Temporal patterns in gut microbiome and resistome of broilers: diversity and function analysis

Temporal patterns in gut microbiome and resistome of broilers: diversity and function analysis | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Temporal patterns in gut microbiome and resistome of broilers: diversity and function analysis Jin-Xin Meng, Ming-Han Li, Hany M Elsheikha, Xiao-Man Li, Xiang-Yu Wang, and 9 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4623220/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Understanding the dynamics and stability of gut microbiota throughout the production cycle of broiler chickens can help identify microbial features associated with better health and productivity. In the present study, we profile changes in the composition and stability of gut microbiota of commercially raised broilers at nine distinct time points using shotgun metagenomics and culturomics approaches. We demonstrate that within the first week post-hatching, there is a rapid decline in pioneer microbial species, accompanied by a substantial decrease in both microbial richness and diversity. This is followed by a gradual increase and stabilization in microbial diversity and population structure, persisting until the broilers reach marketing age. Throughout the production cycle, key bacterial families such as Lachnospiraceae , Bacteroidaceae , and Ruminococcaceae were identified. However, significant shifts at lower taxonomic levels occur at different production stages, influencing the functional capacities and resistance profiles of the microbiota. During the rapid growth phase, enzymes crucial to vitamin and amino acid metabolism dominate, whereas enzymes associated with carbohydrate and energy metabolism are notably more abundant during the fattening stage. Many predicted antibiotic resistance genes are detected in association with typical commensal bacterial species in the gut microbiota, indicating sustained resistance to antibiotic classes such as aminoglycosides and tetracyclines, which persists even in the absence of antibiotic selection pressure. Our research has important implications for the management and health surveillance of broiler production. Metagenomics culturomics broiler gut microbiota temporal fluctuation resistome Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Introduction The development of gut microbiota is intricately regulated by a complex interplay between host and environmental factors. This interplay profoundly influences the maturation and functions of the host's immune system, which in turn impacts the health and outcome of disease[ 1 , 2 ]. Broilers, as a major global source of animal protein, fulfil the rising human demand for protein due to their efficient growth rate and relatively short breeding cycle [ 3 , 4 ]. The rapid growth during the chick stage is crucial for subsequent productivity[ 5 , 6 ]. The colonization of microorganisms in early life is therefore essential for optimizing feed conversion efficiency[ 4 , 7 ], developing the immune system[ 8 ], gut physiology and enhancing resistance to infection[ 9 ]. The changes that occur in the structure and function of the gut microbiota during the entire production cycle of broilers remains unknown. Therefore, a comprehensive understanding of the dynamic changes that occur in gut microbiota over time may reveal potential connections between the intestinal metabolism, physiological functions, and disease risks. Chickens and mammals undergo distinct embryonic development processes. In mammals, embryonic development typically occurs within the maternal body, whereas avian embryos develop within eggs[ 10 ]. The egg yolk contains nutrients necessary for the embryo’s development, eliminating the need for reliance on the microbial presence. This leads to a relatively delayed development of the gut microbiota in birds[ 11 ]. Previous studies revealed a considerable diversity and richness in the gut microbiota of broilers immediately after birth, followed a marked decline and subsequent reestablishment during the production cycle when exposed to the external environment. This phenomenon mirrors the patterns observed in human infants and some juvenile mammals, suggesting that the composition of gut microbiota is subject to significant age-related dynamic changes after birth[ 12 – 14 ]. In chicken gut microbiota studies, initial fungal colonization is attributed to the hatchery environment, and these communities become swiftly supplanted by feed-derived fungi within three days[ 15 ]. Additionally, the microbial composition and antibiotic resistance genes (ARGs) in laying hens fluctuate diurnally, influenced by key species, mobile genetic elements (MGEs), and metabolic products[ 16 ]. Further research into the chicken oviduct and the produced eggs reveals a positive correlation between the hen’s age and increased bacterial genus diversity, which significantly contributes to the embryonic cecal bacterial community[ 11 ]. In spite of the temporal fluctuations of gut microbiota in broilers production cycle we had been characterized ever using amplicon-based metagenomic method, the absence of genome-based species-resolved fine depiction hinders the understanding that effects of both symbiotic bacterial taxa and their associated functions on birds production cycle and growth performance. In study, we analyze fecal samples from 105 Ross 308 broilers at nine distinct developmental stages within the broiler production cycle. Our work elucidates the structure and function of the broiler gut microbiota across the entire production cycle, and reveal its relationship with the resistome. The metagenomic sequencing coupled with traditional culture-based methods were used to identify age-specific bacterial taxa patterns in the broiler intestine. Our study provides new insight into the shifts that occur within the broiler gut microbiota across various the developmental stages and uncovers distinctive patterns in microbial composition, metabolic activity, and antibiotic resistance gene profiles, especially during the first week post-hatching. The data offer valuable insights into the optimization of the microbial communities, which could significantly improve husbandry practices and production efficiency of broilers. Methods Collection of samples The chicken farm details are previously described[ 3 ]. In brief, Ross 308 mixed-sex broilers raised in cages since birth were selected from a commercial hatchery in Shandong, China. To ensure continuity of sampling, we designated randomly 15 Ross 308 broilers in different location in a chicken room. A total of 105 fecal samples were collected from these birds at nine different ages (1, n = 9; 3, n = 13; 5, n = 11; 7, n = 12; 14, n = 12; 21, n = 12; 28, n = 12; 35, n = 12; and 42-day-old, n = 12) The final age of this study is only a young stage for chickens, but considering that the actual production cycle of most broilers is ~ 6 weeks, we opt to analyze the samples up to the age of 6 weeks. At least 2 g of fecal samples were collected and transported to the laboratory in secure containers at 4°C, then stored at − 80°C until DNA extraction. For culturomic, 20 fecal samples (~ 5 g) were collected from 35d and 42d chicken using AnaeroGen Sachet (Thermo Scientific, USA), and immediately transported to the laboratory in secure containers at 4°C for bacterial isolation. The gut microbiota in this time period was considered that more diverse and stabilized compared to the ever times. All broiler chickens were grown up in the same environment under the same breeding system, including diet and management. The farm breeder confirmed that no antibiotic treatments or growth-promoting antibiotics were administrated to the broilers. Disinfection was carried out on the chicken coops prior to the start of the production cycle, and routine disinfection on chicken farms was conducted twice a week. The farm relied on quaternary ammonium compounds and hydrogen peroxide as primary disinfectants. Bacterial isolation and identification The culture medium used for the isolation are listed below: AB: Anaerobic Broth (Land Bridge Tech., Co., Ltd, Beijing, China). AN: Anaerobic Agar Base (Solarbio Sci. & Tech., Co., Ltd, Beijing, China); BAB: Blood Agar Base (Solarbio Sci. & Tech., Co., Ltd, Beijing, China). BHI: Brain Heart Infusion (Hope Bio-Tech., Co., Ltd, Qingdao, China. The same below). BS: Bifidobacterium BS Medium. CBB: Columbia Blood Agar Base. GAM: Gifu Anaerobic Medium. BGAM: GAM + blood. LBB: Lactose Bile Broth. LBS: LBS agar. MRS: MRS Broth. RCM: Reinforced Clostridium Medium. All media were supplemented with 15g/L agar (Biosharp, Labgic Tech., Co., Ltd, Beijing, China) whenever appropriate. All media requiring the addition of blood were supplemented with 5% (v/v) defibrinated sheep blood (Solarbio Sci. & Tech., Co., Ltd, Beijing, China). The culture medium preparation was performed according to instructions provided by manufacturer. Under sterile conditions, the fecal samples were serially diluted (10 2 -10 6 ) by phosphate buffer solution (PBS), then 100µL of the suspension were inoculated immediately onto the surface of each agar plate containing bacterial culture medium. The plates were incubated at 37℃ for 24-36h in an anaerobic incubator (LAI-3, Longyue Instrument Equipment Co., Ltd. Shanghai, China) containing an atmosphere of N 2 (89.3%), CO 2 (6%), and H 2 (4.7%)[ 17 ] and in a constant temperature incubator, respectively. Phenotypically distinct colonies were picked from each incubated agar plate on a fresh medium for further purification, with 2 ~ 3 repeats. All the isolated bacterial strains were inoculated into anaerobic liquid media in Hungate tubes and then incubated at 220 rpm, 37 ℃ for 1–3 days for proliferating. All isolated bacterial strains were stored at -80℃ in 25% glycerol. Bacteria from overnight cultures were pelleted with centrifugation, then genomic DNA were extracted using Bacterial Genomic DNA Extraction Kit (Solarbio Sci. & Tech., Co., Ltd, Beijing, China). The bacterial 16S rRNA genes were amplified using universal primers 27F (5’- AGAGTTTGATCCTGGCTCAG’) and 1492R (5’- TACGGCTACCTTGTACGACTT-3’). After examination of PCR productions using 1% agarose gel electrophoresis, Sanger sequencing was employed to obtain the nearly full-length 16S rRNA gene sequences. Finally, the sequences were aligned against SILVA rRNA database[ 18 ] (release 138) using BLAST (v2.13.0) to determine the taxonomy of the bacterial strains. Genome sequencing, processing and assembly of bacterial isolates The 16S rRNA gene sequences were clustered using CD-HIT[ 19 ] (v4.6.8) with a threshold of identity > 99%[ 20 ]. For each cluster, a longest sequence in a cluster were chosen as the representation for whole-genome shotgun sequencing. Following the DNA library preparation and addition of index codes, the DNA was fragmented through sonication to achieve a size of approximately 350 bp. The qualified libraries were pooled and subjected to sequence on NovaSeq 6000 platforms with PE150 strategy (Novogene Tech., Co., Ltd, Beijing, China). The unqualified reads were filtered using FASTP[ 21 ] (v0.23.2) program with the default parameters. The high-quality reads were then subjected to de novo assembly using SPAdes[ 22 ] (v3.13.0) with the parameter “--careful”. The subsequent analysis of the isolate genomes followed a methodology similar to that of metagenome-assembled genomes (MAGs). DNA extraction, metagenome sequencing, and bioinformatic analysis Genomic DNA was extracted for each fecal samples DNA extraction kits (TIANamp Stool DNA Kit, TIANGEN, China) following the manufacturer’s instructions. The examination of DNA purity and concentration was conducted using 1% agarose gel electrophoresis. After DNA library preparation and addition of index codes, the DNA was fragmented using sonication to achieve a size of approximately 350 bp. The libraries were pooled and sequenced on BGISEQ-500 platform with PE150 strategy (Novogene Tech., Co., Ltd, Beijing, China). A total of 1.28 terabase pairs (Tbp) of raw reads were generated and used for subsequent analysis. The raw reads were filtered to remove unqualified reads using FASTP (v0.23.2) with options “-q 20 -u 30 -l 80 -y”. The reads that aligned with the host genomic sequence (NCBI RefSeq assembly: GCF_016699485.2) were removed using bowtie2[ 23 ] (v2.4.4) with default parameters. The high-quality reads were assembled for each sample by MEGAHIT[ 24 ] (v1.2.9). Reads were mapped to contigs with a length > 2,000 bp using the BWA MEM program[ 25 ] (v0.7.17-r1188). SAMtools[ 26 ] (v1.18) program was used for converting SAM files to BAM files and sorting the aligned results. The jgi_summarize_BAM_contig_depth script was used to generate the files containing the sequencing depths of contigs. We used MetaBAT2[ 27 ] with options “-m 2000 -s 200000 --seed 2023” for binning. The quality of metagenomic bins and isolate genomes were evaluated using CheckM2[ 28 ] (v1.0.1). Only bins with completeness ≥ 70%, contamination ≤ 5% and quality score[ 29 ] (defined as completeness-5×contamination) > 55 were selected. The isolate’s genomes that met this criterion were used for subsequent analysis together. The dRep[ 30 ] (v3.4.2) program was used to eliminate genome redundancy for both raw MAGs and the isolate genomes with the options “-pa 0.9 -sa 0.99 -nc 0.30 -cm larger --S_algorithm fastANI”, resulting in 694 genome-based strains. Taxonomy assignment and the construction of phylogenetic trees were performed using the standard workflow in GTDB-Tk[ 31 ] (v2.3.2) and the GTDB[ 32 ] database (release 214), based on 120 marker genes. The phylogenetic tree was annotated and visualized using iTOL (v6.0)[ 33 ]. The genomes were re-clustered using dRep with a threshold of ANI (average nucleotide identity) > 95%, resulting in 349 genomospecies. We profiled the relative abundance of genomospecies using bowtie2 (v2.4.4) and CoverM (v 0.6.1, Woodcroft et al., unpublished, https://github.com/wwood/CoverM ) with the default parameters. On average, 65.93% (± 14.68%) of the reads from the fecal samples could be assigned as bacterial sequences (Additional files 1: Table S1 ). The relative abundances for higher taxonomic levels were determined by aggregating the abundances of their daughter clades. Gene prediction and functional annotation Prodigal[ 34 ] (v2.6.3) was used to predict the open reading frames (ORFs) of the metagenome contigs with the parameter “-p meta”. The parameter '-p single' was employed for predicting ORFs in the isolated genomes. ORFs with lengths < 100bp and incomplete genes were discarded, and the others were clustered using MMseqs2[ 35 ] (v14-7e284) easy-cluster workflows with following parameter settings: “--cluster-mode 2 --min-seq-id 0.9 --cov-mode 1 -c 0.9 --kmer-per-seq-scale 0.8”, resulting in a nonredundant microbial gene catalogue comprising 2.16 million genes. Entries in the microbial gene catalogue were subjected to taxonomic assignment using blastn (v 2.13.0) searches against the NCBI-NT (v5.0, September 2023, prokaryote and viruses). The protein-coding genes were subjected to functional assignment by comparing them against the Kyoto Encyclopedia of Genes and Genomes[ 36 ] (KEGG, release 106.0) and carbohydrate-active enzymes[ 37 ] (CAZymes, August 2022) databases using DIAMOND[ 38 ] (v2.1.8.162) with the parameters of “--min-score 60 --query-cover 50 ”. The hit with the highest bit score was selected as the representative alignment for the taxonomic and functional assignment of the ORFs. Gene abundance in each sample was profiled by mapping the high-quality reads (20 million reads) against the non-redundant gene catalogue using Bowtie2 (v2.4.4). Subsequently, read counts in each sample were transformed to transcript per million (TPM). The abundances of KEGG orthologous groups (KOs) and CAZymes were calculated based on the abundances of genes assigning to them. Linear discriminant analysis (LDA) effect size (LEfSe) analysis was used to identify the key characteristics of CAZymes in the gut microbiomes across all groups using ‘microeco’[ 39 ] (v1.1.1) package in R. Q values were used for multiple testing correction and generated by the Benjamini‒Hochberg method. LDA scores > 2.0 and q < 0.05 were considered statistically significant. Using the database of CAZyme subfamilies for substrate annotations ( https://bcb.unl.edu/dbCAN_sub/ ), we determined the preferred substrates of the CAZymes. The differential enrichment KEGG modules were identified according to their adjusted reporter score[ 40 , 41 ]. ARG and MGE prediction and profiling The putative amino acid sequences of the ORFs were aligned with Comprehensive Antibiotic Resistance Database[ 42 ] (CARD, v3.2.6) using DIAMOND, with a coverage > 75% and identity > 80%. The predicted genes were identified as MGE-like genes by searching ORFs against the custom MGE database created by Parnanen, et al[ 43 ], using blastn program (v2.13.0+) with an evalue ≤ 10 − 5 , coverage ≥ 80% and identity ≥ 70%. The hit with the highest bit score was selected as the representative alignment for the assignment of ARG and MGE ORFs. Likewise, the same procedure was conducted for each genome to explore the distribution of ARGs and MGEs on themselves. GCView services ( https://proksee.ca/ ) was used to visualize genome and to mark targeted genes. The abundances of ARGs and MGEs were calculated based on the abundances of genes assigning to them. For evaluating the prevalence of ARG and MGE, a threshold of TPM > 10 was used to determine whether a ARG or MGE was present in a sample. Rarefaction curve and diversity analysis Rarefaction curves were generated using R package ‘vegan’ (v2.5-7). The Shannon and Richness indices were calculated using the abundance profiles of the taxonomic and functional features. To assess the β-diversity, Principal Coordinate Analysis (PCoA) was perfromed based on the Bray-Curtis distance, and the significance of group differences was determined using permutational multivariate analysis of variance (PERMANOVA). The Wilcoxon rank-sum test was performed to evaluate the significant difference in the diversity indices and abundance of taxa and functional feature between pairwise groups. Linear regression analysis was employed to determine the optimal trend line that represents the variation in the diversity indices, taxonomic abundance, and functional features across various groups. Correlation analysis Procrustes association analysis was performed using the 'procrustes' function in the 'vegan' package. Mantel tests were performed using the 'mantel_test' function in the 'LinkET' (v0.7.4) R package, available at https://github.com/Hy4m/linkET . Spearman's correlation analysis was carried out to assess the relationships among diversity indices of the gut microbiota, mobilomes, and resistomes, as well as the co-abundance of ARGs and MGEs. Statistical analysis and visualization Statistical analyses were carried out in an R 4.2.1 environment. The ‘mfuzz’ function in the ‘mfuzz’[ 44 ] R package (v2.60) was utilized for soft clustering of the bacterial abundance changes across nine time points. All heatmaps were visualized using the ‘ComplexHeatmap’ (v2.8.0) R package. Sankey plot was constructed using the ‘ggsankey’ package (v0.0.9, https://github.com/davidsjoberg/ggsankey ). Gene arrow maps were constructed using the ‘gggenes’ (v0.4.1, https://github.com/wilkox/gggenes ) package. Network graphs were visualized using the R package ‘ggraph’ (v2.1.0, https://github.com/thomasp85/ggraph ). All other visualizations were produced using the ggplot2 package (v3.3.6). Results Collection of genomes and genes related to the production cycle of broilers To track the temporal fluctuations in microbial composition, we employed a combination of culture-based and metagenomic sequencing methods to obtain a genome collection and a gene catalogue of the broiler gut microbiota during the production cycle. A total of 899 isolates were obtained using culture-based methods and identified by using Sanger sequencing, and morphological images of some isolates were shown in Figure S1 . These isolates belonged to five phyla: Bacillota (also called Firmicutes , n = 704, 78.3%), Pseudomonadota (also called Proteobacteria , n = 134, 14.9%), Bacteroidota (n = 48, 5.3%), Actinomycetota (also called Actinobacteriota , n = 11, 1.2%), and Fusobacteriota (n = 2, 0.2%, Fig. S2 a, Additional files 1: Table S2 ). Bacillaceae (n = 304, 33.8%), Lactobacillaceae (n = 258. 