Svoboda | Graniru | BBC Russia | Golosameriki | Facebook
Next Article in Journal
Effects of Artificial Light at Night on Fitness-Related Traits of Sea Urchin (Heliocidaris crassispina)
Next Article in Special Issue
Precision Glycan Supplementation Improves Gut Microbiota Diversity, Performance, and Disease Outbreak Resistance in Broiler Chickens
Previous Article in Journal
Effects of an Appeasing Substance Application at Weaning on Growth, Stress, Behavior, and Response to Vaccination of Bos indicus Calves
Previous Article in Special Issue
Phenotype Alterations in the Cecal Ecosystem Involved in the Asymptomatic Intestinal Persistence of Paratyphoid Salmonella in Chickens
 
 
Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
Journals
Animals
Volume 13
Issue 19
10.3390/ani13193034
animals-logo
Submit to this Journal Review for this Journal Propose a Special Issue
► Article Menu

Article Menu

share Share announcement Help format_quote Cite question_answer Discuss in SciProfiles thumb_up
...
Endorse textsms
...
Comment
first_page
settings
Order Article Reprints
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:
Background:
Article

Integrated Analysis of the Effects of Cecal Microbiota and Serum Metabolome on Market Weights of Chinese Native Chickens

by
Shenghong Yang
,
Yongxian Yang
,
Xiaoxia Long
,
Hui Li
,
Fuping Zhang
and
Zhong Wang
*
Key Laboratory of Animal Genetics, Breeding and Reproduction in the Plateau Mountainous Region, Ministry of Education, College of Animal Science, Guizhou University, Guiyang 550025, China
*
Author to whom correspondence should be addressed.
Animals 2023, 13(19), 3034; https://doi.org/10.3390/ani13193034
Submission received: 7 August 2023 / Revised: 20 September 2023 / Accepted: 21 September 2023 / Published: 27 September 2023
(This article belongs to the Special Issue EBM Collection: Composition and Function of Poultry Gut Microbiota)
Browse Figures
Versions Notes

Abstract

:

Simple Summary

Native chickens generally have the characteristics of low growth performance, which has also become a limiting factor for the breeding of yellow feather broilers. More and more studies have shown that gut microbiota plays an important role in the growth of livestock farming. The Guizhou yellow chicken is a breed of yellow-feathered broiler chicken with excellent meat quality and good flavor currently being cultivated in Guizhou Province, China. In order to explore the role of gut microbiota on the growth performance of native chickens, the Guizhou yellow chicken was taken as a representative, and high-market-weight and low-market-weight chicken groups were established according to their market weights. By integrating microbial 16S rRNA gene sequencing and non-targeted serum metabolome data, five key cecal microbes associated with high body weight in chickens and one key microbe associated with low body weight were identified. In addition, the results also showed that specific gut microbes might positively affect the growth rate of chickens by regulating vitamin and other metabolic pathways. These findings might improve understanding of the role of gut microbiota in chicken growth traits and their underlying metabolic mechanisms.

Abstract

The gut microbiota plays an important role in the physiological activities of the host and affects the formation of important economic traits in livestock farming. The effects of cecal microbiota on chicken weights were investigated using the Guizhou yellow chicken as a model. Experimental cohorts from chickens with high- (HC, n = 16) and low-market-weights (LC, n = 16) were collected. Microbial 16S rRNA gene sequencing and non-targeted serum metabolome data were integrated to explore the effect and metabolic mechanism of cecal microbiota on market weight. The genera Lachnoclostridium, Alistipes, Negativibacillus, Sellimonas, and Ruminococcus torques were enriched in the HC group, while Phascolarctobacterium was enriched in the LC group (p < 0.05). Metabolomic analysis determined that pantothenic acid (vitamin B5), luvangetin (2H-1-benzopyran-6-acrylic acid), and menadione (vitamin K3) were significantly higher in HC serum, while beclomethasone dipropionate (a glucocorticoid) and chlorophene (2-benzyl-4-chlorophenol) were present at higher levels in the LC group. The microbes enriched in HC were significantly positively correlated with metabolites, including pantothenic acid and menadione, and negatively correlated with beclomethasone dipropionate and chlorophene. These results indicated that specific cecal bacteria in Guizhou yellow chickens alter the host metabolism and growth performance. This study provides a reference for revealing the mechanism of cecal microbe actions that affect chicken body weight.

1. Introduction

Chicken meat is a major protein source throughout the world [1], and the improvement of chicken production performance is necessary to meet increasing demand. Market weight is a key growth trait in chickens, and high market weight can increase the turnover in the chicken production pen and reduce labor costs for farmers [2]. Several factors, such as genetics [3], management [4], and nutrition [5], can affect market weight, thus it can be scientifically altered.
Gut microbes might be involved in the regulation of chicken market weight. For example, the presence of Lachnospiraceae in the cecum was found to be related to high growth performance (body weight) for chickens, while the presence of Escherichia had the opposite effect [6]. Cecal microbiota possessing Microbacterium and Sphingomonas in Turpan gamecock progeny × White Leghorn chickens were significantly correlated with high body weight, while Slackia was enriched in the ceca of low-market-weight chickens [7]. In addition, high Lactobacilli abundance in the chicken jejunum was beneficial to growth, while Comamonas enrichment produced a negative outcome on growth rates [8]. The addition of exogenous Bacillus subtilis and Bacillus licheniformis to chicken feed also promoted growth performance [9,10]. However, the conclusions have not been completely consistent in determining which microbes produce the greatest effect on chicken growth performance.
In spite of numerous studies focused on unraveling the mechanisms for market weight increases, this complex trait has yet to be fully understood [11,12,13]. Since metabolite production might profoundly alter host physiological functions [14,15,16] and thereby affect host phenotypes, such as feed efficiency [17], disease resistance [18], meat quality [19], and gut microbiome can influence host metabolism [15], it is deduced that the influence of gut microbiota on host metabolism might be one of the mechanisms to regulate growth performance of chickens. However, few researches have been found. Only one study proved that cecal microbiota could affect chicken growth performance by regulation of fat metabolism [7]. Unfortunately, there was a limitation in this study, for it focused on only a few specific lipid metabolites. As well known, there are several host metabolites, and the regulatory role of other metabolites remains unclear. Thus, more advanced techniques are necessary for a more comprehensive evaluation.
As a modern omics technology, metabolomics allows high-throughput, multi-dimensional analysis of a large number of metabolites with high sensitivity and can detect very small metabolic changes [20]. Although it has not been directly employed for study on chicken growth performance, it has been applied to fill the information gap between gene and phenotype [21]. With this technology, more comprehensive metabolite information can be obtained. A combination of microbiomes and metabolomics linked gut microbes to the regulation of metabolite production, which is becoming an important means of analyzing complex traits of livestock farming [22,23,24,25]. Based on this, researchers have explored the mechanisms of fat deposition in pigs and milk protein formation in cows. For example, Prevotella is a key bacterial genus that affects pig intramuscular fat deposition via the production of lipopolysaccharides, branched-chain amino acids (BCAAs), and arachidonic acids [26]. Prevotella abundance in the rumen altered amino acid metabolism, resulting in increased milk protein content. In contrast, the enrichment of methanogens in the rumen was not conducive to increasing the milk protein content [27]. In chicken, multi-omics technology was used for the characterization of bacillus spp. probiotics isolated from European broilers to improve their growth performance [28]. Therefore, integrating gut microbiome and metabolomics is helpful for the identification of key microbes related to growth traits and the analysis of their metabolic mechanisms in chickens [29,30].
Guizhou yellow chicken is a breed of yellow-feathered broiler chicken with excellent meat quality and good flavor currently being cultivated in Guizhou Province, China. This synthetic chicken breed was developed by crossing parental breeds (Guizhou Weining chicken as female parent and Golden Plymouth Rock chicken as male parent) [31]. However, compared with commercial broiler breeds such as Ross and Cobb [32], the growth rate of this breed is slow [33]. Furthermore, the factors that influence their growth rates have not yet been fully elucidated.
In this study, it was hypothesized that the specific cecal microbiota might be involved in regulating the market weight of Guizhou yellow chickens by influencing host metabolism. Microbial 16S rDNA gene sequencing and untargeted metabolomics were combined to explore the effect of gut microbiome on the growth performance of Guizhou yellow chickens and to identify key microbes associated with market weight and preliminary explore the possible metabolic mechanisms for improved growth performance. The results of this study could give insight into the analysis of chicken growth performance and provide research references for the discovery of growth-promoting probiotics.

2. Materials and Methods

2.1. Ethics Statement

The Animal Care and Use Committee at Guizhou University approved this project (approval number: EAE-GZU-2022-T050). All animal works were conducted according to the guidelines for the care and use of experimental animals established by the Ministry and Rural Affairs of the People’s Republic of China.

2.2. Experimental Animals and Sample Collection

The animals involved in this study were Guizhou Yellow chickens bred in our laboratory and raised in the chicken farm of Guizhou University (research farm) from June to October 2022. All chickens (n = 49, 24 female + 25 male) were hatched on the same day and were raised in cages. Stocking density was as follows: at the age of 0–4 weeks, 4–10 weeks, and 10–18 weeks, 16 chickens (male and female), 8 chickens (male and female), and 1 chicken were kept in each cage, respectively. The ambient feeding temperature was set at 26 °C when the animals were 0–4 weeks old; after that, chickens were transferred to the roller-curtain natural ventilation cooling chicken house without a temperature regulation system and raised at natural room temperature. The temperature was 20–35 °C in June-August and 15–25 °C in September–October. All chickens were fed at 5 AM and 5 PM every day. The light duration was about 16 h per day, and the humidity was 60% to 65%. All chickens were raised in the same environment. The chicken house was cleaned and disinfected regularly according to the sanitary and epidemic prevention requirements of the farm to keep them clean, dry, and ventilated. All the chickens were allowed to eat and drink freely. At different growth stages, the animals were fed brood feed, nursery feed, and growing feed according to different nutritional requirements. The nutritional composition is shown in Table 1. The chickens were weighed at the same time on the morning of the weighing day using an electronic scale every 2 weeks (±5 g). At 18 weeks of age, 8 roosters and 8 hens with the highest body weight were selected into the high-market-weight group (HC, n = 16), and 8 roosters and 8 hens with the lowest body weight were selected into the low-market-weight group (LC, n = 16). Namely, in each group, 16 replicates were involved.
No antibiotics were used within one month before slaughter. At the end of the experiment (18-week-old), serum samples and cecal contents were collected from all 32 chickens (16 in the high-market-weight group and 16 in the low-market-weight group). Whole blood was obtained from wing veins for serum separation before the chickens were euthanized by CO2 asphyxiation [34], and then ~2 g cecal content was collected at the same position as their cecum. Serum samples and cecal content were immediately put into liquid nitrogen for quick freezing after isolation or collection and were stored at −80 °C for further analyses. Thirty-two serum samples (16 from HC, 16 from LC) for untargeted metabolomics and 32 cecal content samples (16 from HC, 16 from LC) for 16S rDNA gene sequencing were performed at Shanghai Applied Protein Technology (Aptbio, Shanghai, China) and Shanghai Majorbio Biopharm Technology (Majorbio, Shanghai, China), respectively. The experimental flow chart is shown in Figure 1.

