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Storage of biological specimens is crucial in the life and medical sciences. Storage conditions for samples can be different for a number of reasons, and it is unclear what effect this can have on the inferred microbiome composition in metagenomics analyses. Here, we assess the effect of common storage temperatures (deep freezer, −80°C; freezer, −20°C; refrigerator, 5°C; room temperature, 22°C) and storage times (immediate sample processing, 0 h; next day, 16 h; over weekend, 64 h; longer term, 4, 8, and 12 months) as well as repeated sample freezing and thawing (2 to 4 freeze-thaw cycles). We examined two different pig feces and sewage samples, unspiked and spiked with a mock community, in triplicate, respectively, amounting to a total of 438 samples (777 Gbp; 5.1 billion reads). Storage conditions had a significant and systematic effect on the taxonomic and functional composition of microbiomes. Distinct microbial taxa and antimicrobial resistance classes were, in some situations, similarly affected across samples, while others were not, suggesting an impact of individual inherent sample characteristics. With an increasing number of freeze-thaw cycles, an increasing abundance of Firmicutes, Actinobacteria, and eukaryotic microorganisms was observed. We provide recommendations for sample storage and strongly suggest including more detailed information in the metadata together with the DNA sequencing data in public repositories to better facilitate meta-analyses and reproducibility of findings. IMPORTANCE Previous research has reported effects of DNA isolation, library preparation, and sequencing technology on metagenomics-based microbiome composition; however, the effect of biospecimen storage conditions has not been thoroughly assessed. We examined the effect of common sample storage conditions on metagenomics-based microbiome composition and found significant and, in part, systematic effects. Repeated freeze-thaw cycles could be used to improve the detection of microorganisms with more rigid cell walls, including parasites. We provide a data set that could also be used for benchmarking algorithms to identify and correct for unwanted batch effects. Overall, the findings suggest that all samples of a microbiome study should be stored in the same way. Furthermore, there is a need to mandate more detailed information about sample storage and processing be published together with DNA sequencing data at the International Nucleotide Sequence Database Collaboration (ENA/EBI, NCBI, DDBJ) or other repositories.

The bovine udder is colonized by a huge quantity of microorganisms that constitute the intramammary ecosystem, with a specific role in modulating not only udder homeostasis and mastitis susceptibility, but also the quality of the dairy products. However, generating high-quality bacterial DNA can be critical, especially starting from a complex biological matrix like milk, characterized by high fat, protein, and calcium contents. Here, bacterial DNA was recovered from a commercial ultra-high-temperature (UHT) milk sample artificially spiked with a predetermined mock community composition and from three bulk tank milk (raw milk) samples. The DNA was isolated using three different protocols to evaluate the effect of the extraction procedures on the milk microbiota composition. In the mock community experiment, the bacterial profiles generated by the three DNA extraction protocols were profoundly different, with the genera Staphylococcus, Lactobacillus, Listeria, and Salmonella underestimated by all the protocols. Only one protocol revealed values close to the expected abundances for Escherichia/Shigella spp., Bacillus spp., Enterococcus spp., and Pseudomonas spp. On the other hand, the nonspiked UHT milk sample exhibited a similar microbiota composition, revealing the prevalence of Acinetobacter spp., for all the DNA extraction protocols. For the raw milk samples, the three DNA extraction kits performed differently, revealing significant separations in both the microbial richness (alpha diversity) and composition (beta diversity). Our study highlights the presence of significant differences among these procedures, probably due to the different DNA extracting capacities and to the different properties of the milk samples, revealing that the selection of DNA extraction protocol is a critical point. IMPORTANCE The advance of high-throughput technologies has increased our knowledge of the world of microorganisms, especially of microbial populations inhabiting living animals. This study provides evidence that milk, as other complex sources, could be critical for generating high-quality DNA for microbiota analysis. In addition, it demonstrates that the microbial population highlighted by metagenomic studies changes in relation to different DNA extraction procedures, revealing that attention should be paid especially when comparing different studies.

The unprecedented ongoing biodiversity decline necessitates scalable means of monitoring in order to fully understand the underlying causes. DNA metabarcoding has the potential to provide a powerful tool for accurate and rapid biodiversity monitoring. Unfortunately, in many cases, a lack of universal standards undermines the widespread application of metabarcoding. One of the most important considerations in metabarcoding of plants, aside from selecting a potent barcode marker, is primer choice. Our study evaluates published ITS primers in silico and in vitro, through mock communities and presents newly designed primers. We were able to show that a large proportion of previously available ITS primers have unfavourable attributes. Our combined results support the recommendation of the introduced primers ITS-3p62plF1 and ITS-4unR1 as the best current universal plant specific ITS2 primer combination. We also found that PCR optimisation, such as the addition of 5% DMSO, is essential to obtain meaningful results in ITS2 metabarcoding. Finally, we conclude that continuous quality assurance is indispensable for reliable metabarcoding results.

