Unsupervised multiple kernel learning for heterogeneous data integration

Motivation

Recent high-throughput sequencing advances have expanded the breadth of available omics datasets and the integrated analysis of multiple datasets obtained on the same samples has allowed to gain important insights in a wide range of applications. However, the integration of various sources of information remains a challenge for systems biology since produced datasets are often of heterogeneous types, with the need of developing generic methods to take their different specificities into account.

Results

We propose a multiple kernel framework that allows to integrate multiple datasets of various types into a single exploratory analysis. Several solutions are provided to learn either a consensus meta-kernel or a meta-kernel that preserves the original topology of the datasets. We applied our framework to analyse two public multi-omics datasets. First, the multiple metagenomic datasets, collected during the TARA Oceans expedition, was explored to demonstrate that our method is able to retrieve previous findings in a single kernel PCA as well as to provide a new image of the sample structures when a larger number of datasets are included in the analysis. To perform this analysis, a generic procedure is also proposed to improve the interpretability of the kernel PCA in regards with the original data. Second, the multi-omics breast cancer datasets, provided by The Cancer Genome Atlas, is analysed using a kernel Self-Organizing Maps with both single and multi-omics strategies. The comparison of these two approaches demonstrates the benefit of our integration method to improve the representation of the studied biological system.

Availability and implementation

Proposed methods are available in the R package mixKernel, released on CRAN. It is fully compatible with the mixOmics package and a tutorial describing the approach can be found on mixOmics web site http://mixomics.org/mixkernel/.

Contact

jerome.mariette@inra.fr or nathalie.villa-vialaneix@inra.fr.

Supplementary information

Supplementary data are available at Bioinformatics online.

Large-Scale Measurement of mRNA Degradation in Escherichia coli: To Delay or Not to Delay

In this study, we compared different computational methods used for genome-wide determination of mRNA half-lives in Escherichia coli with a special focus on the impact on considering a delay before the onset of mRNA decay after transcription arrest. A wide variety of datasets were analyzed coming from different technical methods for mRNA quantification (microarrays, RNA-seq, and RT-qPCR) and different bacterial growth conditions. The exponential decay of mRNA levels was fitted using both linear and exponential models and with or without a delay. We showed that for all the models, independently of mRNA quantification methods and growth conditions, ignoring the delay resulted in only a modest overestimation of the half-life. For approximately 80% of the mRNAs, differences in mRNA half-life values were less than 34s. The correlation between half-lives estimated with and without a delay was extremely high. However, the slope of the linear regression between the half-lives with and without a delay tended to decrease with the delay. For the few mRNAs for which taking into account the delay influenced the estimated half-life, the impact was dependent on the model and the growth condition. The smallest impact was obtained for the linear model.

Time course study of the response to LPS targeting the pig immune gene networks

Background

Stress is a generic term used to describe non-specific responses of the body to all kinds of challenges. A very large variability in the response can be observed across individuals, depending on numerous conditioning factors like genetics, early influences and life history. As a result, there is a wide range of individual vulnerability and resilience to stress, also called robustness. The importance of robustness-related traits in breeding strategies is increasing progressively towards the production of animals with a high level of production under a wide range of climatic conditions and management systems, together with a lower environmental impact and a high level of animal welfare. The present study aims at describing blood transcriptomic, hormonal, and metabolic responses of pigs to a systemic challenge using lipopolysaccharide (LPS). The objective is to analyze the individual variation of the biological responses in relation to the activity of the HPA axis measured by the levels of plasma cortisol after LPS and ACTH in 120 juvenile Large White (LW) pigs. The kinetics of the response was measured with biological variables and whole blood gene expression at 4 time points. A multilevel statistical analysis was used to take into account the longitudinal aspect of the data.

Results

Cortisol level reaches its peak 4 h after LPS injection. The characteristic changes of white blood cell count to LPS were observed, with a decrease of total count, maximal at t=+4 h, and the mirror changes in the respective proportions of lymphocytes and granulocytes. The lymphocytes / granulocytes ratio was maximal at t=+1 h. An integrative statistical approach was used and provided a set of candidate genes for kinetic studies and ongoing complementary studies focused on the LPS-stimulated inflammatory response.