28.7%), Enterobacteriaceae (n = 134, 14.9%) and Enterococcaceae (n = 100, 11.1%) were dominated families, accounting for nearly 88.5% of total isolates (Fig. S2 b). A total of 98 representative isolates were selected for the whole-genome sequencing and de novo assembly. After excluding 16 genomes with high contamination (< 10%), the remaining 82 genomes, together with the original MAGs, underwent unified analysis. Using metagenomic sequencing, assembly, and binning, a total of 4,282 original MAGs were generated. Following quality assessment and redundancy removal at the 99% ANI level, a total of 694 genomes meeting or exceeding quality standards were generated and included in subsequent analysis (Fig. 1 , Additional files 1: Table S3 ). These genomes had a genome size ranging from 0.66 to 5.98 Mbp (average 2.39 Mbp) and an GC content ranging from 24 to 72.1% (average 47.23%). The mean completeness of the genomes was 87.58%, and the mean contamination was 1.33%. Out of these, a remarkable 299 genomes (43.1%) met the high-quality standard, exhibiting completeness levels of 90% or higher and contamination levels below 5%. Additionally, 349 genomospecies were further determined by clustering with ANI threshold of 95% and a coverage fraction threshold of 30%. The culture-dependent and culture-independent methods contributed 42 and 326 genomospecies, respectively, with 19 genomospecies detected by both two methods (Fig. 1 ). The taxonomic classification revealed that all genomes were classified into bacterial lineages, spanning cross 7 phyla, 63 families, and 189 genera. Among these, the phyla Bacillota (n = 556, 83.1%), Bacteroidota (n = 55, 7.9%), Actinomycetota (n = 42, 6.1%) and Pseudomonadota (n = 27, 3.9%) were dominant in the intestinal tract of commercial broilers. It is worth mentioning that 12 assemblies and 1 isolate could not be classified to any known species using the latest reference genome databases (Additional files 1: Table S3 ), suggesting that the presence of potentially novel species. A non-redundant microbial gene catalogue was generated containing 2.16 million genes with an average length of 789.8 bp, all of which possessed complete ORFs. The genome collection and the gene catalogue serve as crucial tool for studying the taxonomic and functional profiles of broiler gut microbiota. Disappearance and reconstruction of microbial community in the broiler gut microbiota The intestine is a complex organ, and the microbial community is crucial for maintaining the health of poultry gut, affecting feed conversion rates and, consequently, the animal productivity. Our previous research has demonstrated the dynamic nature of gut microbiota throughout broiler production cycle[ 45 ], and here we further explore the details of temporal species-level fluctuations. Rarefaction curves analysis indicated that the cumulative sequencing data reached saturation, suggesting thorough coverage of the microbial genomes by the sequencing analysis (Fig. 2 c). Noteworthy, we observed a distinct microbial profile in the meconium of 1-day-old chicks, which exhibited a microbial structure absent at other time points-a phenomenon previously overlooked in broilers studies. The meconium is primarily comprised the phyla Bacillota (58.8%), Bacteroidota (36.7%), Pseudomonadota (2.3%), and Actinomycetota (1.5%) (Fig. 2 a, Additional file 1: Table S4 ). At the family level, it was mainly comprised Lachnospiraceae (33.9%) and Bacteroidaceae (29.6%) (Fig. 2 b). With the intervention of biological and environmental factors, linear regression analysis showed a rapid trend of microbial species richness and diversity decrease persisting until day 5 (Richness: R²=0.43, p < 0.001; Shannon: R²=0.63, p < 0.001; Fig. 2 d-e). After the rapid turnover of the microbial community in the first week, until the 42nd day when the broilers were ready for the market, we observed a significant increasing trend in the richness and diversity of the broiler gut microbiota (Richness: R²=0.11, p < 0.004; Shannon: R²=0.06, p = 0.035). PCoA revealed that the age of broilers significantly influenced the temporal fluctuations in the gut bacterial community (PERMANOVA, R²=0.4838, p < 0.001, Fig. 2 g). The black solid line depicted the apparent succession trajectory of the gut microbiota, originating from the upper right corner of the cartesian coordinate system and advancing to the lower right corner. Additionally, pairwise comparisons between groups indicated significant differences in almost all cases ( p < 0.05, Fig. S3 ). Next, we sought to identify the bacterial taxa involved in the disappearance and subsequent reconstruction of microbial community. During the first week, the microbial community underwent the most dramatic fluctuations. Accompanying this, there was a significant decrease in the relative abundance of Actinomycetota (R²=0.16, p = 0.021), Bacteroidota (R²=0.66, p < 0.001), Desulfobacterota (R²=0.63, p < 0.001), Fusobacteriota (R²=0.46, p < 0.001), and Pseudomonadota (R²=0.44, p < 0.001), while the relative abundance of Bacillota (R²=0.47, p < 0.001) and Cyanobacteriota (R²=0.34, p < 0.001) significantly increased (Fig. S3 ). The changes in the composition of this microbial community partially occurred later in the life cycle, reflected in the recovery and significant increase in the relative abundance of Bacteroidota (R²=0.11, p = 0.004), Desulfobacterota (R²=0.53, p < 0.001), and Pseudomonadota (R²=0.21, p < 0.001), while Bacillota (R²=0.07, p = 0.030) showed a decreasing trend in relative abundance. Throughout the entire 42-day production cycle, aside from the continuous decrease in Fusobacteriota (R²=0.21, p < 0.001), bacterial phyla such as Actinomycetota (R²=0.06, p = 0.009), Bacillota (R²=0.08, p = 0.004), Cyanobacteriota (R²=0.47, p < 0.001), and Desulfobacterota (R²=0.08, p = 0.003) all showed an upward trend in relative abundance. At the lower taxonomic level of family, dominant families include Lactobacillaceae , Enterococcaceae , and Lachnospiraceae , which together had an average cumulative relative abundance exceeding 80% in each sample. These bacterial taxa were represented dominant gut microbial communities in the broiler production cycle, maintaining a certain elastic colonization ability during the microbial succession. Furthermore, leveraging the advantages of genome assembly, we conducted an in-depth analysis at the species level. We grouped species based on the similarity of relative abundance change patterns and conducted comparative analyses within 12 clusters. Interestingly, as the age increased, we observed a rapid disappearance of genera under the family Lachnospiraceae in the gut of broilers, including Egerieimonas , Sellimonas , Merdimonas , Scatomonas , Eisenbergiella , Lachnoclostridium , and Fimimorpha (Fig. 2 g and Fig. S4 -6). Additionally, several other families exhibited partially similar trends, and we classified them as cluster 12. The cluster 12 represents 32 families and 72 genera, including Lachnospiraceae , Ruminococcaceae , Bacteroidaceae , Acutalibacteraceae , and Burkholderiaceae (Additional file 1: Table S5 ). In the search for bacterial families showing a resurgence in the later stages of production, we observed an upward trend in clusters 7 and 8 in the mid-term. Representatives of these clusters are also dominated by Lachnospiraceae, Ruminococcaceae , and Acutalibacteraceae . Within Lachnospiraceae , the genera changed to Mediterraneibacter , Limivivens , Scatomonas , Egerieimonas , Coladousia . In the later stages of production, we found a sustained increase in bacteria from cluster 5. Surprisingly, the representative families in this cluster remained the same as in the early to mid-term. However, the genera within Lachnospiraceae had changed to Anaerobutyricum , Blautia , Caccovicinus , Choladocola , and Fusicatenibacter . Although there was no change at the family level, the microbial composition at the genus level was almost entirely different. Several other representative clusters also exhibited similar phenomena. As predicted, Lachnospiraceae and several other bacterial families were important microbial participants throughout the entire production cycle. However, the dominant genera involved in colonization differed at different times, showing a trend of microbial succession. These findings showed some taxa with subtle colonization advantage in certain stages, highlighting their significant role in host growth and development. Study on the temporal fluctuation of microbial function To explore the temporal fluctuation in microbial function over time, we annotated the gene catalogue using KO and CAZyme classifications. In general, 52.95% (1,141,468/2,155,595) of protein-coding genes were assigned to 8,233 KOs. Notably, akin to the taxonomic changes, microbial functions also followed a pattern, with greater fluctuations in the first week after hatching compared to other time points. Alongside the rapid disappearance of microbial community diversity in the early stages of life, we observed a significant decrease in the richness and diversity of microbial functions in the first week after hatching in broiler chickens (Linear regression: p < 0.001, Fig. 3 a-b), indicating that changes in the broiler’s gut microbiota significantly influenced fluctuations in microbial functions (PERMANOVA: 49.77%, p < 0.001, Fig. 3 c-d). The disappearance of this functional diversity was accompanied by a substantial reduction in microbial functions related to metabolism, specifically in amino acid, carbohydrate, energy, secondary metabolites, and cofactor and vitamin metabolism (Linear regression: p < 0.001, Fig. 3 e, Fig. S7 a). After the first week, except for genetic information processing, the fluctuations in various functions began to stabilize, while nucleotide and lipid functions continued to exhibit a significant increase (Linear regression: p < 0.01). Furthermore, we explored the contributions of the microbial communities to various functions and found that Bacillota was predominant in contributing to a variety of microbial functions, followed closely by Bacteroidota , Pseudomonadota , and Actinomycetota (Fig. S7 b). Particularly in the early life stages of broilers, the contribution of Bacteroidota , Lachnospiraceae and Bacteroidaceae to microbial functions was significantly higher than at other times (Wilcoxon rank-sum: p < 0.001, Fig. S7 c). Accompanied by a rapid colonization on 3rd day after birth, Lactobacillaceae , Enterococcaceae and Planococcaceae were contribute the vast majority of microbial functions together. After this time point, Lactobacillaceae evolved as the main contributor for microbial composition and functions. Microbial carbohydrate degradation activities also conferred significant benefits to the host. Therefore, we compared and analyzed the intestinal CAZymes functions of broilers at different ages. Among the 2,155,595 predicted proteins, 12.6% (313,568) were predicted to have at least one CAZy function, including 15 auxiliary activities (AAs), 81 carbohydrate-binding modules (CBMs), 19 carbohydrate esterases (CEs), 155 glycoside hydrolases (GHs), 100 glycosyltransferases (GTs), and 31 polysaccharide lyases (PLs). LEfSe analysis revealed that the broiler’s gut microbiome significantly enriched the highest number of CAZymes in the early stages, particularly on day 1 (144/276) (LDA > 2, q < 0.05) (Fig. 3 f-g, Additional file 1: Table S6 ). These early-enriched CAZymes targeted a broader spectrum of substrates, including host glycan, pectin, xylan, β-glucan, among others. Comparative analysis of microbial functional enrichment between rapid growth phase and fattening phase in broilers The rapid growth phase, the initial stage of broilers’ life cycle, occurs within the first few weeks post-hatching and its duration depends on the breed and management practices[ 5 , 6 ]. This period is characterized by substantial development of skeletal muscles. During this stage, broilers exhibit a marked increase in the growth rate, underpinned by accelerated bone and muscle development to accommodate the weight gain[ 46 ]. Concurrently, there is an elevated demand for high-protein and high-energy diets to support this rapid growth[ 46 ]. Subsequently, the growth rate moderates, in transitioning into the fattening phase, where the focus shifts to fat and muscle deposition to enhance the meat quality[ 47 ]. In the present study, broilers at 21 days of age were designated as the threshold between these developmental stages. We focused on comparing the differences in microbial function between the two stages. By employing reporter Z-score-based enrichment analysis of the KEGG metabolic pathways, we identified 40 and 17 functional modules significantly enriched during the rapid growth and fattening phases, respectively (Fig. S8 , Additional file 1: Table S7 ). Metabolic modules enriched in the rapid growth phase were predominantly related to the metabolism of vitamins and amino acids, including proline, lysine, methionine, cysteine and threonine biosynthesis, as well as leucine and tyrosine degradation. In contrast, the fattening phase was primarily associated with carbohydrate and energy metabolism, featuring modules such as pyruvate oxidation, fumarate reductase, reductive acetyl-CoA pathway, fatty acid biosynthesis and methanogenesis. We visualized the modules related to amino acid and carbohydrate metabolism and their role in the growth of broilers are depicted in Fig. 4 . Characteristics and temporal fluctuation of gut resistome of broilers A total of 1,132 ORFs were predicted to be associated with antibiotic resistance ontology (ARO) after alignment with the CARD, representing 425 unique ARGs (Additional file 1: Table S10 ). The ARGs for aminoglycoside antibiotics were notably prominent in the gut microbiota, followed by peptide, tetracycline and other antibiotics (Fig. S9 a). Meanwhile, antibiotic inactivation was the most common mechanism to potentially confer resistance (30.6%), followed by efflux and target alteration (Fig. S9 b). We found that the predicted ARGs were primarily carried by four bacterial phyla: Pseudomonadota , Bacillota , Actinomycetota and Bacteroidota , all of which were also predominant in the host intestine (Fig. 5 a). Notably, Escherichia coli was found to harbor up to 101 ARGs, with 45% of these gene conferring multidrug resistance (Fig. S9 c). Meanwhile, we investigated the distribution of ARG-encoding genes across 694 genomes and found over half of the strains did not carry any ARGs, while 141 strains carried only one ARG (Additional file 1: Table S3 ). However, a significant number of strains carried a variety of ARGs, particularly several strains belonged to Escherichia coli, Escherichia fergusonii, Enterobacter hormaechei, Enterococcus faecium, Staphylococcus epidermidis and Staphylococcus saprophyticus , which harbored more than 20 ARGs, with some containing up to 119 ARGs. We analyzed several strains to illustrate the distribution and location of ARGs on their genomes (Fig. 5 b and Fig. S9 g). For instance, Escherichia coli HC271 harbored 89 ARGs, 29 MGEs (highlighted in the following section), with multiple ARGs and MGEs co-located on the same contigs, such as those marked at Location 1 and 2. Aligned with the aims of this study, we conducted an exploration into the diversity and structural changes of the ARGs and their corresponding drug resistance phenotypes. We evaluated the overall abundance of ARGs within each gut microbiome sample, revealing an average ARG abundance of 0.45% (± 0.20%). This indicates that ARGs are prevalently distributed throughout the gut microbiota. Contrary to the taxonomic changes, we observed a significant increase in the overall ARG abundance during the first three days, which then significantly decreased until the end of the observation phase (Linear regression: p 20% (Fig. 5 c). Notably, the abundance of the genes tetL , tetW , tetOW , tetWNW and vatH consistently increased in the gut microbiota of broilers over time. ARGs such as tetL , poxtA and apmA were present at the third day and at subsequent time points. In contrast, others like tetQ , ErmF , lnuC , which were more abundant at the first day, showed a reduction in the following days. Additionally, some predicted ARGs exhibited a consistent pattern of change, which may indicate an interrelationship, such as a synergistic transfer mechanism at play. Furthermore, based on phenotypes of predicted ARGs, we preliminarily evaluated the dynamic changes in drug resistance phenotypes based on their relative abundance to obtain deeper insights into the environmental pressure and the potential for resistance dissemination (Fig. 5 d). Over time, the resistance levels to antibiotics such as diaminopyrimidine, elfamycin, glycopeptide, and pleuromutilin demonstrated a generally significant downward trend from the initial day (Linear regression: p < 0.001). In contrast, resistance to antimicrobial agents, including aminoglycosides, cephalosporins, MLS (lincosamide, macrolide and streptogramin), nucleosides, phenicols, and tetracyclines, showed a significant upward trend (Linear regression: p < 0.05). Notably, there were no signs of carbapenem resistance detected in the gut microbiome of the broilers during the first three days of their lives. Gut microbiota and mobilome related to resistome landscape To understand the impact of the composition and temporal fluctuation of the gut microbiota on the resistome characteristics, we investigated the correlation between microbial communities and antibiotic resistance profiles. Our findings indicate a significant positive correlation between the Shannon index of the gut microbiota and the resistome (Fig. S10 a, R = 0.56, p < 0.001). Furthermore, the Procrustes association analysis uncovered a tight correlation between gut microbiota and the resistome (Fig. S10 f, M 2 = 0.2444, p 0.3, p < 0.05, Fig. S11 , Additional file 1: Table S9 ), indicating their potential key role in the shifts observed within the resistome composition. Specifically, Escherichia coli and Enterococcus gallinarum , as representative species of their respective families, were primarily responsible for shaping the composition and variability of resistance to antimicrobial drugs (Fig. 6 a). Additionally, resistance to certain antibiotics, such as peptides, oxazolidinones, and aminocoumarins, were most notably affected by alterations in the microbial community. These results underscore the profound influence of the microbial community on the features of the gut resistome and highlight the important role played by certain key bacterial species in this interplay. MGEs are key facilitators in the interspecies exchange of ARGs among bacteria. Elucidating the relationship between MGEs and ARGs is crucial for understanding the temporal fluctuation in resistome. We identified a total of 611 MGEs within our gene pool, predominantly composed of transposons (61.43%) and plasmids (19.01%; Fig. S10 e, Additional file 1: Table S10 ). Association analysis indicated that the impact of the mobilome on the composition and variations of the gut resistome is more pronounced than that of the gut microbiota (Fig. S10 c-d and S10g). Consequently, we examined in detail the co-occurrence relationships between MGEs and ARGs at the gene level. We searched for MGEs within a 5 kilobase range surrounding ARGs and considered these ARG-MGE complexes as potential mobile resistance genes. In total, 1,877 ARG-MGE combinations were identified across 1,144 contigs (Fig. 6 b-c, Additional file 1: Table S11 ). The sul1 and qacEdelta combinations were the most abundant. Aminoglycoside resistance and multi-drug resistance were associated with a variety of transposons. Considering that fragmented contigs might underrepresent the links between ARGs and MGEs, we investigated the co-abundance relationships and found significant correlations among 245 ARGs and 168 MGEs in terms of their abundances (Spearman correlation: r ≥ 0.5, q < 0.05, Additional file 1: Table S12). Among these, an overwhelming majority were positively associated (99.8%). In line with prior observations, ARGs and MGEs exhibiting co-occurrence relationships also showed a strong positive correlation in their co-abundance. These findings not only suggest an increased potential for ARGs to be transferred across different genomic locations and between distinct bacterial strains, but also imply that these ARG-MGE combinations could be key in driving the shifts in antibiotic resistance profiles throughout the production cycle. Discussion In this study, we conducted a investigation of the gut microbiota of commercial broilers from birth to market, aiming to elucidate the relationship between distinct gut microbiota patterns and age throughout the entire production cycle. We observed that the gut microbiota of broilers during the first week post hatching was influenced by a combination of environmental and biological factors, initially underwent depletion of maternally derived microbiota. This was accompanied by the disappearance of bacterial taxa with priority effects and a significant decrease in microbial diversity. Subsequently, after transitioning from the rapid growth stage to the fattening phase, we observed a reconstruction of microbial community with the emergence of other species in the gut microbial community. Microbial community depletion and subsequent reconstruction have not been investigated in previous studies on chickens, and there has been a lack of emphasis on exploring this phenomenon. Unlike the host genome, which is encoded at birth and remains largely stable throughout life, each of these unique microbiota changes can be modifiable over time[ 8 ]. Utilizing metagenomic and genomic sequencing, we observed temporal fluctuations in the gut microbiota of broilers as they age, with significant differences in the microbial community structure among chickens of different ages. We observed a significant decrease in the diversity and richness of the gut microbiota in broilers during the first week post-hatching. Unexpectedly, similar trends are reported in the gut microbiota of infants to those observed in broilers[ 48 , 49 ]. In the early life stages, the gut microbiota is predominantly composed of facultative anaerobes. However, as they deplete oxygen, obligate anaerobes and some oxygen-tolerant bacteria begin to colonize the intestinal environment, facilitating a rapid succession of the microbial community in the early stages of life[ 48 , 49 ]. In a study of the gut microbiota of new-born calves, microbial diversity, richness, and bacterial developmental diversity were highest at birth, and significantly decreased by day 3[ 50 ]. Therefore, we infer that, similar to most mammals, the initial colonization of the chicken microbiota is likely through maternal transmission[ 14 ]. These microbes may influence the immune system of chicks and potentially have significant effects on the subsequent changes in the microbial community. However, this maternal microbial influence can be rapidly overshadowed by environmental factors (such as diet, water source, housing conditions, disinfectants, etc.) and biological factors (including innate and adaptive immunity) during the early stages of life[ 49 , 51 , 52 ]. This drastic fluctuation stabilizes after the first week, with microbial community diversity and richness beginning to show an upward trend. At the family level, we observed a rapid turnover of bacterial taxa such as Lachnospiraceae , Bacteroidaceae , and Ruminococcaceae , with the emergence of bacterial families including Lactobacillaceae and Enterococcaceae . This phenomenon indicates their indispensable importance in the growth and development of broilers. While the microbial community structures still differ at different ages, they become relatively stable compared to the first week. Interestingly, in this dynamic microbial succession, significant changes occur in lower-level taxa of major producers of SCFAs and vitamins, such as Lachnospiraceae , which remains abundant throughout the production cycle[ 53 ]. However, we noted substantial changes in lower-level genera, possibly due to different involvement in biological functions and metabolic cycles across production cycles. Similar phenomena were observed in taxa like Ruminococcaceae and Acutalibacteraceae . The reconstruction of gut microbial community may be related to the interactions between these bacterial taxa, with the successive turnover of different bacterial taxa contributing to formation and maintenance of host microbiota[ 2 , 54 ]. During the rapid growth phase of broilers, microbial functional enrichment was primarily associated with vitamin and amino acid metabolism, which was significantly higher than during the fattening phase. Thiamine biosynthesis was notably abundant, and thiamine serves as a crucial coenzyme in glucose metabolism, participating in the oxidation of glucose to provide the necessary energy for cellular processes[ 55 ]. Adequate energy supply is vital for the normal growth of bones and muscles. Proline biosynthesis also exhibits significant microbial functional activity[ 56 ]. The presence of proline may influence the synthesis and structural maintenance of collagen, potentially affecting the health of bones and joints[ 56 ]. Furthermore, we observed the enrichment of various amino acid metabolic pathways during the