The Effects of Exercise and Estrogen on Gut Microbiota In

THE EFFECTS OF EXERCISE AND ESTROGEN ON GUT MICROBIOTA IN

FEMALE MICE

REBECCA MELVIN

A thesis submitted to the

Graduate School-New Brunswick

Rutgers, The State University of New Jersey

In partial fulfillment of the requirements

For the degree of

Master of Science

Graduate Program in Kinesiology and Applied Physiology

Written under the direction of

Dr. Sara Campbell

And approved by

______

New Brunswick, New Jersey

May, 2016

ABSTRACT OF THE THESIS

The Effects of Exercise and Estrogen on Gut Microbiota in Female Mice

By REBECCA MELVIN

Thesis Director:

Dr. Sara C. Campbell

The gut microbiota has recently been acknowledged as having an impact on overall systemic health and obesity. To date, research has primarily focused on male mice due to the unknown effects of the menstrual cycle in females. Exercise is a known mediator of obesity related diseases, and the literature demonstrates an effect on the microbiome in males thus far. As post-menopausal obesity continues to rise, there is a need to explore the relationship between estrogen and the microbiome, with exercise as a possible moderator. In this study, female mice either had an ovariectomy, to simulate estrogen deficiency, or a sham procedure. Mice were placed into either a continuous exercise group, high intensity, or sedentary control group for six weeks. Microbial analysis was completed to view differences between groups. The estrogen deficient group had higher body weight and body fat percentages, regardless of exercise. Microbial analysis indicated a decrease in diversity in the estrogen deficient group, as well as a higher Firmicutes/Bacteriodetes ratio. These results are similar to obesity studies, suggesting that changes in the microbiome may be one mechanism that promotes obesity

in the estrogen deficient state. Exercise interventions increased microbial diversity, with a higher percent of Bacteroidetes compared to Firmicutes.

Keywords: estrogen, female, microbiome, exercise, gut,

iii

Acknowledgements: I would like to acknowledge Dr. Sara Campbell, an amazing mentor and friend, for constant support, motivation, and encouragement throughout the past years. None of this would be possible without you. I wish you and the lab continued luck and success in the future.

To both Dr. Greg Henderson and Dr. Marc Tuazon, whose initial study design and brilliance created this opportunity for me to pursue.

To Dr. Brandon Alderman and Dr. Sue Shapses for assisting with this process and serving on my committee.

To Dr. Lee Kerkhof and Lora McGuinness, for your patience and assistance in microbial analysis. Thank you for teaching me different techniques and allowing me to work in your lab.

Dedication:

I would like to dedicate this thesis to my loving parents, Rod and Laurie Melvin. Thank you for always pushing me to do my best and encouraging me to pursue all of my dreams, no matter how big. Thank you for always feeding my constant need to learn.

Mom, thank you for being my person to talk to, for sending care packages, and pushing me to always look to the stars. Dad, thank you for teaching me that the world is full of adventures, and I should see as many as I can. Because of you both, I will be successfully moving on to my next adventure soon.

And to Austin, for keeping me sane during the duration of my Master’s program, thank you. You will always be my rock.

Table of Contents Title Page i Abstract ii Acknowledgement/Dedications iv List of Figures vii Introduction 1 Methods 5 Results 7 Discussion 9 Figures 13 References 24

List of Figures Figure 1 Weekly Body Weights Figure 2 Weekly Food Intake Figure 3 Biweekly Body Fat Percentage Figure 4 Sorensen Similarity Index 1.0% Figure 5 Bray Curtis Dissimilarity Index 1.0% Figure 6 Bacteria Phyla Composition Figure 7 OX1 Species Analysis Figure 8 OX2 Species Analysis Figure 9 OX3 Species Analysis Figure 10 SH1 Species Analysis Figure 11 SH2 Species Analysis Figure 12 SH3 Species Analysis Figure 13 Microbial Heat Map

Introduction

Obesity was recognized as a disease in 2013 and affects more than two thirds of

United States adults.1,2 Widely considered a preventable cause of death, obesity related medical treatments resulted in an excess of $147 billion in health care expenditure.1