2.3. Sequence Splicing and ASV Annotation

Amplicon sequencing was conducted using an Illumina MiSeq platform (Illumina, San Diego, CA, USA). The reads were first filtered and assembled into tags according to overlap relationships between the paired-end reads. Tags were then clustered into amplified sequence variants (ASVs) [35]. Data was optimized using the DADA2 to obtain the representative ASV sequence and abundance information [36]. Representative sequences of each ASV were annotated using the Silva database (https://www.arb-silva.de/) (accessed on 22 February 2023) with a taxonomic confidence level of 0.7 [37].

2.4. Extraction of Serum Samples

Serum samples were thawed at 4 °C, and an appropriate amount was added to pre-cooled a methanol/acetonitrile/water solution (2:2:1, v/v) and vortexed and ultrasonicated at low temperature for 30 min and kept at −20 °C for 10 min. Samples were then centrifuged at 14,000× g at 4 °C for 20 min. Supernatants were transferred into clean tubes and dried under vacuum, and the residue was suspended in 100 μL 50% acetonitrile for UPLC-QTOF/MS analysis [38].

2.5. Chromatography-Mass Spectrometry Analysis

2.5.1. Chromatographic Conditions

Sample compounds were separated using an Agilent (Agilent Technologies, Santa Clara, CA, USA) 1290 Infinity LC ultra-high performance liquid chromatography system using a hydrophilic interaction (HILIC) column using the mobile phases as follows: A (25 mM ammonium acetate and 25 mM ammonia in H2O) and B (acetonitrile). The gradient elution procedure is as follows: 0–0.5 min, 95% B; 0.5–7 min, B 95 to 65%; 7–8 min, B 65 to 40%; 8–9 min, B 40%; 9–9.1 min, B 40 to 95%; 9.1–12 min, B 95%. During this process, the proportion of mobile phase A changed accordingly. Samples were placed in an autosampler tray at 4 °C. QC samples were inserted into the sample queues to monitor and evaluate the stability of the system and the reliability of experimental data [39].

2.5.2. Data Processing

Proteo Wizard MS Convert was used to convert the raw MS data to MzXML files before importing it into freely available XCMS software (v 1.52.0) [40]. For peak picking, the following parameters were used: centWave m/z = 10 ppm, peakwidth = c (10, 60), prefilter = c (10, 100). For peak grouping, bw = 5, mzwid = 0.025, minfrac = 0.5 were used. Isotopes and adducts were annotated by CAMERA (Collection of Algorithms of MEtabolite pRofile Annotation) [41]. In the extracted ion features, only the variables with more than 50% of the nonzero measurement values in at least one group were kept [41]. Compound identification of metabolites was conducted by comparing accuracy m/z value (<10 ppm) and MS/MS spectra with an in-house database established with available authentic standards [42].

2.6. Statistical Analysis

2.6.1. Analysis of Microbiota Diversity and Composition Differences

Analysis for taxonomic clusters utilized the ASVs, and quality control was carried out under conditions of relative abundance >0.05% and detection of ASVs in >80% of the samples [43]. ASV α-diversity with Shannon, Simpson, Chao1, Faith’s phylogenetic diversity (PD), and ACE indices were calculated using Mothur software v 1.31.2 [44]. The Wilcoxon rank sum test was used to compare α-diversity differences between the two groups. Principal coordinates analysis (PCoA) [45] was performed to evaluate the discrepancy of the phylogenetic compositions of cecal microbiota between HC and LC. Linear discriminant analysis with difference contribution analysis (LEfSe) was performed with LDA > 2 and p < 0.05 as thresholds to identify bacterial composition differences between the two groups [46].

2.6.2. Construction of Cecal Microbial Co-Abundance Groups

The quality-controlled ASVs were used to construct co-abundance groups (CAGs) of cecal microbiota. The correlation matrix between ASVs was calculated based on the SparCC algorithm [47] using the SpiecEasi package in the R [48]. Paired ASVs with a correlation coefficient >0.5 were used for further analyses. The correlation coefficient values were converted into correlation distances (1-correlation coefficient), and ASVs were clustered into CAGs based on the Ward algorithm with the Vegan package in the R [49]. Cytoscape v 3.9.1 [50] was used for the visualization of cecal microbiota CAGs.

2.6.3. Serum Metabolomics Analysis

After sum-normalization, the online analysis platform MetaboAnalyst 5.0 (https://www.metaboanalyst.ca/) (accessed on 14 March 2023) was employed to analyze the processed data by multivariate data analysis, including Pareto-scaled principal component analysis (PCA) and orthogonal partial least-squares discriminant analysis (OPLS-DA) [51]. The robustness of the model was evaluated using 7-fold cross-validation and response permutation testing. The variable importance in the projection (VIP) value of each variable in the OPLS-DA model was calculated to indicate its contribution to the classification [52]. In order to determine the significance of differences between two groups of independent samples, the Student’s t-test was applied. VIP > 1 and p < 0.05 were used to screen significantly changed metabolites. Pearson’s correlation analysis was performed to determine the correlation between two variables [49].

3. Results

3.1. Growth Performance of the Study Chickens

The weight gain trends for the HC and LC groups of chickens were consistent from 0 to 18 weeks of age when the animals were provided with identical environments and feed and water access. There was no significant difference in body weights between the two groups from 0 to 6 weeks of age. In contrast, from week 8 onwards, body weight differences reached the level of statistical significance (p < 0.05) (Figure 2A). By 18 weeks of age, the body weights for HC (2187.81 ± 232.58 g) were significantly (p < 0.01) greater than the LC group (1817.69 ± 199.30 g) (Figure 2B and Table 2).

3.2. Cecal Microbial Diversity in High- versus Low-Market-Weight Chickens

A total of 32 microbial DNA samples from HC and LC chickens were used for 16S rDNA gene sequencing, 1,535,941 clean reads were generated after QC, and 1955 ASVs were identified for all samples (Table S1). The Shannon and Simpson of α-diversity indices between the two groups were of no significant differences (Figure 3A,B). PCoA analysis showed a lack of significant differences in β-diversity of the HC and LC cecal microbiota, although a certain aggregation effect was evident (Figure 3C).

3.3. Bacteria Differentially Abundant in HC versus LC

At the phylum level and based on relative abundance, the cecal microbiota of the Guizhou yellow chicken was primarily composed of Firmicutes (46.88%), Bacteroidota (46.28%), Actinobacteriota (3.91%), Desulfobacterota (0.86%), and Synergistota (0.85%) (Figure 4A and Table S2). Comparison based on abundance for HC and LC indicated that Verrucomicrobiota were present at significantly higher levels in HC (Wilcoxon test, p = 0.017) while Desulfobacterota were significantly (p = 0.029) higher in the LC group (Figure 4C and Table S3). At the genus level, the dominant bacteria were Bacteroides (25.15%), Megamonas (8.05%), Prevotellaceae UCG-001 (5.98%), Ruminococcus torques group (5.93%) and Phascolarctobacterium (5.92%) (Figure 4B). A comparison by abundance for HC and LC identified 17 genera. In the HC group, the most abundant were the Ruminococcus torques group, unclassified Bacteroidales, Parabacteroides, Bifidobacterium, and Alistipes. The LC group was enriched in eight genera, including Phascolarctobacterium, Rikenellaceae RC9 gut group, Lactobacillus, Desulfovibrio, and Bacillus (Figure 4D).
Characteristics of the cecal microbiota for the LC and HC groups were further explored at the ASV level using LEfSe analysis. Twenty-two out of two hundred and thirty-three (22/223) ASVs displayed differential abundances, among which eight ASVs were significantly enriched in LC, and fourteen were significantly enriched in HC (LDA > 2, p < 0.05). Again, Ruminococcus torques group (ASV20), Alistipes (ASV26, ASV169), Lachnoclostridium (ASV482) were enriched in HC, and Phascolarctobacterium (ASV5, ASV7), Lactobacillus (ASV17, ASV19) and Bacillus (ASV487) were enriched in LC and also displayed significant differences at the genus level (Figure 4E, Table 3).

3.4. Identification of Co-Abundance Groups (CAG) Associated with Body Weight

Another goal of this study was to explore the gut microbiota clusters associated with chicken body weight [53]. The identified 223 ASVs were clustered into 20 CAGs, and the average relative abundance of each CAG was compared between groups using the Wilcoxon test. Four CAGs were identified that significantly differed between the two groups: CAG6, CAG9, CAG16, and CAG17 (Figure 5A). CAG6 was enriched in LC and contained 22 ASVs, and Phascolarctobacterium ASV5 was the most enriched in LC. This suggested that CAG6 with Phascolarctobacterium as the core has a potentially negative effect on growth performance. In contrast, CAG17 was enriched in HC and included Ruminococcus torques group ASV642, Bifidobacterium ASV279, Alistipes ASV26, Alistipes ASV239, and others. In addition, CAG9 was centered on Ruminococcus torques group ASV20, while CAG16 contained bacteria such as Parabacteroides that were enriched in HC at the genus level (Figure 5B and Table S4).

3.5. Differential Serum Metabolites between HC and LC

Untargeted metabolome assays were performed on serum samples from all 32 chickens, and a total of 766 annotated serum metabolites were detected, including 494 positive ion metabolites and 272 negative ion metabolites. Metabolomics data analysis was performed on the MetaboAnalyst 5.0 online platform, and the PLS-DA results indicated that the metabolites of HC and LC were significantly separated (Figure 6A). The metabolites annotated by the positive and negative ion modes were combined, and the differential metabolites were compared at a threshold of VIP > 1, p < 0.05. There were 44 metabolites showing significant differences between the two groups, including 13 metabolites such as pantothenic acid, luvangetin (2H-1-benzopyran-6-acrylic acid), metyrapone (methyl-1,2-di-pyridyl-1-propanone) and sempervirine (16,17,18,19-tetrahydroyohimban) that were at higher levels in group HC. In LC, we identified 31 significant metabolites including phosphocreatine, glufosinate (2-amino-4-[hydroxy(methyl)phosphoryl] butanoic acid), hesperetin ((2S)-5,7-dihydroxy-2-(3-hydroxy-4-methoxyphenyl)-2,3-dihydrochromen-4-one and chlorophene (2-benzyl-4-chlorophenol) (Figure 6B, Table 4). Pathway enrichment analyses were then performed with these differential metabolites. In the HC group, four metabolic pathways were enriched, including ubiquinone and other terpenoid-quinone biosynthesis, pantothenate, and CoA biosynthesis (Figure 6C). In the LC group, five metabolic pathways were enriched, including riboflavin metabolism, phosphonate, and phosphinate metabolism (Figure 6D).

