Page 1 of 39 Diabetes
Restructuring of the gut microbiome by intermittent fasting prevents retinopathy
and prolongs survival in db/db mice
Eleni Beli1*, Yuanqing Yan2*, Leni Moldovan3, Cristiano P. Vieira4, Ruli Gao5, Yaqian
Duan6, Ram Prasad4, Ashay Bhatwadekar7, Fletcher A. White8, Steven Townsend9,
Luisa Chan10, Caitlin N. Ryan10, Daniel Morton10, Emil G. Moldovan11, Fang I Chu12,
Gavin Y. Oudit13, Hartmut Derendorf14, Luciano Adorini15, Xiaoxin X. Wang16, Carmella
Evans Molina1,6,17, Raghavendra G. Mirmira1,6,17, Michael E. Boulton4, Mervin C.
Yoder1, Qiuhong Li18, Moshe Levi19, Julia V. Busik20, and Maria B. Grant4
1Department of Pediatrics, Indiana University School of Medicine, Indianapolis, IN,
USA
2Department of Neurosurgery, The University of Texas Health Science Center at
Houston, Houston, TX, USA
3Department of Surgery, Indiana University School of Medicine, Indianapolis, IN, USA
4Department of Ophthalmology, University of Alabama, Birmingham, AL, USA
5Department of Genetics, The University of Texas MD Anderson Cancer Center,
Houston, TX, USA
6Department of Cellular and Integrative Physiology, Indiana University School of
Medicine, Indianapolis, IN, USA
7Department of Ophthalmology, Indiana University School of Medicine, Indianapolis,
IN, USA
8Department of Anesthesia, Indiana University, Indianapolis, IN, USA
9Department of Chemistry, Vanderbilt University, Nashville, TN, USA
10Second Genome, Inc., San Francisco, CA, USA
11Northeastern University, Boston, MA, USA
12Department of Radiation Oncology, University of California, Los Angeles, CA, USA
1
Diabetes Publish Ahead of Print, published online April 30, 2018 Diabetes Page 2 of 39
13Department of Medicine, University of Alberta, Edmonton, Alberta, Canada
14Department of Pharmaceutics, University of Florida, Gainesville, FL, USA
15Intercept Pharmaceuticals, New York, NY, USA
16Department of Medicine, University of Colorado, Denver, CO, USA
17Department of Medicine, Indiana University School of Medicine, Indianapolis, IN, USA
18Department of Ophthalmology, University of Florida, Gainesville, FL, USA
19Department of Biochemistry and Molecular & Cellular Biology, Georgetown
University, Washington, DC, USA
20Department of Physiology, Michigan State University, East Lansing, MI, USA
*co authors contributed equally to the manuscript
Correspondence:
Maria B. Grant M.D.
University of Alabama
VH 490 | 1720 2nd Ave S | Birmingham, AL 35294 0001
office: 205 996 8673
RUNNING TITLE: Intermittent fasting affects gut-retina axis in diabetes
Total Word Count of the Manuscript: 4,329
Figure count: 8
Keywords:
1. Intermittent Fasting
2. Diabetes
2 Page 3 of 39 Diabetes
3. Diabetic Retinopathy
4. Microbiome/ Microbiota
5. TUDCA
6. TGR5
3 Diabetes Page 4 of 39
Abstract
Intermittent fasting (IF) protects against the development of metabolic diseases and cancer, but whether it can prevent diabetic microvascular complications is not known. In db/db mice, we examined the impact of long term IF on diabetic retinopathy
(DR). Despite no change in glycated hemoglobin, db/db mice on the IF regimen displayed significantly longer survival and a reduction in DR endpoints, including acellular capillaries and leukocyte infiltration. We hypothesized that IF mediated changes in the gut microbiota would produce beneficial metabolites and prevent the development of DR. Microbiome analysis revealed increased levels of Firmicutes and decreased
Bacteroidetes and Verrucomicrobia. Compared to db/db mice on ad libitum (AL) feeding, changes in the microbiome of the db/db mice on IF were associated with increases in gut mucin, goblet cell number and villi length and reductions in plasma peptidoglycan.
Consistent with the known modulatory effects of Firmicutes on bile acid (BA) metabolism, measurement of BAs demonstrated a significant increase of tauroursodeoxycholate (TUDCA), a neuroprotective BA, in db/db on IF but not in db/db on AL feeding. TGR 5, the TUDCA receptor, was found in neural cells of the retina primary ganglion cells. Expression of TGR5 did not change with IF or diabetes.
However, IF reduced retinal TNF α mRNA, which is a key downstream target of TGR 5 activation. Pharmacological activation of TGR5 using INT 767 prevented DR in a second diabetic mouse model. These findings support the concept that IF prevents DR by restructuring the microbiota towards species producing TUDCA and subsequent retinal protection by TGR5 activation.
4 Page 5 of 39 Diabetes
Introduction
Diabetes related blindness is a personal catastrophe for the individual and
affects over 4.2 million Americans (1). Although DR is commonly classified as a
microvascular complication of diabetes, it is now recognized that changes in the neural
cells of the retina occur early in the natural history of the disease and these changes
precede alterations in the microvasculature (2; 3). Current therapies for DR, such as
laser photocoagulation, injection of anti VEGF antibodies, or vitreoretinal surgery (4),
only treat late stage disease and selectively target the microvascular component of the
disease, while therapies for the early stages are limited to preventive strategies, such as
life style changes.
Chronic calorie restriction (CR) and intermittent fasting (IF) represent non
pharmacological strategies for prevention and treatment of diseases such as diabetes,
obesity, and aging. However, CR can lead to nutrient deficiencies and fatigue (5; 6),
low metabolic rate and increased risk for subsequent obesity (7), infertility (5),
suppression of immunity (8) and increased susceptibility to viral infections (9; 10). IF
prevents the adverse effects of CR as it restricts feeding to only a part of the day or to
certain days per week. Thus, IF may be a more beneficial strategy compared to chronic
CR as it combines the beneficial effects elicited by fasting physiology and a recovery
period during the feeding phase which facilitates regeneration and promotes tissue
repair (11).
Accumulating evidence indicates that the gut microbiota contributes to key
physiological functions such as energy metabolism, metabolic signaling, and regulation
of integrity of the gut barrier (12 14). Individuals with chronic diseases such as diabetes
exhibit altered bacterial composition (15), which likely not only influences glucose
metabolism but also, we reasoned, may contribute to development of complications.
5 Diabetes Page 6 of 39
Gut microbes have a symbiotic relationship with the host: they digest exogenous,
indigestible foods generating secondary metabolites that can affect host physiology,
and they metabolize endogenous substrates in the absence of food during prolonged
fasting.
