Background: Careful consideration of experimental artefacts is required in order to successfully apply high-throughput 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 thedecontampackage 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.

Multiple types of solid waste in developing countries is disposed of together in dumpsites where there is interaction between humans, animals and the bacteria in the waste. To study the bacteria at the dumpsite and the associated risks, previous studies have focused on culturable, leaving behind a great number of unculturable bacteria. This study focuses on a more comprehensive approach to study bacteria at the dumpsite. Since the site comprised of unsorted wastes, a qualitative survey was first performed to identify the variety of solid waste as this has influence on the microbial composition. Thus, domestic (Dom), biomedical (Biom), river sludge (Riv), and fecal material of pigs scavenging on the dumpsite (FecD) were sampled. Total DNA was extracted from 78 samples and the v4-16S rRNA amplicons was characterized using an Illumina MiSeq platform. A total of 8,469,294 sequences passed quality control. Catchall analysis predicted a mean of 8243 species per sample. Diversity was high with an average InvSimpson index of 44.21 ± 1.44. A total of 35 phyla were detected and the predominant were Firmicutes (38 %), Proteobacteria (35 %), Bacteroidetes (13 %) and Actinobacteria (3 %). Overall 76,862 OTUs were detected, however, only 20 % were found more than 10 times. The predominant OTUs were Acinetobacter (12.1 %), Clostridium sensu stricto (4.8 %), Proteinclasticum and Lactobacillus both at (3.4 %), Enterococcus (2.9 %) and Escherichia/Shigella (1.7 %). Indicator analysis (P ≤ 0.05, indicator value ≥ 70) shows that Halomonas, Idiomarina, Tisierella and Proteiniclasticum were associated with Biom; Enterococcus, Bifidobacteria, and Clostridium sensu stricto with FecD and Flavobacteria, Lysobacter and Commamonas to Riv. Acinetobacter and Clostridium sensu stricto were found in 62 % and 49 % of all samples, respectively, at the relative abundance of 1 %. None of OTUs was found across all samples. This study provides a comprehensive report on the abundance and diversity bacteria in municipal dumpsite. The species richness reported here shows the complexity of this man-made ecosystem and calls for further research to assess for a link between human diseases and the dumpsite. This would provide insight into proper disposal of the waste, as well as, limit the risks to human health associated with the dumpsite.

With more microbiome studies being conducted by African-based research groups, there is an increasing demand for knowledge and skills in the design and analysis of microbiome studies and data. However, high-quality bioinformatics courses are often impeded by differences in computational environments, complicated software stacks, numerous dependencies, and versions of bioinformatics tools along with a lack of local computational infrastructure and expertise. To address this, H3ABioNet developed a 16S rRNA Microbiome Intermediate Bioinformatics Training course, extending its remote classroom model. The course was developed alongside experienced microbiome researchers, bioinformaticians, and systems administrators, who identified key topics to address. Development of containerised workflows has previously been undertaken by H3ABioNet, and Singularity containers were used here to enable the deployment of a standard replicable software stack across different hosting sites. The pilot ran successfully in 2019 across 23 sites registered in 11 African countries, with more than 200 participants formally enrolled and 106 volunteer staff for onsite support. The pulling, running, and testing of the containers, software, and analyses on various clusters were performed prior to the start of the course by hosting classrooms. The containers allowed the replication of analyses and results across all participating classrooms running a cluster and remained available posttraining ensuring analyses could be repeated on real data. Participants thus received the opportunity to analyse their own data, while local staff were trained and supported by experienced experts, increasing local capacity for ongoing research support. This provides a model for delivering topic-specific bioinformatics courses across Africa and other remote/low-resourced regions which overcomes barriers such as inadequate infrastructures, geographical distance, and access to expertise and educational materials.

Culture-independent molecular techniques have advanced the characterization of environmental and human samples including the human milk (HM) bacteriome. However, extraction of high-quality genomic DNA that is representative of the bacterial population in samples is crucial. Lipids removal from HM prior to DNA extraction is common practice, but this may influence the bacterial population detected. The objective of this study was to compare four commercial DNA extraction kits and lipid removal in relation to HM bacterial profiles. Four commercial DNA extraction kits, QIAamp^® DNA Microbiome Kit, ZR Fungal/Bacterial DNA MiniPrep™, QIAsymphony DSP DNA Kit and ZymoBIOMICS™ DNA Miniprep Kit, were assessed using milk collected from ten healthy lactating women. The kits were evaluated based on their ability to extract high quantities of pure DNA from HM and how well they extracted DNA from bacterial communities present in a commercial mock microbial community standard spiked into HM. Finally, the kits were evaluated by assessing their extraction repeatability. Bacterial profiles were assessed using Illumina MiSeq sequencing targeting the V4 region of the 16S rRNA gene. The ZR Fungal/Bacterial DNA MiniPrep™ and ZymoBIOMICS™ DNA Miniprep (Zymo Research Corp., Irvine, CA, USA) kits extracted the highest DNA yields with the best purity. DNA extracted using ZR Fungal/Bacterial DNA MiniPrep™ best represented the bacteria in the mock community spiked into HM. In un-spiked HM samples, DNA extracted using the QIAsymphony DSP DNA kit showed statistically significant differences in taxa prevalence from DNA extracted using ZR Fungal/Bacterial DNA MiniPrep™ and ZymoBIOMICS™ DNA Miniprep kits. The only difference between skim and whole milk is observed in bacterial profiles with differing relative abundances of Enhydrobacter and Acinetobacter. DNA extraction, but not lipids removal, substantially influences bacterial profiles detected in HM samples, emphasizing the need for careful selection of a DNA extraction kit to improve DNA recovery from a range of bacterial taxa.

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.

Background: Lower respiratory tract infections (LRTIs) in infants are caused by viral and bacterial infections. We investigated associations between LRTI and nasopharyngeal (NP) viruses and bacteria in South African infants. Methods: In a birth cohort, LRTI cases were identified prospectively and age-matched with controls. NP swabs were tested using polymerase chain reaction and 16S RNA gene sequencing. We calculated adjusted conditional odds ratios (aORs) and used mixed-effects models to identify differentially abundant taxa and explore viral–bacterial interactions. Results: A total of 888 case-control samples were tested. Respiratory syncytial virus (RSV) (aOR, 5.69 [95% confidence interval, 3.03–10.69]), human rhinovirus (HRV) (1.47 [1.03–2.09]), parainfluenza virus (3.46 [1.64–7.26]), adenovirus (1.99 [1.08–3.68]), enterovirus (2.32 [1.20–4.46]), Haemophilus influenzae (1.72 [1.25–2.37]), Klebsiella pneumoniae (2.66 [1.59–4.46]), and high-density Streptococcus pneumoniae (1.53 [1.01–2.32]) were associated with LRTI. LRTI was associated with decreased relative abundance of Dolosigranulum (q = .001), Corynebacterium (q = .091), and Neisseria (q = .004). In samples positive for RSV, Staphylococcus and Alloprevotella relative abundance was higher in controls compared to cases. In samples positive for parainfluenza virus or HRV, Haemophilus relative abundance was higher in cases. Detection of cytomegalovirus (CMV) in controls was associated with reduced Corynebacterium, Dolosigranulum, and Staphylococcus. Conclusions: The associations between bacteria, viruses and LRTIs are similar to those from high-income countries. Haemophilus is a major bacterial driver of LRTIs, acting synergistically with viruses. Dolosigranulum and Corynebacterium may reduce LRTI risk, while Staphylococcus may reduce the risk of RSV-related LRTIs. CMV is associated with dysbiotic nasopharyngeal microbiota.