Cold Stress Activates the UCP3 Pathway via Gut Microbiota in Piglet

doi:10.21203/rs.3.rs-388612/v1

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Cold Stress Activates the UCP3 Pathway via Gut Microbiota in Piglet

https://doi.org/10.21203/rs.3.rs-388612/v1

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Background

The gut microbiota plays a key role in the host metabolic thermogenesis by activating uncoupling protein 1 (UCP1) and increasing the browning process of white adipose tissue, especially in a cold environment. However, the crosstalk between gut microbiota and pigs, which lack functional UCP1 making them susceptible to cold, has rarely been illustrated. We used male piglets as a model to evaluate the host response to cold stress via the gut microbiota.

Results

We found that host thermogenesis and insulin resistance with increased serum metabolites, such as glycocholic acid (GCA), glycochenodeoxycholate (GCDCA), and chenodeoxycholate (CDCA), and altered cecal microbiota compositions and functions under cold stress. Using transcriptomics technology, we found that cytochrome P450, family 8, subfamily b, polypeptide 1 (CYP8B1), FXR, FFAR2, and FFAR3, which are related to bile acid and short-chain fatty acid metabolism, increased in the liver under cold stress. In addition, the fat lipolysis genes CLPS, PNLIPRP1, carnitine palmitoyltransferase 1B (CPT1B), and uncoupling protein 3 (UCP3) were significantly increased in the fat of piglets under cold stress. However, microbiota depletion via treatment with mixed antibiotics weakened the effect on the genes CYP8B1, FFAR2, FFAR3, and CPT1B genes, indicating that the gut microbiota plays important roles in the host thermogenesis.

Conclusions

Our results demonstrate that the gut microbiota-blood-liver and fat axis may regulate thermogenesis during cold acclimation in piglets.

General Microbiology

cold stress

piglets

gut microbiota

The intestinal microbial community is a complex ecosystem composed of various microorganisms, with which the host interacts from birth [1]. The host provides habitat for specific intestinal microbial communities, which in turn provides nutrition and energy for the host, and promotes the acquisition of food energy and the storage of white fat [2–4]. Changes in ambient temperature are among the strongest physiological stimuli to increase the formation and activity of thermogenic fat [5, 6]. In mammals, nonshivering thermogenesis (NST) generates metabolic heat to maintain body temperature in response to cold [7]. When the temperature drops, the sympathetic nervous system releases b-adrenergic agonists, such as norepinephrine (NE), which stimulates b-adrenergic receptors on brown adipocytes and initiates a series of downstream events, including the transcription of uncoupling protein 1 (UCP 1) [8]. Brown adipose tissue (BAT), which is dependent on the UCP 1 process, undergoes the uncoupling of the mitochondrial electron transport chain to produce heat from the oxidation of carbohydrates and lipids [9]. However, the activation of metabolic heat production in response to cold is not only restricted to BAT. The BAT-like phenotype is available from white adipose tissue (WAT) depots, and brown adipocytes are formed under exposure to cold in a process known as “browning” [8]. The intestinal microbes in mice regulate fat accumulation by promoting thermogenesis [10]. Cold exposure led to significant changes in the composition of intestinal microbes, referred to as cold microbiota, and the relative abundance of Firmicutes was greater than that of Bacteroides. Interestingly, the transplantation of “cold microbiota” increased the browning of white fat and the insulin sensitivity in recipient mice, which indicated that the “cold microbiota”-transplanted mice had resistance to cold, which was partly mediated by the browning of white fat [11]. Further exploration of the role of the intestinal microbiota as a cold-induced heat-producing medium, and showed that cold exposure changed the cecal microbes of Brandt’s voles and increased the concentration of SCFAs [12]. In addition, injection of NE also resulted in long-term changes in the intestinal microbial structure, and it was confirmed that the transplantation of "cold microbiota" increased thermogenesis through activation of cAMP-PKA-pCREB signaling, which might be due to the interaction of the intestinal microbiota with host neurotransmitters and the joint regulation of body heat production and energy consumption when exposed to cold [13]. Generally, intestinal microbes adapt to changes in the external temperature environment by adjusting the browning of white fat and the activity of brown fat [10, 11, 14–16].

Changes in warm environments also play a vital role in the health of piglets [17]. Since the thermoregulatory mechanism of piglets has not been fully developed, the ambient temperature required for piglets is high. Although thermal insulation measures are usually taken during piglet breeding, cold stress syndrome is still very common in piglets in actual production. At the same time, most breeds of pigs have no external brown fat reservoir, only a white fat reservoir. Many pigs have lost the NST effect of UCP1-mediated brown fat decomposition and switched to mainly rely on shivering thermogenesis [18]. The lack of a functional UCP1 gene, which is very important for thermogenesis, leads to a decreased ability to regulate one's body temperature and an increase in cold sensitivity under cold stress. In addition, cold stress causes decreased piglet resistance, induces intestinal damage and microecological imbalance, causes piglet diarrhea and various other diseases, and reduces the growth rate, thus inducing piglet death [19, 20]. Intestinal microbes may relieve cold stress by providing 15–20% of the energy to the host, seriously affecting the production efficiency of pigs. Therefore, maintaining the intestinal health of piglets and reducing cold stress in the environment play important roles in the health of piglets, so it is particularly important to alleviate cold stress in piglets.

Therefore, the purpose of this study was to evaluate the responses of piglet intestinal microbes under cold stress intervention and to preliminarily explore the possible mechanisms by which intestinal microbes mediate cold stress in piglets to provide a theoretical basis and technical support for improving cold resistance and maintaining the intestinal health of piglets.