An example of endogenous metabolites being converted to beneficial
secondary metabolites by the microbiome is the generation of bile acids (BAs). Primary
BAs generated by the liver are highly hydrophobic and can cause colitis; however,
intestinal bacteria convert them to more hydrophilic, less toxic, secondary BAs that are
potent agonists for nuclear receptors and have physiological functions. Using the db/db
mouse and an IF (every other day fasting) regimen for seven months, we showed that
IF prevented development of DR and increased generation of a secondary BA and
neuroprotective agent tauroursodeoxycholate (TUDCA). We further demonstrated the
relevance of TUDCA by demonstrating localization of TGR5, the receptor that mediates
TUDCA’s effects in the retina. In addition, the pharmacological activation of TGR 5
using INT 767 in a second diabetes model prevented development of DR.
Materials and Methods
Animals and intermittent fasting. Male B6.BKS(D) Leprdb/J (stock number:000697)
Homozygous Leprdb/db were diabetic and heterozygous Leprdb/m were used as controls
(denoted as db/db and db/m hereafter). All mice were obtained from The Jackson
Laboratory (Bar Harbor, ME) and housed in the institutional animal care facilities at the
University of Florida (IACUC # 201106420) with strict 12h:12h light: dark cycle; all studies were repeated at Indiana University (IACUC #10604 and #11167). Blood glucose levels were tested every two weeks in db/db mice. Animals were considered as diabetic and used in the IF experiment if the serum glucose level was above 250 mg/dL on two consecutive measurements. The animals were fed ad lib (AL) before the
6 Page 7 of 39 Diabetes
IF was initiated at 4 months of age. The db/m and db/db mice were each divided in two
subgroups, AL feeding (control) and IF, wherein the animals were fasted for 24 hours
every other day for 7 months, beginning at night (Fig. 1A). Glycated hemoglobin was
measured using the A1CNow+ kit (Bayer HealthCare, Sunnyvale, CA) on the day prior
to euthanasia.
Acellular capillaries. The eyes were fixed in 2% formalin and trypsin digest was
performed for analysis of acellular capillaries as previously published (16).
Microbiome analysis. Fecal samples (n=180) were obtained by collection every 4
hours for 48 hours. Genomic DNA was isolated from ~0.1 g using the PowerSoil® DNA
Isolation Kit (MO Bio, Carlsbad, CA), according to manufacturer’s protocol. DNA was
sent to Second Genome, Inc. (South San Francisco, CA) for 16S rRNA gene V4
sequencing with the MiSeq platform. The full report and statistical analysis from
Second Genome is available upon request.
Colon morphometric analysis. Colon tissue was collected at termination of the
experiment, fixed in 10% buffered formalin and paraffin embedded according to
standard procedures at the IUPUI Pathology Core. Ten m sections were cut and
stained by either hematoxylin and eosin or periodic acid Schiff (PAS) for mucin. Entire
sections were scanned with an Axio Scan Z1 (Zeiss) and a 20X objective.
Morphometric analysis of goblet cells, villi length and muscularis thickness was
undertaken using a Lumenera Infinity 1 2C camera and NIS Elements software (Nikon
Instruments, Melville, NY).
Peptidoglycan ELISA. Plasma samples were diluted 1:10 and analyzed for
7 Diabetes Page 8 of 39
peptidoglycan levels using the mouse peptidoglycan ELISA kit (MBS263268,
Mybioscource Inc, San Diego, CA) according to manufacturer’s instructions.
Bile acid analysis. At the termination of the experiment, whole blood was collected by cardiac puncture and transferred in EDTA coated tubes. Plasma was separated and frozen at 80oC. Samples were sent to Metabolon Inc (Morrisville, NC) and global metabolic analysis was performed with their Metabolon Platform using a Waters
ACQUITY ultra performance liquid chromatography (UPLC) and a Thermo Scientific Q
Exactive high resolution/accurate mass spectrometer interfaced with a heated electrospray ionization (HESI II) source and Orbitrap mass analyzer operated at 35,000 mass resolution. The full report and statistical analysis from Metabolon is available upon request.
Quantitative real-time-PCR (qRT-PCR). Total RNA was extracted from mouse retinal tissue using RNeasy Mini kit (Qiagen, Germantown, MD) and reverse transcription was performed according previous published methods (17). Validated primers for Gpbar1
(TGR 5), Tnf, and endogenous controls B2m and Ppia, were purchased from Qiagen and qRT PCR was carried out using SYBR Green (Qiagen).
INT 767 treatment of diabetic animals. 8 week old male DBA2/J mice were obtained from The Jackson Laboratory and maintained on a 12h:12h light:dark cycle. Mice were injected with streptozotocin (STZ; Sigma Aldrich, St. Louis, MO) intraperitoneal [40 mg/kg made freshly in 50 mM sodium citrate buffer, pH 4.5 (vehicle)] for five consecutive days, or with vehicle only. Blood glucose levels were measured 1 week after the last STZ injection and mice with glucose levels >250 mg/dL on two separate
8 Page 9 of 39 Diabetes
days were considered diabetic. DBA/2J mice were fed with a Western diet (WD, 21%
milk fat, 0.2 % cholesterol, TD88137) obtained from Harlan Teklad (Madison, WI) after
diabetes onset in the STZ groups and were treated for 8 weeks with either WD or WD
containing INT 767 (30 mg/kg bw/day) (Intercept Pharmaceuticals, New York, NY)
(18).
Immunofluorescence staining. Enucleated eyes were fixed in 4% paraformaldehyde
overnight at 4oC. The next day, eyes were incubated with 30% sucrose for 48 hours and
then quickly frozen in optical cutting temperature (OCT) compound. Retinal cross
sections (12 m) were cut at 20oC. The following antibodies were used for
immunostaining: rabbit monoclonal to Iba (Wako, 019 19741, 1:500), rat monoclonal to
CD45 (clone 30 F11, R&D Systems, 1:50), rabbit polyclonal to GPCR TGR5 (ABCAM,
ab72608, 1:250), monoclonal murine anti NeuN, (clone A60, EMD Millipore,
MAB377MI, 1:250). Sections were pre incubated with 5% goat serum (Invitrogen) in
PBS for 1h, followed by incubation with primary antibodies (in 1% normal goat serum)
overnight at 4oC. As secondary antibody, Alexa Fluor 488 was used for TGR5 and Iba1+
and Alexa Fluor 594 was used for NeuN and CD45 (Invitrogen). Positive cells were
counted from 3 5 sections at 100 m intervals for each eye, on at least four images per
section at 20X magnification. Retinal sections were imaged using a confocal scanning
laser microscope (ZEISS LSM 700 confocal microscope system with Axio Observer; Carl
Zeiss Mditec, Jena, Germany) and colocalization was analyzed by using Zen lite
software.
Statistical analysis. Survival curve was plotted by Kaplan Meier method and the
significance of the differences was determined by log rank test. The differences in gene
expression, metabolites and acellular capillaries were analyzed by two way ANOVA
followed by Tukey’s honest significant difference post hoc test (GraphPad Prism, La
9 Diabetes Page 10 of 39
Jolla, CA). Statistical analysis methods for microbiome and metabolites are available
upon request.
