Introduction

Village chickens are the most vital and major poultry species owned by smallholder farmers with very low production inputs in rural areas of South Africa1. They are used in festivals and cultural ceremonies; and can be exchanged for work rendered or sometimes handed as a gift to visitors. They exert a substantial influence on the national economy by enhancing the nutritional well-being of the community and increasing the income of numerous smallholder farmers, as well as marginalized landless communities2.

Village chickens are usually not provided with shelter, feed, or water. As a result, they are left to scavenge for feed which leads to them ingesting foreign materials, including the adhered microorganisms (microbes) which play both beneficial and detrimental roles in different contexts and are usually detected in faeces and soil. They often pick up microbes from other habitats as they scavenge for feed and are transferred to the chicken gastrointestinal tract (GIT)3,4 and may subsequently become a source of infection5. Microbes are defined as microbial populations which usually inhabit the GIT6 and constitute at least 100 times more genetic material of microbial cells than that of the somatic cells of the host. The interactions between the chickens and intestinal microbes primarily play a role in the exchange of nutrients, modulation of the host immune system, the physiology of the digestive system, and exclusion of pathogens7.

Hence, it is essential to understand the role of chicken gastrointestinal microbiota. This also helps to identify pathogenic bacteria as they have been reported amongst the gut microbiota, and these include Salomonella enterica, Campylobacter, Clostridium perfringens, and Escherichia coli7. Moreover, it is important to understand the current methods used in microbiome research in order to improve the GIT microbiome in poultry. Hence the introduction of bacterial 16S rRNA gene sequencing has dramatically improved scientists’ understanding of the composition and diversity of the chicken gut microbiota. However, in South Africa, there is a lack of studies profiling the GIT microbiota of village chickens which may harbour pathogenic microbes that can cause infection in humans8. The main aim of the current study was to characterize the village chicken gut microbiome using 16S rRNA amplicon sequencing.

Results

Sequencing processing and data analysis

The de-multiplexed sequences were obtained from 34 samples, with the number of sequences ranging from 42,028 to 750,998 per sample after filtering and trimming for quality. This resulted in a total of 3,407 ASVs with a frequency of 1,060,523 across all samples at the 97% sequence similarity value. The datasets generated and analysed during the current study are available in the Sequence Read Archive of the National Centre for Biotechnology Information repository, https://www.ncbi.nlm.nih.gov/bioproject/PRJNA1070440.

The proportion of amplicon sequence variants (ASVs) shared by male and female chickens across different villages

As shown in Fig. 1, unique and shared ASVs associated with male and female (a) microbiota as well as from different villages (b) are represented in a form of venn diagrams. A total of 1994 unique bacterial ASVs across the three villages were identified: Thakgalang village (144), Itieleni village (1438), and Senwamokgope village (412) and a total of 398 ASVs were shared by all three villages. The results also revealed a total of 782, 129 and 104 ASVs shared between Itieleni and Senwamokgope, Thakgalang and Itieleni, and Thakgalang and Senwamokgope villages respectively. Figure 1a shows the unique and shared ASVs between males and female chickens. The results revealed that for male chickens, 453 ASVs were obtained whilst the highest number of 1738 ASVs were obtained for female chickens. A total of 1216 ASVs were shared between both male and female chickens.

Fig. 1
figure 1

Venn diagram illustrating unique and shared ASVs associated with male and female chicken intestinal microbiota (a) as well as different villages (b). Numbers indicate the number of ASVs within each sector.

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ASVs diversity and taxonomic annotation

The alpha rarefaction curves were obtained using observed species, Shannon and Simpson estimators. The richness of female intestinal microbiota was higher compared to the male microbiota. The rarefaction curve tended to attain the saturation plateau depicting that the microbiota of the 34 samples were large enough to estimate the richness and microbial community diversity at the 97% ASV threshold (Supplementary Fig. 1).

