RibDif: can individual species be differentiated by 16S sequencing?

Strube, Mikael Lenz

doi:10.1093/bioadv/vbab020

Cited by 24 publications

(38 citation statements)

References 9 publications

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“…Dereplication and inference were performed using the DADA2 pipeline. After merging the paired reads and chimera filtering, an ASV table was constructed (to resolve bacteria at the species level [ 25 ]). The ASVs were taxonomically assigned based on the SILVA 16S rRNA gene database (version 132) [ 26 ] using a naive Bayesian classifier method [ 27 ] implemented in DADA2.…”

Section: Methodsmentioning

confidence: 99%

Fecal microbiota dynamics and its relationship to diarrhea and health in dairy calves

Chen

Liu

Huang

et al. 2022

J Animal Sci Biotechnol

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Background Diarrhea is a major cause of morbidity and mortality in young calves, resulting in considerable economic loss for dairy farms. To determine if some gut microbes might have resistance to dysbiotic process with calf diarrhea by dictating the microbial co-occurrence patterns from birth to post-weaning, we examined the dynamic development of the gut microbiota and diarrhea status using two animal trials, with the first trial having 14 Holstein dairy calves whose fecal samples were collected 18 times over 78 d from birth to 15 d post-weaning and the second trial having 43 Holstein dairy calves whose fecal samples were collected daily from 8 to 18 days of age corresponding to the first diarrhea peak of trial 1. Results Metataxonomic analysis of the fecal microbiota showed that the development of gut microbiota had three age periods with birth and weaning as the separatrices. Two diarrhea peaks were observed during the transition of the three age periods. Fusobacteriaceae was identified as a diarrhea-associated taxon both in the early stage and during weaning, and Clostridium_sensu_stricto_1 was another increased genus among diarrheic calves in the early stage. In the neonatal calves, Prevotella_2 (ASV4 and ASV26), Prevotella_9 (ASV43), and Alloprevotella (ASV14) were negatively associated with Clostridium_sensu_stricto_1 (ASV48), the keystone taxa of the diarrhea-phase module. During weaning, unclassified Muribaculaceae (ASV28 and ASV44), UBA1819 (ASV151), Barnesiella (ASV497), and Ruminococcaceae_UCG-005 (ASV254) were identified being associated with non-diarrheic status, and they aggregated in the non-diarrhea module of co-occurrence patterns wherein unclassified Muribaculaceae (ASV28) and Barnesiella (ASV497) had a direct negative relationship with the members of the diarrhea module. Conclusions Taken together, our results suggest that the dynamic successions of calf gut microbiota and the interactions among some bacteria could influence calf diarrhea, and some species of Prevotella might be the core microbiota in both neonatal and weaning calves, while species of Muribaculaceae might be the core microbiota in weaning calves for preventing calf diarrhea. Some ASVs affiliated with Prevotella_2 (ASV4 and ASV26), Prevotella_9 (ASV43), Alloprevotella (AVS14), unclassified Muribaculaceae (ASV28 and ASV44), UBA1819 (ASV151), Ruminococcaceae_UCG-005 (ASV254), and Barnesiella (ASV497) might be proper probiotics for preventing calf diarrhea whereas Clostridium_sensu_stricto_1 (ASV48) might be the biomarker for diarrhea risk in specific commercial farms.

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Section: Methodsmentioning

confidence: 99%

Fecal microbiota dynamics and its relationship to diarrhea and health in dairy calves

Chen

Liu

Huang

et al. 2022

J Animal Sci Biotechnol

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“…We compared taxonomic profiles of phyla, class, family and genus between shotgun metagenomics and 16S rRNA and ITS amplicon sequencing. It has been reported that for ~42% of bacterial genera there will be pairs of congeneric sequences that cannot be easily separated because of the high similarity of their 16S rRNA sequences (Vetrovsky and Baldrian, 2013 ; Strube, 2021 ). Hence, since species and strains are particularly challenging to identify (or unattainable) for the metataxonomic methods used here (Vetrovsky and Baldrian, 2013 ), and are not commonly reported in vermicomposting (e.g., Gopal et al, 2017 ; Dominguez et al, 2019 ), we did not consider lower-than-genus taxonomic levels in our analyses.…”

Section: Methodsmentioning

confidence: 99%

“…Similarly, the internal transcribed spacer (ITS) region of the rRNA operon for fungi (Nilsson et al, 2019a ) is an effective DNA barcode (Schoch et al, 2012 ) to characterize fungal diversity also due to an extensive database of reference sequences (Merget et al, 2012 ; Santamaria et al, 2018 ; Nilsson et al, 2019b ). However, despite their widespread use in microbial diversity studies, these two target genes are not as effective at classifying sequences at the species and strain levels (Vetrovsky et al, 2016 ; Johnson et al, 2019 ; Strube, 2021 ).…”

