Chicken cecal microbiota reduces abdominal fat deposition by regulating fat metabolism

doi:10.21203/rs.3.rs-1763038/v1

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Chicken cecal microbiota reduces abdominal fat deposition by regulating fat metabolism

https://doi.org/10.21203/rs.3.rs-1763038/v1

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Background:

Rapidly developed broilers are often accompanied by excessive fat deposition and markedly decreased feed utilization, which is a severe problem in the poultry industry. Recent studies established that gut microbiota significantly alters chicken fat deposition. However, what kind of cecal bacteria can regulate fat deposition and how they control fat deposition remains unclear.

Results:

The abdominal fat weight and index, and average diameter of adipocytes were markedly higher in high than in low abdominal fat deposition chickens at different time points (1, 4, and 12 months). The higher expression levels of fat synthesis related genes (ACSL1, FADS1, CYP2C45, ACC, and FAS) and a lower expression of fat catabolism genes (CPT-1, and PPARα) in the liver and abdominal fat of high abdominal fat deposition chickens, indicated that an unbalanced fat metabolism leads to excessive abdominal fat deposition. Parabacteroides, Parasutterella, Oscillibacter, and Anaerofustis were found higher in high abdominal fat deposition chickens, while Bacteroides and Sphaerochaeta were higher in low abdominal fat deposition chickens at different time points. Further, Spearman correlation analysis indicated that the relative abundance of Parabacteroides, Parasutterella, Oscillibacter, and Anaerofustis was positively correlated with abdominal fat weight and index, and the expression level of fat anabolism genes, yet the relative abundance of Bacteroides and Sphaerochaeta was negatively correlated with these factors. Furthermore, transferring fecal microbiota from adult chickens with low abdominal fat deposition into one-day-old chicks significantly decreased Parabacteroides, abdominal fat weight and index, and fat anabolism genes, while markedly increased Bacteroides and Sphaerochaeta and fat catabolism genes.

Conclusion:

Our findings highlighted that Bacteroides and Sphaerochaeta might reduce abdominal fat deposition, yet Parabacteroides Parasutterella, Oscillibacter, and Anaerofustis could promote abdominal fat deposition by regulating fat metabolism.

Chickens

cecal microbiota

abdominal fat deposition

fat metabolism

fecal microbiota transplantation

In the poultry industry, genetic breeding technology and nutrition regulation unprecedentedly enhanced the growth rate and feed conversion of broilers [1]. However, rapidly developed broilers are often accompanied by excessive abdominal fat deposition [2], which is an unfavorable trait both for consumers and producers, and physiologically more than 85% of abdominal fat is useless for body functions [3]. A recent report indicated that broilers produced ≈ 3 million tons of abdominal fat around the world annually, which is causing > $2.7 billion economic loss in the poultry industry [4], a key hindrance to profitable farming [5]. Abdominal fat deposition markedly decreases feed utilization, reduces the reproduction performance of laying hens, negatively affects the slaughtering process, and significantly causes environmental pollution [2, 6, 7]. It also increases fat contents in chicken meat, which could be risky for human health by causing cardiovascular diseases [8]. Researchers have found that biologically, the abdominal adipocytes are more active cells exhibiting a higher (0.82) heritability rate than bodyweight, breast, and leg muscles [5], resulting in fat accumulation. It is also reported that abdominal fat weight and body weight had a strong positive correlation, hindering genetic selection against fatness traits in chickens [4]. Therefore, excessive fat deposition has become a puzzle and also an emerging concern in the recent decade, and a strategy at the same time for keeping the body weight and reducing chicken abdominal fat deposition is urgently required.

Host gut harbors around 80% of the symbiont microorganisms, of which 99% are bacteria, called gut microbiota [9, 10, 11]. It has been established that gut microbiota could play a significant regulatory role in fat deposition and obesity [4, 12]. Evidence revealed that colonization of the obese microbiota promoted fat deposition in mice [13]. For example, a higher abundance of Methanobrevibacter and Faecalibacterium, while a lower abundance of Akkermansia increases fat deposition [4, 14]. Further studies indicated that gut microbiota influences and modulates fat metabolism, and importantly contributes to nutrient utilization, generating additional harvestable energy and resulting in abdominal fat deposition [14, 15]. For instance, Enterococcus faecium increases fatty acid synthase (FAS) and acetyl-CoA carboxylase (ACC) secretion in chicken liver [16], and elevated FAS and ACC increases fatty acid production, which incorporates into triglyceride and increases fat deposition [17]. Klebsiella and Escherichia-Shigella possess lipogenesis characteristics, and their higher abundance increases total cholesterol, low-density lipoprotein, and triglyceride concentrations in serum, which facilitate fat accumulation [18]. On the other hand, some microbiota such as Mucispirillum schaedleri decreases fat deposition in chickens [4], and Sphaerochaeta is found enriched in lean chickens [12]. Lactobacillus johnsonii BS15 decreases fat deposition through lipoprotein lipase (LPL) activity and improves fat catabolism in broilers [19]. Abundant Microbacterium and Sphingomonas in chicken were positively related to fat catabolism genes in muscles and liver, which potentially reduce fat storage [20]. Previous studies indicated that gut microbiota not only can increase fat deposition but also can decrease fat deposition. In the complex network of gut microbial communities, dynamically the highest bacterial diversity is observed in the cecum [21]. Therefore, what kind of cecal bacteria could reduce abdominal fat deposition, and how they decrease fat deposition has become an interesting question.