Conclusions

The present study demonstrates the specific biomarkers indicative of an inflammation in swine. Furthermore, these stress responses persist for prolonged periods of time and at significant expression levels, making them good candidate markers for evaluating the efficacy of anti-inflammatory drugs.

Electronic supplementary material

The online version of this article (doi:10.1186/s12864-017-4363-5) contains supplementary material, which is available to authorized users.

1HNMR-Based metabolomic profiling method to develop plasma biomarkers for sensitivity to chronic heat stress in growing pigs

The negative impact of heat stress (HS) on the production performances in pig faming is of particular concern. Novel diagnostic methods are needed to predict the robustness of pigs to HS. Our study aimed to assess the reliability of blood metabolome to predict the sensitivity to chronic HS of 10 F1 (Large White × Creole) sire families (SF) reared in temperate (TEMP) and in tropical (TROP) regions (n = 56±5 offsprings/region/SF). Live body weight (BW) and rectal temperature (RT) were recorded at 23 weeks of age. Average daily feed intake (AFDI) and average daily gain were calculated from weeks 11 to 23 of age, together with feed conversion ratio. Plasma blood metabolome profiles were obtained by Nuclear Magnetic Resonance spectroscopy (^1HNMR) from blood samples collected at week 23 in TEMP. The sensitivity to hot climatic conditions of each SF was estimated by computing a composite index of sensitivity (I_sens) derived from a linear combination of t statistics applied to familial BW, ADFI and RT in TEMP and TROP climates. A model of prediction of sensitivity was established with sparse Partial Least Square Discriminant Analysis (sPLS-DA) between the two most robust SF (n = 102) and the two most sensitive ones (n = 121) using individual metabolomic profiles measured in TEMP. The sPLS-DA selected 29 buckets that enabled 78% of prediction accuracy by cross-validation. On the basis of this training, we predicted the proportion of sensitive pigs within the 6 remaining families (n = 337). This proportion was defined as the predicted membership of families to the sensitive category. The positive correlation between this proportion and I_sens (r = 0.97, P < 0.01) suggests that plasma metabolome can be used to predict the sensitivity of pigs to hot climate.

Molecular Biomarkers for Weight Control in Obese Individuals Subjected to a Multiphase Dietary Intervention

CONTEXT

Although calorie restriction has proven beneficial for weight loss, long-term weight control is variable between individuals.

OBJECTIVE

To identify biomarkers of successful weight control during a dietary intervention (DI).

DESIGN, SETTING, AND PARTICIPANTS

Adipose tissue (AT) transcriptomes were compared between 21 obese individuals who either maintained weight loss or regained weight during the DI. Results were validated on 310 individuals from the same study using quantitative reverse transcription polymerase chain reaction and protein levels of potential circulating biomarkers measured by enzyme-linked immunosorbent assay.

INTERVENTION

Individuals underwent 8 weeks of low-calorie diet, then 6 months of ad libitum diet.

OUTCOME MEASURE

Weight changes at the end of the DI.

RESULTS

We evaluated six genes that had altered expression during DI, encode secreted proteins, and have not previously been implicated in weight control (EGFL6, FSTL3, CRYAB, TNMD, SPARC, IGFBP3), as well as genes for which baseline expression differed between those with good and poor weight control (ASPN, USP53). Changes in plasma concentrations of EGFL6, FSTL3, and CRYAB mirrored AT messenger RNA expression; all decreased during DI in individuals with good weight control. ASPN and USP53 had higher baseline expression in individuals who went on to have good weight control. Expression quantitative trait loci analysis found polymorphisms associated with expression levels of USP53 in AT. A regulatory network was identified in which transforming growth factor β1 (TGF-β1) was responsible for downregulation of certain genes during DI in good controllers. Interestingly, ASPN is a TGF-β1 inhibitor.

CONCLUSIONS

We found circulating biomarkers associated with weight control that could influence weight management strategies and genes that may be prognostic for successful weight control.