Results
IF improved survival without impacting glycated hemoglobin levels.
Four month old db/m and db/db mice were fed either an ad lib diet (AL) or were
fasted every other 24 hour interval (IF) for 7 months (Fig. 1A). Glycated hemoglobin
levels were not affected by the IF regimen and remained higher in both db/db cohorts
compared to controls (Fig. 1B). However, despite no change in glycated hemoglobin,
the survival rate increased significantly in db/db mice on the IF regimen (db/db IF)
compared to db/db mice on AL feeding (db/db AL) (Fig. 1C).
IF prevented development of acellular capillaries and infiltration of inflammatory
cells in the retina of db/db mice
We next examined the impact of IF on development of DR in db/db mice by
enumerating acellular capillaries (Fig. 2A-D)(19). As expected, db/db AL showed
increased numbers of acellular capillaries (Fig. 2C) compared to age matched db/m
AL (Fig. 2A). However, db/db IF mice (Fig. 2D, E) did not demonstrate an increase in
acellular capillaries. Since increases in retinal levels of inflammatory cells are another
feature of DR, cryosections were examined for changes in activated microglia using
Iba 1 staining and differences in infiltrating hematopoietic cells using CD45 staining.
Db/db AL mice showed a significant increase in Iba 1+ cells compared to db/m AL mice and db/db IF mice (Fig. 2F). CD45+ cell infiltration was significantly increased in the db/db AL mice compared to the db/m AL mice and db/db IF mice (Fig. 2G). Overall, IF protected db/db mice from developing histological features of DR.
10 Page 11 of 39 Diabetes
Long-term IF altered the gut microbiome composition
The composition and diversity of the microbiome is integrated with the pathophysiology
of diabetes. For example, certain bacteria are shown to induce diabetes (20), while
others alleviate it (21).Thus, we hypothesized that the beneficial effects of IF on DR
would, at least in part, be mediated through changes in microbiota composition. Fecal
samples were collected and genomic DNA sequencing was used to discover and
distinguish various bacterial taxa. A total of 1,363 filtered operational taxonomic units
(OTUs) were detected. Sample richness and Shannon diversity (Fig. S1A-F) were not
changed by the implementation of IF but beta diversity, a measure of the between
sample diversity, showed clear differences between db/db AL and db/m AL mice (Fig.
3A), db/m IF vs. db/m AL (Fig. 3B), and db/db IF vs. db/db AL (Fig. 3C). Principal
component analysis of bacteria also demonstrated a clear separation of the four main
groups (db/m AL, db/db AL, db/m IF and db/db IF). The AL groups were closer to each
other while the IF groups diverged more (Fig. 3D). This evidence suggests that IF
restructured a distinct microbiome in the diabetic compared to control mice.
The most represented phyla were Bacteroidetes, Firmicutes, Verrucomicrobia,
Tenericutes, Actinobacteria, and Proteobacteria. The relative proportions of these varied
dramatically among the experimental groups but the majority of bacteria belonged to the
Bacteroidetes, Firmicutes and Verrucomicrobia phyla (Fig. 3E). The ratio of
Firmicutes/Bacteroidetes (F/B) has been widely used as an indicator of changes in the
microbiome with obesity (22). The F/B ratio was not altered in the db/m IF mice (Fig.
3E); however, it was dramatically increased in the db/db IF mice due to a significant
increase of Firmicutes and a significant reduction of Bacteroidetes (Fig. 3E). Apart from
changes observed in the F/B ratio, Verrucomicrobia also changed significantly with IF
11 Diabetes Page 12 of 39
showing an increase in db/m IF and a reduction in db/db IF (Fig. 3E). Overall, these data indicate that IF resulted in a more dramatic restructuring of the microbiota composition in the diabetic mice with a significant expansion of Firmicutes at the
expense of Bacteroidetes and Verrucomicrobia.
To understand how the microbiota composition was altered at the genus level,
the differential taxa in each group were assessed (Fig. 4A-C). Among the differential taxa between db/m AL and db/db AL cohorts, the db/db AL mice were enriched in
Lactobacillus, Bifidobacterium, Bacteroides and Akkermansia, and were deficit in species of Oscillospira, and Ruminococcus (Fig. 4A). The IF regimen in the db/db mice caused enrichment of species of the genus Lactobacillus, Oscillospira and
Ruminococcus and, reduction of species of the genus Akkermansia, Bacteroides and
Bifidobacterium (Fig. 4B-C). Interestingly, the difference in the microbiota between fed vs. fasting state in the IF cohorts was very small, limited to only 24 bacteria in db/db IF mice and 69 bacteria in db/m IF mice (Fig. S2. A-B). Yet, we identified certain common species that were increased by fasting in both db/m IF and db/db IF cohorts, mostly species in the Lachnospiraceae and Ruminococcaceae families with representatives from Oscillospira and Ruminococcus genera. On the other hand, species of the genus
Bifidobacterium were commonly reduced by the IF diet. A list of the common and unique enriched bacterial species is shown in Fig. S2C and the common or uniquely reduced bacteria is shown in Fig. S2D.
IF is known to influence diurnal rhythms and selected microbes in the gut exhibit diurnal changes (23; 24). We investigated which taxa displayed diurnal patterns in healthy db/m AL mice and whether these taxa were affected by either the presence of diabetes or IF (25) . Surprisingly, we found only 34 OTUs showing diurnal cycling.
Among these were members of Lachnospiraceae and Ruminococcaceae families and
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specifically, species of Oscillospira and Ruminococcus. Examples of bacteria with
diurnal regulation is shown in Fig. S3.
Although there were few taxa differentially regulated by feeding / fasting or
diurnal cycling, the evolutionary pressure of fasting and refeeding in combination with
the host metabolic response to fasting led to the distinct restructuring of the microbiota in
the db/m IF and the db/db IF mice. Members of the order Clostridiales (Fig. 4D) and
members of the Ruminococcaceae (Fig. 4E) and Lachnospiraceae families (Fig. 4F)
were uniquely equipped to adapt, expand and survive under IF conditions in db/db but
not in db/m mice. Lactobacillus (Fig. 4G) was enriched in both db/m IF and db/db IF,
while the genus Bifidobacterium (Fig. 4H) and Clostridium (Fig. 4I) was reduced in both
groups. On the other hand, Oscillospira (Fig. 4J), Ruminococcus (Fig. 4K), and
Turicibacter (Fig. 4L) were enriched specifically in the db/db IF, while Bacteroides (Fig.
4M), Akkermansia (Fig. 4N) and Allobaculum (Fig. 4O) were reduced only in the db/db
IF mice. We reasoned that these unique changes in the microflora of the db/db IF mice
could promote the integrity of the gut barrier.
IF promoted restoration of diabetes-induced gut pathology
As disruption of the gut barrier integrity is an important mechanism of disease,
we examined whether IF beneficially impacted colon morphology as measured by goblet
cell number and villi length (Fig. 5). Db/db AL had reduced numbers of goblet cells (Fig.