3.6. Correlation Analysis Reveals Relationships between the Cecal Microbiota and Serum Metabolites

The overlaps of differential bacteria at the ASV, genus, and CAG levels were integrated. The Ruminococcus torques group, Lachnoclostridium, Alistipes, Negativibacillus, and Sellimonas were enriched in HC, and Phascolarctobacterium was enriched in LC in all analyses at three levels (Table S5). Spearman’s rank correlation analysis was then performed between these bacteria and differential metabolites, and the Ruminococcus torques group enriched in HC was significantly positively correlated with pantothenic acid (p = 0.029, r = 0.386). In addition, menadione was positively correlated with numerous bacteria enriched in HC. These included Lachnoclostridium ASV482, Alistipes ASV26, Negativibacillus ASV83, and Sellimonas ASV409, suggesting these bacteria might promote the synthesis of menadione and have beneficial effects on the growth performance of the chickens. The detailed relationships between weight-related bacteria and differential metabolites are shown in Figure 7.

4. Discussion

Growth performance is an important trait for chickens, and gut microbiota have been proven to play an important role in the life activities of hosts. However, clear correlations between the gut microbes and chicken growth performance have not been fully revealed. In this study, multi-omics was used to describe the cecal microbiota composition and serum metabolite differences between high- and low-market-weight Guizhou Yellow chicken. The results showed that Lachnoclostridium, Alistipes, Negativibacillus, Sellimonas, and Ruminococcus torques are beneficial for chicken growth by regulating metabolites such as pantothenic acid and menadione, while Phascolarctobacterium might inhibit the growth of chicken.
The composition of the gut microbiota for individual animals also varies by location within the animal, and the diversity and abundance of microbiota in the cecum is the highest. Therefore, cecal microbiota was taken as representative gut microbiota in this study [54,55,56]. Diversity is an important index to evaluate the community structure of microbiota [57]. It has been proved that the diversity of gut microbiota is negatively correlated with weight gain [58]. Decreased diversity of gut microbiota has been linked to inflammatory diseases, which tend to result in fat deposition and increased body weight [26]. However, results from other studies found no significant difference between the cecal microbiota diversity of high-market-weight and low-market-weight chickens [7], which is consistent with the observation in this study. These results are controversial as of now; more studies are therefore needed.
It was found in this study that at the genus level, Lachnoclostridium, Alistipes, Negativibacillus, Sellimonas, and Ruminococcus torques were enriched in the HC group, while Phascolarctobacterium was enriched in the LC group. These specific cecal microbes might be an important factor influencing the body weight of chickens. Alistipes and Lachnoclostridium are considered to be important producers of short-chain fatty acids, including butyric and acetic acids. It has been proved that a reduction in Alistipes abundance is linked to a reduction in the levels of short-chain fatty acids [59,60], and Alistipes finegoldii was specifically proved to promote the growth of broiler chickens [60,61,62]. In mice, a reduced Lachnoclostridium abundance was associated with decreased body weight [63]. The reason might be that in addition to influencing the production of short-chain fatty acids, Lachnoclostridium is also linked to host nutrient absorption, and its reduced abundance will lead to the downregulation of functional pathways such as protein processing and nutrient transport in the host [59,64,65]. Negativibacillus is a Gram-negative Firmicute, and its abundance in the mouse gut was positively correlated with body weight gain [66]. Sellimonas is an obligate anaerobic, non-motile Gram-positive first isolated from human feces in 2016 [67]. In addition, Ruminococcus torques were found to be enriched in chickens with high body weight. It has not been studied much since Ruminococcus torques was first described. However, it is often involved in studies that link human microbiota to disease states. For instance, it has decreased abundance in patients with Crohn’s Disease in comparison to healthy individuals [68]. In contrast, its abundance increased in children with late-onset autism [69] and those with autism spectrum disorders and gastrointestinal disorders [70]. Therefore, the role of Ruminococcus torques in contribution to health or disease is still an open question [71].
Previous studies have found that there are several mechanisms explaining how gut microbiota affect the growth of the host, such as being involved in vitamin synthesis [72,73], dietary fiber degradation [74], inflammatory induction [33], and lipid metabolism [7]. As well known, vitamins play a catalytic role in promoting nutrient synthesis, thereby controlling metabolism and affecting the performance and health of poultry [75]. Humans and animals cannot synthesize most vitamins by themselves and must obtain them from their diet or rely on gut microbiota to synthesize them [76]. Probiotics such as Bifidobacterium and lactobacillus could synthesize a variety of vitamins necessary for human growth and development, such as vitamins B [72], vitamin K [77], and vitamin D [73]. In this study, several vitamin metabolites related to body weight were identified by serum metabolome analyses. For example, pantothenate acid and menadione were enriched in the serum of high-market-weight chickens, while riboflavin was enriched in the serum of low-market-weight chickens, which highlights the importance of vitamin metabolism for the regulation of growth traits in chickens.
Pantothenic acid, also known as vitamin B5, is an indispensable essential nutrient that can be converted into Coenzyme A (CoA) and Acyl carrier protein (ACP) in living organisms, both of which are enzyme cofactors necessary for key pathways of metabolism and energy production in all living cells [78]. Pantothenic acid is involved in the metabolism of sugar, fat, and protein in both humans and animals [79]. In this study, a higher concentration of pantothenic acid was found in the serum of high-market-weight chickens. This indicates that pantothenic acid could promote the growth and development of broilers. Early studies showed that deficiency of pantothenic acid caused symptoms such as loss of appetite, growth retardation, and wasting in sick chickens [80,81]. Lacking pantothenic acid in the diet for chicks could result in a decrease in protein, fat, and energy stores, and the addition of pantothenic acid in the diet could not only enhance the activity of intestinal digestive enzymes but also promote the digestion and absorption of nutrients in the diet, thus promoting the growth of animal body [82]. In addition, it was found in this study that Ruminococcus torques were enriched in high-market-weight chickens and were significantly positively correlated with pantothenic acid, suggesting that Ruminococcus torques might promote the generation of pantothenic acid and, in turn, promote the growth of chickens.
In this study, a significantly higher concentration of menadione (VK3) in high-market-weight chickens was found. It is deduced that the beneficial effects of menadione on bone development and its strong antioxidant effect might be two of the reasons for the chickens’ higher market weight. Menadione is a fully reduced form of vitamin K. In addition to its well-known anticoagulant effects, vitamin K also plays an important role in bone formation and remodeling [75]. It has been proved that supplementation of vitamin K in the diets of starter(8 mg/kg feed) and grower (2 mg/kg feed) broilers promoted the carboxylation of osteocalcin and improved the hydroxyapatite binding ability of serum osteocalcin, in turn, improved the bone quality [83]. What’s more, menadione has a strong antioxidant effect [84]. Oxidative stress adversely affects the growth performance of animals because it can cause disorders of chicken gastrointestinal peristalsis, which tend to result in enteritis and diarrhea, damage of intestinal villi, in turn, lead to poor absorption of nutrients, and reduce the nutrient absorption capacity of chickens [85]. The chickens involved in this study were raised from June to October when the temperature was up to 33 °C; natural ventilation might not cool the chicken house effectively, which might cause heat stress in chickens. Some chickens with poor tolerance to heat stress might grow slower, while the high-market-weight chickens may alleviate heat stress by increasing the synthesis of menadione.
It was early discovered that menadione could be produced by many bacteria, such as Bacillus cereus, B. mycoides, B. subtilis, Chromobacterium prodigiosus, Escherichia coli, Mycobacterium tuberculosis, Sarcina lutea, and Staphylococcus aureus [86], and human gut microbiota (e.g., Bacteroides and Prevotella) might participate the synthesis of menadione [87]. Interestingly, menadione was found to be enriched in high-market-weight chickens and was significantly associated with a variety of high-market-weight-associated bacteria, including Lachnoclostridium, Alistipes, Negativibacillus, and Sellimonas. This suggests that these microbiotas might promote the synthesis of menadione and thus affect the growth of chickens. However, its mechanism needs further study.
In the present study, Phascolarctobacterium was found to be a key microbe that was enriched in the cecum of low-weight chickens. Phascolarctobacterium is an obligate anaerobic originally isolated from koala feces [88]. Another study also found that Phascolarctobacterium abundance was higher in low-feed conversion chickens and was related to a low nutrient absorption capacity of the host. However, the study did not elucidate a detailed mechanism [89]. An additional study found that the abundance of Phascolarctobacterium in the gut increases when chickens are exposed to high temperatures for extended periods, and this exposure also results in elevated levels of heat shock proteins and related inflammatory gene expression [90]. Heat stress altered the structure and function of enzymes in the chicken body, reduced the pH of the blood, and caused metabolic acidosis. These were negative influences on chicken growth [91,92], and intestinal inflammation also decreases nutrient absorption, causing body weight to drop [93,94,95]. The presence of Phascolarctobacterium was correlated with the induction of inflammation under the action of heat stress and other harmful factors in chickens. These results are consistent with those of this study, where Phascolarctobacterium was enriched in the cecum of low-market-weight chickens. As mentioned above, elevated serum levels of some vitamin-related metabolites in high-market-weight chickens might contribute to the relief of heat stress.
In addition, it is worth noting that in this study, 11 beta-hydroxyprogesterone showed higher concentration in the serum of low-market-weight chickens. 11 beta-hydroxyprogesterone is a naturally occurring, endogenous steroid and derivative of progesterone [96]. This might be because at the age of 18 weeks, the low-market-weight chickens were already sexually mature, and the follicular granulosic cells released related sex hormones, making them lay eggs. The onset of sexual maturity of chickens might slow down their growth because the nutrients would be utilized for reproduction instead of growth [97].