Proposed mechanisms include energy imbalance, adipose tissue dysregulation, homeostatic imbalance, and hormonal imbalances, including estrogen.2 Recent evidence suggests that the gut microbiome plays an integral role in the obesity relationship, with studies reporting a higher Firmicutes to Bacteriodetes ratio. 3,4,5 Additionally, evidence suggests that gut microbiota changes occur in response to diet, exercise, stress, and possibly estrogen levels.3,4,5,6,7 To date, diet, exercise and stress have begun to be studied extensively, however, estrogen deficiency is a relatively unexplored area. Estrogen deficiency is a condition that occurs in postmenopausal women whom have stopped having a menstrual cycle. Menopause can occur in women between the ages of 45-58 and due to medical advancements, the average US woman is expected to live until the age of

81. This means women could spend approximately 30 years in an estrogen deficient state.8 Exercise is a known mediator of obesity-related cardiometabolic disorders, and was found to mitigate increases in visceral adipose tissue and lower serum estrogens in postmenopausal women.9 It is plausible to suggest then that exercise could help weight maintenance in the postmenopausal state. Due to lack of female populations within previous studies, microbiome gender differences remain unknown. The purpose of this study is to examine the relationship between estrogen levels, exercise interventions, and microbiome shifts in female mice.

Microbiome

The gut microbiome consists of the collective beneficial and pathogenic bacterial species in the digestive tract. These gut microbes are responsible for nutrient harvesting, immune modulating, and accessing nutrients that the body cannot normally harvest.14

Currently, there is no consensus on a “normal healthy” gut microbiome profile, however, the most common conclusion is a balance between two major phyla, the Firmicutes and

Bacteroidetes. 4,10,11 A higher F/B ratio has been positively correlated with obesity, while lower ratios correlate to healthier, leaner mice 4,10,12 To date, no clear cause and effect have been linked to obesity and microbiome. Some evidence suggests that high fat diets alter the F/B ratio and this may predispose animals to obesity phenotypes. 3,4 While others suggest that lysing of gram negative bacteria produce lipopolycharride, a pro- inflammatory molecule, and that this disrupts the intestinal barrier promoting systemic inflammation predisposing the animal to obesity.11

While majority of gut bacterial species have not been elucidated, research has uncovered a variety of physiological and environmental triggers which influence alterations in gut microbe abundances. Some of these include diet, exercise, environment, and hormonal influence. Evidence demonstrates a correlation between poor dietary pattern and decreased microbial ecology, found to be reversible and altered by caloric restriction or reduced fat diet.13,14 Exercise has a protective role on cardiovascular function, weight maintenance, cognitive health, and microbial diversity, however mechanisms of this still remain unclear. Mice permitted voluntary exercise showed an increase in microbial diversity, compared to their sedentary counterparts.6,7 Mice with exercise wheel access had lower body mass, lower blood glucose, reduced intestinal

3 inflammation, and increased microbial diversity. 6,7 Voluntary exercise has also been found to alter the intestinal short chain fatty acid profile, particularly butyrate, in rats.15

Exercise may protect the integrity of the microbiome, even in the presence of a high fat diet or environmental toxins, such as PCBs. 6,7,42

A large majority of studies to date exploring microbial composition, changes, and effectors have been conducted in male mice. Abundant differences in gender exist across all scientific literature, and we believe the microbiome is no exception. Conflicting evidence suggests that genders have no more than random expected difference in microbiome, while others suggest sex hormones and genetics greatly affect microbiota composition, whereas females have demonstrated species that were absent in males, including Roseburia, Blautia, Coprococcus 1, Parabacteroides, and Bilophilia 16,17 The effects seen in male mice cannot be directly applied to females at this time because of the difference in hormones and physiological pathways.

Estrogen Deficiency

Estrogen is a major regulator of bodily function, including development and maintenance of the reproductive system, adipose metabolism, and cardiovascular health.

Menopause is the cessation of menses, defined by absence of a menstrual period for at least 12 months. This conclusion of menstruation results in less estrogen production, as ovaries become inactive, and the primary source of estrogen secretion becomes aromatization of androgens in adipose tissue.18 Estrogen deficiency is involved in a variety of health concerns such as decreased collagen synthesis, endothelial dysfunction, decline in bone mineral content, increased blood pressure, loss of lean body mass, reduced glucose tolerance, and increase in both total and abdominal mass.19,20 The shift in

4 body composition can lead to further health complications including higher risk for cancers, cardiovascular disorders, and joint issues. Excess adipose tissue is proposed to synthesize estrogen, through aromatization of androgens, although the physiological pathway is not fully understood.21 In addition, obese women have been found to have higher levels of both estrogen and follicular stimulating hormone (FSH) than their lean counterparts.18 These increased hormonal levels may in part be a result of excess adipose with androgens for aromatization.18

