Effect of probiotic on fecal microbiome of broiler chicken reared at two different high altitude locations

Abstract

Poultry farming in high altitude like Leh-Ladakh, India is a challenge due to hypobaric–hypoxia environment and probiotic may support gut health, feed efficiency, production and adaptation in chicken. Therefore, the present study was performed to elucidate the role of probiotic on broiler chicken gut microflora from two different high altitude locations; Leh and Chang La. No change was found on the growth of the broiler with probiotic intervention. The fecal microbiota study revealed that the microbial abundance was higher in Chang La samples as compared to Leh samples. At taxonomic level, Chang La samples were dominated by Firmicutes, Proteobacteria, Bacteroides, Actinobacteria phylum whereas in Leh samples, Elusimicrobia was dominant followed by Nitrospirae. Probiotic treatment had minimal effect on chicken gut microflora at the genus and species level. The probiotic effect on gut microflora of Leh broilers was more diverse as compared to the control group. Contrary, to the Chang La group, the probiotic effect had less diverse microbial population as compared to the control group; maintaining the gut microbiome homeostasis of broiler chicken in such extreme high altitude. The microbial functional annotation revealed differences majorly in pathways related to maintenance of cellular energy homeostasis, glycan and carbohydrate metabolisms, regulatory systems, signaling pathways, environmental processing and cell–cell signaling. The findings of the study are an indicative of diversified gut microflora of broiler chicken from two distinct high altitude locations. The study can further be useful to explore the role of different probiotic strains, their doses or delivery methods for the adaptation of these birds at such extreme environments.

Supplementary Information

The online version contains supplementary material available at 10.1007/s13205-025-04446-8.

Introduction

The Indian poultry market attained a value of nearly 1749.9 billion rupees in the year 2021 and the market is expected to grow at a compound annual growth rate of 8.1% which is 2,897.6 billion rupees by 2027 (https://www.expertmarketresearch.com/reports/india-poultry-market). However, successful poultry rearing still remains a challenge in the extreme environment of high altitude region. Leh is a city and Chang La is a high mountain pass in Indian Union Territory of Ladakh which comes under high altitude regions characterized by hypobaric–hypoxia, extreme temperature and low humidity that make poultry rearing a challenge. The main limitations here are natural hatching and the absence of local chicken germplasm (Swati et al. 2022). The growth and production performance of chickens reared at high altitude are also compromised when compared to plain region chickens (Chuskit et al. 2021) and occurrence of ascites is very common at high altitude (Buys et al. 1999). The demand for fresh chicken meat and eggs is vast due to increasing tourism in Leh. Therefore, rearing chickens from plain region is commonly practiced to fulfil the demand. However, these chickens are not adapted for high altitude and have poor growth and production performances which may be increased with supplements in the form of feed, local additives, antioxidants, pre or probiotics and also to explore gut microflora of these chickens that may have role in adaptation and survivability at extreme environment.

Recently, the role of microbiota in the gastrointestinal tract (GIT) of chicken has attracted a lot of attention as they provide nutrients and aid in food digestion (Choi et al. 2015). The gut microbiota is a collection of the entire microbial community in the gastrointestinal tract, which influences host health and developmental activities. The gastrointestinal tract of chicken contains over 900 bacterial species that are involved in a variety of functions, including food digestion, toxin breakdown, immune system stimulation, pathogen exclusion, endocrine activity, chicken growth, overall health, and feed conversion rates (Apajalahti et al. 2004; Ley et al. 2006; Benson et al. 2010). It is reported that the gut microflora of a chicken changes with their age (Yan et al. 2017), breed (Pandit et al. 2018), gender (Siegerstetter et al. 2017), tissues (Al-Marzooqi et al. 2020), geographic location (Zhou et al. 2016), season (Schofield et al. 2022), feed supplement (Wang et al. 2017). The colonization of commensal bacteria in the chicken GIT is an ongoing process that begins soon after hatching and the complete microbiota of the small intestine is established within 2 weeks (Uni et al. 2003; Lu et al. 2003; Yegani and Korver 2008). It was reported that 36 bacterial genera were found differentially abundant in the fecal metagenomic profile between broiler chicken groups with high and low feed conversion ratios (Singh et al. 2012). Lima et al. (2017) even studied the fecal virome population from healthy chickens and identified the following prominent viral families: Adenoviridae, Caliciviridae, Circoviridae, Parvoviridae, Picobirnaviridae, Picornaviridae and Reoviridae from poultry farms in Brazil. Tibetan chicken is unique in terms of high altitude adaptation and it was observed that the caecal microbiota of Tibetan chicken was slightly diverged due to exposure to different geographic environments (Zhou et al. 2016). However, nothing is known regarding the gut microflora of chickens from high altitude regions from India.

