This document summarizes computational approaches to studying the microbiome and its relationship to obesity, type 2 diabetes, and antibiotic use. It begins by outlining early microscopy-based observations of microbes and recent approaches involving sequencing and bioinformatics. Three manuscripts analyzing microbiome data are then summarized: 1) A study of bariatric surgery patients finding shifts in microbial diversity and function associated with metabolic improvements. 2) A study of leptin-deficient mice identifying specific microbes linked to obesity and calorie-restriction effects. 3) A cross-ethnic study of Danish and Indian people revealing differences in diversity and microbiome markers associated with metabolic traits and type 2 diabetes status between ethnicities.

1. Computational Approaches to Study
Microbiome
towards understanding obesity, type 2 diabetes and long-term impact of antibiotics
Alireza Kashani
November 2017
1

2. Contents
• Introduction
• Approaches towards understanding the role of Microbiome
• Microbiome & Type 2 Diabetes
• Summary of results from each paper
• Perspectives
2

3. Microbiome
• microbes – 150 fold of our genome
• Production of SCFA from indigestible polysaccharides
• Digest starch, plant fiber, proteins like collagen, elastin
• SCFAs (Short Chain Fatty Acids) Acetic acid, propionic acid, butyric acid,
• Metabolic Function
• Vitamins (B, K)
• Repression of pathogenic, microbial growth
• Competition for nutrition, attachment, produce bacteriocins, lactic acid
• Preventing allergy
• C. difficile and S.aureus > bactericides and Bifidobacteria
3

4. Early approaches …
4

5. Early approaches …
Antoni van Leeuwenhoek
(1632-1723)
Micrographia by Robert Hooke
1665
5

6. Early approaches …
Antoni van Leeuwenhoek
(1632-1723)
Micrographia by Robert Hooke
1665
swamp water, blood, insects, tooth tartar
6

7. Early approaches …
Antoni van Leeuwenhoek
(1632-1723)
Micrographia by Robert Hooke
1665
swamp water, blood, insects, tooth tartar
7

8. To characterize any community
• Who is there
• What they do
• How they interact
8

9. • Who is there (Taxonomical)
• What they do (Functional)
• How they interact (Ecological)
Microbiome
Schwager et. Al. 2015
9

10. Microbiome
• Who is there (Taxonomical)
• What they do (Functional)
• How they interact (Ecological)
Schwager et. Al. 2015
10

11. Clarke SF et al. 2012
Schwager et. Al. 2015
taxonomic profiling through sequencing
11

12. • Who is there (Taxonomical)
• What they do (Functional)
• How they interact (Ecological)
Schwager et. Al. 2015
Correlation
Linear methods
Visualization methods
12

13. Functional Profiling through Amplicon sequencing
PICRUSt (Langille et al. 2013b)
Tax4fun (Aßhauer et al. 2015b)
Piphillin (Iwai et al. 2016)
FishTaco (Manor and Borenstein 2017)
More on the page 25 of the thesis Schwager et. Al. 2015
13

14. Microbiome and T2D
• A few overlapping hypotheses
14

15. Microbiome and T2D
• Increased capacity of energy harvest in obese microbiome
Tsukumo et. Al. 2015, Translational research into gut microbiota: new horizons on obesity treatment,
Arch. Endocrinol. Metab.
courtesy of Human Genome wall for SC99 on csm.ornl.gov
More on page 19 and 23 of the thesis15

16. Microbiome and T2D
• Increased capacity of energy harvest in obese microbiome
• Low-grade inflammation triggered by lipopolysaccharide (LPS)
More on page 19 and 23 of the thesis16

17. Microbiome and T2D
• Increased capacity of energy harvest in obese microbiome
• Low-grade inflammation triggered by LPS
• Role of Short Chain Fatty Acids in mediating appetite
More on page 19 and 23 of the thesis
Kuwahara, Front. Endocrinol, 2014
17

18. Microbiome and T2D
• Increased capacity of energy harvest in obese microbiome
• Low-grade inflammation triggered by LPS
• Role of Short Chain Fatty Acids in mediating appetite
• Microbial amino acid metabolism
More on page 19 and 23 of the thesis18

19. Included papers
19

20. … changes in the field
• Sequencing protocol
• Operational Taxonomic Units to Amplicon Sequence Variants
20