At this point, it is unclear why the lower circulating estrogen causes weight gain, as observed after menopause or with ovariectomy in animal models. Further research is needed to explore menopause linked weight gain, while proposed mechanisms include chronic inflammation, hormonal changes, and gut microbiota disruption. Systemic low grade inflammation is found to be associated with increased levels of circulating C- reactive protein (CRP), interleukin-6 (IL-6), tumor necrosis factor (TNF) α, and leptin.22

In obese women, the relationship between elevated levels of inflammation was correlated with fat amount.22 Estrogen deficiency was found to cause insensitivity to central leptin administration, leading to increased food intake and suppressed spontaneous physical activity in female rats.23 Another study found that leptin levels were correlated with body mass index in women of all ages, with women having higher leptin concentrations than men.24 Obese females supplemented with estradiol benzoate had a significant reduction in obesity, hyperglycemia, and spontaneous food intake, suggesting protective effects of estrogen.25

Sex hormones have been found to influence microbial diversity, which inversely has an effect on metabolism and systemic hormone levels.16,26,27 A possible mechanism

5 linking gut bacteria with estrogen status involves the estrogen receptor mediation, specifically estrogen receptor β (ER-B), of gut microbial composition.28 A recent study found that gut ecology, as well as short chain fatty acid production, may mediate the protective effects of aerobic capacity on cardiometabolic risk in female rats.29 It can be proposed that gut microbiota alterations mediated by estrogen causes phenotypic changes in postmenopausal females. While this preliminary research is promising, further research needs to be conducted to examine the receptor-microbiota connection.

Systemic health has an extremely complex nature and an almost limitless number of factors. As demonstrated, microbiome, diet, exercise, and environmental factors all influence obesity but exact mechanisms are yet to be understood. The majority of studies performed previously examined male mice or rats, and these studies cannot be directly translated to females or humans at this point. Due to the lack of female microbiome and exercise studies in the literature, there is a clear need for further preliminary research prior to human application. The purpose of this study is to explore the relationship between estrogen status, different exercise intensities, and microbiome shifts in female mice. Our hypotheses are that: (1) estrogen deficient groups will have decreased microbial diversity and (2) exercise will promote enhanced microbial diversity despite estrogen deficiency.

Methods

Animals, Diets, and Exercise

Female C57BL/6J mice (Jackson Laboratory, Bar Harbor, ME) were exercised in a six week protocol previously described.30 In summary, mice were assigned to either ovariectomy (OVX) to induce estrogen deficiency or sham-operation (SHAM) and placed

6 into sedentary, continuous moderate intensity, or high intensity interval exercise groups

(Exer3/6, Columbus Instruments, Columbus, OH). The high intensity exercise group performed 30 minutes bouts of 30 seconds sprints followed by 60 seconds walking. The continuous exercise group performed 30 minutes bouts of distance and duration matched exercise, at 13.8 m/min. Body weight, spontaneous physical activity, and food intake were measured weekly. Mice had ad libitum access to water and a Labdiet 5K52 diet

(Purina Mills, Richmond, IN). All mice were sacrificed after the completion of six weeks of exercise, during the diestrus phase, confirmed by vaginal smears.

Gut Microbial Community Analysis

Feces were extracted from the cage of each animal, immediately snap frozen, and stored at -80 degrees until analysis. Fecal analysis was completed using an aquaRNA protocol (AquaRNATM Kit). One million cells were harvested and centrifuged at 2000xg for 2 minutes. Excess supernatant was aspirated and discharged, leaving the cell pellet.

Two hundred μL AquaGenomic Solution was added to a 1.5μL tube which was vortexed vigorously for 30-60 seconds, then incubated at room temperature for four minutes.

Sample was again vortexed vigorously for 30-60 seconds and centrifuged at 21,000xg for two minutes to pellet the debris. 200μL isopropanol was added and contents were centrifuged for 15 minutes at 21,000xg in -4 degrees to pellet the DNA. Supernatant was transferred to a new 0.5mL tube and 70% ethanol wash was performed as the tube was centrifuged for 5 minutes at 21,000xg. All liquid was removed and DNA pellet was resuspended in 50μL PCR H2O. Presence of DNA was verified using photo imaging of agarose gel, post ethidium bromide bath, containing 5 μL DNA and lambda for DNA

7 reference. PCR amplification was completed using GoTach at 25 cycles and verified again by agarose gel photo imager.

