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Abstract Exploring the diversity of diazotrophs is key to understanding their role in supplying fixed nitrogen that supports marine productivity. A nested PCR assay using the universal primer set nifH1-nifH4, which targets the nitrogenase (nifH) gene, is a widely used approach for studying marine diazotrophs by amplicon sequencing. Metagenomics, direct sequencing of DNA without PCR, has provided complementary views of the diversity of marine diazotrophs. A significant fraction of the metagenome-derived nifH sequences (e.g. Planctomycete- and Proteobacteria-affiliated) were reported to have nucleotide mismatches with the nifH1-nifH4 primers, leading to the suggestion that nifH amplicon sequencing does not detect specific diazotrophic taxa and underrepresents diazotroph diversity. Here, we report that these mismatches are mostly located in a single-base at the 5′-end of the nifH4 primer, which does not impact detection of the nifH genes. This is demonstrated by the presence of nifH genes that contain the nucleotide mismatches in a recent compilation of global ocean nifH amplicon datasets, with high relative abundances detected in a variety of samples. While the metagenome- and metatranscriptome-derived nifH genes accounted for 4.4% of the total amplicon sequence variants from the global ocean nifH amplicon database, the corresponding amplicon sequence variants can have high relative abundances (accounting for 47% of the reads in the database). These analyses underscore that nifH amplicon sequencing using the nifH1-nifH4 primers is an important tool for studying diversity of marine diazotrophs, particularly as a complement to metagenomics which can provide taxonomic and metabolic information for some dominant groups.
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PPIT: an R package for inferring microbial taxonomy from nifH sequences
Abstract Motivation Linking microbial community members to their ecological functions is a central goal of environmental microbiology. When assigned taxonomy, amplicon sequences of metabolic marker genes can suggest such links, thereby offering an overview of the phylogenetic structure underpinning particular ecosystem functions. However, inferring microbial taxonomy from metabolic marker gene sequences remains a challenge, particularly for the frequently sequenced nitrogen fixation marker gene, nitrogenase reductase (nifH). Horizontal gene transfer in recent nifH evolutionary history can confound taxonomic inferences drawn from the pairwise identity methods used in existing software. Other methods for inferring taxonomy are not standardized and require manual inspection that is difficult to scale. Results We present Phylogenetic Placement for Inferring Taxonomy (PPIT), an R package that infers microbial taxonomy from nifH amplicons using both phylogenetic and sequence identity approaches. After users place query sequences on a reference nifH gene tree provided by PPIT (n = 6317 full-length nifH sequences), PPIT searches the phylogenetic neighborhood of each query sequence and attempts to infer microbial taxonomy. An inference is drawn only if references in the phylogenetic neighborhood are: (1) taxonomically consistent and (2) share sufficient pairwise identity with the query, thereby avoiding erroneous inferences due to known horizontal gene transfer events. We find that PPIT returns a higher proportion of correct taxonomic inferences than BLAST-based approaches at the cost of fewer total inferences. We demonstrate PPIT on deep-sea sediment and find that Deltaproteobacteria are the most abundant potential diazotrophs. Using this dataset we show that emending PPIT inferences based on visual inspection of query sequence placement can achieve taxonomic inferences for nearly all sequences in a query set. We additionally discuss how users can apply PPIT to the analysis of other marker genes. Availability PPIT is freely available to non-commercial users at https://github.com/bkapili/ppit. Installation includes a vignette that demonstrates package use and reproduces the nifH amplicon analysis discussed here. The raw nifH amplicon sequence data have been deposited in the GenBank, EMBL, and DDBJ databases under BioProject number PRJEB37167. Supplementary information Supplementary data are available at Bioinformatics online.
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- Award ID(s):
- 1634297
- PAR ID:
- 10230959
- Editor(s):
- Birol, Inanc
- Date Published:
- Journal Name:
- Bioinformatics
- ISSN:
- 1367-4803
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.1093/bioinformatics/btab100
