals, has attracted attention as an underlying molecular mechanism of β-cell dysfunction. Previously, we reported β-cell dedifferentiation suppression by calorie restriction, not by reducing hyperglycemia using hypoglycemic agents (including sodium-glucose cotransporter inhibitors), in an obese diabetic mice model (db/db). Here, to explore further mechanisms of the effects of food intake on β-cell function, db/db mice were fed either a high-carbohydrate/low-fat diet (db-HC) or a low-carbohydrate/high-fat diet (db-HF) using similar calorie restriction regimens. After one month of intervention, body weight reduced, and glucose intolerance improved to a similar extent in the db-HC and db-HF groups. However, β-cell dedifferentiation did not improve in the db-HC group, and β-cell mass compensatory increase occurred in this group. More prominent fat accumulation occurred in the db-HC group livers. The expression levels of genes related to lipid metabolism, mainly regulated by peroxisome proliferator-activated receptor α and γ, differed significantly between groups. In conclusion, the fat/carbohydrate ratio in food during calorie restriction in obese mice affected both liver lipid metabolism and β-cell dedifferentiation.

Data integration through canonical correlation analysis and its application to OMICs research

The subject of the paper is a review of multidimensional data analysis methods, which is the canonical analysis with its various variants and its use in omics data research. The dynamic development of high-throughput methods, and with them the availability of large and constantly growing data resources, forces the development of new analytical approaches that allow the review of the analyzed processes, taking into account data from various levels of the organization of living organisms. The multidimensional perspective allows for the assessment of the analyzed phenomenon in a more realistic way, as it generally takes into account much more data (including OMICs data). Without omitting the complexity of an organism, the method simplifies the multidimensional view, finally giving the result so that the researcher can draw practical conclusions. This is particularly important in medical sciences, where the study of pathological processes is usually aimed at developing treatment regimens. One of the primary methods for studying biomedical processes in a multidimensional approach is the canonical correlation analysis (CCA) with various variants. The use of CCA unique methodologies for simultaneous analysis of multiset biomolecular data opens up new avenues for studying previously undiscovered processes and interdependencies such as e.g. in the tumor microenvironment (TME) connected to intercellular communication. Because of the huge and still untapped potential of canonical correlation, in this review available implementations of CCA techniques are presented. In particular, the possibility of using the technique of canonical correlation analysis for OMICs data is emphasized.

Parea: Multi-view ensemble clustering for cancer subtype discovery

Multi-view clustering methods are essential for the stratification of patients into sub-groups of similar molecular characteristics. In recent years, a wide range of methods have been developed for this purpose. However, due to the high diversity of cancer-related data, a single method may not perform sufficiently well in all cases. We present Parea, a multi-view hierarchical ensemble clustering approach for disease subtype discovery. We demonstrate its performance on several machine learning benchmark datasets. We apply and validate our methodology on real-world multi-view cancer patient data. Parea outperforms the current state-of-the-art on six out of seven analysed cancer types. We have integrated the Parea method into our developed Python package Pyrea (https://github.com/mdbloice/Pyrea), which enables the effortless and flexible design of ensemble workflows while incorporating a wide range of fusion and clustering algorithms.

Vanin 1 Gene Role in Modulation of iNOS/MCP-1/TGF-β1 Signaling Pathway in Obese Diabetic Patients

INTRODUCTION

Cysteamine, a powerful endogenous antioxidant, is produced mostly by the vanin-1 with pantetheinase activity. With regard to glycemic, inflammatory, and redox factors, the current study sought to evaluate the association between the expression of the vanin-1 gene, oxidative stress, and inflammatory and iNOS signaling pathway in obese diabetic patients.

METHODS

We enrolled 67 male subjects with an average age of 53.5 ± 5.0 years, divided into 4 groups according to the WHO guideline. We determined their plasma levels of glucose, insulin, IRI, HbA1c, TC, TG, HDL-C, TNF- α, MCP-1, TGF-β1, SOD, CAT, and TBARs, as well as expression of the iNOS and Vanin1 genes.

RESULTS

Overweight and obese class I and II diabetics had significantly higher levels of plasma glucose, insulin, HbA1c, TNF-α, MCP-1, TGF-β1, CAT, and TBAR as well as iNOS and vanin-1 gene expression compared to healthy control individuals. In addition, as compared to healthy control individuals, overweight obese class I and II diabetics' plasma HDL-C levels and blood SOD activity were significantly lower. In addition, ultrasound and computed tomography showed that the presence of a mild obscuring fatty liver with mild hepatic echogenicity appeared in overweight, class I and II obese diabetic patients.

CONCLUSION

These findings provide important information for understanding the correlation between Vanin 1 and glycemic, inflammatory, and redox factors in obese patients. Furthermore, US and CT analysis were performed to visualize the observed images of fatty liver due to obesity.

GW9662, a peroxisome proliferator-activated receptor gamma antagonist, attenuates the development of non-alcoholic fatty liver disease

BACKGROUND AND AIMS

Insulin resistance is among the key risk factors for the development of non-alcoholic fatty liver disease (NAFLD). Recently, it has been reported that GW9662, shown to be a potent peroxisome proliferator-activated receptor gamma (PPARγ) antagonist, may improve insulin sensitivity in settings of type 2 diabetes. Here, we determined the effects of GW9662 on the development of NAFLD and molecular mechanisms involved.

METHODS

Female C57BL/6J mice were pair-fed either a liquid control diet (C) or a fat-, fructose- and cholesterol-rich diet (FFC) for 8 weeks while either being treated with GW9662 (1 mg/kg body weight; C+GW9662 and FFC+GW9662) or vehicle (C and FFC) i.p. three times weekly. Indices of liver damage and inflammation, parameters of glucose metabolism and portal endotoxin levels were determined. Lipopolysaccharide (LPS)-challenged J774A.1 cells were treated with 10 μM GW9662.

RESULTS

Despite similar caloric intake the development of NAFLD and insulin resistance were significantly attenuated in FFC+GW9662-treated mice when compared to FFC-fed animals. Bacterial endotoxin levels in portal plasma were almost similarly increased in both FFC-fed groups while expressions of toll-like receptor 4 (Tlr4), myeloid differentiation primary response 88 (Myd88) and interleukin 1 beta (Il1b) as well as nitrite (NO₂^-) concentration in liver were significantly higher in FFC-fed mice than in FFC+GW9662-treated animals. In J774A.1 cells, treatment with GW9662 significantly attenuated LPS-induced expression of Il1b, interleukin 6 (Il6) and inducible nitric oxide synthase (iNos) as well as NO₂^- formation.

CONCLUSION

In summary, our data suggest that the PPARγ antagonist GW9662 attenuates the development of a diet-induced NAFLD and that this is associated with a protection against the activation of the TLR4 signaling cascade.