Study sites and sample set

The breeding experiment was carried out at the experimental base in Zengcheng District, Guangzhou City, Guangdong Province. The experimental animals were reared by turning carts, with each turning cart was divided into pens, and each turning cart including 3 experimental animals. The test animals were provided with feed and water ad libitum; we cleaned up the remaining feed in the trough and added new feed at 08:00 and 18:00, weighed and calculated the feed intake, and cleaned the cart and the environment at the same time. At the beginning and end of the official period, the test animals were weighed on an empty stomach in the morning to calculate the average daily gain, average feed intake, and feed-to-weight ratio. The experimental animals in the nonantibiotic group did not receive any drugs or antibiotics, and the health status of all experimental animals was recorded; the cold stress group was exposed to a low temperature of 18°C for 48 hours.

Blood was collected from the anterior vena cava of each pig to extract serum for subsequent studies; then, each intestinal segment was punctured, carefully cut, and collected contents. Then, the remaining contents were removed, and the cecal wall was cut and washed with 4°C normal saline. A sterile glass slide was used to gently scrape the intestinal mucosa, and other tissue samples were collected in the same way. All the utensils in the sample collection process were sterilized, and each group of samples was divided into 3 sterile centrifuge tubes, quickly placed in liquid nitrogen, brought back to the laboratory, and stored at -80°C for testing. All samples from each pig were collected within 30 minutes after slaughter.

Measurement of in serum

At room temperature, blood naturally coagulates for 10–20 minutes and centrifugates for about 20 minutes (2000–3000 rpm). Collect the supernatant carefully, and centrifuge again if precipitation occurs during storage. Serum IgA, IgG, IL-6, IL-10, IL-18 and NF-kB were quantified via radioimmunoassay using a Porcine ELISA Kit (Shanghai MLBIO Biotechnology Co. Ltd, Shanghai, China) according to the instructions. Serum glucose, insulin, cortisol, leptin, adiponectin, T3, T4, GLP-1 and PYY were quantified via radioimmunoassay using an ELISA Kit (Nanjing Jiancheng Bioengineering Institute, Nanjing, China) according to the instructions.

Histology

Fresh harvested duodenum and caecum was fixed in 10% buffered formalin for 24–48 hours at room temperature, washed twice with PBS, and stored in PBS at 4℃ before paraffin embedding. Tissue was cut and stained with H&E using standard techniques. Each slice of each treatment group was photographed with 40 times visual field, and five intact villi were selected to measure the mucosa thickness, villi length and crypt depth, which were analyzed by Image-pro plus 6.0 (Media Cybernetics, Inc., Rpckvile, MD, USA) processing software.

16S rRNA sequence

The quantified library was sequenced with the Illumina HiSeq PE250 platform for its V3-V4 hypervariable region PCR amplified fragments. PCR amplification, sequencing services, and data quality control were provided by Novogene Co., Ltd. (Beijing, China). QIIME2.2019.10 was used to analyze the feature table analysis. DADA2 was used to analyze the feature table, performing dereplication (dereplication, equivalent to 100% similarity clustering), to obtain amplicon sequence variants (ASVs). These sequences obtained from the samples were abundant [21]. The feature table, map file, and classification tree analyzed by QIIME2.2019.10 were imported into the Phyloseq platform using qiime2r to calculate the alpha diversity, including the Chao1, Shannon, and observed species indices. Two methods, unweighted and weighted, were used to calculate the beta diversity of the bacterial flora in the samples. Using permutational multivariate analysis of variance (PERMANOVA), also known as Adonis analysis, the Bray-Curtis distance matrix was used to decompose the total variance, and analyzed the degree of explanation of piglet age for the gut microbiota structure was analyzed by a linear model. A permutation test was used for significance analysis, with (P < 0.05) indicating significance. Further analysis was carried out for the differences in the composition of the gut microbiota. For the relative abundance at the bacterial phylum level, the Wilcoxon rank-sum test was used for statistical analysis (P < 0.05), and the difference was significant. DeSEq2 was used to analyze the differences in bacterial composition at the family and genus levels. The difference analysis parameters were set as follows: design = ~ Treatment, and the ASVs with padj (corrected P-value) < 0.05 had significant differences at the family and genus levels.

Metagenomic sequencing and data analysis

The intestinal contents and mucosal microorganisms were analyzed by metagenomic sequencing. After the DNA was extracted and quality-tested. All database construction and sequencing services were provided by Novogene Co., Ltd. (Beijing, China). After quality control and filtering of the obtained raw data. The functional prediction of the microbial community was done on the extracted datasets using eggnog-mapper based on EggNOG databases, and HMMER3 was used for sequence homology search to match the orthologous gene. The functional description of the orthologous gene corresponding to the searched sequence was the final annotation result. STAMP was used to analyze the composition differences between the treatments. After FDR correction, P < 0.05 was the threshold for significance. The aim was to identify strains that play a key role under cold stress conditions.

Metabolomics

First, collected serum samples, then pretreated the samples, and finally analyzed by LC/LS using Agilent 1290 (Agilent Technologies). The raw data obtained were converted into mzXML format by Proteo Wizard software. XCMS was then used for retention time correction, peak recognition, peak extraction, peak integration and peak alignment, with minfrac set to 0.5 and cutoff set to 0.3. At the same time, the substances of peaks were identified by using the self-made R package and self-built secondary mass spectrometry database. The differences of the identified metabolite data tables in positive and negative ion state were analyzed by MetatoAnalyst 4.0 comprehensive metabolomics analysis platform. Auto scaling method was used to standardize the data, and statistical analysis module was used to analyze the differences of metabolomics results, including OPLS-DA multivariate analysis, Heatmaps analysis and Random Forest differential metabolite machine learning identification. Among them, Heatmap mapped the top 30 differential metabolites in differential analysis, and identified the metabolites with significant representative characteristics that played an important role in different treatments by using machine learning method.

