A Data Integration Multi-Omics Approach to Study Calorie Restriction-Induced Changes in Insulin Sensitivity

Maria Carlota Dao, Nataliya Sokolovska, Rémi Brazeilles, Séverine Affeldt, Véronique Pelloux, Edi Prifti, Julien Chilloux, Eric O Verger, Brandon D Kayser, Judith Aron-Wisnewsky, Farid Ichou, Estelle Pujos-Guillot, Lesley Hoyles, Catherine Juste, Joël Doré, Marc-Emmanuel Dumas, Salwa W Rizkalla, Bridget A Holmes, Jean-Daniel Zucker, Karine Clément, MICRO-Obes Consortium, Aurélie Cotillard, Sean P Kennedy, Nicolas Pons, Emmanuelle Le Chatelier, Mathieu Almeida, Benoit Quinquis, Nathalie Galleron, Jean-Michel Batto, Pierre Renault, Stanislav Dusko Ehrlich, Hervé Blottière, Marion Leclerc, Tomas de Wouters, Patricia Lepage, Maria Carlota Dao, Nataliya Sokolovska, Rémi Brazeilles, Séverine Affeldt, Véronique Pelloux, Edi Prifti, Julien Chilloux, Eric O Verger, Brandon D Kayser, Judith Aron-Wisnewsky, Farid Ichou, Estelle Pujos-Guillot, Lesley Hoyles, Catherine Juste, Joël Doré, Marc-Emmanuel Dumas, Salwa W Rizkalla, Bridget A Holmes, Jean-Daniel Zucker, Karine Clément, MICRO-Obes Consortium, Aurélie Cotillard, Sean P Kennedy, Nicolas Pons, Emmanuelle Le Chatelier, Mathieu Almeida, Benoit Quinquis, Nathalie Galleron, Jean-Michel Batto, Pierre Renault, Stanislav Dusko Ehrlich, Hervé Blottière, Marion Leclerc, Tomas de Wouters, Patricia Lepage

Abstract

Background: The mechanisms responsible for calorie restriction (CR)-induced improvement in insulin sensitivity (IS) have not been fully elucidated. Greater insight can be achieved through deep biological phenotyping of subjects undergoing CR, and integration of big data. Materials and Methods: An integrative approach was applied to investigate associations between change in IS and factors from host, microbiota, and lifestyle after a 6-week CR period in 27 overweight or obese adults (ClinicalTrials.gov: NCT01314690). Partial least squares regression was used to determine associations of change (week 6 - baseline) between IS markers and lifestyle factors (diet and physical activity), subcutaneous adipose tissue (sAT) gene expression, metabolomics of serum, urine and feces, and gut microbiota composition. ScaleNet, a network learning approach based on spectral consensus strategy (SCS, developed by us) was used for reconstruction of biological networks. Results: A spectrum of variables from lifestyle factors (10 nutrients), gut microbiota (10 metagenomics species), and host multi-omics (metabolic features: 84 from serum, 73 from urine, and 131 from feces; and 257 sAT gene probes) most associated with IS were identified. Biological network reconstruction using SCS, highlighted links between changes in IS, serum branched chain amino acids, sAT genes involved in endoplasmic reticulum stress and ubiquitination, and gut metagenomic species (MGS). Linear regression analysis to model how changes of select variables over the CR period contribute to changes in IS, showed greatest contributions from gut MGS and fiber intake. Conclusion: This work has enhanced previous knowledge on links between host glucose homeostasis, lifestyle factors and the gut microbiota, and has identified potential biomarkers that may be used in future studies to predict and improve individual response to weight-loss interventions. Furthermore, this is the first study showing integration of the wide range of data presented herein, identifying 115 variables of interest with respect to IS from the initial input, consisting of 9,986 variables. Clinical Trial Registration: clinicaltrials.gov (NCT01314690).

Keywords: data integration; insulin sensitivity; lifestyle factors; microbiota; omics.

Figures

Figure 1
Figure 1
Analysis pipeline for data integration. (A) Groups of variables from host, gut microbiota, and lifestyle were considered as input for this analysis. Specifically, the inputs were changes (week 6 – baseline) in the following blocks of variables: clinical parameters (N = 45), including 10 markers of IS/resistance (), lifestyle factors (food groups, N = 26, nutrients N = 34, and physical activity, N = 3), MGS (N = 741, i.e., with more than 700 genes), fecal metabolic features (N = 835), urine metabolic features (N = 562), serum metabolic features (N = 180), and sAT gene expression (N = 7,560 ILMN probes). (B) Two blocks of variables at a time were analyzed using PLSR with canonical mode (MixOmics R package). We have analyzed changes in versus changes in through , and between and . Association coefficient threshold = | 0.7| was selected for analysis with and , and | 0.75| for analysis with , , and . An example of a PLSR network between blocks and is shown. (C) The associations between and host, microbiota, or lifestyle were interpreted. (D) From the PLSR output, the features most strongly associated with improvement in IS after CR were selected (top 20% variables from each PLSR). (E) Visualization of selected features in network reconstruction using SCS (ScaleNet, developed by our group). In hypothetical network shown in E, red nodes: microbiota; blue nodes: host; green nodes: lifestyle factors. Arrows refer to dependency directionality.
Figure 2
Figure 2
Association between changes in IS and factors in host, microbiota, and lifestyle. (A,B) Superimposed PLSR networks associated with change in insulin sensitivity (ΔINS. SEN.), where nodes are arranged by (A) betweenness centrality and (B) variable type. The green edges correspond to positive correlations of change and the red edges correspond to negative correlations of change. (C) Summary of variables from host, microbiota, and lifestyle factors associated with ΔINS. SEN. Association coefficient threshold = [0.7] for lifestyle factors, and | 0.75| for metabolomics and sAT gene expression. NA, not annotated; BCAAs, branched chain amino acids; AAAs, aromatic amino acids; AAs, amino acids; MGS, metagenomic species. No association with change in physical activity or food groups was found above the selected threshold.
Figure 3
Figure 3
Connection between change in IS, BCAAs and other factors from host, lifestyle, and microbiota. (A) ScaleNet network reconstruction showing how changes in variables most strongly associated with improvements in IS are associated with each other. The green edges correspond to positive correlations of change and the red edges correspond to negative correlations of change. (B) Change from baseline to week 6 in select variables from network (highlighted in orange ellipse on the network in A). When significant, adjusted P-values (BH correction) from Wilcoxon Signed Rank test are shown. (C) Linear regression model, where each section of the pie chart shows the relative contribution of change in selected variables (grouped by class) to change in revised QUICKI and HOMA-B. Variables included in the model were those found in the main cluster of the network in part A (except serum metabolic features annotated as glucose). MGS, metagenomic species; metab., metabolic features; DDRGK1, DDRGK domain containing 1 (also known as Dashurin).

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