Reduction in gut-derived MUFAs via intestinal stearoyl-CoA desaturase 1 deletion drives susceptibility to NAFLD and hepatocarcinoma

Nonalcoholic fatty liver disease (NAFLD) is defined by a set of hepatic conditions ranging from steatosis to steatohepatitis (NASH), characterized by inflammation and fibrosis, eventually predisposing to hepatocellular carcinoma (HCC). Together with fatty acids (FAs) originated from adipose lipolysis and hepatic lipogenesis, intestinal‐derived FAs are major contributors of steatosis. However, the role of mono‐unsaturated FAs (MUFAs) in NAFLD development is still debated. We previously established the intestinal capacity to produce MUFAs, but its consequences in hepatic functions are still unknown. Here, we aimed to determine the role of the intestinal MUFA‐synthetizing enzyme stearoyl‐CoA desaturase 1 (SCD1) in NAFLD. We used intestinal‐specific Scd1‐KO (iScd1^−/−) mice and studied hepatic dysfunction in different models of steatosis, NASH, and HCC. Intestinal‐specific Scd1 deletion decreased hepatic MUFA proportion. Compared with controls, iScd1^−/− mice displayed increased hepatic triglyceride accumulation and derangement in cholesterol homeostasis when fed a MUFA‐deprived diet. Then, on Western diet feeding, iScd1^−/− mice triggered inflammation and fibrosis compared with their wild‐type littermates. Finally, intestinal‐Scd1 deletion predisposed mice to liver cancer. Conclusions: Collectively, these results highlight the major importance of intestinal MUFA metabolism in maintaining hepatic functions and show that gut‐derived MUFAs are protective from NASH and HCC.

High-sensitivity pattern discovery in large, paired multiomic datasets

MOTIVATION

Modern biological screens yield enormous numbers of measurements, and identifying and interpreting statistically significant associations among features are essential. In experiments featuring multiple high-dimensional datasets collected from the same set of samples, it is useful to identify groups of associated features between the datasets in a way that provides high statistical power and false discovery rate (FDR) control.

RESULTS

Here, we present a novel hierarchical framework, HAllA (Hierarchical All-against-All association testing), for structured association discovery between paired high-dimensional datasets. HAllA efficiently integrates hierarchical hypothesis testing with FDR correction to reveal significant linear and non-linear block-wise relationships among continuous and/or categorical data. We optimized and evaluated HAllA using heterogeneous synthetic datasets of known association structure, where HAllA outperformed all-against-all and other block-testing approaches across a range of common similarity measures. We then applied HAllA to a series of real-world multiomics datasets, revealing new associations between gene expression and host immune activity, the microbiome and host transcriptome, metabolomic profiling and human health phenotypes.

AVAILABILITY AND IMPLEMENTATION

An open-source implementation of HAllA is freely available at http://huttenhower.sph.harvard.edu/halla along with documentation, demo datasets and a user group.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Feature selection for kernel methods in systems biology

The substantial development of high-throughput biotechnologies has rendered large-scale multi-omics datasets increasingly available. New challenges have emerged to process and integrate this large volume of information, often obtained from widely heterogeneous sources. Kernel methods have proven successful to handle the analysis of different types of datasets obtained on the same individuals. However, they usually suffer from a lack of interpretability since the original description of the individuals is lost due to the kernel embedding. We propose novel feature selection methods that are adapted to the kernel framework and go beyond the well-established work in supervised learning by addressing the more difficult tasks of unsupervised learning and kernel output learning. The method is expressed under the form of a non-convex optimization problem with a ℓ₁ penalty, which is solved with a proximal gradient descent approach. It is tested on several systems biology datasets and shows good performances in selecting relevant and less redundant features compared to existing alternatives. It also proved relevant for identifying important governmental measures best explaining the time series of Covid-19 reproducing number evolution during the first months of 2020. The proposed feature selection method is embedded in the R package mixKernel version 0.8, published on CRAN. Installation instructions are available at http://mixkernel.clementine.wf/.

Dysregulated lipid and fatty acid metabolism link perfluoroalkyl substances exposure and impaired glucose metabolism in young adults

BACKGROUND

Per- and polyfluoroalkyl substances (PFASs) exposure is ubiquitous among the US population and has been linked to adverse health outcomes including cardiometabolic diseases, immune dysregulation and endocrine disruption. However, the metabolic mechanism underlying the adverse health effect of PFASs exposure is unknown.

OBJECTIVE

The aim of this project is to investigate the association between PFASs exposure and altered metabolic pathways linked to increased cardiometabolic risk in young adults.

METHODS

A total of 102 young adults with 82% overweight or obese participants were enrolled from Southern California between 2014 and 2017. Cardiometabolic outcomes were assessed including oral glucose tolerance test (OGTT) measures, body fat and lipid profiles. High-resolution metabolomics was used to quantify plasma exposure levels of three PFAS congeners and intensity profiles of the untargeted metabolome. Fasting concentrations of 45 targeted metabolites involved in fatty acid and lipid metabolism were used to verify untargeted metabolomics findings. Bayesian Kernel Machine Regression (BKMR) was used to examine the associations between PFAS exposure mixture and cardiometabolic outcomes adjusting for covariates. Mummichog pathway enrichment analysis was used to explore PFAS-associated metabolic pathways. Moreover, the effect of PFAS exposure on the metabolic network, including metabolomic profiles and cardiometabolic outcomes, was investigated.

RESULTS

Higher exposure to perfluorooctanoic acid (PFOA) was associated with higher 30-minute glucose levels and glucose area under the curve (AUC) during the OGTT (p < 0.001). PFAS exposure was also associated with altered lipid pathways, which contributed to the metabolic network connecting PFOA and higher glucose levels following the OGTT. Targeted metabolomics analysis indicated that higher PFOA exposure was associated with higher levels of glycerol (p = 0.006), which itself was associated with higher 30-minute glucose (p = 0.006).

CONCLUSIONS

Increased lipolysis and fatty acid oxidation could contribute to the biological mechanisms linking PFAS exposure and impaired glucose metabolism among young adults. Findings of this study warrants future experimental studies and epidemiological studies with larger sample size to replicate.

Integrating multi-OMICS data through sparse canonical correlation analysis for the prediction of complex traits: a comparison study

Motivation

Recent developments in technology have enabled researchers to collect multiple OMICS datasets for the same individuals. The conventional approach for understanding the relationships between the collected datasets and the complex trait of interest would be through the analysis of each OMIC dataset separately from the rest, or to test for associations between the OMICS datasets. In this work we show that integrating multiple OMICS datasets together, instead of analysing them separately, improves our understanding of their in-between relationships as well as the predictive accuracy for the tested trait. Several approaches have been proposed for the integration of heterogeneous and high-dimensional (p≫n) data, such as OMICS. The sparse variant of canonical correlation analysis (CCA) approach is a promising one that seeks to penalize the canonical variables for producing sparse latent variables while achieving maximal correlation between the datasets. Over the last years, a number of approaches for implementing sparse CCA (sCCA) have been proposed, where they differ on their objective functions, iterative algorithm for obtaining the sparse latent variables and make different assumptions about the original datasets.

Results

Through a comparative study we have explored the performance of the conventional CCA proposed by Parkhomenko et al., penalized matrix decomposition CCA proposed by Witten and Tibshirani and its extension proposed by Suo et al. The aforementioned methods were modified to allow for different penalty functions. Although sCCA is an unsupervised learning approach for understanding of the in-between relationships, we have twisted the problem as a supervised learning one and investigated how the computed latent variables can be used for predicting complex traits. The approaches were extended to allow for multiple (more than two) datasets where the trait was included as one of the input datasets. Both ways have shown improvement over conventional predictive models that include one or multiple datasets.