5 B, E) and reduced villi length (Fig. 5G, J). However, IF resulted in preservation of
colon morphology in the db/db IF mice, as goblet cell number (Fig. 5D, E) and villi length
(Fig.5I, J) were similar to nondiabetic mice. No changes with diabetes or IF were
observed in the thickness of the muscularis (Fig. 5K-O).
13 Diabetes Page 14 of 39
Increases in bacterial products in plasma are indicative of intestinal barrier dysfunction or disruption. We measured peptidoglycan in plasma samples from our cohorts. As shown in Fig. 5P, db/db AL demonstrated a significant increase in plasma peptidoglycan levels supportive of increased gut permeability. IF resulted in a decrease in gut permeability when db/db IF mice were in the fasted state as there was a significant reduction of circulating peptidoglycan levels. However, when the db/db IF mice were in the fed state this potentially beneficial change was lost.
IF altered bile acid metabolism
We next tested whether the microbial changes induced by IF could generate
beneficial metabolites. Bile acids are known to alter the gut microbiome composition
(26), and conversely, the microbiome is known to alter the bile acid pool (27). Thus, we
reasoned that the observed microbiome changes induced by IF would impact BA
metabolism in the db/db mice. Metabolic analysis of plasma from the experimental
cohorts identified the primary BA cholate but not chenodeoxycholate. A list of bile acids
found in the plasma is shown in Table S1.
Cholate was increased in the db/db AL mice compared to db/m AL mice and was
reduced in the db/db IF mice (Fig. 6A). Deoxycholate, the secondary BA that is
produced by the activities of 7α dehydroxylase, an enzyme found in intestinal bacteria,
was moderately increased with IF especially during the fasting period (Fig. 6B)
supporting intestinal transformation of BAs. However, the ratio of conjugated to
unconjugated BA was increased in the db/db IF mice compared to db/m IF during
feeding, suggesting that there may be a reduction of bacteria that express bile salt
hydrolase (BSH) activity required to deconjugate primary and secondary bile acids in the
db/db IF mice. A significant reduction of Bifidobacterium (Fig. 4H), Clostridium (Fig 4I)
14 Page 15 of 39 Diabetes
and Bacteroides (Fig. 4M) was observed and these bacteria are known to express BSH,
which may be responsible, in part, for these observed changes in levels of conjugated to
unconjugated secondary BAs.
An increase in BA products indicative of 7α and 7β hydroxysteroid
dehydrogenase (HSDH) enzymatic activity was also found. Taurochenodeoxycholate
(TCDCA) was identified and significantly increased in the db/db IF mice (Fig. 6C).
TCDCA is converted to TUDCA by the actions of both 7α and 7β HSDH (28) and
TUDCA was significantly increased in the plasma of db/db IF mice (Fig. 6D). As taurine
conjugated secondary bile acids are potent activators of TGR5 (29), we hypothesize that
the beneficial effects we observed following IF were due to an increase in TUDCA
synthesis in the gut.
TGR5 activation reduces acellular capillaries and inflammation in the diabetic
retina.
To determine whether TUDCA could mediate beneficial effects in the retina, we
examined TGR5 expression. TGR5 was localized primarily to the ganglion cell layer of
the retina (Fig. 7A). Neither diabetes nor the IF regimen changed the localization or
expression of TGR5 protein (Fig. 7B-F) as assessed by immunohistochemistry nor
changed TGR5 mRNA levels (Fig. 7G). However, TNF α, a downstream target of TGR5
was reduced significantly by IF in the diabetic cohort (Fig. 7H). To further support the
role of retinal TGR 5, we asked if pharmacological activation of TGR5 would prevent DR.
Previously, we characterized the novel highly potent semi synthetic bile acid derivative
and dual TGR5/FXR agonist INT 767 (18). INT 767 was tested in a second diabetes
model, the DBA/2J mice treated with streptozotocin and placed on a high fat diet. This
model exhibits a more rapid progression of the vascular and inflammatory endpoints
15 Diabetes Page 16 of 39
associated with DR (30) and diabetic nephropathy (31), and replicates the natural history
and metabolic characteristics of human metabolic syndrome and Type 2 diabetes (31;
32). INT 767 treatment of diabetic mice resulted in a marked reduction in the number of
acellular capillaries (Fig. 7C) and reduced numbers of CD11b+ macrophages (Fig. 7D),
CD45+ leukocytes (Fig. 7E) and activated Iba1+ microglia within the retina (Fig. 7F).
Discussion
The salient findings of this study suggest that restructuring of the microbiome by IF favors generation of secondary BAs, such as TUDCA, eliciting protective effects in the retina (Fig. 8). Development of acellular capillaries, activation of retinal microglia
and infiltration of peripheral immune cells into the retina were all reduced by IF in the
db/db mice. IF produced a reconfiguration of the microbiota in the db/db mice resulting
in an enrichment in Firmicutes and a reduction in Bacteroides. Interestingly, this was
not observed for db/m mice undergoing IF, indicating that the diabetic
microenvironment benefited Firmicutes expansion. High F/B ratio has been associated
with healthy obesity (33; 34) and increased capacity for harvesting energy (22). In
addition, a high F/B ratio has been implicated in regulating fat storage (35) and is
consistent with our findings wherein IF did not change the weight of the db/db mice but
promoted a healthier obese phenotype with increased longevity.
Another example of a healthy obese phenotype associated with expansion of
Firmicutes is the case of black bears. During the summer when active and feeding,
these bears experience a healthy weight gain and an expansion of Firmicutes (36). Our
study provides support for the clinical observation that some obese individuals are
metabolically healthy (ranging from 3 to 57%, depending on the study) (13) and may
have a gut microbiota structure that contributes to this beneficial outcome.
16 Page 17 of 39 Diabetes
BAs are significant modulators of the gut microbiome as they exhibit antimicrobial
activities and can inhibit bacteria outgrowth (37). Members of the Firmicutes family are
expanded in high BA conditions, while members of Bacteroides and Actinobacteria
(Bifidobacterium) are reduced (26). This finding is particularly relevant, as it appears that
the db/db AL mice have increased cholate compared to db/m AL. The altered BA
metabolism observed in the diabetic mouse effectively drove the restructuring of the
diabetic microbiota towards the unique composition with high F/B ratio.