Section: Introductionmentioning

confidence: 99%

Comparative Analysis of Metagenomics and Metataxonomics for the Characterization of Vermicompost Microbiomes

et al. 2022

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The study of microbial communities or microbiotas in animals and environments is important because of their impact in a broad range of industrial applications, diseases and ecological roles. High throughput sequencing (HTS) is the best strategy to characterize microbial composition and function. Microbial profiles can be obtained either by shotgun sequencing of genomes, or through amplicon sequencing of target genes (e.g., 16S rRNA for bacteria and ITS for fungi). Here, we compared both HTS approaches at assessing taxonomic and functional diversity of bacterial and fungal communities during vermicomposting of white grape marc. We applied specific HTS workflows to the same 12 microcosms, with and without earthworms, sampled at two distinct phases of the vermicomposting process occurring at 21 and 63 days. Metataxonomic profiles were inferred in DADA2, with bacterial metabolic pathways predicted via PICRUSt2. Metagenomic taxonomic profiles were inferred in PathoScope, while bacterial functional profiles were inferred in Humann2. Microbial profiles inferred by metagenomics and metataxonomics showed similarities and differences in composition, structure, and metabolic function at different taxonomic levels. Microbial composition and abundance estimated by both HTS approaches agreed reasonably well at the phylum level, but larger discrepancies were observed at lower taxonomic ranks. Shotgun HTS identified ~1.8 times more bacterial genera than 16S rRNA HTS, while ITS HTS identified two times more fungal genera than shotgun HTS. This is mainly a consequence of the difference in resolution and reference richness between amplicon and genome sequencing approaches and databases, respectively. Our study also revealed great differences and even opposite trends in alpha- and beta-diversity between amplicon and shotgun HTS. Interestingly, amplicon PICRUSt2-imputed functional repertoires overlapped ~50% with shotgun Humann2 profiles. Finally, both approaches indicated that although bacteria and fungi are the main drivers of biochemical decomposition, earthworms also play a key role in plant vermicomposting. In summary, our study highlights the strengths and weaknesses of metagenomics and metataxonomics and provides new insights on the vermicomposting of white grape marc. Since both approaches may target different biological aspects of the communities, combining them will provide a better understanding of the microbiotas under study.

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“…The number of unique copies of the marker gene for each strain and the percent sequence identity among them will inform the expected number of taxa after mock community sequencing. This is especially important when operational taxonomic units (OTUs) are based on 100% sequence identity (e.g., using the amplicon sequence variant (ASV), zOTU, or ESV definitions at 100%; Starke, Pylro, & Morais, 2020; Strube, 2021), as each unique copy of a marker gene will become a distinct taxonomic unit, including any propagated PCR errors. The gene copy numbers for many cultivable strains or close relatives are available in sequenced genomes in the National Center for Biotechnology Information (NCBI) database or, in the case of the 16S rRNA gene, in the rrnDB (https://rrndb.umms.med.umich.edu/; Stoddard et al., 2015).…”

Section: Strategic Planningmentioning

confidence: 99%

Do‐it‐Yourself Mock Community Standard for Multi‐Step Assessment of Microbiome Protocols

et al. 2022

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Microbiomes provide critical functions that support animals, plants, and ecosystems. High‐throughput sequencing (HTS) has become an essential tool for the cultivation‐independent study of microbiomes found in diverse environments, but requires effective and meaningful controls. One such critical control is a mock microbial community, which is used as a positive control for nucleic acid extraction, marker gene amplification, and sequencing. While mock community standards can be purchased, they can be costly and often include only medically relevant microbial strains that are not expected to be major players in non‐human microbiomes. As an alternative, it is possible to design and construct a do‐it‐yourself (DIY) mock community, which can then be used as a positive control that is specifically customized to the protocol needs of a particular study system. In this article, we describe protocols to select appropriate microbial strains for the construction of a mock community. We first describe the steps to verify the identity of community members via Sanger sequencing. Then, we provide guidance on assembling and storing the DIY mock community as viable whole cells. This includes steps to create standard growth curves referenced to plate counts for each member, so that the community members can be quantified and later compared in terms of their “expected versus returned” relative contributions after sequencing. We also describe appropriate methods for the cryostorage of the fully assembled mock community as viable whole cells, so that they can be used as a unit in a microbiome analysis, from the lysis and nucleic acid extraction steps onwards. Finally, we provide an example of returned data and interpretation of DIY mock community sequences, discussing how to assess possible contamination and identify protocol biases for particular members. Overall, DIY mock communities serve to determine success and possible bias in a cultivation‐independent microbiome analysis. © 2022 The Authors. Current Protocols published by Wiley Periodicals LLC. Basic Protocol 1: Strain identification and verification using Sanger sequencing Basic Protocol 2: Creation of glycerol stocks of each mock community strain for long‐term cryostorage Basic Protocol 3: Assessment of strain freezer viability without cryoprotectant Basic Protocol 4: Creation of standard curve to determine CFU/ml of a liquid culture as a function of optical density Basic Protocol 5: Full mock community assembly using community concentration calculations and standard curves

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RibDif: can individual species be differentiated by 16S sequencing?

Cited by 24 publications

References 9 publications

Fecal microbiota dynamics and its relationship to diarrhea and health in dairy calves

Fecal microbiota dynamics and its relationship to diarrhea and health in dairy calves

Comparative Analysis of Metagenomics and Metataxonomics for the Characterization of Vermicompost Microbiomes

Do‐it‐Yourself Mock Community Standard for Multi‐Step Assessment of Microbiome Protocols

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