TRFLP fingerprints were generated using a protocol previously described.31 In summary equal amounts of PCR product (20ng) were digested with Mnl I for 6 hours at

37˚C degrees. For analysis, digests were precipitated and re-suspended with 0.3μl of

ROX 500 Standard in 19.7 μl deionized formamide. Samples were then analyzed on an

ABI 310 Genetic analyzer using Genescan software for peak detection and quantification

(Applied Biosystems, Foster City, CA).31

Genomic DNA was pooled from sample extracts by group (n=1/group) and sent for 454’ pyrosequencing of bacterial community to Mr. DNA (16S rRNA gene sequencing; approx. 3,000 reads/sample, MR. DNA Labs, Shallowater, TX). Barcoded amplican sequencing process (bTEFAP©) was performed by a method previously described.32 In summary, subunit rRNA sequences were generated using a Roche 454 titanium instrument according to manufacturer’s guidelines. Chimeras, barcodes, and primers were removed, data was denoised, and sequences >200bp were removed. OTUs were clustered and defined with a set parameter of 97% similarity (3% divergence).

OTUs were taxonomically classified against a GreenGenes, RDPII and NCBI database by

BLASTn, compiled by both counts and percentages.33,34,35

Results

Uterine Weights

OVX vs SHAM uterine weights were dramatically reduced (20 vs 80 mg, main effect of surgery, p<0.0001) indicating success of the surgery and estrogen deficiency.

Body composition and food intake

Body weight was significantly higher in OVX compared to SHAM overall, and at each week 0-6 throughout the training (p<0.001) (Figure 1). Body fat percentage was elevated in OVX overall, and significantly at weeks 4-6 (p<0.01) (Figure 2). Weekly food intake was lower in OVX vs SHAM when normalized to bodyweight and FFM, but no differences were expressed in overall intake (Figure 3). Results from analysis of food intake indicated that there were no main effects of training and no interactions between factors (Data not shown).

Fecal Analysis

Fecal analysis via TRFLP showed minimal differences between conditions (Data not shown). When comparing merely SHAM vs OVX, levels showed differences in peak

OTUs between the two estrogen statuses. Heat maps of TRFLP data reveal differences in peak presence (Data not shown). Bray Curtis and Sorenson similarity indexes, at exclusion cut offs of .2% and 1.0%, show grouping between mice who underwent sham versus those who underwent ovariectomy (Figure 4,5). Sorenson index uses a simple presence or absence comparison, while Bray Curtis similarity index identifies the quantitative compositional differences between populations.36,37 This evidence suggested justification for deep sequencing as further analysis.

Upon pooling samples per group, 454’ pyrosequencing identified bacterial phyla and species. At the phyla level, the OVX group showed high percentages of Firmicutes, with reduced Bacteriodetes, and minimal levels of other phyla including candidatus saccharibacteria, actinobacteria, verricomicrobia, proteobacteria and tenericutes. The

OVX CE group specifically had higher presence of Tenericutes and Proteobacteria,

3.63% and 4.26%, respectively. SHAM groups collectively had a reduction of Firmicutes,

9 comparatively, and increased Bacteriodetes, with all other phyla comprising <2% of the community, including Tenericutes, Cyanobacteria, Candidatus Saccharibacteria,

Verrucomicrobia, and Actinobacteria. Species level analysis was also completed and heat map shows distinct differences between each group (Figure 13). Attached figures show the bacterial composition of each group (Figures 7-12). Barnesiella spp. was found in all groups, at very different proportions. In OVX CON and HI samples, Barnesiella spp. comprised approximately 35% of the community. The OVX CE group had extremely decreased presence at 3.81%. All SHAM groups had high proportions of

Barnesiella spp. ranging between 55-67%. Helicobacter hepaticus, comprised all 4.26% of the OVX CE group’s Proteobacteria. Akkermansia muciniphilia was found in only the

SHAM CON group. Clostridium spp. was present in highest quantities in the OVX CON,

CE, and HI groups making up 32, 28, and 38% of the communities respectively.

Clostridium spp. was present in all SHAM groups, creating between 11-13% of the microbial community. found in each group, but in less quantities in all SHAM groups and the OVX CE group. Eubacterium spp. was present in each group, highest concentration in OVX CE of 13%, and consistent concentrations in all SHAM groups(1-

3%). Various strains of prevotella and alistipes were found throughout all six groups.