The observed changes in the gut microbiome of db/db IF mice were deemed
advantageous for gut and intestinal barrier integrity. We utilized plasma peptidoglycan as
a surrogate for gut barrier function. Peptidoglycan levels were increased in diabetes and
the IF diet reduced the levels during fasting. This suggests that gut barrier integrity is
compromised by diabetes and that the IF regimen (during the fasting day) can improve
it. Our results are in agreement with a recent study demonstrated that hyperglycemia
drives the increased intestinal permeability observed in diabetes (38). In our model,
when diabetic mice are under IF, glucose remains elevated during the feeding day of the
IF regimen, but is reduced during the fasting phase of the IF regimen. Furthermore, the
composition of the leaked bacteria maybe important in driving the low grade systemic
inflammation in diabetes (39). Peptidoglycan is a component of gram positive bacteria
and endotoxin is mostly derived from gram negative bacteria. Firmicutes are gram
positive, while Bacteroides are gram negative (40). Thus, our results suggest that the
reduction of peptidoglycan occurring in the db/db mouse on the IF regimen signals a
transient restoration/improvement in gut integrity and could also lead to a transient
reduction in endotoxemia. However, this remains to be tested.
In diabetes, cholesterol and BA metabolism is altered (41). Thus, we focused our
efforts in understanding how remodeling of the microbiota by IF affected
17 Diabetes Page 18 of 39
biotransformation of the BA pool. Primary BAs are synthesized from cholesterol in the liver and act as detergents for the emulsification of dietary fats and as endocrine hormones for the regulation of glucose and lipid metabolism (42; 43). Conjugated or unconjugated primary BAs enter the gallbladder and are released in the small intestine, where BA are structurally modified into various secondary BAs that are less toxic to the host and can exert multiple hormonal regulatory roles (44). α HSDH and 7β HSDH are two key enzymes that drive the efficient biosynthesis of secondary BAs such as TUDCA from taurochenodeoxycholate (TCDCA) (28). Thus, the change in microbiome composition affects the enzymatic activity of these pathways and alters the BA pool.
Of interest is the observation that bear bile has long been used in Chinese traditional medicine for the treatment of liver, gallbladder, heart and eye problems.
Bear bile is highly enriched in TUDCA (45). Recently the bear gut microbiome was used to isolated new, undiscovered 7β HSDH enzymes for more efficient production of
TUDCA (28). Little is known regarding the protective effects of TUDCA in the diabetic retina (46 52). However, this hydrophilic BA can cross the blood brain barrier, and likely can cross the blood retinal barrier. In support of the ability of circulating TUDCA to reach the retina and act on retinal cells are the observations that TUDCA can inhibit photoreceptor apoptosis (47; 48; 51), improve retinal pigment epithelial function (49), inhibit proliferation of human retinal endothelial cells (53), promote the recruitment of endothelial progenitor cells (54) and protect against DR (53). At the molecular level,
TUDCA has been shown to inhibit apoptosis through reduction of endoplasmic reticulum stress (ER) (55) and modulation of the unfolded protein response (56).
TUDCA also inhibits mitochondrial membrane depolarization (57). Receptor activation of TGR5 by TUDCA may also decrease the production of pro inflammatory cytokines and induce anti inflammatory responses (58). Although TGR5 protein expression did not change with IF treatment, our studies would support that TGR5 activation is central
18 Page 19 of 39 Diabetes
to the retinal protection observed, possible through eliciting anti inflammatory effects,
as INT 767 effectively prevented DR and reduced infiltration of inflammatory cells in
the retina of STZ/WD mice. While INT 767 is known as a dual agonist of TGR5 and
FXR, the absence of FXR in the retina (59) allowed us to utilize this highly potent agent
as a selective TGR5 agonist in the retina. However, additional studies will be required
to establish a direct link between TGR5 activation and IF.
Our study has certain limitations that must be acknowledged. First, fecal rather
than mucosal bacterial samples were utilized. As such, our analysis may not be
representative of the entire microbiome. However, fecal sampling is typically used in
human studies. Second, we used plasma samples rather than cecum samples for the
analysis of metabolites. Our intention was to identify microbial metabolites that could be
found in peripheral circulation. Specifically, those that could target the retina. Third, we
used the db/db model as a model of type 2 diabetes to study the IF effects on
microbiota. The STZ/WD model of diabetes was used to establish the link between
TGR5 activation and prevention of DR. The STZ/WD represents an accelerated model of
DR that histologically replicates the DR seen in other diabetes models (30) and also has
the natural history and metabolic characteristics of human metabolic syndrome and Type
2 diabetes (31; 32). Finally, although we show an association between increases in
TUDCA and prevention of retinopathy, additional studies using antibiotics or fecal
transfers will be required to demonstrate this conclusively.
In sum, our study shows a novel relationship between changes in the microbiota,
IF and increases in TUDCA. IF likely provides a way to change BA metabolism in favor
of increasing the endogenous generation of TUDCA (Fig. 8). This work highlights
several findings with potential clinical significance. Diet interventions, such as IF, in
diabetes may result in a unique and highly beneficial microbiome response. IF
19 Diabetes Page 20 of 39
represents a physiological way to enhance production of the neuroprotective TUDCA.
Finally, we identify a role for TGR5 and bile acid signaling in the retina that, to our
knowledge, was not previously known and may represent a new therapeutic target for
DR.
Acknowledgements: Studies were supported by the NIH (EY07739, EY12601,
EY025383, EY023629 and HL110170) to MBG, DK093954 to CEM and by the JDRF (3
APF 2017 396 A N) to EB. We would like to thank Tatiana E. Salazar, Dung V. Nguyen,
James M. Dominguez II, Matthew Richardson, Shakir Hasan Hindi, Harkeerat Dhami,
Jared Andre Smith, Joshua Hayes Thomas, Emily Francesca Hutchinson for their technical help with the animal experiments.
Author Contributions: EB, YY, JVB and MBG designed experiments. EB, YY, LM,
CPV, YD and XXW performed experiments. EB, YY, LM, LC, CNR, DM, EGM and FC analyzed the data. EB, YY, LM, FAW, HD, GYO, RGM, QL, ML, MEB, JVB, and MBG discussed the results and interpreted the data. EB, YY, LM, GYO, LA, CEM, FAW,
MEB, MCY, ML and MBG wrote and revised the paper. Dr. Maria B. Grant is the guarantor of this work and, as such, had full access to all the data in the study and takes responsibility for the integrity of the data and the accuracy of the data analysis.
LC, RNC, and DM are associated with Second Genome Inc. LA is associated with
Intercept Pharmaceuticals. No conflict of interest is declared by the rest of the authors.
20 Page 21 of 39 Diabetes
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Fig. 1. IF feeding prolonged survival of db/db mice without improving glycemic control. (A) Depiction of the experimental protocol with the treatment groups. Mice were put on IF diet at 4 months of age and were held of IF for 7 months. ZT represents zeitgeber time, i.e. time when the lights went on. (B) Glycated hemoglobin levels in db/db-AL and db/m- IF mice were higher compared to db/m mice, however, IF did not correct glycated hemoglobin levels. Data represent means ± SEM (n=6-39); *p < 0.05. (C) Db/db-AL had reduced survival compared to db/m mice. IF improved survival rate of db/db-IF mice to that of normal controls; *p < 0.05.
