Unlocking the world of microbiomes
In 2020 we celebrate 75 years of the anniversary of our founding with a year of activities dedicated to demonstrating the impact of microbiologists’ past, present and future – bringing together and empowering communities that help shape the future of microbiology. We are launching new collections of digital content throughout the anniversary year. The first digital hub is Unlocking the world of microbiomes: exploring microbial communities, which will examine the microbiome and human health, agriculture and food microbiomes and environmental and industrial microbiomes.
The ‘Unlocking the world of microbiomes’ collection brings together articles from across our journals exploring microbial communities and examining the microbiome and human health. This collection is an update of a collection by the Microbiology Society and the British Society for Immunology launched for World Microbiome Day; the ‘Microbiome’ collection can be viewed on Science Open.
Collection Contents
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Assessing the comparability of different DNA extraction and amplification methods in gut microbial community profiling
More LessAutomated, high-throughput technologies are becoming increasingly common in microbiome studies to decrease costs and increase efficiency. However, in microbiome studies, small differences in methodology – including storage conditions, wet lab methods, sequencing platforms and data analysis – can influence the reproducibility and comparability of data across studies. There has been limited testing of the effects of high-throughput methods, including microfluidic PCR technologies. In this paper, we compare two extraction methods (the QIAamp DNA Stool Mini Kit and the MoBio PowerSoil DNA Isolation kit), two taq polymerase enzymes (MyTaq HS Red Mix and Accustart II PCR ToughMix), two primer sets (V3–V4 and V4–V5) and two amplification methods (a common two-step PCR protocol and amplicon library preparation on the Fluidigm Access Array system that allows automated multiplexing of primers). Gut microbial community profiles were significantly affected by all variables. While there were no significant differences in alpha diversity measured between the two extraction methods, there was an effect of extraction method on community composition measured by unweighted UniFrac distances. Both amplification method and primers had a significant effect on both alpha diversity and community composition. The relative abundance of Actinobacteria was significantly lower when using the MoBio kit or Fluidigm amplification method, and the relative abundance of Firmicutes was lower when using the Qiagen kit. Microbial community profiles based on Fluidigm-generated amplicon libraries were not comparable to those generated with more commonly used methods. Researchers should carefully consider the limitations and biases that different extraction and amplification methods can introduce into their results. Additionally, more thorough benchmarking of automated and multiplexing methods is necessary to determine the magnitude of the potential trade-off between the quality and the quantity of data.
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Analysis of the effect of smoking on the buccal microbiome using next-generation sequencing technology
Purpose. This study aimed to investigate the effect of smoking on the buccal microbiome and to analyse the descriptive ability of each of the seven hypervariable regions in their 16S rRNA genes.
Methodology. Microbiome compositions of 40 buccal swab samples collected from smokers (n =20) and non-smokers (n =20) were determined using 16S rRNA sequencing. Seven different 16S rRNA hypervariable regions (V2, V3, V4, V6-7, V8 and V9) in each sample were amplified using the Ion Torrent 16S Metagenomics kit and were sequenced on the Ion S5 instrument.
Results. Seven hypervariable regions in the 16S rRNA gene were successfully sequenced for all samples tested. The data obtained with the V2 region was found to be informative but the consensus data generated according to a number of operational taxonomic unit reads gathered from all seven hypervariable regions gave the most accurate result. At the phylum level, no statistically significant difference was found between smokers and non-smokers whereas relative abundances of Veillonella atypica , Streptococcus australis , Prevotella melaninogenica , Prevotella salivae and Rothia mucilaginosa showed significant increases in the smoker group (P-adj=0.05). Alpha diversity results did not show a significant difference between the two groups; however, beta diversity analysis indicated that samples of smoker and non-smoker groups had a tendency to be clustered within themselves.
Conclusion. The results of the current study indicate that smoking is a factor influencing buccal microbiome composition. In addition, sequencing of all seven hypervariable regions yielded more accurate results than those with any of the single variable regions.
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Antibiotic resistomes of healthy pig faecal metagenomes
More LessAntibiotic resistance reservoirs within food-producing animals are thought to be a risk to animal and human health. This study describes the minimum natural resistome of pig faeces as the bacteria are under no direct antibiotic selective pressure. The faecal resistome of 257 different genes comprised 56 core and 201 accessory resistance genes. The genes present at the highest relative abundances across all samples were tetW, tetQ, tet44, tet37, tet40, mefA, aadE, ant(9)−1, ermB and cfxA2. This study characterized the baseline resistome, the microbiome composition and the metabolic components described by the Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways in healthy pig faeces, without antibiotic selective pressures. The microbiome hierarchical analysis resulted in a cluster tree with a highly similar pattern to that of the accessory resistome cluster tree. Functional capacity profiling identified genes associated with horizontal gene transfer. We identified a statistically significant positive correlation between the total antibiotic resistome and suggested indicator genes, which agree with using these genes as indicators of the total resistomes. The correlation between total resistome and total microbiome in this study was positive and statistically significant. Therefore, the microbiome composition influenced the resistome composition. This study identified a core and accessory resistome present in a cohort of healthy pigs, in the same conditions without antibiotics. It highlights the presence of antibiotic resistance in the absence of antibiotic selective pressure and the variability between animals even under the same housing, food and living conditions. Antibiotic resistance will remain in the healthy pig gut even when antibiotics are not used. Therefore, the risk of antibiotic resistance transfer from animal faeces to human pathogens or the environment will remain in the absence of antibiotics.
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