PRESIDENT & DIRECTOR MADRAS DIABETES RESEARCH

FOUNDATION,SIRUSERI, CHENNAI

CHAIRMANDR.MOHAN’S DIABETES SPECIALITIES CENTRE,

GOPALAPURAM, CHENNAI

WHO COLLABORATING CENTRE FOR NONCOMMUNICABLE DISEASES

Dr.V.Mohan., MD., Ph.D., D.Sc., D.Sc (Hon. Causa), FRCP (London, Edinburgh, Glasgow & Ireland), FNASc., FASc., FNA, FACE, FTWAS, MACP

MICROBIOTA AND DIABETES

ICMR CENTRE FOR ADVANCED RESEARCH ON DIABETES

Functions of gut microbiota

Gut microbiota and pathophysiology of

diabetes

Metagenomics

Prebiotics and probiotics

FLOW OF MY PRESENTATION

GUT MICROBIOTA Human gut hosts 100 trillion microorganisms!

Thousands of species!!

Weighing an average around 1.5 kg!!!

Microbiota changes from infancy to adulthood

Bacteria from 3 major groups represent ~ 95% of the total microbiota

Firmicutes Bacteroidetes Actinobacteria

Burcelin R et al. Acta Diabetol. 2011

Multiple site impact of gut microbiota on whole host metabolism

Functions of gut microbiota

Gut microbiota & pathophysiology of

diabetes

Metagenomics

Prebiotics and probiotics

FLOW OF MY PRESENTATION

‘GUT CONNECTION’ TO DIABETES

Type 1 diabetes

Type 2 diabetes

‘GUT CONNECTION’ IN TYPE 1 DIABETES

Gut microflora - Bacteroides thetaiotaomicron

Secretion of antimicrobial molecules defensin / angiogenin

Secreted by intestinal Paneth cells – into gut– fight intestinal microbes

Deficiency of Bacteroides thetaiotaomicron – Type 1 diabetes

‘Altered aggressiveness’ - Type 1 diabetes

Oxidative STRESS & pro-inflammation in type 2 diabetes Several studies from the Madras Diabetes Research Foundation (MDRF),

Chennai have demonstrated the association of oxidative stress and pro-inflammation with type 2 diabetes

Increased Oxidative STRESS in patients with Type 2 diabetes and its vascular complications

Molecular & Cellular Biochemistry, 2006

Int. J. Biochem. Cell Biology, 2007

Diabetic Medicine, 2006

Molecular & Cellular Biochemistry, 2009

Increased inflammatory STRESS in patients with Type 2 diabetes and its vascular complications

‘GUT CONNECTION’ TO TYPE 2 DIABETES

Intricate relationship between intestinal microbiota and development of metabolic diseases like type 2 diabetes.

The ‘storage’ hypothesis.

The ‘inflammatory’hypothesis.

High-fat diet modulates the composition of gut microbiota, promotes metabolic endotoxemia and triggers the development of metabolic disorders

Cani PD et al. Diabetologia, 2007 ; Cani PD et al, Diabetes. 2008

Lipopolysaccharide (LPS)

LPS transport in circulation is possible by two ways

a) facilitated by chylomicrons and b) by loss of intestinal tight junction

Pussinen PJ et al Diabetes Care. 2011;34:392-7

Mea

n LP

S le

vels

[EU

/ml]

Control Subjects(n=45)

Patients with T2DM(n=45)

*

* p<0.05 compared to control subjects

0

0.2

0.4

0.6

0.8

Circulatory levels of LPS from control subjects and patients with type 2 diabetes

Jayashree B, Mohan V ,Balasubramanyam M, et al, Mol Cell Biochem. 2014 ;388:203-10

Mean LPS/HDL Ratio (LPS activity) from control subjects and patients with type 2 diabetes

*

Mea

n LP

S/H

DL

ratio

Since HDL cholesterol is the main factor involved in neutralization of endotoxemia, we used the LPS/HDL ratio as a functional measure of the LPS activity

Control Subjects(n=45)

Patients with T2DM(n=45)

* p<0.05 compared to control subjects

0

0.01

0.02

Jayashree B, Mohan V ,Balasubramanyam M, et al, Mol Cell Biochem. 2014 ;388:203-10

Functions of gut microbiota

Gut microbiota and pathophysiology of

diabetes

Metagenomics

Prebiotics and probiotics

FLOW OF MY PRESENTATION

METAGENOMICSNew understanding of GI microbiota came from culture

free-molecular analysis based on 16S rDNA analysis

Methods: The study included 36 male Swedish adults, among which 18 subjects were diagnosed with type 2 diabetes.