Microbial alpha diversity associated with female and male intestinal microbiota

The microbial diversity within samples was examined using alpha matrices such as Observed (richness), Shannon and Simpson (evenness). The results are depicted in the form of box plots in Fig. 2. The female microbiota was more diverse compared to the male microbiota across all the diversity metrics (Observed ASVs, Shannon and Simpson). No significant differences were observed between male and female intestinal microbiota when using Kruskal–Wallis tests (P > 0.05) (Supplementary Table 1). When comparing the microbial population between villages, the microbiota from Itieleni village was found to be more diverse than that from Senwamokgope and Thakgalang villages (Fig. 2).

Fig. 2
figure 2

Alpha-diversity box plots (using Observed, Shannon and Simpson phylogenetic diversity metrics) showing variation in alpha diversity from male and female chickens sampled from three different villages. The box represents the first (Q1) and third (Q3) quartiles of the distribution, and the line within the box marks the median.

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Similarity and differences in microbial community composition of the female and male intestinal microbiota

The principal coordinates analysis (PCoA) plots indicated that there was no significant variation between the female and male chicken microbial structure. These results indicate that the intestinal microbial community was similar because the female and male microbiota clustered together across 2 PCoA metrics (Fig. 3a). Permutational Analysis of Variance (PERMANOVA) was employed to statistically test the association of microbial composition and revealed that the microbial community between the male and female chickens was not significantly different (F = 1.0972, P = 0.29). Furthermore, the microbial composition was compared between villages (Fig. 3b) and the samples clustered together, which means that there was no significant difference between them (P = 0.42).

Fig. 3
figure 3

Principal coordinate analysis (PCoA) was generated to determine if samples had grouped into distinct clusters due to beta diversity (Bray curtis distances: sex (a) and villages (b)). PCoA plotted against the PC1 vs PC2 axis. The percentages indicate the relative contribution of the two principal coordinates (PC1-PC2).

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Taxonomic composition in the chicken intestine

The microbiota composition of the female and male chickens at the phylum level

Bacterial phyla associated with the chicken intestinal microbiota were obtained. The major top 4 phyla in the chicken intestinal content were Firmicutes, followed by Proteobacteria, Actinobacteria, and Bacteroidota (Fig. 4). The female chicken’s bacterial phylum was dominated by Firmicutes (73%), followed by Bacteroidata (10%), and Proteobacteria (9%) in comparison to the male chickens, which were dominated by Firmicutes (69%), followed by Bacteroidata (8%), and Proteobacteria (5%). Unassigned taxa were labelled as NA (Fig. 4).

Fig. 4
figure 4

Stacked bar plot showing the relative abundance of bacterial phyla in all intestinal samples of mature village chickens.

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Relative abundance of microbial composition of sampled villages

As expected, the majority of the microbial population across all three villages was made up of the genus Lactobacillus (Fig. 5). In Itieleni village, Lactobacillus accounted for 49%, however, it was almost non-existent in one of the samples labelled MR2. The second most abundant bacteria were Escherichia-Shigella (8%), followed by Bacteroides (4%), Enterococcus (2%), and Sphingomonas (1%). Senwamokgope village had the highest microbial composition and was made up of Lactobacillus (61%), Bacteroides (5%), Erysipelatoclostridium (2%), Escherichia-Shigella (1%), Sphingomonas (1%). Thakgalang village had the smallest sample number (6 samples), and it was dominated by Lactobacillus (56%), followed by Clostridium_sensu_stricto_1 (6%), and Sphingomonas (1%). The remaining bacterial genera were relatively lower than 0.09%. The sampled villages, Itieleni, Thakgalang, and Senwamokgope, were mostly dominated by the phylum Firmicutes. The Firmicutes accounted for 90% (Thakgalang), 78% (Senwamokgope), and 63% in Itieleni village.

Fig. 5
figure 5

Stacked bar graph showing microbial abundance of three villages at the genus level.