126x138mm (300 x 300 DPI)
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Fig. 3. IF resulted in distinct changes in the microbiota of db/db mice. (A-C). Beta diversity (Presence/Absence Ordination): Dimensional reduction of the Jaccard distance on presence/absence OTU table, using the PCoA ordination method. (A) db/m-AL (green circles) vs. db/db-AL(blue triangles); samples separated along Axis 2 according to sample type. (B) db/m-AL (green triangles) vs. db/m IF (feeding: red triangles; fasting: red circles); samples separated along axis 2 according to type, which indicated that samples from IF and AL treatments have different incidences of OTUs. (C) db/db-AL (blue triangles) vs. db/db-IF (feeding: purple triangles; fasting: purple circles); samples separated along axis 1 according to type. Samples from db/d-AL had a smaller degree of variation in microbiome beta diversity than db/db-IF samples. p=0.001 in all comparisons; n=5 mice/group. (D) Principal component analysis of microbiota. Relative abundance of bacteria present in at least 10% of the samples (n = 180) was used. Samples grouped by both disease (spheres: db/m; cubes: db/db) and treatment (blue: ad lib; red: IF), but less clearly by feeding/fasting within the IF groups. Note that the AL cohorts are closer to each other, while the IF cohorts were much further apart, suggesting bigger differences in the microbiomes of db/m-IF and db/db- IF. (E) Relative proportions of the 6 most abundant phyla (the aggregate relative abundance for each of the phyla not represented here is less than 0.1). F/B: ratio is Firmicutes: Bacteroidetes. a p < 0.01 vs db/m-AL; b p < 0.001 vs db/m-AL; c p < 0.05 vs. db/db-AL; d p < 0.001 vs. db/db-AL; e p < 0.05 vs. db/m IF (feeding); f p < 0.001 vs. db/m IF (feeding); g p < 0.05 vs. db/db IF (fasting). All OTUs (detected in at least 10% of the samples) from all samples per each group were used, irrespective of ZT.
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Fig. 4. IF effects on microbiome composition at the genus level. (A) Genera that show statistically significant changes in db/db AL vs db/m AL mice. (B) Genera that were statistically different in db/db IF vs db/m IF in the feeding phase. (C) Genera that were statistically different in db/db IF vs db/m IF in the fasting phase. Points are OTUs belonging to each respective genus. Features were considered significant if FDR corrected p value ≤ 0.05, and the absolute value of Log 2 Fold Change ≥ 1. (D O). Time course of relative bacterial abundances of selected taxa during the 48hr cycle of IF regimen are shown. ZT represents zeitgeber time, i.e. time when the lights went on. Diurnal oscillations in selected taxa and IF effects: (D) Order Clostridiales; (E) Family Ruminococcus; (F) Family Lachnospiraceae; (G) Genus Lactobacillus; (H) Genus Bifidobacterium; (I) Genus Clostridium; (J) Genus Oscillospira; (K) Genus Ruminoccocus; (L) Genus Turicibacter; (M) Genus Bacteroides; (N) Genus Akkermansia; (O) Genus Allobaculum. Data are sums of relative abundance of all OTUs within each genus ± SEM.
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Fig. 5. IF treatment affected gut morphology. (A D) PAS staining showing mucin positive goblet cells. Diabetes reduced goblet cell number, but IF prevented this reduction. (E) Quantification of goblet positive cells. Data represent number of goblet cells/villus. *p < 0.05, n=13 30 villi group. (F I) Hematoxylin and eosin staining of colon. The length of villi was reduced significantly with diabetes but IF restored levels to normal. (J) Quantification of the villi length, in pixels. *p < 0.05, n=17 26 villi/group. (K N) Hematoxylin and eosin staining of the muscularis layer. (O) Quantification of the muscularis width. 20 measurements distributed evenly were done on each section, and data represent averages per section in pixels, ± SD; n=4 7 sections/group. In all cases, one section per mouse was analyzed. Magnifications bars: 50 µm. (P) Peptidoglycan, a component of bacterial cell wall, was measured in plasma from the cohorts using ELISA. Data represent means ± SD (n=3 5). p<0.05. Two Way ANOVA.
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Fig. 6. IF enhanced bile acid metabolism with increases in the neuroprotective bile acid TUCDA. Analysis of plasma samples for bile acid metabolism (A) cholate (B) deoxycholate (C) Taurochenodeoxycholate (TCDCA) was identified and significantly increased with IF diet in the db/db IF mice. TCDCA is converted to Tauroursodeoxycholate (TUDCA) by the actions of both 7α and 7β HSDH. (D) TUDCA was significantly increased in the plasma of db/db IF mice. Data represent means ± SD (n=4 6). p<0.05. Two Way ANOVA.
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Fig. 8. A model for IF-induced changes in the microbiome and their potential impact on development of DR. In diabetic mice, IF resulted in significant expansion of bacteria of the Firmicutes phylum that metabolize primary bile acids to secondary bile acids, such as TUDCA. TUDCA then, enters the circulation and crosses the blood retina barrier, targets its receptor, TGR5 in the ganglion cell layer and protects against retinal neurodegeneration and inflammation.
