Multi-omics data integration

Nowadays, high-throughput technologies in genomics, proteomics and metabolomics allow to produce data on a massive scale (Meng et al. 2014), in which each of its omics-discipline is characterized by its complexity. Hereto, the human GIT and its microbiome is a complex ecosystem (Heintz-Buschart, et al.

2016), which is central in understanding the dynamics of health and disease. The integration of different omics-disciplines can provide a global picture of the microbial composition on a systems-level, understanding the host, microbial metabolism and protein expression, as well as the metabolic status of the human gut. The latter is of top priority to understand the impact of diet, especially of varying amounts of non-digestible carbohydrates, on the gut microbiome for health and disease.

2.4.3.1 Supervised ordination approach for multi-omics correlation

A supervised ordination approach (Figure 2.4-30) was applied, evaluating correlations among the genome, proteome and metabolome comparing baseline and the HRS diet (Figure 2.4-30 A). Also, a classification model was created for the comparison of the HRS diet and the LRS diet (Figure 2.4-30 B). This method was able to discriminate metabolites, proteins and OTUs that correlated with each other and with the different diets, gaining a first overview of possible connections (Maier et al. 2017).

Therefore, a 25% limit (black line, shown dashed) of the highest discriminative feature of either the baseline diet or the HRS diet was set to detect highly correlated and highest discriminating features among the different diets. The same procedure was performed comparing the HRS to the LRS diet.

Figure 2.4-30: Multi-Omics integration through supervised ordination approach.

OPLS-DA plot of all data (Features: metabolites, 5552; Proteome, 57 397; OTUs, 1107) for A: baseline (blue, negative x-axis) versus HRS (red, positive x-axis); p = 8.3∙10-6 (CV-ANOVA), R²Y(cum) = 0.96, Q²(cum) = 0.88.

B: OPLS-DA plot for HRS (red, negative x-axis) versus LRS (green, positive x-axis); p = 0.026 (CV-ANOVA), R²Y(cum) = 0.883, Q²(cum) = 0.534. From Maier, T. V.; Lucio, M.; Lee, L. H.; VerBerkmoes, N. C.; Brislawn, C. J.;

Bernhardt, J.; Lamendella, R.; McDermott, J. E.; Bergeron, N.; Heinzmann, S. S.; Morton, J. T.; González, A.;

Ackermann, G.; Knight, R.; Riedel, K.; Krauss, R. M.; Schmitt-Kopplin, P.; Jansson, J. K.: Impact of Dietary Resistant Starch on the Human Gut Microbiome, Metaproteome, and Metabolome. mBio vol. 8 no. 5 e01343-17 (2017). Reprinted and modified from (Maier et al. 2017). Copyright (2017) Maier et al.

The metabolite profile was vastly changed by the HRS diet. Several features were highly correlated and significantly changed by the HRS diet. This included 17.83% of the total metabolome (990 metabolites) and 1.14% of the total proteome (649 proteins), but no OTUs significantly changed through the HRS diet. On the contrary, the baseline diet was characterized through 0.31% of the metabolome (17 metabolites), 0.008% of the total proteome (5 proteins) and 0.18% of the total genome (2 OTUs).

In general, metabolites, such as sterol lipids, fatty acyls, glycerolipids and polyketides correlated with ribosomal proteins (J), adenylosuccinate synthase (F), rubrerythrin (C), glyceraldehyde-3-phosphate

Baseline HRS HRS LRS

Proteins OTU Metabolites

A Multi-omics correlation B

Proteins – OTUs – metabolites Baseline vs. HRS

Multi-omics correlation Proteins – OTUs – metabolites

HRS vs. LRS

-0.0122 0.0094

-0.0070 0.0091

dehydrogenase (G) and fructose/tagatose bisphosphate aldolase (G) predominately in the HRS diet. In the baseline diet, correlations between genera of Lachnospiraceae, Bacteroidetes, ribosomal proteins, FAs and STs were detected. Further, the correlations between the HRS and LRS diet were investigated, which resulted in 0.34% of the metabolome (19 metabolites), 0.012% of the total proteome (7 proteins) and 0.09% of the total genome (1 OTUs) correlated through the LRS diet. In the HRS diet, only metabolites (1.87%) were found to be significantly changed, whereas no correlations between metabolites, proteins and OTUs were detected. The first OTU, detected as correlated with metabolites was a Roseburia sp. with some STs.

2.4.3.2 Mass-difference network analysis

The supervised ordination approach was complemented using a network-based CLR method (Faith et al. 2007) to display potential interactions of the genome, proteome and metabolome and to examine results across the different omics levels for an integrated systems picture (Maier et al. 2017). Differential expression was calculated as the fold change between mean abundance of each component (OTU, protein and metabolite) in the HRS participants versus baseline participants (Figure 2.4-31 A), as well as the LRS participants versus the baseline ones (Figure 2.4-31 B). Both were visualized in Cytoscape as a network. This allowed assigning various areas of the network to different metabolite compound classes (e.g. STs, GLs, GPs and FAs) and connected OTUs, and proteins. This result agreed with the supervised ordination approaches, that the discrimination in both models was mainly driven by metabolites.