Transcriptomics

Total RNA was prepared using RNAprep pure Tissue Kit (TIANGEN) according to manufacturer’s instructions. Then total RNA was poly-A + selected, fragmented by metal-ion hydrolysis and then converted to cDNA using PrimeScript™ RT reagent Kit with gDNA Eraser (TAKARA). The cDNA was end-repaired adenylated and ligated with Illumina sequencing adapters. Finally, the fragments were amplified by PCR. The quantified library was sequenced with the Illumina HiSeq PE2500 platform. Gene-level read counts were generated using featureCounts and GENCODE gene annotation.

βNTI analysis

To evaluate the community assembly processes, the mean nearest taxon dis-tance (MNTD) taxonomic β-diversity metrics (βNTI and Bray-Curtisbased Raup-Crick, RCBray) was calculated. |βNTI| >2 means that the deterministic process was the main factor to influence the microbial community across all samples. In detail, a βNTI with value of <-2 suggests homogeneous selection and > 2 means variable selection. If |βNTI| <2, the RCBray should be calculated: (1) RCBray > 0.95 means dispersal limitations, (2) RCBray<-0.95 reveals homogeneous dispersal, and (3) |RCBray| <0.95 indicates “non-dominant” fractions [22, 23].

Cold Exposure Changes the Blood Metabolism Parameters in Piglet

Cold exposure changes the body's metabolic state to maintain energy homeostasis. To explore the effects of cold exposure on the metabolism of piglets, we tested the energy metabolism parameters of blood serum after 48h of cold exposure with or without addition of broad-range antibiotics (Anti) to drinking water to deplete the intestinal microbiota. Glucose and insulin concentrations were not found to be different between each treatment (Fig. 1a and 1b). Cortisol was lower under cold exposure treatment than at room temperature (RT) (Fig. 1c, P < 0.01). Leptin and adiponectin were lower under cold exposure than under RT treatment regardless of microbiota depletion (Fig. 1d and 1e, P < 0.05). However, under the same treatment, depletion of the gut microbiota did not influence the leptin and adiponectin concentrations in blood serum (P > 0.05). The T3 concentration was inhibited by cold exposure compared with that at RT (Fig. 1f, P < 0.05). After depleting piglet intestinal microbiota with Anti, the T3 concentration was significantly lower than that under RT treatment alone, which means that the intestinal microbiota played an important role in maintaining T3 homeostasis (Fig. 1f, P < 0.05). In response to cold exposure, T4 had the same result as T3 without microbiota depletion; however, there was no difference under RT treatment with or without Anti addition (Fig. 1g). There was a tendency toward decreased GLP-1 concentrations in the blood serum of cold-exposed or RT + Anti treated piglets that bordered significance (Fig. 1h). The PYY concentration had the same trend as the GLP-1 response to cold exposure; however, there was no significant difference with or without the microbiota depletion using Anti (Fig. 1i). Together, these data suggested that cold exposure would selectively affected blood energy metabolism parameters and that most of them were inhibited. The intestinal microbiota played an important role in the same parameters under the RT environment.

Cold Exposure Increased the IgA Levels and Insulin Sensitivity

To investigate whether the cold exposure influences the weight of important organs, after slaughter, we measured the liver, spleen, and thymus weights (Fig. 2a-c). Only liver weight had a decreasing trend under cold stress compared with that at RT (Fig. 2a, P = 0.059). IgA and IL-18 in the blood serum of piglets were increased under cold exposure, while there were no significant differences between other treatments, including with or without depletion of microbiota (Fig. 2d and 2h). IgG, IL-6, IL-10, and NF-kB were not significantly different under the four treatments (Fig. 2e, 2f, 2g, and 2i). We also detected glucose tolerance by measuring the blood sugar concentration at different times after oral administration of glucose (Fig. 2j). The results showed that cold-exposed piglets without microbiota depletion had an elevated glucose peak following glucose gavage compared to that under other treatments after 40 min (Fig. 2j), but also the fastest clearance. Interestingly, there was nearly the same initial glucose peak between the cold and cold + Anti treatments. This suggested that the oral glucose was rapidly taken up in cold-exposed piglets without microbiota depletion. However, piglets subjected to intestinal microbiota depletion under room or cold temperature showed a delayed peak appearance at 60 min, and the speed of clearance was slower than that under cold treatment but higher than that under normal RT treatment. This suggested that a broad range of antibiotics that interrupted the piglet intestinal microbiota would influence glucose uptake and metabolism in the host. In our models, the duodenum mucosal thickness, villus length, crypt depth, and cecal mucosal thickness increased in the cold-exposed piglets compared with those in the RT-treated piglets (Figure S1a, 1b, 1d, and 1e). Interestingly, there was an antibiotic-dependent effect on the above parameters, which were increased upon intestinal microbiota depletion at room temperature. However, under cold exposure with antibiotic treatment, the duodenal mucosal thickness and villus length decreased compared to those under cold exposure without antibiotic treatments (Figure S1a and S1b) but duodenal crypt depth increased. These results suggest that the intestinal morphological response to the environment was also determined by intestinal microbiota homeostasis.