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A Control-IF B Diabetes-IF Feedin Fastin Feedin Fastin
94otu43063 Phylum g g g g Bacteroidetes 94otu1346 Firmicutes
94otu15334
94otu6138
94otu9254
u
n 94otu15375
u
e n 94otu31487
G
s
e
G s 94otu43360
94otu13105
94otu4147
Allobaculum
94otu17070
unclassified
−5.0 −2.5 0.0 2.5 log 2 fold change of D-IF fasting vs.D-IF feeding
Fig. S2. Microbiome analysis. (A) Genera that show statistically significant changes in db/m-IF fasting vs db/m-IF feeding mice. (B) Genera that show statistically significant changes in db/db-IF fasting vs db/db-IF feeding mice. Diabetes Page 38 of 39
Enriched OTUs class order family genus (#OTUs) C Bacteroidia Bacteroidales S24-7 unclassified (2) Coprococcus (2) db/m IF fasting db/db IF feeding Lachnospiraceae unclassified (2) Clostridia Clostridiales Oscillospira (5) Ruminococcaceae Ruminococcus(2) unclassified unclassified(4) Erysipelotrichi Erysipelotrichales Erysipelotrichaceae unclassified(3) Mollicutes RF39 unclassified unclassified (1)
class order family genus (#OTUs) Bacteroidia Bacteroidales S24-7 unclassified (9) Dorea (1) Lachnospiraceae unclassified (14) Oscillospira (7) Clostridia Clostridiales db/m IF fasting db/db IF fasting Ruminococcaceae Ruminococcus (6) unclassified (11) unclassified unclassified (52) class order family genus (#OTUs) Erysipelotrichi Erysipelotrichales Erysipelotrichaceae unclassified (1) Dehalobacteriaceae Dehalobacterium (1) Mollicutes RF39 unclassified unclassified (1) Lachnospiraceae unclassified (6) unclassified (3) class order family genus (#OTUs) Clostridia Clostridiales Ruminococcaceae Oscillospira (7) Lachnospiraceae unclassified (3) Ruminococcus (1) Clostridia Clostridiales Ruminococcaceae unclassified (4) unclassified unclassified (14) unclassified(17) unclassified(17) Erysipelotrichi Erysipelotrichales Erysipelotrichaceae Coprobacillus (1) Mollicutes RF39 unclassified unclassified (1) Mollicutes RF39 unclassified unclassified (1)
Continuing Fig. S2. Microbiome analysis. (C). Venn diagram for the comparison of lists of bacterial species that were significantly increased from their perspective AL diets in order to identify commonly or uniquely increased OTUs among the four data sets of db/m-IF feeding, db/m- IF fasting, db/db-IF feeding, and db/db-IF fasting. Page 39 of 39 Diabetes D class order family genus (#OTUs) Decreased OTUs Actinomycetales Micrococcaceae unclassified (1) Actinobacteria Bifidobacteriales Bifidobacteriaceae Bifidobacterium (1) db/m IF fasting db/db IF feeding Alphaproteobacteria Caulobacterales Caulobacteraceae unclassified (1) Bacteroidia Bacteroidales S24-7 unclassified (3) Clostridiaceae unclassified (1) Clostridia Clostridiales Ruminococcaceae unclassified (2) unclassified unclassified (11) Enterobacteriales Enterobacteriaceae unclassified (1) Gammaproteobacteria Pseudomonadales Moraxellaceae Acinetobacter (1) Mollicutes RF39 unclassified unclassified (1) class order family genus (#OTUs) Actinomycetales Corynebacteriaceae Corynebacterium (1) Actinobacteria Bifidobacteriales Bifidobacteriaceae Bifidobacterium (2) Bacilli Turicibacterales Turicibacteraceae Turicibacter (1) Bacteroidaceae Bacteroides (15) Bacteroidia Bacteroidales S24-7 unclassified (35) Clostridiaceae unclassified (2) db/m IF fasting db/db IF fasting Lachnospiraceae unclassified (6) Peptostreptococcaceae unclassified (1) Clostridia Clostridiales Oscillospira (3) Ruminococcaceae Ruminococcus (3) class order family genus (#OTUs) unclassified (1) Actinobacteria Bifidobacteriales Bifidobacteriaceae Bifidobacterium (6) unclassified unclassified (25) Adlercreutzia (3) Bacteroidia Bacteroidales S24-7 unclassified (1) Coriobacteriia Coriobacteriales Coriobacteriaceae Clostridiaceae unclassified (3) unclassified (1) Erysipelotrichi Erysipelotrichales Erysipelotrichaceae Allobaculum (7) Dorea (1) Lachnospiraceae Gammaproteobacteria Enterobacteriales Enterobacteriaceae unclassified (2) unclassified (4) Mollicutes RF39 unclassified unclassified (4) Clostridia Clostridiales Oscillospira Verrucomicrobiae Verrucomicrobiales Verrucomicrobiaceae Akkermansia (3) Ruminococcaceae Ruminococcus (3) unclassified (2) class order family genus (#OTUs) Prevotellaceae Prevotella (1) unclassified unclassified (11) Bacteroidia Bacteroidales Mollicutes RF39 unclassified unclassified (1) S24-7 unclassified (3) Christensenellaceae unclassified (1) Dorea (1) Lachnospiraceae Clostridia Clostridiales unclassified (1) Ruminococcaceae unclassified (5) unclassified unclassified (3) Coriobacteriia Coriobacteriales Coriobacteriaceae unclassified (1) Verrucomicrobiae Verrucomicrobiales Verrucomicrobiaceae Akkermansia (4)
Continuing Fig. S2. Microbiome analysis. (D) Venn diagram for the comparison of lists of bacteria that were significantly decreased from their perspective AL diets in order to identify commonly or uniquely decreased OTUs . Diabetes Page 40 of 39 G.Bifidobacterium OTU 1259 G.Sutterella OTU 378 A Intermittent Fasting B Intermittent Fasting Ad-lib Feeding Fasting Ad-lib Feeding Fasting 3.00E-05 1.60E-05
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r 1.00E-05 e r 4.00E-06 5.00E-06 2.00E-06
0.00E+00 0.00E+00 12 16 20 0 4 8 12 16 20 0 4 8 12 16 20 0 4 8 12 16 20 0 4 8 12 12 16 20 0 4 8 12 16 20 0 4 8 12 16 20 0 4 8 12 16 20 0 4 8 12 ZT time ZT time
