Peptidomics analysis of in vitro digested wheat breads: Effect of genotype and environment on protein digestibility and release of celiac disease and wheat allergy related epitopes

Wheat proteins can trigger immunogenic reactions due to their resistance to digestion and immunostimulatory epitopes. Here, we investigated the peptidomic map of partially digested bread samples and the fingerprint of epitope diversity from 16 wheat genotypes grown in two environmental conditions. Flour protein content and composition were characterized; gastric and jejunal peptides were quantified using LC-MS/MS, and genotypes were classified into high or low bread protein digestibility. Differences in flour protein content and peptide composition distinguish high from low digestibility genotypes in both growing environments. No common peptide signature was found between high- and low-digestible genotypes; however, the celiac or allergen epitopes were noted not to be higher in low-digestible genotypes. Overall, this study established a peptidomic and epitope diversity map of digested wheat bread and provided new insights and correlations between weather conditions, genotypes, digestibility and wheat sensitivities such as celiac disease and wheat allergy.

Integrative multi-omics analysis of genomic, epigenomic, and metabolomics data leads to new insights for Attention-Deficit/Hyperactivity Disorder

The evolving field of multi-omics combines data and provides methods for simultaneous analysis across several omics levels. Here, we integrated genomics (transmitted and non-transmitted polygenic scores [PGSs]), epigenomics, and metabolomics data in a multi-omics framework to identify biomarkers for Attention-Deficit/Hyperactivity Disorder (ADHD) and investigated the connections among the three omics levels. We first trained single- and next multi-omics models to differentiate between cases and controls in 596 twins (cases = 14.8%) from the Netherlands Twin Register (NTR) demonstrating reasonable in-sample prediction through cross-validation. The multi-omics model selected 30 PGSs, 143 CpGs, and 90 metabolites. We confirmed previous associations of ADHD with glucocorticoid exposure and the transmembrane protein family TMEM, show that the DNA methylation of the MAD1L1 gene associated with ADHD has a relation with parental smoking behavior, and present novel findings including associations between indirect genetic effects and CpGs of the STAP2 gene. However, out-of-sample prediction in NTR participants (N = 258, cases = 14.3%) and in a clinical sample (N = 145, cases = 51%) did not perform well (range misclassification was [0.40, 0.57]). The results highlighted connections between omics levels, with the strongest connections between non-transmitted PGSs, CpGs, and amino acid levels and show that multi-omics designs considering interrelated omics levels can help unravel the complex biology underlying ADHD.

Genomic data integration tutorial, a plant case study

BACKGROUND

The ongoing evolution of the Next Generation Sequencing (NGS) technologies has led to the production of genomic data on a massive scale. While tools for genomic data integration and analysis are becoming increasingly available, the conceptual and analytical complexities still represent a great challenge in many biological contexts.

RESULTS

To address this issue, we describe a six-steps tutorial for the best practices in genomic data integration, consisting of (1) designing a data matrix; (2) formulating a specific biological question toward data description, selection and prediction; (3) selecting a tool adapted to the targeted questions; (4) preprocessing of the data; (5) conducting preliminary analysis, and finally (6) executing genomic data integration.

CONCLUSION

The tutorial has been tested and demonstrated on publicly available genomic data generated from poplar (Populus L.), a woody plant model. We also developed a new graphical output for the unsupervised multi-block analysis, cimDiablo_v2, available at https://forgemia.inra.fr/umr-gdec/omics-integration-on-poplar , and allowing the selection of master drivers in genomic data variation and interplay.

Asterics: a simple tool for the ExploRation and Integration of omiCS data

BACKGROUND

The rapid development of omics acquisition techniques has induced the production of a large volume of heterogeneous and multi-level omics datasets, which require specific and sometimes complex analyses to obtain relevant biological information. Here, we present ASTERICS (version 2.5), a publicly available web interface for the analyses of omics datasets.

RESULTS

ASTERICS is designed to make both standard and complex exploratory and integration analysis workflows easily available to biologists and to provide high quality interactive plots. Special care has been taken to provide a comprehensive documentation of the implemented analyses and to guide users toward sound analysis choices regarding some specific omics data. Data and analyses are organized in a comprehensive graphical workflow within ASTERICS workspace to facilitate the understanding of successive data editions and analyses leading to a given result.

CONCLUSION

ASTERICS provides an easy to use platform for omics data exploration and integration. The modular organization of its open source code makes it easy to incorporate new workflows and analyses by external contributors. ASTERICS is available at https://asterics.miat.inrae.fr and can also be deployed using provided docker images.

Improvement of variables interpretability in kernel PCA

BACKGROUND

Kernel methods have been proven to be a powerful tool for the integration and analysis of high-throughput technologies generated data. Kernels offer a nonlinear version of any linear algorithm solely based on dot products. The kernelized version of principal component analysis is a valid nonlinear alternative to tackle the nonlinearity of biological sample spaces. This paper proposes a novel methodology to obtain a data-driven feature importance based on the kernel PCA representation of the data.

RESULTS

The proposed method, kernel PCA Interpretable Gradient (KPCA-IG), provides a data-driven feature importance that is computationally fast and based solely on linear algebra calculations. It has been compared with existing methods on three benchmark datasets. The accuracy obtained using KPCA-IG selected features is equal to or greater than the other methods' average. Also, the computational complexity required demonstrates the high efficiency of the method. An exhaustive literature search has been conducted on the selected genes from a publicly available Hepatocellular carcinoma dataset to validate the retained features from a biological point of view. The results once again remark on the appropriateness of the computed ranking.

CONCLUSIONS

The black-box nature of kernel PCA needs new methods to interpret the original features. Our proposed methodology KPCA-IG proved to be a valid alternative to select influential variables in high-dimensional high-throughput datasets, potentially unravelling new biological and medical biomarkers.

Autonomic Nervous System Adaptation and Circadian Rhythm Disturbances of the Cardiovascular System in a Ground-Based Murine Model of Spaceflight

Whether in real or simulated microgravity, Humans or animals, the kinetics of cardiovascular adaptation and its regulation by the autonomic nervous system (ANS) remain controversial. In this study, we used hindlimb unloading (HU) in 10 conscious mice. Blood pressure (BP), heart rate (HR), temperature, and locomotor activity were continuously monitored with radio-telemetry, during 3 days of control, 5 days of HU, and 2 days of recovery. Six additional mice were used to assess core temperature. ANS activity was indirectly determined by analyzing both heart rate variability (HRV) and baroreflex sensitivity (BRS). Our study showed that HU induced an initial bradycardia, accompanied by an increase in vagal activity markers of HRV and BRS, together with a decrease in water intake, indicating the early adaptation to fluid redistribution. During HU, BRS was reduced; temperature and BP circadian rhythms were altered, showing a loss in day/night differences, a decrease in cycle amplitude, a drop in core body temperature, and an increase in day BP suggestive of a rise in sympathetic activity. Reloading induced resting tachycardia and a decrease in BP, vagal activity, and BRS. In addition to cardiovascular deconditioning, HU induces disruption in day/night rhythmicity of locomotor activity, temperature, and BP.

