Gut Microbial triggers that influence the obesity associated phenotype Samodha Fernando Department of Animal Science University of Nebraska-Lincoln Gut Microbes www.genome.gov Kinross et al. Genome Medicine 2011 3:14 Influence of gut microbiome on obesity “Two groups of beneficial bacteria are dominant in the human gut, the Bacteroidetes and the Firmicutes. Here we show that the relative proportion of Bacteroidetes is decreased in obese people by comparison with lean people, and that this proportion increases with weight loss on two types of low-calorie diet.” (Nature, 2006; 444, 1022-1023) Influence of gut microbiome on obesity Microbiome of 154 individuals was studies based on: - 9,920 near full-length and 1,937,461 partial bacterial 16S rRNA sequences, - 2.14 gigabases shotgun data (454/Roche). Metabolic pathway-based clustering and analysis of the human gut microbiome of MZ twins The human gut microbiome is shared among family members, but each person’s gut microbial community varies in the specific bacterial lineages present, with a comparable degree of co-variation between adult monozygotic and dizygotic twin pairs. There is an extensive identifiable ‘core microbiome’ at the gene, rather than at the organismal lineage level. Obesity is associated with phylum-level changes in the microbiota, reduced bacterial diversity, and altered representation of bacterial genes and metabolic pathways. There is a core microbiome at a functional level, deviations from this core are associated with different physiologic states (obese versus lean). Turnbaugh et al, Nature 2009 Diet-Induced Obesity Is Linked to Marked but Reversible Alterations in the Mouse Distal Gut Microbiome - 48 Mb of high quality sequence data Conventionalized mice fed a low-fat, high polysaccharide (CHO) or high-fat/high-sugar (Western) diet. Turnbaugh et al, Cell Host and Microbe 2008 The relative abundance of Firmicutes and Bacteroidetes divisions in the distal gut (cecal) microbiota. Diet-induced obesity (DIO) is associated with a marked reduction in the overall diversity. DIO is linked to a bloom of the Mollicutes class of bacteria within the Firmicutes division. Microbiota transplantation experiments reveal that the DIO community has an increased capacity to promote host fat deposition. Significance Studies have helped understand the changes in microbial community structure, and the metabolic potential in obese and lean phenotypes. Failed to illuminate the signals of the gut microbiome that ameliorate the obesity phenotype. Large individual-to-individual variation in the human gut flora Genotypic variations Environmental exposure (current or historical) Inability to control or monitor dietary and caloric intake Consequently, previous studies have failed to identify microbial regulation of host genes that promote deposition of lipids in adipocytes. In addition, how diversity relates to the collective function of the microbiome and its host remains obscure. Significance These factors complicate efforts to understand how the human microbiome, its population structure and function, interacts and affects human pathophysiology Partly due to the lack of a good model system. Cecum cannulated humanized pig model allowing direct sampling from the gut providing new opportunities to understand microbe-microbe interactions, host-microbe interactions and microbial triggers using meta-transcriptomics. Specific Aims Validate a cecum cannulated humanized pig model with capabilities to perform continuous real-time monitoring to evaluate gut microbial community dynamics and microbial gene expression. First animal model that will allow continuous real-time monitoring of the human microbiome Glimpse into the workings of the human microbiome Identify signals of the gut microbiome in high fat, high carbohydrate diets that ameliorate the obesity phenotype, specifically, microbial genes that regulate adipogenesis. New targets and strategies to control obesity Identify the therapeutic potential of the human gut microbiome towards controlling obesity and type 2 diabetes Innovation First ever model that allows continuous, real-time sampling from the gut to perform functional studies Opportunity to bypass the small intestines and directly add substrates and drugs to the large intestines to investigate the influence on the gut microbiota Groundbreaking in its potential to revolutionize the way we study the human gut microbiome A “core” microbial community is yet to be identified A “core set of genes and gene families representing key metabolic functions has been discovered microbial gene expression and its influence on host gene expression Turnbaugh et al, Nature, 2007 Approach To identify the influence of the gut microbiota in obesity and obesity related diseases, it is essential to understand the interaction between diet and the microbiome. Microbial metabolism of dietary molecules (nutrients) in the gut drives