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. 2023 Feb 16;102(5):102598. doi: 10.1016/j.psj.2023.102598

Changed cecal microbiota involved in growth depression of broiler chickens induced by immune stress

Jixuan Ye *,1, Huaao Yang *,1, Weidong Hu *, Keyi Tang †, Anfang Liu ‡, Shicheng Bi *,2
PMCID: PMC10023976  PMID: 36913756

Abstract

A previous study identified genes and metabolites associated with amino acid metabolism, glycerophospholipid metabolism, and inflammatory response in the liver of broilers with immune stress. The present research was designed to investigate the effect of immune stress on the cecal microbiome in broilers. In addition, the correlation between altered microbiota and liver gene expression, the correlation between altered microbiota and serum metabolites were compared using the Spearman correlation coefficients. Eighty broiler chicks were randomly assigned to 2 groups with 4 replicate pens per group and 10 birds per pen. The model broilers were intraperitoneally injected of 250 µg/kg LPS at 12, 14, 33, and 35 d of age to induce immunological stress. Cecal contents were taken after the experiment and kept at −80°C for 16S rDNA gene sequencing. Then the Pearson's correlation between gut microbiome and liver transcriptome, between gut microbiome and serum metabolites were calculated using R software. The results showed that immune stress significantly changed microbiota composition at different taxonomic levels. KEGG pathways analysis suggested that these gut microbiota were mainly involved in biosynthesis of ansamycins, glycan degradation, D-glutamine and D-glutamate metabolism, valine, leucine, and isoleucine biosynthesis and biosynthesis of vancomycin group antibiotics. Moreover, immune stress increased the activities of metabolism of cofactors and vitamins, as well as decreased the ability of energy metabolism and digestive system. Pearson's correlation analysis identified several bacteria were positively correlated with the gene expression while a few of bacteria were negatively correlated with the gene expression. The results identified potential microbiota involvement in growth depression mediated by immune stress and provided strategies such as supplement of probiotic for alleviating immune stress in broiler chickens.

Key words: Escherichia coli lipopolysaccharide, immune stress, 16S rDNA, broiler chick

INTRODUCTION

Chickens, especially the young, are vulnerable to infection by various pathogenic bacteria, since they are continuously exposed to birds’ excreta, litter, and feed (Nolan et al., 2020). Except for increased mortality, chickens with challenge also showed stress characterized by depression and diarrhea, inducing inhibited growth performance and rising veterinary costs (da Rosa et al., 2019). Subtherapeutic supplements of antibiotics to the diets are traditionally used to prevent pathogenic bacteria infection in broiler chickens. However, the Ministry of Agriculture and Rural Affairs of China has banned all in-feed use of antibiotics since 2020 (Bi et al., 2020). Therefore, researchers and feed manufacturers have to elucidate the specific mechanism of immune stress to develop new strategies to control the disease.

There are various methods to induce immune stress in broiler chickens (Jiang et al., 2019; Li et al., 2020a; Wang et al., 2022). In our previous study, internal injection of lipopolysaccharide (LPS) significantly reduced growth performance and enhanced stress-related hormone and inflammatory factor, conforming the model of immune stress. We further investigated the transcriptome changes in liver and found that differentially expressed genes (DEGs) might account for amino acid metabolism, lipid metabolism, defense function, and inflammatory response (Bi et al., 2022c). There is a close functional and physiological link between the gut and the liver (Konturek et al., 2018). However, there were no reports on the relationship between liver and intestinal microbiome and their effect on the immune stress of broilers.

In this study, a Miseq platform was used to investigate the shaping of cecal microbiota in chronic stress of broilers challenged by LPS (Bi et al., 2020). Then, comprehensive analysis of liver transcriptomics and intestine microbiome was performed. The results may help clarify potential mechanisms and provide further information to search for probiotics to alleviate immune stress in the poultry industry.

MATERIALS AND METHODS

Chickens

A total of 80 Shaver brown broilers (1-day-old) were purchased from Sichuan Lihua Poultry Co., Ltd., Sichuan, China and were kept in separate iron cages. The room temperature was maintained at 33°C for 7 d and then ramp down slowly to 26°C by gradually decreasing 1°C every 2 d. All broilers were given sufficient water and feed daily. The animal experiments were conducted with the permission of the Animal Care and Use Committee of Southwest University. The permit number was IACUC-20200701-01.

Reagents

LPS from E. coli serotype O55: B5 was purchased from Sigma-Aldrich Chemical Co. (St. Louis, MO). All other chemicals were of analytical or higher grade.

Experimental Design

The experiment was determined based on our previous studies (Bi et al., 2022a,b). In brief, 80 broiler chicks were randomly assigned to 2 groups with 4 repeats per group, 10 birds per repeat. The model broilers received an intraperitoneal injection of 250 µg/kg LPS at 12, 14, 33, and 35 d of age to induce immunological stress (Table 1). A comparable dose of sterile saline was administered to the control group via injection. At the end of the experiment, chickens were sacrificed and the cecal content was collected and kept at −80°C for 16S rDNA gene sequencing.

Table 1.

Experimental design.

Group n Drug
LPS 40 250 µg/kg LPS
Control 40 250 µg/kg sterile saline
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Cecal Contents Sampling

The cecal contents were taken using a stainless-steel cylindrical driller with a radius of 2.5 cm and stored at −80°C in a portable refrigerator. After delivery to the laboratory, the cecal samples were stored at −80°C in a refrigerator for further experiments.

DNA Extraction

Cecal DNA was well extracted using PowerSoil DNA Isolation Kit (MoBio Laboratories, Carlsbad, CA). Purity and quality of the genomic DNA were verified on 1% agarose gels and a NanoDrop spectrophotometer (Thermo Scientific, Waltham, MA).

