Phylogenetic relationships among and genetic variability within 60 goats from two different indigenous breeds in Narok and Isiolo counties in Kenya and 22 published goat samples were analysed using mitochondrial control region sequences. The results showed that there were 54 polymorphic sites in a 481-bp sequence and 29 haplotypes were determined. The mean haplotype diversity and nucleotide diversity were 0.981 ± 0.006 and 0.019 ± 0.001, respectively. The phylogenetic analysis in combination with goat haplogroup reference sequences from GenBank showed that all goat sequences were clustered into two haplogroups (A and G), of which haplogroup A was the commonest in the two populations. A very high percentage (99.90%) of the genetic variation was distributed within the regions, and a smaller percentage (0.10%) distributed among regions as revealed by the analysis of molecular variance (amova). This amova results showed that the divergence between regions was not statistically significant. We concluded that the high levels of intrapopulation diversity in Isiolo and Narok goats and the weak phylogeographic structuring suggested that there existed strong gene flow among goat populations probably caused by extensive transportation of goats in history.

Analysis of shotgun metagenomic data generated from next generation sequencing platforms can be done through a variety of bioinformatic pipelines. These pipelines employ different sets of sophisticated bioinformatics algorithms which may affect the results of this analysis. In this study, we compared two commonly used pipelines for shotgun metagenomic analysis: MG-RAST and Kraken 2, in terms of taxonomic classification, diversity analysis, and usability using their primarily default parameters. Overall, the two pipelines detected similar abundance distributions in the three most abundant taxa Proteobacteria, Firmicutes, and Bacteroidetes. Within bacterial domain, 497 genera were identified by both pipelines, while an additional 694 and 98 genera were solely identified by Kraken 2 and MG-RAST, respectively. 933 species were detected by the two algorithms. Kraken 2 solely detected 3550 species, while MG-RAST identified 557 species uniquely. For archaea, Kraken 2 generated 105 and 236 genera and species, respectively, while MG-RAST detected 60 genera and 88 species. 54 genera and 72 species were commonly detected by the two methods. Kraken 2 had a quicker analysis time (~4 hours) while MG-RAST took approximately 2 days per sample. This study revealed that Kraken 2 and MG-RAST generate comparable results and that a reliable high-level overview of sample is generated irrespective of the pipeline selected. However, Kraken 2 generated a more accurate taxonomic identification given the higher number of “Unclassified” reads in MG-RAST. The observed variations at the genus level show that a main restriction is using different databases for classification of the metagenomic data. The results of this research indicate that a more inclusive and representative classification of microbiomes may be achieved through creation of the combined pipelines.

The objective of this study was to investigate the effect of varying roughage and concentrate proportions, in diet of crossbreed dairy cattle, on the composition and associated functional genes of rumen and fecal microbiota. We also explored fecal samples as a proxy for rumen liquor samples. Six crossbred dairy cattle were reared on three diets with an increasing concentrate and reducing roughage amount in three consecutive 10-day periods. After each period, individual rumen liquor and fecal samples were collected and analyzed through shotgun metagenomic sequencing. Average relative abundance of identified Operational Taxonomic Units (OTU) and microbial functional roles from all animals were compared between diets and sample types (fecal and rumen liquor). Results indicated that dietary modifications significantly affected several rumen and fecal microbial OTUs. In the rumen, an increase in dietary concentrate resulted in an upsurge in the abundance of Proteobacteria, while reducing the proportions of Bacteroidetes and Firmicutes. Conversely, changes in microbial composition in fecal samples were not consistent with dietary modification patterns. Microbial functional pathway classification identified that carbohydrate metabolism and protein metabolism pathways dominated microbial roles. Assessment of dietary effects on the predicted functional roles of these microbiota revealed that a high amount of dietary concentrate resulted in an increase in central carbohydrate metabolism and a corresponding reduction in protein synthesis. Moreover, we identified several microbial stress-related responses linked to dietary changes. Bacteroides and Clostridium genera were the principal hosts of these microbial functions. Therefore, the roughage to concentrate proportion has more influence on the microbial composition and microbial functional genes in rumen samples than fecal samples. As such, we did not establish a significant relationship between the rumen and fecal metagenome profiles, and the rumen and fecal microbiota from one animal did not correlate more than those from different animals.

