Browsing by Author "Mwaikono, Kilaza S."
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- ItemHost and microbiome genome-wide association studies : current state and challenges(Frontiers Media, 2019) Awany, Denis; Allali, Imane; Dalvie, Shareefa; Hemmings, Sian M. J.; Mwaikono, Kilaza S.; Thomford, Nicholas E.; Gomez, Andres; Mulder, Nicola; Chimusa, Emile R.The involvement of the microbiome in health and disease is well established. Microbiome genome-wide association studies (mGWAS) are used to elucidate the interaction of host genetic variation with the microbiome. The emergence of this relatively new field has been facilitated by the advent of next generation sequencing technologies that enable the investigation of the complex interaction between host genetics and microbial communities. In this paper, we review recent studies investigating host–microbiome interactions using mGWAS. Additionally, we highlight the marked disparity in the sampling population of mGWAS carried out to date and draw attention to the critical need for inclusion of diverse populations.
- ItemOptimizing 16S rRNA gene profile analysis from low biomass nasopharyngeal and induced sputum specimens(BMC (part of Springer Nature), 2020-05-12) Claassen-Weitz, Shantelle; Gardner-Lubbe, Sugnet; Mwaikono, Kilaza S.; Du Toit, Elloise; Zar, Heather J.; Nicol, Mark P.Background: Careful consideration of experimental artefacts is required in order to successfully apply highthroughput 16S ribosomal ribonucleic acid (rRNA) gene sequencing technology. Here we introduce experimental design, quality control and “denoising” approaches for sequencing low biomass specimens. Results: We found that bacterial biomass is a key driver of 16S rRNA gene sequencing profiles generated from bacterial mock communities and that the use of different deoxyribonucleic acid (DNA) extraction methods [DSP Virus/Pathogen Mini Kit® (Kit-QS) and ZymoBIOMICS DNA Miniprep Kit (Kit-ZB)] and storage buffers [PrimeStore® Molecular Transport medium (Primestore) and Skim-milk, Tryptone, Glucose and Glycerol (STGG)] further influence these profiles. Kit-QS better represented hard-to-lyse bacteria from bacterial mock communities compared to Kit-ZB. Primestore storage buffer yielded lower levels of background operational taxonomic units (OTUs) from low biomass bacterial mock community controls compared to STGG. In addition to bacterial mock community controls, we used technical repeats (nasopharyngeal and induced sputum processed in duplicate, triplicate or quadruplicate) to further evaluate the effect of specimen biomass and participant age at specimen collection on resultant sequencing profiles. We observed a positive correlation (r = 0.16) between specimen biomass and participant age at specimen collection: low biomass technical repeats (represented by < 500 16S rRNA gene copies/μl) were primarily collected at < 14 days of age. We found that low biomass technical repeats also produced higher alpha diversities (r = − 0.28); 16S rRNA gene profiles similar to no template controls (Primestore); and reduced sequencing reproducibility. Finally, we show that the use of statistical tools for in silico contaminant identification, as implemented through the decontam package in R, provides better representations of indigenous bacteria following decontamination. Conclusions: We provide insight into experimental design, quality control steps and “denoising” approaches for 16S rRNA gene high-throughput sequencing of low biomass specimens. We highlight the need for careful assessment of DNA extraction methods and storage buffers; sequence quality and reproducibility; and in silico identification of contaminant profiles in order to avoid spurious results.