Integrative Multi-omics Analysis of Childhood Aggressive Behavior

This study introduces and illustrates the potential of an integrated multi-omics approach in investigating the underlying biology of complex traits such as childhood aggressive behavior. In 645 twins (cases = 42%), we trained single- and integrative multi-omics models to identify biomarkers for subclinical aggression and investigated the connections among these biomarkers. Our data comprised transmitted and two non-transmitted polygenic scores (PGSs) for 15 traits, 78,772 CpGs, and 90 metabolites. The single-omics models selected 31 PGSs, 1614 CpGs, and 90 metabolites, and the multi-omics model comprised 44 PGSs, 746 CpGs, and 90 metabolites. The predictive accuracy for these models in the test (N = 277, cases = 42%) and independent clinical data (N = 142, cases = 45%) ranged from 43 to 57%. We observed strong connections between DNA methylation, amino acids, and parental non-transmitted PGSs for ADHD, Autism Spectrum Disorder, intelligence, smoking initiation, and self-reported health. Aggression-related omics traits link to known and novel risk factors, including inflammation, carcinogens, and smoking.

Multi-omics Data Integration in the Context of Plant Abiotic Stress Signaling

In order to answer new biological questions, high-throughput data generated by new biotechnologies can be very meaningful but require specific and adapted statistical treatments. Thus, in the context of abiotic stress signaling studies, understanding the integration of cascading mechanisms from stress perception to biochemical and physiological adjustments necessarily entails efficient and valid analysis of multilevel and heterogeneous data. In this chapter, we propose examples to manage, analyze, and integrate multi-omics heterogeneous data. This workflow suggests and follows different general biological questions or issues answered with detailed code, data analysis, multiple visualizations, and always followed by brief interpretations. We illustrated this using the mixOmics package for the R software, as it specifically provides tools to address vertical and horizontal data integration issues. In order to illustrate this workflow, we used the usual omics datasets biologists can generate (phenomics, metabolomics, proteomics, and transcriptomics). These data were collected from two organs (leaf rosettes, floral stems) of five ecotypes of the model plant Arabidopsis thaliana exposed to two temperature growth conditions. They are available in the R package WallOmicsData. The workflow presented here is not limited to Arabidopsis thaliana and can be applied to any plant species. It can even be largely deployed to whatever the organisms of interest and the biological questions may be.

Multivariate Analysis with the R Package mixOmics

The high-dimensional nature of proteomics data presents challenges for statistical analysis and biological interpretation. Multivariate analysis, combined with insightful visualization can help to reveal the underlying patterns in complex biological data. This chapter introduces the R package mixOmics which focuses on data exploration and integration. We first introduce methods for single data sets: both Principal Component Analysis, which can identify the patterns of variance present in data, and sparse Partial Least Squares Discriminant Analysis, which aims to identify variables that can classify samples into known groups. We then present integrative methods with Projection to Latent Structures and further extensions for discriminant analysis. We illustrate each technique on a breast cancer multi-omics study and provide the R code and data as online supplementary material for readers interested in reproducing these analyses.

Longitudinal liver proteome profiling in dairy cows during the transition from gestation to lactation: Investigating metabolic adaptations and their interactions with fatty acids supplementation via repeated measurements ANOVA-simultaneous component analysis

Repeated measurements analysis of variance - simultaneous component analysis (ASCA) has been developed to handle complex longitudinal omics datasets and combine novel information with existing data. Herein, we aimed at applying ASCA to 64 liver proteomes collected at 4-time points (day -21, +1, +28, and + 63 relative to parturition) from 16 Holstein cows treated from 9 wk. antepartum to 9 wk. postpartum (PP) with coconut oil (CTRL) or a mixture of essential fatty acids (EFA) and conjugated linoleic acid (CLA) (EFA + CLA). The ASCA modeled 116, 43, and 97 differentially abundant proteins (DAP) during the transition to lactation, between CTRL and EFA + CLA, and their interaction, respectively. Time-dependent DAP were annotated to pathways related to the metabolism of carbohydrates, FA, and amino acid in the PP period. The DAP between FA and the interaction effect were annotated to the metabolism of xenobiotics by cytochrome P450, drug metabolism - cytochrome P450, retinol metabolism, and steroid hormone biosynthesis. Collectively, ASCA provided novel information on molecular markers of metabolic adaptations and their interactions with EFA + CLA supplementation. Bioinformatics analysis suggested that supplemental EFA + CLA amplified hepatic FA oxidation; cytochrome P450 was enriched to maintain metabolic homeostasis by oxidation/detoxification of endogenous compounds and xenobiotics. SIGNIFICANCE: This report is among the first ones applying repeated measurement analysis of variance-simultaneous component analysis (ASCA) to deal with longitudinal proteomics results. ASCA separately identified differentially abundant proteins (DAP) in 'transition time', 'between fatty acid treatments', and 'their interaction'. We first identified the molecular signature of hepatic metabolic adaptations during postpartum negative energy balance; the enriched pathways were well-known pathways related to mobilizing fatty acids (FA) and amino acids to support continuous energy production through fatty acid oxidation, TCA cycle, and gluconeogenesis. Some of the DAP were not previously reported in transition dairy cows. Secondly, we provide novel information on the mechanisms by which supplemented essential FA and conjugated linoleic acids interact with hepatic metabolism. In this regard, FA amplified hepatic detoxifying and oxidation capacity through ligand activation of nuclear receptors. Finally, we briefly compared the strengths and weaknesses of the ASCA model with PLS-DA and outlined why these methods are complementary.

Quantitative Analysis of Cell Aggregation Dynamics Identifies HDAC Inhibitors as Potential Regulators of Cancer Cell Clustering

Simple Summary

Metastases formation involves the formation, circulation and seeding of cohesive group of tumor cells called circulating tumors cells clusters at distant organs from the primary tumor. These clusters have a much higher metastatic potential than individual circulating tumor cell, it is therefore important to understand the molecular mechanisms involved in their formation. To this aim, in this study, from the analysis of the relationship between in vitro aggregation quantitative characterization of 25 cancer cell lines and their expression data, we identified genes significantly associated with aggregation. Interestingly, we found that these genes were strongly correlated with the transcriptional signature induced by HDAC inhibitors treatment and we finally showed experimentally that two HDAC inhibitors inhibits tumor cells cluster formation in vitro. These results open new therapeutic perspectives to prevent metastasis formation.