Cold Exposure Changes the Microbiota Composition and the Content and Epithelial Surface of the Caecum

To investigate whether the cold stress causes changes in the intestinal microbiota, we collected the cecal content and epithelium-attached samples of cold-exposed and RT-treated piglets (Fig. 3 and Figure S2). Determination of the composition by 16S rRNA marker gene sequencing, followed by principal coordinates analysis (PCoA) based on the weighted UniFrac distance, showed the large alterations in the microbiota compositions under cold exposure (Fig. 3a). We observed that the abundant phyla were Firmicutes, Bacteroidetes, Proteobacteria, Epsilonbacteraeota, and Actinobacteria. Firmicutes was the most abundant phylum in all samples (on average 72.16%), followed by Bacteroidetes (on average 23.13%) in all samples (Fig. 3b). At the family level, Lachnospiraceae, Ruminococcaceae, and Prevotellaceae were the most abundant families in all samples, accounting for 36.95%, 22.77%, and 20.43%, respectively, on average. The alpha diversity was increased after cold exposure, but only the Shannon index was significantly higher than that under the RT treatment (Fig. 3c). However, there were no significant differences found between the cold and RT treatments at the phylum level (Fig. 3d-3j). We further investigated whether the differences existed in ASVs using STAMP software and FDR adjusted P-values. We found that 39 ASVs were significantly different between the cold and RT treatments, while most of them, with 20 ASVs were higher under cold exposure than under RT treatment (Fig. 3k, P < 0.05). A total of 20 ASVs were distributed into Lachnospiraceae, Ruminococcaceae, Prevotellaceae, Rikenellaceae, Muribaculaceae. Unique ASVs were higher in the RT treatment and belonged to Eggerthellaceae, Campylobacteraceae, and Helicobacteraceae three families (Fig. 3k, P < 0.05).

The intestinal epithelium surface, where the key signal transporters, such as the short fatty acid receptor, were located, was the most important place for the interaction between intestinal microbiota and the host. Thus, we also detected the cecal epithelium-attached microbiota using 16S rRNA sequencing (Figure S2). The PCoA showed that the gut microbiota under cold exposure and RT treatment had a little overlap between samples (Figure S2a). All alpha diversities also had an increasing trend for the cold-exposed piglets, but no significant index was found (Figure S2c). The most abundant phylum in the intestinal epithelium-attached microbiota was the same as that in the cecum. However, the order of the relative abundance of these phyla was different from those in the cecum and included Bacteroidetes, Firmicutes, Epsilonbacteraeota, Proteobacteria, and Spirochaetes with averages of 50.02%, 22.44%, 17.29%, 8.73%, and 1.00%, respectively. The most abundant families were Prevotellaceae, Lachnospiraceae, Ruminococcaceae, and Muribaculaceae, at 44.29%, 9.81%, 6.55%, and 2.18%, respectively. The phyla Firmicutes and Spirochaetes showed an increasing trend compared with their levels under RT treatment (Figure S2f and S2i, P = 0.071, and 0.056, respectively). The other phyla were not influenced under cold exposure (Figure S2d, g, h, and j). At the ASV level, only 14 ASVs were found to be significantly different, 12 of which were higher than those under RT treatment (Figure S2e, P < 0.05). A total of 12 ASVs belonged to Prevotella, Alloprevotella, Ruminococcaceae, Muribaculaceae, and Erysipelotrichaceae (Figure S2e).

Cold Exposure Changes the Functions of Microbiota in the Cecum

The functions of the gut microbiome were confirmed using metagenomics sequencing. A total of 99 Kyoto Encyclopedia of Genes and Genomes (KEGG) orthology (KO) genes in the metabolism catalog showed significant differences between cold and room conditions (P < 0.05), while 72 of these genes were more highly expressed in the cecal microbiota under cold exposure than at room temperature (Fig. 4a). Changes in gene abundances are shown with pathway representation (Fig. 4b). Among the most differentially abundant pathways, purine metabolism, arginine biosynthesis, fructose and mannose metabolism, sulfur metabolism, biotin metabolism, N-glycan biosynthesis, arginine and proline metabolism, glyoxylate and dicarboxylate metabolism, methane metabolism, oxidative phosphorylation, pentose and glucuronate interconversions, nitrogen metabolism, and glycosaminoglycan degradation were found to be associated with cold exposure (Fig. 4b). More production of purines, including adenine, hypoxanthine, xanthine, and guanine, might be associated with higher energy requirements for microbiome growth and metabolism. Gas production in the intestine is important for energy balance and even for signal transport. Under cold exposure, hydrogen, and trimethylamine for methane metabolism, ammonia for nitrogen and glyoxylate and dicarboxylate metabolism and sulfide and succinate for sulfur metabolism were higher under cold stress, which means that the microbiome might exhaust more energy to produce these products. However, porphyrin and chlorophyll metabolism were found to be significantly associated with RT conditions, and is responsible for producing vitamin B12 coenzymes (Fig. 4b). In the epithelial surface of the cecum, the total number of significantly different KO genes was only 24 under the metabolism category. Eleven of these genes were higher under cold exposure than at room temperature and were mainly (7/11) associated with amino acid metabolism (Fig. 4a). Nine of 13 KO genes were dominant at RT and related to carbohydrate metabolism and glycan biosynthesis and metabolism.

A total of 140 other KO genes were found to be significantly different between the two treatments in the cecum (P < 0.05) (Figure S3). In contrast, 56/140 KO genes were higher at room temperature, and 84/140 KO genes were dominant under cold stress (Figure S3a). For room temperature treatment, the adherence, spore germination, and motility functions at protein families: signaling and cellular processes pathways were stronger than under cold stress, which means the microbiome had more power capacity to establish or move to the proper microenvironment. For protein families: genetic information processing, 16/35 were higher under RT, and 19/35 were higher under cold stress. The functions for the cecal epithelium are shown in Figure S3b. These differences might not be just from the microbiome, but also from including the host.