PCR Amplification

The V3-4 hypervariable region of bacterial 16S rRNA gene were amplified as previously reported (Munyaka et al., 2015). Two common primers 806R (5′-GGACTACNNGGGTATCTAAT-3′) and 338F (5′-ACTCCTACGGGAGGCAGCAG-3′) were added with a 8-digit barcode sequence. A Mastercycler Gradient (Eppendorf, Germany) was employed to perform the PCR. The reaction volumes were 25 μL, involving 12.5 μL 2 × Taq PCR MasterMix, 3 μL BSA (2 ng/μL), 1 μL Forward Primer (5 μM), 1 μL Reverse Primer (5 μM), 2 μL template DNA, and 5.5 μL ddH2O. The PCR process was set as follow: 95°C for 5 min, 28 cycles of 95°C for 45 s, 55°C for 50 s and 72°C for 45 s, extension at 72°C for 10 min. The Agencourt AMPure XP Kit for PCR was provided by Beijing Allwegene Company, Beijing, China.

High Throughput Sequencing

A Miseq platform (Allwegene Company, Beijing, China) was used for deep sequencing. Then, Illumina Analysis Pipeline Version 2.6. was applied for image analysis, base calling and error estimation.

Hepatic Transcriptomic Analysis and Serum LC-MS/MS Analysis

The methods for transcriptomic analysis and metabolites analysis were described in our previous studies (Bi et al., 2022b,c).

Data Analyses

The original data were first checked and sequences were taken away from consideration if they were no more than 230 bp, got a low quality score (≤20), contained ambiguous bases or did not exactly match to primer sequences and barcode tags, and separated with the sample-specific barcode sequences. Qualified reads were aggregated into operational taxonomic units (OTUs) at a similarity level of 97% using Uparse algorithm of Vsearch (v2.7.1) software (Edgar, 2013). All sequences were classified into different taxonomic groups against SILVA128 database using the Ribosomal Database Project (RDP) Classifier tool (Cole et al., 2009).

QIIME (v1.8.0) was used to generate rarefaction curves and to calculate the richness and diversity indices on the basis of the OTU information. Heatmaps were generated with the top 20 OTUs using Mothur for the purpose of comparing the membership and structure of communities in different samples (Jami et al., 2013). Based on taxonomic annotation and relative abundance results, bar-plot diagram analysis was performed using R (v3.6.0) software. To examine the similarity between different samples, clustering analyses and PCA were analyzed by R (v3.6.0) based on the OTU information from each sample (Wang et al., 2012). The evolution distances between microbial communities from each sample were calculated using the Bray Curtis algorithms and represented as an unweighted pair group method with arithmetic mean (UPGMA) clustering tree describing the dissimilarity (1—similarity) between multiple samples (Jiang et al., 2013). A Newick-formatted tree file was generated based on this analysis.

The correlation analysis between hepatic genes and microbiota were based on our previously-published transcriptome study (Bi et al., 2022c). The correlation analysis between serum metabolites and microbiota were based on the previously-published metabolome data (Bi et al., 2022b).

RESULTS

Diversity of Microbial Community in Broilers

Partial least squares discrimination analysis (PLS-DA) was used to determine the difference of microbiota diversity among different groups at OTU level. PLS-DA revealed that samples from the LPS separated and clustered distinctly from control groups (Figure 1). The trend of rarefaction curves demonstrated that the curve of each group progressively flattened out with the increasing of sequencing volume, indicating that sequencing data were reasonable (Figure 2A). Rank abundance curves further verified that the sample size in this study was abundant for sequencing and the species in the sample had a wide coverage (Figure 2B). As shown in Figure 2C and D, the Chao 1 index decreased (P = 6.597E−1) while the level of Shannon index rose (P = 1.972E−1) in the LPS group compared with the control group.

Figure 1.

Figure 1

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Partial least squares discrimination analysis (PLS-DA) of cecal bacterial community. Individual sample was represented as spot with red (LPS) and blue (control) (*P > 0.05).

Figure 2.

Figure 2

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Luteolin modulated the structure and diversity of gut microbiota. (A) Rarefaction curves. (B) Rank abundance curves. (C) Chao1 indexes (P > 0.05, Tukey test). (D) Shannon indexes (P > 0.05, Tukey test).

Composition of Gut Microbiota in Broilers at Taxonomic Levels

As illustrated in Figure 3A, the dominant phyla were Firmicutes, Bacteroidota in all the groups. However, Bacteroidaceae and Ruminococcaceae were found as the dominant microbiota at the family level (Figure 3B). Bacteroides and Faecalibacterium were primary genera in all the groups (Figure 3C).

Figure 3.

Figure 3

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Species classification and abundance analysis. (A–C) The corresponding histograms of species profiling were produced for each group at the classification level of phylum (A), family (B), and genus (C).

Wilcoxon test was performed to confirm top 20 significant alterations in cecal microbial composition between the LPS group and control group at the phylum level, the class level, the order level, the family level, and the genus and OTU level, respectively. As exhibited in Figure S1, the relative abundance of Actinobacteriota at the level of phylum was reduced in the LPS group (red) compared with the control group (blue) (P = 3.998E−2). At the class level, more Bacilli (P = 3.791E−2) and Rhodothermia (P = 4.858E−3) were observed in LPS group than in control group (Figure S2). Among orders differentially affected, the most notable changes were Enterobacterales (P = 4.662E−3) and Lactobacillales (P = 2.067E−2), both enhanced relative abundances in the LPS group (Figure S3). A tendency toward an increase in Enterobacteriaceae (P = 6.993E−3), Erysipelatoclostridiaceae (P = 1.041E−2), Leuconostocaceae (P = 1.123E−2), and Lactobacillaceae (P = 4.662E−3) at the family level was also shown in LPS group (Figure S4). At the genus level, LPS treatment was characterized by a markedly increase in Escherichia-Shigella (P = 1.564E−2), Olsenella (P = 4.017E−2), Lactobacillus (P = 4.662E−3), and Flavonifractor (P = 4.988E−2) and lack of Blautia (P = 6.216E−4) (Figure S5).