21. Identifying taxa based on 16S rRNA gene
depends on two factors
21

22. Identifying taxa based on 16S rRNA gene
depends on two factors
22

23. Identifying taxa based on 16S rRNA gene
depends on two factors
23

24. Identifying taxa based on 16S rRNA gene
depends on two factors
24

25. Identifying taxa based on 16S rRNA gene
depends on two factors
25

26. characterizing 16S rRNA gene
Targeting V3+V4 with 2x250bp
26

27. characterizing 16S rRNA gene
Targeting V3+V4 with 2x250bp
27

28. characterizing 16S rRNA gene
Targeting V4 with 2x250bp
Targeting V3+V4 with 2x250bp
28

29. charactering 16S rRNA gene using
only V4 region
• (pros) Two MiSeq reads (2x250bp) almost cover the
whole region
– Reduces sequencing noise
– Trade of between the length / quality of reads
29

30. charactering 16S rRNA gene using
only V4 region
• (pros) Nearly overlap of the two MiSeq reads (2x250bp)
– Reduces sequencing noise
– Trade of between the length / quality of reads
• (cons) It is still 250bp
– Less information contained compare to multiple
regions
– Hard to annotate OTU in lower biological orders
(genus/species)
30

31. Manuscript I
31

32. 32
Roux-en-Y Gastric Bypass (RYGB)
how the microbiome responds to this drastic change and identify taxa that are associated with improved
beneficial metabolic effects.
Metagenomics 32

33. Phenotypic changes
33

34. 0.2
0.1
0.0
0.1
0.2
0.3
0.4
0.20 0.15 0.10 0.05 0.00 0.05 0.10 0.15 0.20 0.25 0.30 0.35
PC1 34.14 %
PC223.37%
Time
1y
3m
pre
Clinical Parameters
a
a
a
a
a
a
a
a
a
age
bmi
whr
HbA1c
f.glu
f.ins
auc.glu
auc.ins
auc.glp1
Phenotypic changes
34

35. Phenotypic changes
• Microbial Diversity
• Transporters
• phosphotransferase systems
• aerobic respiration
• use of amino acids and fatty acids
0.2
0.1
0.0
0.1
0.2
0.3
0.4
0.20 0.15 0.10 0.05 0.00 0.05 0.10 0.15 0.20 0.25 0.30 0.35
PC1 34.14 %
PC223.37%
Time
1y
3m
pre
Clinical Parameters
a
a
a
a
a
a
a
a
a
age
bmi
whr
HbA1c
f.glu
f.ins
auc.glu
auc.ins
auc.glp1
35

36. • Increase in the diversity of the gut microbiota along with the
alteration in microbial composition following the metabolic
improvement after RYGB.
• We observed increased potential in transporters and
phosphotransferase systems. These changes may indicate
microbiome is assimilating to multiple energy sources.
• We also reported an increased capacity for aerobic respiration in
addition to a shift from protein degradation to putrefaction.
• Furthermore, we observed increased use of amino acids and fatty
acids as long-lasting energy sources.
36

37. Manuscript II
37

38. Factors shaping the gut microbiota
and metabolism in leptin- deficient
ob/ob mice
Kashani A, Jin C, Kern TS, Nygaard A, Nielsen LA, Gerber GK, Holm J-C, Hansen T, Holst B,
Arumugam M., in preparation
38

39. Leptin Deficient Ob/Ob mouse
Animal model for obesity
l Mice with deficiency of the leptin gene
l Develop high blood sugar, increased level of insulin
l Instrumental in suggesting links between gut microbiota and obesity
courtesy of Human Genome wall for SC99 on csm.ornl.gov
39

40. Design of the Experiment
Ob/Ob
Ad libitum
Wild Type
Ad libitum
40

41. Design of the Experiment
Ob/Ob
Ad libitum
Wild Type
Ad libitum
Ob/Ob
Pair-fed
Fecal samples from 9 weeks and the cecum material
V4 region, Illumina MiSeq
41

42. Development of Phenotype
42

43. 43

44. 44

45. 45

46. 46

47. Upstream Analysis
l V4 region – Illumina MiSeq Sequencing - DADA2
l Motivated with substantial changes in the field
47

48. 0
5
10
15
w1 w2 w3 w4 w5 w6 w7 w8 w9 cecum
bacteroidetestofirmicutes
Control Ob/Ob ad libitum Ob/Ob calorie−restricted
Bacteroidetes to Firmicutes ratio
48

49. Beta Diversity
49

50. −0.2
0.0
0.2
1 2 3 4 5 6 7 8 9 cecum
PC1
condition
Ob/Ob
ad libitum
Control
Ob/Ob
calorie−restricted
PCOA bray
Beta Diversity
50