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Microbial composition of female and male chickens at genus level

The relative abundance of microbiota composition in the intestine of mature chickens was evaluated at the genus level (Fig. 6). Lactobacillus was the most abundant genus in the female chickens, with a proportion of 58%, followed by Escherichia-Shigella (6%), Bacteroides (3%), Enterococcus (2%), Cellulosilyticum (1%), Clostridium_sensu_stricto_1 (1%), and Sphingomonas (1%). The male chicken's microbiota composition was dominated by Lactobacillus (48%), Bacteroides (6%), Erysipelothrix (2%), Ureaplasma (2%), and Sphingomonas (1%). Sample RT2 was mostly dominated by the genus Actinomyces.

Fig. 6
figure 6

Microbial composition of top taxa in male and female chickens at genus level. The top bar on the legend represents the remaining microbial genera less than 0.01%.

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Heat map associated with gut microbial abundance and similarity in the intestine of each chicken

To determine the richness and diversity of bacterial community composition between male and female chickens from three different villages, the hierarchically clustered heatmap analysis was performed at the phylum, class, order, family and genus levels. The female and male microbiota did not cluster together, indicating that they harbour different and diverse microbial communities. The phylogenetic results corroborated the PCoA results which indicated the similarities and difference in the diversity of the bacterial communities found in chicken gut. As shown in Fig. 7, the dominant genus was the Lactobacillus in both the female and male groups. The hierarchical clustering on the heatmap also indicated that the majority of samples from Senwamokgope grouped together, showing that they harbour similar microbial communities.

Fig. 7
figure 7

The hierarchically clustered heat map analysis associated with the similarity of microbial community composition between male and female chickens from three different villages was performed. Rows represent the 30 predominant bacterial taxa (average abundance 0.01%). Columns represent the 34 samples. The legend is indicated in the top right corner of the figure.

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Functional prediction of microbial communities in female and male chickens

Figure 8 shows significant differences of predicted inferred MetaCyc pathways of bacterial communities in male and female chickens (p < 0.05). The male chicken exhibited notably higher relative abundance in methylaspartate cycle, chlorophyllide a biosynthesis II (anaerobic), chlorophyllide a biosynthesis III (aerobic, light independent), l-histidine degradation II, catechol degradation III (ortho-cleavage pathway), and aromatic compounds degradation via & beta-ketoadipate (Fig. 8). Conversely, methylaspartate cycle, aromatic compounds degradation via & beta-ketoadipate, catechol degradation III (ortho-cleavage pathway), superpathway of salicylate degradation, l-histidine degradation II, and catechol degradation to & beta-ketoadipate (Fig. 8). Overall, the male chickens had more enriched functional pathways compared to female chickens.

Fig. 8
figure 8

The functional prediction of microbial communities in female and male chickens. (A) MetaCyc pathways of bacterial communities in male and female chickens. (B) MetaCyc pathways description of bacterial communities in male and female chickens.

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Discussion

This study aimed to compare and understand the microbiome composition of male and female village chickens from three villages. It is evident that the prevalence and colonization of gut microbiota of village chickens have not been sufficiently characterized, more especially in South Africa. This metagenomic study was conducted on free-range village chickens as opposed to most of some studies that were conducted on chickens that are reared under regulated and controlled feeding systems20,21. Village chickens are primarily reared for provision of meat and/or eggs for household consumption, and generation of cash which subsequently alleviate poverty and food security22,23,24. They are also admired for their exceptional genetic diversity that allows them to thrive under harsh environmental conditions. However, the village chicken production system practiced in rural areas of South Africa is characterized by a lack of production inputs (feed, housing, and medication), low body weight, lack of government extension support, and production falls below average1,23,25. Scavenging chickens are exposed to the open air and environment and have greater contact with host organisms such as insects and the earthworm where they can be infested and act as intermediate hosts of some endoparasites26.

The chicken gut microbiota plays a significant role in chickens by facilitating the digestion of food, exclusion of disease-causing pathogens, stimulation and modulation of the immune system and metabolism5. Furthermore, it is much needed that the gut microbiota and the host interact continuously in order to provide stimuli for the host’s immune system. This will have a positive impact by keeping it activated and thus always ready for an immediate and appropriate response to viral, bacterial and/or protozoan pathogens.