Discussion/Further Research

The purpose of our study was to explore the relationship between estrogen deficiency, exercise, and microbial alterations in the gut of female mice. Upon completion of ovariectomy or sham procedure, uterine weight differences showed the efficacy of the surgical procedure. Following the six week protocol, body weights and body fat indicate that OVX mice stored more fat than their SHAM counterparts. (Figure

1, 2) These findings support the literature that suggests estrogen deficiency is related to excess adipose tissue, contributing to obesity.25 Microbial composition in the gut strongly demonstrated the differences in the OVX vs SHAM group. The relative shift of higher

Firmicutes/Bacteriodetes ratio in estrogen deficient mice with higher body fat is consistent with the body of literature,suggesting that microbial shifts may influence the phenotypic shifts in postmenopausal women. . 3,10,12

Proteobacteria were only found in the OVX CE group, due to one specific bacteria. The presence of helicobacter hepaticus, known to be related to liver and colon cancer in mice, in the OVX CE may suggest that one or more of the mice in the group was sick, seeing as it was found at 0% in all other groups.38 It is also plausible that exercise was not beneficial to this mouse, or group of mice, which resulted in growth of this species. Being the samples were pooled, there is no way to see which mouse was sick, however, nothing in the literature suggests H. hepaticus could be brought on by the exercise condition or treatment group. Even with this harmful bacteria present, the CE group showed increased diversity and had a slight protective effect on F/B ratio. It is unclear at this time what effect the presence of Helicobacter hepaticus had on the rest of the community.

Tenericutes were found in higher proportions in the OVX CE, SHAM CE, and

SHAM HI, suggesting an effect of exercise, especially in the continuous exercise groups.

Verrucomicrobioais present in more significant amounts only the SHAM CON and HI groups, specific species being akkermansia muciniphilia. Minimal amounts were seen in

OVX CON, OVX HI, and SHAM CE. A. muciniphilia is a known mucin degrader in the colon and it has recently been identified to be correlated with positive systemic

11 health.39,40 Higher presence in the SHAM groups suggests a protective effect of estrogen, and deficiency is correlated with minimal amounts.

The two most significant species across all groups include Barnesiella spp and

Clostridium sp. Changes in Barnesiella spp. are notable between groups. SHAM groups contain more than 50% Barnesiella spp, which is suggested to be a beneficial bacteria as recently, it has been found to suppress antibiotic-resistant bacteria in the human gut.41.

Our findings suggest that estrogen deficiency reduced population of this helpful bacteria.

In addition, Clostridium spp. was a dominant species found in all groups. The species comprised approximately 1/3 of the community of the OVX CON and OVX HI groups while this was reduced by 22% in the OVX CE group. All SHAM groups had reduced concentration, between 10-13% of the community, demonstrating an effect of estrogen.

Clostridium spp. is a gram-positive bacteria, responsible for a large portion of the

Firmicutes major phyla changes in these mice, possibly implicated with negative health implications in higher amounts. 3,10,12

These distinct microbial shifts suggest the gut microbiota acts as a mediator to a phenotypic shift in post-menopausal women. Exercise did not have as profound of an effect as previously seen in the literature, however, it did increase microbial diversity correlated with positive systemic health. Fecal samples were collected from the cages of each animal, depending on the time in between collection some species may have had the opportunity to grow or die. A more accurate collection method may be collecting feces during sacrifice straight out of the colon, giving us a better look at what exists inside of the intestines. In the literature, there is still no standard of healthy microbiota and it is possible that a healthy microbiota is dependent on individual factors. Various strains of

12 different bacteria may have a different effect on immunity, health, and body composition and this should be explored in future research. While this preliminary research demonstrates an influence of both exercise and estrogen, this complex relationship needs to be further explored in hopes for postmenopausal human application.

25 Body Weights 24

Weight (g) Weight 22

20 -1 0 1 2 3 4 5 6 Week

Sham-CON Sham-CE Sham-HIIE OVX-CON OVX-CE OVX-HIIE

Figure 1.

1.8 Body Fat % 1.6

1.4

1.2

Fold Change Training Start ofvs Fold Change

1.0 0 2 4 6 Week

Sham-CON Sham-CE Sham-HIIE OVX-CON OVX-CE OVX-HIIE

Figure 2.

35 Weekly Food Intake

Food Intake Intake Food (g) 25

20 0 1 2 3 4 5 6 Week

Sham-CON Sham-CE Sham-HIIE OVX-CON OVX-CE OVX-HIIE

Figure 3.