51. −0.2
0.0
0.2
1 2 3 4 5 6 7 8 9 cecum
PC1
condition
Ob/Ob
ad libitum
Control
Ob/Ob
calorie−restricted
PCOA bray
Beta Diversity
• Age, genetic, and calorie-restriction
• PERMANOVA on Jensen Shannon Divergence
• Leptin-deficiency explained 13.8% - 7.2% of the variance (R2)
• Calorie-restriction explained 13.1% - 6.5% of the variance (R2)
• Age explained 3% of the variance
51

52. Leptin-deficient microbiome signature
• Two ASVs under Lactobacillus (L. reuteri)
• Antibiotic reduced the level of Lactobacillus -> reduced metabolic endotoxemia (Cani, P. et. al,
2008)
• L.reuteri was significantly enriched in obese subjects (Million, M. et. Al, 2012)
52

53. Leptin-deficient microbiome signature
• Two ASVs under Lactobacillus (L. reuteri)
• Antibiotic reduced the level of Lactobacillus -> reduced metabolic endotoxemia (Cani, P. et. al,
2008)
• L.reuteri was significantly enriched in obese subjects (Million, M. et. Al, 2012)
• Five ASVs under Lachnospiraceae
• One is under Anaerostipes genera (AJ110941) (Kameyama and Itoh 2014)
• Lachnospiraceae family appears to induce monocyte/macrophage mobilization that led to colitis
(Nakanishi, Sato, and Ohteki 2015).
53

54. Leptin-deficient microbiome signature
• Two ASVs under Lactobacillus (L. reuteri)
• Antibiotic reduced the level of Lactobacillus -> reduced metabolic endotoxemia (Cani, P. et. al,
2008)
• L.reuteri was significantly enriched in obese subjects (Million, M. et. Al, 2012)
• Five ASVs under Lachnospiraceae
• One is under Anaerostipes genera (AJ110941) (Kameyama and Itoh 2014)
• Lachnospiraceae family appears to induce monocyte/macrophage mobilization that led to colitis
(Nakanishi, Sato, and Ohteki 2015).
• Depletion of Oscillibacter/Oscillospira
• (Tims et al. 2013) in a twin study
• (Walker et al. 2011) 14 individuals
54

55. Leptin-deficient microbiome signature
• Two ASVs under Lactobacillus (L. reuteri)
• Antibiotic reduced the level of Lactobacillus -> reduced metabolic endotoxemia (Cani, P. et. al,
2008)
• L.reuteri was significantly enriched in obese subjects (Million, M. et. Al, 2012)
• Five ASVs under Lachnospiraceae
• One is under Anaerostipes genera (AJ110941) (Kameyama and Itoh 2014)
• Lachnospiraceae family appears to induce monocyte/macrophage mobilization that led to colitis
(Nakanishi, Sato, and Ohteki 2015).
• Depletion of Oscillibacter/Oscillospira
• (Tims et al. 2013) in a twin study
• (Walker et al. 2011) 14 individuals
• Lower abundance of Akkermansia muciniphila
• A. muciniphila prevents the development of obesity and negatively correlated with
metabolic endotoxemia (Everard et al. 2013)
• (Plovier et al. 2016) demonstrate that Amuc_1100 from the outer membranes of this species,
interacted with Toll-like Receptor 2
55

56. 0.00
0.02
0.04
1 2 3 4 5 6 7 8 9 cecum
RelativeAbundance
condition
ad libitum
control
pair fed
Akkermansia muciniphila
Trajectory of Akkermansia Muciniphila
abundance
56

57. Functional Signature in the Ob/Ob mice
• Lower abundant of KEGG modules related to glycan degradation
• Decreasing production of glycan-degrading enzymes led to increased
expression of amino acid and sugar transporters (Mahowald et al. 2009)
• Enrichment of 9 KEGG modules related to microbial Amino Acid
production and transport system
• Impact insulin resistance and type 2 diabetes (Neis, Dejong, and Rensen
2015)
• elevated levels of aromatic and branched-chain amino acids
More on page 23 57

58. Summary
• Identified Ob/Ob gut microbial signature
• Assessed previously reported biomarkers
• Assessed previously raised microbiome-obesity interplay
58

59. Manuscript III
59

60. Cross-ethnic microbiome study
reveals novel aspects of type 2
diabetes pathophysiology
Kashani A, MicrobDiab consortium, Hansen T, Arumugam M, Vestegaard H, Hansen T, Pedersen O.
60