5. Conclusions

In this study, 16S rRNA gene sequencing with untargeted serum metabolomics was combined to identify cecal microbes associated with body weight in chickens and to preliminarily identify the metabolic mechanism of cecal microbiota affecting growth performance. The effect of cecal microbiota composition on chicken growth performance and its potential mechanism was investigated. The genera Lachnoclostridium, Alistipes, Negativibacillus, Sellimonas, Sellimonas, and Ruminococcus torques group had beneficial effects on the growth performance of chickens, while Phascolarctobacterium correlated with low growth performance. A correlation analysis revealed links between specific gut microbiota and serum metabolites. In conclusion, certain cecal microbiotas might increase the market weight of chickens by promoting the utilization of pantothenic acid and menadione.
The results of this study provide new insights into the role of gut microbiota in regulating the growth performance of chickens and also lay the foundation for the subsequent development of chicken growth-promoting probiotics or prebiotic-related products. However, there are some limitations that should be addressed or avoided in subsequent research. Firstly, this research was a small sample, single-center, cross-sectional study. Secondly, only the cecal microbiota and serum metabolites at the time of slaughter were analyzed, and samples were not collected at different growth stages. Therefore, the causal mechanism between gut microbiota and growth traits needs to be further studied through larger sample sizes, using a multi-center design and applying the innovative research techniques of integrated omics technology.

Supplementary Materials

The following supporting information can be downloaded at: https://www.mdpi.com/article/10.3390/ani13193034/s1, Table S1: Sequencing results of cecal microbiota from Guizhou yellow chickens; Table S2: Relative abundance of cecal microbiota at the phylum level; Table S3: The relative abundance of cecal microbiota at the genus level; Table S4: Co-abundance groups (CAGs) enriched in the high-market-weight chickens or low-market-weight chickens; Table S5: The overlap of differential bacteria groups at ASV, genus and CAG levels.

Author Contributions

Conceptualization, Z.W.; Data curation, H.L.; Formal analysis, Y.Y.; Funding acquisition, Z.W.; Investigation, S.Y., Y.Y., X.L. and Z.W.; Methodology, S.Y.; Resources, F.Z. and Z.W.; Supervision, H.L.; Visualization, S.Y.; Writing—original draft, S.Y.; Writing—review and editing, Z.W. All authors have read and agreed to the published version of the manuscript.

Funding

This work was supported by the National Natural Science Foundation of China (32260829), Guizhou Provincial Science and Technology Project (QKH-ZK2022-113), the Natural Science Research Project of Guizhou Provincial Department of Education (QJJ-ZK2022-061), and Guizhou Provincial Science and Technology Project (QKH-ZC2022-key34).

Institutional Review Board Statement

All animal works were conducted according to the guidelines for the care and use of experimental animals established by the Ministry of Agriculture and Rural Affairs of the People’s Republic of China. Animal Care and Use Committee in Guizhou University approved this project (EAE-GZU-2022-T050).

Informed Consent Statement

Not applicable.

Data Availability Statement

The datasets generated from this study have been submitted to the CNGB Sequence Archive (CNSA) of China National GeneBank DataBase (CNGBdb) with accession code CNP0003145.

Conflicts of Interest

The authors declare no conflict of interest.