Cold Exposure Elevated the Amino Acid and Bile Acid Metabolism level

To determine which metabolites are related to cold exposure in the host, we detected the blood metabolites using metabolomics. There were 133 significant metabolites in the positive model between the two treatments (Fig. 5a), while 120/133 were dominant under cold stress. The OPLS score also showed that the two treatments were significantly separated (Fig. 5b). We also used the microbe-metabolite vector (mmvec) to confirm the correlation between microbiota and metabolites according to a previous report [24]. After analysis, we found that stearidonic acid, Arg-Thr, oxaprozin, glucosamine, His-Cys, Pro-Tyr, N-Oleoylethanolamine, D-4-Hydroxyphenylglycine, and glutamic acid were higher under cold stress than RT, and a higher correlation was observed between metabolites and the microbiome in the cecum (Fig. 8a and c). Stearidonic acid was positively correlated with the Prevotellaceae NK3B31 group, Ruminantium group, Alloprevotella, and Prevotella 9 (Fig. 8c). Oxaprozin was most correlated with Prevotella 9 and Alloprevotella. Glucosamine and His-Cys were correlated with Prevotella 9 and Ruminococcaceae UCG-005. N − Oleoylethanolamine was correlated with the Christensenellaceae R-7 group and Alloprevotella. D-4-Hydroxyphenylglycine was correlated with Prevotella 2. Phenol, His-Ala, sphinganine, and sulfanilamide were more abundant among the blood metabolites and had a high correlation with the microbiome of the mucosal surfaces of the cecum (Fig. 5a and d). Phenol was correlated with Alloprevotella and Prevotellaceae UCG-001. His-Ala was correlated with Muribaculaceae. Sphinganine and sulfanilamide were both correlated with the Prevotellaceae and Rikenellaceae RC9 gut groups.

The significant metabolites numbered 67 in the negative model between the two treatments (Figure S4a), while 28/67 were higher under the cold stress treatment. However, only a few metabolites, including 2-Dehydro-3-deoxy-D-gluconate, Anthranilic acid (Vitamin L1), and 3-Hexanone, were found to be higher under cold stress and to have a strong correlation with the microbiome. 2-Dehydro-3-deoxy-D-gluconate was strongly correlated with Prevotella 9 in cecum (Figure S4c). Anthranilic acid (vitamin L1) was correlated with Prevotellaceae UCG-003 in the mucosal surfaces of the cecum (Figure S4d). 3-Hexanone was correlated with Parabacteroides and Prevotellaceae UCG-003 in the mucosal surfaces of the cecum.

Cold stress increased the expression of UCP3 in fat

To uncover the mechanism of the microbiota-liver and fat cross-talk responsible for the energy balance, we deep sequenced the transcriptome from the liver and fat. Pathway enrichment analysis revealed that changes in pathways involved in butanoate, propanoate, and pyruvate metabolism, pentose and glucuronate interconversions, and glycolysis/gluconeogenesis related to carbohydrate metabolism were decreased in the liver under cold stress when compared to RT (Figure S5a). Glycerolipids, fatty acid degradation and metabolism, ascorbate and alternate metabolism, and retinol metabolism related to vitamins were also decreased in the liver under cold stress (Figure S5a). Tryptophan metabolism, valine, leucine, and isoleucine degradation related to amino acid metabolism and steroid hormone biosynthesis related to hormone biosynthesis were also decreased under cold stress when compared to RT (Figure S5a). Under cold stress, most signaling pathways, including cytosolic DNA-sensing, TNF, NOD − like receptor, and IL-17 signaling pathways, increased compared to those under RT. Pyrimidine metabolism, RNA degradation, RNA polymerase, proteasome, and ribosome biogenesis in eukaryotes were also increased under cold stress, compared to RT pigs (Figure S5b). The immune signaling pathways in the fat were decreased under cold stress, compared to RT pigs (Figure S5c). However, in the fat, the pathways related to the energy supply, including PPAR, cGMP-PKG, AMPK, adipocytokine, calcium signaling pathways, increased compared to those under RT. Regarding substrate metabolism aspects, aspects of energy supply, including protein digestion and absorption, vitamin digestion and absorption, glycerolipid metabolism, fat digestion, and absorption, were also increased under cold stress, compared to RT (Figure S5d). We found that the CYP8B1 involved in the main bile acid synthesis pathway was 16 times higher under cold stress in the liver than under RT (Fig. 6a). However, after treatment with mixed antibiotics, the increasing trend was suppressed, and significant differences were found between cold and cold + Anti treatments, indicating that the gut microbiota might play a very important role in manipulating the bile acid metabolism pathway (P < 0.05). Nr0b2 and FXR receptors, which could combine with bile acid to regulate the bile acid cycle, increased under cold stress in the liver compared to those under RT. The genes Adcy9, Nceh1, and Abce4, which are involved in the bile secretion pathway, also increased under cold stress. Adcy9 encodes neutral cholesterol ester hydrolase 1 responsible for transferring cholesterol ester to cholesterol, which is an important precursor for bile acid synthesis. However, we examined the genes responsible for the secondary bile acid biosynthesis in the gut microbiome, including cbd, baiB, and baiN, and they were not influenced by cold stress (P > 0.05, Figure S6). Other important microbial metabolites include short chain fatty acids, which play a very important role in host energy balance regulation through the receptors Ffar2, Ffar3, and Ffar4. We found that the genes Ffar2 and Ffar3 were upregulated by cold stress in the livers of pigs (P < 0.05, (Fig. 6a), while the Ffar4 was downregulated slightly.

The expression of genes Bcl2l1 and Mcl1 involved in antiapoptotic function increased under cold stress in the liver, compared to RT in pigs (Fig. 6b). The pro-apoptotic genes Casp6, Casp8, Casp10, and Fas also increased under cold stress. However, TNF signaling promoted apoptosis, which was activated by bacteria (Fig. 6b). Thus, these proapoptotic genes were significantly suppressed in all microbiota-depleted pigs (Fig. 6b). The FAS, Slc2a8b, and Slc2a12 genes involved in glucose transporters were upregulated under cold stress, indicating that glucose metabolism can be enhanced by the microbiota under cold stress.