Time course of the response to ACTH in pig: biological and transcriptomic study

Background

HPA axis plays a major role in physiological homeostasis. It is also involved in stress and adaptive response to the environment. In farm animals in general and specifically in pigs, breeding strategies have highly favored production traits such as lean growth rate, feed efficiency and prolificacy at the cost of robustness. On the hypothesis that the HPA axis could contribute to the trade-off between robustness and production traits, we have designed this experiment to explore individual variation in the biological response to the main stress hormone, cortisol, in pigs. We used ACTH injections to trigger production of cortisol in 120 juvenile Large White (LW) pigs from 28 litters and the kinetics of the response was measured with biological variables and whole blood gene expression at 4 time points. A multilevel statistical analysis was used to take into account the longitudinal aspect of the data.

Results

Cortisol level reached its peak 1 h after ACTH injection. White blood cell composition was modified with a decrease of lymphocytes and monocytes and an increase of granulocytes (FDR<0.05). Basal level of cortisol was correlated with birth and weaning weights. Microarray analysis identified 65 unique genes of which expression responded to the injection of ACTH (adjusted P<0.05). These genes were classified into 4 clusters with distinctive kinetics in response to ACTH injection. The first cluster identified genes strongly correlated to cortisol and previously reported as being regulated by glucocorticoids. In particular, DDIT4, DUSP1, FKBP5, IL7R, NFKBIA, PER1, RGS2 and RHOB were shown to be connected to each other by the glucocorticoid receptor NR3C1. Most of the differentially expressed genes that encode transcription factors have not been described yet as being important in transcription networks involved in stress response. Their co-expression may mean co-regulation and they could thus provide new patterns of biomarkers of the individual sensitivity to cortisol.

Conclusions

We identified 65 genes as biological markers of HPA axis activation at the gene expression level. These genes might be candidates for a better understanding of the molecular mechanisms of the stress response.

Electronic supplementary material

The online version of this article (doi:10.1186/s12864-015-2118-8) contains supplementary material, which is available to authorized users.

The structure of a gene co-expression network reveals biological functions underlying eQTLs

What are the commonalities between genes, whose expression level is partially controlled by eQTL, especially with regard to biological functions? Moreover, how are these genes related to a phenotype of interest? These issues are particularly difficult to address when the genome annotation is incomplete, as is the case for mammalian species. Moreover, the direct link between gene expression and a phenotype of interest may be weak, and thus difficult to handle. In this framework, the use of a co-expression network has proven useful: it is a robust approach for modeling a complex system of genetic regulations, and to infer knowledge for yet unknown genes. In this article, a case study was conducted with a mammalian species. It showed that the use of a co-expression network based on partial correlation, combined with a relevant clustering of nodes, leads to an enrichment of biological functions of around 83%. Moreover, the use of a spatial statistics approach allowed us to superimpose additional information related to a phenotype; this lead to highlighting specific genes or gene clusters that are related to the network structure and the phenotype. Three main results are worth noting: first, key genes were highlighted as a potential focus for forthcoming biological experiments; second, a set of biological functions, which support a list of genes under partial eQTL control, was set up by an overview of the global structure of the gene expression network; third, pH was found correlated with gene clusters, and then with related biological functions, as a result of a spatial analysis of the network topology.

Phenotypic prediction based on metabolomic data for growing pigs from three main European breeds

Predicting phenotypes is a statistical and biotechnical challenge, both in medicine (predicting an illness) and animal breeding (predicting the carcass economical value on a young living animal). High-throughput fine phenotyping is possible using metabolomics, which describes the global metabolic status of an individual, and is the closest to the terminal phenotype. The purpose of this work was to quantify the prediction power of metabolomic profiles for commonly used production phenotypes from a single blood sample from growing pigs. Several statistical approaches were investigated and compared on the basis of cross validation: raw data vs. signal preprocessing (wavelet transformation), with a single-feature selection method. The best results in terms of prediction accuracy were obtained when data were preprocessed using wavelet transformations on the Daubechies basis. The phenotypes related to meat quality were not well predicted because the blood sample was taken some time before slaughter, and slaughter is known to have a strong influence on these traits. By contrast, phenotypes of potential economic interest (e.g., lean meat percentage and ADFI) were well predicted (R(2) = 0.7; P < 0.0001) using metabolomic data.