Five bacterial phyla including Firmicutes, Bacteroidetes, Proteobacteria, Actinobacteria and Verrumicrobia, were sequenced.

The proportions of phylum Firmicutes and class Clostridia were significantly reduced in the diabetic group compared to the control group.

Furthermore, the ratios of Bacteroidetes to Firmicutes as well as the Bacteroides-Prevotella group to Clostridium coccoides - Eubacteria rectale group correlated positively and significantly with plasma glucose concentrations.

Similarly, class Lactobacilli species was highly enriched in diabetic compared to non-diabetic persons and positively correlated with plasma glucose.

Chinese patients with type 2 diabetes were characterized

1. by a moderate degree of gut microbial dysbiosis,

2. a decrease in the abundance of some universal butyrate-producing bacteria and

3. increase in Clostridium.

Qin et al. Nature. 2012 

Consistency Depletion of butyrate producing gut commensals in type 2 diabetes

Based upon metagenomic clusters both studies reported that type 2 diabetes patients and controls can be distinguished with high accuracy although with very different microbial entities

Divergence Chinese study reported an enrichment of several Clostridium species in type 2 diabetes

Swedish study reported an enrichment of several Lactobacilli species in type 2 diabetes

CONSISTENCY AND DIVERGENCE IN REPORTED OUTCOMES OF TYPE 2 DIABETES GUT MICROBIOME ANALYSES

The metformin-associated gut microbiome was more similar to the healthy gut microbiome than

the microbiome of T2D metformin-naïve patients – especially with

respect to abundances of Subdoligranulum and Akkermansia

Nature 528, 262-266, 2015

Are these changes due to T2DM or due to Metformin effect ?

As Gut Microbiota seem to be

ethnic specific, there is a need

for Indian studies ……….

MicroDiab - Studies of interactions between the gut Microbiome and the human host biology to elucidate novel aspects of the pathophysiology and

pathogenesis of type 2 Diabetes

Indo-Danish Collaborative Research Grant

Supported by DBT

Madras Diabetes Research Foundation, Chennai

Translational Health Science and Technological Institute, Delhi

TCS Innovation Labs, Tata Consultancy Services,

Pune

The Novo Nordisk Foundation Center for Basic Metabolic

Research, Copenhagen

COLLABORATORS

Investigators from Indian SideDr. V. Mohan, MDRF, (PI)

Dr. G. Balakrish Nair, THSTI Dr. Sharmila Mande, TCS Dr. Bhabatosh Das, THSTI

Dr. M. Balasubramanyam, MDRF Dr. Radha Venkatesan, MDRF

Dr. R. M Anjana, MDRF

Investigators from Danish SideProf. Oluf B Pedersen, (PI)

Prof. Torben HansenDr. Henrik Vestergaard

To identify gut microbiome signatures in Indian subjects associated with pre-diabetes and type 2 diabetes thereby enabling development of novel biomarkers for early diagnosis of people at high risk of progression to overt type 2 diabetes and compare this with the Danish results.

Looking at trans ethnic differences in gut microbial signatures (Indians/Danes)

OBJECTIVE OF THE STUDY

DENMARKINDIA

NGT

n= 150

PRE DIABETES

n=150

DIABETES

n=150

NGT

n= 150

PRE DIABETES

n= 150

DIABETES

n= 150

Total Individuals

n= 900

SAMPLING

Fecal Samples collected and stored by MDRF, Chennai.

Extraction of bacterial Genomic DNA from Feces using INRA method at MDRF.

Quality checking done performed by spectrophotometric analysis and Gel electrophoresis at MDRF.

16S rDNA seqeuncing: V1-V5 regions sequenced at THSTI, Gurgaon.