References

  1. Gilbert, M.; Conchedda, G.; Van Boeckel, T.P.; Cinardi, G.; Linard, C.; Nicolas, G.; Thanapongtharm, W.; D’Aietti, L.; Wint, W.; Newman, S.H.; et al. Income disparities and the global distribution of intensively farmed chicken and pigs. PLoS ONE 2015, 10, e0133381. [Google Scholar] [CrossRef] [PubMed]
  2. Mahoro, J.; Muasya, T.K.; Mbuza, F.; Mbuthia, J.; Kahi, A.K. Farmers’ breeding practices and traits of economic importance for indigenous chicken in RWANDA. Trop. Anim. Health Prod. 2018, 50, 121–128. [Google Scholar] [CrossRef]
  3. Li, F.; Han, H.; Lei, Q.; Gao, J.; Liu, J.; Liu, W.; Zhou, Y.; Li, H.; Cao, D. Genome-wide association study of body weight in Wenshang Barred chicken based on the SLAF-seq technology. J. Appl. Genet. 2018, 59, 305–312. [Google Scholar] [CrossRef] [PubMed]
  4. Wu, Y.; Zhou, X.; Wang, M.; Wang, W.; Yang, Y. Effect of light intensity on growth performance and bone development of tibia in broilers. J. Anim. Physiol. Anim. Nutr. 2023, 107, 192–199. [Google Scholar] [CrossRef]
  5. Manyeula, F.; Mlambo, V.; Marume, U.; Sebola, N.A. Nutrient digestibility, haemo-biochemical parameters and growth performance of an indigenous chicken strain fed canola meal-containing diets. Trop. Anim. Health Prod. 2019, 51, 2343–2350. [Google Scholar] [CrossRef]
  6. Diaz Carrasco, J.M.; Casanova, N.A.; Fernández Miyakawa, M.E. Microbiota, gut health and chicken productivity: What is the connection? Microorganisms 2019, 7, 374. [Google Scholar] [CrossRef] [PubMed]
  7. Zhang, X.; Hu, Y.; Ansari, A.R.; Akhtar, M.; Chen, Y.; Cheng, R.; Cui, L.; Nafady, A.A.; Elokil, A.A.; Abdel Kafy, E.S.M.; et al. Caecal microbiota could effectively increase chicken growth performance by regulating fat metabolism. Microb. Biotechnol. 2022, 15, 844–861. [Google Scholar] [CrossRef]
  8. Zhang, X.; Akhtar, M.; Chen, Y.; Ma, Z.; Liang, Y.; Shi, D.; Cheng, R.; Cui, L.; Hu, Y.; Nafady, A.A.; et al. Chicken jejunal microbiota improves growth performance by mitigating intestinal inflammation. Microbiome 2022, 10, 107. [Google Scholar]
  9. Xu, Y.; Yu, Y.; Shen, Y.; Li, Q.; Lan, J.; Wu, Y.; Zhang, R.; Cao, G.; Yang, C. Effects of Bacillus subtilis and Bacillus licheniformis on growth performance, immunity, short chain fatty acid production, antioxidant capacity, and cecal microflora in broilers. Poult. Sci. 2021, 100, 101358. [Google Scholar] [CrossRef]
  10. Fathima, S.; Shanmugasundaram, R.; Adams, D.; Selvaraj, R.K. Gastrointestinal microbiota and their manipulation for improved growth and performance in chickens. Foods 2022, 11, 1401. [Google Scholar] [CrossRef]
  11. Deng, S.; Xing, T.; Li, C.; Xu, X.; Zhou, G. The effect of breed and age on the growth performance, carcass traits and metabolic profile in breast muscle of Chinese indigenous chickens. Foods 2022, 11, 483. [Google Scholar] [CrossRef] [PubMed]
  12. Li, Y.; Luo, C.; Wang, J.; Guo, F. Effects of different raising systems on growth performance, carcass, and meat quality of medium-growing chickens. J. Appl. Anim. Res. 2017, 1, 326–330. [Google Scholar] [CrossRef]
  13. Zhang, Y.; Wang, Y.; Li, Y.; Wu, J.; Wang, X.; Bian, C.; Tian, Y.; Sun, G.; Han, R.; Liu, X.; et al. Genome-wide association study reveals the genetic determinism of growth traits in a Gushi-Anka F2 chicken population. Heredity 2021, 126, 293–307. [Google Scholar] [CrossRef] [PubMed]
  14. Visconti, A.; Le Roy, C.I.; Rosa, F.; Rossi, N.; Martin, T.C.; Mohney, R.P.; Li, W.; de Rinaldis, E.; Bell, J.T.; Venter, J.C.; et al. Interplay between the human gut microbiome and host metabolism. Nat. Commun. 2019, 10, 4505. [Google Scholar] [CrossRef] [PubMed]
  15. Fujisaka, S.; Watanabe, Y.; Tobe, K. The gut microbiome: A core regulator of metabolism. J. Endocrinol. 2023, 256, e220111. [Google Scholar] [CrossRef]
  16. Sonnenburg, J.L.; Bäckhed, F. Diet–microbiota interactions as moderators of human metabolism. Nature 2016, 535, 56–64. [Google Scholar] [CrossRef]
  17. Urgessa, O.E.; Woldesemayat, A.A. OMICs approaches and technologies for understanding low-high feed efficiency traits in chicken: Implication to breeding. Anim. Biotechnol. 2023, 1–20. [Google Scholar] [CrossRef]
  18. Xu, P.; Liu, P.; Zhou, C.; Shi, Y.; Wu, Q.; Yang, Y.; Li, G.; Hu, G.; Guo, X. A Multi-Omics Study of Chicken Infected by Nephropathogenic Infectious Bronchitis Virus. Viruses 2019, 11, 1070. [Google Scholar] [CrossRef]
  19. Li, J.; Zhang, D.; Yin, L.; Li, Z.; Yu, C.; Du, H.; Jiang, X.; Yang, C.; Liu, Y. Integration analysis of metabolome and transcriptome profiles revealed the age-dependent dynamic change in chicken meat. Food Res. Int. 2022, 156, 111171. [Google Scholar] [CrossRef]
  20. Liu, J.; Wang, J.; Zhou, Y.; Han, H.; Liu, W.; Li, D.; Li, F.; Cao, D.; Lei, Q. Integrated omics analysis reveals differences in gut microbiota and gut-host metabolite profiles between obese and lean chickens. Poult. Sci. 2022, 101, 102165. [Google Scholar] [CrossRef]
  21. Wu, Y.; Zeng, J.; Zhang, F.; Zhu, Z.; Qi, T.; Zheng, Z.; Lloyd-Jones, L.R.; Marioni, R.E.; Martin, N.G.; Montgomery, G.W.; et al. Integrative analysis of omics summary data reveals putative mechanisms underlying complex traits. Nat. Commun. 2018, 9, 918. [Google Scholar] [CrossRef] [PubMed]
  22. Wen, C.; Yan, W.; Mai, C.; Duan, Z.; Zheng, J.; Sun, C.; Yang, N. Joint contributions of the gut microbiota and host genetics to feed efficiency in chickens. Microbiome 2021, 9, 126. [Google Scholar] [CrossRef]
  23. Sun, H.Z.; Zhou, M.; Wang, O.; Chen, Y.; Liu, J.X.; Guan, L.L. Multi-omics reveals functional genomic and metabolic mechanisms of milk production and quality in dairy cows. Bioinformatics 2020, 36, 2530–2537. [Google Scholar] [CrossRef] [PubMed]
  24. Wen, C.; Yan, W.; Sun, C.; Ji, C.; Zhou, Q.; Zhang, D.; Zheng, J.; Yang, N. The gut microbiota is largely independent of host genetics in regulating fat deposition in chickens. ISME J. 2019, 13, 1422–1436. [Google Scholar] [CrossRef]
  25. Schrimpe-Rutledge, A.C.; Codreanu, S.G.; Sherrod, S.D.; McLean, J.A. Untargeted metabolomics strategies-Challenges and emerging directions. J. Am. Soc. Mass Spectr. 2016, 27, 1897–1905. [Google Scholar] [CrossRef] [PubMed]
  26. Chen, C.; Fang, S.; Wei, H.; He, M.; Fu, H.; Xiong, X.; Zhou, Y.; Wu, J.; Gao, J.; Yang, H.; et al. Prevotella copri increases fat accumulation in pigs fed with formula diets. Microbiome 2021, 9, 175. [Google Scholar] [CrossRef] [PubMed]
  27. Xue, M.Y.; Sun, H.Z.; Wu, X.H.; Liu, J.X.; Guan, L.L. Multi-omics reveals that the rumen microbiome and its metabolome together with the host metabolome contribute to individualized dairy cow performance. Microbiome 2020, 8, 64. [Google Scholar] [CrossRef]
  28. Susanti, D.; Volland, A.; Tawari, N.; Baxter, N.; Gangaiah, D.; Plata, G.; Nagireddy, A.; Hawkins, T.; Mane, S.P.; Kumar, A. Multi-omics characterization of host-derived bacillus spp. Probiotics for improved growth performance in poultry. Front. Microbiol. 2021, 12, 747845. [Google Scholar] [CrossRef]
  29. Wang, R.; Li, B.; Lam, S.M.; Shui, G. Integration of lipidomics and metabolomics for in-depth understanding of cellular mechanism and disease progression. J. Genet. Genom. 2020, 47, 69–83. [Google Scholar] [CrossRef]
  30. Yang, Y.; Misra, B.B.; Liang, L.; Bi, D.; Weng, W.; Wu, W.; Cai, S.; Qin, H.; Goel, A.; Li, X.; et al. Integrated microbiome and metabolome analysis reveals a novel interplay between commensal bacteria and metabolites in colorectal cancer. Theranostics 2019, 9, 4101–4114. [Google Scholar] [CrossRef]
  31. Li, W.; Mou, T.; Rao, Y.; Yang, D.; Long, G.; Lin, J.; Fu, Z.; Zhang, F. Determination and correlation analysis of body size, slaughter performance, meat quality of Guizhou yellow chicken. Jiangsu Agric. Sci. 2021, 49, 163–166. (In Chinese) [Google Scholar]
  32. Sakomura, N.K.; Gous, R.M.; Marcato, S.M.; Fernandes, J.B. A description of the growth of the major body components of 2 broiler chicken strains. Poult. Sci. 2011, 90, 2888–2896. [Google Scholar] [CrossRef]
  33. Wang, L.; Zhang, F.; Li, H.; Yang, S.; Chen, X.; Long, S.; Yang, S.; Yang, Y.; Wang, Z. Metabolic and inflammatory linkage of the chicken cecal microbiome to growth performance. Front. Microbiol. 2023, 14, 1060458. [Google Scholar] [CrossRef]
  34. Haetinger, V.S.; Park, C.S.; Adeola, O. Energy values of copra meal and cornstarch for broiler chickens. Poult. Sci. 2021, 100, 858–864. [Google Scholar] [CrossRef]
  35. Magoc, T.; Salzberg, S.L. FLASH: Fast length adjustment of short reads to improve genome assemblies. Bioinformatics 2011, 27, 2957–2963. [Google Scholar] [CrossRef] [PubMed]
  36. Callahan, B.J.; McMurdie, P.J.; Rosen, M.J.; Han, A.W.; Johnson, A.J.; Holmes, S.P. DADA2: High-resolution sample inference from Illumina amplicon data. Nat. Methods 2016, 13, 581–583. [Google Scholar] [CrossRef] [PubMed]
  37. Pruesse, E.; Quast, C.; Knittel, K.; Fuchs, B.M.; Ludwig, W.; Peplies, J.; Glockner, F.O. SILVA: A comprehensive online resource for quality checked and aligned ribosomal RNA sequence data compatible with ARB. Nucleic Acids Res. 2007, 35, 7188–7196. [Google Scholar] [CrossRef] [PubMed]
  38. Gu, Z.; Li, L.; Tang, S.; Liu, C.; Fu, X.; Shi, Z.; Mao, H. Metabolomics Reveals that Crossbred Dairy Buffaloes Are More Thermotolerant than Holstein Cows under Chronic Heat Stress. J. Agric. Food Chem. 2018, 66, 12889–12897. [Google Scholar] [CrossRef]
  39. Siskos, A.P.; Jain, P.; Romisch-Margl, W.; Bennett, M.; Achaintre, D.; Asad, Y.; Marney, L.; Richardson, L.; Koulman, A.; Griffin, J.L.; et al. Interlaboratory Reproducibility of a Targeted Metabolomics Platform for Analysis of Human Serum and Plasma. Anal. Chem. 2017, 89, 656–665. [Google Scholar] [CrossRef]