Abstract

Characterization of the molecular mechanisms involved in tumor cell clustering could open the way to new therapeutic strategies. Towards this aim, we used an in vitro quantitative procedure to monitor the anchorage-independent cell aggregation kinetics in a panel of 25 cancer cell lines. The analysis of the relationship between selected aggregation dynamic parameters and the gene expression data for these cell lines from the CCLE database allowed identifying genes with expression significantly associated with aggregation parameter variations. Comparison of these transcripts with the perturbagen signatures from the Connectivity Map resource highlighted that they were strongly correlated with the transcriptional signature of most histone deacetylase (HDAC) inhibitors. Experimental evaluation of two HDAC inhibitors (SAHA and ISOX) showed that they inhibited the initial step of in vitro tumor cell aggregation. This validates our findings and reinforces the potential interest of HDCA inhibitors to prevent metastasis spreading.

Rv0180c contributes to Mycobacterium tuberculosis cell shape and to infectivity in mice and macrophages

Mycobacterium tuberculosis, the main causative agent of human tuberculosis, is transmitted from person to person via small droplets containing very few bacteria. Optimizing the chance to seed in the lungs is therefore a major adaptation to favor survival and dissemination in the human population. Here we used TnSeq to identify genes important for the early events leading to bacterial seeding in the lungs. Beside several genes encoding known virulence factors, we found three new candidates not previously described: rv0180c, rv1779c and rv1592c. We focused on the gene, rv0180c, of unknown function. First, we found that deletion of rv0180c in M. tuberculosis substantially reduced the initiation of infection in the lungs of mice. Next, we established that Rv0180c enhances entry into macrophages through the use of complement-receptor 3 (CR3), a major phagocytic receptor for M. tuberculosis. Silencing CR3 or blocking the CR3 lectin site abolished the difference in entry between the wild-type parental strain and the Δrv0180c::km mutant. However, we detected no difference in the production of both CR3-known carbohydrate ligands (glucan, arabinomannan, mannan), CR3-modulating lipids (phthiocerol dimycocerosate), or proteins in the capsule of the Δrv0180c::km mutant in comparison to the wild-type or complemented strains. By contrast, we established that Rv0180c contributes to the functionality of the bacterial cell envelope regarding resistance to toxic molecule attack and cell shape. This alteration of bacterial shape could impair the engagement of membrane receptors that M. tuberculosis uses to invade host cells, and open a new perspective on the modulation of bacterial infectivity.

The epidemic efficiency of tuberculosis bacilli is determined by their capacity to transmit via aerosol. Currently, the bacterial functions that favor Mycobacterium tuberculosis seeding in the lung of naïve host remain mostly unknown. Here we implemented a genome-wide approach to identify M. tuberculosis mutants deficient for seeding and early replication in the lung of mice. In addition to genes known to encode virulence factors, we identified three genes not previously described. We used complementary approaches to characterize the phenotype of a M. tuberculosis mutant with insertion within the rv0180c gene. We found that this mutant is impaired for seeding in the lung of mice and for invasion and replication in human macrophages. In macrophages, the defect relies on a lack of engagement of CR3 receptor. Although we did not detect any difference between the wild type strain and the rv0180c mutant with regard to potential CR3-ligand, we found that the bacterial cell envelope is altered in the rv0180c mutant. Our study provides new insight into bacterial genes required for early interaction of M. tuberculosis with the host and perspective to understand the bacterial functions enhancing infectivity.

Integrating independent microbial studies to build predictive models of anaerobic digestion inhibition by ammonia and phenol

Anaerobic digestion (AD) is a process that can efficiently degrade organic waste into renewable energies. AD failure is however common as the underpinning microbial mechanisms are highly vulnerable to a wide range of inhibitory compounds. Sequencing technologies enable the identification of microbial indicators of digesters inhibition, but existing studies are limited. They used different inocula, substrates, sites and types of reactors and reported different or contradictory indicators. Our aim was to identify a robust signature of microbial indicators of phenol and ammonia inhibitions across four independent AD microbial studies. To identify such signature, we applied an original multivariate integrative method on two in-house studies, then validated our approach by predicting the inhibitory status of samples from two other studies with more than 90% accuracy. Our approach shows how we can efficiently leverage on existing studies to extract reproducible microbial community patterns and predict AD inhibition to improve AD microbial management.

A powerful framework for an integrative study with heterogeneous omics data: from univariate statistics to multi-block analysis

High-throughput data generated by new biotechnologies require specific and adapted statistical treatment in order to be efficiently used in biological studies. In this article, we propose a powerful framework to manage and analyse multi-omics heterogeneous data to carry out an integrative analysis. We have illustrated this using the mixOmics package for R software as it specifically addresses data integration issues. Our work also aims at applying the most recent functionalities of mixOmics to real datasets. Although multi-block integrative methodologies exist, we hope to encourage a more widespread use of such approaches in an operational framework by biologists. We have used natural populations of the model plant Arabidopsis thaliana in this work, but the framework proposed is not limited to this plant and can be deployed whatever the organisms of interest and the biological question may be. Four omics datasets (phenomics, metabolomics, cell wall proteomics and transcriptomics) were collected, analysed and integrated to study the cell wall plasticity of plants exposed to sub-optimal temperature growth conditions. The methodologies presented here start from basic univariate statistics leading to multi-block integration analysis. We have also highlighted the fact that each method, either unsupervised or supervised, is associated with one biological issue. Using this powerful framework enabled us to arrive at novel conclusions on the biological system, which would not have been possible using standard statistical approaches.

Sporadic and Familial Variants in NF1: An Explanation of the Wide Variability in Neurocognitive Phenotype?

Background: Cognitive impairment is the most common neurological manifestation in NF1 and occurs in 30–70% of NF1 cases. The onset and severity of each specific cognitive deficit varies greatly from child to child, with no apparent external causes. The wide variability of phenotype is the most complex aspect in terms of management and care. Despite multiple research, the mechanism underlying the high heterogeneity in NF1 has not yet been elucidated. While many studies have focused on the effects of specific and precise genetic mutations on the NF1 phenotype, little has been done on the impact of NF1 transmission (sporadic vs. familial cases). We used a complete neuropsychological evaluation designed to assess five large cognitive areas: general cognitive functions (WISC-IV and EVIP); reading skills (“L'Alouette,” ODEDYS-2 and Lobrot French reading tests); phonological process (ODEDYS-2 test); visual perceptual skills (JLO, Thurstone and Corsi block tests) and attention (CPT-II), as well as psychosocial adjustments (CBCL) to explore the impact of NF1 transmission on cognitive disease manifestation in 96 children affected by NF1 [55 sporadic cases (29♀, 26♂); 41 familial cases (24♀, 17♂)].