Semi-blinded analyses to identify diabetic subject group by TCS R&D, Pune

Diabetes microbiomes from Indian subjectsSTRATEGY EMPLOYED

EXTRACTION OF BACTERIAL DNA FROM EXTRACTION OF BACTERIAL DNA FROM FECES (INRA METHOD)FECES (INRA METHOD)

The 16s rDNA sequence has hypervariable regions, where sequences have

diverged over evolutionary time. Strongly conserved regions often flank these hypervariable regions. Primers are designed to bind to conserved regions and amplify variable regions. By comparing the inferred rRNA sequences, it is possible to estimate the historical

branching order of the species, and also the total amount of sequence

change and also capable of reclassifying bacteria in to completely new species or

even genera (which have never been successfully cultured in laboratories).

Structure of 16s rRNA geneStructure of 16s rRNA gene

V1 – V3 V1 – V5

Rank Genus Name Median Abundance %

1 Prevotella 25.350

2 Faecalibacterium 7.920

3 Lachnospiracea_incertae_sedis 3.871

4 Blautia 2.930

5 Collinsella 2.450

6 Roseburia 2.410

7 Megasphaera 2.314

8 Dorea 2.216

9 Catenibacterium 2.118

10 Lactobacillus 2.034

11 Dialister 1.665

12 Erysipelotrichaceae_incertae_sedis 1.312

13 Coprococcus 1.191

14 Ruminococcus 0.725

15 Streptococcus 0.606

16 Oscillibacter 0.601

17 Mitsuokella 0.556

18 Butyricicoccus 0.523

19 Bacteroides 0.481

20 Ruminococcus2 0.443

21 Gemmiger 0.407

22 Olsenella 0.406

23 Clostridium XlVa 0.406

24 Clostridium sensu stricto 0.242

25 Anaerostipes 0.241

26 Alloprevotella 0.170

27 Clostridium IV 0.155

28 Clostridium XI 0.128

29 Flavonifractor 0.110

Overall taxonomic distribution (major genera) in Indians & Danish Rank Genus Name Median Abundance %

1 Bacteroides 17.079

2 Faecalibacterium 13.595

3 Oscillibacter 5.022

4 Alistipes 3.298

5 Lachnospiracea_incertae_sedis 3.235

6 Prevotella 2.218

7 Ruminococcus 2.009

8 Roseburia 1.951

9 Parabacteroides 1.879

10 Clostridium XlVa 1.362

11 Coprococcus 1.270

12 Clostridium IV 1.256

13 Blautia 1.136

14 Collinsella 0.781

15 Gemmiger 0.772

16 Barnesiella 0.623

17 Dorea 0.557

18 Erysipelotrichaceae_incertae_sedis 0.486

19 Clostridium XVIII 0.448

20 Ruminococcus2 0.446

21 Butyricicoccus 0.403

22 Clostridium XlVb 0.299

23 Phascolarctobacterium 0.298

24 Flavonifractor 0.253

25 Anaerostipes 0.233

26 Odoribacter 0.214

27 Anaerovorax 0.188

28 Sutterella 0.179

29 Rhodococcus 0.145

30 Butyricimonas 0.135

31 Sporobacter 0.114

32 Streptococcus 0.105

Danish samplesIndian samples

Prominent geography specific taxonomic variations in Indian and Danish Gut microbiome data

PCoA plot with JSD distance based on Taxonomic abundance

Taxonomic distribution of NGT, PD & DM (Indians)

DM DM

DMDM

Taxonomic distribution of NGT, PD & DM (Danes)

Differentially abundant genera in Indian NGT/PD/DM subjects

Kruskal-Wallis P-values computed using R

Genera identified in different subject groups

Median abundancein different glucose tolerance

groups

P value(Kruskal-

Wallis test)Overall median

All Subjects (N=429) NGT (N=134) PD (N=140) DM (N=155)

Clostridium sensu stricto 0.329 0.340 0.124 0.0001 0.242Pseudomonas 0.014 0.082 0.023 0.0000 0.038Clostridium XI 0.200 0.161 0.065 0.0001 0.128Achromobacter 0.000 0.065 0.008 0.0004 0.013Escherichia.Shigella 0.041 0.075 0.098 0.0034 0.075Megasphaera 1.662 3.316 2.194 0.0061 2.314

Erysipelotrichaceae_incertae_sedis 1.913 0.828 1.534 0.0177 1.312

Ruminococcus 1.000 0.700 0.670 0.0302 0.725Olsenella 0.337 0.627 0.372 0.0484 0.406Blautia 2.845 2.692 3.222 0.0664 2.930Anaerostipes 0.334 0.233 0.197 0.0695 0.241Butyricicoccus 0.617 0.502 0.533 0.0862 0.523Megamonas 0.010 0.021 0.054 0.0865 0.019