A F. prausnitzii A2-165 strain, assigned as a species for a specific protein, namely phosphotransacetylase, involved in the energy production and conversion was found to be correlated with the HRS diet and showed connections to a glycerophospholipid (PG(15:0/0:0)). The CLR network comparing HRS to the baseline diet showed more features correlated or correlated negatively with the HRS diet compared to baseline than for the LRS diet compared to baseline, which again demonstrates that the HRS diet had a larger impact on the gut microbiome (Maier et al. 2017).

As previously described, genera of Bacteroides and Lachnospiraceae correlated negatively with RS and connected to unsaturated FAs and some STs (Maier et al. 2017). Thousands of metabolites were significantly more abundant or less abundant after the HRS diet, including sterol lipids, GPs, GLs, FAs,

PKs and lots of unknown metabolites, whereas only 3 more proteins (phosphoenolpyruvate carboxykinase (C), ribosomal protein L23 (J) and ABC-type sugar transport system (G) and 2 OTUs (ruminococcaceae) were revealed.

Figure 2.4-31: Multi-Omics integration displayed as CLR network.

A: CLR network displaying correlated features to the HRS diet (red) and negatively correlated to HRS diet (blue).

B: CLR network displaying correlated features to the LRS diet (red) and negatively correlated to LRS diet (blue).

Similarities (edges) within and between species, proteins, and metabolites (circles, squares, triangles, respectively) across participants and time points, including only nodes significantly higher (red) or lower (blue) in HRS or LRS respectively relative to baseline (p < 0.05). Areas of the network assigned to sterol lipids (red), glycerolipids (blue), glycerophospholipids (orange), fatty acyls (pink), polyketides (mint) and unknown metabolites (green). From Maier, T. V.; Lucio, M.; Lee, L. H.; VerBerkmoes, N. C.; Brislawn, C. J.; Bernhardt, J.; Lamendella, R.; McDermott, J. E.;

Bergeron, N.; Heinzmann, S. S.; Morton, J. T.; González, A.; Ackermann, G.; Knight, R.; Riedel, K.; Krauss, R. M.;

Schmitt-Kopplin, P.; Jansson, J. K.: Impact of Dietary Resistant Starch on the Human Gut Microbiome, Metaproteome, and Metabolome. mBio vol. 8 no. 5 e01343-17 (2017).Figure A was reprinted and modified from (Maier et al. 2017). Copyright (2017) Maier et al. Illustration of Figure B was reprinted and modified from (Maier et al. 2017). Copyright (2017) Maier et al., Information about the creator and respective contributions, as well as the original material are available: http://mbio.asm.org/content/8/5/e01343-17.full with the original title: Integrative association network of the microbiome under low resistant starch diet. Licence notice:

https://creativecommons.org/licenses/by/4.0/.

When analyzing the combined CLR data, not only features that were associated with the different diets were observed, but also features that correlated with each other. This systems view of the metabolite composition and clustering confirms results from previous analyses of the influence of dietary RS on some members of the Firmicutes, such as F. prausnitzii (Haenen et al. 2013), and goes beyond them by also identifying correlations of specific species with single metabolites and proteins. For example, F.

prausnitzii was correlated positively with the HRS diet and 14 novel metabolites with putative identifications as polyketides were found that correlated with this microorganism .(Maier et al. 2017)

Ruminococcaceae

When analyzing the CLR network comparing the LRS diet to the baseline diet it was noticeable that comparatively fewer features correlated and a number of features were correlated negatively with the LRS diet. Comparing both networks, an accordance of one species specific protein of the F. prausnitzii A2-165, namely ribosomal protein L23, was observed to be correlated positively with the HRS diet, and to the LRS diet. These results also pointed towards key linkages between several members of the gut microbiome, metabolites and proteins produced in the gut (Maier et al. 2017).

These findings of the multi-omics analyses were summarized to evaluate the main effects of the RS diet on the gut microbiome and functions that they carry out and the multitude of processes that occur through HRS diet. Changes in the starch degradation and metabolism were detected. Glucosidases, proteins involved in sugar transport and in the glycolysis, could be observed. Proteins, such as beta-glucosidases, involved in breaking complex carbohydrates into monomers and proteins involved in transport systems for import of resultant free sugars into the cells were highly increased in the HRS diet.

On the contrary, alpha-glucosidases were in comparison less abundant in the HRS diet. Alpha-glucosidases break down simple starches, but the high amylose cornstarch in the HRS diet was not a substrate for alpha-glucosidase activity. Human alpha-amylase was also significantly less abundant in the HRS diet as expected due to the decrease in readily available starch. This study also reinforced the importance of F. prausnitzii for metabolism of non-dietary carbohydrates in the diet, including enzymes for butyrate production by this organism. In contrast, members of the Bacteroides were reduced in abundance following the HRS diet. A notable strength of the approach used here was that proteins and metabolites were collected from host and microbiome simultaneously, allowing a systems-level approach to observe their interplay. Taken together, our results emphasize the importance of longitudinal, multi-omics study designs for unraveling the effects of nutrition on the microbiome and health (Maier et al. 2017).