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The Journal of experimental biology

Thermo-TRPs and gut microbiota are involved in thermogenesis and energy metabolism during low temperature exposure of obese mice.

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Article Details
Authors
Jing Wen, Tingbei Bo, Xueying Zhang, Zuoxin Wang, Dehua Wang
Journal
The Journal of experimental biology
PM Id
32341176
DOI
10.1242/jeb.218974
Table of Contents
Abstract
Thermo-TRPs And Gut Microbiota Are Involved In Thermogenesis
Mice
Highlights:
Summary Statement:
Abstract
Introduction
Materials And Methods
Animals
Experimental Design
Temperature Preference
Body Temperature
Short-Chain Fatty Acids (SCFAs)
Monoamine Neurotransmitters -NE
Western Blot
Microbiota DNA Extraction
16S RDNA Gene Sequencing Analysis
Statistical Analysis
Results
Discussion
Conclusion
Acknowledgments
SuáRez-Zamorano, N., Fabbiano, S., Chevalier, C., Stojanović, O., Colin, D. J.,
Figures
Tables
Abstract
Ambient temperature and food composition can affect energy metabolism of the host. Thermal transient receptor potential (thermo-TRPs) ion channels can detect temperature signals and are involved in the regulation of thermogenesis and energy homeostasis. Further, the gut microbiota has also been implicated in thermogenesis and obesity. In the present study, we tested the hypothesis that thermo-TRPs and gut microbiota are involved in reducing diet-induced obesity (DIO) during low temperature exposure. C57BL/6J mice in obese (body mass gain >45%), lean (body mass gain <15%), and control (body mass gain < 1%) groups were exposed to high (23 ± 1°C) or low (4 ± 1°C) ambient temperature for 28 days. Our data showed that low temperature exposure attenuated DIO, but enhanced brown adipose tissue (BAT) thermogenesis. Low temperature exposure also resulted in increased norepinephrine (NE) concentrations in the hypothalamus, decreased TRP melastatin 8 (TRPM8) expression in the small intestine, and altered composition and diversity of gut microbiota. In DIO mice, there was a decrease in overall energy intake along with a reduction in TRP ankyrin 1 (TRPA1) expression and an increase in NE concentration in the small intestine. DIO mice also showed increases in Oscillospira, [Ruminococcus], Lactococcus, and Christensenella and decreases in Prevotella, Odoribacter, and Lactobacillus at the genus level in fecal samples. Together, our data suggest that thermos-TRPs and gut microbiota are involved in thermogenesis and energy metabolism during low temperature exposure in DIO mice. Jo ur na l o f E xp er im en ta l B io lo gy • A cc ep te d m an us cr ip t
© 2020. Published by The Company of Biologists Ltd.
Thermo-TRPs and Gut Microbiota Are Involved in Thermogenesis
and Energy Metabolism during Low Temperature Exposure of Obese
Mice
Jing Wen1,2#, Tingbei Bo1,2#, Xueying Zhang1,2, Zuoxin Wang3, Dehua Wang1,2* 1 State Key Laboratory of Integrated Management of Pest Insects and Rodents, Institute of Zoology, Chinese Academy of Sciences, Beijing 100101, China 2 CAS Center for Excellence in Biotic Interactions, University of Chinese Academy of Sciences, Beijing, 100049, China 3 Department of Psychology and Program in Neuroscience, Florida State University, Tallahassee, FL 32306-1270, USA #Contributed equally to this work. Key words: low temperature exposure, gut microbiota, obesity, thermogenesis, transient receptor potential *Correspondence: Dr. De-Hua Wang, Institute of Zoology, Chinese Academy of Sciences, No.1 Beichen Xilu, Chaoyang District, Beijing, 100101, China. Tel: +86-010-64807100; Email: wangdh@ioz.ac.cn Disclosure: The authors declared no conflict of interest. Jo ur na l o f E xp er im en ta l B io lo gy • A cc ep te d m an us cr ip t http://jeb.biologists.org/lookup/doi/10.1242/jeb.218974Access the most recent version at First posted online on 27 April 2020 as 10.1242/jeb.218974
Highlights:
1. Low temperature exposure increased BAT thermogenesis and hypothalamic NE. 2. Low temperature exposure attenuated TRPM8 expression in small intestine. 3. The obese mice showed decreased TRPA1 expression and increased NE content in small intestine. 4. Low temperature and obese conditions interacted to alter the composition of gut microbiota in a genus-specific manner.