rapid growth phase, including lysine, methionine, cysteine and threonine biosynthesis, among others. Amino acids are essential components of proteins, crucial for the synthesis and repair of muscle tissue. Vitamins and amino acids collaborate in the development of muscles and bones, jointly promoting the healthy growth of muscle and skeletal tissues[ 57 ]. In contrast, during the fattening phase, the microbial community is more involved in carbohydrate and energy metabolism, with significant enrichment of pyruvate oxidation and fumarate reductase. During the fattening stage, chickens preferentially utilize carbohydrates, converting a portion into glycogen stored in the liver and muscles[ 58 ]. This serves as the primary energy source, preserving proteins for the development of muscle tissue. Also, acetone produced through metabolic pathways, ultimately converted into fat, can facilitate the rapid weight gain and fat accumulation in broilers[ 59 , 60 ]. We observed that the ARG levels in broilers were relatively high compared to those reported in other animals, such as cows[ 50 ], pigs[ 61 ] and horses[ 62 ], with classes including elfamycin, aminoglycoside, tetracycline, and fluoroquinolone ARGs commonly detected in the chicken intestine. Interestingly, despite not receiving any antibiotic treatment, the broilers in this study still carried a high prevalence of ARGs in their gut microbiome, indicating that the gut microbiome serves as a natural reservoir for ARGs[ 63 , 64 ]. A previous evaluation of human health risk associated with ARGs determined that, among the ARGs identified in the present study, 37 were categorized as "current threats" resistance genes, and 12 ARGs were classified as "future threats" genes. Surprisingly, we also detected the presence of tetX , mcr-1 , and NDM-1 genes, which are known to confer resistance to antibiotics considered as the last resort for treating infections in both human and veterinary medicine[ 50 , 65 ]. These genes not only provide fresh insights into antibiotic resistance research but also carry significant implications for public health policy formulation. Over time, the increase in resistance to aminoglycosides, cephalosporins, MLS, nucleoside, phenicol, and tetracycline antibiotics highlights the importance of monitoring and assessing ARGs in the livestock industry. Aminoglycoside antibiotics, despite playing an important role in treating bacterial diseases, have raised concerns due to the high abundance of their resistance genes potentially disrupting the healthy balance of the gut microbiome[ 66 ]. The abundance of these resistance genes may negatively impact beneficial microbial communities, increasing the proportion of resistant strains, thereby compromising intestinal health[ 67 ]. Furthermore, changes in the composition of the gut microbiota and the role of MGEs in the spread of resistance genes were significant findings in previous studies[ 50 , 68 ]. Enterobacteriaceae and Enterococcaceae may play a key role in the spread of resistance genes, highlighting the need to consider these bacterial groups in antibiotic usage and management strategies[ 51 , 61 , 69 ]. The influence of mobile elements on resistance groups is greater than that of the gut microbiota, suggesting that mobile elements primarily mediate the structure and dynamic changes of resistance groups, which is consistent with previous findings on subtropical estuarial resistome[ 70 ]. Our co-occurrence and co-abundance analysis revealed a close interaction between ARGs and MGE genes, which may be a key driver of changes in the antibiotic resistance spectrum during the broiler production cycle, suggesting that future research should focus on the molecular mechanisms behind these gene interactions and their impact on the gut microbial ecology. Conclusions In this study, we revealed microbial dynamics in broilers’ gut similar to those seen in infants and young mammals. After birth, there was a rapid depletion of maternal-source microbes, followed by the resettlement and reconstruction of new bacterial taxa. As age increased, the microbial community structure stabilized. Due to the dynamic replacement of dominant bacterial genera, different-aged microbial communities exhibited unique taxonomic and functional differences. During the rapid growth phase, the microbial community provided the significant functional potentials related to vitamin and amino acid metabolism, collectively contributing to the healthy development of muscles and bones. During the fattening phase, the microbial community was more involved in the synergistic metabolism of energy and carbohydrates to achieve rapid weight gain and fat accumulation in the broilers. Additionally, we characterized the temporal fluctuations of gut resistome throughout the production cycle, documenting the changes in the abundance of the prevalent ARGs and their associated resistance phenotypes. Furthermore, we analyzed the contributions of gut microbiota and the mobilome to the resistome, which enhanced our understanding of the resistome dynamics. Our study reveals dynamic patterns of gut microbiota alterations throughout the broiler production cycle, enriching the repository of intestinal metagenomic data across diverse stages. These insights will lead to better grasp of age-related microbial shifts and their implications in chicken health, thereby contributing to the advancement of a more sustainable and healthy broiler farming industry. Abbreviations MAG: Metagenome-assembled genome ANI: Average nucleotide identity MGE: Mobile genetic element ARG: Antibiotic resistance gene ORF: open reading frame CAZyme: Carbohydrate-active enzyme AA: Auxiliary activities GH: Glycoside hydrolases CBM: Carbohydrate-binding modules CE: Carbohydrate esterases PL: Polysaccharide lyases GT: glycosyltransferases KEGG: Kyoto Encyclopedia of Genes and Genomes TPM: Transcript per million LEfSe: Linear discriminant analysis effect size PERMANOVA: Permutational multivariate analysis of variance Declarations Ethics approval The complete procedure in this study was approved by the Committee on the Care and Use of Laboratory Animals of the State-Level Animal Experimental Teaching Demonstration Center of Qingdao Agricultural University. The animal experiments were approved by Qingdao Agriculture University Ethics Committee. Consent for publication All subjects provided informed consent to participate in this study and agreed to the publication of the research results Availability of data and materials The raw whole-metagenomic shotgun sequencing data from this study have been deposited in the China Nucleotide Sequence Archive under accession code PRJCA018199. The genome sequences of bacterial isolates are available in the SRA database under accession code PRJNA1123611. All other data supporting the findings of this study are available in the paper and supplemental materials, or from the corresponding author(s) upon request. The codes and the supporting data related to this work are available on GitHub at https://github.com/jinxmeng/24_broiler_mbiome. Competing interests The authors declare that they have no competing interests. Funding The study was supported by the distinguished Scholar Research Fund of Qingdao Agricultural University (1120044). Authors’ contributions X.X.Z. conceived and directed the project. M.H.L and W.L.Y collected the samples. J.X.M., M.H.L. W.L.Y., X.Y.W., X.M.L. and Y.Z.S. conducted culturomic experiments. J.X.M., M.H.L., S.L., Y.Z., and X.Y.W. performed the bioinformatic analyses and prepared figures and texts for the manuscript. J.X.M., M.H.L. and S.L. wrote the first draft of the manuscript. H.M.E., X.X.Z., S.L., H.B.N., H.M., R.L. and X.Y. made substantial revision to the manuscript and participated in discussions. All authors contributed to the revision of the manuscript. Acknowledgements We thank Professor Lesley Hoyles from Nottingham Trent University for their constructive suggestions. References Gacesa R, Kurilshikov A, Vich Vila A, Sinha T, Klaassen MAY, Bolte LA, Andreu-Sánchez S, Chen L, Collij V, Hu S, et al: Environmental factors shaping the gut microbiome in a Dutch population. Nature 2022, 604: 732-739. Xiao F, Zhu W, Yu Y, He Z, Wu B, Wang C, Shu L, Li X, Yin H, Wang J, et al: Host development overwhelms environmental dispersal in governing the ecological succession of zebrafish gut microbiota. npj Biofilms and Microbiomes 2021, 7: 5. Xu S, Li L, Luo X, Chen M, Tang W, Zhan L, Dai Z, Lam TT, Guan Y, Yu G: Ggtree: A serialized data object for visualization of a phylogenetic tree and annotation data. iMeta 2022, 1: e56. Huang P, Zhang Y, Xiao K, Jiang F, Wang H, Tang D, Liu D, Liu B, Liu Y, He X, et al: The chicken gut metagenome and the modulatory effects of plant-derived benzylisoquinoline alkaloids. Microbiome 2018, 6: 211. Jiang L, Zhang J, Tang J, Li M, Zhao X, Zhu H, Yu X, Li Y, Feng T, Zhang X: Analyses of Aerosol Concentrations and Bacterial Community Structures for Closed Cage Broiler Houses at Different Broiler Growth Stages in Winter. Journal of Food Protection 2018, 81: 1557-1564. Khawaja T, Khan SH, Ansari NNJIJops: Effect of different levels of blood meal on broiler performance during two phases of growth. 2007, 6: 860-865. Choi JH, Lee K, Kim DW, Kil DY, Kim GB, Cha CJ: Influence of dietary avilamycin on ileal and cecal microbiota in broiler chickens. Poultry Science 2018, 97: 970-979. Martino C, Dilmore AH, Burcham ZM, Metcalf JL, Jeste D, Knight R: Microbiota succession throughout life from the cradle to the grave. Nature Reviews Microbiology 2022, 20: 707-720. Caballero-Flores G, Pickard JM, Núñez G: Microbiota-mediated colonization resistance: mechanisms and regulation. Nature Reviews Microbiology 2023, 21: 347-360. Hillier LW, Miller W, Birney E, Warren W, Hardison RC, Ponting CP, Bork P, Burt DW, Groenen MAM, Delany ME, et al: Sequence and comparative analysis of the chicken genome provide unique perspectives on vertebrate evolution. Nature 2004, 432: 695-716. Ding J, Dai R, Yang L, He C, Xu K, Liu S, Zhao W, Xiao L, Luo L, Zhang Y, Meng H: Inheritance and Establishment of Gut Microbiota in Chickens. Frontiers in Microbiology 2017, 8 . Ge Y, Zhu W, Chen L, Li D, Li Q, Jie H: The Maternal Milk Microbiome in Mammals of Different Types and Its Potential Role in the Neonatal Gut Microbiota Composition. In Animals , vol. 11; 2021. Ferretti P, Pasolli E, Tett A, Asnicar F, Gorfer V, Fedi S, Armanini F, Truong DT, Manara S, Zolfo M, et al: Mother-to-Infant Microbial Transmission from Different Body Sites Shapes the Developing Infant Gut Microbiome. Cell Host & Microbe 2018, 24: 133-145.e135. Lee S, La T-M, Lee H-J, Choi I-S, Song C-S, Park S-Y, Lee J-B, Lee S-W: Characterization of microbial communities in the chicken oviduct and the origin of chicken embryo gut microbiota. Scientific Reports 2019, 9: 6838. Robinson K, Yang Q, Stewart S, Whitmore MA, Zhang G: Biogeography, succession, and origin of the chicken intestinal mycobiome. Microbiome 2022, 10: 55. Zhang Y, Zhang S, Yuan Y, Li Y, Zhu R, Yang Y, Xing S, Wang Y, Wu Y, Liao X, Mi J: Metagenomic assembly reveals the circadian oscillations of the microbiome and antibiotic resistance genes in a model of laying hens. Science of The Total Environment 2022, 836: 155692. Wylensek D, Hitch TCA, Riedel T, Afrizal A, Kumar N, Wortmann E, Liu T, Devendran S, Lesker TR, Hernández SB, et al: A collection of bacterial isolates from the pig intestine reveals functional and taxonomic diversity. Nature Communications 2020, 11: 6389. Quast C, Pruesse E, Yilmaz P, Gerken J, Schweer T, Yarza P, Peplies J, Glöckner FO: The SILVA ribosomal RNA gene database project: improved data processing and web-based tools. Nucleic Acids Research 2013, 41: D590-D596. Fu L, Niu B, Zhu Z, Wu S, Li W: CD-HIT: accelerated for clustering the next-generation sequencing data. Bioinformatics 2012, 28: 3150-3152. Pedron R, Esposito A, Bianconi I, Pasolli E, Tett A, Asnicar F, Cristofolini M, Segata N, Jousson O: Genomic and metagenomic insights into the microbial community of a thermal spring. Microbiome 2019, 7: 8. Chen S: Ultrafast one-pass FASTQ data preprocessing, quality control, and deduplication using fastp. iMeta 2023, 2: e107. Prjibelski A, Antipov D, Meleshko D, Lapidus A, Korobeynikov A: Using SPAdes De Novo Assembler. Current Protocols in Bioinformatics 2020, 70: e102. Langmead B, Salzberg SL: Fast gapped-read alignment with Bowtie 2. Nature Methods 2012, 9: 357-359. Li D, Liu C-M, Luo R, Sadakane K, Lam T-W: MEGAHIT: an ultra-fast single-node solution for large and complex metagenomics assembly via succinct de Bruijn graph. Bioinformatics 2015, 31: 1674-1676. Li H, Durbin R: Fast and accurate short read alignment with Burrows–Wheeler transform. Bioinformatics 2009, 25: 1754-1760. Danecek P, Bonfield JK, Liddle J, Marshall J, Ohan V, Pollard MO, Whitwham A, Keane T, McCarthy SA, Davies RM, Li H: Twelve years of SAMtools and BCFtools. GigaScience 2021, 10: giab008. Kang DD, Li F, Kirton E, Thomas A, Egan R, An H, Wang Z: MetaBAT 2: an adaptive binning algorithm for robust and efficient genome reconstruction from metagenome assemblies. PeerJ 2019, 7: e7359. Chklovski A, Parks DH, Woodcroft BJ, Tyson GW: CheckM2: a rapid, scalable and accurate tool for assessing microbial genome quality using machine learning. Nature Methods 2023, 20: 1203-1212. Parks DH, Rinke C, Chuvochina M, Chaumeil P-A, Woodcroft BJ, Evans PN, Hugenholtz P, Tyson GW: Recovery of nearly 8,000 metagenome-assembled genomes substantially expands the tree of life. Nature Microbiology 2017, 2: 1533-1542. Olm MR, Brown CT, Brooks B, Banfield JF: dRep: a tool for fast and accurate genomic comparisons that enables improved genome recovery from metagenomes through de-replication. The ISME Journal 2017, 11: 2864-2868. Chaumeil P-A, Mussig AJ, Hugenholtz P, Parks DH: GTDB-Tk: a toolkit to classify genomes with the Genome Taxonomy Database. Bioinformatics 2020, 36: 1925-1927. Parks DH, Chuvochina M, Rinke C, Mussig AJ, Chaumeil P-A, Hugenholtz P: GTDB: an ongoing census of bacterial and archaeal diversity through a phylogenetically consistent, rank normalized and complete genome-based taxonomy. Nucleic Acids Research 2022, 50: D785-D794. Letunic I, Bork P: Interactive Tree Of Life (iTOL) v5: an online tool for phylogenetic tree display and annotation. Nucleic Acids Research 2021, 49: W293-W296. Hyatt D, Chen G-L, LoCascio PF, Land ML, Larimer FW, Hauser LJ: Prodigal: prokaryotic gene recognition and translation initiation site identification. BMC Bioinformatics 2010, 11: 119. Steinegger M, Söding J: MMseqs2 enables sensitive protein sequence searching for the analysis of massive data sets. Nature Biotechnology 2017, 35: 1026-1028. Kanehisa M, Goto S: KEGG: Kyoto Encyclopedia of Genes and Genomes. Nucleic Acids Research 2000, 28: 27-30. Drula E, Garron M-L, Dogan S, Lombard V, Henrissat B, Terrapon N: The carbohydrate-active enzyme database: functions and literature. Nucleic Acids Research 2022, 50: D571-D577. Buchfink B, Xie C, Huson DH: Fast and sensitive protein alignment using DIAMOND. Nature Methods 2015, 12: 59-60. Liu C, Cui Y, Li X, Yao M: microeco: an R package for data mining in microbial community ecology. FEMS Microbiology Ecology 2021, 97: fiaa255. Zhang X-X, Lv Q-B, Yan Q-L, Zhang Y, Guo R-C, Meng J-X, Ma H, Qin S-Y, Zhu Q-H, Li C-Q, et al: A Catalog of over 5,000 Metagenome-Assembled Microbial Genomes from the Caprinae Gut Microbiota. Microbiology Spectrum 2022, 10: e02211-02222. Patil KR, Nielsen J: Uncovering transcriptional regulation of metabolism by using metabolic network topology. Proceedings of the National Academy of Sciences 2005, 102: 2685-2689. Alcock BP, Huynh W, Chalil R, Smith KW, Raphenya Amogelang R, Wlodarski MA, Edalatmand A, Petkau A, Syed SA, Tsang KK, et al: CARD 2023: expanded curation, support for machine learning, and resistome prediction at the Comprehensive Antibiotic Resistance Database. Nucleic Acids Research 2023, 51: D690-D699. Pärnänen K, Karkman A, Hultman J, Lyra C, Bengtsson-Palme J, Larsson DGJ, Rautava S, Isolauri E, Salminen S, Kumar H, et al: Maternal gut and breast milk microbiota affect infant gut antibiotic resistome and mobile genetic elements. Nature Communications 2018, 9: 3891. Kumar L, Futschik MEJB: Mfuzz: a software package for soft clustering of microarray data. 2007, 2: 5. Li M-H, Meng J-X, Wang W, He M, Zhao Z-Y, Ma N, Lv Q-B, Qin Y-F, Geng H-L, Zhao Q, et al: Dynamic description of temporal changes of gut microbiota in broilers. Poultry Science 2022, 101: 102037. Julian RJ: Rapid growth problems: ascites and skeletal deformities in broilers. Poultry Science 1998, 77: 1773-1780. Birkle I, Klink-Lehmann J, Hartmann M: Different and alike: Level and determinants of public acceptance of fattening pig, beef cattle and broiler farming in Germany. Meat Science 2022, 193: 108946. Chu DM, Ma J, Prince AL, Antony KM, Seferovic MD, Aagaard KM: Maturation of the infant microbiome community structure and function across multiple body sites and in relation to mode of delivery. Nature Medicine 2017, 23: 314-326. Dominguez-Bello MG, Costello EK, Contreras M, Magris M, Hidalgo G, Fierer N, Knight R: Delivery mode shapes the acquisition and structure of the initial microbiota across multiple body habitats in newborns. Proceedings of the National Academy of Sciences 2010, 107: 11971-11975. Liu J, Taft DH, Maldonado-Gomez MX, Johnson D, Treiber ML, Lemay DG, DePeters EJ, Mills DA: The fecal resistome of dairy cattle is associated with diet during nursing. Nature Communications 2019, 10: 4406. Zaneveld JR, McMinds R, Vega Thurber R: Stress and stability: applying the Anna Karenina principle to animal microbiomes. Nature Microbiology 2017, 2: 17121. Dini-Andreote F, Stegen JC, van Elsas JD, Salles JF: Disentangling mechanisms that mediate the balance between stochastic and deterministic processes in microbial succession. Proceedings of the National Academy of Sciences 2015, 112: E1326-E1332. Abdugheni R, Wang W-Z, Wang Y-J, Du M-X, Liu F-L, Zhou N, Jiang C-Y, Wang C-Y, Wu L, Ma J, et al: Metabolite profiling of human-originated Lachnospiraceae at the strain level. iMeta 2022, 1: e58. Debray R, Herbert RA, Jaffe AL, Crits-Christoph A, Power ME, Koskella B: Priority effects in microbiome assembly. Nature Reviews Microbiology 2022, 20: 109-121. Bâ A: Effects of thiamine deficiency on food intake and body weight increment in adult female and growing rats. Behavioural Pharmacology 2012, 23 . Li P, Wu G: Roles of dietary glycine, proline, and hydroxyproline in collagen synthesis and animal growth. Amino Acids 2018, 50: 29-38. Rizzoli R, Stevenson JC, Bauer JM, van Loon LJC, Walrand S, Kanis JA, Cooper C, Brandi M-L, Diez-Perez A, Reginster J-Y: The role of dietary protein and vitamin D in maintaining musculoskeletal health in postmenopausal women: A consensus statement from the European Society for Clinical and Economic Aspects of Osteoporosis and Osteoarthritis (ESCEO). Maturitas 2014, 79: 122-132. Mahmood T, Guo Y: Dietary fiber and chicken microbiome interaction: Where will it lead to? Animal Nutrition 2020, 6: 1-8. den Besten G, Lange K, Havinga R, van Dijk TH, Gerding A, van Eunen K, Müller M, Groen AK, Hooiveld GJ, Bakker BM, Reijngoud D-J: Gut-derived short-chain fatty acids are vividly assimilated into host carbohydrates and lipids. American Journal of Physiology-Gastrointestinal and Liver Physiology 2013, 305: G900-G910. Santangelo A, Corsello A, Spolidoro GC, Trovato CM, Agostoni C, Orsini A, Milani GP, Peroni DG: The Influence of Ketogenic Diet on Gut Microbiota: Potential Benefits, Risks and Indications. In Nutrients , vol. 15; 2023. Zhou Y, Fu H, Yang H, Wu J, Chen Z, Jiang H, Liu M, Liu Q, Huang L, Gao J, Chen C: Extensive metagenomic analysis of the porcine gut resistome to identify indicators reflecting antimicrobial resistance. Microbiome 2022, 10: 39. Li C, Li X, Guo R, Ni W, Liu K, Liu Z, Dai J, Xu Y, Abduriyim S, Wu Z, et al: Expanded catalogue of metagenome-assembled genomes reveals resistome characteristics and athletic performance-associated microbes in horse. Microbiome 2023, 11: 7. Cao J, Hu Y, Liu F, Wang Y, Bi Y, Lv N, Li J, Zhu B, Gao GF: Metagenomic analysis reveals the microbiome and resistome in migratory birds. Microbiome 2020, 8: 26. Khachatryan Artashes R, Hancock Dale D, Besser Thomas E, Call Douglas R: Role of Calf-Adapted Escherichia coli in Maintenance of Antimicrobial Drug Resistance in Dairy Calves. Applied and Environmental Microbiology 2004, 70: 752-757. Zhang Z, Zhang Q, Wang T, Xu N, Lu T, Hong W, Penuelas J, Gillings M, Wang M, Gao W, Qian H: Assessment of global health risk of antibiotic resistance genes. Nature Communications 2022, 13: 1553. Redpath A, Hallowell GD, Bowen IM: Use of aminoglycoside antibiotics in equine clinical practice; a questionnaire-based study of current use. Veterinary Medicine and Science 2021, 7: 279-288. Zhang L, Li H, Gao J, Gao J, Wei D, Qi Y: Identification of drug-resistant phenotypes and resistance genes in Enterococcus faecalis isolates from animal feces originating in Xinjiang, People’s Republic of China. Canadian Journal of Animal Science 2020, 100: 674-682. Partridge Sally R, Kwong Stephen M, Firth N, Jensen Slade O: Mobile Genetic Elements Associated with Antimicrobial Resistance. Clinical Microbiology Reviews 2018, 31: 10.1128/cmr.00088-00017. Yang J, Tong C, Xiao D, Xie L, Zhao R, Huo Z, Tang Z, Hao J, Zeng Z, Xiong W: Metagenomic Insights into Chicken Gut Antibiotic Resistomes and Microbiomes. Microbiology Spectrum 2022, 10: e01907-01921. Zhou L, Xu P, Gong J, Huang S, Chen W, Fu B, Zhao Z, Huang X: Metagenomic profiles of the resistome in subtropical estuaries: Co-occurrence patterns, indicative genes, and driving factors. Science of The Total Environment 2022, 810: 152263. Additional Declarations No competing interests reported. Supplementary Files Additionalfile1.xlsx FigureS1.pdf FigureS2.pdf FigureS3.pdf FigureS4.pdf FigureS5.pdf FigureS6.pdf FigureS7.pdf FigureS8.pdf FigureS9.pdf FigureS10.pdf FigureS11.pdf SupplementaryyFigureslegend.docx Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-4623220","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":321326257,"identity":"0141d506-3b67-4a4c-ae27-dbcdea09d278","order_by":0,"name":"Jin-Xin Meng","email":"","orcid":"","institution":"Qingdao Agricultural University","correspondingAuthor":false,"prefix":"","firstName":"Jin-Xin","middleName":"","lastName":"Meng","suffix":""},{"id":321326260,"identity":"69f2a2ce-8120-43c1-be49-3cb8ef73ae35","order_by":1,"name":"Ming-Han Li","email":"","orcid":"","institution":"Jilin Agricultural University","correspondingAuthor":false,"prefix":"","firstName":"Ming-Han","middleName":"","lastName":"Li","suffix":""},{"id":321326262,"identity":"2a884c2e-0452-45e2-a6f7-f5a01eaf7eed","order_by":2,"name":"Hany M Elsheikha","email":"","orcid":"","institution":"University of