Metagenomic data insights into chicken microbiome diversity across various regions of Kazakhstan

Abstract

Understanding the gut microbiome of poultry is essential for ensuring the health, productivity, and safety of poultry products. This study aimed to assess the regional diversity and composition of chicken microbiota in Kazakhstan using high-throughput metagenomic sequencing. Tracheal and cloacal swabs were collected from chickens on private farms in five geographic locations. Pooled DNA and RNA samples were sequenced using the Ion Torrent PGM platform, and taxonomic classification was performed using Kaiju, with subsequent alpha and beta diversity analyses in R. The results revealed considerable differences in the microbial profiles between regions. Notably, Chlamydia was abundant in the Shymkent samples (>48 %) but was nearly absent elsewhere. In contrast, Pseudomonas was disproportionately dominant in Almaty (32.7 %), suggesting possible dysbiosis. This study provides the first metagenomic characterization of poultry microbiota in Kazakhstan. This highlights region-specific microbial risks and underscores the importance of spatial microbiome monitoring in poultry health management. These findings provide a basis for future strategies aimed at preventing disease outbreaks and controlling zoonotic pathogens in poultry.

Keywords: Microbiota; Next-generation sequencing; Poultry.

Fig. 1

Geographic distribution of sampling locations in poultry farms across four regions of Kazakhstan: Almaty, Shymkent (1 and 2), Turkestan, and Saryagash. Coordinates represent approximate sampling centroids. Each point marks a location where tracheal and cloacal swabs were collected. Samples labeled as Shymkent_1 and Shymkent_2 were obtained from the same geographic location but differ by host age: birds in Shymkent_1 were 623 days old, while those in Shymkent_2 were 197 days old. These differences are summarized in Table 1.

Fig. 2

Relative abundance of the most prevalent bacterial genera identified in pooled metagenomic samples collected from laying hens in different regions of Kazakhstan (Almaty, Shymkent_1, Shymkent_2, Turkestan, Saryagash). Taxonomic classification was performed at the genus level using Kaiju. Each bar represents the pooled microbiota profile of a regional sample. Y-axis denotes percentage of classified reads.

Fig. 3

Alpha diversity indices (Simpson and Shannon) of gut microbiota in laying hens across different Kazakhstan regions. Each dot represents a diversity index for a pooled sample from one region. Y-axis indicates alpha diversity value. Higher values reflect greater bacterial richness and evenness.

Fig. 4

Non-metric multidimensional scaling (NMDS) ordination plot of beta diversity (Bray-Curtis dissimilarity) among poultry gut microbiota from four sampling regions. Each dot represents a pooled sample from a region. Distances between points reflect dissimilarity in microbiota composition.

Fig. 5

Principal Coordinates Analysis (PCoA) of beta diversity in poultry gut microbiota across four Kazakhstan regions based on Bray–Curtis dissimilarity. Each dot represents a regional sample; axes show percentage of variation explained (PCoA1=53.3 %, PCoA2=28.4 %).