61. Why MicrobDiab
• At any given BMI, East Asians have a greater amount of body fat
and a tendency to visceral adiposity.
• Diabetes develops at a younger age and is characterized by early β
cell dysfunction in the setting of insulin resistance.
• Genetic environment interaction
61

62. Study Design
V1-V5 region
Roche 454 sequencing
62

63. Methods
• Wilcoxon rank sum test
• DESeq2
• Linear mixed models
• LASSO – Random forest
63

64. Results
• Danish participants have significantly higher α-diversity relative to
Indian subjects, unlike previous studies.
64

65. Results
• Danish participants have significantly higher α-diversity relative to
Indian subjects, unlike previous studies.
• Characterizing both common and ethnic specific gut microbiome
patterns
65

66. Results
• Danish participants have significantly higher α-diversity relative to
Indian subjects, unlike previous studies.
• Characterizing both common and ethnic specific gut microbiome
patterns
• Examining the association between microbial profile and various
metabolic markers
66

67. Results
• Danish participants have significantly higher α-diversity relative to
Indian subjects, unlike previous studies.
• Characterizing both common and ethnic specific gut microbiome
patterns
• Examining the association between microbial profile and various
metabolic markers
• Identified microbiome markers that discriminate between (NGT /
T2D).
67

68. Results
• Danish participants have significantly higher α-diversity relative to
Indian subjects, unlike previous studies.
• Characterizing both common and ethnic specific gut microbiome
patterns
• Examining the association between microbial profile and various
metabolic markers
• Identified microbiome markers that discriminate between (NGT /
T2D).
• Metformin signature independent of ethnicity and diet.
68

69. Results
• An elevated abundance of Enterobacteriaceae in T2D patients
treated with metformin and depletion of Clostridiaceae in patients
not treated with metformin.
• T2D signature that are independent of ethnicity and diet.
69

70. Manuscript IV
70

71. Gut resistome modulates
resilience and recovery of gut
microbiota after broad-
spectrum antibiotic treatment
Palleja A*, Mikkelsen KH*, Forslund K*, Kashani A, Allin K, Nielsen T, Hansen T, Liang S,
Feng Q, Pyl P, et al. submitted
71

72. 72
Metagenomics 72

73. Beta Diversity
73

74. 74
74

75. 75
75

76. Summary
• Computational approach within association microbial studies
• Changes in microbial composition and functional potential following
RYGB
• Investigated the microbiome of the genetic-induced obesity model
• Profiled the first cross-ethnic T2D microbiome study
• Long-term effect of antibiotic intake
76

77. Perspectives
77

78. Perspectives
• What are the major source of variance in the microbiome data ?
78

79. Perspectives
• What are the major source of variance in the microbiome data ?
• What are the emerging technologies in gut microbiota research ?
79

80. Perspectives
• What are the major source of variance in the microbiome data ?
• What are the emerging technologies in gut microbiota research ?
• How much our computational methods reflect the underlying
biological phenomena ?
80

81. Acknowledgments
Albert Palleja
Timo Kern
Paul Pyl
Thorsten Barch
Tue Hansen
Chunyu Jin / Andreas Nygaard / Birgitte Holst
Manimozhian Arumugam / Torben Hansen / Oluf Pedersen
Gerber’s lab at Harvard Medical School / BWH
MicrobDiab consortia
81

82. Supplementary Slides
82

83. 16S rRNA
From reads to the abundance table
I) From the sequencing machine
83

84. 16S rRNA
From reads to the abundance table
II) Trimming
I) From the sequencing machine
84

85. 16S rRNA
From reads to the abundance table
II) Trimming
III) Sorting & Pooling
I) From the sequencing machine
85

86. 16S rRNA
From reads to the abundance table
II) Trimming
III) Sorting & Pooling
IV) OTU findingI) From the sequencing machine
86

87. 16S rRNA
From reads to the abundance table
II) Trimming
III) Sorting & Pooling
IV) OTU findingI) From the sequencing machine
V) remapping
87

88. A. muciniphila
Aerobic respiration
HOST METABOLISM
G
U
T
L
U
M
E
N
STOMACH
O2 pH
E. coli
K.pneumoniae
Streptococcus spp.
E. faecalis
Oxidative stress /
peroxides
Aerotolerant Proteobacteria
Glutathione
Transit
time
Gut
motility
Putrefaction
Putrescine
GABA
GLP1
Microbial
diversity
Inflammation Satiety Adiposity BMI Glucose tolerance
Veillonella spp.
B.dentium
F.nucleatum
Oral microbiota
Mucin degradation
Size
Acid
secretions
Alistipes spp.
Metabolic interpretation
88