Results: Familial and Sporadic form of NF1 only differ in IQ expression. The families' socioeconomic status (SES) impacts IQ performance but not differently between sporadic and familial variants. However, SES is lower in familial variants than in the sporadic variant of NF1. No other cognitive differences emerge between sporadic and familial NF1.

Conclusions: Inheritance in NF1 failed to explain the phenotype variability in its entirety. IQ differences between groups seems in part linked to the environment where the child grows up. Children with NF1, and especially those that have early diagnoses (most often in inherited cases), must obtain careful monitoring from their early childhood, at home to strengthen investment in education and in school to early detect emerging academic problems and to quickly place them into care.

Trial Registration: IDRCB, IDRCB2008-A01444-51. Registered 19 January 2009.

Urinary Amine and Organic Acid Metabolites Evaluated as Markers for Childhood Aggression: The ACTION Biomarker Study

Biomarkers are of interest as potential diagnostic and predictive instruments in personalized medicine. We present the first urinary metabolomics biomarker study of childhood aggression. We aim to examine the association of urinary metabolites and neurotransmitter ratios involved in key metabolic and neurotransmitter pathways in a large cohort of twins (N = 1,347) and clinic-referred children (N = 183) with an average age of 9.7 years. This study is part of ACTION (Aggression in Children: Unraveling gene-environment interplay to inform Treatment and InterventiON strategies), in which we developed a standardized protocol for large-scale collection of urine samples in children. Our analytical design consisted of three phases: a discovery phase in twins scoring low or high on aggression (N = 783); a replication phase in twin pairs discordant for aggression (N = 378); and a validation phase in clinical cases and matched twin controls (N = 367). In the discovery phase, 6 biomarkers were significantly associated with childhood aggression, of which the association of O-phosphoserine (β = 0.36; SE = 0.09; p = 0.004), and gamma-L-glutamyl-L-alanine (β = 0.32; SE = 0.09; p = 0.01) remained significant after multiple testing. Although non-significant, the directions of effect were congruent between the discovery and replication analyses for six biomarkers and two neurotransmitter ratios and the concentrations of 6 amines differed between low and high aggressive twins. In the validation analyses, the top biomarkers and neurotransmitter ratios, with congruent directions of effect, showed no significant associations with childhood aggression. We find suggestive evidence for associations of childhood aggression with metabolic dysregulation of neurotransmission, oxidative stress, and energy metabolism. Although replication is required, our findings provide starting points to investigate causal and pleiotropic effects of these dysregulations on childhood aggression.

The antibody response induced FMDV vaccines in sheep correlates with early transcriptomic responses in blood

Foot and mouth disease (FMD) is a highly contagious viral disease with high economic impact, representing a major threat for cloven-hooved mammals worldwide. Vaccines based on adjuvanted inactivated virus (iFMDV) induce effective protective immunity implicating antibody (Ab) responses. To reduce the biosafety constraints of the manufacturing process, a non-replicative human adenovirus type 5 vector encoding FMDV antigens (Ad5-FMDV) has been developed. Here we compared the immunogenicity of iFMDV and Ad5-FMDV with and without the ISA206VG emulsion-type adjuvant in sheep. Contrasted Ab responses were obtained: iFMDV induced the highest Ab levels, Ad5-FMDV the lowest ones, and ISA206VG increased the Ad5-FMDV-induced Ab responses to protective levels. Each vaccine generated heterogeneous Ab responses, with high and low responders, the latter being considered as obstacles to vaccine effectiveness. A transcriptomic study on total blood responses at 24 h post-vaccination revealed several blood gene module activities correlating with long-term Ab responses. Downmodulation of T cell modules’ activities correlated with high responses to iFMDV and to Ad5-FMDV+ISA206VG vaccines as also found in other systems vaccinology studies in humans and sheep. The impact of cell cycle activity depended on the vaccine types, as it positively correlated with higher responses to iFMDV but negatively to non-adjuvanted Ad5-FMDV. Finally an elevated B cell activity at 24 h correlated with high Ab responses to the Ad5-FMDV+ISA206VG vaccine. This study provides insights into the early mechanisms driving the Ab response induced by different vaccine regimens including Ad5 vectors and points to T cell modules as early biomarker candidates of different vaccine-type efficacy across species.

Foot and mouth disease virus (FMDV) is a serious pathogen of cloven hoofed mammals and is of high economic and veterinary importance. Inactivated vaccine (iFMDV) is effective but difficult to produce because of high biosafety level requirements; non-replicating adenovirus vectors carrying key FMDV antigens (Ad5-FMDV) might therefore represent an attractive alternative. Isabelle Schwartz-Cornil and colleagues use sheep to systematically compare vaccination with adjuvanted iFMDV, adjuvanted Ad5-FMDV, or non-adjuvanted Ad5-FMDV. All vaccines produce neutralizing antibody responses which are stable to at least one year, however the iFMDV group elicits the strongest response, followed by the adjuvanted Ad5-FMDV. Ad5-FMDV alone produces weak antibody titers. Blood transcriptomic analysis performed in the first 24 h following vaccination identifies a reduced T cell gene expression module as a correlate of high neutralizing antibody titers. Blood gene expression might therefore offer insights into the mechanistic underpinnings of humoral immunity as well as provide useful biomarker correlates of protection.

Proteomic Data Integration Highlights Central Actors Involved in Einkorn (Triticum monococcum ssp. monococcum) Grain Filling in Relation to Grain Storage Protein Composition

Albumins and globulins (AGs) of wheat endosperm represent about 20% of total grain proteins. Some of these physiologically active proteins can influence the synthesis of storage proteins (SPs) (gliadins and glutenins) and consequently, rheological properties of wheat flour and processing. To identify such AGs, data, (published by Bonnot et al., 2017) concerning abundance in 352 AGs and in the different seed SPs during grain filling and in response to different nitrogen (N) and sulfur (S) supply, were integrated with mixOmics R package. Relationships between AGs and SPs were first unraveled using the unsupervised method sparse Partial Least Square, also known as Projection to Latent Structure (sPLS). Then, data were integrated using a supervised approach taking into account the nutrition and the grain developmental stage. We used the block.splda procedure also referred to as DIABLO (Data Integration Analysis for Biomarker discovery using Latent variable approaches for Omics studies). These approaches led to the identification of discriminant and highly correlated features from the two datasets (AGs and SPs) which are not necessarily differentially expressed during seed development or in response to N or S supply. Eighteen AGs were correlated with the quantity of SPs per grain. A statistical validation of these proteins by genetic association analysis confirmed that 5 out of this AG set were robust candidate proteins able to modulate the seed SP synthesis. In conclusion, this latter result confirmed that the integrative strategy is an adequate way to reduce the number of potentially relevant AGs for further functional validation.

