Genomic characterization of fecal microbial communities in severely sick humans and broiler chickens using next generation sequencing

Background: Microbiota plays an important role in food safety and its alteration poses a serious threat to humans. Comparative microbiome profiling using next-generation sequencing (NGS) enabled the understanding of microbial diversity and similarity between different species. In this study, we used NGS to profile the fecal microbiota of sick human and broiler chickens. A total of 26 fecal samples were collected from severely sick human subjects (n= 13) and broiler chickens (n=13) with similar symptoms. Results: The total number of microbial species detected in broiler chickens fecal microbiota was higher than that of humans. Phylum Proteobacteria was the most abundant in both human and broiler chickens fecal microbiota while Tenericutes was found to be least abundant in both species. Phylum Actinobacteria was found only in the human fecal microbiota. In both humans and broiler chickens, E.coli was found to be phylogenetically related suggesting a microbial association between both species. Conclusion: NGS based taxonomic profiling revealed the association of microbial dysbiosis with extreme sickness in both humans and broiler chickens. The dominance of phylum Proteobacteria in both the species ascertains their altered gut microbiota. Both human and broiler chickens microbial communities were found to be genetically related indicating horizontal transfer of microbes between the two species. ,

Genetic analysis of E.coli found in both HS and BC fecal samples was carried out against the reference strain E.coli K-12 substr. MG1655 (NC 000913. 3) on the basis of sequence similarity. E.coli dominated in BC fecal microbial profile was found to be 99.48% similar to the reference E.coli strain. Conversely, E.coli dominated in HS fecal samples was found to be 98.2% similar to the reference strain. Upon comparing the sequence similarity of E.coli dominated in the HS and BC fecal samples with respect to each other, the similarity was found to be 98% (Figure 8).

Discussion
In this study, we used NGS shotgun metagenomics to profile HS and BC fecal microbiota of severely sick HS and BC with similar symptoms. Phylogenetic analysis was performed to investigate the bacterial association between humans and poultry. Metagenomic profiling of HS fecal microbiota revealed a predominance of phylum Proteobacteria, Bacteroidetes, Firmicutes, Actinobacteria while Tenericutes was the least dominant group. Similarly, BC fecal microbiota was dominated by Proteobacteria, Firmicutes, Bacteroidetes while Tenericutes constitute the least abundant group. We observed that Proteobacteriawas dominantly present in both HS and BC fecal microbiota. The higher abundance of Proteobacteria in this study reflects the state of the altered gut microbiome of both humans and poultry [28,29]. Our findings are in line with a previous study that reported a significantly higher abundance of phylum Proteobacteria in the hospitalized Israeli population [30].
Similarly, another study found that the distribution of various bacterial phyla in pigs was altered after 14 days of antibiotics intake with a considerable increase in Proteobacteria in comparison with the group raised without antibiotics using the approach of 16S rDNA amplicon sequencing [31].
Furthermore, a recent study found a significantly higher percentage of Proteobacteria in fecal samples of both layers and BC speculating that antibiotic intake might be the contributing factor for the alteration of gut microbiota [32]. The predominance of Proteobacteria has been documented as a diagnostic marker of various diseases and dysbiosis [33]. An altered gut microbiome is termed as dysbiosis which may lead to various pathological conditions such as reducing the barrier function of the intestine, reduced nutrient digestive potential, increased risk of bacterial translocation and various inflammatory conditions [34]. In addition, the predominance of Proteobacteria has been reported to be associated with the emergence of opportunistic pathogens [35,36].
During the taxonomic profiling of the samples, we observed that phylum Actinobacteria was present only in HS microbiota. The complete loss of Actinobacteria in BC fecal microbiota can be attributed to the high usage of antibiotics as growth promoters in BC feed. Our suspicion is supported by a recent report indicating a decrease in Actinobacteria in chicken fed with antibiotics as growth promoters [37,38]. In our study, we observed that phylum Tenericutes was the least abundant in both HS and BC fecal microbiota. This could be explained by a recent report indicating that antibiotic treatment drastically affected the abundance of Tenericutes [39].
Looking at bacterial class level diversity, we found 10 different classes in HS fecal whereas only 6 classes were detected in BC fecal samples ( Fig. 3 and 4). Various factors are reported to affect the diversity of gut microbiota including age, intestinal transit time, diet and use of antibiotics as growth promoters in chicken feed [40][41][42]. Mortality at an early age can possibly be a reason for lower microbial diversity in BC fecal microbiota. A recent report demonstrated the enrichment of intestinal microbial diversity with increasing age in chickens [6].
We found common useful and pathogenic genera between HS and BC fecal samples. Useful common genera in HS and BC fecal samples were Bacteroides, Faecalibacterium, Alistipes, and Ruminococcus while Escherichia and Salmonella were the common pathogenic genera. Our result of a greater abundance of Bacteroides and Escherichia in HS microbiota is in agreement with a previous report indicating a higher abundance of Escherichia and Bacteroidetes in elderly subjects indicating gut dysbiosis [43]. The results of this study based on the genera distribution of BC fecal microbiota are not in agreement with the previous studies [44][45][46][47]. The variation of our results from the previous studies could possibly be explained by the fact that their studies were based on 16S rDNA amplicon sequencing using healthy chickens raised under various conditions. In our study, the presence of a higher abundance of pathogenic genera in both HS and BC samples is in line with a recent report indicating that a higher abundance of the particular pathogen within the intestinal communities causes greater liability to be affected by those particular pathogens. Furthermore, a higher abundance of commensal E. coli was attributed to an increased level of Salmonella enterica infections 9 [48].
A number of common useful and pathogenic bacterial species were identified at the species level in HS and BC fecal microbiota. Useful common species in HS and BC were Bacteroides fragilis, The various pathogenic bacterial species identified in BC fecal microbiota have been reported to be zoonotic pathogens transmitted to humans through contaminated food [49][50][51]. Humans acquire the majority of foodborne infections upon direct or indirect exposure to contaminated food from a variety of host species including poultry, dogs, cats and livestock [52]. The co-dominance of various foodborne pathogens such as E.coli and Salmonella enteric in both HS and BC fecal microbiota indicates a possible route through which these pathogenic bacteria are transferred between the two species. Among the different pathogenic bacteria, E.coli has been considered as an important source of serious infections in both humans and poultry [53]. E.coli is the causative agent of infections designated as extraintestinal pathogenic E. coli (ExPEC) [54]. Among the various forms of ExPEC, avian-pathogenic E.coli (APEC) causes severe poultry infections collectively called as avian Colibacillosis [55]. Similarly, the intestinal tract of humans is considered as the main reservoir of ExPEC, however, multiple recent reports have indicated that BC can act as an external source of ExPEC transmitted to humans through the food chain [56] [57][58].

