Héctor Maldonado-Pérez Taylor Sehein Katia Chadaideh

The ecosystem inside us The Microbiome = microbes, their genomes, and the habitat they are found The gut microbiome: The most diverse (over 100 trillion bacteria) Implicated in a range of metabolic processes The gut microbiome is a diverse, quickly-evolving, and responsive community -Implicated as a contributor to health and disease, and its major role is in metabolic functions -Acts as a commensal metabolic organ, which provides the host with abilities to break down energy sources that or own physiology can’t

Influences on Gut Microbial Composition Factors that have been found to affect the composition of the gut microbiome include environmental conditions (ie: birth mode, activity level), antibiotics usage, genetics and diet -Major dichotomy as of late has been the prevalent impact of either diet or host genetics as the predominant driving force

Main Driver: Diet or Genetics? In a previous paper by Turnbaugh, et al (2009) showed that switching from a low-fat, plant polysaccharide-rich diet to a high-fat, high-sugar "Western" diet shifted the structure of the microbiota within a single day, changed the representation of metabolic pathways in the microbiome, and altered microbiome gene expression. -Figure shows 16s rRNA sequences of germ free mice inoculated with human gut microbiota, beginning from 1 day post colonization, then -dpc == days post colonization with human microbiome -dpd = days post diet switch from a LFPP to a “Western” HFHS diet Turnbaugh et al. (2009)

Main Driver: Diet or Genetics? Goodrich, et al. (2014) provided evidence that host genetics is an alternative driver for gut microbial variation. Mammals exhibit marked interindividual variations in their gut microbiota, but it remained unclear if this is primarily driven by host genetics or by extrinsic factors like dietary intake. Goodrich et al. (2014)

Outline Part 1: Examining the effects of Diet vs Host Genotype Part 2: Evaluating Responsiveness to Diet Part 3: Assessing Memory associated with Past Diets Evaluation and Critiques Conclusions and Significance Part 1: Relative abundance of gut microbial OTUs under different diets, and between different inbred/outbred mouse strains Part 2: Oscillations from one type of diet to another and tracking relative abundance of microbial OTUs with each diet change (one mouse strain: c57BL6J) Part 3: Isolating microbial OTUs with signature increases or decreases that are amplified with each re-introduction of diet type

Part 1: Diet vs Host Genotype In order to determine the relative impacts of host diet and host genotypic variation on the microbiome, the first step of this study involved systematic testing of the relative impacts of dietary intake and host genetics on the gut microbiota, through the combined analysis of five inbred mouse strains, four transgenic lines, and a recently developed outbred mouse resource, the Diversity Outbred (DO) population

Part 1: Computational Methods Data generated by 16s rRNA Illumina Sequencing Analyses: Operational taxonomic unit (OTU) relative abundance Bray-Curtis dissimilarity-based principal coordinate analysis Seq Details: Illumina HiSeq platform (PCR clusters and fluorescence detection); analyzed on the Harvard Odyssey cluster using the QIIME (Quantitative Insights Into Microbial Ecology) software package and custom Perl scripts (bioinformatic pipeline)- translates raw sequences, quality controls, assigns OTUs; LefSe was used to identify taxa associated with each experimental group- finds biomarkers between 2 or more groups using relative abundances Bray-Curtis dissimilarity: a statistic used to quantify the compositional dissimilarity between two different sites, based on counts at each site

Part 1: Diet drives gut microbial variance PCA- orthogonal transformation of the correlated diet and subsequently by genotype. Figure 1A provide a platform in which there are only 2 diets and multiple phenotype to evaluate, this allow the lustering network to be better understoof in terms of the process. Mice strains vary depending on their nature, here mice strains that are WT, and trangenic were dosed and from the supplemental data observed in the literature we see that there is no real difference in among strain but rather diet. PCoA- microbial community structure primarily determined by diet, secondary cluster is host genotype; accounts for 48% of variance; based on 16S count data Two groups of bacteria, Bacteroidetes and Firmicutes, consistent indicators of different diets (Firmicutes have greater relative abundance under a high fat diet) Repeated with transgenic and outbred mice with the same results