As shown in Figure 4, the OTU 277 (Bacteroides vulgatus dnLKV7) (P = 1.476E−2), OTU 1492 (Faecalibacterium) (P = 3.850E−3), OTU 1062 (Faecalibacterium bacterium ic1379) (P = 3.253E−3), and OTU 279 (Parabacteroides goldsteinii dnLKV18) (P = 1.518E−2) were less in the LPS group. Meanwhile, OTU 17 (Lactobacillus aviarius) (P = 2.953E−3), OTU 288 (human gut metagenome) (P = 2.953E−3), OTU 35 (E. coli) (P = 1.564E−2) and OTU 307 (B. fragilis str. 3725 D9 ii) (P = 4.042E−2) showed statistically significant increases in LPS group. To a lower extent, OTU 469 (UCG-005) (P = 4.988E−2), OTU 1204 (metagenome) (P = 5.351E−3), OTU 473 (Clostridia vadinBB60 group) (P = 4.887E−2), OTU 486 (Christensenellaceae R-7 group) (P = 1.345E−2) and OTU 888 (uncultured organism) (P = 4.584E−2) had a declined relative proportion in LPS group, while OTU 626 (Oscillospiraceae) (P = 2.067E−2), OTU 232 (Erysipelatoclostridiaceae) (P = 3.910E−2), OTU 482 (Flavonifractor) (P = 4.988E−2), OTU 554 (Butyricicoccus) (P = 4.988E−2), OTU 1180 (metagenome) (P = 3.230E−3) and OTU 514 (Intestinimonas timonensis) (P = 4.042E−2) had a higher relative proportion in control group.

Figure 4.

Figure 4

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Bar plot of compositional differences at the OTU level in the gut microbiome of chicks in the LPS group vs. the control group by the Wilcoxon test. Data are expressed as mean ± SD.

Linear Discriminant Analysis Effect Size (LEfSe) and Functional Predictive Analyses of Gut Microbiota

As shown in Figure 5A and B, the most differentially abundant taxa enriched in microbiota between 2 groups was shown on cladogram generated from LEfSe analysis. The results suggested that LPS treatment altered the cecal microbiota composition and population structure. In addition, KEGG analysis was performed to find out the potential function of cecal microbiota. The KEGG pathways between the LPS and control group were shown in Table 2A. The gut microbiota was chiefly associated with biosynthesis of ansamycins (P = 2.813E−2) and other glycan degradation (P = 4.988E−2). In comparison with the control group, the activities of metabolism of cofactors and vitamins (P = 6.993E−3) and cardiovascular disease (P = 3.792E−2) were increased in the LPS group. However, the ability of energy metabolism (P = 2.067E−2) and digestive system (P = 1.476E−2) were decreased in the LPS group.

Figure 5.

Figure 5

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Linear discriminant analysis effect size (LEfSe) analysis (A). LEfSe identified the abundance of cecal microbiota. The specific differential cecal microbial taxa that focused on each group were characterized with different colors. Control group (red); LPS group (green). The cladogram showed differential colonic microbial taxa among the control and LPS group (B) (*P < 0.05).

Table 2.

PICRUST 2 compared the KEGG database to get the microbiome abundance of each pathway at KEGG pathway level 2 (A) and level 3 (B) between the LPS group and control group.