Summary Statement:
Thermo-TRPs and gut microbiota are involved in attenuating the diet-induced obese (DIO) during low temperature exposure. Jo ur na l o f E xp er im en ta l B io lo gy • A cc ep te d m an us cr ip t
Abstract
Ambient temperature and food composition can affect energy metabolism of the host. Thermal transient receptor potential (thermo-TRPs) ion channels can detect temperature signals and are involved in the regulation of thermogenesis and energy homeostasis. Further, the gut microbiota has also been implicated in thermogenesis and obesity. In the present study, we tested the hypothesis that thermo-TRPs and gut microbiota are involved in reducing diet-induced obesity (DIO) during low temperature exposure. C57BL/6J mice in obese (body mass gain >45%), lean (body mass gain <15%), and control (body mass gain < 1%) groups were exposed to high (23 ± 1°C) or low (4 ± 1°C) ambient temperature for 28 days. Our data showed that low temperature exposure attenuated DIO, but enhanced brown adipose tissue (BAT) thermogenesis. Low temperature exposure also resulted in increased norepinephrine (NE) concentrations in the hypothalamus, decreased TRP melastatin 8 (TRPM8) expression in the small intestine, and altered composition and diversity of gut microbiota. In DIO mice, there was a decrease in overall energy intake along with a reduction in TRP ankyrin 1 (TRPA1) expression and an increase in NE concentration in the small intestine. DIO mice also showed increases in Oscillospira, [Ruminococcus], Lactococcus, and Christensenella and decreases in Prevotella, Odoribacter, and Lactobacillus at the genus level in fecal samples. Together, our data suggest that thermos-TRPs and gut microbiota are involved in thermogenesis and energy metabolism during low temperature exposure in DIO mice. Jo ur na l o f E xp er im en ta l B io lo gy • A cc ep te d m an us cr ip t
Introduction
Obesity is a common illness accompanied by a series of metabolic disorders (Ley et al., 2005; Nudel and Sanchez, 2019). Chronic energy intake that exceeds energy expenditure leads to energy accumulation, which has been considered a main driver of obesity (Romieu et al., 2017; Yoo et al., 2014). Intake of high-fat food can quickly cause fat accumulation. Conversely, low temperature exposure can increase thermogenesis in brown adipocytes of mice, rats, hamsters, and humans and attenuate obesity (Masayuki et al., 2015; Rothwell and Stock, 1979; Suárez-Zamorano et al., 2015; Zietak et al., 2016). Low temperature exposure in small mammals typically results in behavioral and physiological responses that minimize heat dissipation (e.g. vasoconstriction, huddling) and increase heat production (e.g. shivering, non-shivering thermogenesis (NST)) (Bo et al., 2019; Hoffstaetter et al., 2018; Ravussin et al., 2014). In addition, low temperature exposure can promote adrenergic release from sympathetic nerves in brown adipose tissue (BAT), which, in turn, can be activated by norepinephrine (NE) stimulation (Rossato et al., 2014). Activation of this pathway can stimulate uncoupling protein 1 (UCP1), increase the capacity of NST, and decrease energy accumulation (Cannon and Nedergaard, 2004; Li et al., 2019a). It has been shown that low temperatures can be sensed by transient receptor potential melastatin 8 (TRPM8) and/or ankyrin 1 (TRPA1), both of which are expressed in peripheral cutaneous nerve endings. Their activations can induce adrenergic release, thereby activating BAT thermogenesis (Bachman et al., 2002; Barbatelli et al., 2010). TRPs multigene superfamily has 8 sub-families (Li, 2017) and is expressed in almost all tissue, including BAT and the small intestine (Nilius and Owsianik, 2011). As non-selective cation channels, TRPs can be activated by temperature and are involved in regulating thermogenesis and energy homeostasis (Senaris et al., 2018; Song et al., 2016; Vay et al., 2012). Low temperatures are mainly sensed by TRPM8 and TRPA1, which are activated by temperatures below 28°C and 17°C, respectively (Bödding et al., 2007). Moreover, TRPM8 and TRPA1 participate in BAT Jo ur na l o f E xp er im en ta l B io lo gy • A cc ep te d m an us cr ip t thermogenesis activation induced by low temperature exposure (Saito et al., 2015) and enhance energy metabolism in mice (Uchida et al., 2017). TRPM8 is expressed in brown adipocytes and can increase UCP1 expression and heat production (Ma et al., 2012; Rossato et al., 2014). Although the thermo-sensation of TRPA1 remains controversial, recent data have shown that TRPA1 takes part in energy homeostasis, including glucose homeostasis (Senaris et al., 2018). These two thermo-TRPs have been considered as potential therapeutic targets for preventing and treating obesity and its related metabolic disorders. Recently, the gut microbiota has been implicated in the regulation of energy homeostasis and body mass (Tremaroli and Bäckhed, 2012). The gut microbiota affects DIO via different pathways involved in triglyceride storage and fatty acid oxidation (Fredrik et al., 2007). Obesity is found to be associated with changes in microbial diversity and relative proportions between the Firmicutes and Bacteroidetes phyla in both mice and humans (Ley et al., 2005; Ley et al., 2006; Turnbaugh et al., 2008). Transplantation of caecal contents from cold-exposed mice can significantly reduce obesity and improve insulin sensitivity in the host (Chevalier et al., 2015). Data have also shown that germ-free mice have impaired thermogenesis of BAT, which is associated with limited increases in UCP1 expression and reduced browning of white adipose tissue (WAT) (Bo et al., 2019; Li et al., 2019a). Collectively, these data suggest that both BAT and gut microbiota contribute to thermogenesis and inhibition of obesity at low temperature. Recent studies have also linked changes in the gut microbiota in cold environments to the thermogenesis of BAT via exogenous NE (Bo et al., 2019), bile acid (Zietak et al., 2016), and butyric acid (Li et al., 2019b). However, it remains unclear how gut microbiota in the small intestine perceive temperature signals. The aim of the present study was to illustrate a gut microbial signature for obese mice under low temperature, to explore the mechanisms by which intestinal cells or gut microbiota sense the host environment and animal’s physiological changes, and to reveal the possible relationship between gut microbiota and TRPs in thermogenesis and metabolism. We hypothesized that TRPs play an important role in attenuating DIO Jo ur na l o f E xp er im en ta l B io lo gy • A cc ep te d m an us cr ip t during low temperature exposure by sensing ambient temperature and are also involved in regulating metabolism and changes in gut microbiota composition and structure.
Materials and methods
Animals
In total, 85 C57BL/6J male mice (5 wks old, 22.0-28.0 g) were housed individually in plastic cages (29×18×16 cm) with sawdust bedding in a temperature-controlled room (23±1°C), under a 16:8 h light-dark cycle (lights on at 0400). Water and standard rodent chow (6.2% fat, 35.6% carbohydrate, 20.8% protein, and 17.6 gross energy kJ·g-1) (Beijing Keao Xieli Feed Co., China) or high-fat food (60% fat, 20% carbohydrate, 20% protein, and 22.0 gross energy kJ·g-1) (Research Diet Inc., D12492, USA) were provided ad libitum. The animal procedures were approved by the Animal Care and Use Committee of the Institute of Zoology, Chinese Academy of Sciences (CAS).