Nottingham","correspondingAuthor":false,"prefix":"","firstName":"Hany","middleName":"M","lastName":"Elsheikha","suffix":""},{"id":321326264,"identity":"c8bf95ff-25bb-4d62-8fc2-157f77e618f8","order_by":3,"name":"Xiao-Man Li","email":"","orcid":"","institution":"Qingdao Agricultural University","correspondingAuthor":false,"prefix":"","firstName":"Xiao-Man","middleName":"","lastName":"Li","suffix":""},{"id":321326270,"identity":"43ba5061-43f0-4053-bca9-d0e20776e505","order_by":4,"name":"Xiang-Yu Wang","email":"","orcid":"","institution":"Qingdao Agricultural University","correspondingAuthor":false,"prefix":"","firstName":"Xiang-Yu","middleName":"","lastName":"Wang","suffix":""},{"id":321326275,"identity":"a85bbcc8-6725-4819-9d9b-306c882a29ff","order_by":5,"name":"Wei-Lan Yan","email":"","orcid":"","institution":"Qingdao Agricultural University","correspondingAuthor":false,"prefix":"","firstName":"Wei-Lan","middleName":"","lastName":"Yan","suffix":""},{"id":321326277,"identity":"a319aae3-7367-4e10-a825-0b2b64232ce3","order_by":6,"name":"Shenghui Li","email":"","orcid":"","institution":"Puensum Genetech Institute","correspondingAuthor":false,"prefix":"","firstName":"Shenghui","middleName":"","lastName":"Li","suffix":""},{"id":321326278,"identity":"4411ae9c-d5e5-4dbc-a459-20cd85939dc2","order_by":7,"name":"Yue Zhang","email":"","orcid":"","institution":"Puensum Genetech Institute","correspondingAuthor":false,"prefix":"","firstName":"Yue","middleName":"","lastName":"Zhang","suffix":""},{"id":321326279,"identity":"6f8c8c71-a485-4c16-bda5-43762fa78a24","order_by":8,"name":"Hong-Bo Ni","email":"","orcid":"","institution":"Qingdao Agricultural University","correspondingAuthor":false,"prefix":"","firstName":"Hong-Bo","middleName":"","lastName":"Ni","suffix":""},{"id":321326280,"identity":"270b4c0d-23f8-4e0c-a77e-76028e21a355","order_by":9,"name":"He Ma","email":"","orcid":"","institution":"Qingdao Agricultural University","correspondingAuthor":false,"prefix":"","firstName":"He","middleName":"","lastName":"Ma","suffix":""},{"id":321326281,"identity":"dba95166-59fd-4001-ab14-fa5c3e962f0b","order_by":10,"name":"Rui Liu","email":"","orcid":"","institution":"Qingdao Agricultural University","correspondingAuthor":false,"prefix":"","firstName":"Rui","middleName":"","lastName":"Liu","suffix":""},{"id":321326282,"identity":"2099c7c5-98f4-4efa-be30-cd1ade504972","order_by":11,"name":"Yu-Zhe Sun","email":"","orcid":"","institution":"Qingdao Agricultural University","correspondingAuthor":false,"prefix":"","firstName":"Yu-Zhe","middleName":"","lastName":"Sun","suffix":""},{"id":321326283,"identity":"ff303718-8e38-4479-bb04-96f7a485143c","order_by":12,"name":"Xing Yang","email":"","orcid":"","institution":"Dali University","correspondingAuthor":false,"prefix":"","firstName":"Xing","middleName":"","lastName":"Yang","suffix":""},{"id":321326284,"identity":"53fc7b6e-24e0-40fc-858e-f64f41c689bc","order_by":13,"name":"Xiao-Xuan Zhang","email":"data:image/png;base64,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","orcid":"","institution":"Qingdao Agricultural University","correspondingAuthor":true,"prefix":"","firstName":"Xiao-Xuan","middleName":"","lastName":"Zhang","suffix":""}],"badges":[],"createdAt":"2024-06-22 21:38:17","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4623220/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4623220/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":60424217,"identity":"146aad9f-5410-4b24-8833-627802ab8f72","added_by":"auto","created_at":"2024-07-16 15:18:43","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":1016175,"visible":true,"origin":"","legend":"\u003cp\u003eThe phylogenetic relationship among the 694 bacterial genomes. The color coding of each clade corresponds to the phylum-level classification of the genomes. Representative species are indicated by the white dot at the terminal ends of the branches. The first outer ring denotes the genome source, distinguishing between isolates and metagenome-assembled genomes. The second outer ring indicates whether the taxonomy of the genome has been determined by GTDB. The third outer ring shows the classification of genomes at the family level. The fourth and fifth rings are bar charts representing the GC content and the genome size of each genome, respectively. The Venn diagram illustrates the contribution of the culture-dependent and culture-independent to the identification of genomospecies.\u003c/p\u003e","description":"","filename":"Figure1.png","url":"https://assets-eu.researchsquare.com/files/rs-4623220/v1/1a59d47c548e760ab9f29381.png"},{"id":60424916,"identity":"53400ca7-db03-4855-a4bd-50ea227482e7","added_by":"auto","created_at":"2024-07-16 15:26:43","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":985175,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003e(a-b)\u003c/strong\u003eStacked bar plots display community composition of the gut microbiota at both the phylum and family level. \u003cstrong\u003e(c)\u003c/strong\u003e Rarefaction curve analysis shows the accumulation richness as a function of the increasing sample size. \u003cstrong\u003e(d-e)\u003c/strong\u003eBoxplots depict the species Richness and Shannon indices across all groups, respectively. The levels of significance were determined using Wilcoxon rank-sum test: *, \u003cem\u003ep\u003c/em\u003e\u0026lt;0.05; **, \u003cem\u003ep\u003c/em\u003e\u0026lt;0.01, ***, \u003cem\u003ep\u003c/em\u003e\u0026lt;0.001. The purple, orange, and cyan lines represent the best-fit lines obtained through linear regression based on observations throughout the entire life cycle, the first five days after hatching, and from days 7 to 42, respectively. The gray background indicates a 95% confidence interval for each regression line. \u003cstrong\u003e(f)\u003c/strong\u003e The scatter plot exhibits β-diversity, representing the compositional variation of gut microbiome across all groups. Samples are plotted along the first and second principal coordinates (PCoA1 and PCoA2), with the associated explained variance ratio for these coordinates. The center point within each group represents the mean coordinate value of samples belonging to that specific group. Ellipsoids represent 80% confidence interval surrounding each group. PERMANOVA results reveal the overall effect size of different laying phases. P values were calculated using the adonis test with 1,000 permutations in R. \u003cstrong\u003e(g)\u003c/strong\u003e Line plots depict 12 distinct patterns of temporal fluctuations in the bacterial species abundance. The x-axis represents nine different time points from 1 to 42, and the y-axis represents the scaled relative abundance of the microbial species.\u003c/p\u003e","description":"","filename":"Figure2.png","url":"https://assets-eu.researchsquare.com/files/rs-4623220/v1/b463f949cacbae0d3803f5fe.png"},{"id":60424215,"identity":"872b76fc-5405-41b4-8d91-9c07f558a167","added_by":"auto","created_at":"2024-07-16 15:18:43","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":4493,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003e(a-b)\u003c/strong\u003e Boxplots show the KOs Richness and Shannon indices across all groups, respectively. \u003cstrong\u003e(c)\u003c/strong\u003e The scatter plot exhibits β-diversity of microbial functions across all groups based on the abundance of KO profile. \u003cstrong\u003e(d)\u003c/strong\u003e The results of the PERMANOVA analysis for microbial functions on the each pairwise groups are presented. Darker points indicate smaller \u003cem\u003ep\u003c/em\u003e values, while larger points correspond to larger R\u003csup\u003e2\u003c/sup\u003e values. \u003cstrong\u003e(e)\u003c/strong\u003e The boxplots display the temporal fluctuations of different functional categories. The purple, orange, and cyan lines represent the best-fit lines obtained through linear regression based on observations during the different phases. The gray background indicates 95% confidence interval for each regression line. \u003cstrong\u003e(f)\u003c/strong\u003e The heatmap illustrates the abundance (log10-transformed TPM) of each CAZyme across samples. The bar plot above the heatmap presents the LDA scores for each CAZyme, identifying those with significant differences. \u003cstrong\u003e(g)\u003c/strong\u003e The bar plot enumerates the CAZyme enriched across different groups. Concurrently, the pie chart details the proportion of various substrates degraded by these enriched CAZymes as observed on day 1. For a detailed description of the plot's features and analytical methodology, see the legend of Figure 1.\u003c/p\u003e","description":"","filename":"Figure3.png","url":"https://assets-eu.researchsquare.com/files/rs-4623220/v1/7be380e2539de6321b02d615.png"},{"id":60424218,"identity":"12277dfa-1e3f-4eb7-a9c8-8353821c4ae0","added_by":"auto","created_at":"2024-07-16 15:18:43","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":552957,"visible":true,"origin":"","legend":"\u003cp\u003eMetabolic modules associated with amino acid metabolism were enriched during the rapid growth phase, while several carbohydrate-related modules showed enrichment during the flattening phase. The cells on the left indicate gene abundances during the rapid growth phase, while those on the right correspond to the fattening phase. A deeper color intensity signifies greater gene abundance. Notably, the gene abundance levels exhibit statistically significant differences between the two phases, as determined by Wilcoxon rank-sum testing (\u003cem\u003ep\u003c/em\u003e \u0026lt; 0.05). Further details on these enriched modules can be found in Additional file 1: Table S8.\u003c/p\u003e","description":"","filename":"Figure4.png","url":"https://assets-eu.researchsquare.com/files/rs-4623220/v1/08e10543b4706e07adf228fd.png"},{"id":60424917,"identity":"384956a8-0122-4b32-8bbc-be52cfcb3dc7","added_by":"auto","created_at":"2024-07-16 15:26:43","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":1179900,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003e(a) \u003c/strong\u003eSankey diagram illustrates the associations between taxa across different taxonomic levels (phylum, family, genus, and species) and type of ARGs, which are categorized by drug class and resistance mechanism. \u003cstrong\u003e(b)\u003c/strong\u003e The genome circle map for\u003cem\u003e Escherichia coli \u003c/em\u003eHC271. The outer rings are delineated by red and green stripes, representing ARGs and MGEs, respectively. The insets provide detailed views of two genetic clusters where ARGs and MGEs are interspersed. \u003cstrong\u003e(c)\u003c/strong\u003e The heatmap displays log10-transformed average abundance of ARGs that have a prevalence exceeding 20%. A plus or minus sign in each cell indicates whether the abundance is higher or lower on a certain day compared to the previous day. The asterisk denotes a significant difference in abundance comparison. \u003cstrong\u003e(d)\u003c/strong\u003e Temporal fluctuations in predicted antibiotic resistance phenotypes are presented throughout the entire lifecycle, with the exception of carbapenems, which were not detected during the first three days. The lines represent the best-fit lines obtained through linear regression. The three lines of text in the figure represent the coefficients of the linear regression analysis for the phases from day 1 to 5, day 7 to 42, and the entire phase, respectively. For additional details on the plot's characteristics, refer to Figure 1.\u003c/p\u003e","description":"","filename":"Figure5.png","url":"https://assets-eu.researchsquare.com/files/rs-4623220/v1/b5e2c74db00fc9333e341ce8.png"},{"id":60424223,"identity":"4e74eca9-ca69-45dd-be6c-b437ce8bc03d","added_by":"auto","created_at":"2024-07-16 15:18:43","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":976614,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003e(a)\u003c/strong\u003eMantel tests were used to analyze the correlations between microbial families with the resistome. \u003cstrong\u003e(b)\u003c/strong\u003e The network diagram illustrates the co-occurrence patterns of ARGs and MGEs, represented as combinations. Line thickness corresponds to the detection frequency of these combinations in the gut microbiome. Distinctly colored nodes represent different resistance phenotypes and MGE types. \u003cstrong\u003e(c)\u003c/strong\u003e The arrow diagrams delineate specific combinations of ARGs and MGEs within contigs.\u003c/p\u003e","description":"","filename":"Figure6.png","url":"https://assets-eu.researchsquare.com/files/rs-4623220/v1/e782149a115d36191b49d24e.png"},{"id":63453609,"identity":"8f293c26-e9f6-4f0f-ae33-ad5ac51d63aa","added_by":"auto","created_at":"2024-08-28 09:52:38","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":6298115,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4623220/v1/3e71e535-b56f-490a-9d4a-5c27c82b7a5f.pdf"},{"id":60424221,"identity":"cc895bb9-25f1-4f6d-a061-07db90d951ee","added_by":"auto","created_at":"2024-07-16 15:18:43","extension":"xlsx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":1016647,"visible":true,"origin":"","legend":"","description":"","filename":"Additionalfile1.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-4623220/v1/0c2664e77754e148b32ddac0.xlsx"},{"id":60424219,"identity":"696660bc-e49b-4e77-af33-b44e6032dadb","added_by":"auto","created_at":"2024-07-16 15:18:43","extension":"pdf","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":1431286,"visible":true,"origin":"","legend":"","description":"","filename":"FigureS1.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4623220/v1/5575d971014ecd333140aa9e.pdf"},{"id":60425552,"identity":"5edde528-2d6d-4476-816b-43d7638fbc3a","added_by":"auto","created_at":"2024-07-16 15:34:43","extension":"pdf","order_by":3,"title":"","display":"","copyAsset":false,"role":"supplement","size":171735,"visible":true,"origin":"","legend":"","description":"","filename":"FigureS2.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4623220/v1/d6689a592a0546af7eccf7fe.pdf"},{"id":60424231,"identity":"26158a10-e65f-40ed-b913-41bd7bfebb67","added_by":"auto","created_at":"2024-07-16 15:18:44","extension":"pdf","order_by":4,"title":"","display":"","copyAsset":false,"role":"supplement","size":6121555,"visible":true,"origin":"","legend":"","description":"","filename":"FigureS3.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4623220/v1/4e187b58d849bff9270afbf1.pdf"},{"id":60424227,"identity":"849492ce-7561-454a-94ef-7fde8aaa87b1","added_by":"auto","created_at":"2024-07-16 15:18:44","extension":"pdf","order_by":5,"title":"","display":"","copyAsset":false,"role":"supplement","size":5465798,"visible":true,"origin":"","legend":"","description":"","filename":"FigureS4.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4623220/v1/61ab2b89ac9decd08cd03d48.pdf"},{"id":60424229,"identity":"f4657929-d601-42f6-9edb-de3a1a9790e8","added_by":"auto","created_at":"2024-07-16 15:18:44","extension":"pdf","order_by":6,"title":"","display":"","copyAsset":false,"role":"supplement","size":5647047,"visible":true,"origin":"","legend":"","description":"","filename":"FigureS5.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4623220/v1/bc163c36b36edf2fa3ede106.pdf"},{"id":60424920,"identity":"6ea9060f-7ffe-4dd4-9526-64e3581a34bb","added_by":"auto","created_at":"2024-07-16 15:26:44","extension":"pdf","order_by":7,"title":"","display":"","copyAsset":false,"role":"supplement","size":5370920,"visible":true,"origin":"","legend":"","description":"","filename":"FigureS6.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4623220/v1/da5572c08db811c3068651ba.pdf"},{"id":60424919,"identity":"12b002e4-ed1e-4a55-9f91-ee34e91108f0","added_by":"auto","created_at":"2024-07-16 15:26:44","extension":"pdf","order_by":8,"title":"","display":"","copyAsset":false,"role":"supplement","size":5941480,"visible":true,"origin":"","legend":"","description":"","filename":"FigureS7.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4623220/v1/4d0f8a5729bdc8ebd6cc11de.pdf"},{"id":60424226,"identity":"462fa4e9-6c93-4ba3-886d-76b4e4a19f19","added_by":"auto","created_at":"2024-07-16 15:18:44","extension":"pdf","order_by":9,"title":"","display":"","copyAsset":false,"role":"supplement","size":5036015,"visible":true,"origin":"","legend":"","description":"","filename":"FigureS8.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4623220/v1/54b5877b43b517f10fbb613a.pdf"},{"id":60424232,"identity":"71c275ff-98e6-4135-8d44-63a2e36509cc","added_by":"auto","created_at":"2024-07-16 15:18:44","extension":"pdf","order_by":10,"title":"","display":"","copyAsset":false,"role":"supplement","size":5698079,"visible":true,"origin":"","legend":"","description":"","filename":"FigureS9.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4623220/v1/5b84851859993d5c071eed97.pdf"},{"id":60424225,"identity":"9463cc40-571c-459a-b59c-45984b0ce948","added_by":"auto","created_at":"2024-07-16 15:18:44","extension":"pdf","order_by":11,"title":"","display":"","copyAsset":false,"role":"supplement","size":749589,"visible":true,"origin":"","legend":"","description":"","filename":"FigureS10.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4623220/v1/bca10bf71476ebc9ebc6b15a.pdf"},{"id":60424230,"identity":"d04c3ccd-2ddc-4b7b-b142-a18fb3b6b40e","added_by":"auto","created_at":"2024-07-16 15:18:44","extension":"pdf","order_by":12,"title":"","display":"","copyAsset":false,"role":"supplement","size":713576,"visible":true,"origin":"","legend":"","description":"","filename":"FigureS11.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4623220/v1/b7fba08d2221946afda4cdf8.pdf"},{"id":60424224,"identity":"bf1c61c1-2570-4d46-ae1b-b7252760e8b7","added_by":"auto","created_at":"2024-07-16 15:18:43","extension":"docx","order_by":13,"title":"","display":"","copyAsset":false,"role":"supplement","size":16083,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryyFigureslegend.docx","url":"https://assets-eu.researchsquare.com/files/rs-4623220/v1/871fabfdea6568989cb833e7.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Temporal patterns in gut microbiome and resistome of broilers: diversity and function analysis","fulltext":[{"header":"Introduction","content":"\u003cp\u003eThe development of gut microbiota is intricately regulated by a complex interplay between host and environmental factors. This interplay profoundly influences the maturation and functions of the host's immune system, which in turn impacts the health and outcome of disease[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. Broilers, as a major global source of animal protein, fulfil the rising human demand for protein due to their efficient growth rate and relatively short breeding cycle [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. The rapid growth during the chick stage is crucial for subsequent productivity[\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. The colonization of microorganisms in early life is therefore essential for optimizing feed conversion efficiency[\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e], developing the immune system[\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e], gut physiology and enhancing resistance to infection[\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. The changes that occur in the structure and function of the gut microbiota during the entire production cycle of broilers remains unknown. Therefore, a comprehensive understanding of the dynamic changes that occur in gut microbiota over time may reveal potential connections between the intestinal metabolism, physiological functions, and disease risks.\u003c/p\u003e \u003cp\u003eChickens and mammals undergo distinct embryonic development processes. In mammals, embryonic development typically occurs within the maternal body, whereas avian embryos develop within eggs[\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. The egg yolk contains nutrients necessary for the embryo\u0026rsquo;s development, eliminating the need for reliance on the microbial presence. This leads to a relatively delayed development of the gut microbiota in birds[\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. Previous studies revealed a considerable diversity and richness in the gut microbiota of broilers immediately after birth, followed a marked decline and subsequent reestablishment during the production cycle when exposed to the external environment. This phenomenon mirrors the patterns observed in human infants and some juvenile mammals, suggesting that the composition of gut microbiota is subject to significant age-related dynamic changes after birth[\u003cspan additionalcitationids=\"CR13\" citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. In chicken gut microbiota studies, initial fungal colonization is attributed to the hatchery environment, and these communities become swiftly supplanted by feed-derived fungi within three days[\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. Additionally, the microbial composition and antibiotic resistance genes (ARGs) in laying hens fluctuate diurnally, influenced by key species, mobile genetic elements (MGEs), and metabolic products[\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. Further research into the chicken oviduct and the produced eggs reveals a positive correlation between the hen\u0026rsquo;s age and increased bacterial genus diversity, which significantly contributes to the embryonic cecal bacterial community[\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. In spite of the temporal fluctuations of gut microbiota in broilers production cycle we had been characterized ever using amplicon-based metagenomic method, the absence of genome-based species-resolved fine depiction hinders the understanding that effects of both symbiotic bacterial taxa and their associated functions on birds production cycle and growth performance.\u003c/p\u003e \u003cp\u003eIn study, we analyze fecal samples from 105 Ross 308 broilers at nine distinct developmental stages within the broiler production cycle. Our work elucidates the structure and function of the broiler gut microbiota across the entire production cycle, and reveal its relationship with the resistome. The metagenomic sequencing coupled with traditional culture-based methods were used to identify age-specific bacterial taxa patterns in the broiler intestine. Our study provides new insight into the shifts that occur within the broiler gut microbiota across various the developmental stages and uncovers distinctive patterns in microbial composition, metabolic activity, and antibiotic resistance gene profiles, especially during the first week post-hatching. The data offer valuable insights into the optimization of the microbial communities, which could significantly improve husbandry practices and production efficiency of broilers.