A
KEGG term Mean of LPS Mean of control KEGG pathway
Carbohydrate metabolism 14.42286302 14.41865598 Metabolism
Amino acid metabolism 12.18329667 12.16065171 Metabolism
Metabolism of cofactors and vitamins 11.63813352 12.31190714 Metabolism
Metabolism of terpenoids and polyketides 9.662242952 9.890765499 Metabolism
Metabolism of other amino acids 7.003209668 6.71137746 Metabolism
Lipid metabolism 6.702214917 6.353163336 Metabolism
Replication and repair 5.97386487 5.658709089 Genetic information processing
Energy metabolism 5.600360789 5.698108063 Metabolism
Glycan biosynthesis and metabolism 5.290240477 6.237291053 Metabolism
Xenobiotics biodegradation and metabolism 3.321699907 3.2359613 Metabolism
Translation 3.216391297 3.042512498 Genetic information processing
Folding, sorting, and degradation 3.159091206 3.047476356 Genetic information processing
Biosynthesis of other secondary metabolites 2.217043895 2.178616457 Metabolism
Nucleotide metabolism 1.975640765 1.886912585 Metabolism
Cell motility 1.80307641 1.5560721 Cellular processes
Membrane transport 1.671946349 1.494762771 Environmental information processing
Cell growth and death 1.485182464 1.468883424 Cellular processes
Transcription 1.154216436 1.107532407 Genetic information processing
Signal transduction 0.346516839 0.322106796 Environmental information processing
Transport and catabolism 0.278569027 0.358083804 Cellular processes
Environmental adaptation 0.19510448 0.187462282 Organismal systems
Cellular community—prokaryotes 0.17739013 0.173235207 Cellular processes
Infectious disease: bacterial 0.167656241 0.149592454 Human diseases
Drug resistance: antimicrobial 0.129372715 0.116400133 Human diseases
Endocrine system 0.085309414 0.0775328 Organismal systems
Immune system 0.062017203 0.053602214 Organismal systems
Digestive system 0.057502562 0.085504624 Organismal systems
Infectious disease: parasitic 0.013541309 0.013602308 Human diseases
Cancer: overview 0.004687948 0.002348253 Human diseases
Cardiovascular disease 0.001153199 0.000405166 Human diseases
Neurodegenerative disease 0.000435491 0.000721022 Human diseases
Immune disease 0.0000126 0.0000342 Human diseases
Excretory system 0.00000837 0.00000370 Organismal systems
Signaling molecules and interaction 6.84E−06 5.80E−06 Environmental information processing
B
KEGG term Mean of LPS Mean of control KEGG pathway
Biosynthesis of ansamycins 5.550594795 5.152785235 Metabolism of terpenoids and polyketides
Other glycan degradation 2.787746092 2.073974257 Glycan biosynthesis and metabolism
D-Glutamine and D-glutamate metabolism 1.969064527 2.067786545 Metabolism of other amino acids
Valine, leucine and isoleucine biosynthesis 2.019431155 2.052404698 Amino acid metabolism
Biosynthesis of vancomycin group antibiotics 1.82221872 1.945391134 Metabolism of terpenoids and polyketides
Peptidoglycan biosynthesis 1.736034617 1.867314173 Glycan biosynthesis and metabolism
Fatty acid biosynthesis 1.701672753 1.828375617 Lipid metabolism
Alanine, aspartate and glutamate metabolism 1.846384358 1.80689758 Amino acid metabolism
Streptomycin biosynthesis 1.744185307 1.792920144 Biosynthesis of other secondary metabolites
Pentose phosphate pathway 1.745488813 1.750228909 Carbohydrate metabolism
Secondary bile acid biosynthesis 1.565483277 1.75014793 Lipid metabolism
One carbon pool by folate 1.723298985 1.700143815 Metabolism of cofactors and vitamins
Lysine biosynthesis 1.557774363 1.62260193 Amino acid metabolism
Pantothenate and CoA biosynthesis 1.629928763 1.618106864 Metabolism of cofactors and vitamins
Mismatch repair 1.52076776 1.615105289 Replication and repair
D-Alanine metabolism 1.459712896 1.61004722 Metabolism of other amino acids
Aminoacyl-tRNA biosynthesis 1.477613056 1.577352516 Translation
Ribosome 1.462695439 1.534269092 Translation
Thiamine metabolism 1.544106351 1.526013144 Metabolism of cofactors and vitamins
Carbon fixation in photosynthetic organisms 1.556776125 1.513354074 Energy metabolism
C5-Branched dibasic acid metabolism 1.458827327 1.486054352 Carbohydrate metabolism
Homologous recombination 1.416299455 1.46990979 Replication and repair
Cell cycle—Caulobacter 1.417220607 1.444951184 Cell growth and death
Protein export 1.385297325 1.39887449 Folding, sorting, and degradation
Drug metabolism—other enzymes 1.375116011 1.379766881 Xenobiotics biodegradation and metabolism
Biotin metabolism 1.629294232 1.353530461 Metabolism of cofactors and vitamins
Cysteine and methionine metabolism 1.252429753 1.319857082 Amino acid metabolism
Histidine metabolism 1.337980357 1.307807274 Amino acid metabolism
Terpenoid backbone biosynthesis 1.219721106 1.255154428 Metabolism of terpenoids and polyketides
Pyruvate metabolism 1.203718389 1.244340396 Carbohydrate metabolism
Galactose metabolism 1.304353556 1.241669162 Carbohydrate metabolism
Amino sugar and nucleotide sugar metabolism 1.187586374 1.204681509 Carbohydrate metabolism
DNA replication 1.121966036 1.175640842 Replication and repair
Starch and sucrose metabolism 1.152237336 1.160056586 Carbohydrate metabolism
RNA polymerase 1.107531898 1.154214975 Transcription
Pyrimidine metabolism 1.080985697 1.138523975 Nucleotide metabolism
Glycolysis/Gluconeogenesis 1.059329713 1.117924911 Carbohydrate metabolism