  40. Huan, T.; Forsberg, E.M.; Rinehart, D.; Johnson, C.H.; Ivanisevic, J.; Benton, H.P.; Fang, M.; Aisporna, A.; Hilmers, B.; Poole, F.L.; et al. Systems biology guided by XCMS Online metabolomics. Nat. Methods 2017, 14, 461–462. [Google Scholar] [CrossRef]
  41. Kuhl, C.; Tautenhahn, R.; Bottcher, C.; Larson, T.R.; Neumann, S. CAMERA: An integrated strategy for compound spectra extraction and annotation of liquid chromatography/mass spectrometry data sets. Anal. Chem. 2012, 84, 283–289. [Google Scholar] [CrossRef] [PubMed]
  42. Wang, B.; Wang, Y.; Zuo, S.; Peng, S.; Wang, Z.; Zhang, Y.; Luo, H. Untargeted and Targeted Metabolomics Profiling of Muscle Reveals Enhanced Meat Quality in Artificial Pasture Grazing Tan Lambs via Rescheduling the Rumen Bacterial Community. J. Agric. Food Chem. 2021, 69, 846–858. [Google Scholar] [CrossRef] [PubMed]
  43. Liu, H.; Chen, X.; Hu, X.; Niu, H.; Tian, R.; Wang, H.; Pang, H.; Jiang, L.; Qiu, B.; Chen, X.; et al. Alterations in the gut microbiome and metabolism with coronary artery disease severity. Microbiome 2019, 7, 68. [Google Scholar] [CrossRef] [PubMed]
  44. Cole, J.R.; Wang, Q.; Cardenas, E.; Fish, J.; Chai, B.; Farris, R.J.; Kulam-Syed-Mohideen, A.S.; McGarrell, D.M.; Marsh, T.; Garrity, G.M.; et al. The Ribosomal Database Project: Improved alignments and new tools for rRNA analysis. Nucleic Acids Res. 2009, 37, 141–145. [Google Scholar] [CrossRef]
  45. Lozupone, C.; Knight, R. UniFrac: A new phylogenetic method for comparing microbial communities. Appl. Environ. Microbiol. 2005, 71, 8228–8235. [Google Scholar] [CrossRef]
  46. Segata, N.; Izard, J.; Waldron, L.; Gevers, D.; Miropolsky, L.; Garrett, W.S.; Huttenhower, C. Metagenomic biomarker discovery and explanation. Genome Biol. 2011, 12, R60. [Google Scholar] [CrossRef]
  47. Reverter, A.; Chan, E.K.F. Combining partial correlation and an information theory approach to the reversed engineering of gene co-expression networks. Bioinformatics 2008, 24, 2491–2497. [Google Scholar] [CrossRef]
  48. Kurtz, Z.D.; Muller, C.L.; Miraldi, E.R.; Littman, D.R.; Blaser, M.J.; Bonneau, R.A. Sparse and compositionally robust inference of microbial ecological networks. PLoS Comput. Biol. 2015, 11, e1004226. [Google Scholar] [CrossRef]
  49. Wang, Z.; Fu, H.; Zhou, Y.; Yan, M.; Chen, D.; Yang, M.; Xiao, S.; Chen, C.; Huang, L. Identification of the gut microbiota biomarkers associated with heat cycle and failure to enter oestrus in gilts. Microb. Biotechnol. 2021, 14, 1316–1330. [Google Scholar] [CrossRef]
  50. Shannon, P.; Markiel, A.; Ozier, O.; Baliga, N.S.; Wang, J.T.; Ramage, D.; Amin, N.; Schwikowski, B.; Ideker, T. Cytoscape: A software environment for integrated models of biomolecular interaction networks. Genome Res. 2003, 13, 2498–2504. [Google Scholar] [CrossRef]
  51. Pang, Z.; Zhou, G.; Ewald, J.; Chang, L.; Hacariz, O.; Basu, N.; Xia, J. Using MetaboAnalyst 5.0 for LC-HRMS spectra processing, multi-omics integration and covariate adjustment of global metabolomics data. Nat. Protoc. 2022, 17, 1735–1761. [Google Scholar] [CrossRef] [PubMed]
  52. Gao, Y.; Li, J.T.; Li, X.; Li, X.; Yang, S.W.; Chen, N.H.; Li, L.; Zhang, L. Tetrahydroxy stilbene glycoside attenuates acetaminophen-induced hepatotoxicity by UHPLC-Q-TOF/MS-based metabolomics and multivariate data analysis. J. Cell Physiol. 2021, 236, 3832–3862. [Google Scholar] [CrossRef] [PubMed]
  53. Ke, S.; Fang, S.; He, M.; Huang, X.; Yang, H.; Yang, B.; Chen, C.; Huang, L. Age-based dynamic changes of phylogenetic composition and interaction networks of health pig gut microbiome feeding in a uniformed condition. BMC Vet. Res. 2019, 15, 172. [Google Scholar] [CrossRef] [PubMed]
  54. Xiao, Y.; Xiang, Y.; Zhou, W.; Chen, J.; Li, K.; Yang, H. Microbial community mapping in intestinal tract of broiler chicken. Poult. Sci. 2017, 96, 1387–1393. [Google Scholar] [CrossRef] [PubMed]
  55. Lkhagva, E.; Chung, H.J.; Hong, J.; Tang, W.; Lee, S.I.; Hong, S.T.; Lee, S. The regional diversity of gut microbiome along the GI tract of male C57BL/6 mice. BMC Microbiol. 2021, 21, 44. [Google Scholar] [CrossRef] [PubMed]
  56. Yeoman, C.J.; Chia, N.; Jeraldo, P.; Sipos, M.; Goldenfeld, N.D.; White, B.A. The microbiome of the chicken gastrointestinal tract. Anim. Health Res. Rev. 2012, 13, 89–99. [Google Scholar] [CrossRef] [PubMed]
  57. Huang, C.; Feng, S.; Huo, F.; Liu, H. Effects of Four Antibiotics on the Diversity of the Intestinal Microbiota. Microbiol. Spectr. 2022, 10, e0190421. [Google Scholar] [CrossRef]
  58. Han, G.G.; Kim, E.B.; Lee, J.; Lee, J.Y.; Jin, G.; Park, J.; Huh, C.S.; Kwon, I.K.; Kil, D.Y.; Choi, Y.J.; et al. Relationship between the microbiota in different sections of the gastrointestinal tract, and the body weight of broiler chickens. Springerplus 2016, 5, 911. [Google Scholar] [CrossRef]
  59. Nogal, A.; Louca, P.; Zhang, X.; Wells, P.M.; Steves, C.J.; Spector, T.D.; Falchi, M.; Valdes, A.M.; Menni, C. Circulating Levels of the Short-Chain Fatty Acid Acetate Mediate the Effect of the Gut Microbiome on Visceral Fat. Front. Microbiol. 2021, 12, 711359. [Google Scholar] [CrossRef]
  60. Parker, B.J.; Wearsch, P.A.; Veloo, A.; Rodriguez-Palacios, A. The Genus Alistipes: Gut Bacteria with Emerging Implications to Inflammation, Cancer, and Mental Health. Front. Immunol. 2020, 11, 906. [Google Scholar] [CrossRef]
  61. Rau, M.; Rehman, A.; Dittrich, M.; Groen, A.K.; Hermanns, H.M.; Seyfried, F.; Beyersdorf, N.; Dandekar, T.; Rosenstiel, P.; Geier, A. Fecal SCFAs and SCFA-producing bacteria in gut microbiome of human NAFLD as a putative link to systemic T-cell activation and advanced disease. United Eur. Gastroent. 2018, 6, 1496–1507. [Google Scholar] [CrossRef] [PubMed]
  62. Song, Y.; Kononen, E.; Rautio, M.; Liu, C.; Bryk, A.; Eerola, E.; Finegold, S.M. Alistipes onderdonkii sp. nov. and Alistipes shahii sp. nov., of human origin. Int. J. Syst. Evol. Microbiol. 2006, 56, 1985–1990. [Google Scholar] [CrossRef] [PubMed]
  63. Hernández-Calderón, P.; Wiedemann, L.; Benítez-Páez, A. The microbiota composition drives personalized nutrition: Gut microbes as predictive biomarkers for the success of weight loss diets. Front. Nutr. 2022, 9, 1006747. [Google Scholar] [CrossRef] [PubMed]
  64. Yutin, N.; Galperin, M.Y. A genomic update on clostridial phylogeny: Gram-negative spore formers and other misplaced clostridia. Environ. Microbiol. 2013, 15, 2631–2641. [Google Scholar] [CrossRef] [PubMed]
  65. Zhao, J.; Bai, Y.; Tao, S.; Zhang, G.; Wang, J.; Liu, L.; Zhang, S. Fiber-rich foods affected gut bacterial community and short-chain fatty acids production in pig model. J. Funct. Foods 2019, 57, 266–274. [Google Scholar] [CrossRef]
  66. Yan, H.; Lu, J.; Wang, Y.; Gu, W.; Yang, X.; Yu, J. Intake of total saponins and polysaccharides from Polygonatum kingianum affects the gut microbiota in diabetic rats. Phytomedicine 2017, 26, 45–54. [Google Scholar] [CrossRef]
  67. Seo, B.; Yoo, J.E.; Lee, Y.M.; Ko, G. Sellimonas intestinalis gen. nov., sp. nov., isolated from human faeces. Int. J. Syst. Evol. Microbiol. 2016, 66, 951–956. [Google Scholar] [CrossRef]
  68. Takahashi, K.; Nishida, A.; Fujimoto, T.; Fujii, M.; Shioya, M.; Imaeda, H.; Inatomi, O.; Bamba, S.; Sugimoto, M.; Andoh, A. Reduced Abundance of Butyrate-Producing Bacteria Species in the Fecal Microbial Community in Crohn’s Disease. Digestion 2016, 93, 59–65. [Google Scholar] [CrossRef]
  69. Finegold, S.M.; Molitoris, D.; Song, Y.; Liu, C.; Vaisanen, M.L.; Bolte, E.; McTeague, M.; Sandler, R.; Wexler, H.; Marlowe, E.M.; et al. Gastrointestinal microflora studies in late-onset autism. Clin. Infect. Dis. 2002, 35, S6–S16. [Google Scholar] [CrossRef]
  70. Wang, L.; Christophersen, C.T.; Sorich, M.J.; Gerber, J.P.; Angley, M.T.; Conlon, M.A. Increased abundance of Sutterella spp. and Ruminococcus torques in feces of children with autism spectrum disorder. Mol. Autism 2013, 4, 42. [Google Scholar] [CrossRef]
  71. La Reau, A.J.; Suen, G. The Ruminococci: Key symbionts of the gut ecosystem. J. Microbiol. 2018, 56, 199–208. [Google Scholar] [CrossRef] [PubMed]
  72. Yoshii, K.; Hosomi, K.; Sawane, K.; Kunisawa, J. Metabolism of dietary and microbial vitamin b family in the regulation of host immunity. Front. Nutr. 2019, 6, 48. [Google Scholar] [CrossRef] [PubMed]
  73. Thomas, R.L.; Jiang, L.; Adams, J.S.; Xu, Z.Z.; Shen, J.; Janssen, S.; Ackermann, G.; Vanderschueren, D.; Pauwels, S.; Knight, R.; et al. Vitamin D metabolites and the gut microbiome in older men. Nat. Commun. 2020, 11, 5997. [Google Scholar] [CrossRef]
  74. Liu, H.; Liao, C.; Wu, L.; Tang, J.; Chen, J.; Lei, C.; Zheng, L.; Zhang, C.; Liu, Y.Y.; Xavier, J.; et al. Ecological dynamics of the gut microbiome in response to dietary fiber. ISME J. 2022, 16, 2040–2055. [Google Scholar] [CrossRef] [PubMed]
  75. Alagawany, M.; Elnesr, S.S.; Farag, M.R.; Tiwari, R.; Yatoo, M.I.; Karthik, K.; Michalak, I.; Dhama, K. Nutritional significance of amino acids, vitamins and minerals as nutraceuticals in poultry production and health—A comprehensive review. Vet. Q. 2020, 41, 1–29. [Google Scholar] [CrossRef]
  76. Pham, V.T.; Dold, S.; Rehman, A.; Bird, J.K.; Steinert, R.E. Vitamins, the gut microbiome and gastrointestinal health in humans. Nutr. Res. 2021, 95, 35–53. [Google Scholar] [CrossRef] [PubMed]
  77. Ellis, J.L.; Karl, J.P.; Oliverio, A.M.; Fu, X.; Soares, J.W.; Wolfe, B.E.; Hernandez, C.J.; Mason, J.B.; Booth, S.L. Dietary vitamin K is remodeled by gut microbiota and influences community composition. Gut Microbes 2021, 13, 1887721. [Google Scholar] [CrossRef] [PubMed]