To address this concern, chickens at three different ages (1 month, 4 months, and 12 months) with significantly different abdominal fat deposition were used in the present study. The fat metabolism levels, cecal microbial communities, and the abundances of different bacteria were compared between high and low abdominal fat deposition chickens. Spearman correlation analysis was used to find the relationship between cecal microbiota and abdominal fat deposition. Besides, transferring fecal microbiota from adult healthy chickens with low abdominal fat deposition into one-day-old chicks was performed to verify whether gut microbiota could regulate chicken fat deposition, and the fat metabolism levels in the liver and abdominal adipose tissues were also compared.

2.1 The abdominal fat deposition is significantly different between high and low abdominal fat deposition chickens

Based on the abdominal fat index, the chickens were divided into high (H) and low (L) abdominal fat deposition chickens of different ages (1 month old, 4 months old, 12 months old). The abdominal fat volume (Fig. 1A), abdominal fat weight (H vs L, 1 month old: 4.33 ± 0.31 g vs 1.12 ± 0.09 g; 4 months old: 9.58 ± 0.56 g vs 1.15 ± 0.08 g; 12 months old: 63.77 ± 6.19 g vs 19.46 ± 2.77 g) (p < 0.0001) (Fig. 1B), and abdominal fat index (1 month old: 1.63 ± 0.12% vs 0.48 ± 0.45%; 4 months old: 1.04 ± 0.07% vs 0.13 ± 0.01%; 12 months old: 3.11 ± 0.22% vs 0.94 ± 0.13%) (p < 0.0001) (Fig. 1C) were significantly higher in high abdominal fat deposition chickens. Hematoxylin and eosin (HE) staining results showed that the average diameter of abdominal adipocytes was significantly higher in high abdominal fat deposition chickens than that in low abdominal fat deposition chickens (P < 0.0001) (Fig. 1D). The above results showed that there were significant differences in fat deposition between high and low abdominal fat deposition chickens.

2.2 The fat metabolism is significantly different between high and low abdominal fat deposition chickens

It has been established that unbalanced fat metabolism is closely related to abdominal fat deposition, so the fat metabolism levels in the blood, abdominal fat, and liver were compared between high and low abdominal fat deposition chickens of different ages (1 month old, 4 months old, 12 months old). In blood, the concentrations of triglycerides (TG), total cholesterol (TC), and low-density lipoprotein cholesterol (LDL-C) were markedly higher in high abdominal fat deposition chickens at some time points, yet the concentration of high-density lipoprotein cholesterol (HDL-C) was significantly higher in low abdominal fat deposition chickens at all time points (p < 0.05) (Fig. 2A). In abdominal fat, the relative mRNA expression of fat synthesis related genes, such as acetyl-CoA carboxylase (ACC), fatty acid synthase (FAS), and lipoprotein lipase (LPL), was markedly higher in high abdominal fat deposition chickens at all time points (Fig. 2B), yet the relative mRNA expression of fat catabolism related gene hormone-sensitive lipase (HSL) was significantly higher in low abdominal fat deposition chickens at the age of 4 and 12 months (p < 0.05) (Fig. 2C). In the liver, the number of hollow vesicular fat was more in high abdominal fat deposition chickens (Fig. 3A). The relative mRNA expression of fat synthesis related genes including acyl-CoA synthetase long chain family member 1 (ACSL1), fatty acid desaturase 1 (FADS1), and cytochrome P450 2C45 (CYP2C45) was significantly higher in high abdominal fat deposition chickens at all time points (p < 0.05) (Fig. 3B). Yet the relative mRNA expression of fat transport related gene apolipoprotein A-I (APOAI) (Fig. 3C) was significantly (p < 0.05) higher and fat catabolism related genes including peroxisome proliferator activated receptor alpha (PPARα), carnitine palmitoyl transferase 1 (CPT-1), leptin receptor (LEPR), Janus kinase 2 (JAK2), and signal transducer and activator of transcription 3 (STAT3) was higher in low abdominal fat deposition chickens at different time points (Fig. 3D). Besides, the protein expression levels of p-JAK2 and p-STAT3 were significantly higher in low abdominal fat deposition chickens at all time points (p < 0.05) (Fig. 4).

2.3 The cecal microbiota is significantly different between high and low abdominal fat deposition chickens

16S rRNA gene sequencing was used to compare the cecal microbiota composition between high and low abdominal fat deposition chickens at different time points. Alpha diversity analysis showed that the microbial diversity (Fig. 5A) and community abundance (Fig. 5B) in high abdominal fat deposition were higher than low abdominal fat deposition chickens. Beta diversity showed distinct separation between high and low abdominal fat deposition chickens at different time points (p < 0.05) (Fig. 5C). At the phylum level, Firmicutes were more abundant in high abdominal fat deposition chickens, while Bacteroidetes were more abundant in low abdominal fat deposition chickens at all time points (Fig. 6A). The ratio of Firmicutes to Bacteroidetes was significantly higher in high abdominal fat deposition chickens at 4 months (p < 0.01) and 12 months (p < 0.001) (Fig. 6B). At the genus level, the relative abundance of Parabacteroides, Parasutterella, Oscillibacter, and Anaerofustis was higher in high abdominal fat deposition chickens (Fig. 6C), while the relative abundance of Bacteroides and Sphaerochaeta was higher in low abdominal fat deposition chickens (Fig. 6D) (Fig. S2).