Sulfur relay system 1.037571692 1.115114503 Folding, sorting, and degradation
Carbon fixation pathways in prokaryotes 1.144409137 1.110323034 Energy metabolism
Bacterial chemotaxis 0.928965433 1.072521694 Cell motility
Phenylalanine, tyrosine and tryptophan biosynthesis 1.142041453 1.072385934 Amino acid metabolism
Glycine, serine and threonine metabolism 1.047522291 1.051897679 Amino acid metabolism
Vitamin B6 metabolism 1.095978252 1.036813495 Metabolism of cofactors and vitamins
Fructose and mannose metabolism 1.01269474 1.019973337 Carbohydrate metabolism
Nicotinate and nicotinamide metabolism 1.003934939 1.015386994 Metabolism of cofactors and vitamins
Selenocompound metabolism 0.996936885 0.997400288 Metabolism of other amino acids
Citrate cycle (TCA cycle) 0.980549374 0.937314932 Carbohydrate metabolism
Base excision repair 0.856127838 0.918980928 Replication and repair
Folate biosynthesis 1.039746113 0.889420937 Metabolism of cofactors and vitamins
Sulfur metabolism 0.881663768 0.853457984 Energy metabolism
Purine metabolism 0.805926888 0.83711679 Nucleotide metabolism
Pentose and glucuronate interconversions 0.944522721 0.831248327 Carbohydrate metabolism
Lipoic acid metabolism 0.870625718 0.804928953 Metabolism of cofactors and vitamins
Riboflavin metabolism 0.818374506 0.783066516 Metabolism of cofactors and vitamins
Glycosaminoglycan degradation 0.974747315 0.782333209 Glycan biosynthesis and metabolism
Bacterial secretion system 0.731754682 0.77750339 Membrane transport
Nucleotide excision repair 0.70397027 0.74313523 Replication and repair
Arginine and proline metabolism 0.759171318 0.731347955 Amino acid metabolism
Flagellar assembly 0.627106666 0.730554715 Cell motility
Propanoate metabolism 0.650482452 0.699075048 Carbohydrate metabolism
Taurine and hypotaurine metabolism 0.708283506 0.678809706 Metabolism of other amino acids
Glyoxylate and dicarboxylate metabolism 0.692037795 0.669965895 Carbohydrate metabolism
Zeatin biosynthesis 0.689302718 0.659106067 Metabolism of terpenoids and polyketides
Butanoate metabolism 0.618635253 0.648325375 Carbohydrate metabolism
Nitrogen metabolism 0.634023763 0.612675101 Energy metabolism
Porphyrin and chlorophyll metabolism 0.624160594 0.612572314 Metabolism of cofactors and vitamins
ABC transporters 0.563800651 0.609122585 Membrane transport
RNA degradation 0.574612652 0.593532487 Folding, sorting, and degradation
Photosynthesis 0.515066016 0.556433472 Energy metabolism
Glycerophospholipid metabolism 0.485024638 0.534990168 Lipid metabolism
Lipopolysaccharide biosynthesis 0.687742024 0.527040708 Glycan biosynthesis and metabolism
Cyanoamino acid metabolism 0.390200145 0.492359562 Metabolism of other amino acids
Methane metabolism 0.496294216 0.488184733 Energy metabolism
Sphingolipid metabolism 0.635235796 0.477936177 Lipid metabolism
Chloroalkane and chloroalkene degradation 0.425192334 0.470601084 Xenobiotics biodegradation and metabolism
Oxidative phosphorylation 0.46935296 0.465735567 Energy metabolism
Glycerolipid metabolism 0.430773874 0.461463402 Lipid metabolism
Glutathione metabolism 0.460205538 0.45430514 Metabolism of other amino acids
beta-Alanine metabolism 0.440959318 0.407775379 Metabolism of other amino acids
Valine, leucine and isoleucine degradation 0.359058929 0.369814577 Amino acid metabolism
Tropane, piperidine and pyridine alkaloid biosynthesis 0.388244182 0.359667083 Biosynthesis of other secondary metabolites
Biosynthesis of unsaturated fatty acids 0.335150603 0.357839165 Lipid metabolism
Two-component system 0.322106105 0.34651664 Signal transduction
Synthesis and degradation of ketone bodies 0.268429193 0.345131072 Lipid metabolism
Fatty acid degradation 0.346803712 0.342486857 Lipid metabolism
Linoleic acid metabolism 0.342898482 0.341448602 Lipid metabolism
Phosphotransferase system (PTS) 0.199207437 0.285320373 Membrane transport
Bisphenol degradation 0.35186384 0.274870063 Xenobiotics biodegradation and metabolism
Phenylalanine metabolism 0.300373933 0.273789072 Amino acid metabolism
Ubiquinone and other terpenoid-quinone biosynthesis 0.279629391 0.257149235 Metabolism of cofactors and vitamins
Tyrosine metabolism 0.246937361 0.256962574 Amino acid metabolism
Tetracycline biosynthesis 0.208775192 0.247854294 Metabolism of terpenoids and polyketides
Ascorbate and aldarate metabolism 0.202289911 0.208615133 Carbohydrate metabolism
Inositol phosphate metabolism 0.205902226 0.203389152 Carbohydrate metabolism
Nitrotoluene degradation 0.172897251 0.20173248 Xenobiotics biodegradation and metabolism
Peroxisome 0.235250149 0.201722271 Transport and catabolism
Polyketide sugar unit biosynthesis 0.183657223 0.196158763 Metabolism of terpenoids and polyketides
Plant–pathogen interaction 0.187462282 0.19510448 Environmental adaptation
Primary bile acid biosynthesis 0.174258783 0.194704611 Lipid metabolism
Phosphonate and phosphinate metabolism 0.210623597 0.193985285 Metabolism of other amino acids
Dioxin degradation 0.132156389 0.192271489 Xenobiotics biodegradation and metabolism
Biofilm formation—Vibrio cholerae 0.173235207 0.17739013 Cellular community—prokaryotes
Lysine degradation 0.156908886 0.163490452 Amino acid metabolism
