Predictive phage therapy for Escherichia coli urinary tract infections: Cocktail selection for therapy based on machine learning models.

This study supports the development of predictive bacteriophage (phage) therapy: the concept of phage cocktail selection to treat a bacterial infection based on machine learning (ML) models. For this purpose, ML models were trained on thousands of measured interactions between a panel of phage and sequenced bacterial isolates. The concept was applied to Escherichia coli associated with urinary tract infections. This is an important common infection in humans and companion animals from which multidrug-resistant (MDR) bloodstream infections can originate. The global threat of MDR infection has reinvigorated international efforts into alternatives to antibiotics including phage therapy. E. coli exhibit extensive genome-level variation due to horizontal gene transfer via phage and plasmids. Associated with this, phage selection for E. coli is difficult as individual isolates can exhibit considerable variation in phage susceptibility due to differences in factors important to phage infection including phage receptor profiles and resistance mechanisms. The activity of 31 phage was measured on 314 isolates with growth curves in artificial urine. Random Forest models were built for each phage from bacterial genome features, and the more generalist phage, acting on over 20% of the bacterial population, exhibited F1 scores of >0.6 and could be used to predict phage cocktails effective against previously untested strains. The study demonstrates the potential of predictive ML models which integrate bacterial genomics with phage activity datasets allowing their use on data derived from direct sequencing of clinical samples to inform rapid and effective phage therapy.

Genome-wide diversity and admixture of five indigenous cattle populations from the Tigray region of northern Ethiopia.

The Tigray region, where we found around eight per cent of the indigenous cattle population of Ethiopia, is considered as the historic centre of the country, with the ancient pre-Aksumite and Aksumite civilisations in contact with the civilisations of the Fertile Crescent and the Indian subcontinent. Here, we used whole genome sequencing data to characterise the genomic diversity, relatedness, and admixture of five cattle populations (Abergelle, Arado, Begait, Erob, and Raya) indigenous to the Tigray region of Ethiopia. We detected 28 to 29 million SNPs and 2.7 to 2.9 million indels in each population, of which 7% of SNPs and 34% of indels were novel. Functional annotation of the variants showed around 0.01% SNPs and 0.22%-0.27% indels in coding regions. Enrichment analysis of genes overlapping missense private SNPs revealed 20 significant GO terms and KEGG pathways that were shared by or specific to breeds. They included important genes associated with morphology (SCN4A, TAS1R2 and KCNG4), milk yield (GABRG1), meat quality (MMRN2, VWC2), feed efficiency (PCDH8 and SLC26A3), immune response (LAMC1, PCDH18, CELSR1, TLR6 and ITGA5), heat resistance (NPFFR1 and HTR7) and genes belonging to the olfactory gene family, which may be related to adaptation to harsh environments. Tigray indigenous cattle are very diverse. Their genome-wide average nucleotide diversity ranged from 0.0035 to 0.0036. The number of heterozygous SNPs was about 0.6-0.7 times higher than homozygous ones. The within-breed average number of ROHs ranged from 777.82 to 1000.45, with the average sum of the length of ROHs ranging from 122.01 Mbp to 163.88 Mbp. The genomic inbreeding coefficients differed among animals and breeds, reaching up to 10% in some Begait and Raya animals. Tigray indigenous cattle shared a common ancestry with Asian indicine (85.6%-88.7%) and African taurine (11.3%-14.1%) cattle, with very small, if any, European taurine introgression. This study identified high within-breed genetic diversity representing an opportunity for breeding improvement programs and, also, significant novel variants that could increase the number of known cattle variants, an important contribution to the knowledge of domestic cattle genetic diversity.

Ecological niche modelling as a tool to identify candidate indigenous chicken ecotypes of Tigray (Ethiopia).

The Tigray region is an ancient entry route for the domestic chickens into Africa. The oldest African chicken bones were found in this region at Mezber, a pre-Aksumite rural farming settlement. They were dated to around 800-400 BCE. Since then, the farming communities of the region have integrated chicken into their livelihoods. The region is also recognised for its high chicken-to-human population ratio and diverse and complex geography, ranging from 500 to 4,000 m above sea level (m.a.s.l.). More than 15 agro-ecological zones have been described. Following exotic chicken introductions, the proportion of indigenous chicken is now 70% only in the region. It calls for the characterisation of indigenous Tigrayan chicken ecotypes and their habitats. This study reports an Ecological Niche Modelling using MaxEnt to characterise the habitats of 16 indigenous village chicken populations of Tigray. A total of 34 ecological and landscape variables: climatic (22), soil (eight), vegetation, and land cover (four), were included. We applied Principal Component Analysis correlation, and MaxentVariableSelection procedures to select the most contributing and uncorrelated variables. The selected variables were three climatic (bio5 = maximum temperature of the warmest month, bio8 = mean temperature of the wettest quarter, bio13 = precipitation of the wettest month), three vegetation and land cover (grassland, forest land, and cultivated land proportional areas), and one soil (clay content). Following our analysis, we identified four main chicken agro-ecologies defining four candidates indigenous Tigrayan chicken ecotypes. The study provides baseline information for phenotypic and genetic characterisation as well as conservation interventions of indigenous Tigrayan chickens.