Experimental design
The mice were acclimated at 23±1°C for 3 weeks. Thereafter, 10 animals were randomly chosen to be in the control group and fed standard rodent chow. The remaining animals were fed high-fat food for 8 weeks. By the end of the 8 weeks, all animals fed with high-fat food were rank-ordered by their body mass. Fourteen animals from both the low and high ends of body mass measurements were assigned as either lean (body mass gain less than 15%) or obese (body mass gain more than 45%) groups. Significant differences were found in body mass between Control (29.24 ± 0.60 g), Lean (33.31 ± 0.53 g), and Obese (42.54 ± 1.17 g) groups (F(2,36) = 57.70, P < 0.0001). Mice within each group were subsequently and randomly assigned to be exposed to either 23°C ambient temperature or 4°C ambient temperature for 4 weeks. In total, 6 experimental groups with the combination of temperature and degree of obesity were created: 23°C-Control (n=5), 4°C-Control (n=5), 23°C-Obesity (n=7), 4°C-Obesity (n=7), 23°C-Lean (n=7), and 4°C-Lean (n=7). Jo ur na l o f E xp er im en ta l B io lo gy • A cc ep te d m an us cr ip t Body mass and gross energy intake (GEI) Body mass of the mice were measured at 1800 h each day. Daily food intake was calculated as the mass of food offered minus food remained in the hopper and food mixed in the bedding. The gross energy content of the food was then determined using a C2000 oxygen bomb calorimeter (PARR 1281, USA) and GEI calculation was as follows: GEI (kJ·day-1) = food intake (g·day-1) × dry matter content of the diet (g DM·g FM-1) × energy content of food (kJ·g-1).
Temperature preference
The apparatus consists of two plastic cages (35×25×20 cm) with one cage filled with ice while the other cage remained empty. The two cages were placed next to each other, separated by a board of polyethylene foam, and covered with an opaque plastic plate. The room temperature was maintained at 23±1ºC. Subjects were placed in the middle line of the plastic plate and allowed to roam freely. After 5 mins of acclimation, the subject’s time spent in each area of the plate was recorded for 10 mins. The duration of time spent on the cold surface (above the ice cage) was used as an index of cold tolerance. Resting metabolic rate (RMR) and non-shivering thermogenesis (NST) RMR and maximum non-shivering thermogenesis (NSTmax) were estimated by measuring the rate of oxygen consumption using an open-flow respirometry system (TSE, Germany) at the end of the 4-week temperature exposure. After drying, air was pumped at a rate of 1000 mL·min-1 through a cuboid sealed Perspex chamber. Gas leaving the chamber was dried using a special drier (TSE system, Germany) and passed Jo ur na l o f E xp er im en ta l B io lo gy • A cc ep te d m an us cr ip t through an oxygen analyzer at a flow rate of 380 mL·min-1. The data were recorded and averaged every 10 s by a computer connected via an analogue-to-digital converter that converted the changes of air composition to digital signal (TSE system, Germany), then analyzed using Labmaster software (TSE system, Germany). The mice had access to food and water prior to the measurements of RMR and NSTmax during which subjects were deprived of food and water. RMR was measured for 3 h at 30 ± 0.5°C and calculated from the lowest rate of oxygen consumption over 10 min. RMR measurements for all subjects were completed during a 2-day window. The accuracy of the oxygen analysis was checked periodically using the standard gas by an experienced technician. NSTmax was the maximum rate of oxygen consumption in response to NE and was induced by a subcutaneous injection of NE at 15±0.5°C. The mass-dependent dosage of NE was calculated according to the equation: NE (mg·kg-1) = 6.6BM0.458(BM, body mass in gram) (Heldmaier, 1971). NSTmax was calculated from continuous stable maximal recordings over 10 min. All measurements were performed between 06:00 and 19:00 during the light phase of the photoperiod.
Body temperature
Rectal temperatures (Tcore) were recorded by inserting a temperature probe (TES 1310) 3 cm into the rectum daily during the temperature exposure period. At the end of the experiment, surface body temperatures (Tsurface) were read with an infrared camera (FLIR E60, UK) from a distance of 30 cm, and the data were analyzed by FLIR Tools software. The highest temperatures of shell and tail in an image were selected. Body composition, body fat content and fat volume Animals were sacrificed between 14:00-16:00 h at the end of the experiment. Interscapular brown adipose tissue (BAT), small intestines, and brains were quickly Jo ur na l o f E xp er im en ta l B io lo gy • A cc ep te d m an us cr ip t excised and immediately frozen in liquid nitrogen following the collection of trunk blood. Next, the caecum, rectum, stomach, liver, lung, and kidneys were separated from the remainder of the carcass and dried in an oven at 60°C to determine dry mass. Total body fat was extracted from the dried carcass by ether extraction in a Soxhlet apparatus (2055 SOXTEC Manual Extraction Unit). The body fat volume was calculated by an in vivo imaging system for living animals (small animal CT) (PE Quantum FX).