There have been several bacterial and yeast species that are commonly used in potential probiotic formulations for broiler chickens, like Bacillus, Lactobacillus, Enterococcus, Bifidobacterium, Pediococcus, Escherichia and Saccharomyces cerevisiae (Rodrigues et al. 2020). All the known commercial probiotic formulations have been tried in chicken reared at plain region, specific probiotic formulation for chicken performance and adaptability at high altitude is yet to be explored. It may be hypothesized that the use of probiotic may improve the growth and adaptability of these broiler chicken in high altitude condition by restoring gut microbiota composition disturbed due to cold stress. Probiotics may increase the population of beneficial bacteria, and at the same time may reduce pathogenic bacteria. This change in microbial diversity may support better nutrient absorption, immune function, and intestinal barrier integrity. Therefore, probiotic supplementation can improve body weight gain and can assist in physiological adaptation to high-altitude stress. Therefore, the present study was undertaken with the objective of studying the effect of probiotic intervention on gut microflora of broiler chicken reared at two distinct high altitude regions.

Materials and methods

Experimental design

The trial was performed on 120 broiler chickens (CHABRO breed) of age 5 weeks for a duration of 1 month. Sixty birds each at two different altitude regions, i.e., Chang La (5330 m) and Leh (3500 m) were reared. Further sixty birds from each altitude were grouped into control (30 no’s) and probiotic treatment (30 no’s) in the deep litter system. Therefore, the study consists of four groups i.e. Chang La control (CC), Chang La treatment (CT), Leh control (LC) and Leh treatment (LT). Biovet probiotic (1gm/L; ACME Pharmaceuticals, Ahmedabad, India) is a commercial readymade probiotic which contained Lactobacillus acidophilus (50 × 10 cfu/kg); Bacillus subtilis (1 × 10 cfu/kg); Bacillus licheniformis (1 × 10 cfu/kg) and Saccharomyces boulardii (10 × 10 cfu/kg) bacterial strains (colony forming units-cfu) and were provided to the broiler as per the manufacturer’s instructions. The birds were fed with the ready-made ration and were supplied with fresh water throughout the trial. The average body weight of the birds was recorded on 30th day of the trial and fresh fecal droppings were collected early in the morning before feeding the birds from both locations at the same day. The fecal samples were collected in RNAlater solution (Invitrogen) and stored at – 80 ⁰C until further analysis. For metagenomic analysis, four pooled samples (feces from random three broilers) representing four groups of treatment and altitude were sequenced. The pooling strategy ensured sufficient DNA yield for high-quality microbiome sequencing and focused on differences between treatment groups and locations rather than individual-level heterogeneity. This pooling strategy to compare groups is commonly reported in various metagenomic studies (Lamendella et al. 2011; Zhou et al. 2016).

Body weight

The body weight of the broiler chicken was measured on 30th day of the trial from both the locations. The average body weight was calculated and presented as mean ± standard error. The data were analyzed using SPSS Statistics version 16.0 software.

DNA isolation

DNA was extracted from the chicken fecal samples using the QIAamp DNA stool mini kit (Qiagen, Hilden, Germany) according to the manufacturer’s instructions. The quantity and quality of isolated DNA were measured using a NanoDrop ND-1000 spectrophotometer (Thermo Fisher Scientific, Waltham, MA, USA) and agarose gel electrophoresis, respectively. In addition, DNA concentrations were also measured using the Qubit double-stranded DNA (dsDNA) BR assay kit on a Qubit 2.0 fluorometer (Invitrogen, Carlsbad, CA). All the extracted DNA samples were stored at − 20 °C until further use.

