Investigating the diversity of the 18S SSU rRNA hyper-variable region of Theileria in cattle and Cape buffalo (Syncerus caffer) from southern Africa using a next generation sequencing approach

Molecular classification and systematics of the Theileria is based on the analysis of the 18S rRNA gene. Reverse line blot or conventional sequencing approaches have disadvantages in the study of 18S rRNA diversity and a next-generation 454 sequencing approach was investigated. The 18S rRNA gene was amplified using RLB primers coupled to 96 unique sequence identifiers (MIDs). Theileria positive samples from African buffalo (672) and cattle (480) from southern Africa were combined in batches of 96 and sequenced using the GS Junior 454 sequencer to produce 825711 informative sequences. Sequences were extracted based on MIDs and analysed to identify Theileria genotypes. Genotypes observed in buffalo and cattle were confirmed in the current study, while no new genotypes were discovered. Genotypes showed specific geographic distributions, most probably linked with vector distributions. Host specificity of buffalo and cattle specific genotypes were confirmed and prevalence data as well as relative parasitemia trends indicate preference for different hosts. Mixed infections are common with African buffalo carrying more genotypes compared to cattle. Associative or exclusion co-infection profiles were observed between genotypes that may have implications for speciation and systematics: specifically that more Theileria species may exist in cattle and buffalo than currently recognized. Analysis of primers used for Theileria parva diagnostics indicate that no new genotypes will be amplified by the current primer sets confirming their specificity. T. parva SNP variants that occur in the 18S rRNA hypervariable region were confirmed. A next generation sequencing approach is useful in obtaining comprehensive knowledge regarding 18S rRNA diversity and prevalence for the Theileria, allowing for the assessment of systematics and diagnostic assays based on the 18S gene.The Theileria diagnostics project ARC-OVI (OV15/08/1P01) and the Department of Agriculture, Forestry and Fisheries (OV21/03/C148).http://www.elsevier.com/locate/ttbdis2017-07-31hb2016Veterinary Tropical Disease

Determining the risk-factors for molecular clustering of drug-resistant tuberculosis in South Africa

Abstract Background Drug-resistant tuberculosis (DR-TB) epidemic is driven mainly by the effect of ongoing transmission. In high-burden settings such as South Africa (SA), considerable demographic and geographic heterogeneity in DR-TB transmission exists. Thus, a better understanding of risk-factors for clustering can help to prioritise resources to specifically targeted high-risk groups as well as areas that contribute disproportionately to transmission. Methods The study analyzed potential risk-factors for recent transmission in SA, using data collected from a sentinel molecular surveillance of DR-TB, by comparing demographic, clinical and epidemiologic characteristics with clustering and cluster sizes. A genotypic cluster was defined as two or more patients having identical patterns by the two genotyping methods used. Clustering was used as a proxy for recent transmission. Descriptive statistics and multinomial logistic regression were used. Result The study identified 277 clusters, with cluster size ranging between 2 and 259 cases. The majority (81.6%) of the clusters were small (2–5 cases) with few large (11–25 cases) and very large (≥ 26 cases) clusters identified mainly in Western Cape (WC), Eastern Cape (EC) and Mpumalanga (MP). In a multivariable model, patients in clusters including 11–25 and ≥ 26 individuals were more likely to be infected by Beijing family, have XDR-TB, living in Nelson Mandela Metro in EC or Umgungunglovo in Kwa-Zulu Natal (KZN) provinces, and having history of imprisonment. Individuals belonging in a small genotypic cluster were more likely to infected with Rifampicin resistant TB (RR-TB) and more likely to reside in Frances Baard in Northern Cape (NC). Conclusion Sociodemographic, clinical and bacterial risk-factors influenced rate of Mycobacterium tuberculosis (M. tuberculosis) genotypic clustering. Hence, high-risk groups and hotspot areas for clustering in EC, WC, KZN and MP should be prioritized for targeted intervention to prevent ongoing DR-TB transmission

Distribution and clonality of drug-resistant tuberculosis in South Africa

BACKGROUND: Studies have shown that drug-resistant tuberculosis (DR-TB) in South Africa (SA) is clonal and is caused mostly by transmission. Identifying transmission chains is important in controlling DR-TB. This study reports on the sentinel molecular surveillance data of Rifampicin-Resistant (RR) TB in SA, aiming to describe the RR-TB strain population and the estimated transmission of RR-TB cases. METHOD: RR-TB isolates collected between 2014 and 2018 from eight provinces were genotyped using combination of spoligotyping and 24-loci mycobacterial interspersed repetitive-units-variable-number tandem repeats (MIRU-VNTR) typing. RESULTS: Of the 3007 isolates genotyped, 301 clusters were identified. Cluster size ranged between 2 and 270 cases. Most of the clusters (247/301; 82.0%) were small in size (< 5 cases), 12.0% (37/301) were medium sized (5–10 cases), 3.3% (10/301) were large (11–25 cases) and 2.3% (7/301) were very large with 26–270 cases. The Beijing genotype was responsible for majority of RR-TB cases in Western and Eastern Cape, while the East-African-Indian-Somalian (EAI1_SOM) genotype accounted for a third of RR-TB cases in Mpumalanga. The overall proportion of RR-TB cases estimated to be due to transmission was 42%, with the highest transmission-rate in Western Cape (64%) and the lowest in Northern Cape (9%). CONCLUSION: Large clusters contribute to the burden of RR-TB in specific geographic areas such as Western Cape, Eastern Cape and Mpumalanga, highlighting the need for community-wide interventions. Most of the clusters identified in the study were small, suggesting close contact transmission events, emphasizing the importance of contact investigations and infection control as the primary interventions in SA.The President’s Emergency Plan for AIDS Relief (PEPFAR) through the Centers for Disease Control and Prevention (CDC), CTB and NICD.http://www.biomedcentral.com/bmcmicrobiolpm2021Medical Microbiolog