Short-chain fatty acids (SCFAs)
At the end of experiment, feces were collected from each animal using sterilized tools and were placed into super-clean tubes, frozen immediately in liquid nitrogen, and stored at −80°C. Acetic, propionic, butyric, isobutyric, valeric, and isovaleric acids in the feces were measured by high-performance gas chromatography (GC, Agilent 7890A; Agilent Technologies, Germany) with modified GC autosampler and FID system (Zhang et al., 2018). Separations were performed in a 30 m × 0.25 mm × 0.25 μm DB-WAX column (Agilent Technologies) using 99.998% hydrogen as carrier gas at a flow rate of 1.0 mL·min-1 (Zhang et al., 2018). The system operated at 250°C. Injections were performed at 230°C in the splitless mode, with 0.5 μL per injection. The oven temperature was programmed from 60°C (1 min) to 200°C at 5°C·min-1 and then from 200 to 230°C at 10°C·min-1. The total running time of each sample was 32 min.
Monoamine neurotransmitters -NE
High performance liquid chromatography with electrochemical detection (HPLC-ECD) was used to measure NE in the small intestine and hypothalamic samples. The 3,4- dihydroxybelzyamine (DHBA) was used as internal standard. Samples were homogenized in 0.1 M cold perchloric acid, oscillated with ultrasound for 10 s, followed by centrifugation at 13,000 rpm at 4°C for 30 min. Supernatant was filtered Jo ur na l o f E xp er im en ta l B io lo gy • A cc ep te d m an us cr ip t using 0.2 mm nylon filter. Aliquots of 30 μl were manually injected using Hamilton syringe.
Western blot
ting was performed on whole tissue lysates and were probed with primary antibodies: UCP1 (ab155117, Abcam), β-tubulin (A01030HRP, Abbkine), GAPDH (A01020, Abbkine). Anti-TRPA1 (PA1-46159) and Anti-TRPM8 (ab3243) were purchased from Thermo Fisher Scientific and Abcam (UK). The secondary antibodies used were either peroxidase-conjugated goat anti-rabbit IgG (111-035-003, Jackson) or peroxidase-conjugated goat anti-mice IgG (115-035-003, Jackson). Samples from the small intestine (0.2 g) and BAT (0.2 g) were homogenized in RIPA buffer using established methods (Bo et al., 2019). The total protein was separated by SDS-PAGE using a Mini Protean apparatus (Bio-Rad Laboratories, PA, USA) and transferred onto PVDF membranes which were then blocked with 5% skimmed milk for 1.5 h at room temperature. Thereafter, the membranes were incubated with the primary antibodies for about 12 h at 4˚C, followed by incubation with an appropriate horseradish peroxidase-conjugated secondary antibody for 2 h at room temperature. Antibody concentrations were determined based on the literature and our pilot experiments. The reaction products were revealed by chemiluminescence (ECL, Yesen).
Microbiota DNA extraction
DNA from fecal samples were extracted by 2×CTAB (cetyltrimethyl ammonium bromide), phenol chloroform mixture (phenol:chloroform:isoamyl alcohol=25:24:1) and SanPrep Column DNA Gel Extraction Kit (Sangon Biotech, China). DNA purity and concentrations were assessed by absorbance on a Nanodrop 2000 (Thermo Fisher Scientific, Carlsbad, CA, USA) via measuring the A260/A280 ratio. Only DNA with an A260/A280 ratio of 1.8-2.0 were used. Jo ur na l o f E xp er im en ta l B io lo gy • A cc ep te d m an us cr ip t
16S rDNA gene sequencing analysis
The 16S sequence paired-end data set was joined and quality filtered using the FLASH method described by Magoč and Salzberg (2011). Sequencing was done on an Illumina HiSeq 2500. All sequence analyses were provided in the Quantitative Insights Into Microbial Ecology (QIIME, version 1.9.1) software suite according to the Qiime tutorial (http://qiime.org/) with modified methods. Sequences that did not match any entries in this reference were subsequently clustered into de novo OTUs at 97% similarity with UCLUST. The hierarchical clustering on the basis of population profiles of most common and abundant taxa was performed using UPGMA clustering (Unweighted Pair Group Method with Arithmetic Mean, also known as average linkage) on the distance matrix of OTU abundance.
Statistical analysis
SPSS 20.0 software was used for statistical analyses. For intra- and inter-group analyses, repeated measures ANOVA or two-way ANOVA (Fat × Ta) was applied when appropriate, followed by Tukey’s post hoc test. All data are presented as mean ± standard error of mean (SEM), and p value <0.05 was deemed statistically significant. For microbiota data, the OTUs that reached a nucleotide-similarity level of 97% were used for alpha diversity (Shannon). Principal coordinate analyses (PCoA) based on weighted and unweighted UniFrac distances were used to visualize the variation of bacterial structure across different groups using R vegan package. Significance for βdiversity analyses was checked with analysis of similarity (ANOSIM) included in the package “vegan” of the QIIME-incorporated version of “R”. Differences of α-diversity, abundance of phylum and genera were analyzed by two-way ANOVA with Tukey’s post hoc test. Jo ur na l o f E xp er im en ta l B io lo gy • A cc ep te d m an us cr ip t
Results
Energy metabolism
Body mass (BM)
After 4 weeks of exposure to 4°C ambient temperature (Ta), mice showed a significant lower body mass gain compared to their counterparts at 23°C (F1,33=4.915, p<0.05; Fig. 1E). At 4°C, the average body mass in the obese group was significantly lower than the other two groups, which did not differ from each other (F2,33=35.517, p<0.01; Fig. 1F). There were significant interactions between days-fat (F56,924=6.656, p<0.01) and days-Ta (F28,924=11.811, p<0.01), but not between fat-Ta (F2,33=1.995, p>0.05) (Fig. 1A, B).