Carbon sources and XlnR-dependent transcriptional landscape of CAZymes in the industrial fungus Talaromyces versatilis: when exception seems to be the rule

Background

Research on filamentous fungi emphasized the remarkable redundancy in genes encoding hydrolytic enzymes, the similarities but also the large differences in their expression, especially through the role of the XlnR/XYR1 transcriptional activator. The purpose of this study was to evaluate the specificities of the industrial fungus Talaromyces versatilis, getting clues into the role of XlnR and the importance of glucose repression at the transcriptional level, to provide further levers for cocktail production.

Results

By studying a set of 62 redundant genes representative of several categories of enzymes, our results underlined the huge plasticity of transcriptional responses when changing nutritional status. As a general trend, the more heterogeneous the substrate, the more efficient to trigger activation. Genetic modifications of xlnR led to significant reorganisation of transcriptional patterns. Just a minimal set of genes actually fitted in a simplistic model of regulation by a transcriptional activator, and this under specific substrates. On the contrary, the diversity of xlnR⁺ versus ΔxlnR responses illustrated the existence of complex and unpredicted patterns of co-regulated genes that were highly dependent on the culture condition, even between genes that encode members of a functional category of enzymes. They notably revealed a dual, substrate-dependant repressor-activator role of XlnR, with counter-intuitive transcripts regulations that targeted specific genes. About glucose, it appeared as a formal repressive sugar as we observed a massive repression of most genes upon glucose addition to the mycelium grown on wheat straw. However, we also noticed a positive role of this sugar on the basal expression of a few genes, (notably those encoding cellulases), showing again the strong dependence of these regulatory mechanisms upon promoter and nutritional contexts.

Conclusions

The diversity of transcriptional patterns appeared to be the rule, while common and stable behaviour, both within gene families and with fungal literature, the exception. The setup of a new biotechnological process to reach optimized, if not customized expression patterns of enzymes, hence appeared tricky just relying on published data that can lead, in the best scenario, to approximate trends. We instead encourage preliminary experimental assays, carried out in the context of interest to reassess gene responses, as a mandatory step before thinking in (genetic) strategies for the improvement of enzyme production in fungi.

Electronic supplementary material

The online version of this article (10.1186/s12934-019-1062-8) contains supplementary material, which is available to authorized users.

Phenotypic Trait Variation as a Response to Altitude-Related Constraints in Arabidopsis Populations

Natural variations help in identifying genetic mechanisms of morphologically and developmentally complex traits. Mountainous habitats provide an altitudinal gradient where one species encounters different abiotic conditions. We report the study of 341 individuals of Arabidopsis thaliana derived from 30 natural populations not belonging to the 1001 genomes, collected at increasing altitudes, between 200 and 1800 m in the Pyrenees. Class III peroxidases and ribosomal RNA sequences were used as markers to determine the putative genetic relationships among these populations along their altitudinal gradient. Using Bayesian-based statistics and phylogenetic analyses, these Pyrenean populations appear with significant divergence from the other regional accessions from 1001 genome (i.e., from north Spain or south France). Individuals of these populations exhibited varying phenotypic changes, when grown at sub-optimal temperature (22 vs. 15°C). These phenotypic variations under controlled conditions reflected intraspecific morphological variations. This study could bring new information regarding the west European population structure of A. thaliana and its phenotypic variations at different temperatures. The integrative analysis combining genetic, phenotypic variation and environmental datasets is used to analyze the acclimation of population in response to temperature changes. Regarding their geographical proximity and environmental diversity, these populations represent a tool of choice for studying plant response to temperature variation.

HIGHLIGHTS

-Studying the natural diversity of A. thaliana in the Pyrenees mountains helps to understand European population structure and to evaluate the phenotypic trait variation in response to climate change.

Support media can steer methanogenesis in the presence of phenol through biotic and abiotic effects

A wide variety of inhibitors can induce anaerobic digester disruption. To avoid performance losses, support media can be used to mitigate inhibitions. However, distinguishing the physico-chemical from the biological mechanisms of such strategies remains delicate. In this framework, the impact of 10  g/L of different types of zeolites and activated carbons (AC) on microbial community dynamics during anaerobic digestion of biowaste in the presence of 1.3 g/L of phenol was evaluated with 16 S rRNA gene sequencing. In the presence of AC, methanogenesis inhibition was rapidly removed due to a decrease of phenol concentration. This abiotic effect related to the physico-chemical properties of AC led to increased final CH4 and CO2 productions by 29-31% compared to digesters incubated without support. Interestingly, although zeolite did not adsorb phenol, final CH4 and CO2 production reached comparable levels as with AC. Nevertheless, compared to digesters incubated without support, methanogenesis lag phase duration was less reduced in the presence of zeolites (5 ± 1 days) than in the presence of activated carbons (12 ± 2 days). Both types of support induced biotic effects. AC and zeolite both allowed the preservation of the major representative archaeal genus of the non-inhibited ecosystem, Methanosarcina. By contrast, they distinctly shaped bacterial populations. OTUs belonging to class W5 became dominant at the expense of OTUs assigned to orders Clostridiales, Bacteroidales and Anaerolinales in the presence of AC. Zeolite enhanced the implantation of OTUs assigned to bacterial phylum Cloacimonetes. This study highlighted that supports can induce biotic and abiotic effects within digesters inhibited with phenol, showing potentialities to enhance anaerobic digestion stability under disrupting conditions.

Caloric Restriction and Diet-Induced Weight Loss Do Not Induce Browning of Human Subcutaneous White Adipose Tissue in Women and Men with Obesity

Caloric restriction (CR) is standard lifestyle therapy in obesity management. CR-induced weight loss improves the metabolic profile of individuals with obesity. In mice, occurrence of beige fat cells in white fat depots favors a metabolically healthy phenotype, and CR promotes browning of white adipose tissue (WAT). Here, human subcutaneous abdominal WAT samples were analyzed in 289 individuals with obesity following a two-phase dietary intervention consisting of an 8 week very low calorie diet and a 6-month weight-maintenance phase. Before the intervention, we show sex differences and seasonal variation, with higher expression of brown and beige markers in women with obesity and during winter, respectively. The very low calorie diet resulted in decreased browning of subcutaneous abdominal WAT. During the whole dietary intervention, evolution of body fat and insulin resistance was independent of changes in brown and beige fat markers. These data suggest that diet-induced effects on body fat and insulin resistance are independent of subcutaneous abdominal WAT browning in people with obesity.