Availability and implementation

https://github.com/theorod93/sCCA.

Supplementary information

Supplementary data are available at Bioinformatics online.

Cloning of the gene encoding acyl-CoA thioesterase 11 and its functional characterization in hybrid yellow catfish (Pelteobagrus fulvidraco ♀ × Pelteobagrus vachelli ♂) under heat stress

Members of the ACOT (acyl-CoA thioesterase) family hydrolyze fatty acyl-CoA to form free fatty acids (FFAs) and coenzyme A (CoA). These enzymes play important roles in fatty acid metabolism. Here, we report the cloning and functional analysis of acot11β in hybrid yellow catfish (Pelteobagrus fulvidraco ♀ × P. vachelli ♂). The open reading frame of acot11β was found to be 594 bp in length, encoding 198 amino acids. We determined the transcript levels of acot11β in ten tissues of hybrid yellow catfish by qRT-PCR and found that it was highly expressed in the liver, so we chose the liver for further analysis. We determined the transcript levels of acot11β in hybrid yellow catfish under heat stress conditions, and analyzed the changes in serum biochemical parameters, liver biochemical parameters, and transcript levels of lipid metabolism-related genes. Healthy yellow catfish were subjected to heat stress at 35 °C for 96 h, and the experimental results were compared with those from fish in a control group (28 °C). The levels of glucose (GLU), total cholesterol (TC), and triglyceride (TG) in serum were significantly increased in the heat-stressed group compared with the control group (P < 0.05). Acute heat stress led to decreased liver glycogen contents, but significantly increased TC and TG contents in the liver (P < 0.05). The transcript levels of acot11β, acc, and fas were significantly reduced, while that of pparα was significantly increased in hybrid yellow catfish exposed to heat stress (P < 0.05). Our results indicate that acot11β plays an important role in regulating lipid metabolism in hybrid yellow catfish, and this metabolic process is greatly affected by temperature. These results may be useful for developing effective strategies to prevent or reduce metabolic disorders of yellow catfish caused by high temperature.

OmicsON - Integration of omics data with molecular networks and statistical procedures

A huge amount of atomized biological data collected in various databases and the need for a description of their relation by theoretical methods causes the development of data integration methods. The omics data analysis by integration of biological knowledge with mathematical procedures implemented in the OmicsON R library is presented in the paper. OmicsON is a tool for the integration of two sets of data: transcriptomics and metabolomics. In the workflow of the library, the functional grouping and statistical analysis are applied. Subgroups among the transcriptomic and metabolomics sets are created based on the biological knowledge stored in Reactome and String databases. It gives the possibility to analyze such sets of data by multivariate statistical procedures like Canonical Correlation Analysis (CCA) or Partial Least Squares (PLS). The integration of metabolomic and transcriptomic data based on the methodology contained in OmicsON helps to easily obtain information on the connection of data from two different sets. This information can significantly help in assessing the relationship between gene expression and metabolite concentrations, which in turn facilitates the biological interpretation of the analyzed process.

Peroxisome Proliferator-Activated Receptors and Their Novel Ligands as Candidates for the Treatment of Non-Alcoholic Fatty Liver Disease

Non-alcoholic fatty liver disease (NAFLD) is a major health issue worldwide, frequently associated with obesity and type 2 diabetes. Steatosis is the initial stage of the disease, which is characterized by lipid accumulation in hepatocytes, which can progress to non-alcoholic steatohepatitis (NASH) with inflammation and various levels of fibrosis that further increase the risk of developing cirrhosis and hepatocellular carcinoma. The pathogenesis of NAFLD is influenced by interactions between genetic and environmental factors and involves several biological processes in multiple organs. No effective therapy is currently available for the treatment of NAFLD. Peroxisome proliferator-activated receptors (PPARs) are nuclear receptors that regulate many functions that are disturbed in NAFLD, including glucose and lipid metabolism, as well as inflammation. Thus, they represent relevant clinical targets for NAFLD. In this review, we describe the determinants and mechanisms underlying the pathogenesis of NAFLD, its progression and complications, as well as the current therapeutic strategies that are employed. We also focus on the complementary and distinct roles of PPAR isotypes in many biological processes and on the effects of first-generation PPAR agonists. Finally, we review novel and safe PPAR agonists with improved efficacy and their potential use in the treatment of NAFLD.

Identification of a hormone response element that mediates suppression of APOF by LXR and PPARα agonists

Apolipoprotein F (ApoF) regulates cholesteryl ester transfer protein activity. We previously observed that hepatic APOF mRNA levels are decreased by high fat, cholesterol-enriched diets. Here we show in human liver C3A cells that APOF mRNA levels are reduced by agonists of LXR and PPARα nuclear receptors. This negative regulation requires co-incubation with the RXR agonist, retinoic acid. Bioinformatic analysis of the ~2 kb sequence upstream of the APOF promoter identified one potential LXR and 4 potential PPARα binding sites clustered between nucleotides -2007 and -1961. ChIP analysis confirmed agonist-dependent binding of LXRα, PPARα, and RXRα to this hormone response element complex (HREc). A luciferase reporter containing the 2 kb 5' APOF sequence was negatively regulated by LXR and PPARα ligands as seen in cells. This regulation was maintained in constructs lacking the ~1700 nucleotides between the HREc and the APOF proximal promoter. Mutations of the HREc that disrupted LXRα and PPARα binding led to the loss of reporter construct inhibition by agonists of these nuclear receptors. siRNA knockdown studies showed that APOF gene regulation by LXRα or PPARα agonists did not require an interaction between these two nuclear receptors. Thus, APOF is subject to negative regulation by agonist-activated LXR or PPARα nuclear receptors binding to a regulatory element ~1900 bases 5' to the APOF promoter. High fat, cholesterol-enriched diets likely reduce APOF gene expression via these receptors interacting at this regulatory site.

A whitening approach to probabilistic canonical correlation analysis for omics data integration

BACKGROUND

Canonical correlation analysis (CCA) is a classic statistical tool for investigating complex multivariate data. Correspondingly, it has found many diverse applications, ranging from molecular biology and medicine to social science and finance. Intriguingly, despite the importance and pervasiveness of CCA, only recently a probabilistic understanding of CCA is developing, moving from an algorithmic to a model-based perspective and enabling its application to large-scale settings.

RESULTS

Here, we revisit CCA from the perspective of statistical whitening of random variables and propose a simple yet flexible probabilistic model for CCA in the form of a two-layer latent variable generative model. The advantages of this variant of probabilistic CCA include non-ambiguity of the latent variables, provisions for negative canonical correlations, possibility of non-normal generative variables, as well as ease of interpretation on all levels of the model. In addition, we show that it lends itself to computationally efficient estimation in high-dimensional settings using regularized inference. We test our approach to CCA analysis in simulations and apply it to two omics data sets illustrating the integration of gene expression data, lipid concentrations and methylation levels.

