In this study, indigenous chickens were collected from eight different regions in Kenya and kept at InCIP-Egerton University. These were studied using eighteen microsatellite markers to determine ...genetic variation. Statistics related to genetic variation were estimated using GenALEx6. Mean percentage polymorphic loci (PPL) was 96.71% and 4% genetic variance (p≥0.003) was seen between the eight populations. MCW0123 marker had the highest genetic variance of 13% among populations (p≥0.003) at 95% CI. Mean He ranged from 0.351±0.031 (SIB) to 0.434±0.022 (BM) with a grand mean He of 0.399±0.011 across the populations using the microsatellite markers. Nei’s genetic distance ranged from 0.016 (SIB and WP) to 0.126 (NR and SIB). DARwin6.501 analysis software was used to draw the population dendrogram and two major population clusters were observed, also seen with PCoA. This study found a lot of genetic variation and relatedness within and among populations. Based on the phylogenetic tree result, it is concluded that the clustering of the chicken populations in the present study is not based on geographical proximity. The microsatellite markers used in this study were suitable for the measurement of the genetic biodiversity and relationship of Kenyan chicken populations. These results can therefore serve as an initial step to plan the conservation of indigenous chickens in Kenya.
Since the first isolation of Rift Valley fever virus (RVFV) in the 1930s, there have been multiple epizootics and epidemics in animals and humans in sub-Saharan Africa. Prospective climate-based ...models have recently been developed that flag areas at risk of RVFV transmission in endemic regions based on key environmental indicators that precede Rift Valley fever (RVF) epizootics and epidemics. Although the timing and locations of human case data from the 2006-2007 RVF outbreak in Kenya have been compared to risk zones flagged by the model, seroprevalence of RVF antibodies in wildlife has not yet been analyzed in light of temporal and spatial predictions of RVF activity. Primarily wild ungulate serum samples from periods before, during, and after the 2006-2007 RVF epizootic were analyzed for the presence of RVFV IgM and/or IgG antibody. Results show an increase in RVF seropositivity from samples collected in 2007 (31.8%), compared to antibody prevalence observed from 2000-2006 (3.3%). After the epizootic, average RVF seropositivity diminished to 5% in samples collected from 2008-2009. Overlaying maps of modeled RVF risk assessments with sampling locations indicated positive RVF serology in several species of wild ungulate in or near areas flagged as being at risk for RVF. Our results establish the need to continue and expand sero-surveillance of wildlife species Kenya and elsewhere in the Horn of Africa to further calibrate and improve the RVF risk model, and better understand the dynamics of RVFV transmission.
Poultry production plays an important role in the economy and livelihoods of rural households in Kenya. As part of a surveillance program, avian influenza virus (AIV)-specific real-time RT-PCR ...(RRT-PCR) was used to screen 282 oropharyngeal swabs collected from chickens at six live bird markets (LBMs) and 33 backyard poultry farms in Kenya and 8 positive samples were detected. Virus was isolated in eggs from five samples, sequenced, and identified as H9N2 low pathogenic AIV (LPAIV) G1 lineage, with highest nucleotide sequence identity (98.6–99.9%) to a 2017 Ugandan H9N2 isolate. The H9N2 contained molecular markers for mammalian receptor specificity, implying their zoonotic potential. Virus pathogenesis and transmissibility was assessed by inoculating low and medium virus doses of a representative Kenyan H9N2 LPAIV isolate into experimental chickens and exposing them to naïve uninfected chickens at 2 -days post inoculation (dpi). Virus shedding was determined at 2/4/7 dpi and 2/5 days post placement (dpp), and seroconversion determined at 14 dpi/12 dpp. None of the directly-inoculated or contact birds exhibited any mortality or clinical disease signs. All directly-inoculated birds in the low dose group shed virus during the experiment, while only one contact bird shed virus at 2 dpp. Only two directly-inoculated birds that shed high virus titers seroconverted in that group. All birds in the medium dose group shed virus at 4/7 dpi and at 5 dpp, and they all seroconverted at 12/14 dpp. This is the first reported detection of H9N2 LPAIV from Kenya and it was shown to be infectious and transmissible in chickens by direct contact and represents a new disease threat to poultry and potentially to people.
•First detection and characterization of avian influenza virus (AIV) in Kenya.•Viruses were characterized as H9N2 low pathogenic AIV (LPAIV) from G1 lineage.•Kenyan H9N2 LPAIV contain markers for virulence and adaptation in mammalian hosts.•Kenyan H9N2 LPAIV is infectious and transmissible by direct contact in chickens.
The avian gamma-coronavirus infectious bronchitis virus (AvCoV, IBV;
family) causes upper respiratory disease associated with severe economic losses in the poultry industry worldwide. Here, we report ...for the first time in Kenya and the Eastern African region two novel AvCoVs, designated IBV/ck/KE/1920/A374/2017 (A374/17) and AvCoV/ck/KE/1922/A376/2017 (A376/17), inadvertently discovered using random nontargeted next-generation sequencing (NGS) of cloacal swabs collected from indigenous chickens. Despite having genome organization (5'UTR-Rep1a/1ab-S-3a-3b-E-M-4b-4c-5a-5b-N-6b-3'UTR), canonical conservation of essential genes and size (~27.6 kb) typical of IBVs, the Kenyan isolates do not phylogenetically cluster with any genotypes of the 37 IBV lineages and 26 unique variants (UVs). Excluding the spike gene, genome sequences of A374/17 and A376/17 are only 93.1% similar to each other and 86.7-91.4% identical to genomes of other AvCoVs. All five non-spike genes of the two isolates phylogenetically cluster together and distinctly from other IBVs and turkey coronaviruses (TCoVs), including the indigenous African GI-26 viruses, suggesting a common origin of the genome backbone of the Kenyan isolates. However, isolate A376/17 contains a TCoV-like spike (S) protein coding sequence and is most similar to Asian TCoVs (84.5-85.1%) compared to other TCoVs (75.6-78.5%), whereas isolate A374/17 contains an S1 gene sequence most similar to the globally distributed lineage GI-16 (78.4-79.5%) and the Middle Eastern lineage GI-23 (79.8-80.2%) viruses. Unanswered questions include the actual origin of the Kenyan AvCoVs, the potential pathobiological significance of their genetic variations, whether they have indeed established themselves as independent variants and subsequently spread within Kenya and to the neighboring east/central African countries that have porous live poultry trade borders, and whether the live-attenuated Mass-type (lineage GI-1)-based vaccines currently used in Kenya and most of the African countries provide protection against these genetically divergent field variants.