It is known that data from both 16S and shotgun metagenomics studies are subject to biases that cause the observed relative abundances of taxa to differ from their true values. Model community analyses, in which the relative abundances of all taxa in the sample are known by construction, seem to offer the hope that these biases can be measured. However, it is unclear whether the bias we measure in a mock community analysis is the same as we measure in a sample in which taxa are spiked in at known relative abundance, or if the biases we measure in spike-in samples is the same as the bias we would measure in a real (e.g., biological) sample. Here, we consider these questions in the context of 16S rRNA measurements on three sets of samples: the commercially available Zymo cells model community; the Zymo model community mixed with Swedish Snus, a smokeless tobacco product that is virtually bacteria-free; and a set of commercially available smokeless tobacco products. Each set of samples was subject to four different extraction protocols. The goal of our analysis is to determine whether the patterns of bias observed in each set of samples are the same, i.e., can we learn about the bias in the commercially available smokeless tobacco products by studying the Zymo cells model community?

International audience; Fish eDNA metabarcoding is usually performed from filtered water samples. The volume of filtered water depends on the study scope and can rapidly become time consuming according to the number of samples that have to be processed. To avoid time allocated to filtration, passive DNA samplers have been used to recover fish eDNA from marine environments faster. In freshwater ecosystems, aquatic biofilms were used to catch eDNA from macroinvertebrates. Here, we test the capacity of aquatic biofilms to entrap fish eDNA in a large lake and, therefore, the possibility to perform fish eDNA metabarcoding from this matrix compared to the traditional fish eDNA approach from filtered water samples. Methodological aspects of the use of aquatic biofilms for fish eDNA metabarcoding (e.g. PCR replicates, biological replicates, bioinformatics pipeline, reference database and taxonomic assignment) were validated against a mock community. When using biofilms from habitats sheltered from wind and waves, biofilm and water approach provided similar inventories. Richness and diversity were comparable between both approaches. Approaches differed only for rare taxa. Our results illustrate the capacity of aquatic biofilms to act as passive eDNA samplers of fish eDNA and, therefore, the possibility to use biofilms to monitor fish communities efficiently from biofilms. Furthermore, our results open up avenues of research to study a diversity of biological groups (among which bioindicators as diatoms, macroinvertebrates and fish) from eDNA isolated from a single environmental matrix reducing sampling efforts, analysis time and costs.

Despite the routine use of standards and blanks in virtually all chemical or physical assays and most biological studies (a kind of “control”), microbiome analysis has traditionally lacked such standards. Here we show that unexpected problems of unknown origin can occur in such sequencing runs and yield completely incorrect results that would not necessarily be detected without the use of standards. Assuming that the microbiome sequencing analysis works properly every time risks serious errors that can be detected by the use of mock communities., Mock communities have been used in microbiome method development to help estimate biases introduced in PCR amplification and sequencing and to optimize pipeline outputs. Nevertheless, the strong value of routine mock community analysis beyond initial method development is rarely, if ever, considered. Here we report that our routine use of mock communities as internal standards allowed us to discover highly aberrant and strong biases in the relative proportions of multiple taxa in a single Illumina HiSeqPE250 run. In this run, an important archaeal taxon virtually disappeared from all samples, and other mock community taxa showed >2-fold high or low abundance, whereas a rerun of those identical amplicons (from the same reaction tubes) on a different date yielded “normal” results. Although obvious from the strange mock community results, we could have easily missed the problem had we not used the mock communities because of natural variation of microbiomes at our site. The “normal” results were validated over four MiSeqPE300 runs and three HiSeqPE250 runs, and run-to-run variation was usually low. While validating these “normal” results, we also discovered that some mock microbial taxa had relatively modest, but consistent, differences between sequencing platforms. We strongly advise the use of mock communities in every sequencing run to distinguish potentially serious aberrations from natural variations. The mock communities should have more than just a few members and ideally at least partly represent the samples being analyzed to detect problems that show up only in some taxa and also to help validate clustering. IMPORTANCE Despite the routine use of standards and blanks in virtually all chemical or physical assays and most biological studies (a kind of “control”), microbiome analysis has traditionally lacked such standards. Here we show that unexpected problems of unknown origin can occur in such sequencing runs and yield completely incorrect results that would not necessarily be detected without the use of standards. Assuming that the microbiome sequencing analysis works properly every time risks serious errors that can be detected by the use of mock communities.