  78. Ottenhof, H.H.; Ashurst, J.L.; Whitney, H.M.; Saldanha, S.A.; Schmitzberger, F.; Gweon, H.S.; Blundell, T.L.; Abell, C.; Smith, A.G. Organisation of the pantothenate (vitamin B5) biosynthesis pathway in higher plants. Plant J. 2004, 37, 61–72. [Google Scholar] [CrossRef]
  79. Ebner, F.; Heller, A.; Rippke, F.; Tausch, I. Topical use of dexpanthenol in skin disorders. Am. J. Clin. Dermatol. 2002, 3, 427–433. [Google Scholar] [CrossRef]
  80. Hegsted, D.M.; Perry, R.L. Nutritional studies with the duck; riboflavin and pantothenic acid requirements. J. Nutr. 1948, 35, 411–417. [Google Scholar] [CrossRef]
  81. Hegsted, D.M.; Riggs, T.R. The pantothenic acid requirements of chicks receiving a purified diet. J. Nutr. 1949, 37, 361–367. [Google Scholar] [CrossRef] [PubMed]
  82. Qian, Y.; Li, X.F.; Zhang, D.D.; Cai, D.S.; Tian, H.Y.; Liu, W.B. Effects of dietary pantothenic acid on growth, intestinal function, anti-oxidative status and fatty acids synthesis of juvenile blunt snout bream Megalobrama amblycephala. PLoS ONE 2015, 10, e0119518. [Google Scholar] [CrossRef] [PubMed]
  83. Zhang, C.; Li, D.; Wang, F.; Dong, T. Effects of dietary vitamin K levels on bone quality in broilers. Arch. Tierernahr. 2003, 57, 197–206. [Google Scholar] [CrossRef]
  84. Mishima, E.; Ito, J.; Wu, Z.; Nakamura, T.; Wahida, A.; Doll, S.; Tonnus, W.; Nepachalovich, P.; Eggenhofer, E.; Aldrovandi, M.; et al. A non-canonical vitamin K cycle is a potent ferroptosis suppressor. Nature 2022, 608, 778–783. [Google Scholar] [CrossRef]
  85. Liang, F.; Jiang, S.; Mo, Y.; Zhou, G.; Yang, L. Consumption of Oxidized Soybean Oil Increased Intestinal Oxidative Stress and Affected Intestinal Immune Variables in Yellow-feathered Broilers. Asian Australas. J. Anim. Sci. 2015, 28, 1194–1201. [Google Scholar] [CrossRef]
  86. Bentley, R.; Meganathan, R. Biosynthesis of vitamin K (menaquinone) in bacteria. Microbiol. Rev. 1982, 46, 241–280. [Google Scholar] [CrossRef] [PubMed]
  87. Karl, J.P.; Meydani, M.; Barnett, J.B.; Vanegas, S.M.; Barger, K.; Fu, X.; Goldin, B.; Kane, A.; Rasmussen, H.; Vangay, P.; et al. Fecal concentrations of bacterially derived vitamin K forms are associated with gut microbiota composition but not plasma or fecal cytokine concentrations in healthy adults. Am. J. Clin. Nutr. 2017, 106, 1052–1061. [Google Scholar] [CrossRef] [PubMed]
  88. Wu, F.; Guo, X.; Zhang, J.; Zhang, M.; Ou, Z.; Peng, Y. Phascolarctobacterium faecium abundant colonization in human gastrointestinal tract. Exp. Ther. Med. 2017, 14, 3122–3126. [Google Scholar] [CrossRef]
  89. Singh, K.M.; Shah, T.; Deshpande, S.; Jakhesara, S.J.; Koringa, P.G.; Rank, D.N.; Joshi, C.G. High through put 16S rRNA gene-based pyrosequencing analysis of the fecal microbiota of high FCR and low FCR broiler growers. Mol. Biol. Rep. 2012, 39, 10595–10602. [Google Scholar] [CrossRef]
  90. Yang, Y.; Li, X.; Cao, Z.; Qiao, Y.; Lin, Q.; Liu, J.; Zhao, Z.; An, Q.; Zhang, C.; Zhang, H.; et al. Effects of different ambient temperatures on caecal microbial composition in broilers. Pol. J. Microbiol. 2021, 70, 33–43. [Google Scholar] [CrossRef]
  91. Zaboli, G.; Huang, X.; Feng, X.; Ahn, D.U. How can heat stress affect chicken meat quality?—A review. Poult. Sci. 2019, 98, 1551–1556. [Google Scholar] [CrossRef] [PubMed]
  92. Goel, A. Heat stress management in poultry. J. Anim. Physiol. Anim. Nutr. 2021, 105, 1136–1145. [Google Scholar] [CrossRef] [PubMed]
  93. Vich, V.A.; Imhann, F.; Collij, V.; Jankipersadsing, S.A.; Gurry, T.; Mujagic, Z.; Kurilshikov, A.; Bonder, M.J.; Jiang, X.; Tigchelaar, E.F.; et al. Gut microbiota composition and functional changes in inflammatory bowel disease and irritable bowel syndrome. Sci. Transl. Med. 2018, 10, eaap8914. [Google Scholar] [CrossRef] [PubMed]
  94. Pittayanon, R.; Lau, J.T.; Leontiadis, G.I.; Tse, F.; Yuan, Y.; Surette, M.; Moayyedi, P. Differences in Gut Microbiota in Patients with vs. wthout Inflammatory Bowel Diseases: A Systematic Review. Gastroenterology 2020, 158, 930–946.e1. [Google Scholar] [CrossRef] [PubMed]
  95. Zhang, W.; Quan, K.; Feng, C.; Zhang, T.; He, Q.; Kwok, L.; Chen, Y. The Lactobacillus gasseri G098 strain mitigates symptoms of DSS-induced inflammatory bowel disease in mice. Nutrients 2022, 14, 3745. [Google Scholar] [CrossRef] [PubMed]
  96. Yudaev, N.A.; Afinogenova, S.A. Role of 11 beta-hydroxyprogesterone in biosynthesis of corticosterone from progesterone in guinea pig adrenals. Endocrinol. Exp. 1975, 9, 114–120. [Google Scholar]
  97. Lee, K.A.; Volentine, K.K.; Bahr, J.M. Two steroidogenic pathways present in the chicken ovary: Theca layer prefers delta 5 pathway and granulosa layer prefers delta 4 pathway. Domest. Anim. Endocrinol. 1998, 15, 1–8. [Google Scholar] [CrossRef]
Animals 13 03034 g001
Figure 1. Experimental flow chart. The experimental cohort comprises 49 healthy Guizhou yellow chickens; 8 roosters and 8 hens that possessed the highest body weights in the group constituted the high-market-weight group (HC, n = 16), and a similar group was selected for the low-market-weight group (LC, n = 16). Cecal samples were collected and subjected to 16S rRNA sequencing to infer microbial profiles. Concurrent blood samples were collected to perform untargeted metabolomics detection. Cecal and serum metabolites were identified by statistical analysis.
Figure 1. Experimental flow chart. The experimental cohort comprises 49 healthy Guizhou yellow chickens; 8 roosters and 8 hens that possessed the highest body weights in the group constituted the high-market-weight group (HC, n = 16), and a similar group was selected for the low-market-weight group (LC, n = 16). Cecal samples were collected and subjected to 16S rRNA sequencing to infer microbial profiles. Concurrent blood samples were collected to perform untargeted metabolomics detection. Cecal and serum metabolites were identified by statistical analysis.
Animals 13 03034 g001
Animals 13 03034 g002
Figure 2. Body weights of Guizhou yellow chickens. (A) Changes in body weight of ages 0 to 18 weeks. * p < 0.05, ** p < 0.01, *** p < 0.001. (B) Body weight distribution of experimental chickens at 18 weeks of age. HC, high-market-weight chicken group (n = 16). LC, low-market-weight chicken group (n = 16).
Figure 2. Body weights of Guizhou yellow chickens. (A) Changes in body weight of ages 0 to 18 weeks. * p < 0.05, ** p < 0.01, *** p < 0.001. (B) Body weight distribution of experimental chickens at 18 weeks of age. HC, high-market-weight chicken group (n = 16). LC, low-market-weight chicken group (n = 16).
Animals 13 03034 g002
Animals 13 03034 g003
Figure 3. Differences of cecal microbiota diversity between high- and low-market-weight chickens. Comparison of α-diversity in cecal microbiota between HC and LC using the (A) Shannon and (B) Simpson indices. (C) PCoA of cecal microbiota from HC and LC. HC, high-market-weight chicken group (n = 16). LC, low-market-weight chicken group (n = 16).
Figure 3. Differences of cecal microbiota diversity between high- and low-market-weight chickens. Comparison of α-diversity in cecal microbiota between HC and LC using the (A) Shannon and (B) Simpson indices. (C) PCoA of cecal microbiota from HC and LC. HC, high-market-weight chicken group (n = 16). LC, low-market-weight chicken group (n = 16).
Animals 13 03034 g003
Animals 13 03034 g004
Figure 4. Differences of cecal microbiota between high- and low-market-weight chickens. Comparison of cecal microbiota composition between HC/LC at the (A) phylum and (B) genus levels (relative abundance > 0.05%). Cecal microbiota showing different abundances at the (C) phylum and (D) genus levels between HC and LC (relative abundance > 0.05%). (E) Differential ASVs identified by LEfSe analysis between HC and LC (relative abundance > 0.05%). HC, high-market-weight chicken group (n = 16). LC, low-market-weight chicken group (n = 16).
Figure 4. Differences of cecal microbiota between high- and low-market-weight chickens. Comparison of cecal microbiota composition between HC/LC at the (A) phylum and (B) genus levels (relative abundance > 0.05%). Cecal microbiota showing different abundances at the (C) phylum and (D) genus levels between HC and LC (relative abundance > 0.05%). (E) Differential ASVs identified by LEfSe analysis between HC and LC (relative abundance > 0.05%). HC, high-market-weight chicken group (n = 16). LC, low-market-weight chicken group (n = 16).
Animals 13 03034 g004
Animals 13 03034 g005
Figure 5. Co-abundance groups (CAG) of cecal microbiota associated with high- and low-market-weight chickens. (A) Association of CAGs with HC or LC. CAG6 was enriched in the cecal microbiota of LC, and CAG9, CAG16, and CAG17 were enriched in the cecum of HC. (B) Comparison of mean relative abundance in four differential CAGs between HC and LC. HC, high-market-weight chicken group (n = 16). LC, low-market-weight chicken group (n = 16).
Figure 5. Co-abundance groups (CAG) of cecal microbiota associated with high- and low-market-weight chickens. (A) Association of CAGs with HC or LC. CAG6 was enriched in the cecal microbiota of LC, and CAG9, CAG16, and CAG17 were enriched in the cecum of HC. (B) Comparison of mean relative abundance in four differential CAGs between HC and LC. HC, high-market-weight chicken group (n = 16). LC, low-market-weight chicken group (n = 16).
Animals 13 03034 g005
Animals 13 03034 g006