Kenyan poultry consists of ~80% free-range indigenous chickens kept in small flocks (~30 birds) on backyard poultry farms (BPFs) and they are traded via live bird markets (LBMs). Newcastle disease ...virus (NDV) was detected in samples collected from chickens, wild farm birds, and other domestic poultry species during a 2017-2018 survey conducted at 66 BPFs and 21 LBMs in nine Kenyan counties. NDV nucleic acids were detected by rRT-PCR L-test in 39.5% (641/1621) of 1621 analyzed samples, of which 9.67% (62/641) were NDV-positive by both the L-test and a fusion-test designed to identify the virulent virus, with a majority being at LBMs (64.5%; 40/62) compared to BPFs (25.5%; 22/62). Virus isolation and next-generation sequencing (NGS) on a subset of samples resulted in 32 complete NDV genome sequences with 95.8-100% nucleotide identities amongst themselves and 95.7-98.2% identity with other east African isolates from 2010-2016. These isolates were classified as a new sub-genotype, V.3, and shared 86.5-88.9% and 88.5-91.8% nucleotide identities with subgenotypes V.1 and V.2 viruses, respectively. The putative fusion protein cleavage site (
R-Q-K-R↓F
) in all 32 isolates, and a 1.86 ICPI score of an isolate from a BPF chicken that had clinical signs consistent with Newcastle disease, confirmed the high virulence of the NDVs. Compared to genotypes V and VI viruses, the attachment (HN) protein of 18 of the 32 vNDVs had amino acid substitutions in the antigenic sites. A time-scaled phylogeographic analysis suggests a west-to-east dispersal of the NDVs via the live chicken trade, but the virus origins remain unconfirmed due to scarcity of continuous and systematic surveillance data. This study reveals the widespread prevalence of vNDVs in Kenyan backyard poultry, the central role of LBMs in the dispersal and possibly generation of new virus variants, and the need for robust molecular epidemiological surveillance in poultry and non-poultry avian species.
Newcastle disease (ND) is a major constraint to Kenya’s poultry production, which is comprised of approximately 80% indigenous chickens (ICs; caged and free-range system) and 20% exotic chickens ...(intensive system). This study analyzed cases reported as suspected ND in Kenya between 2005 and 2015. Of the suspected 332 ND reported cases from the three production systems in 27 locations within six Kenyan Agro-Ecological Zones (AEZs), 140 diagnosed as infected with avian orthoavulavirus 1 (AOaV-1; formerly Newcastle disease virus) were present in every year in all AEZs. The numbers of AOaV-1-positive cases differed significantly (
p
< 0.05) between the production systems across the years depending on the season, climate, and location. In the free-range system, both ambient temperatures and season associated significantly (
p
= 0.001 and 0.02, respectively) with the number of cases, while in the intensive and caged systems, the positive cases correlated significantly with season and relative humidity, respectively (
p
= 0.05). Regardless of the production systems, the numbers of clinically sick birds positively correlated with the ambient temperatures (
r
= 0.6;
p
< 0.05). Failure to detect AOaV-1 in 58% of the ND cases reported, and mortalities exceeding the observed numbers of clinically sick birds suggest deficiencies in the current ND reporting and diagnostic system. Intensive farmers were the slowest in reporting the cases and diagnostic deficiencies were most evident by failure to test the exposure of ICs to natural infection with AOaV-1 and for the AOaV-1-negative cases lack of testing for other pathogens and/or AOaV-1 variants. This study indicates a need for improved surveillance and diagnostics in Kenyan domestic poultry.
Since the first isolation of Rift Valley fever virus (RVFV) in the 1930s, there have been multiple epizootics and epidemics in animals and humans in sub-Saharan Africa. Prospective climate-based ...models have recently been developed that flag areas at risk of RVFV transmission in endemic regions based on key environmental indicators that precede Rift Valley fever (RVF) epizootics and epidemics. Although the timing and locations of human case data from the 2006-2007 RVF outbreak in Kenya have been compared to risk zones flagged by the model, seroprevalence of RVF antibodies in wildlife has not yet been analyzed in light of temporal and spatial predictions of RVF activity. Primarily wild ungulate serum samples from periods before, during, and after the 2006-2007 RVF epizootic were analyzed for the presence of RVFV IgM and/or IgG antibody. Results show an increase in RVF seropositivity from samples collected in 2007 (31.8%), compared to antibody prevalence observed from 2000-2006 (3.3%). After the epizootic, average RVF seropositivity diminished to 5% in samples collected from 2008-2009. Overlaying maps of modeled RVF risk assessments with sampling locations indicated positive RVF serology in several species of wild ungulate in or near areas flagged as being at risk for RVF. Our results establish the need to continue and expand sero-surveillance of wildlife species Kenya and elsewhere in the Horn of Africa to further calibrate and improve the RVF risk model, and better understand the dynamics of RVFV transmission.
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