Metatranscriptomic analysis of the Gut microbial community

Gut Microbial triggers that
influence the obesity associated
Samodha Fernando
Department of Animal Science
University of Nebraska-Lincoln
Gut Microbes
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
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)
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.
 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.
 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
 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
 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
 microbial gene expression and its influence on host gene expression
Turnbaugh et al, Nature, 2007
 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
 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
 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
 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
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
 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”
Analysis – Gut microbial community
 Operational Taxonomic Unit (OUT) based analysis
 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
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
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
 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
 Major portion is facultative
 Use of cannula in rumen microbiology studies
Specific Aim #2 – Experimental Design
 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
 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.

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