Dynamics of the human microbiome in response to a resistant starch diet: proteome level

Individuals harbor their specific microbiomes, whose composition depends on a variety of factors, including the individual diet, health and physical fitness. Therefore, the proteome data sets of all 8 participants were clustered at the experimental stage „baseline“ by using TIGR multiple experiment viewer (TMEV / MEV) (Saeed et al. 2006) on species summarized protein counts. Then Voronoi treemaps of the participant specific microbiomes were built by using the taxonomic levels (from root via brunches to leafs): kingdom, phylum, class, order, family, genus, species (Figure 2.4-27). Participant‘s microbiomes shown below are dominated by Bacteroidetes bacteria, especially Bacteroidetes vulgatus and uniformis, and those shown in the middle were less dominated by B. vulgatus or uniformis, but showed increased levels of species of the Prevotellaceae. The bottom microbiomes show a more Firmicutes dominated composition with Eubacterium rectale, Ruminocuccus bromii, Faecalibacterium prausnitzii and Roseburia species as main constituents.

Figure 2.4-27: Voronoi Treemaps at experimental stage „baseline“ with their specific microbiomes.

Participants’ microbiomes shown on top are dominated by Bacteroidetes bacteria, especially Bacteroidetes vulgatus and uniformis. The bottom left microbiomes show a more Firmicutes dominated composition with Eubacterium rectale, Ruminocuccus bromii and Roseburia species as main constituents. Voronoi treemaps of the participant specific microbiomes were built by using the taxonomic levels (from root via brunches to leafs): kingdom, phylum, class, order, family, genus, species. Cell sizes are proportionally sized according the species summarized protein counts. Colors specify bacteria on phylum level – Firmicutes (greenish grey); Actinobacteria (carmin red);

Bacteroidetes (steel blue); Proteobacteria (yellow); Fusobacteria (plum); Tenericutes (antique pink); Spirochaetes (ocre); Cyanobacteria (green); Verrucomicrobia (pink). 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).Illustration 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: Individual response to resistant starch. Licence notice: https://creativecommons.org/licenses/by/4.0/.

This illustration emphases inter-individual differences in the gut microbiome and highlights different starting points of each individual in the RS dietary intervention. Next, we investigated common and mean changes in the individuals after RS intervention. Through high resistant dietary starch intake, a significant increase of species specific proteins in the Firmicutes phylum were observed (Figure 2.4-28), as already detected in the genome data. Here, the butyrate-producing bacterium, Eubacterium sp. (E.

rectale, E. eligens), Subdoligranulum sp. (S. variabile), Faecalibacterium sp. (F. prausnitzii), Blautia sp., Coprococcus sp. (C. comes), Anaerostipes sp. and Clostridium sp. were the most abundant species and correlated positively with a high amount of RS. On the contrary, most species of the Bacteroidetes phylum were negatively correlated to the HRS diet, except species of genera Odoribacter (O.

splanchnicus) and Parabacteroides (P. johnsonii).

Bacteroidetes

Relationship of participant specific microbiome levels

Participants

Figure 2.4-28: Taxonomic treemap of averaged species specific summarized protein counts.

Taxonomic treemaps from all experimental stages (baseline; LRS; HRS), as encoded by the cell sizes. Colors within the explanatory hierarchy treemaps in the top line (labels specifiy phylum, order, genus (from left to right)) specify bacteria on phylum level – Firmicutes (greenish grey); Actinobacteria (carmin red); Bacteroidetes (steel blue); Proteobacteria (yellow); Fusobacteria (plum); Tenericutes (antique pink); Spirochaetes (ocre);

Cyanobacteria (green); Verrucomicrobia (pink). Cell colors of the large treemap show the correlation (Pearson correlation; -1 dark blue; -0.5 light blue; 0 medium grey; 0.5 orange; 1 dark red) of all participant specific z-scores (participant specific mean centering and standard deviation normalization) of the species summarized protein counts with the resistant starch regime with 0; 0.05 and 1 for baseline; LRS and HRS respectively. Z-scoring made sure that only changes of the bacterial amount in response to the resistant starch diet but not the different levels the bacteria occur in the individual gut floras were taken into account for the determination of the degree of correlation.