16S V3–V4 amplification, library preparation and sequencing

V3–V4 hypervariable region of the 16S rRNA gene was amplified and sequenced using 341 F and 805R universal primers targeting a region of approximately 464 bp. The sequences of the primers are listed in Table 1.

Table 1.

Sequence of forward and reverse primers

Illumina_16S_341F	5′-TCGTCGGCAGCGTCAGATGTGTATAAGAGACAGCCTACGGGNGGCWGCAG
Illumina_16S_805R	5′-GTCTCGTGGGCTCGGAGATGTGTATAAGAGACAGGACTACHVGGGTATCTAATCC

The final amplified amplicon libraries were purified using AMPure XT beads (Beckman Coulter Genomics, Danvers, MA, USA), and the amplicon library were further assessed by Bioanalyser (Agilent Technologies, Santa Clara, CA, USA) for its size and quantity and the library quantification kit for Illumina (Kapa Biosciences, Woburn, MA, USA), respectively. The library was constructed on 250PE (paired end) on an Illumina Hiseq 2500 platform (San Diego, CA, USA).

Sequencing data analysis

Raw sequences obtained from all the samples were first filtered for reads with the adapter contamination at the end of the reads using trim galore (v0.4.1). Subsequently, reads with low quality (quality score < 20) were removed by the program FastX tool kit (http://hannonlab.cshl.edu/fastx_toolkit/). The chicken fecal DNA was decontaminated by mapping cleaned data to the chicken genome using BWA and samtools. Sequencing for microbiota generated approximately a total of 176 GB data for all the samples.

Analysis of classification and abundance

Taxonomic profiling was performed from the raw metagenomic sequencing reads using various applications for different steps. Quality filtering and trimming of the raw reads was done using FastX toolkit 0.0.13, followed by screening of reads to a custom database (Chicken genome) by BWA mem to remove chicken DNA contamination. The obtained high-quality reads were assembled into contigs and scaftigs (IDBA assembler), and gene prediction from the contigs was done using MetaGeneMark 1.0. The same filtered reads were mapped to a MetaPhlAn database using the MetaPhlAn pipeline to estimate taxonomic abundance.

Metagenomic assembly and gene prediction

The resulting cleaned sequences were de novo assembled into contigs using IDBA assembler (https://github.com/loneknightpy/idba), set as presets Kmer (10, 20, 30, 40, and 50), and then generated scaffolds for assembly. Scaffolds longer than 200 bp were used for further analysis (Peng et al. 2012). Open reading frames (ORFs) within contigs were used for gene prediction using MetaGeneMark (http://exon.gatech.edu/meta_gmhmmp.cgi). Subsequently, the nonredundant genes were identified by further clustering the predicted genes using CD-HIT (http://www.bioinformatics.org/cd-hit/) with a 90% nucleotide identity. Functional pathway annotation of all ORFs was conducted using FMAP (https://github.com/jiwoongbio/FMAP) against the Uniprot database, with an e value ≤ 10 − 5. The Welch’s t test was used to quantitate differential taxonomic and functional pathway abundance between groups (control versus treatment) with the STAMP program (http://kiwi.cs.dal. ca/Software/STAMP).

Microbial diversity and taxonomic analysis

Cumulative sum scaling (CSS) was followed to remove biases introduced by total sum standardization (TSS) and log² transformation to account for the non-normal distribution of taxonomic counts data. Taxonomic abundance data was then used in Calypso web server-based tool (Zakrzewski et al. 2017) and Microbiome Analyst (https://www.microbiomeanalyst.ca/) for α-diversity analysis across all groups and was calculated using Shannon and Simpson’s index based on phylum, genus and species profiles. Differentially abundant and significant taxa were evaluated using the Wilcoxon rank sum test at a p value cutoff of 0.05. p value < 0.05 was considered to be statistically significant for reproducibility. β-diversity was calculated using weighted UniFrac distances based on the principal coordinate analysis (PCoA).

Microbial function and CAZymes annotation

Function analysis was done using STAMP (Statistical Analysis of Metagenomic Profiles), a software package for analyzing and graphical representation for inferring the biological relevance of features in a metagenomic profile. CAZymes (carbohydrate active enzymes) were predicted from amino acid sequences using the Hmmscan software in the HMMER 3.0 package to produce alignments with family-specific CAZymes HMMs in the dbCAN database. Alignments were filtered at e value < 1e–5 and coverage > 0.30. A heat map of CAZymes genes was constructed using the pheatmap R package.