2.4 Metagenomic analysis revealed distinct functional differences of the cecal microbiota between high and low abdominal fat deposition chickens

The association between cecal microbiota and carbohydrate active enzymes (CAZymes) including glycoside hydrolases (GHs), glycosyltransferases (GTs), carbohydrate esterases (CEs), auxiliary activities (AAs), carbohydrate-binding modules (CBMs), and polysaccharide lyases (PLs) was analyzed. Firmicutes and Bacteroidetes encoded more than 85% of the main CAZymes. Compared with the high abdominal fat deposition chickens, the Firmicutes encoded fewer CAZymes, yet the Bacteroidetes encoded more CAZymes in low abdominal fat deposition chickens (Fig. 7A). Further analysis indicated that 25 CAZymes were upregulated in high abdominal fat deposition chickens, and 19 of them are GHs. Other 25 CAZymes were upregulated in low abdominal fat deposition chickens, 12 of them are GHs, and 10 of them are GTs (Fig. 7B). KEGG analysis showed that the differentially expressed genes were annotated to 58 different pathways. Carbohydrate metabolism pathways including starch and sucrose metabolism, pyruvate metabolism, pentose and glucuronate interconversion, C5 branched chain dibasic acid metabolism, and propanoate metabolism were upregulated in high abdominal fat deposition chickens. Lipid metabolism pathways including fatty acid biosynthesis and fatty acid degradation were upregulated in low abdominal fat deposition chickens (Fig. 7C).

2.5 Cecal microbiota was differentially related to abdominal fat deposition in chickens

Spearman correlation analysis was used to analyze the correlation between cecal microbiota and abdominal fat weight, abdominal fat index, and fat metabolism levels. The results indicated that the abundance of Parabacteroides, Parasutterella, Oscillibacter, and Anaerofustis was positively correlated with abdominal fat weight, abdominal fat index, and the expression of fat synthesis related genes in the liver and abdominal fat, while negatively correlated with the expression of fat transport and catabolism related genes in the liver and abdominal fat. On the other hand, the abundance of Bacteroides and Sphaerochaeta was positively correlated with the expression of fat transport and catabolism related genes in the liver and abdominal fat and negatively correlated with abdominal fat weight, and abdominal fat index (Fig. 8).

2.6 Fecal microbiota transplantation from low abdominal fat deposition chicken significantly decreased the abdominal fat deposition of the recipients

In order to verify the effects of gut microbiota on chicken abdominal fat deposition, fecal microbiota transplantation (FMT) from adult chickens with low abdominal fat deposition into one-day-old chicks was performed. Interestingly, an increasing trend of body weight, breast/leg muscle weight, and breast/leg muscle index was observed in the FMT group compared with the control group (Fig. 9A, B, C). The results also indicated that four weeks of FMT significantly decreased the abdominal fat volume (Fig. 9D), abdominal fat weight (FMT: 15.18 ± 1.05 g vs Con: 18.19 ± 0.79 g) (p < 0.05) (Fig. 9E), and abdominal fat index (FMT: 1.02 ± 0.06% vs Con: 1.23 ± 0.04%) (p < 0.01) (Fig. 9F). HE staining results indicated that the average diameter of abdominal adipocytes was significantly lower in FMT group than that of the control group (P < 0.0001) (Fig. 9G, H). Besides, in abdominal fat, FMT significantly down-regulated the relative mRNA expression of fat synthesis related genes (FAS and LPL), and up-regulated the relative mRNA expression of HSL (P < 0.05) (Fig. 9I, J).

2.7 Fecal microbiota transplantation from low abdominal fat deposition chicken reshaped the cecal microbiota of the recipients

16S rRNA gene sequencing results showed that FMT significantly increased cecal microbial community abundance (P < 0.0001) (Fig. 10A), changed cecal microbiota composition (P < 0.001) (Fig. 10B), increased the relative abundance of Bacteroidetes, decreased the relative abundance of Firmicutes (Fig. 10C), and reduced the ratio of Firmicutes to Bacteroidetes (P < 0.05) (Fig. 10D). Further, FMT increased the relative abundances of Bacteroides and Sphaerochaeta, and decreased the relative abundance of Parabacteroides in the FMT group compared with the control group (Fig. 10E) (Fig. S3).

2.8 Reshaped cecal microbiota modulated the fat metabolism levels of the recipients

In order to verify the effects of the reshaped cecal microbiota on fat metabolism of the recipients, the fat metabolism levels in the liver and abdominal fat were investigated. HE staining results indicated the number of hollow vesicular fat was markedly less in FMT group (Fig. 11A). Q-PCR results showed that FMT significantly down-regulated the relative mRNA expression of fat synthesis related genes (FAS, ACC), and up-regulated the expression of APOAI and fat catabolism related genes (PPARα, CPT-1, LEPR, JAK2, and STAT3) (P < 0.05) (Fig. 11B). IHC staining results indicated that FMT significantly up-regulated the protein expression of p-JAK2 and p-STAT3 (P < 0.05) (Fig. 11C).

