Integrative analysis of longitudinal metabolomics data from a personal multi-omics profile

Abstract

The integrative personal omics profile (iPOP) is a pioneering study that combines genomics, transcriptomics, proteomics, metabolomics and autoantibody profiles from a single individual over a 14-month period. The observation period includes two episodes of viral infection: a human rhinovirus and a respiratory syncytial virus. The profile studies give an informative snapshot into the biological functioning of an organism. We hypothesize that pathway expression levels are associated with disease status. To test this hypothesis, we use biological pathways to integrate metabolomics and proteomics iPOP data. The approach computes the pathways' differential expression levels at each time point, while taking into account the pathway structure and the longitudinal design. The resulting pathway levels show strong association with the disease status. Further, we identify temporal patterns in metabolite expression levels. The changes in metabolite expression levels also appear to be consistent with the disease status. The results of the integrative analysis suggest that changes in biological pathways may be used to predict and monitor the disease. The iPOP experimental design, data acquisition and analysis issues are discussed within the broader context of personal profiling.

Figure 1

The timeline of the study. The subject was monitored for 726 days. Days of human rhinovirus (HRV) and respiratory syncytial virus (RSV) infections are marked in red and green, respectively. The red and green bars represent the onset of the infections. The light blue bar shows the period of high glucose levels, and the dark blue one indicates lifestyle changes, including (1) increased exercise, (2) ingestion of 81 mg of acetylsalicylic acid and ibuprofen each day (the latter only during the first six weeks of this period) and (3) substantially reduced sugar intake. Circled days indicate fasted time points.

Figure 2

The dendrogram sharpening algorithm.

Figure 3

(Left to right) dendrogram trees for the full, once- and twice-sharpened data containing 1,098, 545 and 293 complete data points on metabolic compounds.

Figure 4

Eight distinct clusters of the metabolome profiles in the personal omics profile (iPOP) study showing individual (grey) and mean (blue) metabolite time courses for each cluster. Also marked are periods of HRV and RSV infections.

Figure 5

Changes in functional pathway scores over time. Each pathway contained both measured metabolites and proteins. The pathways are color-coded according to the legend.

Figure 6

Three out of eight clusters showing distinct temporal patterns of the pathway scores for serum metabolome and PBMC proteome. Individual pathway scores are shown in grey; mean scores are in blue.

Figure S1

Pathway networks for Clusters 2,3,6,7,8 (counterclockwise from the left top corner). The node size reflects the total number of components in a pathway; the node color reflects the p-value of the pathway representation analysis (a darker color corresponds to lower p-values); the edge width corresponds to a relative number of shared compounds between the pathways; and the edge color reflects the absolute number in the overlap.