Statistical analysis

Differential abundance of phylum, genus, and species was tested by Wilcoxon rank-sum test between control and treatment samples in Chang La and Leh group using R script (version 3.5.2). Statistical analysis like principal component analysis (PCA) and principal coordinate analysis (PCoA) were executed using Calypso software. Unifrac is selected as the distance for statistical analysis for Calypso software. A p value is also computed using the Wilcoxon rank sum test to indicate significant taxa associated with community composition. For all the analysis, p value < 0.05 was considered statistically significant.

Results

Body weight

Probiotic intervention showed no differences in the body weight of the broiler chicken from both the location (Supplemental Table 1). However, less growth was observed in Chang La birds as compared to Leh birds, which could be due to the stress associated with the extreme high altitude environment at Chang La.

Alteration of the chicken gut microbiome due to probiotic intervention and different locations

The total of 176 GB raw data were sequenced from this run. A total of 584,488,565 (× 2 paired end) reads for the microbiota were analyzed with a mean of 14,922,423 and 15,145,132 reads for the Chang La and Leh groups, respectively. In terms of microbial composition, the microbiota between the control and probiotic treatment from each location was found to be distinct, which indicated the impact of probiotic treatment on the gut microflora of the broiler chicken. Significant differences were observed between the control and probiotic treated samples from the Chang La trial; 6 at the phylum level, 76 at the genus level and 237 at the species level (p value < 0.05). Simultaneously, in Leh samples, it was found that significant changes occurred; 4 at the phylum level, 56 at the genus level and 230 at the species level (p value < 0.05) between the control and probiotic treated samples. In Chang La control samples, phyla Firmicutes was found to be the most abundant (15.21%), followed by Proteobacteria (11.99%), Bacteroides (11.74%), Actinobacteria (11.34%). In contrast, in the probiotic treated group, Dictyoglomi phyla was the most abundant (15.13%). Probiotic treatments had minimal effect on chicken gut microflora at the genus and species level. The detailed percentage of microbiota composition at phylum, genus, and species level found in Chang La and Leh control and probiotic treated samples are shown in Fig. 1a, b, respectively. Changes in microbial composition were also observed from broiler chicken reared at two different locations. A significant difference at the phylum level was observed between the Chang La and Leh groups, possibly due to the differences in geographical locations with different altitudes. The microbial abundance was higher in Chang La samples as compared to the Leh samples. In Chang La samples, Firmicutes (15.21%) was the most predominant phyla whereas, in Leh samples Elusimicrobia (15.38%) was predominant. Lower level of Actinobacteria (8.10%) and Bacteroides (7.61%) was found in Leh samples as compared to Chang La samples. The most abundant phyla in the control group were also present in the treatment group for both Chang La and Leh samples, albeit at lower abundance which may still influence the composition at the genus and species levels. It was clearly found that there was a major microbial diversity shift showing a distinct gut microflora in adaptation of birds at different environments. However, no significant differences were observed at the genus and species level from both locations.

Fig. 1.

a Microbiota composition at different levels in Control and Probiotic treated samples at Chang La at phylum, genus and species level, respectively. b Microbiota composition at different levels in Control and Probiotic treated samples at Leh at phylum, genus and species level, respectively