Stable fat metabolism liberates energy to increase growth, yet unstable fat metabolism often results in an unnecessary fat deposition [22]. Fat metabolism is a complex biochemical mechanism in which fat digestion, assimilation, and transportation occur through several anabolic and catabolic reactions [20]. The produced fatty acids and glycerol are absorbed in the intestinal epithelium and transported through blood circulation to the liver, adipose tissues, and other organs [20]. Fat synthesis in the organs is regulated by fat synthesis-related genes, including FAS, ACSL1, FADS1, CYP2C45, and LPL [23]. In the present study, a significantly higher relative mRNA expression of FAS, ACSL1, FADS1, CYP2C45, and LPL was found in the liver and abdominal fat of high abdominal fat deposition chickens, suggesting more fat synthesis. Elevated ACSL1, ACC, and FAS are associated with fat deposition through increasing serum TG, TC, and LDL-C levels [24, 25]. In the present study, the increased serum TG, TC, and LDL-C levels and decreased serum HDL-C levels in high abdominal fat deposition chickens are consistent with the findings in mice [26], and in chickens [27, 28]. Simultaneously, CPT-1 and PPARα are the catabolic genes that stimulate fatty acid’s oxidation, resulting in energy production for chicken growth [29]. In the present study, downregulated hepatic mRNA expression of CPT-1 and PPARα and a lower JAK2 expression via STAT3 activation in high abdominal fat deposition chickens increased abdominal fat deposition in broilers [30, 31]. Notably, a significantly higher expression of APOAI and HSL and higher serum HDLC levels in low abdominal fat deposition chickens at all time points in our study facilitate fat excretion because HSL acts as a cleaner and, along with APOAI, transports cholesterol/fatty acids from fat depots to the liver for lipolysis, thus are essential to reduce fat accumulation in broilers [32, 33, 34]. Additionally, the upregulation of genes related to the differentiation of adipocytes most probably increases their proliferation and contributes to abdominal fat deposition in chickens [5, 20], and a significantly higher average diameter of abdominal adipocytes in high abdominal fat deposition chickens in our study, indicating the crucial role of adipocytes differentiation in fat deposition [35]. Therefore, more fat synthesis and less fat catabolism result in excessive fat deposition and vice versa.

It has been established that gut microbiota could control abdominal fat deposition by regulating fat metabolism [14]. It is also well-known that an obese host harbors more Firmicutes than Bacteroidetes and a higher Firmicutes/Bacteroidetes ratio [12, 36]. In the present study, we found more Firmicutes than Bacteroidetes along with a significantly higher Firmicutes/Bacteroidetes ratio in high abdominal fat deposition chickens compared with low abdominal fat deposition chickens at 4 and 12 months, which are in accordance with the above findings. Parabacteroides are related to several gut disorders in humans and chickens [37], and are directly linked with the obese individuals [38]. Parasutterella causes irritable bowel syndrome and immunosuppression in chickens [39], and increases abdominal fat percentage and deposition [40]. Oscillibacter is abundant in fat line chickens [41], and is associated with obesity and metabolic syndrome [42]. Likewise, an increased abundance of Anaerofustis in the cecum of high fat diet mice [43], and of broilers during Clostridium perfringens-induced infection [44], is linked with fat metabolism. Typically, these bacteria have a significant contribution in fat accumulation and are consistent with our findings of high abdominal fat deposition chickens. Interestingly, Bacteroides could also reduce diet-induced obesity in the obese host [45], and are associated with lipid metabolism [20]. Bacteroides abundance was found higher in low abdominal fat deposition chickens in our study and is positively associated with leanness traits in porcine [46], significantly higher in lean rats [47], and along with Bacteroides, Sphaerochaeta was also found enriched in lean chickens [12]. In the present study, higher abundances of Parabacteroides, Parasutterella, Oscillibacter, and Anaerofustis are predicted to an increasing fat deposition trend in high abdominal fat deposition chickens through fat anabolism at all time points, whereas higher abundances of Bacteroides and Sphaerochaeta are expected to the decreasing fat deposition trend in low abdominal fat deposition chickens through fat catabolism at all time points.

Firmicutes and Bacteroidetes could encode carbohydrate active enzymes (CAZymes) to degrade resistant starch and dietary fibers through hydrolysis [12, 48]. Present study found that both of these bacteria encode 85% of these enzymes, indicating their significance in nutrient metabolism. It is observed that Firmicutes are more efficient in calories absorption and thus associated with obesity [49]. In the obese microbiome, Firmicutes are enriched in genes related to synthesis of butyrate, acetate and succinate through galactosidases activity and produce nutrient transporters to facilitate their transportation [50]. Evidence revealed that Firmicutes contain phosphotransferase system (PTS) for the phosphorylation of monosaccharides/disaccharides or sugar derivatives, while Bacteroidetes are deficient in PTS, inferring that Firmicutes have additional capabilities to transport more (metabolized) carbohydrates because obesity-associated enzymes belong to PTS [51]. Both Anaerofustis and Oscillibacter are Firmicutes and in the present study, both of them are found enriched in high abdominal fat deposition chickens, suggesting that these bacteria extracted extra energy and transported it to the adipose tissues. Other studies also found a positive association of Anaerofustis and Oscillibacter with fiber digestibility and obesity [52, 53]. On the other hand, Bacteroidetes use type VI secretion system in competition with their competitors to harvest energy from the shared resources. They possess polysaccharide utilization loci and accomplish this process through activating GHs, PLs, GTs, and CEs [54], exhibiting a distinct behavior from Firmicutes. In the present study, Bacteroides through GHs, GTs, and CEs activities efficiently utilized harvested energy for chicken growth instead of storing in fat depots of the low abdominal fat deposition chickens. Typically, carbohydrate metabolism is interlinked with fat metabolism because high carbohydrates of higher calories might also cause de novo lipogenesis and obesity [42], following a huge conversion of glucose into pyruvate (glycolysis) or into TG, indicating how both metabolic pathways together contribute to fat deposition [22, 55]. Thus, Firmicutes-mediated CAZymes activities induced fat deposition, yet Bacteroidetes-mediated CAZymes activities reduced fat deposition.