db/m-AL db/db-AL db/m-IF db/db-IF db/m-AL db/db-AL db/m-IF db/db-IF
C D G.Coprococcus OTU 182 G.Oscillospira OTU 13 Intermittent Fasting Intermittent Fasting Ad-lib Feeding Fasting Ad-lib Feeding Fasting
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e 3.50E-04
2.50E-02 c e n c
a 3.00E-04 n d
a 2.00E-02 u d b 2.50E-04 a u
e b v
a 1.50E-02
i 2.00E-04
t e a l v e 1.50E-04 i r t 1.00E-02 a l
1.00E-04 e r 5.00E-05 5.00E-03 0.00E+00 0.00E+00 12 16 20 0 4 8 12 16 20 0 4 8 12 16 20 0 4 8 12 16 20 0 4 8 12 121620 0 4 8 121620 0 4 8 121620 0 4 8 121620 0 4 8 12 ZT time ZT time db/m-AL db/db-AL db/m-IF db/db-IF db/m-AL db/db-AL db/m-IF db/db-IF
Fig. S3. Examples of OTUs that show diurnal variations in the AL regimen. The JTK cycle was used to examine whether a circadian rhythm exists for relative abundance of bacteria in db/m and db/db for each OTU. The comparison of the pattern in relative abundance among (db/m IF, db/db-IF), (db/m-AL, db/db-AL), (db/m-IF, db/m-AL), and (db/db-IF, db/db-AL) was conducted using Kolmogorov–Smirnov test, a nonparametric test of equality of probability distribution. (A) c_Actinobacteria, o_Bifidobacteriales, f_Bifidobacteriaceae, g_Bifidobacterium, s_unclassified_otu1259. (B) c_Betaproteobacteria, o_Burkholderiales, f_Alcaligenaceae, g_Sutterella, s_97otu21533_otu378. (B) c_Clostridia, o_Clostridiales, f_ Lachnospiraceae, g_ Coprococcus, s_unclassified_otu182. (C) c_Clostridia, o_Clostridiales, f_ Ruminococcaceae, g_Oscillospira, s_97otu13166_otu13. Green lines: db/m-AL; blue lines: db/db-AL; red lines: db/m-IF; purple lines: db/db-IF. c_(class), o_(order), f_(family), g_(genus), s_(species). Page 41 of 39 Diabetes
T a ble 1S . Analysis of bile acid content in plasma interm itte nt fa sting Ad-libitum fee ding fa sting bile a cids
db/m (n=4) db/db (n= 4) db/m (n= 5 ) db/db (n= 4-5 ) db/m (n= 5) db/db (n=4 -6) cholate (CA) 0.166 ± 0.04 11.986 ± 6.3 2.840 ± 1.69 3.923 ± 1.71 4.001 ± 1.96 4.851 ± 1.53 alpha-muricholate (α-MCA) 0.192 ± 0.00 1.173 ± 0.62 1.025 ± 0.55 0.920 ± 0.37 3.374 ± 1.90 1.396 ± 0.48 unconjug ated beta-muricholate (β-MCA) 0.560 ± 0.13 2.120 ± 1.55 2.275 ± 0.96 1.656 ± 0.67 7.648 ± 3.51 4.304 ± 2.23 a ll prim a ry unconjug a ted bile a cids: 0.91 7 ± 0.1 6 15 .279 ± 7 .73 6.1 40 ± 3.17 6.49 9 ± 2.6 9 15 .023 ± 7 .35 10.551 ± 4.03 glycocholate (GCA) 0.257 ± 0.10 2.376 ± 1.16 0.438 ± 0.18 2.146 ± 0.60 0.355 ± 0.13 0.941 ± 0.38 Prim a ry bile a cids tauro-beta-muricholate (Tβ-MCA) 0.809 ± 0.20 2.243 ± 0.73 0.418 ± 0.15 5.927 ± 2.47 0.576 ± 0.15 3.405 ± 1.17 conjug a ted taurochenodeoxycholate (TCDCA) 0.697 ± 0.12 1.776 ± 0.52 0.556 ± 0.19 8.961 ± 4.54 0.482 ± 0.11 3.092 ± 1.19 taurocholate (TCA) 0.382 ± 0.16 4.868 ± 1.79 0.661 ± 0.27 4.545 ± 1.41 0.426 ± 0.14 2.076 ± 0.60 a ll prim a ry conjug a ted bile acids: 2.14 4 ± 0.5 6 11 .264 ± 3 .70 2.0 72 ± 0.75 1 9.93 6 ± 8.3 0 1 .839 ± 0 .49 9.5 15 ± 2.86 a ll prim a ry bile a cids (conjug a ted a nd unconjug a ted): 3.06 2 ± 0.5 5 26 .543 ± 1 1.1 3 8.2 12 ± 3.74 2 6.43 5 ± 7.9 1 16 .862 ± 7 .78 20.0 66 ± 6.45 12-dehydrocholate (12-DCA) 0.053 ± 0.00 0.237 ± 0.11 2.663 ± 1.50 0.686 ± 0.44 9.607 ± 4.09 0.435 ± 0.23 3-dehydrocholate (3-DCA) 0.097 ± 0.00 1.103 ± 0.59 0.892 ± 0.55 0.523 ± 0.27 1.711 ± 0.80 0.456 ± 0.18 7-ketodeoxycholate (7KDCA) 0.563 ± 0.15 0.974 ± 0.54 5.309 ± 2.89 0.794 ± 0.43 14.628 ± 6.52 2.096 ± 1.25 unconjug atedd eoxycholate (DCA) 0.209 ± 0.03 0.876 ± 0.27 0.906 ± 0.37 1.524 ± 0.22 2.374 ± 0.96 2.387 ± 0.72 ursocholate (UCA) 0.141 ± 0.02 1.228 ± 0.64 1.852 ± 0.92 0.582 ± 0.25 3.773 ± 1.46 0.862 ± 0.28 ursodeoxycholate (UDCA) 0.544 ± 0.14 0.631 ± 0.28 3.104 ± 1.26 0.928 ± 0.41 6.770 ± 3.40 2.196 ± 0.97 S econda ry bile a cid a ll se conda ry unconjug a te d bile a cids : 1.60 6 ± 0.2 7 5 .048 ± 2 .32 14.7 27 ± 7.38 5.03 8 ± 1.8 3 38 .863 ± 1 6.9 3 8.4 31 ± 3.13 taurodeoxycholate (TDCA) 0.265 ± 0.09 1.737 ± 0.65 0.415 ± 0.20 1.946 ± 0.39 0.765 ± 0.22 2.280 ± 0.59 taurohyodeoxycholic acid (THDCA) 0.638 ± 0.19 2.990 ± 1.25 0.465 ± 0.15 1.717 ± 0.46 0.687 ± 0.27 2.805 ± 1.09 conjug a ted tauroursodeoxycholate (TUDCA) 1.024 ± 0.16 0.913 ± 0.26 0.645 ± 0.10 2.668 ± 1.02 0.664 ± 0.18 2.063 ± 0.28 a ll se conda ry conjug a ted bile a cids: 1.92 7 ± 0.3 7 5 .641 ± 1 .80 1.5 25 ± 0.44 5.97 3 ± 1.6 5 2 .116 ± 0 .61 6.6 30 ± 1.78 a ll seconda ry bile a cids : 3.53 4 ± 0.4 4 10 .689 ± 4 .06 16.2 52 ± 7.82 1 1.01 1 ± 2.6 2 40 .980 ± 1 7.0 2 15.0 61 ± 4.62 a ll bile a cids (prim a ry a nd se conda ry ) 6.59 5 ± 0.8 9 43 .490 ± 1 9.5 6 24.4 64 ± 11 .54 3 7.44 6 ± 10.13 57 .842 ± 2 4.6 8 35.1 27 ± 11 .05 a ll conjug a te d bile a cids (prim ary a nd se conda ry ) 4.07 2 ± 0.8 8 16 .905 ± 5 .49 3.5 97 ± 1.11 2 5.90 9 ± 9.9 0 3 .956 ± 0 .97 16.1 45 ± 4.59 a ll unconjug a ted bile a cids(prim a ry a nd seconda ry ) 2.52 4 ± 0.4 0 20 .327 ± 1 0.0 4 20.8 67 ± 10 .54 1 1.53 7 ± 4.4 8 53 .886 ± 2 4.1 6 18.9 82 ± 7.07 ra tio of conjug a ted/ unconjug a ted 1.88 9 ± 0.5 1 1 .663 ± 0 .61 0.2 56 ± 0.05 5.02 8 ± 3.2 2 0 .211 ± 0 .13 0.9 20 ± 0.18 ra tio of D CA /CA (indica tion of 7 -α dehy drox y la tion) 1.57 9 ± 0.3 6 0 .213 ± 0 .09 0.4 99 ± 0.09 1.87 7 ± 1.3 7 1 .202 ± 0 .41 0.6 53 ± 0.22 ra tio of conjug a ted /unconjug a ted seconda ry bile a cids (indica tion of B S H ) 1.45 3 ± 0.4 3 1 .720 ± 0 .51 0.1 77 ± 0.05 1.85 5 ± 0.7 1 0 .169 ± 0 .11 0.9 18 ± 0.21