Figure 4.

Figure 5.

Bacterial Phyla Presence 120

100

0 OX1 OX2 OX3 SH1 SH2 SH3

actinobacteria firmicutes verrucomicrobia candidatus saccharibacteria proteobacteria cyanobacteria bacteroidetes tenericutes

Figure 6.

Figure 7.

Figure 8.

Figure 9.

Figure 10.

Figure 11.

Figure 12.

OX1 OX2 OX3 SH1 SH2 SH3 helicobacter hepaticus 0 4.257983719 0 0 0 0 alistipes spp. 0 0 0 0 0.051015203 0.115085835 ruminococcus spp. 0.115765102 0.313087038 0.482879719 0.344622698 0.214263851 0.028771459 acetivibrio cellulolyticus 0.031572301 0.814026299 0.204858063 0.083184789 0.173451689 0.105495349 anaerotruncus colihominis 0.0105241 0 0.029265438 0.011883541 0.020406081 0.009590486 bacteroides gallinarum 0.031572301 0 0.014632719 0.047534165 0 0.07672389 coprobacter fastidiosus 0 0 0 0 0 0.038361945 ruminococcus sp. 0.231530204 0.062617408 0.146327188 0.106951872 0 0.163038266 eubacterium rectale 0.094716902 0.438321853 0.190225344 0.118835413 0.040812162 0.364438477 pseudoflavonifractor bacteroides capillosus 0.042096401 0 0.014632719 0.059417706 0.030609122 0.047952431 parvibacter caecicola 0 0 0.058530875 0.023767083 0.010203041 0 peptococcus sp. 0.0105241 0.125234815 0.043898156 0.071301248 0.020406081 0.258943128 lachnoclostridium clostridium phytofermentans 0.0210482 0.062617408 0.043898156 0 0 0.038361945 sporobacterium spp. 0 0 0.087796313 0 0 0 prevotella spp. 0.473584509 5.009392611 1.038923032 0.5466429 0.336700337 4.373261724 lachnoclostridium clostridium symbiosum 0 0 0.043898156 0.047534165 0 0 enterorhabdus caecimuris 0.052620501 0 0.073163594 0.059417706 0.010203041 0 tyzzerella clostridium lactatifermentans 0.031572301 0.125234815 0.043898156 0.035650624 0.010203041 0 eubacterium plexicaudatum 0 0 0 0 0 0.028771459 roseburia spp. 0.136813302 0 0.11706175 0 0 0 planococcus spp. 0.136813302 0 0.058530875 0 0 0 roseburia faecis 3.89391707 0 1.273046532 0.225787285 0.071421284 0.019180972 bacteroides sp. 1.620711429 0.125234815 1.112086626 0.689245395 0.051015203 1.841373358 barnesiella intestinihominis 0 0 0 0.011883541 0.153045608 0 lachnospira spp. 0 0 0 0 0 0.038361945 blautia sp. 0.115765102 0.062617408 0.160959906 0 0.010203041 0.028771459 alistipes sp. 0 0 0 0.011883541 0 0.067133404 pseudobutyrivibrio spp. 0 0 0 0.09506833 0.030609122 0 lachnoclostridium clostridium celerecrescens 0.0105241 0 0.029265438 0.011883541 0.091827365 0.038361945 lachnoclostridium clostridium saccharolyticum 0 0 0.087796313 0 0 0 barnesiella spp. 36.07661545 3.819661866 34.21129646 67.33214498 57.51453933 54.87676225 lachnoclostridium clostridium oroticum 0.031572301 0 0 0.023767083 0 0 butyricicoccus pullicaecorum 0 0 0 0 0.040812162 0.354847991 anaerostipes sp. 0.063144601 0 0.11706175 0 0 0 akkermansia muciniphila 0.168385603 0 0.029265438 1.901366607 0.030609122 0.479524312 clostridium sp. 32.10902968 22.85535379 38.55721393 11.51515152 10.74380165 13.54176657 oribacterium sp. 0.0210482 0 0.014632719 0 0 0 lachnoclostridium clostridium xylanolyticum 0 0 0 0 0.030609122 0 bacteroides xylanolyticus 0.0210482 0 0.029265438 0 0.030609122 0 catabacter hongkongensis 0.063144601 0.876643707 0.014632719 0.09506833 0 0.057542917 oscillibacter spp. 0.031572301 0.125234815 0 0.059417706 0.010203041 0.038361945 hallella spp. 0.031572301 0 0.087796313 0.023767083 0.020406081 0.086314376 flavonifractor clostridium orbiscindens 0.031572301 0 0.043898156 0 0.010203041 0.019180972 acetanaerobacterium spp. 0.031572301 0.062617408 0.043898156 0.047534165 0.010203041 0.009590486 bacteroides acidofaciens 0.063144601 0 0.102429031 0 0 0.124676321 eubacterium sp. 1.147126921 13.40012523 0.907228563 3.588829471 2.367105397 2.033183082 lactobacillus spp. 0 0 0 0.023767083 0 0.134266807 clostridium spp. 0.852452115 1.127113338 1.126719344 0.356506239 0.448933782 0.728876954 intestinimonas butyriciproducens 1.031361819 0.062617408 0.863330407 0.356506239 0.19385777 0.15344778