CONCLUSIONS

Our whitening approach to CCA provides a unifying perspective on CCA, linking together sphering procedures, multivariate regression and corresponding probabilistic generative models. Furthermore, we offer an efficient computer implementation in the "whitening" R package available at https://CRAN.R-project.org/package=whitening .

Insights into the role of hepatocyte PPARα activity in response to fasting

The liver plays a central role in the regulation of fatty acid metabolism. Hepatocytes are highly sensitive to nutrients and hormones that drive extensive transcriptional responses. Nuclear hormone receptors are key transcription factors involved in this process. Among these factors, PPARα is a critical regulator of hepatic lipid catabolism during fasting. This study aimed to analyse the wide array of hepatic PPARα-dependent transcriptional responses during fasting. We compared gene expression in male mice with a hepatocyte specific deletion of PPARα and their wild-type littermates in the fed (ad libitum) and 24-h fasted states. Liver samples were acquired, and transcriptome and lipidome analyses were performed. Our data extended and confirmed the critical role of hepatocyte PPARα as a central for regulator of gene expression during starvation. Interestingly, we identified novel PPARα-sensitive genes, including Cxcl-10, Rab30, and Krt23. We also found that liver phospholipid remodelling was a novel fasting-sensitive pathway regulated by PPARα. These results may contribute to investigations on transcriptional control in hepatic physiology and underscore the clinical relevance of drugs that target PPARα in liver pathologies, such as non-alcoholic fatty liver disease.

Decreased Fatty Acid β-Oxidation Is the Main Cause of Fatty Liver Induced by Polyunsaturated Fatty Acid Deficiency in Mice

Insufficient intake of polyunsaturated fatty acids (PUFA) causes fatty liver. The mechanism responsible is primarily related to increased lipogenesis and decreased FA degradation based on rodent studies. However, these studies were limited by the fact that the typical PUFA-deficient diets contained insufficient amounts of long-chain FA, the PUFA-containing diets were primarily composed of n-3 PUFA-enriched oil, and the intake of PUFA was excessive compared with the physiological requirement. To address these issues, mice were fed a PUFA-deficient diet containing long-chain FA at a standard fed level and then were orally fed a n-3/n-6-balanced PUFA-containing oil [PUFA (+)] or a PUFA-deficient oil [PUFA (-)] at physiological relevant levels (0.1 mL/mouse/2d). We compared these groups and examined whether fatty liver in PUFA deficiency was attributable to both the effects of increased lipogenesis and decreased FA catabolism. Compared with the PUFA (+) group, the PUFA (-) group showed increases in liver triglyceride and serum FA content. Hepatic gene expression of several mitochondrial β-oxidation enzymes, the serum 3-hydroxybutyrate level, and DNA-binding ability of peroxisome proliferator-activated receptor α (PPARα) were increased in the PUFA (+) group, whereas these adaptive responses were significantly attenuated in the PUFA (-) group. The hepatic expression of typical lipogenesis genes did not differ between the groups. Therefore, fatty liver in PUFA deficiency is attributable to suppression of the FA-degrading system probably from decreased PPARα adaptive responsiveness, and PUFA may be an essential factor for PPARα functioning. This finding is helpful for managing clinical situations having a risk of PUFA deficiency.

Qualitative and Quantitative Evaluation of Dietary Intake in Patients with Non-Alcoholic Steatohepatitis

There are very few reports about the intake of nutrients for the development or progression of non-alcoholic steatohepatitis (NASH). The aim of this study was to identify the dietary habits and the nutrient intake in patients with NASH, in comparison to chronic hepatitis C (HCV)-related patients. We prospectively evaluated the intake of macronutrients and micronutrients in 124 NAFLD and 162 HCV patients, compared to 2326 subjects as a control group. We noticed major differences in macro- and micronutrients intakes in NASH and HCV patients compared to controls. Proteins, carbohydrate (glucose, fructose, sucrose, maltose and amide), saturated fatty acid (SFA), monounsaturated fatty acid (MUFA), folic acid, vitamin A and C (p < 0.0001), and thiamine (p < 0.0003) ingestion was found to be higher in patients with NASH, while total lipids, polyunsaturated fatty acid (PUFA), riboflavin and vitamin B6 daily intake were lower compared to controls (p < 0.0001). Similarly, NASH patients had significantly reduced carbohydrate intake (p < 0.0001) and an increased intake of calcium (p < 0.0001) compared to HCV positive patients. Finally, we showed in NASH males an increase in the intake of SFA, PUFA, soluble carbohydrates (p < 0.0001) and a decrease in the amount of fiber (p < 0.0001) compared to control males. In NASH female population, we showed an increase of daily total calories, SFA, MUFA, soluble carbohydrates, starch and vitamin D ingested (p < 0.0001) with a reduction of fibers and calcium (p < 0.0001) compared to control females. This study showed how NASH patients’ diets, in both male and females, is affected by a profound alteration in macro- and micronutrients intake.

Intervention in prediction measure: a new approach to assessing variable importance for random forests

Background

Random forests are a popular method in many fields since they can be successfully applied to complex data, with a small sample size, complex interactions and correlations, mixed type predictors, etc. Furthermore, they provide variable importance measures that aid qualitative interpretation and also the selection of relevant predictors. However, most of these measures rely on the choice of a performance measure. But measures of prediction performance are not unique or there is not even a clear definition, as in the case of multivariate response random forests.

Methods

A new alternative importance measure, called Intervention in Prediction Measure, is investigated. It depends on the structure of the trees, without depending on performance measures. It is compared with other well-known variable importance measures in different contexts, such as a classification problem with variables of different types, another classification problem with correlated predictor variables, and problems with multivariate responses and predictors of different types.

Results

Several simulation studies are carried out, showing the new measure to be very competitive. In addition, it is applied in two well-known bioinformatics applications previously used in other papers. Improvements in performance are also provided for these applications by the use of this new measure.

Conclusions

This new measure is expressed as a percentage, which makes it attractive in terms of interpretability. It can be used with new observations. It can be defined globally, for each class (in a classification problem) and case-wise. It can easily be computed for any kind of response, including multivariate responses. Furthermore, it can be used with any algorithm employed to grow each individual tree. It can be used in place of (or in addition to) other variable importance measures.

Electronic supplementary material

The online version of this article (doi:10.1186/s12859-017-1650-8) contains supplementary material, which is available to authorized users.

Sparse kernel canonical correlation analysis for discovery of nonlinear interactions in high-dimensional data

BACKGROUND

Advance in high-throughput technologies in genomics, transcriptomics, and metabolomics has created demand for bioinformatics tools to integrate high-dimensional data from different sources. Canonical correlation analysis (CCA) is a statistical tool for finding linear associations between different types of information. Previous extensions of CCA used to capture nonlinear associations, such as kernel CCA, did not allow feature selection or capturing of multiple canonical components. Here we propose a novel method, two-stage kernel CCA (TSKCCA) to select appropriate kernels in the framework of multiple kernel learning.