Alpha diversity was reported as Chao1, richness, Shannon, and Simpson’s index. Alpha-diversity indices of the bacterial communities based on Shannon and Simpson Index from all the groups is shown in Supplemental Table 2. Alpha diversity results suggested that the richness and diversity of gut microbiota were influenced by the probiotic intervention. The alpha diversity analysis between the control and treatment group revealed significant difference in the Chang La and Leh groups. The probiotic group at Leh was more diverse as compared to the control group (Fig. 2a). On the contrary, in Chang La group, probiotic group has less diverse microbial population as compared to the control group (Fig. 2b). The rarefaction curve at all the diversity levels shows the number of species against the number of samples (Fig. 3). Generally, it initially grows rapidly as the most common species are found and then slightly flattens as the rarest species remain to be sampled at all the hierarchical levels. The combined groups from both locations when compared, showed that Chang La samples possessed higher microbial abundance than Leh group. Highest microbial abundance was found in the control group of Chang La than its probiotic treated samples (Fig. 4). Firmicutes phyla and Clostridium botulinum species were found to be the most abundant in all groups. It was evident that the probiotic intervention in Chang La group is either lowering the microbial diversity or maintaining the gut microbiome homeostasis of broiler chicken in such extreme high altitude. It was found that the probiotic intervention responded differently in chickens from these two locations which could possibly be because of the difference in geographical locations and, depending on host physiology, probiotic strains may behave differently under varying oxygen and temperature conditions. This differential location specific response could also be possible due to the probiotic strain interaction that can influence overall adaptability and survivability of broiler chicken. In extreme high-altitude environment with pronounced hypobaric hypoxia, severe colder temperatures, and greater physiological stress the gut may also prioritize stability and resilience over diversity. This area could further be explored to check the potential of probiotic effect on an individual on different altitude in order to come up with the best combination of probiotic formulations for adaptation at such extreme environments for both animals and humans. Alpha diversity based on the Shannon and Simpson indexes showed no significant differences (p value < 0.05) among all groups (Fig. 5). No evident clustering was observed in PCoA plot for beta diversity; however, clustering of microbes distinct for each location was observed in PCA plot (Fig. 6).

Fig. 2.

a Differences in bacterial diversity (Chao1, Richness, Shannon and Simpson’s Index) between the Leh control (Lcontrol) and Leh probiotic treated (Ltreatment) groups at all hierarchical levels. b Differences in bacterial diversity (Chao1, Richness, Shannon and Simpson’s Index) between the Chang La control (Ccontrol) and Chang La probiotic treated (Ctreatment) groups at all hierarchical levels

Fig. 3.

Rarefaction curve showing probiotic effect in both Chang La and Leh groups at phylum, genus and species level. Chang La control (Ccontrol), Chang La probiotic treated (Ctreatment), Leh control (Lcontrol), Leh probiotic treated (Ltreatment)

Fig. 4.

Microbial abundance plot at all the taxonomic level from Chang La and Leh groups. Chang La control samples are more abundant than any other groups. (Chang La control CC; Chang La treatment CT; Leh control LC; Leh treatment LT)

Fig. 5.

Shannon and Simpson index represented microbial alpha diversity of gut microbiome within the groups and between the groups with probiotic intervention in both locations. No significant difference was observed (p < 0.05). PCA plot of chicken gut microbiome within the groups. (Chang La control CC; Chang La treatment CT; Leh control LC; Leh treatment LT at all the taxonomic level)

Fig. 6.

PCoA plot represented microbial beta diversity of gut microbiome within the groups and between the groups with probiotic intervention in both locations. PCA plot of chicken gut microbiome within the groups. (Chang La control CC; Chang La treatment CT; Leh control LC; Leh treatment LT at all the taxonomic level)

Annotation of microbial function

In order to investigate the influence of environmental conditions and probiotic intervention on the functional performance of the chicken fecal microbiota, we performed microbial function analysis. The KEGG pathways generally shows three levels of hierarchy and the differentials at each level of KEGG pathways were analyzed (Levels 2 and 3 were considered). The PCA plots were determined to evaluate the similarity of the microbial functions between the probiotic and control treatments at both locations. PCA analysis revealed no evident clustering among the samples. However, based on the abundance profiling of microbial pathways, there were differences (two-side Welch’s t test; p < 0.05) between the microbial metabolic activities in probiotic treatments and the control groups (Fig. 7). In case of Leh samples, differences in microbial functional analysis in control and probiotic treated samples occurred in pathways mainly related to maintenance of cellular energy homeostasis, glycan and carbohydrate metabolisms, regulatory systems, and signaling pathways. However, no significant differences occurred in the samples from Chang La in control and probiotic treated samples except in the environmental processing and cell–cell signaling pathways.

Fig. 7.