Cell wall modifications of two Arabidopsis thaliana ecotypes, Col and Sha, in response to sub-optimal growth conditions: An integrative study

With the global temperature change, plant adaptations are predicted, but little is known about the molecular mechanisms underlying them. Arabidopsis thaliana is a model plant adapted to various environmental conditions, in particular able to develop along an altitudinal gradient. Two ecotypes, Columbia (Col) growing at low altitude, and Shahdara (Sha) growing at 3400m, have been studied at optimal and sub-optimal growth temperature (22°C vs 15°C). Macro- and micro-phenotyping, cell wall monosaccharides analyses, cell wall proteomics, and transcriptomics have been performed in order to accomplish an integrative analysis. The analysis has been focused on cell walls (CWs) which are assumed to play roles in response to environmental changes. At 15°C, both ecotypes presented characteristic morphological traits of low temperature growth acclimation such as reduced rosette diameter, increased number of leaves, modifications of their CW composition and cuticle reinforcement. Altogether, the integrative analysis has allowed identifying several candidate genes/proteins possibly involved in the cell wall modifications observed during the temperature acclimation response.

Integration of Biochemical, Biophysical and Transcriptomics Data for Investigating the Structural and Nanomechanical Properties of the Yeast Cell Wall

The yeast cell is surrounded by a cell wall conferring protection and resistance to environmental conditions that can be harmful. Identify the molecular cues (genes) which shape the biochemical composition and the nanomechanical properties of the cell wall and the links between these two parameters represent a major issue in the understanding of the biogenesis and the molecular assembly of this essential cellular structure, which may have consequences in diverse biotechnological applications. We addressed this question in two ways. Firstly, we compared the biochemical and biophysical properties using atomic force microscopy (AFM) methods of 4 industrial strains with the laboratory sequenced strain BY4743 and used transcriptome data of these strains to infer biological hypothesis about differences of these properties between strains. This comparative approach showed a 4–6-fold higher hydrophobicity of industrial strains that was correlated to higher expression of genes encoding adhesin and adhesin-like proteins and not to their higher mannans content. The second approach was to employ a multivariate statistical analysis to identify highly correlated variables among biochemical, biophysical and genes expression data. Accordingly, we found a tight association between hydrophobicity and adhesion events that positively correlated with a set of 22 genes in which the main enriched GO function was the sterol metabolic process. We also identified a strong association of β-1,3-glucans with contour length that corresponds to the extension of mannans chains upon pulling the mannosyl units with the lectin-coated AFM tips. This association was positively correlated with a group of 27 genes in which the seripauperin multigene family was highly documented and negatively connected with a set of 23 genes whose main GO biological process was sulfur assimilation/cysteine biosynthetic process. On the other hand, the elasticity modulus was found weakly associated with levels of β-1,6-glucans, and this biophysical variable was positively correlated with a set of genes implicated in microtubules polymerization, tubulin folding and mitotic organization.

Modular bioink for 3D printing of biocompatible hydrogels: sol–gel polymerization of hybrid peptides and polymers

Inorganic polymerization as a cross-linking method for 3D printing of PEG–peptide hydrogels.

1H NMR metabolomic signatures in five brain regions of the AβPPswe Tg2576 mouse model of Alzheimer's disease at four ages

In the quest for biomarkers of onset and progression of Alzheimer's disease, a 1H NMR-based metabolomic study was performed on the simple single-transgenic Tg2576 mouse model. These mice develop a slow cognitive decline starting by 6 months and express amyloid plaques from 10 months of age. The metabolic profiles of extracts from five brain regions (frontal cortex, rhinal cortex, hippocampus, midbrain, and cerebellum) of Tg2576 male mice were compared to those of controls, at 1, 3, 6 and 11 months of age. The most obvious differences were due to brain regions. Age was also a discriminating parameter. Metabolic perturbations were already detected in the hippocampus and the rhinal cortex of transgenic mice as early as 1 month of age with decreased concentrations of glutamate (Glu) and N-acetylaspartate (NAA) compared to those in wild-type animals. Metabolic changes were more numerous in the hippocampus and the rhinal cortex of 3 month-old transgenic mice and involved Glu, NAA, myo-inositol, creatine, phosphocholine, and γ-aminobutyric acid (only in the hippocampus) whose concentrations decreased. A metabolic disruption characterized by an increase in the hippocampal concentrations of Glu, creatine, and taurine was detected in 6 month-old transgenic mice. At this time point, the chemical profile of the cerebellum was slightly affected. At 11 months, all the brain regions analyzed (except the frontal cortex) were metabolically altered, with mainly a marked increase in the formation of the neuroprotective metabolites creatine and taurine. Our findings demonstrate that metabolic modifications occur long before the onset of behavioral impairment.

Adipose tissue CIDEA is associated, independently of weight variation, to change in insulin resistance during a longitudinal weight control dietary program in obese individuals

AIM

Weight loss reduces risk factors associated with obesity. However, long-term metabolic improvement remains a challenge. We investigated quantitative gene expression of subcutaneous adipose tissue in obese individuals and its relationship with low calorie diet and long term weight maintenance induced changes in insulin resistance.

RESEARCH DESIGN

Three hundred eleven overweight and obese individuals followed a dietary protocol consisting of an 8-week low calorie diet followed by a 6-month ad libitum weight-maintenance diet. Individuals were clustered according to insulin resistance trajectories assessed using homeostasis model assessment of insulin resistance (HOMA-IR) index. Adipose tissue mRNA levels of 267 genes selected for regulation according to obesity, metabolic status and response to dieting was assessed using high throughput RT-qPCR. A combination of discriminant analyses was used to identify genes with regulation according to insulin resistance trajectories. Partial correlation was used to control for change in body mass index.

RESULTS

Three different HOMA-IR profile groups were determined. HOMA-IR improved during low calorie diet in the 3 groups. At the end of the 6-month follow-up, groups A and B had reduced HOMA-IR by 50%. In group C, HOMA-IR had returned to baseline values. Genes were differentially expressed in the adipose tissue of individuals according to groups but a single gene, CIDEA, was common to all phases of the dietary intervention. Changes in adipose tissue CIDEA mRNA levels paralleled variations in insulin sensitivity independently of change in body mass index. Overall, CIDEA was up-regulated in adipose tissue of individuals with successful long term insulin resistance relapse and not in adipose tissue of unsuccessful individuals.