Increasing evidences indicated that reshaping gut microbiota by fecal microbiota transplantation (FMT) could reduce abdominal fat deposition [12, 56]. It was reported that Bacteroides and Sphaerochaeta enriched in lean chickens, and Parabacteroides enriched in obese individuals [12, 38]. Another study reported that FMT could increase Bacteroides, which maintain gut homeostasis [57]. Interestingly, a higher abundance of Sphaerochaeta and Bacteroides, and a lower abundance of Parabacteroides were observed in the FMT group of the present study, which are consistent with our results in low abdominal fat deposition chickens. Other studies also described that FMT could significantly attenuate fat deposition in high fat diet mice [58] because lower LPL level decreases adipogenesis by reducing triglyceride hydrolysis in the adipose tissues [59]. In the present study, the decreased anabolism (FAS, LPL in abdominal fat and ACC, FAS in liver), and increased catabolism (hepatic PPARα, CPT-1, LEPR, JAK2, and STAT3) gene’s expressions were found in the FMT group compared with the control. Besides, a significantly higher expression of HSL in abdominal fat, and APOAI, p-JAK2, and p-STAT3 in the liver of FMT group rather than the control group was also observed. Thus, in our study, the findings of FMT and control groups are consistent with the results of low and high abdominal fat deposition chickens respectively, indicating that FMT might be applied to reshape gut microbiota, which in turn might reduce fat deposition by modulating fat metabolism [60].

Taken together, the current findings indicated that the unbalanced fat metabolism leads to excessive abdominal fat deposition. Early colonization of Parabacteroides, Parasutterella, Oscillibacter, and Anaerofus upregulates the expression of fat anabolism genes, which increases abdominal fat deposition. However, early colonization of Bacteroides and Sphaerochaeta upregulates the expression of fat catabolism genes, which reduces abdominal fat deposition and benefits the muscle growth of the chickens. Moreover, FMT significantly decreased Parabacteroides, increased Bacteroides and Sphaerochaeta, and upregulated the expression of fat catabolism genes. FMT might be applied as a strategy in reducing abdominal fat deposition and at the same time promoting the growth of muscles.

4.1 Animals

The Institutional Animal Care and Use Committee of Huazhong Agricultural University (HZAUCH-2018-008), Wuhan, China) approved all the animal procedures, and all methods were performed in accordance with the relevant guidelines and regulations.

Newly hatched chickens (Turpan cockfighting × White Leghorn) were reared under similar husbandry conditions on the chicken farm of Huazhong Agricultural University. At the age of 1, 4, and 12 months, 120 chickens were randomly selected for each time point. Based on the abdominal fat index, the chickens at each time point were categorized into two groups, namely the high abdominal fat deposition group (H) and the low abdominal fat deposition group (L) (n = 10, 5 males and 5 females). For the fecal microbiota transplantation (FMT) experiment, the chickens with high body weight and low abdominal fat deposition were selected as FMT donors. 60 one-day-old white feather broilers were selected as recipients.

4.2 Selection of FMT donors

Two adult female white Leghorn chicken × Turpan fighting chicken possibly having high or low abdominal fat deposition were scanned with computed tomography (CT) instrument (Aquilion PRIME Tsx-303A, Canon Medical, Japan). Pari software was used to mark the abdominal fat in different frame images of each chicken (Fig. S1), and then Python language was used to write programs to analyze the images and calculate the volume of the body and abdominal fat of each chicken. The volume of the body was 2.22 dm³ and 2.50 dm³, and the volume of abdominal fat was 0.06 dm³ and 0.15 dm³, respectively. Similarly, the volume percentage of abdominal fat was 2.66% and 5.92%. The chicken with less abdominal fat volume percentage was selected as FMT donor. After FMT experiment, the two chickens were dissected to get the abdominal fat weight and index. The abdominal fat weight was 74.3 g and 161.2 g, and the abdominal fat index was 3.12% and 5.78%, respectively, which are consistent with the CT results and indicated that the FMT donor has low abdominal fat deposition.

4.3 Preparation of fecal suspension

Every morning, once the donor chickens defecated, the white part of the fecal materials was removed as it contains uric acid. Then 10 g of feces were collected in the sterile tube (50 mL) and gently mixed with 60 mL of 0.75% normal saline. The mixture was kept on the ice for settling down the precipitates. The supernatant was obtained and filtered with the sterile gauze to get fecal suspension.

4.4 Animal treatment

60 one-day-old white feather broilers were selected as recipients and randomly divided into FMT group and control group (n = 30). Broilers in the FMT group were orally administrated with 1 mL fecal microbiota suspension, while 1 mL 0.75% saline was used as a substitute in the control group for 28 days. At the age of 42 days, they were sacrificed and samples were collected.