RESULTS

TSKCCA first selects relevant kernels based on the HSIC criterion in the multiple kernel learning framework. Weights are then derived by non-negative matrix decomposition with L1 regularization. Using artificial datasets and nutrigenomic datasets, we show that TSKCCA can extract multiple, nonlinear associations among high-dimensional data and multiplicative interactions among variables.

CONCLUSIONS

TSKCCA can identify nonlinear associations among high-dimensional data more reliably than previous nonlinear CCA methods.

Bezafibrate ameliorates diabetes via reduced steatosis and improved hepatic insulin sensitivity in diabetic TallyHo mice

OBJECTIVE

Recently, we have shown that Bezafibrate (BEZ), the pan-PPAR (peroxisome proliferator-activated receptor) activator, ameliorated diabetes in insulin deficient streptozotocin treated diabetic mice. In order to study whether BEZ can also improve glucose metabolism in a mouse model for fatty liver and type 2 diabetes, the drug was applied to TallyHo mice.

METHODS

TallyHo mice were divided into an early (ED) and late (LD) diabetes progression group and both groups were treated with 0.5% BEZ (BEZ group) or standard diet (SD group) for 8 weeks. We analyzed plasma parameters, pancreatic beta-cell morphology, and mass as well as glucose metabolism of the BEZ-treated and control mice. Furthermore, liver fat content and composition as well as hepatic gluconeogenesis and mitochondrial mass were determined.

RESULTS

Plasma lipid and glucose levels were markedly reduced upon BEZ treatment, which was accompanied by elevated insulin sensitivity index as well as glucose tolerance, respectively. BEZ increased islet area in the pancreas. Furthermore, BEZ treatment improved energy expenditure and metabolic flexibility. In the liver, BEZ ameliorated steatosis, modified lipid composition and increased mitochondrial mass, which was accompanied by reduced hepatic gluconeogenesis.

CONCLUSIONS

Our data showed that BEZ ameliorates diabetes probably via reduced steatosis, enhanced hepatic mitochondrial mass, improved metabolic flexibility and elevated hepatic insulin sensitivity in TallyHo mice, suggesting that BEZ treatment could be beneficial for patients with NAFLD and impaired glucose metabolism.

PPARs and Mitochondrial Metabolism: From NAFLD to HCC

Metabolic related diseases, such as type 2 diabetes, metabolic syndrome, and nonalcoholic fatty liver disease (NAFLD), are widespread threats which bring about a significant burden of deaths worldwide, mainly due to cardiovascular events and cancer. The pathogenesis of these diseases is extremely complex, multifactorial, and only partially understood. As the main metabolic organ, the liver is central to maintain whole body energetic homeostasis. At the cellular level, mitochondria are the metabolic hub connecting and integrating all the main biochemical, hormonal, and inflammatory signaling pathways to fulfill the energetic and biosynthetic demand of the cell. In the liver, mitochondria metabolism needs to cope with the energetic regulation of the whole body. The nuclear receptors PPARs orchestrate lipid and glucose metabolism and are involved in a variety of diseases, from metabolic disorders to cancer. In this review, focus is placed on the roles of PPARs in the regulation of liver mitochondrial metabolism in physiology and pathology, from NAFLD to HCC.

Hepatic Fasting-Induced PPARα Activity Does Not Depend on Essential Fatty Acids

The liver plays a central role in the regulation of fatty acid metabolism, which is highly sensitive to transcriptional responses to nutrients and hormones. Transcription factors involved in this process include nuclear hormone receptors. One such receptor, PPARα, which is highly expressed in the liver and activated by a variety of fatty acids, is a critical regulator of hepatic fatty acid catabolism during fasting. The present study compared the influence of dietary fatty acids and fasting on hepatic PPARα-dependent responses. Pparα^−/− male mice and their wild-type controls were fed diets containing different fatty acids for 10 weeks prior to being subjected to fasting or normal feeding. In line with the role of PPARα in sensing dietary fatty acids, changes in chronic dietary fat consumption influenced liver damage during fasting. The changes were particularly marked in mice fed diets lacking essential fatty acids. However, fasting, rather than specific dietary fatty acids, induced acute PPARα activity in the liver. Taken together, the data imply that the potent signalling involved in triggering PPARα activity during fasting does not rely on essential fatty acid-derived ligand.

n -3 Fatty acids preserve muscle mass and insulin sensitivity in a rat model of energy restriction

In obese subjects, the loss of fat mass during energy restriction is often accompanied by a loss of muscle mass. The hypothesis thatn-3 PUFA, which modulate protein homoeostasis via effects on insulin sensitivity, could contribute to maintain muscle mass during energy restriction was tested in rats fed a high-fat diet (4 weeks) rich in 18 : 1n-9 (oleic acid, OLE-R), 18 : 3n-3 (α-linolenic acid, ALA-R) orn-3 long-chain (LC-R) fatty acid and then energy restricted (8 weeks). A control group (OLE-ad libitum(AL)) was maintained with AL diet throughout the study. Rats were killed 10 min after an i.v. insulin injection. All energy-restricted rats lost weight and fat mass, but only the OLE-R group showed a significant muscle loss. TheGastrocnemiusmuscle was enriched with ALA in the ALA-R group and with LC-PUFA in the ALA-R and LC-R groups. The proteolytic ubiquitin–proteasome system was differentially affected by energy restriction, with MAFbx and muscle ring finger-1 mRNA levels being decreased in the LC-R group (−30 and −20 %, respectively). RAC-αserine/threonine-protein kinase and insulin receptor substrate 1 phosphorylation levels increased in the LC-R group (+70 %), together with insulin receptor mRNA (+50 %). The ALA-R group showed the same overall activation pattern as the LC-R group, although to a lesser extent. In conclusion, dietaryn-3 PUFA prevent the loss of muscle mass associated with energy restriction, probably by an improvement in the insulin-signalling pathway activation, in relation to enrichment of plasma membranes inn-3 LC-PUFA.

Robust sparse canonical correlation analysis

Background

Canonical correlation analysis (CCA) is a multivariate statistical method which describes the associations between two sets of variables. The objective is to find linear combinations of the variables in each data set having maximal correlation. In genomics, CCA has become increasingly important to estimate the associations between gene expression data and DNA copy number change data. The identification of such associations might help to increase our understanding of the development of diseases such as cancer. However, these data sets are typically high-dimensional, containing a lot of variables relative to the number of objects. Moreover, the data sets might contain atypical observations since it is likely that objects react differently to treatments. We discuss a method for Robust Sparse CCA, thereby providing a solution to both issues. Sparse estimation produces canonical vectors with some of their elements estimated as exactly zero. As such, their interpretability is improved. Robust methods can cope with atypical observations in the data.

Results

We illustrate the good performance of the Robust Sparse CCA method by several simulation studies and three biometric examples. Robust Sparse CCA considerably outperforms its main alternatives in (1) correctly detecting the main associations between the data sets, in (2) accurately estimating these associations, and in (3) detecting outliers.

Conclusions

Robust Sparse CCA delivers interpretable canonical vectors, while at the same time coping with outlying observations. The proposed method is able to describe the associations between high-dimensional data sets, which are nowadays commonplace in genomics.