SummaryKenya indigenous goat breeds (Capra hircus) have not been accurately described. Therefore, there is threat of erosion of unique genotypes such as those associated with adaptability and disease resistance, through indiscriminate crossbreeding. The Kenyan goats classification based on phenotype/morphology identifies three breeds: Small East African (SEA) goats, the Galla goat and crosses of SEA and the Galla. In the present study, we sampled goats from two main geographic regions of Kenya with pastoralist communities, the Maasai and Somali/Boran. DNA was extracted from whole blood and polymerase chain reaction amplified using primers flanking a fragment of Cytocrome-b and D-loop regions of mitochondria DNA. The sequences derived were analysed both within Kenya goat populations and also compared with phylogeographic-related datasets. These data show that the majority of Kenyan indigenous goats are not distinct and their genetic structure is very diverse; however, distinct haplogroups were present. Genetic diversity showed weak positive in Tajima D test for Kenyan indigenous goats, while the Iberian/Mediterranean/Middle-East dataset had a more pronounced negative value indicating that the two populations are under different selection pressure. These analyses enabled phylogenetic relationships between and within species and the comparisons of local goats to related breeds geographically. The information can be applied management of conservation-guided breeding programmes by crossing the indigenous breed's unique genes with high productivity traits from another source.

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Background: Analysis of shotgun metagenomic data generated from next generation sequencing platforms can be done through a variety of bioinformatic pipelines. These pipelines employ different sets of sophisticated bioinformatics algorithms which may affect the results of this analysis. Furthermore, no conventional assessment technique for estimating the precision of each pipeline exists and few studies have been carried out to compare the characteristics, benefits and disadvantages of each pipeline. In this study we compared two commonly used pipelines for shotgun metagenomic analysis: MetaGenome Rapid Annotation using Subsystem Technology (MG-RAST) and Kraken, in terms of taxonomic classification, diversity analysis and usability using their primarily default parametersResults: Overall, the two pipelines detected similar abundance distributions in the three most abundant taxa Proteobacteria, Firmicutes and Bacteroidetes. Within bacterial domain, 497 genera were identified by both pipelines, while an additional 694 and 98 genera were solely identified by Kraken and MG-RAST respectively. 933 species were detected by the two algorithms. Kraken solely detected 3550 species, while MG-RAST identified 557 species uniquely. For archaea, Kraken generated 105 and 236 genera and species respectively while MG-RAST detected 60 genera and 88 species. 54 genera and 72 species were commonly detected by the two methods. Kraken had a quicker analysis time (~4 hours) while MG-RAST took approximately 2 days per sample.Conclusions: This study revealed that Kraken and MG-RAST generate comparable results and that a reliable high-level overview of sample is generated irrespective of the pipeline selected. The observed variations at the genus level show that a main restriction is using different databases for classification of the metagenomic data. Specifically, the pipelines could have been limited because some rumen microbes lack reference genomes. The results of this research indicate that a more inclusive and representative classification of microbiomes may be achieved through creation of combined pipelines.

There is little information about the diversity of bacterial pathogens present in the rumen and feces of healthy cow and the subsequent effects on the performance of the host animal. The objectives of the present study were to genetically characterize the enteric bacterial pathogens found in the rumen fluid and cow feces and to identify the resistant genes responsible for antimicrobial resistance in the detected pathogens. The cow feces and rumen fluid samples (6 rumen fluid and 42 feces) were collected from lactating dairy cows. Using next generation sequencing, the enteric bacterial pathogens detected were screened for antimicrobial resistance genes using ResFinder-2.1 database in the center of Abricate. The characterized enteric bacterial pathogens include Escherichia coli, Salmonella enterica, Streptococcus agalactiae, Streptococcus pyogenes, Campylobacter coli, and Campylobacter fetus among others. Those enteric bacterial pathogens were also drug resistant bacteria except Campylobacter coli. The Campylobacter fetus fetus was identified as the only multidrug resistant bacterial pathogen detected in the cow feces. However, the abundant resistant genes detected confer resistance to tetracycline (17 genes from 209 contigs), beta-lactam (21 genes from 67 contigs), streptomycin (6 genes from 153 contigs), and sulfamethoxazole (2 genes from 72 contigs). This is the first study to identify the diversity of enteric bacterial pathogens from the station based and smallholder dairy cows in Kenya and Tanzania, respectively.

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