4.5 Sample collection

After fasting for 12 hours, the chickens were weighed and sacrificed, then blood, liver, abdominal adipose tissue, and left cecum were collected. The abdominal adipose tissue was measured as well. For gut microbiota analysis, the cecal content (1 to 1.5 g per bird) was collected into two sterilized centrifuge tubes (1.5 mL) and snap-frozen in liquid nitrogen, then stored at -80°C for sequencing. For analysis of lipometabolic parameter, blood samples (3 mL per bird) were centrifuged at 3,000× g at 4°C for 15 min to get the serum, and then it was stored at − 80 ℃ for subsequent blood biochemical index analysis. For histo-morphological analysis, freshly harvested liver and abdominal adipose tissues were fixed in 4% paraformaldehyde solution. For gene expression analysis, parts of freshly harvested liver and abdominal adipose tissues were snap-frozen in liquid nitrogen and then stored at -80°C.

4.6 Muscle or abdominal fat index calculation

The muscle or abdominal fat index was calculated using the following formula: muscle index = muscle weight (g)/ body weight (g) × 100%, abdominal fat index = abdominal fat weight (g)/ body weight (g) × 100%.

4.7 16S rRNA and Metagenomic genes sequencing

Microbial genomic DNA was extracted from the chicken’s cecal content using Fast DNA SPIN extraction kit (MP Biomedicals, Santa Ana, CA, USA), according to manufacturer’s instructions. The hypervariable region V3-V4 of the bacterial 16S rRNA gene was amplified with primer pairs 338F (5'-ACTCCTACGGGAGGCAGCAG-3') and 806R (5'-GGACTACHVGGGTWTCTAAT-3'). The PCR amplification of the 16S rRNA gene was performed as follows: an initial denaturation (3 min) at 95 ℃ following 27 cycles of denaturing (30 s) at 95 ℃, annealing (30 s) at 55 ℃, extension (45 s) at 72 ℃, and single extension (10 min) at 72 ℃, and ended at 4 ℃. The PCR product was extracted from 2% agarose gel and purified using the AxyPrep DNA Gel Extraction Kit (Axygen Biosciences, Union City, CA, USA) according to manufacturer’s instructions and quantified using Quantus™ Fluorometer (Promega, USA). Illumina MiSeq PE300 platform (Illumina, San Diego, USA) was used for 16S rRNA gene sequencing. For 20 chickens at the age of 4 months with metagenomic sequencing, the same DNA extract was fragmented to an average size of about 400 bp using Covaris M220 (Gene Company Limited, China) for paired-end library construction, which was constructed using NEXTFLEX Rapid DNA-Seq (Bioo Scientific, Austin, TX, USA). Illumina NovaSeq platform (Illumina, San Diego, CA, USA) was used for metagenomic sequencing.

4.8 16S rRNA gene sequencing data processing

The raw 16S rRNA gene sequencing reads were demultiplexed, quality-filtered by fastp version 0.20.0, and merged by FLASH version 1.2.7. Operational taxonomic units (OTUs) with 97% similarity cutoff were clustered using UPARSE version 7.1, and chimeric sequences were identified and removed. The taxonomy of each OTU representative sequence was analyzed by RDP Classifier version 2.2 against the 16S rRNA database (Silva 132) using a confidence threshold of 0.7. For α and β diversity measurements, the sequencing depth was minimized by subsampling the readings of each sample. The lowest valid reads of cecal microbiota of high and low abdominal fat deposition chickens at the age of 1 month were 25,339, the lowest effective reading of cecal microbiota of high and low abdominal fat deposition chickens at the age of 4 months was 30,671, and the lowest effective reading of cecal microbiota of high and low abdominal fat deposition chickens at the age of 12 months was 45,053. Similarly, the lowest valid reads of cecal microbiota in the control and FMT chickens were 14,960. The α-diversity was described using the Shannon index and Chao index. Principal coordinates analysis (PCoA) based on Bray-Curtis was used to estimate the dissimilarity in the community structure. The community composition at the phylum level and the change of abundance at the genus level were visualized by bar chart and histogram. Linear discriminant analysis effect size (LEfSe) was performed to detect differentially abundant taxa across groups using the default parameters linear discriminant analysis (LDA > 2).

4.9 Metagenomic sequencing data processing

The low-quality reads (length < 50 bp or with a quality value < 20 or having N bases) were removed by fastp (https://github.com/OpenGene/fastp, version 0.20.0). Reads were aligned to the chicken genome by burrows-wheeler alignment (BWA) tool (http://bio-bwa.sourceforge.net, version 0.7.9a), and any hit associated with the reads and their mated reads were removed. The optimized sequence was spliced and assembled, and contigs ≥ 300 bp were selected as the final assembly result, and then the contigs were used for further gene prediction and annotation. Open reading frames (ORFs) from each assembled contig were predicted using MetaGene (http://metagene.cb.k.u-tokyo.ac.jp/). The predicted ORFs with length ≥ 100 bp were retrieved and translated into amino acid sequences. A non-redundant gene catalog was constructed using CD-HIT (http://www.bioinformatics.org/cd-hit/, version 4.6.1) with 90% sequence identity and 90% coverage. Reads after quality control were mapped to the non-redundant gene catalog with 95% identity using SOAPaligner (http://soap.genomics.org.cn/, version 2.21), and gene abundance in each sample was evaluated. Public data used for taxonomic analysis and gene functional classification included the integrated NCBI-NR database, KEGG database, and CAZy database. Amino acid sequence of non-redundant gene was aligned to NR database and KEGG database respectively with an e-value cutoff of 1e^− 5 using Diamond (http://www.diamondsearch.org/index.php, version 0.8.35), and obtained the species annotation and KEGG function corresponding to the gene. Carbohydrate-active enzymes annotation was conducted using hmmscan (http://hmmer.janelia.org/search/hmmscan) against CAZy database (http://www.cazy.org/) with an e-value cutoff of 1e^− 5.

