Browsing by Author "Du Toit, Elloise"

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    Influence of socio-economic and psychosocial profiles on the human breast milk bacteriome of South African women
    (MDPI, 2019-06-20) Ojo-Okunola, Anna; Claassen-Weitz, Shantelle; Mwaikono, Kilaza S.; Gardner-Lubbe, Sugnet; Stein, Dan J.; Zar, Heather J.; Nicol, Mark P.; Du Toit, Elloise
    The human breast milk (HBM) bacteriome is an important, continuous source of microbes to the neonate in early life, playing an important role in shaping the infant’s intestinal bacteriome. Study of the composition of the HBM bacteriome is an emerging area of research, with little information available, particularly from low- and middle-income countries. The aim of this study was to characterize the diversity of bacterial communities in HBM samples collected between 6–10 weeks postpartum from lactating South African women and to study potential influencing factors of the bacteriome. Using 16S rRNA gene sequencing of samples from 554 women, we demonstrated that the HBM bacteriome was largely dominated by the phyla Firmicutes (mean relative abundance: 71.1%) and Actinobacteria (mean relative abundance: 16.4%). The most abundant genera identified from the HBM bacteriome were Streptococcus (mean relative abundance: 48.6%), Staphylococcus (mean relative abundance: 17.8%), Rothia (mean relative abundance: 5.8%), and Corynebacterium (mean relative abundance: 4.3%). “Core” bacterial genera including Corynebacterium, Streptococcus, Staphylococcus, Rothia, Veillonella, Gemella, Acinetobacter, Micrococcus and a genus belonging to the Enterobacteriaceae family were present in 80% of samples. HBM samples were classified, according to their bacteriome, into three major clusters, dominated by the genera Staphylococcus (cluster 1), a combination of Staphylococcus and Streptococcus (cluster 2), and Streptococcus (cluster 3). The cluster groups differed significantly for Shannon and chao1 richness indices. Bacterial interactions were studied using co-occurrence networks with positive associations observed between the abundances of Staphylococcus and Corynebacteria (members of the skin microflora) and between Streptococcus, Rothia, Veillonella, and Gemella (members of the oral microflora). HBM from older mothers had a higher Shannon diversity index. The study site was associated with differences in HBM bacteriome composition (permutational multivariate analysis of variance using distance matrices (PERMANOVA), p < 0.05). No other tested socio-demographic or psychosocial factors were associated with HBM bacterial composition.
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    Optimizing 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.