89. Introduction notes
RYGB, the most effective treatment:
-rapid weight reduction
-significant improvement of glucose
metabolism
-reduces insulin resistance
-decrease in appetite and meal size
-change in food preferences
-anatomical rearrangement of the gut
-higher secretion of gut hormones (GLP1,
ghrelin and PYY)
-shift in the bile acid metabolism
89

90. Phenotype Data – MicrobDiab paper (PCA)
90

91. Upstream Analysis
l V4 region – Illumina Miseq Sequencing - DADA2
l 359 samples (9 weeks + cecum)
l 348 samples with more than 5000 high quality fragments
l Filtered out taxa with average relative abundance less than 1%
(from 1162 to 528)
l Filtered out taxa were consistently absent in 90% of samples in all
9 weeks of the study (from 528 to 343 taxa)
91

92. x_1f__S24−7/g_?
x_3f__S24−7/g_?
x_4g__Prevotella/s_?
x_8f__S24−7/g_?
x_10g__Bacteroides/s_?
x_12o__Clostridiales/f_?
x_14f__S24−7/g_?
x_15f__Lachnospiraceae/g_?
x_17f__S24−7/g_?
x_22f__S24−7/g_?
x_23f__Rikenellaceae/g_?
x_24f__S24−7/g_?
x_28f__Lachnospiraceae/g_?
x_31g__Allobaculum/s_?
x_32o__Clostridiales/f_?
x_36f__S24−7/g_?
x_37f__S24−7/g_?
x_40g__Lactobacillus/s_?
x_41f__S24−7/g_?
x_46g__Lactobacillus/s_?
x_47f__S24−7/g_?
x_48f__S24−7/g_?
x_49f__S24−7/g_?
x_52o__Bacteroidales/f_?
x_54o__Clostridiales/f_?
x_60o__Clostridiales/f_?
x_61g__Akkermansia/s_?
x_63f__S24−7/g_?
x_65o__Clostridiales/f_?
x_66g__CandidatusArthromitus/s_?
x_75o__Clostridiales/f_?
x_81f__S24−7/g_?
x_82f__Lachnospiraceae/g_?
x_84f__Rikenellaceae/g_?
x_85g__Ruminococcus/s_?
x_88f__Rikenellaceae/g_?
x_89f__Lachnospiraceae/g_?
x_93o__Clostridiales/f_?
x_95g__Lactobacillus/s_?
x_98g__Oscillospira/s_?
x_100o__Clostridiales/f_?
x_101f__S24−7/g_?
x_109o__Clostridiales/f_?
x_110f__Lachnospiraceae/g_?
x_114o__Clostridiales/f_?
x_118f__Lachnospiraceae/g_?
x_119o__Clostridiales/f_?
x_124o__Clostridiales/f_?
x_126f__S24−7/g_?
x_130o__YS2/f_?
x_131g__Oscillospira/s_?
x_133f__Lachnospiraceae/g_?
x_135NA/f_?
x_142g__Butyricicoccus/s_?
x_145o__Clostridiales/f_?
x_147o__RF32/f_?
x_150g__Oscillospira/s_?
x_154f__Lachnospiraceae/g_?
x_157o__Clostridiales/f_?
x_159g__Anaerofilum/s_?
x_161g__Oscillospira/s_?
x_163o__Clostridiales/f_?
x_172g__Clostridium/s_?
x_176g__Ruminococcus/s_?
x_177g__[Ruminococcus]/s_?
x_179g__Oscillospira/s_?
x_183f__Lachnospiraceae/g_?
x_192g__Oscillospira/s_?
x_197g__Coprococcus/s_?
x_201o__Clostridiales/f_?
x_208g__Oscillospira/s_?
x_209f__Lachnospiraceae/g_?
x_213g__Lactobacillus/s_?
x_218g__[Ruminococcus]/s_?
x_219f__Lachnospiraceae/g_?
x_223g__Ruminococcus/s_?
x_229g__Coprococcus/s_?
x_242g__Butyricicoccus/s_?