Additionally, under cold stress, the insulin resistance was higher than that under RT, which was consistent with a previous insulin resistance experiment. Fat is important for thermogenesis, especially UCP genes. However, the UCP1 gene is enriched in brown adipose tissue and is absent in pigs. We also investigated whether the UCP gene involved in thermal energy was significantly different under cold stress in the liver and fat (Fig. 6c). The results revealed that the UCP2 gene counts were decreased under cold stress in the liver; in contrast, UCP3 gene counts were increased in the fat under cold stress compared to those under RT, which suggested that the fat might provide an important energy supply through UCP3 pathways for piglets under short-term cold stress. The genes Arid1b, Smarcd3, Cpt1A, Cpt1B, and Slc25a20, which are involved in fat metabolism for thermogenesis, increased under cold stress in fat (Fig. 6c). In particular, Cpt1B, which is necessary to use fat to supply energy through β-oxidation, increased under cold stress to levels 4 times those under RT. The expression of the Clps and Pnliprt1genes responsible for fat metabolism increased 18-fold under cold stress; however, no difference was found between cells treated with or without using antibiotics under cold stress (Fig. 6c).

Cold stress changed the turnover of the total, abundant, and rare ASV fractions

We calculated ໿βNTI and RC_Bray to determine the assembly processes driving microbial community composition under different treatments (Fig. 7). In the total, abundant, and rare ASVs fractions, βNTI values were all between − 2 and 2, indicating that the stochastic process was critical. With further analysis, the RC_Bray values of the microbial communities in the abundant and most total ASVs, except RT_CeM, were ໿>0.95, suggesting that dispersal limitation predominantly governed the microbial communities. Conversely, the RC_Bray values of the microbial communities of the rare ASVs were only found in C_CeM and C_CeM-RT_CeC to be > 0.95, and in the other pairwise samples, the RC_Bray values were < 0.95, reflecting that drifting alone (weak selection) determined the microbial community.

The gut microbiota is shaped by diet, environmental conditions, and physiological, host, and social influences, which have dual effects on the host and gut microbiota [25]. In the present study, we first investigated the bidirectional effects between the gut microbiota and host in regulating thermogenesis in piglets (Fig. 8). The composition, structure, and functions of the microbiota of the cecum and its mucosal surface were affected by cold stress, and there were correlations between the gut microbiota and thermogenesis through metabolites regulating the signal and gene expression in the liver and fat. After the host experienced cold stress, glucose was first mobilized as energy. However, when the cold stress was prolonged, the pool of the host glucose was consumed, hormones related to glucose metabolism, such as cortisol, T3, and T4, decreased, ໿and the host increased fat metabolism to supply energy. Cold stress increases the SCFA and some secondary bile acid concentrations through metabolites in the blood and their receptors (NR0B2, FXR, FFAR2, and FFAR3), activating the cGMP-PKG, PPAR, AMPK, and calcium signaling pathways in fat and increasing the fat degradation genes, including CLPS, PNLIPEP1, CPT1A/B, and UCP3, thus leading to thermogenesis of the host. These data suggest that gut microbiota-SCFA and bile acid interactions through the host's many and comprehensive signaling pathways in fat mediated host energetics and thermogenesis during cold stress in piglets.

Cold stress could significantly suppresses the production of GLP-1 and PYY, which regulate the food intake and normally reach the peak at 20 min after food intake [26], indicating that the negative feedback regulating on food intake was weaken under cold stress and allowed piglets to consume more food to produce heat and maintain normal body temperature. Cold stress increased insulin resistance; however, after depletion of the microbiome, insulin resistance was lowered, indicating that the gut microbiota plays an important role in this function, which is consistent with the previous reports in mice under cold stress [27–29]. In our study, we also found interesting results that hormones in serum involved energy metabolism, including T3, T4, cortisol, leptin, and adiponectin, were suppressed in pigs under cold stress. Leptin and adiponectin are secreted by fat [30]. The decreased leptin might weaken the suppression of the food intake through the pro-opiomelanocortin (POMC) pathway and decrease the energy exhaustion, indicating that the pigs could store more energy to resist cold stress [26, 31]. However, T3, T4, cortisol, and adiponectin, which play important roles in the energy exhaustion, were increased under cold stress in most previous studies [12, 27]. The reason for this phenomenon might be that the piglets had low energy stores, and after cold stress, the energy was not adequate to exhaust, and liver function was suppressed to decrease T3 and T4 secretion through the hormonal or leptin pathways [32]. Cold stress increased the weight of the critical immune organs the spleen and thymus in pigs, while depletion of the gut microbiota weakened these effects, indicating that gut microbiota may play a very important role in immune functions. IgA, IgG, and IL-18 also increased under cold stress, further confirming that short-term cold stress increases the host immune response and gut microbiota may play a key role in the immune function [33]. Cold stress also increased duodenal villus length and mucosal thickness in pigs, and depletion of gut microbiota would also weak these effects. A previous report showed that the mice had an increased intestinal surface to strengthen the glucose uptake for energy supply after cold microbiota transplantation [27].