Benzoate degradation 0.130709106 0.154388521 Xenobiotics biodegradation and metabolism
Toluene degradation 0.226951419 0.154343247 Xenobiotics biodegradation and metabolism
Tryptophan metabolism 0.134637556 0.154039866 Amino acid metabolism
Epithelial cell signaling in Helicobacter pylori infection 0.133630181 0.133210493 Infectious disease: bacterial
beta-Lactam resistance (ko01501) 0.116400133 0.129372715 Drug resistance: antimicrobial
Aminobenzoate degradation 0.099396758 0.11115085 Xenobiotics biodegradation and metabolism
D-Arginine and D-ornithine metabolism 0.075391048 0.100740543 Metabolism of other amino acids
Geraniol degradation 0.132655551 0.099248644 Metabolism of terpenoids and polyketides
Insulin signaling pathway 0.0775328 0.085309414 Endocrine system
Lysosome 0.122831158 0.076843656 Transport and catabolism
Limonene and pinene degradation 0.041696135 0.068942607 Metabolism of terpenoids and polyketides
Chlorocyclohexane and chlorobenzene degradation 0.049164205 0.067012044 Xenobiotics biodegradation and metabolism
Xylene degradation 0.060222967 0.066537537 Xenobiotics biodegradation and metabolism
NOD-like receptor signaling pathway 0.053602214 0.062017203 Immune system
Penicillin and cephalosporin biosynthesis 0.044010901 0.061981596 Biosynthesis of other secondary metabolites
Metabolism of xenobiotics by cytochrome P450 0.087723182 0.058571569 Xenobiotics biodegradation and metabolism
Protein digestion and absorption 0.085504624 0.057502562 Digestive system
Styrene degradation 0.047308092 0.056042434 Xenobiotics biodegradation and metabolism
RNA transport 0.053502252 0.053393141 Translation
Ethylbenzene degradation 0.043423693 0.05273609 Xenobiotics biodegradation and metabolism
Ribosome biogenesis in eukaryotes 0.048701751 0.051376548 Translation
Protein processing in endoplasmic reticulum 0.049857189 0.051344231 Folding, sorting, and degradation
Nonhomologous end-joining 0.039577729 0.051092789 Replication and repair
Steroid hormone biosynthesis 0.057364299 0.049768089 Lipid metabolism
Retinol metabolism 0.052829295 0.041000788 Metabolism of cofactors and vitamins
Apoptosis 0.051068242 0.039806596 Cell growth and death
N-Glycan biosynthesis 0.051020131 0.039577794 Glycan biosynthesis and metabolism
Biosynthesis of siderophore group nonribosomal peptides 0.040949082 0.036467507 Metabolism of terpenoids and polyketides
Naphthalene degradation 0 0.03345037 Xenobiotics biodegradation and metabolism
Staphylococcus aureus infection 0.014941154 0.032990713 Infectious disease: bacterial
Caprolactam degradation 0.015081781 0.031192436 Xenobiotics biodegradation and metabolism
Arachidonic acid metabolism 0.00835724 0.015988998 Lipid metabolism
Atrazine degradation 0.012706409 0.012590907 Xenobiotics biodegradation and metabolism
Amoebiasis 0.010943335 0.010565855 Infectious disease: parasitic
Pathways in cancer 0.002348253 0.004687948 Cancer: overview
African trypanosomiasis 0.002658973 0.002975453 Infectious disease: parasitic
Fluorobenzoate degradation 0.003939606 0.00284413 Xenobiotics biodegradation and metabolism
Flavonoid biosynthesis 0.001518669 0.001969788 Biosynthesis of other secondary metabolites
Steroid biosynthesis 0.001710684 0.001934228 Lipid metabolism
Polycyclic aromatic hydrocarbon degradation 0.002108258 0.001597775 Xenobiotics biodegradation and metabolism
Bacterial invasion of epithelial cells 0.000977183 0.001439847 Infectious disease: bacterial
Hypertrophic cardiomyopathy (HCM) 0.000405166 0.001153199 Cardiovascular disease
Carotenoid biosynthesis 0.001092591 0.000980961 Metabolism of terpenoids and polyketides
Betalain biosynthesis 0.000516002 0.000457185 Biosynthesis of other secondary metabolites
Parkinson disease 0.000721022 0.000435491 Neurodegenerative disease
Meiosis—yeast 0.000594575 0.000424683 Cell growth and death
Proteasome 0.000137497 0.000225495 Folding, sorting, and degradation
Photosynthesis—antenna proteins 0.000522078 0.000196824 Energy metabolism
Biosynthesis of type II polyketide backbone 8.01E−05 9.25E−05 Metabolism of terpenoids and polyketides
Isoflavonoid biosynthesis 0.000141397 4.81E−05 Biosynthesis of other secondary metabolites
Biosynthesis of type II polyketide products 0 3.28E−05 Metabolism of terpenoids and polyketides
Sesquiterpenoid and triterpenoid biosynthesis 2.23E−05 2.79E−05 Metabolism of terpenoids and polyketides
Systemic lupus erythematosus 3.42E−05 1.26E−05 Immune disease
Vibrio cholerae infection 3.21E−05 1.09E−05 Infectious disease: bacterial
Vasopressin-regulated water reabsorption 3.70E−06 8.37E−06 Excretory system
ECM–receptor interaction 5.80E−06 6.84E−06 Signaling molecules and interaction
Shigellosis 1.18E−05 4.24E−06 Infectious disease: bacterial
Endocytosis 1.20E−06 2.17E−06 Transport and catabolism
Spliceosome 5.09E−07 1.46E−06 Transcription
Phagosome 1.30E−06 9.32E−07 Transport and catabolism
Glycosphingolipid biosynthesis—lacto and neolacto series 4.69E−07 3.37E−07 Glycan biosynthesis and metabolism
Plant hormone signal transduction 2.77E−07 1.99E−07 Signal transduction
Various types of N-glycan biosynthesis 4.06E−07 0 Glycan biosynthesis and metabolism
Wnt signaling pathway 4.14E−07 0 Signal transduction
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The significance test method was Wilcoxon rank-sum test.

