Integrative Personal Omics Profiles during Periods of Weight Gain and Loss - PubMed (original) (raw)

. 2018 Feb 28;6(2):157-170.e8.

doi: 10.1016/j.cels.2017.12.013. Epub 2018 Jan 17.

Wenyu Zhou 1, Kévin Contrepois 1, Hannes Röst 1, Gucci Jijuan Gu Urban 2, Tejaswini Mishra 1, Blake M Hanson 3, Eddy J Bautista 3, Shana Leopold 3, Christine Y Yeh 4, Daniel Spakowicz 3, Imon Banerjee 5, Cynthia Chen 5, Kimberly Kukurba 1, Dalia Perelman 6, Colleen Craig 6, Elizabeth Colbert 6, Denis Salins 1, Shannon Rego 1, Sunjae Lee 7, Cheng Zhang 7, Jessica Wheeler 1, M Reza Sailani 1, Liang Liang 1, Charles Abbott 1, Mark Gerstein 8, Adil Mardinoglu 9, Ulf Smith 10, Daniel L Rubin 5, Sharon Pitteri 11, Erica Sodergren 3, Tracey L McLaughlin 12, George M Weinstock 13, Michael P Snyder 14

Affiliations

Integrative Personal Omics Profiles during Periods of Weight Gain and Loss

Brian D Piening et al. Cell Syst. 2018.

Abstract

Advances in omics technologies now allow an unprecedented level of phenotyping for human diseases, including obesity, in which individual responses to excess weight are heterogeneous and unpredictable. To aid the development of better understanding of these phenotypes, we performed a controlled longitudinal weight perturbation study combining multiple omics strategies (genomics, transcriptomics, multiple proteomics assays, metabolomics, and microbiomics) during periods of weight gain and loss in humans. Results demonstrated that: (1) weight gain is associated with the activation of strong inflammatory and hypertrophic cardiomyopathy signatures in blood; (2) although weight loss reverses some changes, a number of signatures persist, indicative of long-term physiologic changes; (3) we observed omics signatures associated with insulin resistance that may serve as novel diagnostics; (4) specific biomolecules were highly individualized and stable in response to perturbations, potentially representing stable personalized markers. Most data are available open access and serve as a valuable resource for the community.

Keywords: genomics; metabolomics; microbiome; obesity; proteomics; systems biology; type 2 diabetes.

Copyright © 2017 Elsevier Inc. All rights reserved.

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Conflict of interest statement

Declaration of Interests:

M.P.S. is a founder and member of the science advisory board of Personalis and SensOmix and a science advisory board member of Genapsys and AxioMX.

Figures

Fig. 1

Fig. 1

Overview of the multi-omic weight perturbation experiment. (A) Schematic of the weight gain and loss perturbation. The sampling timepoints (T1–T3) are indicated at the specific time in the perturbation when they occur. Inset: SSPG and BMI measurements for IS and IR subjects. (B) The types of ‘omics analyses performed are indicated along with the types of biological materials they are performed on. Listed below each data type are the number of analytes measured per timepoint for each individual, as well as the total number of analytes measured across the study. (C) Circos plot of multi-omic data points from selected assays. The transcriptome, both targeted and untargeted proteome and serum cytokine levels are plotted according to their genomic location as well as the average expression in IR and IS participants (see inset labels). (D) The stool microbiome phylogenetic tree is visualized by GraPhlAn for taxonomies present across all participants along with the respective relative abundance in IR and IS (outer layers).

Fig. 2

Fig. 2

Differences between IR and IS participants at baseline. (A) Heatmap showing differences between IR and IS in baseline molecular abundance for each ‘omic type. Each analyte is normalized according to the average expression in IS and significant differences in the IR group are plotted (red=upregulated in IR, blue=downregulated in IR). (B) Pathways exhibiting significant transcriptomic and proteomics differences between IR and IS. The top Gene Ontology categories are presented and top transcripts and proteins are plotted in a network diagram showing pathway connections. (C) Differences in microbial abundance (%) between IR and IS by both 16S and shotgun metagenomic sequencing. (D) Regression analysis detailing association of multiple metabolites with clinical steady-state-plasma glucose (SSPG). MS signal intensity is plotted versus SSPG (mg/dl) for the selected metabolites indolelactic acid and tetrahydrocortisol glucuronide. Inset are the R2 and p-values for the selected comparisons.

Fig. 3

Fig. 3

Multi-omic differences over the course of weight gain and loss perturbation experiment. (A) Heatmap showing analytes that vary in abundance in response to the weight gain and loss perturbation. (B) Pathways that are significantly different between baseline versus weight gain, and weight gain versus weight loss, respectively. (C) IR or IS-specific microbiome changes are shown for selected taxonomic units confirmed by both methods (16S and shotgun metagenomics).

Fig. 4

Fig. 4

Multiparametric and trend analyses reveal novel responses to weight gain and loss. (A) Longitudinal pattern recognition using fuzzy c-means clustering across all host ‘omes. Data from the transcriptome, proteome, cytokines and metabolites were standardized to z-scores for each analyte and subjected to c-means clustering across all four timepoints. Each subplot shows a unique cluster and the trend for all analytes comprising the cluster. (B) KEGG pathway diagram for analytes implicated in dilated cardiomyopathy, a pathway that was significantly enriched in Cluster 12 (FDR < 0.000004). Elements highlighted in yellow indicate the pathway analytes that comprise Cluster 12. (C) Table showing biological pathway enrichment and association with clinical blood panel analytes for key gene co-expression clusters. (D) Gene expression heatmap for transcripts comprising the yellow module from Fig. S3. The expression for each gene is shown for all timepoints (T1, pink; T2, blue; T3, orange; T4, green.) along with the relative levels for each of the enriched clinical parameters (A1C, LDL, HDL, IGM and bilirubin (TBIL)).

Fig. 5

Fig. 5. Associations of analytes across IR and IS and across ‘omes

(A) Co-varying microbial species are plotted based on whether they are co- or inversely associated (blue or red, respectively), and whether this occurs in IR (upper quadrant) or IS (lower quadrant). (B) Co-variation of microbes and metabolites for IR and IS is plotted for selected associations. Inset are the Spearman’s rho and adjusted p-values after FDR correction for the selected associations. Also, adjusted p values by FDR are shown between IS and IR individuals for the interaction term of linear model describing different trends in two groups.

Fig. 6

Fig. 6. Personal variation of ‘omics data

(A) Variance decomposition analysis of selected ‘omes (see Fig. S9 for others). The variance across all timepoints was deconvolved into experiment-dependent variation (i.e. due to the perturbation), personal variation (within an individual), or other types of variation (technical or unknown sources). The heatmap color (yellow to red) indicates the density of analytes at each particular coordinate. (B) Variation in cytokine/chemokine/adipokine abundance within participants versus across participants. The coefficient of variation for all measured Luminex immunoassays is plotted for across steady-state timepoints (T1 and T4) within an individual (red) and across individuals (blue). (C) Power comparison for longitudinal versus group wise study designs using metabolomics data as an example.

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