Furthermore, the Robust Sparse CCA method allows to characterize outliers.

Electronic supplementary material

The online version of this article (doi:10.1186/s12918-016-0317-9) contains supplementary material, which is available to authorized users.

Inferring differentially expressed pathways using kernel maximum mean discrepancy-based test

Background

Pathway expression is multivariate in nature. Thus, from a statistical perspective, to detect differentially expressed pathways between two conditions, methods for inferring differences between mean vectors need to be applied. Maximum mean discrepancy (MMD) is a statistical test to determine whether two samples are from the same distribution, its implementation being greatly simplified using the kernel method.

Results

An MMD-based test successfully detected the differential expression between two conditions, specifically the expression of a set of genes involved in certain fatty acid metabolic pathways. Furthermore, we exploited the ability of the kernel method to integrate data and successfully added hepatic fatty acid levels to the test procedure.

Conclusion

MMD is a non-parametric test that acquires several advantages when combined with the kernelization of data: 1) the number of variables can be greater than the sample size; 2) omics data can be integrated; 3) it can be applied not only to vectors, but to strings, sequences and other common structured data types arising in molecular biology.

NO synthesis from arginine is favored by α-linolenic acid in mice fed a high-fat diet

Alterations in NO availability and signaling play a pivotal role at early stages of the metabolic syndrome (MetSynd). We hypothesized that dietary α-linolenic acid (ALA, 18:3 n-3) favors NO availability by modulating amino acid metabolism, with a specific impact on the arginine-NO pathway. Mice were fed a hyperlipidic diet (285 g lipid/kg, 51.1 % energy), rich in either saturated fatty acids (SFA, provided by palm oil, PALM group) or ALA (provided by linseed oil, LIN group). We measured whole-body NO synthesis and systemic arginine hydrolysis with a tracer-based method, plasma concentration of related metabolites, and hepatic mRNA level of related enzymes, and the study was completed by a transcriptomic analysis in the liver. As expected with this model, hyperlipidic diets resulted in increased adiposity and glycemia after 5 weeks. As compared to PALM mice, LIN mice had a higher plasma nitrite and nitrate concentration, a higher whole-body conversion of arginine into NO vs urea, and a similar plasma concentration of asymmetric dimethylarginine (ADMA), despite a higher expression of the liver dimethylargininase-1. In LIN mice, there was a higher expression of genes involved in PPARα signaling, but a little impact on gene expression related to amino acids and arginine metabolism. This effect cannot be directly ascribed to changes in arginase activity in the liver or ADMA metabolism, nor to direct regulation of the related target genes. In conclusion, dietary ALA favors NO synthesis, which could contribute to rescue NO availability when jeopardized by the nutritional conditions in relation with the initiation of the MetSynd.

Progress and prospects of long noncoding RNAs in lipid homeostasis

BACKGROUND

Long noncoding RNAs (lncRNAs) are a novel group of universally present, non-coding RNAs (>200 nt) that are increasingly recognized as key regulators of many physiological and pathological processes.

SCOPE OF REVIEW

Recent publications have shown that lncRNAs influence lipid homeostasis by controlling lipid metabolism in the liver and by regulating adipogenesis. lncRNAs control lipid metabolism-related gene expression by either base-pairing with RNA and DNA or by binding to proteins.

MAJOR CONCLUSIONS

The recent advances and future prospects in understanding the roles of lncRNAs in lipid homeostasis are discussed.

DNA microarray integromics analysis platform

BACKGROUND

The study of interactions between molecules belonging to different biochemical families (such as lipids and nucleic acids) requires specialized data analysis methods. This article describes the DNA Microarray Integromics Analysis Platform, a unique web application that focuses on computational integration and analysis of "multi-omics" data. Our tool supports a range of complex analyses, including - among others - low- and high-level analyses of DNA microarray data, integrated analysis of transcriptomics and lipidomics data and the ability to infer miRNA-mRNA interactions.

RESULTS

We demonstrate the characteristics and benefits of the DNA Microarray Integromics Analysis Platform using two different test cases. The first test case involves the analysis of the nutrimouse dataset, which contains measurements of the expression of genes involved in nutritional problems and the concentrations of hepatic fatty acids. The second test case involves the analysis of miRNA-mRNA interactions in polysaccharide-stimulated human dermal fibroblasts infected with porcine endogenous retroviruses.

CONCLUSIONS

The DNA Microarray Integromics Analysis Platform is a web-based graphical user interface for "multi-omics" data management and analysis. Its intuitive nature and wide range of available workflows make it an effective tool for molecular biology research. The platform is hosted at https://lifescience.plgrid.pl/.

Ketone Body Therapy Protects From Lipotoxicity and Acute Liver Failure Upon Pparα Deficiency

Acute liver failure (ALF) is a severe and rapid liver injury, often occurring without any preexisting liver disease, which may precipitate multiorgan failure and death. ALF is often associated with impaired β-oxidation and increased oxidative stress (OS), characterized by elevated levels of hepatic reactive oxygen species (ROS) and lipid peroxidation (LPO) products. Peroxisome proliferator-activated receptor (PPAR)α has been shown to confer hepatoprotection in acute and chronic liver injury, at least in part, related to its ability to control peroxisomal and mitochondrial β-oxidation. To study the pathophysiological role of PPARα in hepatic response to high OS, we induced a pronounced LPO by treating wild-type and Pparα-deficient mice with high doses of fish oil (FO), containing n-3 polyunsaturated fatty acids. FO feeding of Pparα-deficient mice, in contrast to control sunflower oil, surprisingly induced coma and death due to ALF as indicated by elevated serum alanine aminotransferase, aspartate aminotransferase, ammonia, and a liver-specific increase of ROS and LPO-derived malondialdehyde. Reconstitution of PPARα specifically in the liver using adeno-associated serotype 8 virus-PPARα in Pparα-deficient mice restored β-oxidation and ketogenesis and protected mice from FO-induced lipotoxicity and death. Interestingly, administration of the ketone body β-hydroxybutyrate prevented FO-induced ALF in Pparα-deficient mice, and normalized liver ROS and malondialdehyde levels. Therefore, PPARα protects the liver from FO-induced OS through its regulatory actions on ketone body levels. β-Hydroxybutyrate treatment could thus be an option to prevent LPO-induced liver damage.

ONION: Functional Approach for Integration of Lipidomics and Transcriptomics Data

To date, the massive quantity of data generated by high-throughput techniques has not yet met bioinformatics treatment required to make full use of it. This is partially due to a mismatch in experimental and analytical study design but primarily due to a lack of adequate analytical approaches. When integrating multiple data types e.g. transcriptomics and metabolomics, multidimensional statistical methods are currently the techniques of choice. Typical statistical approaches, such as canonical correlation analysis (CCA), that are applied to find associations between metabolites and genes are failing due to small numbers of observations (e.g. conditions, diet etc.) in comparison to data size (number of genes, metabolites). Modifications designed to cope with this issue are not ideal due to the need to add simulated data resulting in a lack of p-value computation or by pruning of variables hence losing potentially valid information. Instead, our approach makes use of verified or putative molecular interactions or functional association to guide analysis. The workflow includes dividing of data sets to reach the expected data structure, statistical analysis within groups and interpretation of results. By applying pathway and network analysis, data obtained by various platforms are grouped with moderate stringency to avoid functional bias. As a consequence CCA and other multivariate models can be applied to calculate robust statistics and provide easy to interpret associations between metabolites and genes to leverage understanding of metabolic response. Effective integration of lipidomics and transcriptomics is demonstrated on publically available murine nutrigenomics data sets. We are able to demonstrate that our approach improves detection of genes related to lipid metabolism, in comparison to applying statistics alone. This is measured by increased percentage of explained variance (95% vs. 75–80%) and by identifying new metabolite-gene associations related to lipid metabolism.