Further functional orthologs were predicted with level 3 KEGG pathway (Table 2B). Pentose and glucuronate interconversions (P = 4.988E−2), arginine and proline metabolism (P = 1.476E−2), phenylalanine metabolism (P = 3.792E−2), benzoate degradation (P = 3.792E−2), n-glycan biosynthesis (P = 1.041E−2), glyoxylate and dicarboxylate metabolism (P = 3.792E−2), riboflavin metabolism (P = 2.067E−2), biotin metabolism (P = 1.476E−2), folate biosynthesis (P = 3.792E−2), zeatin biosynthesis (P = 3.792E−2), nitrogen metabolism (P = 4.988E−2), caprolactam degradation (P = 2.067E−2), tropane, piperidine and pyridine alkaloid biosynthesis (P = 2.067E−2), biosynthesis of ansamycins (P = 2.813E−2), protein digestion and absorption (P = 1.476E−2), Staphylococcus aureus infection (P = 4.988E−2), phenylalanine, tyrosine, and tryptophan biosynthesis (P = 1.088E−3), beta-alanine metabolism (P = 6.993E−3), carbon fixation in photosynthetic organisms (P = 4.662E−3) and vitamin b6 metabolism (P = 6.993E−3) were less frequently expressed in the LPS group.

Correlation Between Changes in Hepatic Genes and Microbiota

The spearman correlation analysis between the microbiome and gene expression indicated that the relative abundance of Bifidobacterium, Lactobacillus were positively correlated (P < 0.05) while Eubacterium eligens group, Lachnospiraceae NC2004 group, ASF356, Lachnospiraceae NK4A136 group were negatively correlated with the changes of ACAA1, EHHADH, ALDH3A2 mRNA expression, which are associated with amino acid metabolism (P < 0.05) (Figure 6). In addition, Hibiscus syriacus and Blautia were strongly positively correlated while Slackia, Defluviitaleaceae UCG 011, and Leuconostoc negatively correlated with IL4I1 expression (P < 0.05). Blautia, ASF356, Lachnospiraceae NK4A136 group were profoundly correlated with inflammation-related genes such as ANXAl, CCL19, CXCLl3L2, EXFABP, ILlR2, and IL22RA2 (P < 0.05). However, Lactobacillus and Defluviitaleaceae UCG 011 were negatively correlated with the expression of inflammation-related mRNA (P < 0.05). As ALDH3A2, AOX2, ACAA1, ACOX1, PDPR, EHHADH, NOX4, DMGDH be genes related with oxidative stress, Slackia, Bifidobacterium, Escherichia-Shigella, Anaerostipes, Defluviitaleaceae UCG 011, and Lactobacillus had a positive association (P < 0.05) while Lachnospiraceae NC2004 group, Eubacterium eligens group, Blautia, and ASF356 a negative association with those mRNA expression (P < 0.05).

Figure 6.

Figure 6

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Heatmap of Spearman's correlation between the gut microbiota and differentially expressed genes of liver. The intensity of the colors represented the degree of association (blue, positive correlation; red, negative correlation). Significant correlations are marked by *P < 0.05; **P < 0.01.

Correlation Between Changes in Metabolites and Microbiota

According to the results of Wilcoxon test, the top 20 significantly different bacterial genera were screened out. The correlations between bacterial genera and serum metabolites are represented in heatmaps (Figure 7). The result revealed that, among the bacteria at genus level, Bifidobacterium (P = 1.480E−2), Escherichia Shigella (P = 2.796E−2), and Anaerostipes (P = 1.205E−3) showed strong positive correlations with the myristoleic acid. In addition, Lachnospiraceae NK4A136 group (P = 4.877E−2), Blautia (P = 7.355E−3), and ASF356 (P = 1.587E−3) were negatively correlated with phenyllactic acid, while Leuconostoc (P = 2.836E−3), Anaerostipes (P = 3.407E−2), and Defluviitaleaceae UCG-011 (P = 4.886E−3) showed positive correlation.

Figure 7.

Figure 7

Open in a new tab

Heatmap of Spearman's correlation between the gut microbiota and serum metabolites. The intensity of the colors represented the degree of association (blue, positive correlation; red, negative correlation). Significant correlations are marked by *P < 0.05; **P < 0.01.

DISCUSSION

Immune stress in broiler chickens is generally characterized by declined appetite, injury of multiple organs, inducing growth inhibition. Bacterial infection and bacterial toxin are constantly responsible for immune stress (Wang et al., 2020; Tiku and Tan, 2021). Our previous study observed increased ACTH and CORT and decreased growth performance, which was consistent with the model of chronic stress (Beckford et al., 2020; Hu et al., 2021). We further identified amino acid metabolism, lipid metabolism of liver in relation to growth inhibition of broiler chickens mediated by immune stress (Bi et al., 2022a). Considering the potential involvement of intestinal microbiota mediating amino acid and lipid absorption and its role in poultry nutrition, 16S rRNA gene sequencing was applied to investigate the shaping of cecal microbiota in broiler chickens with immune stress (Xiao et al., 2017; Martinez-Guryn et al., 2018; Li et al., 2020b). In the present study, immune stress significantly altered the microbial community and function in cecum. After challenge, broilers showed enhanced diversity indices and variability of the microbiome, reduced abundance of SCFA-producing and butyrate-producing bacteria, as well as raised abundance of probiotics. Moreover, immune stress increased the activities of metabolism of cofactors and vitamins, as well as decreased the ability of energy metabolism and digestive system. Pearson's correlation analysis suggested that those bacteria were apparently correlated with the hepatic gene expression. The results may help us clarify the role of intestinal microbiota in growth inhibition induced by immune stress and search for potential therapeutic strategy.