4.10 Blood parameters analysis

For the analysis of different blood parameters, the serum concentrations of triglycerides (TG), total cholesterol (TC), high density lipoprotein cholesterol (HDL-C), and low density lipoprotein cholesterol (LDL-C) were determined using a Rayto Chemistry Analyzer (Chemray 800, China) according to the manufacturer’s instructions with the commercial diagnostic kits (Shenzhen Rayto Life Science Co., Ltd). Briefly, the serum samples were thoroughly mixed with the reaction solution in the recommended proportion and maintained at 37 ° C for 10 minutes. Finally, the absorbance for each sample was measured, and the total concentrations were calculated according to the following formula. Total concentrations = Absorbance of sample / Absorbance of calibration solution × Calibration concentrations (mmol per liter).

4.11 Hematoxylin and eosin (HE) staining

For morphological observation, liver and abdominal fat tissue samples were embedded in paraffin, and the sections were prepared. Liver tissues were cut into 3 µm thick sections, and abdominal fat tissues were cut into 7 µm thick sections with a rotary slicer (LEICA 819, Leica, Germany). HE staining was performed according to the routine protocol, and the stained tissue sections were examined with the light microscope (BH-2, Olympus, Japan) using a digital camera (DP72, Olympus, Japan).

4.12 Real-time quantitative polymerase chain reaction (qPCR)

In order to detect the expression of fat metabolism related genes on mRNA level, total RNA was extracted from abdominal adipose and liver tissues using Trizol reagent (Takara, Japan) following the instructions of the manufacturer. RNA (1µg) from each sample was reverse transcribed into cDNA using the PrimeScript™ RT reagent Kit with gDNA Eraser (Takara, Japan). The qPCR reaction mixture (10 µL) consisted of 5 µL of SYBR (Takara, Japan), 0.4 µL of forward and reverse primer, 3.2 µL of ddH₂O, and 1 µL of template cDNA. The qPCR reaction is carried out on Bio-Rad CFX Connect real-time qPCR detection system (Bio-Rad, Hercules, CA, USA). The steps are as follows: 5 min pre-denaturation at 95°C, following 30 s denaturation at 95°C (40 cycles), 30 s annealing at 60°C, and 15 s elongation at 72°C. The sequences of primers were listed in Table 1 with reference gene (β-actin). Gene expression levels were quantified using the 2 ^−ΔΔCT method.

Table 1

Primers used for real-time qPCR
Gene	Forward Sequence (5’-3’)	Reverse Sequence (5’-3’)	Gene Bank No.
β-actin	TTGTTGACAATGGCTCCGGT	TCTGGGCTTCATCACCAACG	NM_205518.2
ACC	TCCAGCAGAACCGCATTGACAC	GTATGAGCAGGCAGGACTTGGC	NM_205505.2
FAS	GCTCTGCGTCTGCTTCAGTCTAC	GGTACAGGACTCTGCCATCAATGC	NM_205155.4
LPL	TGGACATTGGTGACCTGCTTATGC	TCGCCTGACTTCACTCTGACTCTC	NM_205282.2
ACSL1	GACTAATGGTCACAGGAGCAGCAC	CCAGGCATTGACAGTGAGCATCC	NM_001012578.2
FADS1	CCGTGCCACTGTGGAGAAGATG	GCCTAGAAGCAACGCAGAGAAGAG	XM_040673219.1
CYP2C45	AACAAGCACCACCACACGATACG	GGTCAGCCACGCAAGGTCTTC	NM_001001752.3
APOAI	GTGACCCTCGCTGTGCTCTT	CACTCAGCGTGTCCAGGTTGT	NM_205525.5
PPARα	TGCTGTGGAGATCGTCCTGGTC	CTGTGACAAGTTGCCGGAGGTC	XM_040699549.1
CPT-1	ACAGCGAATGAAAGCAGGGT	GCCATGGCTAAGGTTTTCGT	NM_001012898.1
LEPR	CACTCGCTGGGAACACTTGA	TTCAGCAGCCCATCGTTTCT	NM_204323.2
JAK2	GAGCGTGAGAATGCCACTGAC	TGGAGGACAGCACTTGATGAAC	NM_001030538.3
STAT3	GCCGAATCACAACTACAGACTC	CTGACTTTGGTGGTGAACTGC	NM_001030931.3
HSL	GAGGCACAGCGTCTTCTTTAGG	GGCACGAACTGGAACCCGAG	XM_040695201.1