CONCLUSION

The concomitant change in adipose tissue CIDEA mRNA levels and insulin sensitivity suggests a beneficial role of adipose tissue CIDEA in long term glucose homeostasis, independently of weight variation.

TRIAL REGISTRATION

ClinicalTrials.gov NCT00390637.

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.

Quality assessment of commercial Magnoliae officinalis Cortex by ¹H-NMR-based metabolomics and HPLC methods

INTRODUCTION

The quality control of Magnoliae officinalis Cortex, a commonly used traditional Chinese medicine, is currently based on the assay of the two active compounds, honokiol and magnolol, by TLC or HPLC.

OBJECTIVE

To compare ¹H-NMR-based metabolomics with the HPLC method for controlling the quality of Magnoliae officinalis Cortex. To identify the metabolites contributing to the differences between the samples and to discriminate different medicinal parts and geographic origins of these samples by ¹H-NMR-based metabolomics.

METHODOLOGY

¹H-NMR and several multivariate analysis techniques were applied to analyse the extracts of 18 batches of Magnoliae officinalis Cortex commercial samples, and the contents of honokiol and magnolol in these samples were determined by HPLC. The correlation analysis between the data from ¹H-NMR and HPLC was performed with the mixOmics software based on an unsupervised method.

RESULTS

Honokiol and magnolol were the main compounds responsible for the discrimination of samples from different batches, thus proving that the choice of these two compounds as markers for quality assessment by HPLC is relevant. The two sources of Magnoliae officinalis Cortex recorded in the Chinese Pharmacopoeia, Magnolia officinalis and Magnolia officinalis var. biloba, could be differentiated from ¹H-NMR data, but the pattern recognition analysis by PLS-DA was unsuccessful in discriminating samples from various geographical origins.

CONCLUSION

The combination of ¹H-NMR that gives a comprehensive profile of the metabolites and HPLC that targets two biomarkers is an efficient means for a better quality control of Magnoliae officinalis Cortex.

The gene expression profile of phosphoantigen-specific human γδ T lymphocytes is a blend of αβ T-cell and NK-cell signatures

Global transcriptional technologies have revolutionised the study of lymphoid cell populations, but human γδ T lymphocytes specific for phosphoantigens remain far less deeply characterised by these methods despite the great therapeutic potential of these cells. Here we analyse the transcriptome of circulating TCRVγ(+) γδ T cells isolated from healthy individuals, and their relation with those from other lymphoid cell subsets. We report that the gene signature of phosphoantigen-specific TCRVγ(+) γδ T cells is a hybrid of those from αβ T and NK cells, with more 'NK-cell' genes than αβ T cells have and more 'T-cell' genes than NK cells. The expression profile of TCRVγ(+) γδ T cells stimulated with phosphoantigen recapitulates their immediate physiological functions: Th1 cytokine, chemokine and cytotoxic activities reflect their high mitotic activity at later time points and do not indicate antigen-presenting functions. Finally, such hallmarks make the transcriptome of γδ T cells, whether resting or clonally expanding, clearly distinctive from that of NK/T or peripheral T-cell lymphomas of the γδ subtype.

Clustering time-series gene expression data using smoothing spline derivatives

Microarray data acquired during time-course experiments allow the temporal variations in gene expression to be monitored. An original postprandial fasting experiment was conducted in the mouse and the expression of 200 genes was monitored with a dedicated macroarray at 11 time points between 0 and 72 hours of fasting. The aim of this study was to provide a relevant clustering of gene expression temporal profiles. This was achieved by focusing on the shapes of the curves rather than on the absolute level of expression. Actually, we combined spline smoothing and first derivative computation with hierarchical and partitioning clustering. A heuristic approach was proposed to tune the spline smoothing parameter using both statistical and biological considerations. Clusters are illustrated a posteriori through principal component analysis and heatmap visualization. Most results were found to be in agreement with the literature on the effects of fasting on the mouse liver and provide promising directions for future biological investigations.

integrOmics: an R package to unravel relationships between two omics datasets

Motivation: With the availability of many ‘omics’ data, such as transcriptomics, proteomics or metabolomics, the integrative or joint analysis of multiple datasets from different technology platforms is becoming crucial to unravel the relationships between different biological functional levels. However, the development of such an analysis is a major computational and technical challenge as most approaches suffer from high data dimensionality. New methodologies need to be developed and validated.

Results:integrOmics efficiently performs integrative analyses of two types of ‘omics’ variables that are measured on the same samples. It includes a regularized version of canonical correlation analysis to enlighten correlations between two datasets, and a sparse version of partial least squares (PLS) regression that includes simultaneous variable selection in both datasets. The usefulness of both approaches has been demonstrated previously and successfully applied in various integrative studies.

Availability:integrOmics is freely available from http://CRAN.R-project.org/ or from the web site companion (http://math.univ-toulouse.fr/biostat) that provides full documentation and tutorials.

Contact:k.lecao@uq.edu.au

Supplementary information:Supplementary data are available at Bioinformatics online.

Environmental microarray analyses of Antarctic soil microbial communities

Antarctic ecosystems are fascinating in their limited trophic complexity, with decomposition and nutrient cycling functions being dominated by microbial activities. Not only are Antarctic habitats exposed to extreme environmental conditions, the Antarctic Peninsula is also experiencing unequalled effects of global warming. Owing to their uniqueness and the potential impact of global warming on these pristine systems, there is considerable interest in determining the structure and function of microbial communities in the Antarctic. We therefore utilized a recently designed 16S rRNA gene microarray, the PhyloChip, which targets 8741 bacterial and archaeal taxa, to interrogate microbial communities inhabiting densely vegetated and bare fell-field soils along a latitudinal gradient ranging from 51 degrees S (Falkland Islands) to 72 degrees S (Coal Nunatak). Results indicated a clear decrease in diversity with increasing latitude, with the two southernmost sites harboring the most distinct Bacterial and Archaeal communities. The microarray approach proved more sensitive in detecting the breadth of microbial diversity than polymerase chain reaction-based bacterial 16S rRNA gene libraries of modest size ( approximately 190 clones per library). Furthermore, the relative signal intensities summed for phyla and families on the PhyloChip were significantly correlated with the relative occurrence of these taxa in clone libraries. PhyloChip data were also compared with functional gene microarray data obtained earlier, highlighting numerous significant relationships and providing evidence for a strong link between community composition and functional gene distribution in Antarctic soils. Integration of these PhyloChip data with other complementary methods provides an unprecedented understanding of the microbial diversity and community structure of terrestrial Antarctic habitats.