Whole genome sequences of 234 indigenous African chickens from Ethiopia.

Indigenous chickens predominate poultry production in Africa. Although preferred for backyard farming because of their adaptability to harsh tropical environments, these populations suffer from relatively low productivity compared to commercial lines. Genome analyses can unravel the genetic potential of improvement of these birds for both production and resilience traits for the benefit of African poultry farming systems. Here we report whole-genome sequences of 234 indigenous chickens from 24 Ethiopian populations distributed under diverse agro-climatic conditions. The data represents over eight terabytes of paired-end sequences from the Ilumina HiSeqX platform with an average coverage of about 57X. Almost 99% of the sequence reads could be mapped against the chicken reference genome (GRCg6a), confirming the high quality of the data. Variant calling detected around 15 million SNPs, of which about 86% are known variants (i.e., present in public databases), providing further confidence on the data quality. The dataset provides an excellent resource for investigating genetic diversity and local environmental adaptations with important implications for breed improvement and conservation purposes.

Integrated Environmental and Genomic Analysis Reveals the Drivers of Local Adaptation in African Indigenous Chickens.

Breeding for climate resilience is currently an important goal for sustainable livestock production. Local adaptations exhibited by indigenous livestock allow investigating the genetic control of this resilience. Ecological niche modeling (ENM) provides a powerful avenue to identify the main environmental drivers of selection. Here, we applied an integrative approach combining ENM with genome-wide selection signature analyses (XPEHH and Fst) and genotype-environment association (redundancy analysis), with the aim of identifying the genomic signatures of adaptation in African village chickens. By dissecting 34 agro-climatic variables from the ecosystems of 25 Ethiopian village chicken populations, ENM identified six key drivers of environmental challenges: One temperature variable-strongly correlated with elevation, three precipitation variables as proxies for water availability, and two soil/land cover variables as proxies of food availability for foraging chickens. Genome analyses based on whole-genome sequencing (n = 245), identified a few strongly supported genomic regions under selection for environmental challenges related to altitude, temperature, water scarcity, and food availability. These regions harbor several gene clusters including regulatory genes, suggesting a predominantly oligogenic control of environmental adaptation. Few candidate genes detected in relation to heat-stress, indicates likely epigenetic regulation of thermo-tolerance for a domestic species originating from a tropical Asian wild ancestor. These results provide possible explanations for the rapid past adaptation of chickens to diverse African agro-ecologies, while also representing new landmarks for sustainable breeding improvement for climate resilience. We show that the pre-identification of key environmental drivers, followed by genomic investigation, provides a powerful new approach for elucidating adaptation in domestic animals.

Integrating Genetic and Genomic Analyses of Combined Health Data Across Ecotypes to Improve Disease Resistance in Indigenous African Chickens.

Poultry play an important role in the agriculture of many African countries. The majority of chickens in sub-Saharan Africa are indigenous, raised in villages under semi-scavenging conditions. Vaccinations and biosecurity measures rarely apply, and infectious diseases remain a major cause of mortality and reduced productivity. Genomic selection for disease resistance offers a potentially sustainable solution but this requires sufficient numbers of individual birds with genomic and phenotypic data, which is often a challenge to collect in the small populations of indigenous chicken ecotypes. The use of information across-ecotypes presents an attractive possibility to increase the relevant numbers and the accuracy of genomic selection. In this study, we performed a joint analysis of two distinct Ethiopian indigenous chicken ecotypes to investigate the genomic architecture of important health and productivity traits and explore the feasibility of conducting genomic selection across-ecotype. Phenotypic traits considered were antibody response to Infectious Bursal Disease (IBDV), Marek's Disease (MDV), Fowl Cholera (PM) and Fowl Typhoid (SG), resistance to Eimeria and cestode parasitism, and productivity [body weight and body condition score (BCS)]. Combined data from the two chicken ecotypes, Horro (n = 384) and Jarso (n = 376), were jointly analyzed for genetic parameter estimation, genome-wide association studies (GWAS), genomic breeding value (GEBVs) calculation, genomic predictions, whole-genome sequencing (WGS), and pathways analyses. Estimates of across-ecotype heritability were significant and moderate in magnitude (0.22-0.47) for all traits except for SG and BCS. GWAS identified several significant genomic associations with health and productivity traits. The WGS analysis revealed putative candidate genes and mutations for IBDV (TOLLIP, ANGPTL5, BCL9, THEMIS2), MDV (GRM7), SG (MAP3K21), Eimeria (TOM1L1) and cestodes (TNFAIP1, ATG9A, NOS2) parasitism, which warrant further investigation. Reliability of GEBVs increased compared to within-ecotype calculations but accuracy of genomic prediction did not, probably because the genetic distance between the two ecotypes offset the benefit from increased sample size. However, for some traits genomic prediction was only feasible in across-ecotype analysis. Our results generally underpin the potential of genomic selection to enhance health and productivity across-ecotypes. Future studies should establish the required minimum sample size and genetic similarity between ecotypes to ensure accurate joint genomic selection.