In pigs under cold stress, the diversity of the gut microbiota increased, and the Shannon index was significantly higher than that under RT (Fig. 5c). However, most previous studies in mice showed that the diversity was not influenced by cold stress [12, 27, 34, 35], and cold stress also decreased the alpha diversity, including the Shannon index and observed OTUs. However, after 4 weeks of cold stress in the ໿male Brandt’s voles (Lasiopodomys brandtii), the alpha diversity of the cecal microbiota increased [12], indicating that the changes in the diversity of the gut microbiota under cold stress might be affected by the breed of animal and the duration of exposure to cold stress. The most abundant phyla in the cecum or its mucosal surface in pigs were Firmicutes, Bacteroidetes, Proteobacteria, and Actinomycetes, occupying up to 95%, which was consistent with findings in other mammals, such as mice [27] and humans (Martens et al., 2018). However, at the phylum level, not all bacteria were affected by cold stress, and at the mucosal surface, only Firmicutes showed an increasing trend (P = 0.071), consistent with a previous study in mice under cold stress; additionally, more differences were found at the family, genus, or species levels (Li et al., 2019). However, the number of different ASVs from the mucosal surface of the cecum was higher under cold stress than RT, suggesting that the microbiota in the cecum might be strongly affected under cold stress or that the microbiota on the mucosal surface is limited. Many studies show that environmental temperature affects an animal's gut microbiota [12, 34]. Prevotellaceae and Ruminococcaceae were the most abundant ASVs under cold stress compared to RT, consistent with a previous study in the ໿male Brandt voles and mice under cold stress [28]. These bacteria might play very important roles in the fermentation of residues, including complex carbohydrates that cannot be digested in their original form in the intestine of the hindgut [36]. We also found ໿that Muribaculaceae was higher in both the cecum and mucosal surface under cold stress than under RT. Muribaculaceae is newly named from family S24-7 (phylum Bacteroidetes) and includes versatile microorganisms involved in complex carbohydrate degradation and defined as the main mucosal sugar utilizers to reduce Clostridiodes difficile colonization [37, 38]. ໿Rikenellaceae and ໿Lachnospiraceae, which were increased after cold acclimation in pigs, could also have the potential ability to utilize mucosal sugars and may be associated with the host health [38–41]. Lachnospiraceae is also an important butyrate producer that impacts colonization resistance through antibiotic expression or intestinal acidification and influence host mucosal immune cells and enterocytes [40]. ໿Alloprevotella, which was increased on the mucosal surface of pigs after cold acclimation, is similar to Prevottella and correlated with low cardiovascular disease risk [42]. ໿Erysipelotrichaceae increased at the mucosal-surface of pigs after cold exposure. However, Erysipelotrichaceae decreased after cold exposure in mice, and the reason for this difference might be the different positions of the gut microbiota used [42]. Some bacteria in ໿Erysipelotrichaceae were reported as colitogenic strains [43] but were also reported to be correlated with host health [44].

Using correlation analyses ‘mmvec’ (microbe–metabolite vectors) was used in our study to determine the correlation between microbiomes and serum metabolites [24]. ໿taurolithocholic acid (TLCA), ໿taurodeoxycholic acid (TDCA), taurocholic acid (TCA) were correlated with ໿the Rikenellaceae RC9 gut group and ໿Muribaculaceae. ໿Glycocholic acid (GCA), ໿glycochenodeoxycholate (GCDCA), and ໿chenodeoxycholate (CDCA) were higher in serum under cold stress than under RT. CDCA has the highest FXR receptor affinity [45]. However, we did not find a strong correlation between bacteria and these bile acids, possibly because the main bacterial genera of the gut microbiota involved in BA metabolism occurred in the small intestine [24]. However, the families Ruminococcaceae and Prevotellaceae were the main bacteria that were correlated with metabolites in serum. ໿Cold-exposed microbiota could induce the bile acid synthesis gene CYP8B1 to increase in the liver, which was consistent with the previous studies [28]. However, we did not find a significant difference after cold exposure for the important gene CYP7A1, which was reported to increase after cold exposure in mice [28, 29]. The receptors and the genes involved in bile acid metabolism including NR0B2, FXR, ADCY9, NCEH1, and ABCB4 were higher after cold exposure, which was consistent with the previous studies [28, 29]. The strength of bile acid metabolism might contribute to the host energy homeostasis [29]. SCFAs can also act as signal metabolites via the G-protein-coupled receptors FFAR2, FFAR3, HCA2, HCA1, and SUCNR1 during appetite regulation and thermoregulation [30, 46]. The FFAR2 and FFAR3 genes were higher in the liver under cold stress than under RT, which was consistent with a previous study, and FFAR2 expression in fat was increased in ໿male Brandt’s voles (Lasiopodomys brandtii) [12]. However, a previous study showed that FFAR2 and FFAR3 were not different under different antibiotic treatments to deplete gut microbiota [34]. We found the same result for FFAR3, but the expression of FFAR2 was increased under RT and decreased under cold stress after antibiotic treatment. These differences might need further study to confirm. Increasing evidence indicates that SCFAs can regulate appetite and by increasing ໿glucagon-like peptide-1 (GLP-1)/fasting peptide YY (PYY), a peptide hormone that reduces appetite [30]. However, we found that GLP-1 and PYY decreased under cold stress, which might increase the appetite and thus food intake to maintain energy requirements. Fat is important for the energy supply when carbohydrates or glucose are insufficient, especially in a cold environment [47]. The fat lipolysis genes CLPS and PNLIPRP1 were higher under cold stress than under RT. After fat lipolysis, the fat must be transferred into mitochondria for energy supply through hepatic fatty acid oxidation by CPT1A/B, which increased in the fat of pigs under cold stress in our study, consistent with a previous report about CPT1A in mouse livers under cold stress [28]. The UCP gene was the last and key gene for heat supply. Most studies have shown that under cold stress in most mammalian animals, fat can be transferred to BAT by UCP1, and the gut microbiota also plays a very important role in this process [12, 28, 34]. However, the pig lacks UCP1 genes and cannot be browning of white adipose tissue to balance thermogenesis [48–50]. However, using RNAseq, we detected that UCP2/3 genes and UCP3 were higher under cold stress than under RT, indicating that UCP3 might play a very important role in supplying heat by oxidizing fat in pigs. This result agreed with the finding of higher UCP3 was higher under cold stress in Tibetan pigs [51], indicating that UCP3 also plays a very important role in the thermogenesis in pigs.