Gene expression profiling of human skeletal muscle in response to stabilized weight loss

BACKGROUND

Diet-induced weight reduction promotes a decrease in resting energy expenditure that could partly explain the difficulty in maintaining reduced body mass. Whether this reduction remains after stabilized weight loss is still controversial, and the molecular mechanisms are unknown.

OBJECTIVE

The objective was to investigate the effect of a stabilized 10% weight loss on body composition, metabolic profile, and skeletal muscle gene expression profiling.

DESIGN

Obese women were assigned to a 4-wk very-low-calorie diet, a 3-6-wk low-calorie diet, and a 4-wk weight-maintenance program to achieve a 10% weight loss. Resting energy expenditure, body composition, plasma variables, and skeletal muscle transcriptome were compared before weight loss and during stabilized weight reduction.

RESULTS

Energy restriction caused an 11% weight loss. Stabilization to the new weight was accompanied by an 11% decrease in the resting metabolic rate normalized to the body cellular mass. A large number of genes were regulated with a narrow range of regulation. The main regulated genes were slow/oxidative fiber markers, which were overexpressed, and the gene encoding the glucose metabolism inhibitor PDK4, which tended to be down-regulated. The knowledge-based approach gene set enrichment analysis showed that a set of genes related to long-term calorie restriction was up-regulated, whereas sets of genes related to insulin, interleukin 6, and ubiquitin-mediated proteolysis were down regulated.

CONCLUSIONS

Weight loss-induced decreases in resting metabolic rate persist after weight stabilization. Changes in skeletal muscle gene expression indicate a shift toward oxidative metabolism.

Relationships between sensory and physicochemical measurements in meat of rabbit from three different breeding systems using canonical correlation analysis

Meat from rabbits reared either according to a standard (STAND) or a high quality norm (LABEL) or a low growth breeding (RUSSE) system were submitted to a sensory evaluation and to a large set of physicochemical measurements (weight of retail cuts, colour parameters, ultimate pH, femur flexure test, Warner-Bratzler shear test, water holding capacities and cooking losses). STAND rabbit meat exhibited the most juicy meat in back and in leg (p<0.01). Leg tenderness significantly decreased (p<0.001) in the rank order STAND>LABEL>RUSSE. Canonical correlation analysis showed strong correlations between physicochemical and sensory variables (R(2)=0.73 and 0.68 between the two first pairs of canonical variates). Especially, sensory tenderness and WB shear test variables assessed on raw longissimus muscle (LL) were correlated. Fibrous attribute in back was correlated with cooking loss in LL. When analysed separately only RUSSE rabbits exhibited the same relations between variables as those calculated in whole dataset.

Analysis of the real EADGENE data set: comparison of methods and guidelines for data normalisation and selection of differentially expressed genes (open access publication)

A large variety of methods has been proposed in the literature for microarray data analysis. The aim of this paper was to present techniques used by the EADGENE (European Animal Disease Genomics Network of Excellence) WP1.4 participants for data quality control, normalisation and statistical methods for the detection of differentially expressed genes in order to provide some more general data analysis guidelines. All the workshop participants were given a real data set obtained in an EADGENE funded microarray study looking at the gene expression changes following artificial infection with two different mastitis causing bacteria: Escherichia coli and Staphylococcus aureus. It was reassuring to see that most of the teams found the same main biological results. In fact, most of the differentially expressed genes were found for infection by E. coli between uninfected and 24 h challenged udder quarters. Very little transcriptional variation was observed for the bacteria S. aureus. Lists of differentially expressed genes found by the different research teams were, however, quite dependent on the method used, especially concerning the data quality control step. These analyses also emphasised a biological problem of cross-talk between infected and uninfected quarters which will have to be dealt with for further microarray studies.

Analysis of the real EADGENE data set: multivariate approaches and post analysis (open access publication)

The aim of this paper was to describe, and when possible compare, the multivariate methods used by the participants in the EADGENE WP1.4 workshop. The first approach was for class discovery and class prediction using evidence from the data at hand. Several teams used hierarchical clustering (HC) or principal component analysis (PCA) to identify groups of differentially expressed genes with a similar expression pattern over time points and infective agent (E. coli or S. aureus). The main result from these analyses was that HC and PCA were able to separate tissue samples taken at 24 h following E. coli infection from the other samples. The second approach identified groups of differentially co-expressed genes, by identifying clusters of genes highly correlated when animals were infected with E. coli but not correlated more than expected by chance when the infective pathogen was S. aureus. The third approach looked at differential expression of predefined gene sets. Gene sets were defined based on information retrieved from biological databases such as Gene Ontology. Based on these annotation sources the teams used either the GlobalTest or the Fisher exact test to identify differentially expressed gene sets. The main result from these analyses was that gene sets involved in immune defence responses were differentially expressed.

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

Peroxisome proliferator-activated receptor-alpha (PPARalpha) is a major transcriptional regulator of lipid metabolism. It is activated by diverse chemicals such as fatty acids (FAs) and regulates the expression of numerous genes in organs displaying high FA catabolic rates, including the liver. The role of this nuclear receptor as a sensor of whole dietary fat intake has been inferred, mostly from high-fat diet studies. To delineate its function under low fat intake conditions (4.8% w/w), we studied the effects of five regimens with contrasted FA compositions on liver lipids and hepatic gene expression in wild-type and PPARalpha-deficient mice. Diets containing polyunsaturated FAs reduced hepatic fat stores in wild-type mice. Only sunflower, linseed, and fish oil diets lowered hepatic lipid stores in PPARalpha-/- mice, a model of progressive hepatic triglyceride accumulation. These beneficial effects were associated, in particular, with dietary regulation of Delta9-desaturase in both genotypes, and with a newly identified PPARalpha-dependent regulation of lipin. Furthermore, hepatic levels of 18-carbon essential FAs (C18:2omega6 and C18:3omega3) were elevated in PPARalpha-/- mice, possibly due to the observed reduction in expression of the Delta6-desaturase and of enoyl-coenzyme A isomerases. Effects of diet and genotype were also observed on the xenobiotic metabolism-related genes Cyp3a11 and CAR.

CONCLUSION

Together, our results suggest that dietary FAs represent--even under low fat intake conditions--a beneficial strategy to reduce hepatic steatosis. Under such conditions, we established the role of PPARalpha as a dietary FA sensor and highlighted its importance in regulating hepatic FA content and composition.