4.13 Immunohistochemistry (IHC)

Following the steps described in earlier studies, immunohistochemical staining was performed to observe the protein distribution and expression in the liver. Briefly, the sections were dewaxed twice in xylene and rehydrated in graded series ethanol. The antigen was retrieved in sodium citrate buffer (pH 6.0) using a microwave oven (MYA-2270M, Haier, Qingdao, China) for 18 min, i.e., three min at 700 W and fifteen min at 116 W, and then cooled for 2 to 3 h at room temperature. Endogenous peroxidase was inactivated with 3% hydrogen peroxide (H₂O₂), and tissue sections were incubated with 5% bovine serum albumin (BSA) (boster, China) at 37 ℃ for 30 minutes to block nonspecific binding sites. Then, the sections were incubated with primary antibodies of rabbit anti-JAK2 (1:100) (A11497, ABclonal Technology, Wuhan, China), rabbit anti-p-JAK2 (1:100) (AP0531, ABclonal Technology, Wuhan, China), rabbit anti-STAT-3 (1:100) (A1192, ABclonal Technology, Wuhan, China) and rabbit anti-p-STAT3 (1:100) (AP0474, ABclonal Technology, Wuhan, China). Subsequently, the horseradish peroxidase (HRP)-conjugated secondary antibody (Proteintech, China) was used to incubate the tissue sections for 30 min at 37°C. After diaminobenzidine (DAB) (Proteintech, China) staining, the sections were counterstained with hematoxylin, cleaned and dehydrated until they became transparent, and finally sealed with neutral gum and coverslips. Finally, we used a light microscope (BH-2, Olympus, Japan) with a digital camera (DP72, Olympus, Japan) to examine the sections.

4.14 Statistical analysis

Under 10 x 20 microscope, 10 abdominal fat HE stained sections were selected from each group, and 5 visual fields were randomly selected for the image acquisition. The average diameter of abdominal fat adipocytes was measured with image pro plus 6.0 (Media Cybernetics, USA). Under a 10 x 40 microscope, 10 liver immunohistochemical sections were selected from each group and five positive visual fields were randomly selected from each section for the image acquisition. Image Pro Plus 6.0 was used to calculate the integral optical density of positive signals. GraphPad Prism 6.0 (Media Cybernetics, USA) was used to analyze the test data. The measurement data were expressed as mean ± standard error of the mean (mean ± SEM). The statistical significance of the mean values in the comparisons of two groups was determined using Student’s t-test. Value of p < 0.05 was considered statistically significant.

Ethics approval and consent to participate

The current scientific investigation was conducted following the rules and regulations of the ethics committee for the use of animals, Huazhong Agricultural University (HZAUCH-2018-008), Wuhan, China.

Consent for publication

Not applicable.

Availability of data and materials

The raw 16S rRNA gene and metagenomic sequencing data are available at the NCBI Sequence Read Archive (SRA), under BioProject PRJNA837471.

Competing interests

The authors declare that they have no conflicts of interest.

Funding

The National Key Research and Development Program of China (2017YFE0113700, 2019YFE0115400), National Natural Science Foundation of China Major Projects (31790414), College Students’ Research Funds (S202110504030), and the National Natural Science Foundation of China (30800808) supported this work.

Authors’ contributions

YC, MA, ZYM, TWH, QYL, HP, XLZ, AAN, ARA, E-SMA-K, DSS, and HZL all contributed to the conceptual design for this project and the experiments within the manuscript. YC, MA, ZYM, and TWH performed the experiments and data analysis, contributed to animal handling, samples, and clinical data collection. MA, DSS, XLZ, AAN, ARA, E-SMA-K, and HZL edited and critically revised the manuscript. YC, MA, DSS, and HZL wrote the manuscript. DSS and HZL supervised the writing, experimentation, analysis, reviewing of the manuscript, funding acquisition, and project administration. All authors read and approved the final version of the manuscript.

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No competing interests reported.

FigureS1.tif
Supplementary Fig. 1Analysis of the fecal suspension donor using CT scanning techniqueCT scanning technique was used to analyze the fat deposition-related factors for the donor. A three-dimensional image (A) and a cross section (B) of high body weight and low abdominal fat weight chicken. A three-dimensional image (C) and a cross section (D) of high body weight and high abdominal fat weight chicken. The red area is the abdominal fat in figure A and C. The gray area indicated by the red arrow is the abdominal fat in figures B and D.
FigureS2.tif
Supplementary Fig. 2(A) Analysis of differential microbiota in cecum between high and low abdominal fat deposition chickens at 1 month, (B) at 4 months, and (C) at 12 months. LDA score (log10) > 2.0. H represents high abdominal fat deposition chickens, and L represents low abdominal fat deposition chickens.
FigureS3.tif
Supplementary Fig. 3Analysis of differential microbiota in cecum between the control and FMT groups. LDA score (log10) > 2.0. Con represents the control group, and FMT represents the fecal microbiota transplantation group.

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Chicken cecal microbiota reduces abdominal fat deposition by regulating fat metabolism

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Abstract

Figures

Introduction

Results

2.1 The abdominal fat deposition is significantly different between high and low abdominal fat deposition chickens

2.2 The fat metabolism is significantly different between high and low abdominal fat deposition chickens

2.3 The cecal microbiota is significantly different between high and low abdominal fat deposition chickens

2.5 Cecal microbiota was differentially related to abdominal fat deposition in chickens

2.8 Reshaped cecal microbiota modulated the fat metabolism levels of the recipients

Discussion

Conclusion

Material And Methods

4.1 Animals

4.2 Selection of FMT donors

4.3 Preparation of fecal suspension

4.4 Animal treatment

4.5 Sample collection

4.6 Muscle or abdominal fat index calculation

4.7 16S rRNA and Metagenomic genes sequencing

4.8 16S rRNA gene sequencing data processing

4.9 Metagenomic sequencing data processing

4.10 Blood parameters analysis

Declarations

References

Additional Declarations

Supplementary Files

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