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eCollection of samples\u003c/h2\u003e \u003cp\u003eThe chicken farm details are previously described[\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. In brief, Ross 308 mixed-sex broilers raised in cages since birth were selected from a commercial hatchery in Shandong, China. To ensure continuity of sampling, we designated randomly 15 Ross 308 broilers in different location in a chicken room. A total of 105 fecal samples were collected from these birds at nine different ages (1, n\u0026thinsp;=\u0026thinsp;9; 3, n\u0026thinsp;=\u0026thinsp;13; 5, n\u0026thinsp;=\u0026thinsp;11; 7, n\u0026thinsp;=\u0026thinsp;12; 14, n\u0026thinsp;=\u0026thinsp;12; 21, n\u0026thinsp;=\u0026thinsp;12; 28, n\u0026thinsp;=\u0026thinsp;12; 35, n\u0026thinsp;=\u0026thinsp;12; and 42-day-old, n\u0026thinsp;=\u0026thinsp;12) The final age of this study is only a young stage for chickens, but considering that the actual production cycle of most broilers is ~\u0026thinsp;6 weeks, we opt to analyze the samples up to the age of 6 weeks. At least 2 g of fecal samples were collected and transported to the laboratory in secure containers at 4\u0026deg;C, then stored at \u0026minus;\u0026thinsp;80\u0026deg;C until DNA extraction. For culturomic, 20 fecal samples (~\u0026thinsp;5 g) were collected from 35d and 42d chicken using AnaeroGen Sachet (Thermo Scientific, USA), and immediately transported to the laboratory in secure containers at 4\u0026deg;C for bacterial isolation. The gut microbiota in this time period was considered that more diverse and stabilized compared to the ever times. All broiler chickens were grown up in the same environment under the same breeding system, including diet and management. The farm breeder confirmed that no antibiotic treatments or growth-promoting antibiotics were administrated to the broilers. Disinfection was carried out on the chicken coops prior to the start of the production cycle, and routine disinfection on chicken farms was conducted twice a week. The farm relied on quaternary ammonium compounds and hydrogen peroxide as primary disinfectants.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003eBacterial isolation and identification\u003c/h2\u003e \u003cp\u003eThe culture medium used for the isolation are listed below: AB: Anaerobic Broth (Land Bridge Tech., Co., Ltd, Beijing, China). AN: Anaerobic Agar Base (Solarbio Sci. \u0026amp; Tech., Co., Ltd, Beijing, China); BAB: Blood Agar Base (Solarbio Sci. \u0026amp; Tech., Co., Ltd, Beijing, China). BHI: Brain Heart Infusion (Hope Bio-Tech., Co., Ltd, Qingdao, China. The same below). BS: Bifidobacterium BS Medium. CBB: Columbia Blood Agar Base.\u003c/p\u003e \u003cp\u003eGAM: Gifu Anaerobic Medium. BGAM: GAM\u0026thinsp;+\u0026thinsp;blood. LBB: Lactose Bile Broth. LBS: LBS agar. MRS: MRS Broth. RCM: Reinforced Clostridium Medium. All media were supplemented with 15g/L agar (Biosharp, Labgic Tech., Co., Ltd, Beijing, China) whenever appropriate. All media requiring the addition of blood were supplemented with 5% (v/v) defibrinated sheep blood (Solarbio Sci. \u0026amp; Tech., Co., Ltd, Beijing, China). The culture medium preparation was performed according to instructions provided by manufacturer.\u003c/p\u003e \u003cp\u003eUnder sterile conditions, the fecal samples were serially diluted (10\u003csup\u003e2\u003c/sup\u003e-10\u003csup\u003e6\u003c/sup\u003e) by phosphate buffer solution (PBS), then 100\u0026micro;L of the suspension were inoculated immediately onto the surface of each agar plate containing bacterial culture medium. The plates were incubated at 37℃ for 24-36h in an anaerobic incubator (LAI-3, Longyue Instrument Equipment Co., Ltd. Shanghai, China) containing an atmosphere of N\u003csub\u003e2\u003c/sub\u003e (89.3%), CO\u003csub\u003e2\u003c/sub\u003e (6%), and H\u003csub\u003e2\u003c/sub\u003e (4.7%)[\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e] and in a constant temperature incubator, respectively. Phenotypically distinct colonies were picked from each incubated agar plate on a fresh medium for further purification, with 2\u0026thinsp;~\u0026thinsp;3 repeats. All the isolated bacterial strains were inoculated into anaerobic liquid media in Hungate tubes and then incubated at 220 rpm, 37 ℃ for 1\u0026ndash;3 days for proliferating. All isolated bacterial strains were stored at -80℃ in 25% glycerol.\u003c/p\u003e \u003cp\u003eBacteria from overnight cultures were pelleted with centrifugation, then genomic DNA were extracted using Bacterial Genomic DNA Extraction Kit (Solarbio Sci. \u0026amp; Tech., Co., Ltd, Beijing, China). The bacterial 16S rRNA genes were amplified using universal primers 27F (5\u0026rsquo;- AGAGTTTGATCCTGGCTCAG\u0026rsquo;) and 1492R (5\u0026rsquo;- TACGGCTACCTTGTACGACTT-3\u0026rsquo;). After examination of PCR productions using 1% agarose gel electrophoresis, Sanger sequencing was employed to obtain the nearly full-length 16S rRNA gene sequences. Finally, the sequences were aligned against SILVA rRNA database[\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e] (release 138) using BLAST (v2.13.0) to determine the taxonomy of the bacterial strains.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003eGenome sequencing, processing and assembly of bacterial isolates\u003c/h2\u003e \u003cp\u003eThe 16S rRNA gene sequences were clustered using CD-HIT[\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e] (v4.6.8) with a threshold of identity\u0026thinsp;\u0026gt;\u0026thinsp;99%[\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. For each cluster, a longest sequence in a cluster were chosen as the representation for whole-genome shotgun sequencing. Following the DNA library preparation and addition of index codes, the DNA was fragmented through sonication to achieve a size of approximately 350 bp. The qualified libraries were pooled and subjected to sequence on NovaSeq 6000 platforms with PE150 strategy (Novogene Tech., Co., Ltd, Beijing, China). The unqualified reads were filtered using FASTP[\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e] (v0.23.2) program with the default parameters. The high-quality reads were then subjected to \u003cem\u003ede novo\u003c/em\u003e assembly using SPAdes[\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e] (v3.13.0) with the parameter \u0026ldquo;--careful\u0026rdquo;. The subsequent analysis of the isolate genomes followed a methodology similar to that of metagenome-assembled genomes (MAGs).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003eDNA extraction, metagenome sequencing, and bioinformatic analysis\u003c/h2\u003e \u003cp\u003eGenomic DNA was extracted for each fecal samples DNA extraction kits (TIANamp Stool DNA Kit, TIANGEN, China) following the manufacturer\u0026rsquo;s instructions. The examination of DNA purity and concentration was conducted using 1% agarose gel electrophoresis. After DNA library preparation and addition of index codes, the DNA was fragmented using sonication to achieve a size of approximately 350 bp. The libraries were pooled and sequenced on BGISEQ-500 platform with PE150 strategy (Novogene Tech., Co., Ltd, Beijing, China). A total of 1.28 terabase pairs (Tbp) of raw reads were generated and used for subsequent analysis. The raw reads were filtered to remove unqualified reads using FASTP (v0.23.2) with options \u0026ldquo;-q 20 -u 30 -l 80 -y\u0026rdquo;. The reads that aligned with the host genomic sequence (NCBI RefSeq assembly: GCF_016699485.2) were removed using bowtie2[\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e] (v2.4.4) with default parameters. The high-quality reads were assembled for each sample by MEGAHIT[\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e] (v1.2.9). Reads were mapped to contigs with a length\u0026thinsp;\u0026gt;\u0026thinsp;2,000 bp using the BWA MEM program[\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e] (v0.7.17-r1188). SAMtools[\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e] (v1.18) program was used for converting SAM files to BAM files and sorting the aligned results. The \u003cem\u003ejgi_summarize_BAM_contig_depth\u003c/em\u003e script was used to generate the files containing the sequencing depths of contigs. We used MetaBAT2[\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e] with options \u0026ldquo;-m 2000 -s 200000 --seed 2023\u0026rdquo; for binning. The quality of metagenomic bins and isolate genomes were evaluated using CheckM2[\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e] (v1.0.1). Only bins with completeness\u0026thinsp;\u0026ge;\u0026thinsp;70%, contamination\u0026thinsp;\u0026le;\u0026thinsp;5% and quality score[\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e] (defined as completeness-5\u0026times;contamination)\u0026thinsp;\u0026gt;\u0026thinsp;55 were selected. The isolate\u0026rsquo;s genomes that met this criterion were used for subsequent analysis together.\u003c/p\u003e \u003cp\u003eThe dRep[\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e] (v3.4.2) program was used to eliminate genome redundancy for both raw MAGs and the isolate genomes with the options \u0026ldquo;-pa 0.9 -sa 0.99 -nc 0.30 -cm larger --S_algorithm fastANI\u0026rdquo;, resulting in 694 genome-based strains. Taxonomy assignment and the construction of phylogenetic trees were performed using the standard workflow in GTDB-Tk[\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e] (v2.3.2) and the GTDB[\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e] database (release 214), based on 120 marker genes. The phylogenetic tree was annotated and visualized using iTOL (v6.0)[\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]. The genomes were re-clustered using dRep with a threshold of ANI (average nucleotide identity)\u0026thinsp;\u0026gt;\u0026thinsp;95%, resulting in 349 genomospecies.\u003c/p\u003e \u003cp\u003eWe profiled the relative abundance of genomospecies using bowtie2 (v2.4.4) and CoverM (v 0.6.1, Woodcroft et al., unpublished, \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://github.com/wwood/CoverM\u003c/span\u003e\u003cspan address=\"https://github.com/wwood/CoverM\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) with the default parameters. On average, 65.93% (\u0026plusmn;\u0026thinsp;14.68%) of the reads from the fecal samples could be assigned as bacterial sequences (Additional files 1: Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e). The relative abundances for higher taxonomic levels were determined by aggregating the abundances of their daughter clades.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003eGene prediction and functional annotation\u003c/h2\u003e \u003cp\u003eProdigal[\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e] (v2.6.3) was used to predict the open reading frames (ORFs) of the metagenome contigs with the parameter \u0026ldquo;-p meta\u0026rdquo;. The parameter '-p single' was employed for predicting ORFs in the isolated genomes. ORFs with lengths\u0026thinsp;\u0026lt;\u0026thinsp;100bp and incomplete genes were discarded, and the others were clustered using MMseqs2[\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e] (v14-7e284) easy-cluster workflows with following parameter settings: \u0026ldquo;--cluster-mode 2 --min-seq-id 0.9 --cov-mode 1 -c 0.9 --kmer-per-seq-scale 0.8\u0026rdquo;, resulting in a nonredundant microbial gene catalogue comprising 2.16\u0026nbsp;million genes. Entries in the microbial gene catalogue were subjected to taxonomic assignment using blastn (v 2.13.0) searches against the NCBI-NT (v5.0, September 2023, prokaryote and viruses). The protein-coding genes were subjected to functional assignment by comparing them against the Kyoto Encyclopedia of Genes and Genomes[\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e] (KEGG, release 106.0) and carbohydrate-active enzymes[\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e] (CAZymes, August 2022) databases using DIAMOND[\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e] (v2.1.8.162) with the parameters of \u0026ldquo;--min-score 60 --query-cover 50 \u0026rdquo;. The hit with the highest bit score was selected as the representative alignment for the taxonomic and functional assignment of the ORFs.\u003c/p\u003e \u003cp\u003eGene abundance in each sample was profiled by mapping the high-quality reads (20\u0026nbsp;million reads) against the non-redundant gene catalogue using Bowtie2 (v2.4.4). Subsequently, read counts in each sample were transformed to transcript per million (TPM). The abundances of KEGG orthologous groups (KOs) and CAZymes were calculated based on the abundances of genes assigning to them. Linear discriminant analysis (LDA) effect size (LEfSe) analysis was used to identify the key characteristics of CAZymes in the gut microbiomes across all groups using \u0026lsquo;microeco\u0026rsquo;[\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e] (v1.1.1) package in R. Q values were used for multiple testing correction and generated by the Benjamini‒Hochberg method. LDA scores\u0026thinsp;\u0026gt;\u0026thinsp;2.0 and \u003cem\u003eq\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05 were considered statistically significant. Using the database of CAZyme subfamilies for substrate annotations (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://bcb.unl.edu/dbCAN_sub/\u003c/span\u003e\u003cspan address=\"https://bcb.unl.edu/dbCAN_sub/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e), we determined the preferred substrates of the CAZymes. The differential enrichment KEGG modules were identified according to their adjusted reporter score[\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e, \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eARG and MGE prediction and profiling\u003c/h2\u003e \u003cp\u003eThe putative amino acid sequences of the ORFs were aligned with Comprehensive Antibiotic Resistance Database[\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e] (CARD, v3.2.6) using DIAMOND, with a coverage\u0026thinsp;\u0026gt;\u0026thinsp;75% and identity\u0026thinsp;\u0026gt;\u0026thinsp;80%. The predicted genes were identified as MGE-like genes by searching ORFs against the custom MGE database created by Parnanen, et al[\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e], using blastn program (v2.13.0+) with an evalue\u0026thinsp;\u0026le;\u0026thinsp;10\u003csup\u003e\u0026minus;\u0026thinsp;5\u003c/sup\u003e, coverage\u0026thinsp;\u0026ge;\u0026thinsp;80% and identity\u0026thinsp;\u0026ge;\u0026thinsp;70%. The hit with the highest bit score was selected as the representative alignment for the assignment of ARG and MGE ORFs. Likewise, the same procedure was conducted for each genome to explore the distribution of ARGs and MGEs on themselves. GCView services (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://proksee.ca/\u003c/span\u003e\u003cspan address=\"https://proksee.ca/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) was used to visualize genome and to mark targeted genes. The abundances of ARGs and MGEs were calculated based on the abundances of genes assigning to them. For evaluating the prevalence of ARG and MGE, a threshold of TPM\u0026thinsp;\u0026gt;\u0026thinsp;10 was used to determine whether a ARG or MGE was present in a sample.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003eRarefaction curve and diversity analysis\u003c/h2\u003e \u003cp\u003eRarefaction curves were generated using R package \u0026lsquo;vegan\u0026rsquo; (v2.5-7). The Shannon and Richness indices were calculated using the abundance profiles of the taxonomic and functional features. To assess the β-diversity, Principal Coordinate Analysis (PCoA) was perfromed based on the Bray-Curtis distance, and the significance of group differences was determined using permutational multivariate analysis of variance (PERMANOVA). The Wilcoxon rank-sum test was performed to evaluate the significant difference in the diversity indices and abundance of taxa and functional feature between pairwise groups. Linear regression analysis was employed to determine the optimal trend line that represents the variation in the diversity indices, taxonomic abundance, and functional features across various groups.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003eCorrelation analysis\u003c/h2\u003e \u003cp\u003eProcrustes association analysis was performed using the 'procrustes' function in the 'vegan' package. Mantel tests were performed using the 'mantel_test' function in the 'LinkET' (v0.7.4) R package, available at \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://github.com/Hy4m/linkET\u003c/span\u003e\u003cspan address=\"https://github.com/Hy4m/linkET\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. Spearman's correlation analysis was carried out to assess the relationships among diversity indices of the gut microbiota, mobilomes, and resistomes, as well as the co-abundance of ARGs and MGEs.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eStatistical analysis and visualization\u003c/h2\u003e \u003cp\u003eStatistical analyses were carried out in an R 4.2.1 environment. The \u0026lsquo;mfuzz\u0026rsquo; function in the \u0026lsquo;mfuzz\u0026rsquo;[\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e] R package (v2.60) was utilized for soft clustering of the bacterial abundance changes across nine time points. All heatmaps were visualized using the \u0026lsquo;ComplexHeatmap\u0026rsquo; (v2.8.0) R package. Sankey plot was constructed using the \u0026lsquo;ggsankey\u0026rsquo; package (v0.0.9, \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://github.com/davidsjoberg/ggsankey\u003c/span\u003e\u003cspan address=\"https://github.com/davidsjoberg/ggsankey\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). Gene arrow maps were constructed using the \u0026lsquo;gggenes\u0026rsquo; (v0.4.1, \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://github.com/wilkox/gggenes\u003c/span\u003e\u003cspan address=\"https://github.com/wilkox/gggenes\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) package. Network graphs were visualized using the R package \u0026lsquo;ggraph\u0026rsquo; (v2.1.0, \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://github.com/thomasp85/ggraph\u003c/span\u003e\u003cspan address=\"https://github.com/thomasp85/ggraph\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). All other visualizations were produced using the ggplot2 package (v3.3.6).\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eCollection of genomes and genes related to the production cycle of broilers\u003c/h2\u003e \u003cp\u003eTo track the temporal fluctuations in microbial composition, we employed a combination of culture-based and metagenomic sequencing methods to obtain a genome collection and a gene catalogue of the broiler gut microbiota during the production cycle. A total of 899 isolates were obtained using culture-based methods and identified by using Sanger sequencing, and morphological images of some isolates were shown in Figure \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e. These isolates belonged to five phyla: \u003cem\u003eBacillota\u003c/em\u003e (also called \u003cem\u003eFirmicutes\u003c/em\u003e, n\u0026thinsp;=\u0026thinsp;704, 78.3%), \u003cem\u003ePseudomonadota\u003c/em\u003e (also called \u003cem\u003eProteobacteria\u003c/em\u003e, n\u0026thinsp;=\u0026thinsp;134, 14.9%), \u003cem\u003eBacteroidota\u003c/em\u003e (n\u0026thinsp;=\u0026thinsp;48, 5.3%), \u003cem\u003eActinomycetota\u003c/em\u003e (also called \u003cem\u003eActinobacteriota\u003c/em\u003e, n\u0026thinsp;=\u0026thinsp;11, 1.2%), and \u003cem\u003eFusobacteriota\u003c/em\u003e (n\u0026thinsp;=\u0026thinsp;2, 0.2%, Fig. \u003cspan refid=\"MOESM2\" class=\"InternalRef\"\u003eS2\u003c/span\u003ea, Additional files 1: Table \u003cspan refid=\"MOESM2\" class=\"InternalRef\"\u003eS2\u003c/span\u003e). \u003cem\u003eBacillaceae\u003c/em\u003e (n\u0026thinsp;=\u0026thinsp;304, 33.8%), \u003cem\u003eLactobacillaceae\u003c/em\u003e (n\u0026thinsp;=\u0026thinsp;258. 28.7%), \u003cem\u003eEnterobacteriaceae\u003c/em\u003e (n\u0026thinsp;=\u0026thinsp;134, 14.9%) and \u003cem\u003eEnterococcaceae\u003c/em\u003e (n\u0026thinsp;=\u0026thinsp;100, 11.1%) were dominated families, accounting for nearly 88.5% of total isolates (Fig. \u003cspan refid=\"MOESM2\" class=\"InternalRef\"\u003eS2\u003c/span\u003eb). A total of 98 representative isolates were selected for the whole-genome sequencing and \u003cem\u003ede novo\u003c/em\u003e assembly. After excluding 16 genomes with high contamination (\u0026lt;\u0026thinsp;10%), the remaining 82 genomes, together with the original MAGs, underwent unified analysis.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eUsing metagenomic sequencing, assembly, and binning, a total of 4,282 original MAGs were generated. Following quality assessment and redundancy removal at the 99% ANI level, a total of 694 genomes meeting or exceeding quality standards were generated and included in subsequent analysis (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e1\u003c/span\u003e, Additional files 1: Table \u003cspan refid=\"MOESM3\" class=\"InternalRef\"\u003eS3\u003c/span\u003e). These genomes had a genome size ranging from 0.66 to 5.98 Mbp (average 2.39 Mbp) and an GC content ranging from 24 to 72.1% (average 47.23%). The mean completeness of the genomes was 87.58%, and the mean contamination was 1.33%. Out of these, a remarkable 299 genomes (43.1%) met the high-quality standard, exhibiting completeness levels of 90% or higher and contamination levels below 5%. Additionally, 349 genomospecies were further determined by clustering with ANI threshold of 95% and a coverage fraction threshold of 30%. The culture-dependent and culture-independent methods contributed 42 and 326 genomospecies, respectively, with 19 genomospecies detected by both two methods (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e1\u003c/span\u003e). The taxonomic classification revealed that all genomes were classified into bacterial lineages, spanning cross 7 phyla, 63 families, and 189 genera. Among these, the phyla \u003cem\u003eBacillota\u003c/em\u003e (n\u0026thinsp;=\u0026thinsp;556, 83.1%), \u003cem\u003eBacteroidota\u003c/em\u003e (n\u0026thinsp;=\u0026thinsp;55, 7.9%), \u003cem\u003eActinomycetota\u003c/em\u003e (n\u0026thinsp;=\u0026thinsp;42, 6.1%) and \u003cem\u003ePseudomonadota\u003c/em\u003e (n\u0026thinsp;=\u0026thinsp;27, 3.9%) were dominant in the intestinal tract of commercial broilers. It is worth mentioning that 12 assemblies and 1 isolate could not be classified to any known species using the latest reference genome databases (Additional files 1: Table \u003cspan refid=\"MOESM3\" class=\"InternalRef\"\u003eS3\u003c/span\u003e), suggesting that the presence of potentially novel species. A non-redundant microbial gene catalogue was generated containing 2.16\u0026nbsp;million genes with an average length of 789.8 bp, all of which possessed complete ORFs. The genome collection and the gene catalogue serve as crucial tool for studying the taxonomic and functional profiles of broiler gut microbiota.