PCA and extended bar plot showing differences in functional pathways between probiotic and control groups from two locations Leh and Chang La, respectively. CC vs CT shows significance at Level 2 and 3 and in LC vs LT shows significance only at Level 3. The right side of the graph shows the proportions of functional abundance in 95% confidence interval, respectively Chang La control CC; Chang La treatment CT; Leh control LC; Leh treatment LT)

Significant alterations were observed in probiotic treatment between Leh and Chang La groups, mainly in signal transduction, metabolic pathways (metabolism of cofactors and vitamins, metabolism of other amino acids, glycan biosynthesis and metabolism) and translation. Interestingly, it was also observed that the aging process showed a significant difference between the Leh and Chang La groups. This could be attributed to the response of gut microbiota coping in different environments, different altitude levels, its related stress and survival at different geographical locations (Fig. 8).

Fig. 8.

Extended bar plot showing differences in functional pathways between probiotic treated groups from two locations Leh and Chang La. The right side of the graph shows the proportion of functional abundance in 95% confidence interval, respectively. (Chang La treatment CT; Leh treatment LT)

CAZyme analysis

The CAZyme heatmap shows css + log transformed abundance of the top 20 abundant CAZyme gene families, including carbohydrate-binding module family (CBM), S-layer homology domain family (SLH), glycoside hydrolase family (GH), and glycosyl transferase (GT) family in Chang La groups and Leh groups. The majority of differences were observed in the Chang La group. CAZy genes were filtered at an E value of 0.35 for all bacteria using dbCANHMMdb (Fig. 9).

Fig. 9.

A heatmap of top 20 abundant CAZyme gene family distributions among Leh and Chang La groups. (Chang La control CC; Chang La treatment CT; Leh control LC; Leh treatment)

Discussion

The present study was an attempt to explore the effect of different geographical locations and dietary probiotic intervention on the gut microbial composition of broiler chicken reared at high altitudes. No differences were observed in body growth of the broiler chicken between the control and probiotic treated groups in the present study. There are several studies that confirmed that no changes occurred in the growth performance of chicken when administered with probiotic. Our result is in accordance with the previous study where probiotic supplementation has no positive effects on the growth and production performance of broiler chicken at high altitude (Kalia et al. 2017). The same was also observed by Lee et al. (2010) and Zhang et al. (2011) where no significant differences were found in chicken growth performance. On the contrary, there are several findings that showed positive effect of various probiotic supplementation that promote growth of chicken (Kalavathy et al. 2003; Timmerman et al. 2006; Ashayerizadeh et al. 2009; Awad et al. 2009). However, it is tough to conclude regarding the efficacy of probiotic on chicken’s health, as the phenomenon is dependent on various factors like type of bacterial strains, administration level, concentration of the probiotic, application method, overall chicken diet and farm hygiene (Patterson and Burkholder 2003). No differences were found in the growth of chicken upon probiotic intervention in the present study, however there were differences observed in body weight of broiler chicken among two different locations. The two locations i.e. Leh comes under high altitude region (3500 m) and Chang La pass comes under extreme high altitude region (5330 m) and possess different environmental conditions. The difference in the overall growth of broiler chicken was obvious because of this extreme difference in the location, altitude, harsh environment and their stress associated with it. The overall growth and production performance of the chicken is generally compromised in high altitude due to extreme cold and hypobaric hypoxia. The same was shown in one of our earlier reports where it was observed that the broiler chicken reared at high altitude grow slower as compared to lowland broilers (Chuskit et al. 2021). The same was also observed by Hassanzadeh et al. (2004) were broiler chickens from lowland grow faster than broilers reared at high altitude. However, growth related other parameters like feed intake, feed conversion ratio may be considered in future similar studies to establish the link between changes in the gut microbiota and host response.