Further, the shotgun metaproteomics approach allowed to identify thousands of host and microbial proteins altered through RS. The Clusters of Orthologous Groups (COG) of proteins (Figure 2.4-29) (Tatusov et al. 2000) represented in the protein data are classified into functional groups, including among other those for energy production and conversion (C), amino acid metabolism and transport (E), nucleotide metabolism and transport (F), carbohydrate metabolism and transport (G), lipid metabolism (I) and translation (J). In addition, detected proteins of the COG main class (Figure 2.4-29 left top) and the COG sub class (Figure 2.4-29, left middle) were assigned to the bacterial phyla. This revealed proteins, predominantly assigned to phyla of Firmicutes, Bacteroidetes, Actinobacteria and Proteobacteria, the most abundant microbes in the human gut.

Further, bacterial proteins were correlated (Pearson correlation; -1: dark blue; -0.5: light blue; 0: grey;

0.5: orange; 1: dark red) with the RS regime with 0; 0.05 and 1 for baseline; LRS and HRS respectively (Figure 2.4-29, right).

Figure 2.4-29: Treemap of averaged bacterial protein specific counts.

Treemap from all experimental stages (baseline; LRS; HRS), as encoded by the cell sizes. Colors within the explanatory hierarchy treemaps in the top line (COG main class; COG sub class; bacterial phylum (from top to bottom)). Cell colors of the large treemap show the correlation (Pearson correlation; -1 dark blue; -0.5 light blue; 0 medium grey; 0.5 orange; 1 dark red) of all participant specific z-scores (participant specific mean centering and standard deviation normalization) of the protein counts with the resistant starch regime with 0; 0.05 and 1 for baseline; LRS and HRS respectively. Z-scoring made sure that only changes of the protein amount in response to the resistant starch diet but not the different levels the proteins occur in the individual gut floras were taken into account for the determination of the degree of correlation. 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).In parts reprinted and modified from (Maier et al. 2017). Copyright (2017) Maier et al., original material is available: http://mbio.asm.org/content/8/5/e01343-17.full.

It turned out that a wide array of proteins was highly correlated to RS, but especially proteins of the GOG main classes. This included the carbohydrate metabolism and transport, as well as lipid metabolism, which showed highly correlated features to the HRS diet. The most abundant and highly

-1 1 correlation with resistant starch diet

Firmicutes Actinobacteria Bacteroidetes Proteobacteria Fusobacteria Tenericutes Spriochaetes Cyanobacteria Verrucomicrobia no taxon assignment

correlated proteins were the ABC-type sugar transport systems and glyceraldehyde-3-phosphate dehydrogenase of the carbohydrate metabolism and transport. Further, three enzymes involved in the lipid metabolism were increased in samples of the HRS diet, namely acyl-CoA dehydrogenase, acetyl-CoA acetyltransferase and enoyl-acetyl-CoA hydratase. We observed, that proteins involved in the butyrate metabolism, such as enoyl-CoA hydratase (LRS vs. Baseline: p-value < 0.0001; LRS vs. HRS: p-value

< 0.003) and butyrate kinase (Baseline vs. HRS: p-value < 0.001; HRS vs. LRS: p-value < 0.01) significantly changed with diet (Maier et al. 2017). A targeted quantification of butyrate in the samples revealed trends for increased butyrate accumulation in the HRS diet and to a lesser extent in the LRS diet, although highly variable between individuals (Chapter 2.4.1.5.4) (Maier et al. 2017). Cross-feeding effects between gut microbial populations were previously shown to increase variability between individuals because butyrate producers often take longer to establish after a dietary intervention (Belenguer et al. 2006, Maier et al. 2017).

Additionally, gut proteins were classified according to KEGG Digestion classes and correlated to the amount of RS, whereby alpha-amylase was found to be correlated negatively with the HRS diet. This appears, presumably because amylose-rich RS is not a substrate for α-amylase, the enzyme that hydrolyzes α-1,4 glycosidic linkages in starch (Rendleman 2000, Ramsay et al. 2006). Also, glucosidases, including beta-glucosidases (breaking complex carbohydrates into monomers) and alpha-glucosidases (breaking simple starches) were differently affected by HRS diet.