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eDisappearance and reconstruction of microbial community in the broiler gut microbiota\u003c/h2\u003e \u003cp\u003eThe intestine is a complex organ, and the microbial community is crucial for maintaining the health of poultry gut, affecting feed conversion rates and, consequently, the animal productivity. Our previous research has demonstrated the dynamic nature of gut microbiota throughout broiler production cycle[\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e], and here we further explore the details of temporal species-level fluctuations. Rarefaction curves analysis indicated that the cumulative sequencing data reached saturation, suggesting thorough coverage of the microbial genomes by the sequencing analysis (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e2\u003c/span\u003ec). Noteworthy, we observed a distinct microbial profile in the meconium of 1-day-old chicks, which exhibited a microbial structure absent at other time points-a phenomenon previously overlooked in broilers studies. The meconium is primarily comprised the phyla \u003cem\u003eBacillota\u003c/em\u003e (58.8%), \u003cem\u003eBacteroidota\u003c/em\u003e (36.7%), \u003cem\u003ePseudomonadota\u003c/em\u003e (2.3%), and \u003cem\u003eActinomycetota\u003c/em\u003e (1.5%) (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e2\u003c/span\u003ea, Additional file 1: Table \u003cspan refid=\"MOESM4\" class=\"InternalRef\"\u003eS4\u003c/span\u003e). At the family level, it was mainly comprised \u003cem\u003eLachnospiraceae\u003c/em\u003e (33.9%) and \u003cem\u003eBacteroidaceae\u003c/em\u003e (29.6%) (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e2\u003c/span\u003eb). With the intervention of biological and environmental factors, linear regression analysis showed a rapid trend of microbial species richness and diversity decrease persisting until day 5 (Richness: R\u0026sup2;=0.43, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001; Shannon: R\u0026sup2;=0.63, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001; Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e2\u003c/span\u003ed-e). After the rapid turnover of the microbial community in the first week, until the 42nd day when the broilers were ready for the market, we observed a significant increasing trend in the richness and diversity of the broiler gut microbiota (Richness: R\u0026sup2;=0.11, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.004; Shannon: R\u0026sup2;=0.06, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.035). PCoA revealed that the age of broilers significantly influenced the temporal fluctuations in the gut bacterial community (PERMANOVA, R\u0026sup2;=0.4838, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001, Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e2\u003c/span\u003eg). The black solid line depicted the apparent succession trajectory of the gut microbiota, originating from the upper right corner of the cartesian coordinate system and advancing to the lower right corner. Additionally, pairwise comparisons between groups indicated significant differences in almost all cases (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05, Fig. \u003cspan refid=\"MOESM3\" class=\"InternalRef\"\u003eS3\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eNext, we sought to identify the bacterial taxa involved in the disappearance and subsequent reconstruction of microbial community. During the first week, the microbial community underwent the most dramatic fluctuations. Accompanying this, there was a significant decrease in the relative abundance of \u003cem\u003eActinomycetota\u003c/em\u003e (R\u0026sup2;=0.16, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.021), \u003cem\u003eBacteroidota\u003c/em\u003e (R\u0026sup2;=0.66, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001), \u003cem\u003eDesulfobacterota\u003c/em\u003e (R\u0026sup2;=0.63, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001), \u003cem\u003eFusobacteriota\u003c/em\u003e (R\u0026sup2;=0.46, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001), and \u003cem\u003ePseudomonadota\u003c/em\u003e (R\u0026sup2;=0.44, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001), while the relative abundance of \u003cem\u003eBacillota\u003c/em\u003e (R\u0026sup2;=0.47, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and \u003cem\u003eCyanobacteriota\u003c/em\u003e (R\u0026sup2;=0.34, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001) significantly increased (Fig. \u003cspan refid=\"MOESM3\" class=\"InternalRef\"\u003eS3\u003c/span\u003e). The changes in the composition of this microbial community partially occurred later in the life cycle, reflected in the recovery and significant increase in the relative abundance of \u003cem\u003eBacteroidota\u003c/em\u003e (R\u0026sup2;=0.11, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.004), \u003cem\u003eDesulfobacterota\u003c/em\u003e (R\u0026sup2;=0.53, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001), and \u003cem\u003ePseudomonadota\u003c/em\u003e (R\u0026sup2;=0.21, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001), while \u003cem\u003eBacillota\u003c/em\u003e (R\u0026sup2;=0.07, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.030) showed a decreasing trend in relative abundance. Throughout the entire 42-day production cycle, aside from the continuous decrease in \u003cem\u003eFusobacteriota\u003c/em\u003e (R\u0026sup2;=0.21, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001), bacterial phyla such as \u003cem\u003eActinomycetota\u003c/em\u003e (R\u0026sup2;=0.06, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.009), \u003cem\u003eBacillota\u003c/em\u003e (R\u0026sup2;=0.08, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.004), \u003cem\u003eCyanobacteriota\u003c/em\u003e (R\u0026sup2;=0.47, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001), and \u003cem\u003eDesulfobacterota\u003c/em\u003e (R\u0026sup2;=0.08, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.003) all showed an upward trend in relative abundance. At the lower taxonomic level of family, dominant families include \u003cem\u003eLactobacillaceae\u003c/em\u003e, \u003cem\u003eEnterococcaceae\u003c/em\u003e, and \u003cem\u003eLachnospiraceae\u003c/em\u003e, which together had an average cumulative relative abundance exceeding 80% in each sample. These bacterial taxa were represented dominant gut microbial communities in the broiler production cycle, maintaining a certain elastic colonization ability during the microbial succession.\u003c/p\u003e \u003cp\u003eFurthermore, leveraging the advantages of genome assembly, we conducted an in-depth analysis at the species level. We grouped species based on the similarity of relative abundance change patterns and conducted comparative analyses within 12 clusters. Interestingly, as the age increased, we observed a rapid disappearance of genera under the family \u003cem\u003eLachnospiraceae\u003c/em\u003e in the gut of broilers, including \u003cem\u003eEgerieimonas\u003c/em\u003e, \u003cem\u003eSellimonas\u003c/em\u003e, \u003cem\u003eMerdimonas\u003c/em\u003e, \u003cem\u003eScatomonas\u003c/em\u003e, \u003cem\u003eEisenbergiella\u003c/em\u003e, \u003cem\u003eLachnoclostridium\u003c/em\u003e, and \u003cem\u003eFimimorpha\u003c/em\u003e (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e2\u003c/span\u003eg and Fig. \u003cspan refid=\"MOESM4\" class=\"InternalRef\"\u003eS4\u003c/span\u003e-6). Additionally, several other families exhibited partially similar trends, and we classified them as cluster 12. The cluster 12 represents 32 families and 72 genera, including \u003cem\u003eLachnospiraceae\u003c/em\u003e, \u003cem\u003eRuminococcaceae\u003c/em\u003e, \u003cem\u003eBacteroidaceae\u003c/em\u003e, \u003cem\u003eAcutalibacteraceae\u003c/em\u003e, and \u003cem\u003eBurkholderiaceae\u003c/em\u003e (Additional file 1: Table \u003cspan refid=\"MOESM5\" class=\"InternalRef\"\u003eS5\u003c/span\u003e). In the search for bacterial families showing a resurgence in the later stages of production, we observed an upward trend in clusters 7 and 8 in the mid-term. Representatives of these clusters are also dominated by Lachnospiraceae, \u003cem\u003eRuminococcaceae\u003c/em\u003e, and \u003cem\u003eAcutalibacteraceae\u003c/em\u003e. Within \u003cem\u003eLachnospiraceae\u003c/em\u003e, the genera changed to \u003cem\u003eMediterraneibacter\u003c/em\u003e, \u003cem\u003eLimivivens\u003c/em\u003e, \u003cem\u003eScatomonas\u003c/em\u003e, \u003cem\u003eEgerieimonas\u003c/em\u003e, \u003cem\u003eColadousia\u003c/em\u003e. In the later stages of production, we found a sustained increase in bacteria from cluster 5. Surprisingly, the representative families in this cluster remained the same as in the early to mid-term. However, the genera within \u003cem\u003eLachnospiraceae\u003c/em\u003e had changed to \u003cem\u003eAnaerobutyricum\u003c/em\u003e, \u003cem\u003eBlautia\u003c/em\u003e, \u003cem\u003eCaccovicinus\u003c/em\u003e, \u003cem\u003eCholadocola\u003c/em\u003e, and \u003cem\u003eFusicatenibacter\u003c/em\u003e. Although there was no change at the family level, the microbial composition at the genus level was almost entirely different. Several other representative clusters also exhibited similar phenomena. As predicted, \u003cem\u003eLachnospiraceae\u003c/em\u003e and several other bacterial families were important microbial participants throughout the entire production cycle. However, the dominant genera involved in colonization differed at different times, showing a trend of microbial succession. These findings showed some taxa with subtle colonization advantage in certain stages, highlighting their significant role in host growth and development.\u003c/p\u003e\u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003eStudy on the temporal fluctuation of microbial function\u003c/h2\u003e \u003cp\u003eTo explore the temporal fluctuation in microbial function over time, we annotated the gene catalogue using KO and CAZyme classifications. In general, 52.95% (1,141,468/2,155,595) of protein-coding genes were assigned to 8,233 KOs. Notably, akin to the taxonomic changes, microbial functions also followed a pattern, with greater fluctuations in the first week after hatching compared to other time points. Alongside the rapid disappearance of microbial community diversity in the early stages of life, we observed a significant decrease in the richness and diversity of microbial functions in the first week after hatching in broiler chickens (Linear regression: \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001, Fig.\u0026nbsp;\u003cspan refid=\"Fig10\" class=\"InternalRef\"\u003e3\u003c/span\u003ea-b), indicating that changes in the broiler\u0026rsquo;s gut microbiota significantly influenced fluctuations in microbial functions (PERMANOVA: 49.77%, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001, Fig.\u0026nbsp;\u003cspan refid=\"Fig10\" class=\"InternalRef\"\u003e3\u003c/span\u003ec-d). The disappearance of this functional diversity was accompanied by a substantial reduction in microbial functions related to metabolism, specifically in amino acid, carbohydrate, energy, secondary metabolites, and cofactor and vitamin metabolism (Linear regression: \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001, Fig.\u0026nbsp;\u003cspan refid=\"Fig10\" class=\"InternalRef\"\u003e3\u003c/span\u003ee, Fig. \u003cspan refid=\"MOESM7\" class=\"InternalRef\"\u003eS7\u003c/span\u003ea). After the first week, except for genetic information processing, the fluctuations in various functions began to stabilize, while nucleotide and lipid functions continued to exhibit a significant increase (Linear regression: \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01). Furthermore, we explored the contributions of the microbial communities to various functions and found that \u003cem\u003eBacillota\u003c/em\u003e was predominant in contributing to a variety of microbial functions, followed closely by \u003cem\u003eBacteroidota\u003c/em\u003e, \u003cem\u003ePseudomonadota\u003c/em\u003e, and \u003cem\u003eActinomycetota\u003c/em\u003e (Fig. \u003cspan refid=\"MOESM7\" class=\"InternalRef\"\u003eS7\u003c/span\u003eb). Particularly in the early life stages of broilers, the contribution of \u003cem\u003eBacteroidota\u003c/em\u003e, \u003cem\u003eLachnospiraceae\u003c/em\u003e and \u003cem\u003eBacteroidaceae\u003c/em\u003e to microbial functions was significantly higher than at other times (Wilcoxon rank-sum: \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001, Fig. \u003cspan refid=\"MOESM7\" class=\"InternalRef\"\u003eS7\u003c/span\u003ec). Accompanied by a rapid colonization on 3rd day after birth, \u003cem\u003eLactobacillaceae\u003c/em\u003e, \u003cem\u003eEnterococcaceae\u003c/em\u003e and \u003cem\u003ePlanococcaceae\u003c/em\u003e were contribute the vast majority of microbial functions together. After this time point, \u003cem\u003eLactobacillaceae\u003c/em\u003e evolved as the main contributor for microbial composition and functions.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eMicrobial carbohydrate degradation activities also conferred significant benefits to the host. Therefore, we compared and analyzed the intestinal CAZymes functions of broilers at different ages. Among the 2,155,595 predicted proteins, 12.6% (313,568) were predicted to have at least one CAZy function, including 15 auxiliary activities (AAs), 81 carbohydrate-binding modules (CBMs), 19 carbohydrate esterases (CEs), 155 glycoside hydrolases (GHs), 100 glycosyltransferases (GTs), and 31 polysaccharide lyases (PLs). LEfSe analysis revealed that the broiler\u0026rsquo;s gut microbiome significantly enriched the highest number of CAZymes in the early stages, particularly on day 1 (144/276) (LDA\u0026thinsp;\u0026gt;\u0026thinsp;2, \u003cem\u003eq\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05) (Fig.\u0026nbsp;\u003cspan refid=\"Fig10\" class=\"InternalRef\"\u003e3\u003c/span\u003ef-g, Additional file 1: Table \u003cspan refid=\"MOESM6\" class=\"InternalRef\"\u003eS6\u003c/span\u003e). These early-enriched CAZymes targeted a broader spectrum of substrates, including host glycan, pectin, xylan, β-glucan, among others.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003eComparative analysis of microbial functional enrichment between rapid growth phase and fattening phase in broilers\u003c/h2\u003e \u003cp\u003eThe rapid growth phase, the initial stage of broilers\u0026rsquo; life cycle, occurs within the first few weeks post-hatching and its duration depends on the breed and management practices[\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. This period is characterized by substantial development of skeletal muscles. During this stage, broilers exhibit a marked increase in the growth rate, underpinned by accelerated bone and muscle development to accommodate the weight gain[\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e]. Concurrently, there is an elevated demand for high-protein and high-energy diets to support this rapid growth[\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e]. Subsequently, the growth rate moderates, in transitioning into the fattening phase, where the focus shifts to fat and muscle deposition to enhance the meat quality[\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e]. In the present study, broilers at 21 days of age were designated as the threshold between these developmental stages. We focused on comparing the differences in microbial function between the two stages. By employing reporter Z-score-based enrichment analysis of the KEGG metabolic pathways, we identified 40 and 17 functional modules significantly enriched during the rapid growth and fattening phases, respectively (Fig. \u003cspan refid=\"MOESM8\" class=\"InternalRef\"\u003eS8\u003c/span\u003e, Additional file 1: Table \u003cspan refid=\"MOESM7\" class=\"InternalRef\"\u003eS7\u003c/span\u003e). Metabolic modules enriched in the rapid growth phase were predominantly related to the metabolism of vitamins and amino acids, including proline, lysine, methionine, cysteine and threonine biosynthesis, as well as leucine and tyrosine degradation. In contrast, the fattening phase was primarily associated with carbohydrate and energy metabolism, featuring modules such as pyruvate oxidation, fumarate reductase, reductive acetyl-CoA pathway, fatty acid biosynthesis and methanogenesis. We visualized the modules related to amino acid and carbohydrate metabolism and their role in the growth of broilers are depicted in Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e4\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003eCharacteristics and temporal fluctuation of gut resistome of broilers\u003c/h2\u003e \u003cp\u003eA total of 1,132 ORFs were predicted to be associated with antibiotic resistance ontology (ARO) after alignment with the CARD, representing 425 unique ARGs (Additional file 1: Table \u003cspan refid=\"MOESM10\" class=\"InternalRef\"\u003eS10\u003c/span\u003e). The ARGs for aminoglycoside antibiotics were notably prominent in the gut microbiota, followed by peptide, tetracycline and other antibiotics (Fig. \u003cspan refid=\"MOESM9\" class=\"InternalRef\"\u003eS9\u003c/span\u003ea). Meanwhile, antibiotic inactivation was the most common mechanism to potentially confer resistance (30.6%), followed by efflux and target alteration (Fig. \u003cspan refid=\"MOESM9\" class=\"InternalRef\"\u003eS9\u003c/span\u003eb). We found that the predicted ARGs were primarily carried by four bacterial phyla: \u003cem\u003ePseudomonadota\u003c/em\u003e, \u003cem\u003eBacillota\u003c/em\u003e, \u003cem\u003eActinomycetota\u003c/em\u003e and \u003cem\u003eBacteroidota\u003c/em\u003e, all of which were also predominant in the host intestine (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e5\u003c/span\u003ea). Notably, \u003cem\u003eEscherichia coli\u003c/em\u003e was found to harbor up to 101 ARGs, with 45% of these gene conferring multidrug resistance (Fig. \u003cspan refid=\"MOESM9\" class=\"InternalRef\"\u003eS9\u003c/span\u003ec). Meanwhile, we investigated the distribution of ARG-encoding genes across 694 genomes and found over half of the strains did not carry any ARGs, while 141 strains carried only one ARG (Additional file 1: Table \u003cspan refid=\"MOESM3\" class=\"InternalRef\"\u003eS3\u003c/span\u003e). However, a significant number of strains carried a variety of ARGs, particularly several strains belonged to \u003cem\u003eEscherichia coli, Escherichia fergusonii, Enterobacter hormaechei, Enterococcus faecium, Staphylococcus epidermidis\u003c/em\u003e and \u003cem\u003eStaphylococcus saprophyticus\u003c/em\u003e, which harbored more than 20 ARGs, with some containing up to 119 ARGs. We analyzed several strains to illustrate the distribution and location of ARGs on their genomes (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e5\u003c/span\u003eb and Fig. \u003cspan refid=\"MOESM9\" class=\"InternalRef\"\u003eS9\u003c/span\u003eg). For instance, \u003cem\u003eEscherichia coli\u003c/em\u003e HC271 harbored 89 ARGs, 29 MGEs (highlighted in the following section), with multiple ARGs and MGEs co-located on the same contigs, such as those marked at Location 1 and 2.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eAligned with the aims of this study, we conducted an exploration into the diversity and structural changes of the ARGs and their corresponding drug resistance phenotypes. We evaluated the overall abundance of ARGs within each gut microbiome sample, revealing an average ARG abundance of 0.45% (\u0026plusmn;\u0026thinsp;0.20%). This indicates that ARGs are prevalently distributed throughout the gut microbiota. Contrary to the taxonomic changes, we observed a significant increase in the overall ARG abundance during the first three days, which then significantly decreased until the end of the observation phase (Linear regression: \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001, Fig. \u003cspan refid=\"MOESM9\" class=\"InternalRef\"\u003eS9\u003c/span\u003ee). Subsequently, we evaluated the changes in the abundance of ARGs with prevalence\u0026thinsp;\u0026gt;\u0026thinsp;20% (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e5\u003c/span\u003ec). Notably, the abundance of the genes \u003cem\u003etetL\u003c/em\u003e, \u003cem\u003etetW\u003c/em\u003e, \u003cem\u003etetOW\u003c/em\u003e, \u003cem\u003etetWNW\u003c/em\u003e and \u003cem\u003evatH\u003c/em\u003e consistently increased in the gut microbiota of broilers over time. ARGs such as \u003cem\u003etetL\u003c/em\u003e, \u003cem\u003epoxtA\u003c/em\u003e and \u003cem\u003eapmA\u003c/em\u003e were present at the third day and at subsequent time points. In contrast, others like \u003cem\u003etetQ\u003c/em\u003e, \u003cem\u003eErmF\u003c/em\u003e, \u003cem\u003elnuC\u003c/em\u003e, which were more abundant at the first day, showed a reduction in the following days. Additionally, some predicted ARGs exhibited a consistent pattern of change, which may indicate an interrelationship, such as a synergistic transfer mechanism at play. Furthermore, based on phenotypes of predicted ARGs, we preliminarily evaluated the dynamic changes in drug resistance phenotypes based on their relative abundance to obtain deeper insights into the environmental pressure and the potential for resistance dissemination (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e5\u003c/span\u003ed). Over time, the resistance levels to antibiotics such as diaminopyrimidine, elfamycin, glycopeptide, and pleuromutilin demonstrated a generally significant downward trend from the initial day (Linear regression: \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001). In contrast, resistance to antimicrobial agents, including aminoglycosides, cephalosporins, MLS (lincosamide, macrolide and streptogramin), nucleosides, phenicols, and tetracyclines, showed a significant upward trend (Linear regression: \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05). Notably, there were no signs of carbapenem resistance detected in the gut microbiome of the broilers during the first three days of their lives.