As a component of gram-negative bacteria cell wall, LPS damages the gut barrier and disturbs the microbial composition, leading to impairment in nutrient utilization and the immune balance, thereby inhibiting growth of broilers (Chen and Yu, 2021; Zhang et al., 2021). Interestingly, supplement of jejunal microbiota can improve growth performance of chickens by suppressing intestinal inflammation (Zhang et al., 2022). However, the specific mechanism concerning the relationship between the microbiota and nutrient utilization needs to be elucidated. In this study, the challenge decreased the frequency of SCFA-producing bacteria including Blautia and butyrate producers including Faecalibacterium and Erysipelatoclostridiaceae. Blautia has activities of maintaining intestinal homeostasis such as preventing inflammation and is significantly reduced in patients with colorectal cancer (Nakatsu et al., 2015; Zou et al., 2022). Known as the major energy source to the colonic mucosa, Faecalibacterium plays a critical role in keeping gut balance (Luo et al., 2013). Clostridia vadinBB60 group, a member of the Clostridia class, which showed a positive correlation with the Treg cell counts, also declined after LPS challenge (Deng et al., 2022). Flavonifractor is found to strongly suppress Th2 immune response and attenuated inflammation (Mikami et al., 2020; Ogita et al., 2020). Butyricicoccus is a butyrate producing bacteria and its descent presents inflammatory bowel disease (Eeckhaut et al., 2013). In this study, the abundance of Flavonifractor and Butyricicoccus in the Model group showed a significant decline. The changes of microbiota population in LPS broilers are correlated with lower growth performance and increased inflammatory response (Bi et al., 2022). Therefore, regulating intestinal microbiota or supplementing specific probiotics will help reduce the influence that immune stress has on poultry production. Consistently, a recent study demonstrated that supplement of Blautia subtilis helps maintain normal growth performance in broiler chickens challenged by heat stress (Abdelqader et al., 2020).

It is interesting how microbial changes influence their host. Thus, to obtain more information on the functions performed by the microorganisms in the cecum, PICRUSt was used to compare Model with Control birds in the present study. We found that cecal microbiota were enriched with functions related to biosynthesis of ansamycins, glycan degradation, D-glutamine and D-glutamate metabolism, valine, leucine and isoleucine biosynthesis and biosynthesis of vancomycin group antibiotics. Besides, the LPS challenge promoted the activities of metabolism of cofactors and vitamins but receded the ability of energy metabolism and digestive system. The results obtained here are consistent with our previous work (Bi et al., 2022a). Thus, it is assumed that reduced food digest and nutrients metabolism induced declining feed utilization and growth performance via manipulating the bacterial community composition.

Gut–liver axis, which means a close functional and physiological link between the gut and the liver (Lin et al., 2021; Silveira et al., 2022; Yu et al., 2022). Approximately 70% of the blood that goes to the liver comes from the portal vein exit intestine, the liver is easily exposed to gut-derived compounds when the intestinal barrier is disrupted (Brescia and Rescigno, 2021; Zhong et al., 2021). On the other hand, liver injury impaired the bile acid metabolism and the promotion of intestinal dysmotility, thereby resulting in systemic inflammation and gut dysbiosis (Albillos et al., 2020). By RNA-seq, 129 DEGs were identified between the Model and the Control bird, which were enriched in process such as amino acid metabolism, lipid metabolism and inflammatory response (Bi et al., 2022). In this study, we examined the relationship between the relative abundance of gut microbiome and the liver DEGs using the Spearman correlation. In total, 182 positive correlations and 177 negative correlations were found. For instance, the DEGs including ACAA1, EHHADH, and ALDH3A2, which are involved in valine, leucine, and isoleucine metabolism, were significantly positively associated with the genus Bifidobacterium, Lactobacillus, while they had a strong negative correlation with the genus Eubacterium eligens group, Lachnospiraceae NC2004 group, ASF356, Lachnospiraceae NK4A136 group. Furthermore, ALDH3A2, AOX2, ACAA1, ACOX1, PDPR, EHHADH, NOX4, and DMGDH that are related to oxidoreductase activity had a significantly negative correlation with the genus ASF356. Interestingly, most DEGs related to inflammatory response were positively associated with the genus Blautia, Hibiscus syriacus, ASF356, Lachnospiraceae NK4A136 group. These results suggested that LPS challenge might induce increased inflammatory response, inhibited oxidoreductase activity and declined nutrient metabolism by regulating liver gene expression, which depends on gut microbiota.

Lactobacillus performed an anti-inflammatory effect mediated by TGF-β (Huang et al., 2015), IL-1β (Guo et al., 2015), and NLRP3 inflammasome (Wu et al., 2015; Ren et al., 2017). It was also reported that LPS-induced immune stress can be alleviated by adding Lactobacillus (Murray et al., 2020). In the present study, increased Lactobacillus aviaries was identified in the immune stress broilers. Lactobacillus can produce phenyllactic acid from 3-phenylpropanoic acid and phenyllactic acid. Due to the enhanced abundance of Lactobacillus that response to immune stress, upregulated phenyllactic and downregulated 3-phenylpropanoic acid in association with antibiotic and anti-inflammation were observed in our previous study (Bi et al., 2022c). Therefore, we presumed that Lactobacillus can remit LPS-induced inflammation by enhancing the phenylalanine metabolic bypass pathway. Similar results were reported in previous studies (Murray et al., 2019; Wang et al., 2019; Shini et al., 2020).

CONCLUSIONS

In summary, the results suggested discrepancy in terms of diversity and composition of the cecal microbiota in broiler chicks with immune stress. We also found decreased frequency of SCFA-producing bacteria and butyrate producers, as well as increased probiotic in immune stress birds. In addition, a high positive correlation between a few bacterial genera and DEGs was found in relation to inflammatory response and BCAA metabolism, whereas a high negative correlation was found between a few bacterial genera and these DEGs simultaneously. The results suggested potential microbiota involved in growth depression mediated by immune stress and provided strategies such as supplement of probiotic for alleviating immune stress in broiler chickens.

ACKNOWLEDGMENTS

This work was supported by the National Natural Science Foundation of China (32002325), Chongqing Research Program of Basic and Frontier Technology (cstc2020jcy-jmsxmX0418), and Fundamental Research Funds for the Central Universities (SWU-KT22011).

Availability of Data and Materials: All data generated or analyzed during this study are included in this published article.

DISCLOSURES

The authors declare no competing financial interests.

Footnotes

Supplementary material associated with this article can be found in the online version at doi:10.1016/j.psj.2023.102598.

Appendix. Supplementary materials

mmc1.docx (615.4KB, docx)

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