Profiling of microbial community composition is frequently performed by partial 16S rRNA gene sequencing on benchtop platforms following PCR amplification of specific hypervariable regions within this gene. Accuracy and reproducibility of this strategy are two key parameters to consider, which may be influenced during all processes from sample collection and storage, through DNA extraction and PCR based library preparation to the final sequencing. In order to evaluate both the reproducibility and accuracy of 16S rRNA gene based microbial profiling using the Ion Torrent PGM platform, we prepared libraries and performed sequencing of a well-defined and validated 20-member bacterial DNA mock community on five separate occasions and compared results with the expected even distribution. In general the applied method had a median coefficient of variance of 11.8% (range 5.5–73.7%) for all 20 included strains in the mock community across five separate sequencing runs, with underrepresented strains generally showing the largest degree of variation. In terms of accuracy, mock community species belonging to Proteobacteria were underestimated, whereas those belonging to Firmicutes were mostly overestimated. This could be explained partly by premature read truncation, but to larger degree their genomic GC-content, which correlated negatively with the observed relative abundances, suggesting a PCR bias against GC-rich species during library preparation. Increasing the initial denaturation time during the PCR amplification from 30 to 120 s resulted in an increased average relative abundance of the three mock community members with the highest genomic GC%, but did not significantly change the overall evenness of the community distribution. Therefore, efforts should be made to optimize the PCR conditions prior to sequencing in order to maximize accuracy.

© 2013 Treangen et al. We describe MetAMOS, an open source and modular metagenomic assembly and analysis pipeline. MetAMOS represents an important step towards fully automated metagenomic analysis, starting with next-generation sequencing reads and producing genomic scaffolds, open-reading frames and taxonomic or functional annotations. MetAMOS can aid in reducing assembly errors, commonly encountered when assembling metagenomic samples, and improves taxonomic assignment accuracy while also reducing computational cost. MetAMOS can be downloaded from: https://github.com/treangen/MetAMOS.

Next-generation sequencing has revolutionized our ability to investigate the microbiota composition of diverse and complex environments. However, a number of factors can affect the accuracy of microbial community assessment, such as the DNA extraction method, the hypervariable region of 16S rRNA gene targeted, or the PCR primers used for amplification. The aim of this study was to assess the influence of commercially available DNA extraction kits and different primer pairs to provide a non-biased vision of the composition of bacterial communities present in olive xylem sap. For that purpose, branches from “Picual” and “Arbequina” olive cultivars were used for xylem sap extraction using a Scholander chamber device. The DNA extraction protocol significantly affected xylem sap bacterial community assessment. That resulted in significant differences in alpha (Richness) and beta diversity (UniFrac distances) metrics among DNA extraction protocols, with the 12 DNA extraction kits evaluated being clustered in four groups behaving differently. Although the core number of taxa detected by all DNA extraction kits included four phyla, seven classes, 12 orders, 16 or 21 families, and 12 or 14 genera when using the Greengenes or Silva database for taxonomic assignment, respectively, some taxa, particularly those identified at low frequency, were detected by some DNA extraction kits only. The most accurate depiction of a bacterial mock community artificially inoculated on sap samples was generated when using the PowerPlant DNA extraction kit, the combination of 799F/1193R primers amplifying the hypervariable V5–V7 region, and the Silva 132 database for taxonomic assignment. The DESeq2 analysis displayed significant differences among genera abundance between the different PCR primer pairs tested. Thus, Enterobacter, Granulicatella, Prevotella, and Brevibacterium presented a significant higher abundance in all PCR protocols when compared with primer pair 799F/1193R, while the opposite was true for Pseudomonas and Pectobacterium. The methodological approach followed in this study can be useful to optimize plant-associated microbiome analysis, especially when exploring new plant niches. Some of the DNA extraction kits and PCR primers selected in this study will contribute to better characterize bacterial communities inhabiting the xylem sap of olives or other woody crop species., This study was funded by the project AGL2016-75606-R (Programa Estatal de I+D Orientado a los Retos de la Sociedad from Spanish Government, the Spanish State Research Agency, and FEDER-EU) and Project XF-ACTORS (grant 727987) from the European Union’s Horizon 2020 Framework Research Programme. MA-M is a recipient of a research fellowship BES-2017-082361 from the Spanish Ministry of Economy and Competitiveness.