The importance of conducting a research study that explores the gut microbiome of village chickens is to develop disease control measures to improve the health and productivity of village chickens. The main purpose of this study was to characterize the gut microbiota of mature mixed-gender chickens from three different villages. This computational study was achieved by employing a 16S ribosomal RNA (rRNA) gene amplicon sequencing analysis approach.

The alpha and beta diversity of these chickens was evaluated by comparing male chickens to female chickens. The current results showed that the microbiota of female chickens was more diverse and abundant than that of males. The variations in the microbiome of male and female chickens may be caused by reproductive needs, hormonal changes, and immune response27. Moreover, the higher microbial abundance in females might be attributed to egg development which does not occur in males. However, no significant differences were observed in the current study (P > 0.05). The results of PCoA showed that the microbial community between the male and female chickens and the microbial composition between villages clustered together, which means that there was no significant difference between them (P > 0.05). In addition, the hierarchically clustered heat map analysis showed that the intestinal microbial composition of the male and female were similar.

Several factors are known to influence the composition of bacterial communities in chicken GIT including diet, location and food additives28,29, sex and body weight30, age31 and geography32,33, as extensively reviewed by Kers et al.34. Moreover, host and environmental factors influence the gut microbiota. The current study did not investigate variables such as diet and food additives and age, however, sex and location variables were investigated, and our results indicated that female chickens harbour the greatest microbial diversity compared to males (Fig. 2). The current study showed that the male and female chicken microbiome was dominated by Proteobacteria, Actinobacteria, and Bacteroidota (Fig. 4). These results are in line with those previously observed by Bhogoju et al.6. Similarly, Xiao et al.35 and Xu et al.36 also reported Firmicutes, Bacteriotedes, and Proteobacteria as the most abundant phyla found in the small intestine of broiler chickens.

It is well established in the literature that the environmental factor in microbiota is more important than the host factor37,38. Previous studies have reported a higher proportion of Bacteriotedes, Firmicutes, and Proteobacteria in free-range cecum compared with the indoor group36,39. A recent study reported that extensive chickens had a higher abundance and diversity of microbiota compared to semi-intensive chickens26. Cecal microbiota in free-range mode are also reported to have higher abundance of functions involved in amino acids and glycan metabolic pathways40. Chickens raised indoors are exposed to more stresses, such as feeding density and space. The high microbial diversity in free-range chickens may be due to the earlier contact to the natural environment, thus, the diversity can be established earlier41. This was probably the case with this current study, as the tested samples were from free-range chickens.

Enterococcus spp. are naturally gut-oriented bacteria that prefer intestinal habitats and were reported in this study. These bacteria can be found in the gastrointestinal microbiota of various animals and humans42. They have the ability to tolerate many unfavourable conditions and can survive for several months in environments that are hostile43. They are considered harmless commensal bacteria, however, when the commensal relationship with the host is disturbed, enterococci are capable of causing aggressive infections44.

Escherichia-Shigella is a genus that is found universally in the gut of chickens, and it belongs to the family Enterobacteriaceae. The findings of this study showed the elevated population of Escherichia-Shigella in chickens from two out of three villages. Even though there was no significant variation between the female and male chicken microbial structure, other studies conducted by Lumpkins et al.45 and Zhao et al.27 indicated that the sex of an animal might be the crucial factor affecting gut bacterial communities, representing various bacterial ecosystems between female and male chickens. More studies conducted on a bigger population are required to establish if Escherichia-Shigella is correlated with sex in village chickens.

Apart from Lactobacillus and Escherichia-Shigella, Bacteroides genus appeared to be more dominant. Bacteroides genus belongs to the Bacteroidetes phylum46. In this study, the levels of Bacteroides were not too different in different villages, however, the levels were high in male animals compared to female animals. These findings are in line with the results reported by Lee et al.30 where male chickens were found to be related to the enrichment of Bacteroides. It is possible that Bacteroides might be playing a significant role in the gut of village chickens. A study by Yang et al.47 found Bacteroides to be the dominant taxa in broilers, with Yan et al.48 reporting that Bacteroides dominate the cecum microbiome (21–23%). The abundance of this genus in gut is known to be related to the amount of animal fat and protein in chicken feed. In addition, it is known for its fibre fermenting capacity46. Furthermore, its effect is to control pathogenic microbes such as Clostridium perfringens in chickens49,50.