Energy intake
Mice in the obese group had the lowest average energy intake (F2,33=5.779, p<0.01; Fig. 1G) compared with the energy intake of the other two groups, which did not differ from each other. In addition, mice at 4°C had a higher average energy intake than mice at 23°C (F1,33=7.241, p<0.05; Fig. 1G). A significant fat-Ta interaction was also found (F2, 33=5.174, p<0.01). changed over days in all experimental groups (F88, 924=2.870, p<0.01; Fig. 1C, D). Body fat content and energy metabolism As shown in Fig. 2A, the obese group had the highest level of body fat content (F2,32=82.223, p<0.01), and the lean group also had a higher level of body fat content compared to the control group. In addition, the mice at 23°C showed higher body fat content than those at 4°C (F1,32=19.341, p<0.01), and a significant fat-Ta interaction was also found (F2,32=3.884, p<0.05). The effects of fat (F2,23=1.842, p>0.05), Ta (F1,23=1.207, p>0.05), and fat-Ta interaction (F2,23=1.442, p>0.05) on fat volume were Jo ur na l o f E xp er im en ta l B io lo gy • A cc ep te d m an us cr ip t not significant (Fig. 2B). The obese group had higher body fat volume than the other groups only at 23°C. RMR (F2,32=3.605, p<0.05, Fig. 2C) and GTT-glycemia (F2,32=6.203, p<0.01, Fig. 2F) were affected significantly by body fat content. The RMR in the control group did not differ from that of the obese group but was higher than the lean group. Conversely, the obese group had higher GTT-glycemia than the other two groups, which did not differ from each other. The level of fasting blood glucose was higher in mice at 23°C than at 4°C (F1,23=4.684, p<0.05, Fig. 2E). Relative to 23°C controls, mice at 4°C showed higher NST (F1,33=5.238, p<0.05, Fig. 2D) and higher amounts of UCP1 (F1,33=4.203, p<0.05, Fig. 2G), which was essential for heat production via adaptive thermogenesis in BAT. Body temperature and cold tolerance Rectal temperature was significantly affected by Ta (F1,33=10.746, p<0.01; Fig. 3A and Fig. 3B), but not by fat and fat-Ta interaction (F2,33=1.184, 1.486, respectively; both p>0.05). Meanwhile, shell temperature, tail skin temperature, and BAT skin temperature were significantly affected by Ta (F1,33=54.940, 272.885, 45.196, respectively; all p<0.01) and fat (F2,33=10.765, 8.953, 8.922, respectively; all p<0.01) (Fig. 3C, D, E). Moreover, a fat-Ta interaction was found for tail skin temperature (F2,33=3.305, p<0.01, Fig. 3D). Compared to mice at 23°C, mice at 4°C had better cold tolerance (F2,24=23.615, p<0.01). In particular, mice in the obese group did better than the other two groups at 4°C (Fig. 3F, G).
The expression of TRPs
No group difference was found in protein expression levels of TRPA1 and TRPM8 in BAT (Fig. 4A, B). However, in the small intestine, TRPA1 expression was affected by fat (F2,33=14.570, p<0.01, Fig. 4C), and when mice were challenged with low temperature exposure, TRPM8 expression levels declined (F1,33=5.530, p<0.05, Fig. 4D). Jo ur na l o f E xp er im en ta l B io lo gy • A cc ep te d m an us cr ip t The diversity and composition of gut microbiota and metabolites
Gut microbiota
The OTU-level rarefaction curve of Goods coverage across all samples reached stable values (Fig. S1 A), showing that most of the gut microbial diversity had already been captured in our study. As shown in Fig. 5A, the Shannon showed a significant difference (F2,33=6.700, P<0.01). The control mice had a higher Shannon index (α-diversity) than the obese and the lean mice (F2,33=14.014, p<0.001), but Ta did not affect α-diversity significantly (F1,33=3.980, P=0.054). For β-diversity, analysis based on unweighted UniFrac distance (ANOSIM, r= 0.35, P= 0.001; Fig. 5B) and weighted UniFrac distance (ANOSIM, r= 0.32, P= 0.001; Fig. 5C) showed significant differences among 6 experimental groups. The samples in the control groups (4°C and 23°C) were clustered together, while the other 4 groups were also clustered together (Fig. 5B, C). Further, the effect of high-fat food was significant (ANOSIM, unweighted: r= 0.249, P= 0.002; weighted: r= 0.251, P= 0.001), but not of temperature (ANOSIM, unweighted: r=0.016, P = 0.232; weighted: r=0.008, P = 0.445). The microbial community structures between the 23°C-Control group and the 4°C-Control group were similar (Fig. 5D). We observed differences in OTUs abundance at phylum level in Bacteroidetes, Proteobacteria, and Firmicutes between high-fat content (obese and lean groups) and normal-fat content (control groups) (Fig. 5G, I, J). At the phylum level, the proportions of Firmicutes significantly increased and Bacteroidetes decreased in the obese and lean groups at different temperatures. Actinobacteria (p=0.014) and TM7 (p=0.010) phyla were significantly affected by Ta (Fig. 5F, K). To assess differences in microbial communities affected by fat and Ta, we applied LEfSe method with LDA score > 2 (Fig. 5E). The results identified 26 and 23 discriminative features in the microbiota of the control and obese groups, respectively, and 29 in lean groups. Fat content (obese and lean groups) significantly augmented Oscillospira, [Ruminococcus], Lactococcus, and Christensenella at the genus level when compared with control groups at different temperatures (Fig. 6B, C, D, F), but diminished the levels of Jo ur na l o f E xp er im en ta l B io lo gy • A cc ep te d m an us cr ip t Prevotella, Odoribacter, and Lactobacillus (Fig. 6E, H, I). If effect of temperature was only considered, we found that Adlercreutzia (p=0.025; Fig. 6A), [Ruminococcus] (P=0.011; Fig. 6C), Prevotella (P=0.032; Fig. 6E) and Christensenella (P=0.005; Fig. 6F) differed at 4°C and 23°C. The KEGG analysis indicated that the gut microbiota in the obese and lean groups was significantly different in lipid metabolism compared to the control groups (Table. S1).