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec18\" class=\"Section2\"\u003e \u003ch2\u003eGut microbiota and mobilome related to resistome landscape\u003c/h2\u003e \u003cp\u003eTo understand the impact of the composition and temporal fluctuation of the gut microbiota on the resistome characteristics, we investigated the correlation between microbial communities and antibiotic resistance profiles. Our findings indicate a significant positive correlation between the Shannon index of the gut microbiota and the resistome (Fig. \u003cspan refid=\"MOESM10\" class=\"InternalRef\"\u003eS10\u003c/span\u003ea, R\u0026thinsp;=\u0026thinsp;0.56, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Furthermore, the Procrustes association analysis uncovered a tight correlation between gut microbiota and the resistome (Fig. \u003cspan refid=\"MOESM10\" class=\"InternalRef\"\u003eS10\u003c/span\u003ef, M\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;0.2444, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Mantel analysis showed that 2 families \u003cem\u003eEnterobacteriaceae\u003c/em\u003e and \u003cem\u003eEnterococcaceae\u003c/em\u003e had significant correlations with resistance phenotypes to 17 different antimicrobial drugs (r\u0026thinsp;\u0026gt;\u0026thinsp;0.3, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05, Fig. \u003cspan refid=\"MOESM11\" class=\"InternalRef\"\u003eS11\u003c/span\u003e, Additional file 1: Table \u003cspan refid=\"MOESM9\" class=\"InternalRef\"\u003eS9\u003c/span\u003e), indicating their potential key role in the shifts observed within the resistome composition. Specifically, \u003cem\u003eEscherichia coli\u003c/em\u003e and \u003cem\u003eEnterococcus gallinarum\u003c/em\u003e, as representative species of their respective families, were primarily responsible for shaping the composition and variability of resistance to antimicrobial drugs (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e6\u003c/span\u003ea). Additionally, resistance to certain antibiotics, such as peptides, oxazolidinones, and aminocoumarins, were most notably affected by alterations in the microbial community. These results underscore the profound influence of the microbial community on the features of the gut resistome and highlight the important role played by certain key bacterial species in this interplay.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eMGEs are key facilitators in the interspecies exchange of ARGs among bacteria. Elucidating the relationship between MGEs and ARGs is crucial for understanding the temporal fluctuation in resistome. We identified a total of 611 MGEs within our gene pool, predominantly composed of transposons (61.43%) and plasmids (19.01%; Fig. \u003cspan refid=\"MOESM10\" class=\"InternalRef\"\u003eS10\u003c/span\u003ee, Additional file 1: Table \u003cspan refid=\"MOESM10\" class=\"InternalRef\"\u003eS10\u003c/span\u003e). Association analysis indicated that the impact of the mobilome on the composition and variations of the gut resistome is more pronounced than that of the gut microbiota (Fig. \u003cspan refid=\"MOESM10\" class=\"InternalRef\"\u003eS10\u003c/span\u003ec-d and S10g). Consequently, we examined in detail the co-occurrence relationships between MGEs and ARGs at the gene level. We searched for MGEs within a 5 kilobase range surrounding ARGs and considered these ARG-MGE complexes as potential mobile resistance genes. In total, 1,877 ARG-MGE combinations were identified across 1,144 contigs (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e6\u003c/span\u003eb-c, Additional file 1: Table \u003cspan refid=\"MOESM11\" class=\"InternalRef\"\u003eS11\u003c/span\u003e). The \u003cem\u003esul1\u003c/em\u003e and \u003cem\u003eqacEdelta\u003c/em\u003e combinations were the most abundant. Aminoglycoside resistance and multi-drug resistance were associated with a variety of transposons. Considering that fragmented contigs might underrepresent the links between ARGs and MGEs, we investigated the co-abundance relationships and found significant correlations among 245 ARGs and 168 MGEs in terms of their abundances (Spearman correlation: r\u0026thinsp;\u0026ge;\u0026thinsp;0.5, \u003cem\u003eq\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05, Additional file 1: Table S12). Among these, an overwhelming majority were positively associated (99.8%). In line with prior observations, ARGs and MGEs exhibiting co-occurrence relationships also showed a strong positive correlation in their co-abundance. These findings not only suggest an increased potential for ARGs to be transferred across different genomic locations and between distinct bacterial strains, but also imply that these ARG-MGE combinations could be key in driving the shifts in antibiotic resistance profiles throughout the production cycle.\u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eIn this study, we conducted a investigation of the gut microbiota of commercial broilers from birth to market, aiming to elucidate the relationship between distinct gut microbiota patterns and age throughout the entire production cycle. We observed that the gut microbiota of broilers during the first week post hatching was influenced by a combination of environmental and biological factors, initially underwent depletion of maternally derived microbiota. This was accompanied by the disappearance of bacterial taxa with priority effects and a significant decrease in microbial diversity. Subsequently, after transitioning from the rapid growth stage to the fattening phase, we observed a reconstruction of microbial community with the emergence of other species in the gut microbial community. Microbial community depletion and subsequent reconstruction have not been investigated in previous studies on chickens, and there has been a lack of emphasis on exploring this phenomenon.\u003c/p\u003e \u003cp\u003eUnlike the host genome, which is encoded at birth and remains largely stable throughout life, each of these unique microbiota changes can be modifiable over time[\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. Utilizing metagenomic and genomic sequencing, we observed temporal fluctuations in the gut microbiota of broilers as they age, with significant differences in the microbial community structure among chickens of different ages. We observed a significant decrease in the diversity and richness of the gut microbiota in broilers during the first week post-hatching. Unexpectedly, similar trends are reported in the gut microbiota of infants to those observed in broilers[\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e, \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e]. In the early life stages, the gut microbiota is predominantly composed of facultative anaerobes. However, as they deplete oxygen, obligate anaerobes and some oxygen-tolerant bacteria begin to colonize the intestinal environment, facilitating a rapid succession of the microbial community in the early stages of life[\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e, \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e]. In a study of the gut microbiota of new-born calves, microbial diversity, richness, and bacterial developmental diversity were highest at birth, and significantly decreased by day 3[\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e]. Therefore, we infer that, similar to most mammals, the initial colonization of the chicken microbiota is likely through maternal transmission[\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. These microbes may influence the immune system of chicks and potentially have significant effects on the subsequent changes in the microbial community. However, this maternal microbial influence can be rapidly overshadowed by environmental factors (such as diet, water source, housing conditions, disinfectants, etc.) and biological factors (including innate and adaptive immunity) during the early stages of life[\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e, \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e, \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e]. This drastic fluctuation stabilizes after the first week, with microbial community diversity and richness beginning to show an upward trend. At the family level, we observed a rapid turnover of bacterial taxa such as \u003cem\u003eLachnospiraceae\u003c/em\u003e, \u003cem\u003eBacteroidaceae\u003c/em\u003e, and \u003cem\u003eRuminococcaceae\u003c/em\u003e, with the emergence of bacterial families including \u003cem\u003eLactobacillaceae\u003c/em\u003e and \u003cem\u003eEnterococcaceae\u003c/em\u003e. This phenomenon indicates their indispensable importance in the growth and development of broilers. While the microbial community structures still differ at different ages, they become relatively stable compared to the first week. Interestingly, in this dynamic microbial succession, significant changes occur in lower-level taxa of major producers of SCFAs and vitamins, such as \u003cem\u003eLachnospiraceae\u003c/em\u003e, which remains abundant throughout the production cycle[\u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e]. However, we noted substantial changes in lower-level genera, possibly due to different involvement in biological functions and metabolic cycles across production cycles. Similar phenomena were observed in taxa like \u003cem\u003eRuminococcaceae\u003c/em\u003e and \u003cem\u003eAcutalibacteraceae\u003c/em\u003e. The reconstruction of gut microbial community may be related to the interactions between these bacterial taxa, with the successive turnover of different bacterial taxa contributing to formation and maintenance of host microbiota[\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eDuring the rapid growth phase of broilers, microbial functional enrichment was primarily associated with vitamin and amino acid metabolism, which was significantly higher than during the fattening phase. Thiamine biosynthesis was notably abundant, and thiamine serves as a crucial coenzyme in glucose metabolism, participating in the oxidation of glucose to provide the necessary energy for cellular processes[\u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e]. Adequate energy supply is vital for the normal growth of bones and muscles. Proline biosynthesis also exhibits significant microbial functional activity[\u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e56\u003c/span\u003e]. The presence of proline may influence the synthesis and structural maintenance of collagen, potentially affecting the health of bones and joints[\u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e56\u003c/span\u003e]. Furthermore, we observed the enrichment of various amino acid metabolic pathways during the rapid growth phase, including lysine, methionine, cysteine and threonine biosynthesis, among others. Amino acids are essential components of proteins, crucial for the synthesis and repair of muscle tissue. Vitamins and amino acids collaborate in the development of muscles and bones, jointly promoting the healthy growth of muscle and skeletal tissues[\u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e57\u003c/span\u003e]. In contrast, during the fattening phase, the microbial community is more involved in carbohydrate and energy metabolism, with significant enrichment of pyruvate oxidation and fumarate reductase. During the fattening stage, chickens preferentially utilize carbohydrates, converting a portion into glycogen stored in the liver and muscles[\u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e58\u003c/span\u003e]. This serves as the primary energy source, preserving proteins for the development of muscle tissue. Also, acetone produced through metabolic pathways, ultimately converted into fat, can facilitate the rapid weight gain and fat accumulation in broilers[\u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e59\u003c/span\u003e, \u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e60\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eWe observed that the ARG levels in broilers were relatively high compared to those reported in other animals, such as cows[\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e], pigs[\u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e61\u003c/span\u003e] and horses[\u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e62\u003c/span\u003e], with classes including elfamycin, aminoglycoside, tetracycline, and fluoroquinolone ARGs commonly detected in the chicken intestine. Interestingly, despite not receiving any antibiotic treatment, the broilers in this study still carried a high prevalence of ARGs in their gut microbiome, indicating that the gut microbiome serves as a natural reservoir for ARGs[\u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e63\u003c/span\u003e, \u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e64\u003c/span\u003e]. A previous evaluation of human health risk associated with ARGs determined that, among the ARGs identified in the present study, 37 were categorized as \"current threats\" resistance genes, and 12 ARGs were classified as \"future threats\" genes. Surprisingly, we also detected the presence of \u003cem\u003etetX\u003c/em\u003e, \u003cem\u003emcr-1\u003c/em\u003e, and \u003cem\u003eNDM-1\u003c/em\u003e genes, which are known to confer resistance to antibiotics considered as the last resort for treating infections in both human and veterinary medicine[\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e, \u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e65\u003c/span\u003e]. These genes not only provide fresh insights into antibiotic resistance research but also carry significant implications for public health policy formulation. Over time, the increase in resistance to aminoglycosides, cephalosporins, MLS, nucleoside, phenicol, and tetracycline antibiotics highlights the importance of monitoring and assessing ARGs in the livestock industry. Aminoglycoside antibiotics, despite playing an important role in treating bacterial diseases, have raised concerns due to the high abundance of their resistance genes potentially disrupting the healthy balance of the gut microbiome[\u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e66\u003c/span\u003e]. The abundance of these resistance genes may negatively impact beneficial microbial communities, increasing the proportion of resistant strains, thereby compromising intestinal health[\u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e67\u003c/span\u003e]. Furthermore, changes in the composition of the gut microbiota and the role of MGEs in the spread of resistance genes were significant findings in previous studies[\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e, \u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e68\u003c/span\u003e]. \u003cem\u003eEnterobacteriaceae\u003c/em\u003e and \u003cem\u003eEnterococcaceae\u003c/em\u003e may play a key role in the spread of resistance genes, highlighting the need to consider these bacterial groups in antibiotic usage and management strategies[\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e, \u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e61\u003c/span\u003e, \u003cspan citationid=\"CR69\" class=\"CitationRef\"\u003e69\u003c/span\u003e]. The influence of mobile elements on resistance groups is greater than that of the gut microbiota, suggesting that mobile elements primarily mediate the structure and dynamic changes of resistance groups, which is consistent with previous findings on subtropical estuarial resistome[\u003cspan citationid=\"CR70\" class=\"CitationRef\"\u003e70\u003c/span\u003e]. Our co-occurrence and co-abundance analysis revealed a close interaction between ARGs and MGE genes, which may be a key driver of changes in the antibiotic resistance spectrum during the broiler production cycle, suggesting that future research should focus on the molecular mechanisms behind these gene interactions and their impact on the gut microbial ecology.\u003c/p\u003e"},{"header":"Conclusions","content":"\u003cp\u003eIn this study, we revealed microbial dynamics in broilers\u0026rsquo; gut similar to those seen in infants and young mammals. After birth, there was a rapid depletion of maternal-source microbes, followed by the resettlement and reconstruction of new bacterial taxa. As age increased, the microbial community structure stabilized. Due to the dynamic replacement of dominant bacterial genera, different-aged microbial communities exhibited unique taxonomic and functional differences. During the rapid growth phase, the microbial community provided the significant functional potentials related to vitamin and amino acid metabolism, collectively contributing to the healthy development of muscles and bones. During the fattening phase, the microbial community was more involved in the synergistic metabolism of energy and carbohydrates to achieve rapid weight gain and fat accumulation in the broilers. Additionally, we characterized the temporal fluctuations of gut resistome throughout the production cycle, documenting the changes in the abundance of the prevalent ARGs and their associated resistance phenotypes. Furthermore, we analyzed the contributions of gut microbiota and the mobilome to the resistome, which enhanced our understanding of the resistome dynamics. Our study reveals dynamic patterns of gut microbiota alterations throughout the broiler production cycle, enriching the repository of intestinal metagenomic data across diverse stages. These insights will lead to better grasp of age-related microbial shifts and their implications in chicken health, thereby contributing to the advancement of a more sustainable and healthy broiler farming industry.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003eMAG: Metagenome-assembled genome\u003c/p\u003e\n\u003cp\u003eANI: Average nucleotide identity\u003c/p\u003e\n\u003cp\u003eMGE:\u0026nbsp;Mobile genetic element\u003c/p\u003e\n\u003cp\u003eARG: Antibiotic resistance gene\u003c/p\u003e\n\u003cp\u003eORF: open reading frame\u003c/p\u003e\n\u003cp\u003eCAZyme: Carbohydrate-active enzyme\u003c/p\u003e\n\u003cp\u003eAA: Auxiliary activities\u003c/p\u003e\n\u003cp\u003eGH: Glycoside hydrolases\u003c/p\u003e\n\u003cp\u003eCBM: Carbohydrate-binding modules\u003c/p\u003e\n\u003cp\u003eCE: Carbohydrate esterases\u003c/p\u003e\n\u003cp\u003ePL: Polysaccharide lyases\u003c/p\u003e\n\u003cp\u003eGT: glycosyltransferases\u003c/p\u003e\n\u003cp\u003eKEGG: Kyoto Encyclopedia of Genes and Genomes\u003c/p\u003e\n\u003cp\u003eTPM: Transcript per million\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eLEfSe: Linear discriminant analysis effect size\u003c/p\u003e\n\u003cp\u003ePERMANOVA: Permutational multivariate analysis of variance\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe complete procedure in this study was approved by the Committee on the Care and Use of Laboratory Animals of the State-Level Animal Experimental Teaching Demonstration Center of Qingdao Agricultural University. The animal experiments were approved by Qingdao Agriculture University Ethics Committee.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll subjects provided informed consent to participate in this study and agreed to the publication of the research results\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe raw whole-metagenomic shotgun sequencing data from this study have been deposited in the China Nucleotide Sequence Archive under accession code PRJCA018199. The genome sequences of bacterial isolates are available in the SRA database under accession code PRJNA1123611. All other data supporting the findings of this study are available in the paper and supplemental materials, or from the corresponding author(s) upon request. The codes and the supporting data related to this work are available on GitHub at https://github.com/jinxmeng/24_broiler_mbiome.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe study was supported by the distinguished Scholar Research Fund of Qingdao Agricultural University (1120044).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026rsquo; contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eX.X.Z. conceived and directed the project. M.H.L and W.L.Y collected the samples. J.X.M., M.H.L. W.L.Y., X.Y.W., X.M.L. and Y.Z.S. conducted culturomic experiments. J.X.M., M.H.L., S.L., Y.Z., and X.Y.W. performed the bioinformatic analyses and prepared figures and texts for the manuscript. J.X.M., M.H.L. and S.L. wrote the first draft of the manuscript. H.M.E.,\u0026nbsp;X.X.Z., S.L., H.B.N., H.M., R.L. and X.Y. made substantial revision to the manuscript and participated in discussions. All authors contributed to the revision of the manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe thank Professor Lesley Hoyles from Nottingham Trent University for their constructive suggestions.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eGacesa R, Kurilshikov A, Vich Vila A, Sinha T, Klaassen MAY, Bolte LA, Andreu-S\u0026aacute;nchez S, Chen L, Collij V, Hu S, et al: \u003cstrong\u003eEnvironmental factors shaping the gut microbiome in a Dutch population.\u003c/strong\u003e \u003cem\u003eNature \u003c/em\u003e2022, \u003cstrong\u003e604:\u003c/strong\u003e732-739.\u003c/li\u003e\n\u003cli\u003eXiao F, Zhu W, Yu Y, He Z, Wu B, Wang C, Shu L, Li X, Yin H, Wang J, et al: \u003cstrong\u003eHost development overwhelms environmental dispersal in governing the ecological succession of zebrafish gut microbiota.\u003c/strong\u003e \u003cem\u003enpj Biofilms and Microbiomes \u003c/em\u003e2021, \u003cstrong\u003e7:\u003c/strong\u003e5.\u003c/li\u003e\n\u003cli\u003eXu S, Li L, Luo X, Chen M, Tang W, Zhan L, Dai Z, Lam TT, Guan Y, Yu G: \u003cstrong\u003eGgtree: A serialized data object for visualization of a phylogenetic tree and annotation data.\u003c/strong\u003e \u003cem\u003eiMeta \u003c/em\u003e2022, \u003cstrong\u003e1:\u003c/strong\u003ee56.\u003c/li\u003e\n\u003cli\u003eHuang P, Zhang Y, Xiao K, Jiang F, Wang H, Tang D, Liu D, Liu B, Liu Y, He X, et al: \u003cstrong\u003eThe chicken gut metagenome and the modulatory effects of plant-derived benzylisoquinoline alkaloids.\u003c/strong\u003e \u003cem\u003eMicrobiome \u003c/em\u003e2018, \u003cstrong\u003e6:\u003c/strong\u003e211.\u003c/li\u003e\n\u003cli\u003eJiang L, Zhang J, Tang J, Li M, Zhao X, Zhu H, Yu X, Li Y, Feng T, Zhang X: \u003cstrong\u003eAnalyses of Aerosol Concentrations and Bacterial Community Structures for Closed Cage Broiler Houses at Different Broiler Growth Stages in Winter.\u003c/strong\u003e \u003cem\u003eJournal of Food Protection \u003c/em\u003e2018, \u003cstrong\u003e81:\u003c/strong\u003e1557-1564.\u003c/li\u003e\n\u003cli\u003eKhawaja T, Khan SH, Ansari NNJIJops: \u003cstrong\u003eEffect of different levels of blood meal on broiler performance during two phases of growth.\u003c/strong\u003e 2007, \u003cstrong\u003e6:\u003c/strong\u003e860-865.\u003c/li\u003e\n\u003cli\u003eChoi JH, Lee K, Kim DW, Kil DY, Kim GB, Cha CJ: \u003cstrong\u003eInfluence of dietary avilamycin on ileal and cecal microbiota in broiler chickens.\u003c/strong\u003e \u003cem\u003ePoultry Science \u003c/em\u003e2018, \u003cstrong\u003e97:\u003c/strong\u003e970-979.\u003c/li\u003e\n\u003cli\u003eMartino C, Dilmore AH, Burcham ZM, Metcalf JL, Jeste D, Knight R: \u003cstrong\u003eMicrobiota succession throughout life from the cradle to the grave.\u003c/strong\u003e \u003cem\u003eNature Reviews Microbiology \u003c/em\u003e2022, \u003cstrong\u003e20:\u003c/strong\u003e707-720.\u003c/li\u003e\n\u003cli\u003eCaballero-Flores G, Pickard JM, N\u0026uacute;\u0026ntilde;ez G: \u003cstrong\u003eMicrobiota-mediated colonization resistance: mechanisms and regulation.