The functional prediction of bacterial communities in male and female chickens reveals significant differences in their metabolic pathways, as illustrated in Fig. 8. These differences underscore the distinct roles that microbial communities play in the physiology of each sex. Understanding these differences can help optimize nutritional strategies and overall management in poultry farming. In this study, the male chickens exhibit a higher enrichment of functional pathways, suggesting a more diverse and metabolically active gut microbiome. The enriched metabolic pathways include Methylaspartate Cycle, Chlorophyllide a Biosynthesis II and III, L-Histidine Degradation II, Catechol Degradation III, Aromatic Compounds Degradation via β-Ketoadipate, and Superpathway of Salicylate Degradation. This could be attributed to the higher growth rates and different nutritional requirements of males, necessitating a broader range of microbial functions to support rapid protein synthesis and energy production51,52.

Conclusions

Village chickens play an important role in the livelihood of the many families in marginalized communities of South Africa. Metagenome analysis of village chickens is necessary to explore the impact of local fauna and flora on the health of chickens. Even though this study had several limitations which included different sample size between the three villages, lack of data on body weight and the actual age of the sampled chickens, the results of this study revealed the composition and diversity of bacterial microbiota that plays significant roles in chicken health and productivity. These findings can form baseline information that can be used to improve the overall productivity of chickens by developing disease control measures and increasing production inputs.

Methods

Study sites and sample collection

All experimental protocols for this study were approved by the UNISA-CAES Animal Research Ethics Committee (2020/CAES_AREC/165). Furthermore, all experiments were performed in accordance with SANS 10381 guidelines and regulations and all methods are reported in accordance with ARRIVE guidelines. The study was conducted in Itieleni, Thakgalang, and Senwamokgope villages in Limpopo Province, Mopani Districts, South Africa. Each village was divided into 4 sections and from each section smallholder farmers were randomly selected based on the availability of chickens. The distance between the three villages were between 8 and 17 km. Limpopo Province covers a total area of 125 755 km2 which is located in the savannah biome, a region with mixed grassland and trees commonly known as bushveld, with summer rainfall of 577 mm to 1000 mm/annum9,10. The Northern and Eastern areas of Limpopo Province are subtropical, with hot and humid summer and winter is mild and generally frost-free11. This province is located at 24.0000° S, 29.50000° E12.

Fifty live adult non-descript mixed-gender village chickens were purchased from Itieleni (19), Thakgalang (11), and Senwamokgope (20) villages in Mopani District, Limpopo Province. The apparently healthy chickens were left to scavenge for their feed to meet their nutritional need. The chickens were then slaughtered by cervical dislocation, and the gastrointestinal tract was removed from the proventriculus to the cloaca using a scissor. The small intestine region was cut open by dissection following the World Association for the Advancement of Veterinary Parasitology (W.A.A.V.P) guidelines13. The contents of the ileal part of the small intestine were placed inside collection tubes with 95% ethanol and immediately preserved in a freezer for subsequent microbial analysis. A pair of scissors was used to open the whole intestine in a longitudinal section. Gastrointestinal parasites visible to the naked eye were identified based on the morphological parameters using helminthological keys14 and chickens that were positive for parasites were excluded from the downstream analysis. A total of 34 live non-descript mixed gender chickens were found to be negative for parasites and were used for this study.