Part 2: Responsiveness to Shifts in Diet The next step was to determine the reversibility and reproducibility of changes in the microbiome in response to diet. To do this, they designed a second leg of this experiment, where daily fecal samples were collected from inbred C57BL/6J mice (n = 15) oscillating between LFPP and HFHS diets every 3 days. All animals were maintained on a LFPP diet in individual cages prior to the beginning of the experiment at 7 weeks of age. In total, they analyzed four groups, representing two sets of oscillating mice staggered by 3 days, and two control groups maintained on either a continuous LFPP or a continuous HFHS diet.

Part 2: Computational Methods Use of OTU relative abundance Analysis: Microbial Counts Trajectories Infinite Mixture Model Engine (MC-TIMME) modeling -To analyze the impact of these successive dietary shifts on the time-dependent responses of species-level microbial OTUs, they used a modified MC-TIMME algorithm to model temporal signatures using simple linear models with constant levels for each dietary regimen (LFPP and HFHS) and linear increases or decreases with dietary switches (oscillation number) -MC-TIMME is a Bayesian nonparametric hierarchical generative probability model that extrapolates latent variables that assign observed sets of counts to prototype signatures. For this study, they modified the MC-TIMME model so that it assigned each OTU to a prototype signature, which induces a clustering of OTUs into groups that share similar dynamics

Part 2: Diet drives reproducible microbial shifts

Part 3: Memory Associated with Past Diets Oscillation of number dependent or stable abundances for OTUs Determination of “microbial memory” -Given the rapid and reproducible microbial response to diet, they next wanted to test whether this phenomenon could be attributed fully to current dietary intake or whether there were lingering effects of past dietary history.

Part 3: Computational Methods Relative abundance of species-level OTUs Analysis: Normalization of specific OTU trends per 10k reads in individual mice Detailed plots are shown for representative figures were also constructed based on these data that display oscillation number dependent or stable abundances for OTUs in individual mice.

Part 3: Memory trends in specific OTUs Phylum Bacteroidetes: Phylum Firmicutes:

Part 3: Memory trends in specific OTUs Dashed red and blue lines represent median inferred signatures on the HFHS and LFPP diets respectively (widths of line segments are proportional to length of each dietary regimen for oscillator group); shading represents 95% credible intervals for estimates. Vertical axes represent model estimates of counts per 10K sequencing reads.

Part 3: Memory trends in specific OTUs

A Critical Appraisal No reported conflicts of interest Sources of funding: the NIH Brigham and Women’s General Mills Bell Institute This work was supported by the NIH, the Brigham and Women’s Department of Pathology and Center for Clinical and Translational Metagenomics, and the General Mills Bell Institute of Health and Nutrition, which funds doctorate and masters-level scientists in fields ranging from nutrition science to public health to food science to microbiology

Conclusions and Significance Dietary factors provide greatest impact on gut microbiome Reproducible Past-diet impacts Future directions: Functional significance of OTUs Public health implications Trends in obesity linked to microbiome Cross-cultural variation Increased Firmicutes:Bacteroidetes ratio linked to increased adiposity in both mice and humans Yang, et al (2009)

References Carmody, Rachel N., et al. "Diet dominates host genotype in shaping the murine gut microbiota." Cell host & microbe 17.1 (2015): 72-84. Turnbaugh, Peter J., et al. "The effect of diet on the human gut microbiome: a metagenomic analysis in humanized gnotobiotic mice." Science translational medicine 1.6 (2009): 6ra14-6ra14. Goodrich, Julia K., et al. "Human genetics shape the gut microbiome." Cell. 159.4 (2014): 789-799. Yang, Xing, et al. "More than 9,000,000 unique genes in human gut bacterial community: estimating gene numbers inside a human body." PLoS One 4.6 (2009): e6074.