x_244g__Oscillospira/s_?
x_245g__Coprococcus/s_?
x_249f__S24−7/g_?
x_256g__Oscillospira/s_?
x_258o__Clostridiales/f_?
x_270f__Erysipelotrichaceae/g_?
x_280o__Clostridiales/f_?
x_285f__Lachnospiraceae/g_?
x_288NA/f_?
x_295o__Clostridiales/f_?
x_298g__Coprococcus/s_?
x_306g__Coprobacillus/s_?
x_307o__RF39/f_?
x_312g__Coprobacillus/s_?
x_324f__Coriobacteriaceae/g_?
x_333f__Lachnospiraceae/g_?
x_353f__Ruminococcaceae/g_?
w6 WT/OB
w7 WT/OB
w8 WT/OB
w9 WT/OB
cecum WT/OB
w6 WT/OB−PF
w7 WT/OB−PF
w8 WT/OB−PF
w9 WT/OB−PF
cecum WT/OB−PF
w6 OBOB−PF
w7 OBOB−PF
w8 OBOB−PF
w9 OBOB−PF
cecum OBOB−PF
−10
−5
0
5
10
x_4g__Prevotella/s_?
x_15f__Lachnospiraceae/g_?
x_28f__Lachnospiraceae/g_?
x_31g__Allobaculum/s_?
x_32o__Clostridiales/f_?
x_40g__Lactobacillus/s_?
x_46g__Lactobacillus/s_?
x_54o__Clostridiales/f_?
x_61g__Akkermansia/s_?
x_66g__CandidatusArthromitus/s_?
x_82f__Lachnospiraceae/g_?
x_85g__Ruminococcus/s_?
x_93o__Clostridiales/f_?
x_95g__Lactobacillus/s_?
x_98g__Oscillospira/s_?
x_109o__Clostridiales/f_?
x_110f__Lachnospiraceae/g_?
x_114o__Clostridiales/f_?
x_118f__Lachnospiraceae/g_?
x_119o__Clostridiales/f_?
x_124o__Clostridiales/f_?
x_131g__Oscillospira/s_?
x_133f__Lachnospiraceae/g_?
x_135NA/f_?
x_142g__Butyricicoccus/s_?
x_147o__RF32/f_?
x_150g__Oscillospira/s_?
x_157o__Clostridiales/f_?
x_159g__Anaerofilum/s_?
x_161g__Oscillospira/s_?
x_163o__Clostridiales/f_?
x_172g__Clostridium/s_?
x_176g__Ruminococcus/s_?
x_177g__[Ruminococcus]/s_?
x_179g__Oscillospira/s_?
x_183f__Lachnospiraceae/g_?
x_192g__Oscillospira/s_?
x_197g__Coprococcus/s_?
x_201o__Clostridiales/f_?
x_208g__Oscillospira/s_?
x_209f__Lachnospiraceae/g_?
x_213g__Lactobacillus/s_?
x_218g__[Ruminococcus]/s_?
x_219f__Lachnospiraceae/g_?
x_223g__Ruminococcus/s_?
x_229g__Coprococcus/s_?
x_242g__Butyricicoccus/s_?
x_244g__Oscillospira/s_?
x_245g__Coprococcus/s_?
x_256g__Oscillospira/s_?
x_258o__Clostridiales/f_?
x_270f__Erysipelotrichaceae/g_?
x_280o__Clostridiales/f_?
x_288NA/f_?
x_295o__Clostridiales/f_?
x_298g__Coprococcus/s_?
x_306g__Coprobacillus/s_?
x_307o__RF39/f_?
x_312g__Coprobacillus/s_?
x_324f__Coriobacteriaceae/g_?
x_353f__Ruminococcaceae/g_?
w6 WT/OB
w7 WT/OB
w8 WT/OB
w9 WT/OB
cecum WT/OB
w6 WT/OB−PF
w7 WT/OB−PF
w8 WT/OB−PF
w9 WT/OB−PF
cecum WT/OB−PF
w6 OBOB−PF
w7 OBOB−PF
w8 OBOB−PF
w9 OBOB−PF
cecum OBOB−PF
−10
−5
0
5
10
92

93. −0.2
−0.1
0.0
0.1
0.2
1 2 3 4 5 6 7 8 9 cecum
PC1
condition
Ob/Ob
ad libitum
Control
Ob/Ob
calorie−restricted
PCOA jsd
−0.2
−0.1
0.0
0.1
0.2
1 2 3 4 5 6 7 8 9 cecum
PC1
condition
Ob/Ob
ad libitum
Control
Ob/Ob
calorie−restricted
PCOA unifrac
93

94. 94