Metabolites
The total concentration of SCFAs in the control groups was slightly higher than in the obese and the lean groups (Fig. 7A). Isovaleric acid and valeric acid levels were significantly affected by fat (F2,33 = 31.67, 25.27, respectively; both p < 0.01). Control groups had lower isovaleric acid and valeric acid than the obese and lean groups, which did not differ from each other (Fig. 7B).
NE concentration in the hypothalamus and small intestine
The levels of NE in the hypothalamus were significantly increased by low Ta (F1,33=5.452, p<0.05), but not by fat and fat-Ta interaction (F2,33=1.502, 0.790, respectively; both p>0.05) (Fig. 8A). The concentration of NE in the small intestine was affected by fat (F1,33=9.376, p<0.01), but not by Ta and fat-Ta interaction (F2,33=1.969, 2.497, respectively; both p>0.05) (Fig. 8B).
Discussion
In the present study, mice under a low ambient temperature showed significant increases in GEI, but decreases in total body mass gain and body fat content in comparison to their control counterparts. As low temperature exposure causes rapid increases in heat dissipation and thermogenesis, less energy is available for fat Jo ur na l o f E xp er im en ta l B io lo gy • A cc ep te d m an us cr ip t deposition, leading to the low temperature-attenuated DIO. Indeed, our data illustrate low temperature-augmented increases in glucose and lipid metabolism. Low temperature exposure not only increased NST and UCP1 expression in the interscapular BAT (iBAT), but also elevated NE content in the hypothalamus. Increased NE activity in the hypothalamus and in BAT by cold stress are implicated in regulating sympathetic outflow in rats (Gotoh and Smythe, 1991). Thus, our data provide further evidence to support the notion that low temperature-enhanced thermogenesis capacity involves the activation of BAT thermogenesis by participation of hypothalamic NE. One interesting finding is that although a fat-Ta interaction on GEI was found, obese and lean groups did not increase their GEI during low temperature exposure. One possibility is that the obese and lean groups tended to utilize adipose tissues to meet their energy demand during low temperature exposure. This is supported by our data showing an overall decrease in fat content during low temperature exposure in mice. Alternatively, adipose tissue has good thermal insulation properties, so the obese and lean groups with higher fat content can protect themselves from low temperature without additional heat production. This is also supported by our data showing decreased RMR in obese and lean mice and no changes in fat contents between the two control groups. It is possible that control and obese mice had different energy utilization strategies when they were exposed to the low ambient temperature. DIO mice with metabolic dysfunction can progress to a status of passive fat accumulation, leading to body weight gain. The increase in fat content is also a plausible, causal factor of insulin resistance in DIO mice (Belfiore et al., 1979). However, in our study, the obese groups showed reduced GEI without significant increases in RMR (compared to the control groups). We speculate that the low energy demand of obese mice is due to the lower mass-specific metabolic rate (calculated as the ratio between RMR and body mass; energy expenditure) and the dry weight of digestive organs (energy expending organs, Table S1). These data are supported by data from a previous study showing that DIO striped hamsters didn’t change energy intake, but decreased BMR (Shi et al., 2017). Jo ur na l o f E xp er im en ta l B io lo gy • A cc ep te d m an us cr ip t TRPM8 is known for its central role in low temperature detection (Colburn et al., 2007; Senaris et al., 2018), although the expression of TRPM8 in the mouse small intestine remains controversial (Penuelas et al., 2007; Zhang et al., 2004). Our data show that chronic low temperature exposure resulted in a significant decrease in TRPM8 expression in the small intestine, with a drop in rectal temperature (~0.9ºC) and an increase in cold tolerance. It has been suggested that TRPM8 may play a role in thermoregulation. For example, while wild-type mice display a strong avoidance of cold environments, this behavior is blunted in TRPM8-deficient mice (Bautista et al., 2007). TRPM8-deficient mice also show an increase in tail heat loss and a fall in core body temperature (~0.7 ºC), leading to hypothermia (Reimundez et al., 2018). It has also been demonstrated that deletion of TRPM8 leads to an increase in food intake in mice (Reimundez et al., 2018). This is supported by our data showing that low temperature-related decreases in TRPM8 expression in the small intestine was associated with an increased GEI. Nevertheless, the role of TRPM8 in food intake/digestion and thermoregulation during low temperature exposure needs to be further studied. TRPA1 channels are located in the gut and other tissues (Fothergill et al., 2016). Data have shown that TRPA1 can attenuate spontaneous neurogenic contractions and transit of the colon (Poole et al., 2011). Pharmacological activation of TRPA1 can reduce energy intake and ghrelin secretion, delay gastric emptying, and increase insulin release (Ahn et al., 2014; Cao et al., 2012; Senaris et al., 2018). These data indicate that activation of enterocyte TRPA1 has the potential to impede nutrient absorption and obesity (Fothergill et al., 2016). However, our data show that DIO was associated with decreased TRPA1 expression in the small intestine. At the moment, we can only speculate that there might be a disassociation