0-0.5 % 0.51-1.0% 1.01-10.0% 10.01-20.0% 20.01-30.0% >30.01

anaeroplasma spp. 0.094716902 2.755165936 0.058530875 0.356506239 0.826446281 0.479524312 porphyromonas spp. 0.031572301 0 0.014632719 0.118835413 0.234669932 0.028771459 thermincola spp. 0.0105241 0 0 0.09506833 0 0 desulfonispora spp. 0 0.062617408 0.014632719 0.035650624 0.010203041 0.009590486 lactobacillus johnsonii 0.368343507 0 0.263388938 0.047534165 0.153045608 0.537067229 turicibacter spp. 0.084192802 0.125234815 0 0.213903743 0.010203041 0 hydrogenoanaerobacterium sp. 0.147337403 0.125234815 0.11706175 0.166369578 0.061218243 0.086314376 dehalobacterium spp. 0.063144601 0 0.043898156 0.023767083 0 0.038361945 butyrivibrio spp. 0.063144601 0.250469631 0.11706175 0 0.010203041 0.009590486 sporobacter termitidis 0 0.125234815 0 0 0.030609122 0.019180972 prevotella ruminicola 0 0.626174076 0 0 0.061218243 0.479524312 fusicatenibacter saccharivorans 0.0210482 0 0.014632719 0 0 0 alistipes indistinctus 2.041675437 9.392611146 0.380450688 0.308972074 4.091419243 1.400210991 tannerella spp. 0 0 0.029265438 0 0 0 oscillibacter sp. 0 0 0 0 0.061218243 0 turicibacter sp. 0.368343507 0 0 0.047534165 0.357106418 0.009590486 coprococcus catus 0.042096401 0 0.11706175 0.09506833 0 0 lachnoclostridium clostridium glycyrrhizinilyticum 0 0 0 0 0 0.028771459 candidatus dorea massiliensis 0.031572301 0 0 0.047534165 0 0 oscillospira spp. 6.68280362 2.254226675 7.345624817 2.448009507 0.99989797 1.208401266 alistipes putredinis 0.063144601 0 0.204858063 0 0.040812162 0.067133404 butyrivibrio sp. 0.094716902 0.500939261 0.278021656 0.190136661 0.010203041 0.038361945 blautia ruminococcus gnavus 0.263102505 0.187852223 0.263388938 0.071301248 0.030609122 0.134266807 prevotella oris 1.357608924 0 0.951126719 1.152703506 3.387409448 0.182219239 ruminiclostridium eubacterium siraeum 0.084192802 2.191609267 0.014632719 0.035650624 0.061218243 0.441162367 lachnoclostridium clostridium jejuense 0.073668701 0 0.029265438 0.202020202 0.020406081 0.009590486 lachnospira pectinoschiza 0.042096401 0.062617408 0.321919813 0.083184789 0.040812162 0 faecalitalea eubacterium cylindroides 0 0 0 0 0.030609122 0 pseudobacteroides cellulosolvens 0.042096401 0.688791484 0.131694469 0.035650624 0.418324661 1.428982449 coprococcus spp. 0.031572301 0 0 0 0 0 eubacterium ventriosum 0.105241002 0 0 1.057635175 0 0.172628752 eubacterium ruminantium 0 0.062617408 0 0.154486037 0 0 bacteroides acidifaciens 0.399915807 0 0.35118525 0.118835413 0.316294256 0.412390908 bacteroides spp. 0.147337403 0.125234815 0.087796313 0.071301248 0.214263851 0.517886257 eubacterium xylanophilum 0 0 0 0.154486037 0 0 clostridium hveragerdense 0.042096401 0.250469631 0.087796313 0.011883541 0 0.07672389 lachnoclostridium clostridium bolteae 0.157861503 0.125234815 0.307287094 0 0 0.047952431 vagococcus spp. 0 0 0.043898156 0 0.418324661 0 syntrophomonas spp. 0 0 0 0 0 0.028771459 parasporobacterium paucivorans 0.189433803 0 0.526777875 0.106951872 0.051015203 0 alistipes finegoldii 1.494422227 5.197244834 0.556043313 0.594177065 3.111927354 1.457753908 blautia producta 1.599663229 1.252348153 1.990049751 0.665478313 0.255076013 0.38361945 pleurocapsa spp. 0 0.062617408 0 0 0.020406081 0.115085835 allobaculum sp 0.221006104 0 0 0.439691028 0.244872972 0.009590486 lachnoclostridium clostridium aminophilum 0.168385603 0 0.307287094 0.047534165 0.010203041 0.038361945 candidatus bacilloplasma &apos 0 0.876643707 0 0 0.061218243 0.96863911 helcococcus sp. 0.084192802 1.001878522 0.029265438 0.035650624 0.438730742 1.544068284 parabacteroides distasonis 0 0 0 0 0 0.028771459 lachnoclostridium clostridium scindens 0.0105241 0 0.058530875 0.047534165 0.030609122 0