N-3 fatty acids improve body composition and insulin sensitivity during energy restriction in the rat

The hypothesis that n-3 polyunsaturated fatty acids (PUFA) could contribute to maintain muscle mass during energy restriction aiming to weight loss was tested in the rat, with special attention paid to insulin signalling. After 10 weeks on a diet rich in lipids and sucrose, male rats were energy restricted and fed diets rich in 18:1 n-9 (OLE), 18:3 n-3 (ALA) or n-3 long-chain (LC, >18 carbons) PUFA. After 4 weeks, they were killed after an insulin injection. Red blood cells, liver, and Gastrocnemius muscle were enriched in ALA in the ALA group, and in LC-PUFA in the ALA and LC groups. The LC diet resulted in a higher weight loss, without negative impact on the muscle weight. In parallel, hepatic phosphorylation of insulin receptor and IRS1 was the highest in this group. This suggests that the trend we observed in the preservation of protein homeostasis in the LC group is mediated, at least partly, by an enhancement of the early steps of insulin signalling resulting from cell membrane enrichment in n-3 PUFA.

The effect of a moderate zinc deficiency and dietary fat source on the activity and expression of the Δ(3)Δ (2)-enoyl-CoA isomerase in the liver of growing rats

Auxiliary enzymes participate in β-oxidation of unsaturated fatty acids. The objective of the study was to investigate the impact of a moderate zinc deficiency and a high intake of polyunsaturated fat on Δ(3)Δ(2)-enoyl-CoA isomerase (ECI) in the liver and other tissues. Five groups of eight weanling rats each were fed moderately zinc-deficient (ZD) or zinc-adequate (ZA) semisynthetic diets (7 or 50 mg Zn/kg) enriched with 22 % cocoa butter (CB) or 22 % safflower oil (SO) for 4 weeks: (1) ZD-CB, fed free choice; (2) ZA-CBR, ZA-CB diet fed in equivalent amounts consumed by the ZD-CB group; (3) ZD-SO, fed free choice; (4) ZA-SOR, ZA-SO diet fed in equivalent amounts consumed by the ZD-SO group; and (5) ZA-SO, fed free choice. Growth and Zn status markers were markedly reduced in the ZD groups. ECI activity in the liver of the animals fed the ZD- and ZA-SO diets were significantly higher (approximately 2- and 3-fold, respectively) as compared with the CB-fed animals, whereas activities in extrahepatic tissues (kidneys, heart, skeletal muscle, testes, adipose tissue) were not altered by dietary treatments. Transcript levels of the mitochondrial Eci gene in the liver did not significantly differ between ZD and ZA rats, but were 1.6-fold higher in the ZA-SO- than in the ZD-CB-fed animals (P < 0.05). It is concluded that diets enriched with safflower oil as a source high in linoleic acid induce markedly increased hepatic ECI activities and that a moderate Zn deficiency does not affect transcription of the mitochondrial Eci gene in the liver.

A Moderate Zinc Deficiency Does Not Impair Gene Expression of PPARα, PPARγ, and Mitochondrial Enoyl-CoA Delta Isomerase in the Liver of Growing Rats

The aim of the study was to investigate the impact of a moderate zinc deficiency and a high intake of polyunsaturated fat on the mRNA expression of peroxisome-proliferator-activated receptor alpha (PPARα), peroxisome-proliferator-activated receptor gamma (PPARγ), and mitochondrial Δ3Δ2-enoyl-CoA isomerase (ECI) in the liver. Weanling rats were assigned to five groups (eight animals each) and fed semi-synthetic, low-carbohydrate diets containing 7 or 50 mg Zn/kg (low-Zn (LZ) or high-Zn (HZ)) and 22% cocoa butter (CB) or 22% safflower (SF) oil for four weeks. One group each was fed the LZ-CB, LZ-SF, or HZ-SF diet free choice, and one group each was fed the HZ-CB and HZ-SF diets in restricted amounts according to intake of the respective LZ diets. The LZ diets markedly lowered growth and zinc concentrations in plasma and femur. Hepatic mRNA levels of PPARα, PPARγ, and ECI were not reduced by the moderate zinc deficiency. Overall, ECI-mRNA abundance was marginally higher in the SF-fed than in the CB-fed animals.

Kernel-PCA data integration with enhanced interpretability

Background

Nowadays, combining the different sources of information to improve the biological knowledge available is a challenge in bioinformatics. One of the most powerful methods for integrating heterogeneous data types are kernel-based methods. Kernel-based data integration approaches consist of two basic steps: firstly the right kernel is chosen for each data set; secondly the kernels from the different data sources are combined to give a complete representation of the available data for a given statistical task.

Results

We analyze the integration of data from several sources of information using kernel PCA, from the point of view of reducing dimensionality. Moreover, we improve the interpretability of kernel PCA by adding to the plot the representation of the input variables that belong to any dataset. In particular, for each input variable or linear combination of input variables, we can represent the direction of maximum growth locally, which allows us to identify those samples with higher/lower values of the variables analyzed.

Conclusions

The integration of different datasets and the simultaneous representation of samples and variables together give us a better understanding of biological knowledge.

Integrated physiology and systems biology of PPARα

The Peroxisome Proliferator Activated Receptor alpha (PPARα) is a transcription factor that plays a major role in metabolic regulation. This review addresses the functional role of PPARα in intermediary metabolism and provides a detailed overview of metabolic genes targeted by PPARα, with a focus on liver. A distinction is made between the impact of PPARα on metabolism upon physiological, pharmacological, and nutritional activation. Low and high throughput gene expression analyses have allowed the creation of a comprehensive map illustrating the role of PPARα as master regulator of lipid metabolism via regulation of numerous genes. The map puts PPARα at the center of a regulatory hub impacting fatty acid uptake, fatty acid activation, intracellular fatty acid binding, mitochondrial and peroxisomal fatty acid oxidation, ketogenesis, triglyceride turnover, lipid droplet biology, gluconeogenesis, and bile synthesis/secretion. In addition, PPARα governs the expression of several secreted proteins that exert local and endocrine functions.