between the amount of TRPA1 and their function. As thermosensitive channels, TRPA1 can also be activated by fatty acids – especially for animals receiving high-fat food (Terada et al., 2011). TRPA1 activation can induce adrenaline secretion via sensory nerve activation and ultimately inhibit fat deposition by increasing thermogenesis and energy expenditure (Iwasaki et al., 2008; Jo ur na l o f E xp er im en ta l B io lo gy • A cc ep te d m an us cr ip t Terada et al., 2011; Watanabe and Terada, 2015). In our obese mice, TRPA1 expression in the small intestine was reduced, but NE content changed in the opposite direction. NE levels in the small intestine can be transmitted as a signal molecule to gut microbes, which, in turn, release short-chain fatty acids (SCFAs) to form a feedback loop (Bo et al., 2019). Therefore, it is possible that the low levels of TRPA1 in the small intestine had a higher activation rate and still played a role in mediating low temperatureattenuated DOI. This speculation needs to be examined in further studies. Gut microbiota could be linked to the development of obesity and related comorbidities (Moreno-Indias et al., 2016). Our data show that the abundance of Firmicutes was significantly increased, but that of Bacteroidetes was decreased at the phyla level in DIO mice. Previous studies have demonstrated that an increase in the ratio of Firmicutes/Bacteroidetes was positively correlated with body fat content (Ley et al., 2005; Turnbaugh et al., 2008). Bacteria belonging to Firmicutes are enriched in mice with high-fat diet and are usually associated with obesity (Turnbaugh et al., 2008). Conversely, bacteria belonging to Bacteroidetes are usually higher in control groups and are associated with leanness (Goodrich et al., 2014). We also observed that the abundance of Oscillospira, [Ruminococcus], Lactococcus, and Christensenella at the genus level increased, and the abundance of Prevotella, Odoribacter, and Lactobacillus at the genus levels decreased in DIO mice, which is consistent with previous studies in rats and mice (Lin et al., 2019; Tung et al., 2018). In addition, Spearman's correlation analyses have negatively linked Oscillospira with most metabolic parameters (Li et al., 2019b). Although our data support the notion that alterations in these bacteria may serve as the markers of morbid obesity, it should not be ignored that group differences in dietary composition/intake may also be attributing to differences seen in fecal bacteria composition between the obese and non-obese groups in our present study. Changes in gut microbiota cause changes in SCFAs. Evidence indicates that isovaleric acid (42.11%) increases, while valeric acid (23.92%) decreases in colon contents in obese mice (Li et al., 2018). In the present study, isovaleric acid and valeric acid in fecal samples were significantly increased in both the obese and the lean groups. Fat-Ta Jo ur na l o f E xp er im en ta l B io lo gy • A cc ep te d m an us cr ip t interactions also influenced gut microbiota structure of mice. The α diversity of gut microbiota decreased in DIO mice, which was conserved by low temperature exposure. Consistent with the study of Zietak et al (2016), we identified that the abundance of Actinobacteria and TM7 at the phylum level were decreased when mice were exposed to low temperature, suggesting that the changes in these bacteria may contribute to the cold-induced phenotype and inhibit adiposity in mice (Zietak et al., 2016). The mechanism of gut microbiota influencing adiposity may include modulation of NE concentrations (Bo et al., 2019). NE can alleviate obesity through increased thermogenesis (Gotoh and Smythe, 1991).
Conclusion
In the present study, we found that BAT thermogenesis was associated with increased NE concentrations in the hypothalamus and enhanced TRPM8 expression in the small intestine during low temperature exposure. Energy intake and TRPA1 in the small intestine were reduced, and NE in the small intestine was increased in DIO mice. Further, we found that ambient temperature and DIO can interact in altering the composition and diversity of gut microbiota. Together, our data indicate that low temperature-attenuated DIO may be mediated by changes in BAT thermogenesis, TRPs (TRPA1 and TRPM8) activity, and gut microbiota composition and through their interactions. In addition, we suggest that Oscillospira, [Ruminococcus], Lactococcus, and Christensenella at the genus levels are potential bacterial markers related to morbid obesity, while Prevotella, Odoribacter, and Lactobacillus at the genus levels are potential bacterial markers that impede obesity. TRPM8 and TRPA1 may also serve as potential therapeutic targets for preventing/treating obesity and its related metabolic disorders. Jo ur na l o f E xp er im en ta l B io lo gy • A cc ep te d m an us cr ip t
Acknowledgments
We thank all the members of Animal Physiological Ecology Group of the Institute of Zoology, Chinese Academy of Sciences, for their helpful discussion. This work was supported by the National Natural Science Foundation of China (No. 31970417 and 31772461) to DHW and No.31770440 to XYZ. We thank Qing-gang Qiao for his encouragement. Jo ur na l o f E xp er im en ta l B io lo gy • A cc ep te d m an us cr ip t
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Figures