Further, to study the changes in the gut microbial composition of broiler chicken reared at two different altitudes, i.e., Chang La and Leh, gut metagenomic sequencing approach was performed. The microbial composition of an organism is said to change with different locations. It has been reported that adult passerine birds showed differences in the cloacal microbial communities due to changes in geographic location, diet, and season (Klomp et al. 2008). In the present study, the influence of the altitude on chicken broiler gut microflora was mainly observed at the phylum level and Firmicutes, Proteobacteria, and Bacteroides were found to be dominant in the Chang La sample which may contribute the chickens to sustain in such extreme environment. Firmicutes are known to survive in extreme environments (Filippidou et al. 2016). A recent study from our institute, also reported the significant predominance of the phyla Firmicutes in the gut microbiome of high altitude adapted chickens similar to that from other high altitude adapted livestock like yak, cattle, antelope, sheep, chicken, dog from Tibet (Bhagat et al. 2025). Also reported significantly higher Firmicutes/Bacteroidetes (F/B) ratio, which is beneficial for energy harvesting in animals and can help the animal to maintain a metabolic balance and body homeostasis at high altitudes (Bhagat et al. 2025). Chang La groups possessed more diverse gut microflora than other groups, showing the effect of extreme high altitude. There are very few reports regarding gut microflora of chickens from high altitude region and it will be very interesting to further explore the effect of one's microflora on survivability and adaptability to high altitude conditions. However, there is one report on the cecal microbiota of Tibetan chickens that inhabit 2200–4100 m and are naturally adapted to high altitudes. It was revealed that there were differences in the composition of the intestinal microbiota between the Tibetan chickens and low-altitude chickens which were mainly associated with geographic conditions (Zhou et al. 2016). It was reported from the same study, that the major phyla found were Bacteroidetes, Firmicutes, Spirochaetes, and Proteobacteria in Tibetan chicken, which was also observed in our study indicating the presence of these major phyla in the gut microbiome of chickens reared at high altitude. However, there were less similarities in the intestinal microbial composition of the Tibetan chicken and the broiler chicken reared at high altitude from the current study at the genus level, which could be summed up as the Tibetan chickens are native and well adapted to high altitude and possess distinct gut microflora due to long term geographic dispersion and separation that occurred in their intestinal microbial composition (Zhou et al. 2016).

The abundance and diversity of the gut microbiome of any host changes in response to changes in the season or environment and act like a bridge between the host's adaptability to a particular environment. Environment plays an important factor in shaping one’s homeostasis. The difference in microflora composition will be obvious in high altitude adapted animals than those from lowlanders. Ma et al. (2019) found that the genera Ruminococcus, Oscillospira, and Clostridium were common taxa in the gut microbiota of indigenous high altitude herbivores at the height of 4300 m. In the same study, the functional annotation significantly showed the energy, metabolism, and DNA repair mechanism as high altitude adaptation as these Tibetan indigenous animals have co-evolved with the host genome for high altitude. It was reported that the Tibetans residing at higher altitudes possessed higher levels of Firmicutes, Clostridiales, and Ruminococcus bacterial families than those residing from the lower altitudes and Ruminococcaceae was one of the three bacterial families that was most frequently found in the gut microbiota of Tibetans (Lan et al. 2017; Kang et al. 2016). Gao et al. (2020) found that the Bacteroidetes and Firmicutes were the dominant phyla and that the overall structure and abundance of the gut microbiota of the wild Tibetan wild ass differed significantly between seasons and environments. A previous study reported that a higher ratio of Firmicutes to Bacteroidetes in a microbial community correlates with higher food energy absorption (Murphy et al. 2010). It was hypothesized that the Tibetan chickens with higher Bacteroidetes phylum than the Firmicutes group may be more adapted to harsh environments (Du et al. 2022).

The second part of the study was to evaluate the probiotic effect on gut microbiome of the broiler chicken reared at two distinct locations at high altitude which showed that the probiotic supplementation had the maximum effect at the phylum level in the broiler from the Chang La group. No significant differences were observed in other experimental groups, and they possessed similar taxon abundances between the probiotic treated groups. It was reported that the tested probiotic formulations neither elicited significant changes in the cecal microbiota nor they altered the growth parameters of broiler chicken however, differences were observed in the predicted metabolic activities which indicated that probiotic supplementation may play role in the microbial energy metabolism (Rodrigues et al. 2020). Probiotics seem to have the most significant effect during the initial development of the chicken microbiota (Oakley et al. 2014); therefore, an immediate supplementation of probiotics post hatch is more critical to achieve the best performance in poultry (Kabir Lutful 2009). Probiotic species belonging to Lactobacillus, Bacillus, Bifidobacterium, Streptococcus, Enterococcus, Saccharomyces, Aspergillus and Candida are said to have a positive effect on overall broiler performances (Kabir Lutful 2009). Therefore, the best combination of the bacterial strain for the ideal probiotic and manipulation of gut microflora with the aim to increase the performance and adaptability of chickens reared at high altitude should further be explored in order to boost up the commercial poultry farming in such extreme environment.