Differentially abundant genera in Danish NGT/PD/DM subjects

Kruskal-Wallis P-values computed using R

Genera identified in different subject groups

Median abundancein different glucose tolerance groups

P value(Kruskal-

Wallis test)Overall median

All Subjects (N=429) NGT (N=159) PD (N=155) DM (N=112)

Escherichia Shigella 0.021 0.023 0.082 0.033 0.0000Clostridium XI 0.093 0.086 0.013 0.070 0.0000Alistipes 4.100 2.868 2.648 3.298 0.0001Catenibacterium 0.000 0.000 0.006 0.000 0.0004Anaerovorax 0.221 0.207 0.135 0.188 0.0014Barnesiella 0.829 0.499 0.492 0.623 0.0022Clostridium.sensu.stricto 0.040 0.035 0.007 0.027 0.0033Pelomonas 0.025 0.023 0.037 0.027 0.0057Coprococcus 1.652 1.283 0.845 1.270 0.0065Flavonifractor 0.208 0.263 0.290 0.253 0.0131Acetivibrio 0.010 0.005 0.000 0.006 0.0585Streptococcus 0.107 0.127 0.069 0.105 0.0587Sporobacter 0.133 0.112 0.102 0.114 0.0604Oxalobacter 0.006 0.000 0.000 0.000 0.0671Ruminococcus 2.014 2.140 1.403 2.009 0.0769

• Functions of gut microbiota

• Gut microbiota and pathophysiology of diabetes

• Metagenomics

• Prebiotics and probiotics

FLOW OF MY PRESENTATION

WHAT ARE PREBIOTICS AND PROBIOTICS?

PROBIOTICS : Live micro-organisms which, when administered in

adequate amounts, confer a health benefit on the host.

Source: Found in foods such as yogurt, other dairy products and also

available as dietary supplements.

PREBIOTICS : Non-digestible substances that provide a beneficial

physiological effect for the host by selectively stimulating the

favorable growth or activity of a limited number of indigenous.

Basically food for probiotics.

Source: Found in whole grains, bananas, onions, garlic, honey and also

available as dietary supplements.

Cani PD et al. Diabetologia, 2007; Cani PD et al, Diabetes, 2008

ROLE OF PREBIOTICS / PROBIOTICS

GLP–1

RESULTS OF THE ORAL GLUCOSE TOLERANCE TEST (OGTT)

Compared to control animals, mice fed with high fat showed increased area under curve (AUC) of the OGTT i.e., impaired glucose tolerance

Probiotic culture MTCC5689 significantly improved the glucose tolerance in HFD mice and this is comparable to the results obtained with positive control (LGG) and anti-diabetic drugs Metformin & Vildagliptin

Balakumar et al 2016, Eur J Nutr, Published online : 18 October 2016

RESULTS OF THE INSULIN TOLERANCE TEST (ITT)

Compared to control animals, mice fed with high fat showed impaired insulin sensitivity as revealed by decrease in % kITT

Probiotic cultures (both MTCC 5690 & MTCC5689) significantly improved the insulin tolerance as evident from the increased kITT value (increased glucose disposal rate) and this is comparable to the results obtained with positive control (LGG) and anti-diabetic drugs metformin and vildagliptin

Balakumar et al 2016, Eur J Nutr, Published online : 18 October 2016

Alterations in microbiota are associated with disease states. This is understood better in type 2 diabetes than type 1 diabetes.

Gut Microbiota in Indian and Danish populations are completely different.

In Indian diabetic patients, Clostridium sensu stricto and Clostridium XI are decreased markedly, whereas pre-diabetes subjects, Megasphaera is increased 3 fold especially in males.

Among Danish diabetic patients, Escherichia Shigella and Clostridium XI are reduced.

Pre/probiotics have a beneficial role, but more studies are

needed !

SUMMARY

Is to apply metagenomic and integrative metabolomic approaches to better understand the underlying causes of metabolic diseases in the context of gut microbiota. Develop novel therapeutic options including targeting drugs to microbial genes or co- regulated host pathways or modifying the gut microbiota through diet, (probiotic and prebiotic interventions), bariatric surgery, fecal transplants, or ecological engineering.

The Challenge………………..

A whole new field is waiting to be explored!