Figure 6. Identification of the serum metabolites enriched in high- and low-market-weight chickens. (A) Partial least squares discriminant analysis (PLS-DA) of untargeted serum metabolome data from HC and LC. The pink and blue markers represent HC and LC, respectively. (B) Variable importance in projection (VIP > 1) scores for the top serum metabolites contributing to variation in metabolic profiles of HC and LC (Only showed the top 30, the annotation of metabolites are showed in Table 4). Metabolic pathways enriched in (C) HC and (D) LC. HC, high-market-weight chicken group (n = 16). LC, low-market-weight chicken group (n = 16).
Figure 6. Identification of the serum metabolites enriched in high- and low-market-weight chickens. (A) Partial least squares discriminant analysis (PLS-DA) of untargeted serum metabolome data from HC and LC. The pink and blue markers represent HC and LC, respectively. (B) Variable importance in projection (VIP > 1) scores for the top serum metabolites contributing to variation in metabolic profiles of HC and LC (Only showed the top 30, the annotation of metabolites are showed in Table 4). Metabolic pathways enriched in (C) HC and (D) LC. HC, high-market-weight chicken group (n = 16). LC, low-market-weight chicken group (n = 16).
Animals 13 03034 g006
Animals 13 03034 g007
Figure 7. Correlation between differential serum metabolites and weight-related cecal microbes. Red indicates a positive correlation, while blue indicates a negative correlation. * p < 0.05, ** p < 0.01.
Figure 7. Correlation between differential serum metabolites and weight-related cecal microbes. Red indicates a positive correlation, while blue indicates a negative correlation. * p < 0.05, ** p < 0.01.
Animals 13 03034 g007
Table 1. Composition and nutrient level (air-dried basis).
Table 1. Composition and nutrient level (air-dried basis).
Item0–6 Weeks6–18 Weeks
Ingredients, %
Corn56.3058.62
Soybean meal18.5225.00
Rapeseed meal10.000.00
Corn gluten meal6.333.05
Wheat bran2.945.63
Soybean oil1.633.34
Limestone1.181.18
Phytase0.040.00
Choline chloride0.150.00
Methionine0.150.10
Lysine0.220.32
NaCl0.150.15
CaHPO41.891.61
Premix 10.051.00
Total100.00100.00
Nutrients 2, %
CP21.1819.05
ME, MJ/kg12.1212.56
Ca1.00.90
AP0.450.40
Met + Cys0.900.72
Lys1.060.90
1 Provides per kg of diet: 0–6 weeks: Vitamin A, 6000 IU; Vitamin B1, 2.0 mg; Vitamin B2, 4.0 mg; Vitamin B5, 42 mg; Vitamin B6, 4.0 mg; Vitamin B12, 0.01 g; Vitamin D3, 2000 IU; Vitamin E 30 IU; Vitamin K3, 1.8 mg; Calcium pantothen-ate, 10.0 mg; Biotin, 0.15 mg; Folic acid, 0.85 mg; Fe, 80 mg; Cu, 8.0 mg; Mn, 80 mg; Zn, 65 mg; I, 0.50 mg; Se, 0.25 mg. 6–18 weeks: Vitamin A, 10,000 IU; Vitamin B1, 2.50 mg; VB2, 7.5 mg; Vitamin B6, 3 mg; Vitamin D, 2000 IU; Vitamin E, 25 mg; Vitamin K, 2.8 mg; Nicotinamide 40 mg; Calcium pantothenate, 25 mg; Biotin 0.20 mg; Folic acid 1.5 mg; Vitamin B12, 0.015 mg, Fe, 80 mg; Cu, 8 mg, Mn, 100 mg; Zn, 60 mg; I, 0.35 mg; Se 0.3 mg. 2 CP was a measured value, while the others were calculated values.
Table 2. Body weights of Guizhou yellow chickens at the age of 18 weeks.
Table 2. Body weights of Guizhou yellow chickens at the age of 18 weeks.
IDGroupSexWeights (g)Average Weights (g)
0 Week2 Weeks4 Weeks6 Weeks8 Weeks10 Weeks12 Weeks14 Weeks16 Weeks18 WeeksAgeLCHCp-Value (Wilcoxon Test)
GH2951LCF35.2154.5337512844.4109511731327146414900 week33.1835.870.665
GH3047LCF29130.4251553808.910051116122714601513
GH2802LCF37.594.8190387559.57641060120715511637
GH2863LCF36.1164.7364666922.996112731401160316582 weeks155.13160.790.616
GH2923LCF35.1147.7318368821.810141231133416001675
GH2992LCF32.8138310506794.810281221142716461726
GH2952LCF33.3149287507776.4104512751465170217374 weeks335.06345.300.220
GH3066LCF31.7140.9311582953.711631339151216221739
GH3015LCM42.2166.64217431055.512121404149317781829
GH3034LCM31.9184.2352391870118112731415175618566 weeks563.50592.530.132
GH2850LCM31.2164.8365543906.312461349152118611886
GH2929LCM36.7178.8345690952.811821315153918771967
GH2999LCM32.5182.5342584933.9116913061478191520448 weeks878.04922.150.023
GH3098LCM29.5163376622903.910351321157320162073
GH3096LCM28.3159.7401642937.712131521168720372124
GH2987LCM27.9162.53917201006.21084131615741992212910 weeks1087.311162.010.005
GH2957HCF34.6135.8319509823.59611276149817551869
GH2876HCF60.6147.8321608893.310981276147718401897
GH2874HCF32.5175.5374616946.91252141715741851194312 weeks1280.811390.63<0.001
GH2845HCF32.1283.135264993411341506166318841948
GH2895HCF27.8160.7342548900.111641345156118411952
GH2899HCF55.9142.7219443719.91016124314751891201114 weeks1448.751596.17<0.001
GH2907HCF32.8179.6395598974.912451545177320032076
GH2825HCF34.1165.23457131059.514021693191221172196
GH2931HCM32.2156.2344616922.71175136516002050226516 weeks1742.501899.35<0.001
GH2808HCM25.9180.14176961105.113081562190422022279
GH2848HCM31.5131.33557011013.413081563194121312310
GH2973HCM36.2179380642976.512411542180921652313
GH3001HCM35.3144.34107481037.31301167519172180234818 weeks1817.691997.14<0.001
GH3042HCM35.1160.53507521075.714121669190322232384
GH2917HCM32.5175.94015331138.214001704200324712576
GH2872HCM34.8151.2375602983.314451736203524522638
F: female, M: male.
Table 3. The ASVs showing different abundances between high- and high-market-weight chicken groups.
Table 3. The ASVs showing different abundances between high- and high-market-weight chicken groups.
ASVsAverage Relative Abundance(%)Enriched GroupsPhylumFamilyGenus
LCHC
ASV201.99 3.28 HCFirmicutesLachnospiraceaeRuminococcus_torques_group
ASV4820.15 0.36 HCFirmicutesLachnospiraceaeLachnoclostridium
ASV4090.20 0.35 HCFirmicutesLachnospiraceaeSellimonas
ASV1710.05 0.15 HCFirmicutesLachnospiraceaeSellimonas
ASV7550.07 0.21 HCFirmicutesLachnospiraceaeunclassified_Lachnospiraceae
ASV4860.08 0.77 HCFirmicutesRuminococcaceaeFaecalibacterium
ASV830.32 0.56 HCFirmicutesRuminococcaceaeNegativibacillus
ASV4120.06 0.10 HCFirmicutesRuminococcaceaenorank_Ruminococcaceae
ASV1000.33 0.71 HCFirmicutesEubacterium_coprostanoligenes_groupnorank_Eubacterium_coprostanoligenes_group
ASV510.06 0.20 HCFirmicutesOscillospiraceaeOscillibacter
ASV4930.05 0.32 HCBacteroidotaMarinifilaceaeOdoribacter
ASV260.11 0.33 HCBacteroidotaRikenellaceaeAlistipes
ASV1690.05 0.28 HCBacteroidotaRikenellaceaeAlistipes
ASV4030.03 0.09 HCBacteroidotaFlavobacteriaceaenorank_Flavobacteriaceae
ASV56.33 2.91 LCFirmicutesAcidaminococcaceaePhascolarctobacterium
ASV72.13 1.25 LCFirmicutesAcidaminococcaceaePhascolarctobacterium
ASV170.66 0.04 LCFirmicutesLactobacillaceaeLactobacillus
ASV190.16 0.01 LCFirmicutesLactobacillaceaeLactobacillus
ASV1010.13 0.05 LCFirmicutesRuminococcaceaeunclassified_Ruminococcaceae
ASV4870.10 0.02 LCFirmicutesBacillaceaeBacillus
ASV890.23 0.06 LCFirmicutesLachnospiraceaeunclassified_Lachnospiraceae
ASV6730.18 0.03 LCFusobacteriotaFusobacteriaceaeFusobacterium
Table 4. Annotation of metabolites enriched in high- and low-market-weight-chicken groups.
Table 4. Annotation of metabolites enriched in high- and low-market-weight-chicken groups.
IDMetabolitesp ValueVIP ValueEnrichment GroupIDMetabolitesp ValueVIP ValueEnrichment Group
M374T30N-arachidonoyl-l-alanine0.001 1.32HCM335T31Docosatrienoic acid0.021 3.17LC
M276T184Luvangetin0.003 1.71HCM230T22Acetaminophen sulfate0.021 2.52LC
M357T532Beta-estradiol 17-valerate0.003 1.39HCM377T208Riboflavin0.019 3.51LC
M429T49(1-acetyloxy-3-hydroxy-6,8a-dimethyl-7-oxo-3-propan-2-yl-2,3a,4,8-tetrahydro-1h-azulen-4-yl) 4-hydroxybenzoate0.004 2.70HCM339T24Gly-His-Lys0.019 2.24LC
M173T114Menadione0.008 2.59HCM353T154pGlu-Glu-Pro-amide0.019 3.33LC
M202T126Pantothenic acid0.008 2.54HCM291T59Trimethoprim0.017 1.86LC
M227T180Metyrapone0.014 1.44HCM339T154Cilastatin0.017 3.87LC
M393T31Chenodeoxycholate0.015 1.08HCM325T24Hydroquinidine0.017 2.10LC
M233T60Ile-Thr0.017 1.14HCM311T25Thymol-beta-d-glucoside0.015 2.05LC
M175T31Alpha-ionone0.023 1.64HCM259T281,3-benzenediol, 5-methyl-2-[(1r,6r)-3-methyl-6-(1-methylethenyl)-2-cyclohexen-1-yl]0.014 1.65LC
M273T255Sempervirine0.023 1.47HCM201T21Bergaptol0.014 2.75LC
M249T20D-myo-inositol-3,4,5,6-tetraphosphate0.026 1.82HCM338T30Erucamide0.012 1.44LC
M431T69Eplerenone hydroxy acid0.047 2.60HCM233T38Riluzole0.012 3.12LC
M438T196N-arachidonoyldopamine0.047 1.25LCM242T134Etodolac0.010 2.87LC
M285T1573′,4′-dimethoxy-2′-hydroxychalcone0.043 2.32LCM482T190_21-hexadecyl-sn-glycero-3-phosphocholine0.008 2.14LC
M503T239Beclomethasone dipropionate0.043 1.27LCM306T255His-Lys0.007 2.24LC
M210T31Phosphocreatine0.043 2.29LCM297T25Ostruthin0.007 2.15LC
M329T10911beta-hydroxyprogesterone0.035 1.99LCM162T245-hydroxy-3,4-dihydro-2(1h)-quinolinone0.006 3.69LC
M170T4051-methylhistidine0.032 1.88LCM290T99Fingolimod0.005 2.79LC
M363T154Glufosinate0.032 4.22LCM294T306Asn-Tyr0.002 2.37LC
M303T201Hesperetin0.032 2.07LCM312T3445-hydroxydiclofenac0.001 3.02LC
M217T25Chlorophene0.029 2.61LCM249T2872-aminoethylphosphonic acid0.000 2.85LC
Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.

Share and Cite

MDPI and ACS Style

Yang, S.; Yang, Y.; Long, X.; Li, H.; Zhang, F.; Wang, Z. Integrated Analysis of the Effects of Cecal Microbiota and Serum Metabolome on Market Weights of Chinese Native Chickens. Animals 2023, 13, 3034. https://doi.org/10.3390/ani13193034

AMA Style

Yang S, Yang Y, Long X, Li H, Zhang F, Wang Z. Integrated Analysis of the Effects of Cecal Microbiota and Serum Metabolome on Market Weights of Chinese Native Chickens. Animals. 2023; 13(19):3034. https://doi.org/10.3390/ani13193034

Chicago/Turabian Style

Yang, Shenghong, Yongxian Yang, Xiaoxia Long, Hui Li, Fuping Zhang, and Zhong Wang. 2023. "Integrated Analysis of the Effects of Cecal Microbiota and Serum Metabolome on Market Weights of Chinese Native Chickens" Animals 13, no. 19: 3034. https://doi.org/10.3390/ani13193034

Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.

Article Metrics

Back to TopTop