In the present study, the KEGG analysis on the functional capacities of the gut microbial communities in the broiler chicken reared at high altitude annotated for almost 70% of the genes related to metabolism and genetic information processing pathways like transcription, translation, and DNA repair. Furthermore, it was found that high altitudes have a higher abundance of gut microbiota involved in the energy and glycan biosynthesis metabolism pathways. Gut microbes may contribute to the host's adaptation to high altitude environments by increasing host energy and glycan biosynthesis. The vital aging process was also observed in our study, which could be the high altitude effect and its related stress at both locations. It is reported that the microbiome usually transduces environmental signals, metabolic and neurological functions, and processes like aging (Ghosh et al. 2022). Most CAZyme gene families observed in the present study were associated with essential metabolic maintenance and survivability of a host. A more metabolically active and functionally diverse microbiota supports resilience to high-altitude stress by maintaining intestinal integrity and modulating immune responses. The Chang La group showed differences in the level of expression of some of these CAZyme gene families, which may be in order to survive at such extreme locations and maintain homeostasis. It is reported that the diversity of gut microbes and CAZyme-associated functional genes make the basis for maintaining gut microbiome homeostasis and host health (Kriss et al. 2018). The purpose of CAZyme analysis in probiotic research is to understand the probiotic strain’s ability to utilize complex carbohydrates which may vary due to high altitude stress at different locations. It has been reported that some food products fermented by probiotics show potential in reducing levels of certain carbohydrates and proteins (Iqbal et al. 2021; Lau and Quek 2024). Gut microbiota may contribute to breaking down complex plant derived carbohydrates into absorbable monosaccharides and thereby improve nutrient utilization. Enhanced glycan metabolism may directly support better feed efficiency by enabling broilers to extract more energy from indigestible dietary fibers.

The microbial gene encoding for carbohydrate active enzymes can mainly regulate the way in which the host interacts with diverse carbohydrate sources thereby influencing the host's overall health (Onyango et al. 2021). In conclusion, the role of probiotic and different high altitude locations in shaping the gut microflora of broiler chicken were studied. Probiotic showed no positive effect on the body growth of the broilers; however, its effect was observed in the gut microflora which changed especially in Chang La samples due the extreme environment. On another hand, it was found that the abundance and diversity of chicken gut microflora also changed with locations. The functional annotation showed significant alteration in signal transduction, metabolic and translation pathways. Therefore, it is proposed that the role of different probiotic strains, their doses must be further explored that will be most effective for the chickens to overcome high altitude challenges like ascites, poor feed conversion and adaptation at high altitude regions. Incorporating symbiotic i.e. combinations of probiotic and prebiotic to enhance microbial resilience and nutrient absorption may also prove instrumental in mitigating high-altitude-stress. Encapsulation methods such as microencapsulation or nanoencapsulation for probiotic delivery could also improve microorganism viability during feed processing and passage through the gastrointestinal tract, especially in cold-stress environments.

Supplementary Information

Below is the link to the electronic supplementary material.

Author contributions

S and MK conducted the experiment in Chang La and DC conducted the experiment in Leh simultaneously. S wrote the manuscript. AKS and JS were responsible for interpreting the data/results. OPS provided the logistical support and permission. LG designed the study and helped in editing the manuscript. All authors reviewed and approved the final manuscript.

Data availability

All the supporting data are available in the manuscript and its supplementary file. The metagenomic sequencing data generated from this study were deposited in NCBI database and PRJNA1178376 accession number was obtained.

Declarations

Conflict of interest

The authors declare that they have no competing interests.

Ethical approval

The work was approved by the Institutional Animal Ethical Committee (S&T/14–15/DIH-123/P-2). Fecal samples were used in this study.

Merit and limitations

The metagenomic approach to study the gut microflora of broiler chicken from high altitude from India is first of its kind and that too from Chang La pass which is located at extreme high altitude location. The cecal sample represent the actual intestinal microbiome of an organism which could be a limitation of the present work however, the work was conducted on fecal sample which also shows pen picture of organism’s gut microflora.