the release of bioactive compounds (including lipid metabolites and short chain fatty acids), which interacts with host cellular targets to control energy metabolism To assess the relevance of the gut microbiome to obesity, it is important to understand how the gut microbiome interacts with the host, with respect to metabolic response to the diet. The microbial metabolites produced under different dietary conditions, influence interactions between different gut microbial communities (in situ effects). These metabolites also have systemic effects on host tissues by acting as metabolic regulators. Ex. Diet – Microbes – SCFA – pH – gut microbial ecosystem SCFA – monocarboxylate transporters – metabolic substrates or regulators SCFA as metabolic regulators SCFA produced by the gut microbes has different metabolic features Acetate – fatty acid or cholesterol precursor Butyrate – energy substrates for colonocytes Propionate – Gluconeogenic substrate in the liver Ligands for G-protein coupled receptors (GPR 43 & 41 and fatty acid receptor 1 & 2) – adipose tissue expansion Can play a role in regulating host metabolism. Ex. Conjugated linoleic acids (CLAs), bile acids, gases such as methane and hydrogen sulfide, and polysaccharides such as, lipopolysaccharide (LPS), and peptidoglycan can bind to specific receptors in the host and change gene expression and metabolic activity of the host. LPS is found in higher levels in the serum of obese individuals, can create metabolic endotoxemia that stimulates insulin resistance, obesity and systemic inflammation. CLAs are beneficial. SCFA as metabolic regulators Influence of the gut microbiome on adipogenesis. Different dietary molecules can result in changes in the gut microbial flora. The fermentation of carbohydrates and other nutrients by the gut microbiota on a high fat/high carbohydrate diet can result in an increase SCFA concentration and can result in increased absorption. The SCFA absorbed can promote fat storage via activation of GPR43 and 41 receptors. The presence of gut microbiota can suppress the intestinal synthesis of FIAF (fasting-induced adipose factor), and influence the activity of lipoprotein lipase (LPL) and the fat storage in the adipose tissue. In addition, hepatic and muscle fatty acid oxidation can be influenced/altered by the gut microbiota via the 5’ adenosinmonophosphate-activated protein kinase (AMPK)-dependent mechanism. Finally, low grade inflammation and insulin resistance observed in obesity can be triggered by alteration of the gut barrier (namely, by a decrease in tight junction proteins (ZO-I and Occludin) by activating the endocannabinoid (eCB) system tone, leading to higher plasma lipopolysaccharide levels (LPS). These events contribute to fat deposition mainly in high fat diets. Gut microbial stimuli and transcriptional control of adipogenesis Gut Microbial Stimulation The proposed influence of the gut microbial stimuli on transcriptional control of adipogenesis (modified from Rosen et al. Genes and Dev., 2000) The transcription proteins are expressed in a network in which microbial gene expression or metabolites (SCFAs) can activate, C/EBPβ and C/EBPδ, followed by PPARγ, directly PPARγ, or C/EBPα. Microbial stimuli can also activate ADD1/SREBP1 followed by PPARγ activation. All of these factors contribute to the expression of genes that lead to the obesity phenotype. Central hypothesis In the obesity phenotype, signals of the gut microbiome influences host gene expression resulting in increased differentiation of preadipocytes to adipocytes, increasing the susceptibility to type 2 diabetes. To test the above hypothesis… A new cannulated humanized pig model will be established and validated. The influence of high starch and high fat diets on microbial gene expression and the influence of microbial gene expression on host gene expression, specifically on the transcriptional cascade involving the nuclear receptor PPAR, members of the C/EBPs family and basic helixloop-helix family (ADD1/SREBP1c) that regulates adipogenesis, will be investigated. Specific Aim #1 – Experimental Design Week 3 Week 4 Week 5 Week 6 Specific Aim #1 – Experimental Design Daily sampling Week 8 Week 9 Week 10 Week 11 Weekly body weight Back-fat thickness Intake Weekly Biopsy from sub-cutaneous fat and gastrointestinal wall to monitor host responses. Analysis – Gut microbial community DNA and RNA extraction from donor and recipient fecal and recipient cecal microbiota Amplification and sequencing of the V1-V3 region of microbial 16S rRNA gene (using 454-pyrosequencing) At CAGE under the supervision of Dr. Andy Benson (secondary mentor) Microbial community analysis Quality filter reads (primer seq. barcode, length, quality) Daisy chopper – unequal sampling Taxonomy based analysis “Classifier” NCBI-NR Analysis – Gut microbial community Operational Taxonomic Unit (OUT) based analysis MOTHUR Align with SILVA Pre-cluster – pseudo linkage clustering UCHIME – chimera Cluster at 97% to generate OTUs AMOVA, ACE, Chao1, and rarefaction curves Weighted UniFrac – clearcut and FastUnifrac Ordination plots – PCoA and NMDS (non-metric multidimensional scaling) Metagenomic analysis of the Gut microbial community Metagenome sequencing will be done using 454-pyrosequencing Metabolic potential and as a scaffold for metatranscriptome analysis Assembled into contigs via velvet and SOAPdenovo (short oligonucleotide assembly package) Gene prediction via MATAGENEMark – Bacterial Fungal gene prediction via Dr. Etsuko Moriyama’s (primary mentor) fungal genome database and CAZy database MuMer – compare metagenome reads to reference bacterial and fungal genomes KEGG, COG and NCBI-nr using BLASTX Blasted to each other MEGAN Metatranscriptomic analysis of the Gut microbial community Total RNA Poly A mRNA Identify responses of the fungi, and protozoa in the gut RNA (18S,16S, 23S, 28S, 5S) + Bacterial mRNA Bacterial total RNA (16S, 23S, 5S, mRNA) Bacterial mRNA & 5S RNA Metatranscriptome sequencing will be done using Illumina Bacterial mRNA Metagenome as a scaffold BFAST Similar to metagenome analysis Metatranscriptome analysis Host responses Transcriptomic analysis of biopsy samples Total RNA – poly A Isolation High throughput Illumina sequencing Align to genome (NCBI build 3.1, based on Sscrofa 10) for annotation Quantitative real-time PCR (qPCR) analysis Quantify gene expression of transcription factors C/EBP, C/EBP, C/EBP, PPAR, and C/EBP, GPR43 and 41 SCFA analysis Statistical analysis – Dr. Steve Kachman (secondary mentor) Expected results and interpretations Differences in microbial species composition in lean and obese individuals and recipient animals Differences in SCFA composition with high lactic acid levels under high carbohydrate dietary condition Differential gene expression is expected, with more carbohydrate utilization genes under high carbohydrate diet Host gene expression in the gut wall and sub cutaneous tissue are also expected to change – genes regulating adipogenesis is expected to be upregulated in obese recipient pigs Potential problems Cecum cannulations – None expected (Dr. Doug Hostetler) How sampling will effect anaerobic environment? Quick sampling, limited exposure to oxygen, oxygen scavenged by facultatives Major portion is facultative Use of cannula in rumen microbiology studies Specific Aim #2 – Experimental Design Hig h Low Low Hig h Low Hig h NO TRANSPLANTATION Analysis Microbial community analysis, metagenomics and meta transcriptomic analysis performed as described in Aim #1 To identify differentially expressed genes that influence obesity Critical to understand origin of observed changes Independently estimate transcript abundance and genome abundance Expression level of a gene; o(gene,genome) = e(gene,genome) * a(genome) (observable gene expression o(gene,genome) is a product of two factors: the gene expression level within the host genome, e(gene,genome); and the abundance of that genome in the environment, a(genome)) Host transcriptomics, realtime PCR analysis and SCFA profiles will be monitored as before. Expected results and interpretations Pinpoint specific genes and pathways that are influenced by microbial gene expression Dissect the influence of diet and microbiome Obese microbiota on high fat/ high carb diet will gain more than lean microbiota on High fat/ high carb diet Down-regulation of host adipogenic genes under above conditions Obese microbiota on low fat/ low carb diet will gain more than lean microbiota on low fat/ low carb diet Up-regulation of host adipogenic genes under above conditions Obese pigs that receive lean microbiota and the low fat/low carb diet is expected to reduce fat deposition and down-regulate adipogenic genes Lean pigs that receive obese microbiota and the high fat/high carb diet is expected to increase fat deposition and up-regulate adipogenic genes Potential problems Removing a majority of the cecum microbiota during transplantation Stirpump will be used to rapidly remove cecum contents Summary and future directions Powerful tool to gain insight into the workings of the human microbiome and identify microbial genes and populations that influence obesity Provide a glimpse into the metabolic and molecular interactions that occur between diet, host and its gut microflora. Expand the model to study other metabolic diorders ( metabolic syndrome, Inflammatory bowel disease etc.) Use of core facilities Computational and data sharing core Computational analysis Storage Platform for collaboration and data sharing Core for applied Genomics and Ecology (CAGE) Fully equipped for molecular biology and genome-based studies All 454-pyrosequencing will be performed in this facility CAGE Director Dr. Andy Benson (secondary mentor) Data analysis pipelines for quality filtering and to perform taxonomy and phylogeny based analysis.