Biology subjects

Mackenzie, G.

Publications and source records attributed to Mackenzie, G..

2 recordsLinked to original sources

bioRxiv2021

Invasive atypical non-typhoidal Salmonella in The Gambia

BackgroundInvasive non-typhoidal Salmonella (iNTS) disease continues to be a significant public health problem in sub-Saharan Africa. Common clinical misdiagnosis, antimicrobial resistance, high case fatality and lack of a vaccine make iNTS a priority for global health research. Using whole genome sequence analysis of 164 invasive Salmonella isolates obtained through population-based surveillance between 2008 and 2016, we conducted genomic analysis of the serovars causing invasive Salmonella diseases in rural Gambia. ResultsThe incidence of iNTS varied over time. The proportion of atypical serovars causing disease increased over time from 40% to 65% compared to the typical serovars Enteritidis and Typhimurium decreasing from 30% to 12%. Overall iNTS case fatality was 10% with 10% fatality in cases of atypical iNTS. Genetic virulence factors were identified in 14/70 (20%) typical serovars and 45/68 (66%) of the atypical serovars and were associated with: invasion, proliferation and/or translocation (Clade A); and host colonization and immune modulation (Clade G). Among Enteritidis isolates, 33/40 were resistant to [≥]4 the antimicrobials tested, except for ciprofloxacin, to which all isolates were susceptible. Resistance was low in Typhimurium isolates, however, all16 isolates were resistant to gentamicin. ConclusionThe increase in incidence and proportion of iNTS disease caused by atypical serovars is concerning. The increased proportion of atypical serovars and the high associated case fatality may be related to acquisition of specific genetic virulence factors. These factors may provide a selective advantage to the atypical serovars. Investigations should be conducted elsewhere in Africa to identify potential changes in the distribution iNTS serovars and the extent of these virulence elements.

genomics↗

bioRxiv2020

Varia: Prediction, analysis and visualisation of variable genes

Assessing the diversity or expression of variable gene families in pathogens can inform about immune escape mechanisms or host interaction phenotypes of clinical relevance. However, obtaining the sequences and quantifying their expression is a challenge. Here, we present a tool, which based on unique sequence tag similarity between members of a gene family, predicts the domains encoded by the queried gene. As an example, we are using the var gene family, encoding the major virulence proteins (PfEMP1) of the human malaria parasite, Plasmodium falciparum. We developed Varia, which predicts the likely var gene sequence and encoded protein domain composition of a gene from short sequence tags. We provide a new extended annotated var genome database, in which Varia identifies genes with identical tag sequences and compares these to return the most probable domain composition of the query gene. Varias ability to predict correct PfEMP1 domain compositions from short var sequence tags was tested in two complementary pipelines to (a) return the putative gene sequences and domain compositions of the query gene from any partial sequence provided, thereby enabling detailed assessment of specific genes putative function and experimental validation of these (b) to accommodate rapid profiling of var gene expression in complex patient samples, by compiling the overall domain prevalence among var transcripts predicted identified and quantified by next generation sequencing of so-called var DBL-sequence tags. Availability and implementationVaria is available on GitHub (https://github.com/GCJMacken-zie/Varia) under the MIT license. Contactthomasl@sund.ku.dk, thomasdan.otto@glasgow.ac.uk

bioinformatics↗