Fig. 1. Effects of DIO and low temperature exposure on body mass (A-B), energy intake (C-D), body mass gain ratio (E-F) and gross energy (G) in C57BL/6J mice. Data are presented as mean ± SEM. Different letters indicate significant between-group differences determined by Tukey post-hoc test (P<0.05). Pfat, **, significant effect by DIO (P<0.01). *P<0.05. Jo ur na l o f E xp er im en ta l B io lo gy • A cc ep te d m an us cr ip t Jo ur na l o f E xp er im en ta l B io lo gy • A cc ep te d m an us cr ip t Jo ur na l o f E xp er im en ta l B io lo gy • A cc ep te d m an us cr ip t Jo ur na l o f E xp er im en ta l B io lo gy • A cc ep te d m an us cr ip t Fig. 5. DIO and low temperature exposure alter the diversity and composition of fecal microbiota at the phylum level. Shannon index (α diversity) of gut microbiota Jo ur na l o f E xp er im en ta l B io lo gy • A cc ep te d m an us cr ip t (A). Principal coordinates analysis (PCoA) plots based on unweighted UniFrac distance (B) and weighted UniFrac distance (C). Relative abundance at the phylum level in fecal microbiota community of the six groups (D). Differential bacterial taxonomy selected by LEfSe analysis with LDA score > 2 in fecal microbiota community of the six groups (E). Relative abundance of Actinobacteria (F), Bacteroidetes (G), Deferribacteres (H), Proteobacteria (I), Firmicute (J), and TM7 (K) in fecal microbiota community of the six groups at phylum levels. In panels from G to L, the black crosses indicate the mean of data. Different letters indicate significant between-group differences determined by Tukey post-hoc test (P<0.05). *, P<0.05; ***, P<0.001. Jo ur na l o f E xp er im en ta l B io lo gy • A cc ep te d m an us cr ip t Fig. 6. DIO and low temperature exposure alter the diversity and composition of fecal microbiota at the genus level. Relative abundance of Adlercreutzia (A), Oscillospira (B), [Ruminococcus] (C), Lactococcus (D), Prevotella (E), Christensenlla (F), Acinetobacter (G), Odoribacter (H) and Lactobacillus (I), in fecal microbiota community of the six groups at genus levels. In panels from A to I, the black crosses indicate the mean of data. *, P<0.05; **, P<0.01; ***, P<0.001. Jo ur na l o f E xp er im en ta l B io lo gy • A cc ep te d m an us cr ip t Jo ur na l o f E xp er im en ta l B io lo gy • A cc ep te d m an us cr ip t Jo ur na l o f E xp er im en ta l B io lo gy • A cc ep te d m an us cr ip t
Tables
Data are means ± SEM. Pfat, effect of DIO, PTa, effect of ambient temperature, **, P<0.01; ns, non-significant (P>0.05). Different letters indicate significant betweengroup differences determined by Tukey post-hoc test (P <0.05). Table 1 Dry weight of digestive tract Fat Ambient temperature (Ta) Con Obese Lean Pfa t 23°C 4°C PT a Caecum 0.031±0.002b 0.015±0.001a 0.015±0.001a ** 0.019±0.002 0.019±0.002 ns Rectum 0.053±0.002b 0.039±0.002a 0.037±0.002a ** 0.042±0.002 0.041±0.002 ns Stomach 0.047±0.003b 0.045±0.002ab 0.040±0.002a ** 0.045±0.002 0.043±0.002 ns Jo ur na l o f E xp er im en ta l B io lo gy • A cc ep te d m an us cr ip t Data are means ± SEM. Pfat, effect of DIO, PTa, effect of ambient temperature, **, P<0.01; ns, non-significant (P>0.05). Different letters indicate significant betweengroup differences determined by Tukey post-hoc test (P <0.05). Table 2 Dry weight of metabolic active organs Fat Ambient temperature (Ta) Con Obese Lean Pfa t 23°C 4°C PT a Liver 0.329±0.009ab 0.359±0.029b 0.274±0.008a ** 0.330±0.024 0.312±0.012 ns Lung 0.038±0.002 0.044±0.002 0.042±0.002 ns 0.043±0.001 0.041±0.002 ns Kidneys 0.114±0.003 0.131±0.005 0.122±0.005 ns 0.116±0.004 0.131±0.003 ** Jo ur na l o f E xp er im en ta l B io lo gy • A cc ep te d m an us cr ip t Table S1 Prediction of the function of the gut microbiota associated with lipid metabolism Fat Ambient temperature (Ta) Con Obesity Lean P fat 23°C 4°C P Ta Adipocytokine signaling pathway 35193.800±2059. 739a 28156.571±168 5.519b 27483.000±174 0.797b * 30142.610±151 3.148 30412.971±148 5.072 ns Fatty acid biosynthesis 153708.900±910 5.456b 254233.982±74 51.151a 245999.143±76 95.515a ** 216635.914±66 89.149 219325.436±65 65.036 ns Lipid biosynthesis proteins 226159.100±109 39.766b 307230.089±89 52.197a 297295.714±92 45.790a ** 274898.848±80 36.690 278891.088±78 87.573 ns Lipid metabolism 35798.600±2962. 246b 64158.991±242 4.057a 61717.571±250 3.555a ** 53089.067±217 6.158 54694.375±213 5.780 ns Glycerolipid metabolism 149049.800±102 42.972b 224724.429±83 81.999a 214273.500±86 56.892a ** 192695.076±75 24.804 199336.743±73 85.185 ns Glycerophosph olipid metabolism 204349.400±985 1.181b 281218.071±80 61.390a 274066.429±83 25.768a ** 249782.657±72 36.982 256639.943±71 02.704 ns Fatty acid metabolism 74009.100±5996. 628b 130701.429±49 07.143a 120999.143±50 68.076a ** 109195.371±44 05.308 107944.410±43 23.570 ns Data are means ± SEM. Pfat, effect of DIO, PTa, effect of ambient temperature, **, P<0.01; ns, non-significant (P > 0.05). Different letters indicate significant between-group differences determined by Tukey post-hoc test (P<0.05). Journal of Experimental Biology: doi:10.1242/jeb.218974: Supplementary information Jo ur na l o f E xp er im en ta l B io lo gy • S up pl em en ta ry in fo rm at io n Fig. S1. DIO and cold exposure alter the diversity and composition of fecal microbiota. The OTU-level rarefaction curve of Goods coverage across all samples has reached stable values(A). Principal coordinates analysis (PCoA) plots based on unweighted UniFrac distance (B-E). Cladogram representing taxa enriched in fecal microbiota community of the six groups detected by the LEfSe tool. Differences were represented by the color of the most abundant class (F). A B F C D E Journal of Experimental Biology: doi:10.1242/jeb.218974: Supplementary information Jo ur na l o f E xp er im en ta l B io lo gy • S up pl em en ta ry in fo rm at io n
 
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