\u003c/strong\u003e \u003cem\u003eNature Reviews Microbiology \u003c/em\u003e2023, \u003cstrong\u003e21:\u003c/strong\u003e347-360.\u003c/li\u003e\n\u003cli\u003eHillier LW, Miller W, Birney E, Warren W, Hardison RC, Ponting CP, Bork P, Burt DW, Groenen MAM, Delany ME, et al: \u003cstrong\u003eSequence and comparative analysis of the chicken genome provide unique perspectives on vertebrate evolution.\u003c/strong\u003e \u003cem\u003eNature \u003c/em\u003e2004, \u003cstrong\u003e432:\u003c/strong\u003e695-716.\u003c/li\u003e\n\u003cli\u003eDing J, Dai R, Yang L, He C, Xu K, Liu S, Zhao W, Xiao L, Luo L, Zhang Y, Meng H: \u003cstrong\u003eInheritance and Establishment of Gut Microbiota in Chickens.\u003c/strong\u003e \u003cem\u003eFrontiers in Microbiology \u003c/em\u003e2017, \u003cstrong\u003e8\u003c/strong\u003e.\u003c/li\u003e\n\u003cli\u003eGe Y, Zhu W, Chen L, Li D, Li Q, Jie H: \u003cstrong\u003eThe Maternal Milk Microbiome in Mammals of Different Types and Its Potential Role in the Neonatal Gut Microbiota Composition.\u003c/strong\u003e In \u003cem\u003eAnimals\u003c/em\u003e, vol. 11; 2021.\u003c/li\u003e\n\u003cli\u003eFerretti P, Pasolli E, Tett A, Asnicar F, Gorfer V, Fedi S, Armanini F, Truong DT, Manara S, Zolfo M, et al: \u003cstrong\u003eMother-to-Infant Microbial Transmission from Different Body Sites Shapes the Developing Infant Gut Microbiome.\u003c/strong\u003e \u003cem\u003eCell Host \u0026amp; Microbe \u003c/em\u003e2018, \u003cstrong\u003e24:\u003c/strong\u003e133-145.e135.\u003c/li\u003e\n\u003cli\u003eLee S, La T-M, Lee H-J, Choi I-S, Song C-S, Park S-Y, Lee J-B, Lee S-W: \u003cstrong\u003eCharacterization of microbial communities in the chicken oviduct and the origin of chicken embryo gut microbiota.\u003c/strong\u003e \u003cem\u003eScientific Reports \u003c/em\u003e2019, \u003cstrong\u003e9:\u003c/strong\u003e6838.\u003c/li\u003e\n\u003cli\u003eRobinson K, Yang Q, Stewart S, Whitmore MA, Zhang G: \u003cstrong\u003eBiogeography, succession, and origin of the chicken intestinal mycobiome.\u003c/strong\u003e \u003cem\u003eMicrobiome \u003c/em\u003e2022, \u003cstrong\u003e10:\u003c/strong\u003e55.\u003c/li\u003e\n\u003cli\u003eZhang Y, Zhang S, Yuan Y, Li Y, Zhu R, Yang Y, Xing S, Wang Y, Wu Y, Liao X, Mi J: \u003cstrong\u003eMetagenomic assembly reveals the circadian oscillations of the microbiome and antibiotic resistance genes in a model of laying hens.\u003c/strong\u003e \u003cem\u003eScience of The Total Environment \u003c/em\u003e2022, \u003cstrong\u003e836:\u003c/strong\u003e155692.\u003c/li\u003e\n\u003cli\u003eWylensek D, Hitch TCA, Riedel T, Afrizal A, Kumar N, Wortmann E, Liu T, Devendran S, Lesker TR, Hern\u0026aacute;ndez SB, et al: \u003cstrong\u003eA collection of bacterial isolates from the pig intestine reveals functional and taxonomic diversity.\u003c/strong\u003e \u003cem\u003eNature Communications \u003c/em\u003e2020, \u003cstrong\u003e11:\u003c/strong\u003e6389.\u003c/li\u003e\n\u003cli\u003eQuast C, Pruesse E, Yilmaz P, Gerken J, Schweer T, Yarza P, Peplies J, Gl\u0026ouml;ckner FO: \u003cstrong\u003eThe SILVA ribosomal RNA gene database project: improved data processing and web-based tools.\u003c/strong\u003e \u003cem\u003eNucleic Acids Research \u003c/em\u003e2013, \u003cstrong\u003e41:\u003c/strong\u003eD590-D596.\u003c/li\u003e\n\u003cli\u003eFu L, Niu B, Zhu Z, Wu S, Li W: \u003cstrong\u003eCD-HIT: accelerated for clustering the next-generation sequencing data.\u003c/strong\u003e \u003cem\u003eBioinformatics \u003c/em\u003e2012, \u003cstrong\u003e28:\u003c/strong\u003e3150-3152.\u003c/li\u003e\n\u003cli\u003ePedron R, Esposito A, Bianconi I, Pasolli E, Tett A, Asnicar F, Cristofolini M, Segata N, Jousson O: \u003cstrong\u003eGenomic and metagenomic insights into the microbial community of a thermal spring.\u003c/strong\u003e \u003cem\u003eMicrobiome \u003c/em\u003e2019, \u003cstrong\u003e7:\u003c/strong\u003e8.\u003c/li\u003e\n\u003cli\u003eChen S: \u003cstrong\u003eUltrafast one-pass FASTQ data preprocessing, quality control, and deduplication using fastp.\u003c/strong\u003e \u003cem\u003eiMeta \u003c/em\u003e2023, \u003cstrong\u003e2:\u003c/strong\u003ee107.\u003c/li\u003e\n\u003cli\u003ePrjibelski A, Antipov D, Meleshko D, Lapidus A, Korobeynikov A: \u003cstrong\u003eUsing SPAdes De Novo Assembler.\u003c/strong\u003e \u003cem\u003eCurrent Protocols in Bioinformatics \u003c/em\u003e2020, \u003cstrong\u003e70:\u003c/strong\u003ee102.\u003c/li\u003e\n\u003cli\u003eLangmead B, Salzberg SL: \u003cstrong\u003eFast gapped-read alignment with Bowtie 2.\u003c/strong\u003e \u003cem\u003eNature Methods \u003c/em\u003e2012, \u003cstrong\u003e9:\u003c/strong\u003e357-359.\u003c/li\u003e\n\u003cli\u003eLi D, Liu C-M, Luo R, Sadakane K, Lam T-W: \u003cstrong\u003eMEGAHIT: an ultra-fast single-node solution for large and complex metagenomics assembly via succinct de Bruijn graph.\u003c/strong\u003e \u003cem\u003eBioinformatics \u003c/em\u003e2015, \u003cstrong\u003e31:\u003c/strong\u003e1674-1676.\u003c/li\u003e\n\u003cli\u003eLi H, Durbin R: \u003cstrong\u003eFast and accurate short read alignment with Burrows\u0026ndash;Wheeler transform.\u003c/strong\u003e \u003cem\u003eBioinformatics \u003c/em\u003e2009, \u003cstrong\u003e25:\u003c/strong\u003e1754-1760.\u003c/li\u003e\n\u003cli\u003eDanecek P, Bonfield JK, Liddle J, Marshall J, Ohan V, Pollard MO, Whitwham A, Keane T, McCarthy SA, Davies RM, Li H: \u003cstrong\u003eTwelve years of SAMtools and BCFtools.\u003c/strong\u003e \u003cem\u003eGigaScience \u003c/em\u003e2021, \u003cstrong\u003e10:\u003c/strong\u003egiab008.\u003c/li\u003e\n\u003cli\u003eKang DD, Li F, Kirton E, Thomas A, Egan R, An H, Wang Z: \u003cstrong\u003eMetaBAT 2: an adaptive binning algorithm for robust and efficient genome reconstruction from metagenome assemblies.\u003c/strong\u003e \u003cem\u003ePeerJ \u003c/em\u003e2019, \u003cstrong\u003e7:\u003c/strong\u003ee7359.\u003c/li\u003e\n\u003cli\u003eChklovski A, Parks DH, Woodcroft BJ, Tyson GW: \u003cstrong\u003eCheckM2: a rapid, scalable and accurate tool for assessing microbial genome quality using machine learning.\u003c/strong\u003e \u003cem\u003eNature Methods \u003c/em\u003e2023, \u003cstrong\u003e20:\u003c/strong\u003e1203-1212.\u003c/li\u003e\n\u003cli\u003eParks DH, Rinke C, Chuvochina M, Chaumeil P-A, Woodcroft BJ, Evans PN, Hugenholtz P, Tyson GW: \u003cstrong\u003eRecovery of nearly 8,000 metagenome-assembled genomes substantially expands the tree of life.\u003c/strong\u003e \u003cem\u003eNature Microbiology \u003c/em\u003e2017, \u003cstrong\u003e2:\u003c/strong\u003e1533-1542.\u003c/li\u003e\n\u003cli\u003eOlm MR, Brown CT, Brooks B, Banfield JF: \u003cstrong\u003edRep: a tool for fast and accurate genomic comparisons that enables improved genome recovery from metagenomes through de-replication.\u003c/strong\u003e \u003cem\u003eThe ISME Journal \u003c/em\u003e2017, \u003cstrong\u003e11:\u003c/strong\u003e2864-2868.\u003c/li\u003e\n\u003cli\u003eChaumeil P-A, Mussig AJ, Hugenholtz P, Parks DH: \u003cstrong\u003eGTDB-Tk: a toolkit to classify genomes with the Genome Taxonomy Database.\u003c/strong\u003e \u003cem\u003eBioinformatics \u003c/em\u003e2020, \u003cstrong\u003e36:\u003c/strong\u003e1925-1927.\u003c/li\u003e\n\u003cli\u003eParks DH, Chuvochina M, Rinke C, Mussig AJ, Chaumeil P-A, Hugenholtz P: \u003cstrong\u003eGTDB: an ongoing census of bacterial and archaeal diversity through a phylogenetically consistent, rank normalized and complete genome-based taxonomy.\u003c/strong\u003e \u003cem\u003eNucleic Acids Research \u003c/em\u003e2022, \u003cstrong\u003e50:\u003c/strong\u003eD785-D794.\u003c/li\u003e\n\u003cli\u003eLetunic I, Bork P: \u003cstrong\u003eInteractive Tree Of Life (iTOL) v5: an online tool for phylogenetic tree display and annotation.\u003c/strong\u003e \u003cem\u003eNucleic Acids Research \u003c/em\u003e2021, \u003cstrong\u003e49:\u003c/strong\u003eW293-W296.\u003c/li\u003e\n\u003cli\u003eHyatt D, Chen G-L, LoCascio PF, Land ML, Larimer FW, Hauser LJ: \u003cstrong\u003eProdigal: prokaryotic gene recognition and translation initiation site identification.\u003c/strong\u003e \u003cem\u003eBMC Bioinformatics \u003c/em\u003e2010, \u003cstrong\u003e11:\u003c/strong\u003e119.\u003c/li\u003e\n\u003cli\u003eSteinegger M, S\u0026ouml;ding J: \u003cstrong\u003eMMseqs2 enables sensitive protein sequence searching for the analysis of massive data sets.\u003c/strong\u003e \u003cem\u003eNature Biotechnology \u003c/em\u003e2017, \u003cstrong\u003e35:\u003c/strong\u003e1026-1028.\u003c/li\u003e\n\u003cli\u003eKanehisa M, Goto S: \u003cstrong\u003eKEGG: Kyoto Encyclopedia of Genes and Genomes.\u003c/strong\u003e \u003cem\u003eNucleic Acids Research \u003c/em\u003e2000, \u003cstrong\u003e28:\u003c/strong\u003e27-30.\u003c/li\u003e\n\u003cli\u003eDrula E, Garron M-L, Dogan S, Lombard V, Henrissat B, Terrapon N: \u003cstrong\u003eThe carbohydrate-active enzyme database: functions and literature.\u003c/strong\u003e \u003cem\u003eNucleic Acids Research \u003c/em\u003e2022, \u003cstrong\u003e50:\u003c/strong\u003eD571-D577.\u003c/li\u003e\n\u003cli\u003eBuchfink B, Xie C, Huson DH: \u003cstrong\u003eFast and sensitive protein alignment using DIAMOND.\u003c/strong\u003e \u003cem\u003eNature Methods \u003c/em\u003e2015, \u003cstrong\u003e12:\u003c/strong\u003e59-60.\u003c/li\u003e\n\u003cli\u003eLiu C, Cui Y, Li X, Yao M: \u003cstrong\u003emicroeco: an R package for data mining in microbial community ecology.\u003c/strong\u003e \u003cem\u003eFEMS Microbiology Ecology \u003c/em\u003e2021, \u003cstrong\u003e97:\u003c/strong\u003efiaa255.\u003c/li\u003e\n\u003cli\u003eZhang X-X, Lv Q-B, Yan Q-L, Zhang Y, Guo R-C, Meng J-X, Ma H, Qin S-Y, Zhu Q-H, Li C-Q, et al: \u003cstrong\u003eA Catalog of over 5,000 Metagenome-Assembled Microbial Genomes from the Caprinae Gut Microbiota.\u003c/strong\u003e \u003cem\u003eMicrobiology Spectrum \u003c/em\u003e2022, \u003cstrong\u003e10:\u003c/strong\u003ee02211-02222.\u003c/li\u003e\n\u003cli\u003ePatil KR, Nielsen J: \u003cstrong\u003eUncovering transcriptional regulation of metabolism by using metabolic network topology.\u003c/strong\u003e \u003cem\u003eProceedings of the National Academy of Sciences \u003c/em\u003e2005, \u003cstrong\u003e102:\u003c/strong\u003e2685-2689.\u003c/li\u003e\n\u003cli\u003eAlcock BP, Huynh W, Chalil R, Smith KW, Raphenya Amogelang R, Wlodarski MA, Edalatmand A, Petkau A, Syed SA, Tsang KK, et al: \u003cstrong\u003eCARD 2023: expanded curation, support for machine learning, and resistome prediction at the Comprehensive Antibiotic Resistance Database.\u003c/strong\u003e \u003cem\u003eNucleic Acids Research \u003c/em\u003e2023, \u003cstrong\u003e51:\u003c/strong\u003eD690-D699.\u003c/li\u003e\n\u003cli\u003eP\u0026auml;rn\u0026auml;nen K, Karkman A, Hultman J, Lyra C, Bengtsson-Palme J, Larsson DGJ, Rautava S, Isolauri E, Salminen S, Kumar H, et al: \u003cstrong\u003eMaternal gut and breast milk microbiota affect infant gut antibiotic resistome and mobile genetic elements.\u003c/strong\u003e \u003cem\u003eNature Communications \u003c/em\u003e2018, \u003cstrong\u003e9:\u003c/strong\u003e3891.\u003c/li\u003e\n\u003cli\u003eKumar L, Futschik MEJB: \u003cstrong\u003eMfuzz: a software package for soft clustering of microarray data.\u003c/strong\u003e 2007, \u003cstrong\u003e2:\u003c/strong\u003e5.\u003c/li\u003e\n\u003cli\u003eLi M-H, Meng J-X, Wang W, He M, Zhao Z-Y, Ma N, Lv Q-B, Qin Y-F, Geng H-L, Zhao Q, et al: \u003cstrong\u003eDynamic description of temporal changes of gut microbiota in broilers.\u003c/strong\u003e \u003cem\u003ePoultry Science \u003c/em\u003e2022, \u003cstrong\u003e101:\u003c/strong\u003e102037.\u003c/li\u003e\n\u003cli\u003eJulian RJ: \u003cstrong\u003eRapid growth problems: ascites and skeletal deformities in broilers.\u003c/strong\u003e \u003cem\u003ePoultry Science \u003c/em\u003e1998, \u003cstrong\u003e77:\u003c/strong\u003e1773-1780.\u003c/li\u003e\n\u003cli\u003eBirkle I, Klink-Lehmann J, Hartmann M: \u003cstrong\u003eDifferent and alike: Level and determinants of public acceptance of fattening pig, beef cattle and broiler farming in Germany.\u003c/strong\u003e \u003cem\u003eMeat Science \u003c/em\u003e2022, \u003cstrong\u003e193:\u003c/strong\u003e108946.\u003c/li\u003e\n\u003cli\u003eChu DM, Ma J, Prince AL, Antony KM, Seferovic MD, Aagaard KM: \u003cstrong\u003eMaturation of the infant microbiome community structure and function across multiple body sites and in relation to mode of delivery.\u003c/strong\u003e \u003cem\u003eNature Medicine \u003c/em\u003e2017, \u003cstrong\u003e23:\u003c/strong\u003e314-326.\u003c/li\u003e\n\u003cli\u003eDominguez-Bello MG, Costello EK, Contreras M, Magris M, Hidalgo G, Fierer N, Knight R: \u003cstrong\u003eDelivery mode shapes the acquisition and structure of the initial microbiota across multiple body habitats in newborns.\u003c/strong\u003e \u003cem\u003eProceedings of the National Academy of Sciences \u003c/em\u003e2010, \u003cstrong\u003e107:\u003c/strong\u003e11971-11975.\u003c/li\u003e\n\u003cli\u003eLiu J, Taft DH, Maldonado-Gomez MX, Johnson D, Treiber ML, Lemay DG, DePeters EJ, Mills DA: \u003cstrong\u003eThe fecal resistome of dairy cattle is associated with diet during nursing.\u003c/strong\u003e \u003cem\u003eNature Communications \u003c/em\u003e2019, \u003cstrong\u003e10:\u003c/strong\u003e4406.\u003c/li\u003e\n\u003cli\u003eZaneveld JR, McMinds R, Vega Thurber R: \u003cstrong\u003eStress and stability: applying the Anna Karenina principle to animal microbiomes.\u003c/strong\u003e \u003cem\u003eNature Microbiology \u003c/em\u003e2017, \u003cstrong\u003e2:\u003c/strong\u003e17121.\u003c/li\u003e\n\u003cli\u003eDini-Andreote F, Stegen JC, van Elsas JD, Salles JF: \u003cstrong\u003eDisentangling mechanisms that mediate the balance between stochastic and deterministic processes in microbial succession.\u003c/strong\u003e \u003cem\u003eProceedings of the National Academy of Sciences \u003c/em\u003e2015, \u003cstrong\u003e112:\u003c/strong\u003eE1326-E1332.\u003c/li\u003e\n\u003cli\u003eAbdugheni R, Wang W-Z, Wang Y-J, Du M-X, Liu F-L, Zhou N, Jiang C-Y, Wang C-Y, Wu L, Ma J, et al: \u003cstrong\u003eMetabolite profiling of human-originated Lachnospiraceae at the strain level.\u003c/strong\u003e \u003cem\u003eiMeta \u003c/em\u003e2022, \u003cstrong\u003e1:\u003c/strong\u003ee58.\u003c/li\u003e\n\u003cli\u003eDebray R, Herbert RA, Jaffe AL, Crits-Christoph A, Power ME, Koskella B: \u003cstrong\u003ePriority effects in microbiome assembly.\u003c/strong\u003e \u003cem\u003eNature Reviews Microbiology \u003c/em\u003e2022, \u003cstrong\u003e20:\u003c/strong\u003e109-121.\u003c/li\u003e\n\u003cli\u003eB\u0026acirc; A: \u003cstrong\u003eEffects of thiamine deficiency on food intake and body weight increment in adult female and growing rats.\u003c/strong\u003e \u003cem\u003eBehavioural Pharmacology \u003c/em\u003e2012, \u003cstrong\u003e23\u003c/strong\u003e.\u003c/li\u003e\n\u003cli\u003eLi P, Wu G: \u003cstrong\u003eRoles of dietary glycine, proline, and hydroxyproline in collagen synthesis and animal growth.\u003c/strong\u003e \u003cem\u003eAmino Acids \u003c/em\u003e2018, \u003cstrong\u003e50:\u003c/strong\u003e29-38.\u003c/li\u003e\n\u003cli\u003eRizzoli R, Stevenson JC, Bauer JM, van Loon LJC, Walrand S, Kanis JA, Cooper C, Brandi M-L, Diez-Perez A, Reginster J-Y: \u003cstrong\u003eThe role of dietary protein and vitamin D in maintaining musculoskeletal health in postmenopausal women: A consensus statement from the European Society for Clinical and Economic Aspects of Osteoporosis and Osteoarthritis (ESCEO).\u003c/strong\u003e \u003cem\u003eMaturitas \u003c/em\u003e2014, \u003cstrong\u003e79:\u003c/strong\u003e122-132.\u003c/li\u003e\n\u003cli\u003eMahmood T, Guo Y: \u003cstrong\u003eDietary fiber and chicken microbiome interaction: Where will it lead to?\u003c/strong\u003e \u003cem\u003eAnimal Nutrition \u003c/em\u003e2020, \u003cstrong\u003e6:\u003c/strong\u003e1-8.\u003c/li\u003e\n\u003cli\u003eden Besten G, Lange K, Havinga R, van Dijk TH, Gerding A, van Eunen K, M\u0026uuml;ller M, Groen AK, Hooiveld GJ, Bakker BM, Reijngoud D-J: \u003cstrong\u003eGut-derived short-chain fatty acids are vividly assimilated into host carbohydrates and lipids.\u003c/strong\u003e \u003cem\u003eAmerican Journal of Physiology-Gastrointestinal and Liver Physiology \u003c/em\u003e2013, \u003cstrong\u003e305:\u003c/strong\u003eG900-G910.\u003c/li\u003e\n\u003cli\u003eSantangelo A, Corsello A, Spolidoro GC, Trovato CM, Agostoni C, Orsini A, Milani GP, Peroni DG: \u003cstrong\u003eThe Influence of Ketogenic Diet on Gut Microbiota: Potential Benefits, Risks and Indications.\u003c/strong\u003e In \u003cem\u003eNutrients\u003c/em\u003e, vol. 15; 2023.\u003c/li\u003e\n\u003cli\u003eZhou Y, Fu H, Yang H, Wu J, Chen Z, Jiang H, Liu M, Liu Q, Huang L, Gao J, Chen C: \u003cstrong\u003eExtensive metagenomic analysis of the porcine gut resistome to identify indicators reflecting antimicrobial resistance.\u003c/strong\u003e \u003cem\u003eMicrobiome \u003c/em\u003e2022, \u003cstrong\u003e10:\u003c/strong\u003e39.\u003c/li\u003e\n\u003cli\u003eLi C, Li X, Guo R, Ni W, Liu K, Liu Z, Dai J, Xu Y, Abduriyim S, Wu Z, et al: \u003cstrong\u003eExpanded catalogue of metagenome-assembled genomes reveals resistome characteristics and athletic performance-associated microbes in horse.\u003c/strong\u003e \u003cem\u003eMicrobiome \u003c/em\u003e2023, \u003cstrong\u003e11:\u003c/strong\u003e7.\u003c/li\u003e\n\u003cli\u003eCao J, Hu Y, Liu F, Wang Y, Bi Y, Lv N, Li J, Zhu B, Gao GF: \u003cstrong\u003eMetagenomic analysis reveals the microbiome and resistome in migratory birds.\u003c/strong\u003e \u003cem\u003eMicrobiome \u003c/em\u003e2020, \u003cstrong\u003e8:\u003c/strong\u003e26.\u003c/li\u003e\n\u003cli\u003eKhachatryan Artashes R, Hancock Dale D, Besser Thomas E, Call Douglas R: \u003cstrong\u003eRole of Calf-Adapted Escherichia coli in Maintenance of Antimicrobial Drug Resistance in Dairy Calves.\u003c/strong\u003e \u003cem\u003eApplied and Environmental Microbiology \u003c/em\u003e2004, \u003cstrong\u003e70:\u003c/strong\u003e752-757.\u003c/li\u003e\n\u003cli\u003eZhang Z, Zhang Q, Wang T, Xu N, Lu T, Hong W, Penuelas J, Gillings M, Wang M, Gao W, Qian H: \u003cstrong\u003eAssessment of global health risk of antibiotic resistance genes.\u003c/strong\u003e \u003cem\u003eNature Communications \u003c/em\u003e2022, \u003cstrong\u003e13:\u003c/strong\u003e1553.\u003c/li\u003e\n\u003cli\u003eRedpath A, Hallowell GD, Bowen IM: \u003cstrong\u003eUse of aminoglycoside antibiotics in equine clinical practice; a questionnaire-based study of current use.\u003c/strong\u003e \u003cem\u003eVeterinary Medicine and Science \u003c/em\u003e2021, \u003cstrong\u003e7:\u003c/strong\u003e279-288.\u003c/li\u003e\n\u003cli\u003eZhang L, Li H, Gao J, Gao J, Wei D, Qi Y: \u003cstrong\u003eIdentification of drug-resistant phenotypes and resistance genes in Enterococcus faecalis isolates from animal feces originating in Xinjiang, People\u0026rsquo;s Republic of China.\u003c/strong\u003e \u003cem\u003eCanadian Journal of Animal Science \u003c/em\u003e2020, \u003cstrong\u003e100:\u003c/strong\u003e674-682.\u003c/li\u003e\n\u003cli\u003ePartridge Sally R, Kwong Stephen M, Firth N, Jensen Slade O: \u003cstrong\u003eMobile Genetic Elements Associated with Antimicrobial Resistance.\u003c/strong\u003e \u003cem\u003eClinical Microbiology Reviews \u003c/em\u003e2018, \u003cstrong\u003e31:\u003c/strong\u003e10.1128/cmr.00088-00017.\u003c/li\u003e\n\u003cli\u003eYang J, Tong C, Xiao D, Xie L, Zhao R, Huo Z, Tang Z, Hao J, Zeng Z, Xiong W: \u003cstrong\u003eMetagenomic Insights into Chicken Gut Antibiotic Resistomes and Microbiomes.\u003c/strong\u003e \u003cem\u003eMicrobiology Spectrum \u003c/em\u003e2022, \u003cstrong\u003e10:\u003c/strong\u003ee01907-01921.\u003c/li\u003e\n\u003cli\u003eZhou L, Xu P, Gong J, Huang S, Chen W, Fu B, Zhao Z, Huang X: \u003cstrong\u003eMetagenomic profiles of the resistome in subtropical estuaries: Co-occurrence patterns, indicative genes, and driving factors.\u003c/strong\u003e \u003cem\u003eScience of The Total Environment \u003c/em\u003e2022, \u003cstrong\u003e810:\u003c/strong\u003e152263.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Metagenomics, culturomics, broiler, gut microbiota, temporal fluctuation, resistome","lastPublishedDoi":"10.21203/rs.3.rs-4623220/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4623220/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eUnderstanding the dynamics and stability of gut microbiota throughout the production cycle of broiler chickens can help identify microbial features associated with better health and productivity. In the present study, we profile changes in the composition and stability of gut microbiota of commercially raised broilers at nine distinct time points using shotgun metagenomics and culturomics approaches. We demonstrate that within the first week post-hatching, there is a rapid decline in pioneer microbial species, accompanied by a substantial decrease in both microbial richness and diversity. This is followed by a gradual increase and stabilization in microbial diversity and population structure, persisting until the broilers reach marketing age. Throughout the production cycle, key bacterial families such as \u003cem\u003eLachnospiraceae\u003c/em\u003e, \u003cem\u003eBacteroidaceae\u003c/em\u003e, and \u003cem\u003eRuminococcaceae\u003c/em\u003e were identified. However, significant shifts at lower taxonomic levels occur at different production stages, influencing the functional capacities and resistance profiles of the microbiota. During the rapid growth phase, enzymes crucial to vitamin and amino acid metabolism dominate, whereas enzymes associated with carbohydrate and energy metabolism are notably more abundant during the fattening stage. Many predicted antibiotic resistance genes are detected in association with typical commensal bacterial species in the gut microbiota, indicating sustained resistance to antibiotic classes such as aminoglycosides and tetracyclines, which persists even in the absence of antibiotic selection pressure. Our research has important implications for the management and health surveillance of broiler production.\u003c/p\u003e","manuscriptTitle":"Temporal patterns in gut microbiome and resistome of broilers: diversity and function analysis","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-07-16 15:18:38","doi":"10.21203/rs.3.rs-4623220/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"13204261-c7a1-4d2a-b4e3-f88f607e075f","owner":[],"postedDate":"July 16th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2024-08-28T09:44:23+00:00","versionOfRecord":[],"versionCreatedAt":"2024-07-16 15:18:38","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-4623220","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4623220","identity":"rs-4623220","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}