ruminiclostridium clostridium methylpentosum 0 0 0.014632719 0 0.010203041 0.009590486 alloprevotella prevotella sp. 0 0 0 0 0.051015203 0 alistipes senegalensis 1.347084824 5.82341891 0.731635938 0.415923945 3.111927354 1.409801477 eubacterium desmolans 0.073668701 0.062617408 0.043898156 0 0 0.07672389 catonella sp. 0.0105241 1.941139637 0.029265438 0.011883541 0 0.038361945 eubacterium spp. 0.063144601 0 0.058530875 0.083184789 0.102030405 0.038361945 prevotella sp. 0.273626605 2.504696306 0.980392157 0.784313725 4.672992552 3.490936991 coprococcus eutactus 0.031572301 0 0.160959906 0.035650624 0 0.086314376 eubacterium coprostanoligenes 0.136813302 0.375704446 0.131694469 0.178253119 0.061218243 0.009590486 lachnoclostridium clostridium hathewayi 0 1.06449593 0 0.011883541 0.010203041 0.009590486 acetivibrio spp. 0.199957904 0.187852223 0.014632719 0.249554367 0.040812162 0.086314376 alistipes onderdonkii 1.220795622 4.571070758 0.629206907 0.285204991 2.183450668 1.026182027 barnesiella viscericola 0.210482004 0 0.087796313 0.249554367 0.39791858 0.249352642 butyrivibrio clostridium proteoclasticum 0.189433803 0 0.087796313 0.118835413 0 0.057542917 ruminococcus flavefaciens 0.126289202 0.187852223 0.058530875 0 0.081624324 0.086314376 dorea formicigenerans 0.031572301 0 0.014632719 0 0 0 clostridium fusiformis 0 0.250469631 0.073163594 0.023767083 0 0 thermoanaerobacterium thermosaccharolyticum 0.0210482 0 0 0 0 0.009590486 candidatus stoquefichus massiliensis 0.0105241 0 0.029265438 0.083184789 0 0.019180972 sporobacter spp. 0.178909703 0.250469631 0.073163594 0.047534165 0.132639527 0.067133404 candidatus arthromitus sp. sfb_mouse 0.0105241 0.250469631 0 0.059417706 0 0.009590486 paludibacter sp. 0.052620501 0 0 0.011883541 0 0 prevotella scopos 0 0 0 0 0 0.047952431 propionicimonas paludicola 0.0210482 0 0 0 0 0.009590486 lachnoclostridium clostridium amygdalinum 0.136813302 0 0.146327188 0.011883541 0.071421284 0 candidatus saccharimonas aalborgensis 0.0210482 0.062617408 0.014632719 0.011883541 0.010203041 0.009590486 flavobacterium spp. 0 0 0 0 0 0.047952431 christensenella minuta 0.052620501 0.375704446 0.043898156 0.118835413 0.163248648 0.287714587 parabacteroides gordonii 0 0 0 0 0 0.028771459 lachnoclostridium clostridium indolis 0.0210482 0.062617408 0.014632719 0 0 0.028771459 Figure 13.

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