Cold stress is a normal environmental stress in piglets and disturbs the balance of their gut microbiota, affecting host metabolic physiology. The present study illustrates the relationship and possible pathways between the host and microbiota under cold stress in piglets. Our data demonstrate that cold stress induced a wide range of physiological changes, such as increased insulin resistance, IgA, and IgG and decreased cortisol, T3, T4, leptin, adiponectin, GLP-1, and PYY. Cold stress also caused a significant alteration in the content and microbiota of the cecum and its epithelial surface, which was also accompanied by an increase in some secondary bile acids in serum in piglets under cold stress. The most significant and correlated with metabolites in serum were the ASVs belonging to Ruminococcaceae, Prevotellaceae, and Muribaculaceae. Moreover, the transcriptomics results showed that a higher expression of CYP8B1, FXR, FFAR2, and FFAR3, which are related to bile acid and SCFA metabolism, in the liver under cold stress than under RT. In addition, the fat lipolysis genes CLPS and PNLIPRP1, CPT1B, and UCP3 were significantly increased in the fat of piglets under cold stress. However, antibiotic-treated piglets showed a weakened or strengthened cold tolerance phenotype, indicating a link between the host response to cold stress and regulation by gut microbiota. These findings suggest that gut microbiota-blood-liver/fat axes mediate host thermogenesis under cold stress via metabolites produced by bacteria and the host. This sheds a considerable new insight into the communication between piglets and the gut microbiota used to improve the survival or health situation of piglets under cold stress.

Ethics approval and consent to participate

The experimental design and procedures were conducted according to the institutional guidelines for the care and use of experimental procedures involving animals were approved by the Animal Experimental Committee of South China Agricultural University (SYXK2014-0136).

Consent for publication

No applicable.

Availability of data and material

Raw sequence data are deposited in the ENA Sequence Read Archive under accession PRJEB44118.

Competing of interests

The authors declare that they have no conflict of interest.

Funding

This research was supported by the National Natural Science Foundation of China (31802108) and the Natural Science Foundation of Guangdong Province (2019A1515011022).

Authors’ contributions

L-S, XD-L, and JD-M designed the studies. Y-Z, L-S, R-Z, S-L, and SY-Z conducted the experiments. L-S, and Y-Z performed the measurements of data. JD-M, Y-W, SC-X and YB-W analyzed the data. JD-M and Y-Z made the figures. Y-Z, L-S, and JD-M wrote the manuscript. All authors read and approved the final manuscript.

Acknowledgments

No applicable.

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figS1.tif
Cold-exposed piglets show the increased index of the duodenum. a Duodenum mucosa thickness. b Duodenum villi length. c Total no. of goblet cells per mm duodenum villi. d Duodenum crypt depth. e Cecum mucosa thickness. Data are means ± SEM (n = 5 per treatment). *p < 0.05, **p < 0.01, and ***p < 0.001.
figS2.tif
Cold exposure changes the epithelium surface of the cecum composition. a Principal coordinates analysis (PCoA) based on weighted UniFrac analysis. Each symbol represents a single sample of cecum content after 48h of cold exposed (n = 5) or RT controls (n = 5 per group). b Heatmap tree comparing most abundant ASVs from cecum content of 48h cold-exposed (n = 5, inner green rings) and RT controls (n = 5, outer yellow rings) and their phylogenic relationships and the bar represents abundant ASVs. c Alpha diversity of ASVs. d The boxplot of the microbiota of cecum content at the phylum level. e The different ASVs with bar and heat map with p < 0.05. f-j The boxplot of the microbiota of cecum content at the phylum level. Data are means ± SEM (n = 5 per treatment).
figS3.tif
Cold exposure changes the other functions of microbiota in the cecum. The heatmap of the difference at other functions level of the microbiota of (a) content and (b) epithelium surface of the cecum.
figS4.tif
Different metabolomic alterations under the negative model in serum and strong correlation with the microbiome of content and epithelium surface of the cecum. a The heatmap of the difference of serum metabolomic metabolites under cold stress. b Partial least-squares discriminant analysis (PLS-DA) of microbial metabolites in serum. c Metabolite in serum correlation with the microbiome of the content of cecum. d Metabolite correlation with the microbiome of the epithelium surface of the cecum.
figS5.tif
The summary pathways at RNA expression level by RNA seq. a The down-regulated pathways under cold tress compare to RT in liver of piglets. b The up-regulated pathways under cold tress compare to RT in liver of piglets. c The down-regulated pathways under cold tress compare to RT in fat of piglets. d The up-regulated pathways under cold tress compare to RT in fat of piglets.
figS6.tif
The counts of genes involved in the secondary bile acid biosynthesis in the gut microbiota of cecum content by metagenomic sequence. cbh conjugated bile acid hydrolase, baiB, bile acid-coenzyme A ligase, baiN, 3-dehydro-bile acid delta (4,6)-reductase.

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Cold Stress Activates the UCP3 Pathway via Gut Microbiota in Piglet

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Abstract

Figures

Background

Materials And Methods

Study sites and sample set

Measurement of in serum

Histology

16S rRNA sequence

Metagenomic sequencing and data analysis

Metabolomics

Transcriptomics

βNTI analysis

Results

Cold Exposure Changes the Blood Metabolism Parameters in Piglet

Cold Exposure Increased the IgA Levels and Insulin Sensitivity

Cold Exposure Changes the Microbiota Composition and the Content and Epithelial Surface of the Caecum

Cold Exposure Changes the Functions of Microbiota in the Cecum

Cold Exposure Elevated the Amino Acid and Bile Acid Metabolism level

Cold stress increased the expression of UCP3 in fat

Cold stress changed the turnover of the total, abundant, and rare ASV fractions

Discussion

Conclusion

Declarations

References

Supplementary Files

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