Conclusion
The alteration of gut microbiota with various factors is now a well-established fact. In this study, we compared the gut microbiome of severely sick humans and poultry using NGS. Our findings demonstrate the identification of Proteobacteria to be the most abundant phylum in both HS and BC fecal samples indicating an altered gut microbiome. Phylum Actinobacteria was found only in HS fecal samples providing a point of difference between the two species. Moreover, Bacteroides and Escherichia were the most abundant genera in HS and BC fecal samples respectively. Our findings indicate a higher number of bacterial species in BC than that of HS fecal samples. The presence of various pathogenic bacterial species in HS and BC fecal samples reflects their altered gut microbial communities. Furthermore, this study found that human and poultry E.coli are phylogenetically related indicating horizontal transfer of microbes between the two species.

Methods
Fecal samples were used for gut microbiome profiling of human and broiler chickens.

Collection of fecal samples
Initially, we screened 1000 samples for both human and poultry using the records of Microbiology

Sequencing data analysis
Paired-end sequencing generated FASTQ files that were analyzed using various publicly available bioinformatics softwares. NGS sequenced data were analyzed through multiple steps as follows: 1.

2.
By using our custom data analysis pipeline, all the samples were analyzed for microbial characterization (Fig. 1).

3.
Raw data comprising of low quality reads, adapters and technical biases were removed usingTrimmomatic 0.36 [63].

4.
All the microbial reads were decontaminated from human DNA using a computational tool Knead Data 0.6.1 [64].

7.
DIAMOND blast results were interpreted using MEtaGenome ANalyzer (MEGAN 6) software for the taxonomic classification of microbes [67].

Genetic analysis
For genetic analysis, FASTQ files of various bacterial strains detected within the fecal samples of HS and BC were aligned against the reference genome. Samtools mpileup v.1.9 was used to generate FASTA files [68]. Using ClustalW, all the FASTA files were aligned against the reference genome and a phylogenic tree was constructed using the BioNJ distance algorithm [69,70]. The complete bioinformatics workflow of genetic analysis is shown in Fig. 2.

Visualization of microbial profiles
Microbial community abundance was visualized using Pavain to generate cladograms for various taxonomic groups while hclust2 was used to generate heatmaps based on Bray-Curtis dissimilarity for sample clustering [71][72].

Ethics approval and consent to participate
Ethical approval was obtained from the Rehman Medical Institute-Research Ethics Committee (Ref: RMI/RMI-REC/Approval/33) Peshawar, Pakistan. Informed consent was obtained from the all patients who participated in this study.

Consent for publication
Not applicable

Availability of data and materials
The datasets analyzed during the current study are available from the corresponding author on reasonable request.

Competing interests
The authors declare that they have no competing interests.

Authors' contributions
OKA performed experiments, analyzed data, wrote the paper. JA co-supervised and designed the 13 study, provided advice in data analysis, critically discussed results and edited paper. JHC supervised the study, provided advice in study design and co-edited the paper.