DNA extraction, library quantification and sequencing

Microbial DNA was extracted from intestinal contents using the Zymobiomic DNA Miniprep Kit, DNA for microbiome for metagenome analysis (Zymobiomics, UK, 2022) and quantified using a NanoDrop instrument (NanoDrop Technologies, Wilmington) and Qubit Fluorometric Quantification (Thermo Fisher Scientific, Waltham, MA, USA), following manufacturer’s protocol. The DNA concentration was diliuted to get a total of 5 ng/µl per sample before downstream analysis. The highly conserved V3-V4 region of the 16S rRNA gene was amplified by using its universal barcoded PCR primers 338 (F: 5′-CCTACGGGNGGCWGCAG-3′) and 806 (R: 5′GACTACHVGGGTATCTAATCC-3′) (Intergrated DNA Technologies, Coralville, United States). The primers are aimed to combine the Illumina and a sample barcode sequence, in order to enable directional sequencing that covers variable region V3-V4. A total PCR reaction volume of 25 µl consisted of 5 µl of extracted microbial DNA as the template, 5 µl of 10 µM of each primer and 12.5 µl 2× KAPA HiFi HotStart ReadyMix (Kapabiosystems, United States). PCR was performed on BIO-RAD T100™ Thermal Cycler (Bio-Rad Laboratories, United Kingdom) with an initial denaturation at 95 °C for 5 min followed by 30 cycles of denaturation/amplifications (95 °C, 30 s), annealing at 56 °C for 30 s, and elongation at 72 °C for 40 s, and a final 10 min elongation at 72 °C.

Gel electrophoresis was run on PCR products in order to determine the success of amplifications. The barcoded DNA amplicons were analyzed on a 1% (w/v) agarose gel and gel electrophoresis was run at 100 V for 45 minutes. The sizes of the DNA bands were visualized under UV light. An Additional index PCR was carried out by adding 2 sequencing primers to enable dual-index barcoding method on the Miseq. Amplicon libraries were sequenced using Illumina Miseq sequencing platform (ARC-BTP, SA) following the manufacturer’s instructions. The libraries for sequencing were prepared by mixing the pooled amplicons with PhiX control DNA purchased from Illumina. Pooled libraries were denatured with NaOH, diluted with hybridization buffer and heat denatured before MiSeq sequencing. Paired-end reads of 300 x 2 base pairs were generated with addition of 5% PhiX, in length in each direction.

Data processing and taxonomic classification

The fastq files generated as an Illumina sequencing output were analysed using QIIME2 and visualised in R Studio (version 4.1.3). Firstly, demultiplexed paired-end fastq reads and metadata files were imported into the Quantitative Insights Into Mi-crobial Ecology 2 (QIIME2, version 2-2022.2.0) pipeline for data filtering, quality control, and analysis. The Divisive amplicon Denoising Algorithm 2 (DADA2 v1.26.0) software which is wrapped in QIIME2 was used for quality filtering to get operational sequences. All remaining high-quality reads were aligned and clustered into Amplicon Sequence Variants (ASVs) at a similarity threshold of 97%. This function feature classifier was then used to perform taxonomic classification of the obtained ASVs. The ASVs were then categorized into distinct taxonomic levels by the SILVA 138 database. Moreover, the clustered ASVs were also used to construct the rarefaction curves and calculate the Shannon and Simpson diversity indices.

Principal coordinate analysis (PCoA) was carried out by employing the default beta diversity metrics namely: Bray-Curtis distance, weighted UniFrac distance (which takes into consideration taxa abundance) and unweighted UniFrac (which measures the fraction of unique branch length), to determine if samples had grouped into distinct clusters due to beta diversity. The PCoA was conducted based on the weighted Unifrac distance15. QIIME2 was also used to conduct microbial composition. Alpha diversity was assessed using the Simpson, Shannon and Observed ASVs. The diversity index was analysed statistically using Permutational analysis variance and significant differences between group means were determined using the least significant difference Kruskal Wallis test. Kruskal Wallis test is a non-parametric test used to compare two or more independent samples in order to find out whether the samples come from the same distribution16.

Functional prediction of microbial communities

For functional prediction of microbial communities, PICRUSt217 was used which predicts the functions of microbial communities based on marker gene sequencing profiles using MetaCyc Metabolic Pathway Database18. The picrust2_pipeline.py command runs the default pipeline with the filtered sequences and table input for pathway analyses. The statistical analysis of metagenomic profiles (STAMP)19 was used to compare the functional features between male and female chickens using the Kruskal-Wallis test (p < 0.05).