Mode of action framework analysis for receptor-mediated toxicity: The peroxisome proliferator-activated receptor alpha (PPARα) as a case study

Several therapeutic agents and industrial chemicals induce liver tumors in rodents through the activation of the peroxisome proliferator-activated receptor alpha (PPARα). The cellular and molecular events by which PPARα activators induce rodent hepatocarcinogenesis has been extensively studied and elucidated. This review summarizes the weight of evidence relevant to the hypothesized mode of action (MOA) for PPARα activator-induced rodent hepatocarcinogenesis and identifies gaps in our knowledge of this MOA. Chemical-specific and mechanistic data support concordance of temporal and dose-response relationships for the key events associated with many PPARα activators including a phthalate ester plasticizer di(2-ethylhexyl) phthalate (DEHP) and the drug gemfibrozil. While biologically plausible in humans, the hypothesized key events in the rodent MOA, for PPARα activators, are unlikely to induce liver tumors in humans because of toxicodynamic and biological differences in responses. This conclusion is based on minimal or no effects observed on growth pathways, hepatocellular proliferation and liver tumors in humans and/or species (including hamsters, guinea pigs and cynomolgous monkeys) that are more appropriate human surrogates than mice and rats at overlapping dose levels. Overall, the panel concluded that significant quantitative differences in PPARα activator-induced effects related to liver cancer formation exist between rodents and humans. On the basis of these quantitative differences, most of the workgroup felt that the rodent MOA is "not relevant to humans" with the remaining members concluding that the MOA is "unlikely to be relevant to humans". The two groups differed in their level of confidence based on perceived limitations of the quantitative and mechanistic knowledge of the species differences, which for some panel members strongly supports but cannot preclude the absence of effects under unlikely exposure scenarios.

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.

PPAR-α as a key nutritional and environmental sensor for metabolic adaptation

Peroxisome proliferator-activated receptors (PPARs) are transcription factors that belong to the superfamily of nuclear hormone receptors and regulate the expression of several genes involved in metabolic processes that are potentially linked to the development of some diseases such as hyperlipidemia, diabetes, and obesity. One type of PPAR, PPAR-α, is a transcription factor that regulates the metabolism of lipids, carbohydrates, and amino acids and is activated by ligands such as polyunsaturated fatty acids and drugs used to treat dyslipidemias. There is evidence that genetic variants within the PPARα gene have been associated with a risk of the development of dyslipidemia and cardiovascular disease by influencing fasting and postprandial lipid concentrations; the gene variants have also been associated with an acceleration of the progression of type 2 diabetes. The interactions between genetic PPARα variants and the response to dietary factors will help to identify individuals or populations who can benefit from specific dietary recommendations. Interestingly, certain nutritional conditions, such as the prolonged consumption of a protein-restricted diet, can produce long-lasting effects on PPARα gene expression through modifications in the methylation of a specific locus surrounding the PPARα gene. Thus, this review underlines our current knowledge about the important role of PPAR-α as a mediator of the metabolic response to nutritional and environmental factors.

Essential fatty acids deficiency promotes lipogenic gene expression and hepatic steatosis through the liver X receptor

BACKGROUND & AIMS

Nutrients influence non-alcoholic fatty liver disease. Essential fatty acids deficiency promotes various syndromes, including hepatic steatosis, through increased de novo lipogenesis. The mechanisms underlying such increased lipogenic response remain unidentified.

METHODS

We used wild type mice and mice lacking Liver X Receptors to perform a nutrigenomic study that aimed at examining the role of these transcription factors.

RESULTS

We showed that, in the absence of Liver X Receptors, essential fatty acids deficiency does not promote steatosis. Consistent with this, Liver X Receptors are required for the elevated expression of genes involved in lipogenesis in response to essential fatty acids deficiency.

CONCLUSIONS

This work identifies, for the first time, the central role of Liver X Receptors in steatosis induced by essential fatty acids deficiency.

Cholesterol and male fertility: what about orphans and adopted?

The link between cholesterol homeostasis and male fertility has been clearly suggested in patients who suffer from hyperlipidemia and metabolic syndrome. This has been confirmed by the generation of several transgenic mouse models or in animals fed with high cholesterol diet. Next to the alteration of the endocrine signaling pathways through steroid receptors (androgen and estrogen receptors); "orphan" and "adopted" nuclear receptors, such as the Liver X Receptors (LXRs), the Proliferating Peroxisomal Activated Receptors (PPARs) or the Liver Receptor Homolog-1 (LRH-1), have been involved in this cross-talk. These transcription factors show distinct expression patterns in the male genital tract, explaining the large panel of phenotypes observed in transgenic male mice and highlighting the importance of lipid homesostasis and the complexity of the molecular pathways involved. Increasing our knowledge of the roles of these nuclear receptors in male germ cell differentiation could help in proposing new approaches to either treat infertile men or define new strategies for contraception.

Modulation of PPAR-γ by Nutraceutics as Complementary Treatment for Obesity-Related Disorders and Inflammatory Diseases

A direct correlation between adequate nutrition and health is a universally accepted truth. The Western lifestyle, with a high intake of simple sugars, saturated fat, and physical inactivity, promotes pathologic conditions. The main adverse consequences range from cardiovascular disease, type 2 diabetes, and metabolic syndrome to several cancers. Dietary components influence tissue homeostasis in multiple ways and many different functional foods have been associated with various health benefits when consumed. Natural products are an important and promising source for drug discovery. Many anti-inflammatory natural products activate peroxisome proliferator-activated receptors (PPAR); therefore, compounds that activate or modulate PPAR-gamma (PPAR-γ) may help to fight all of these pathological conditions. Consequently, the discovery and optimization of novel PPAR-γ agonists and modulators that would display reduced side effects is of great interest. In this paper, we present some of the main naturally derived products studied that exert an influence on metabolism through the activation or modulation of PPAR-γ, and we also present PPAR-γ-related diseases that can be complementarily treated with nutraceutics from functional foods.

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.

Lipidomic profiling reveals protective function of fatty acid oxidation in cocaine-induced hepatotoxicity

During cocaine-induced hepatotoxicity, lipid accumulation occurs prior to necrotic cell death in the liver. However, the exact influences of cocaine on the homeostasis of lipid metabolism remain largely unknown. In this study, the progression of subacute hepatotoxicity, including centrilobular necrosis in the liver and elevation of transaminase activity in serum, was observed in a three-day cocaine treatment, accompanying the disruption of triacylglycerol (TAG) turnover. Serum TAG level increased on day 1 of cocaine treatment but remained unchanged afterwards. In contrast, hepatic TAG level was elevated continuously during three days of cocaine treatment and was better correlated with the development of hepatotoxicity. Lipidomic analyses of serum and liver samples revealed time-dependent separation of the control and cocaine-treated mice in multivariate models, which was due to the accumulation of long-chain acylcarnitines together with the disturbances of many bioactive phospholipid species in the cocaine-treated mice. An in vitro function assay confirmed the progressive inhibition of mitochondrial fatty acid oxidation after the cocaine treatment. Cotreatment of fenofibrate significantly increased the expression of peroxisome proliferator-activated receptor α (PPARα)-targeted genes and the mitochondrial fatty acid oxidation activity in the cocaine-treated mice, resulting in the inhibition of cocaine-induced acylcarnitine accumulation and other hepatotoxic effects. Overall, the results from this lipidomics-guided study revealed that the inhibition of fatty acid oxidation plays an important role in cocaine-induced liver injury.