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ORIGINAL RESEARCHpublished: 30 April 2018

doi: 10.3389/fmicb.2018.00703

Edited by:Marcus A. Horn,

Leibniz University of Hanover,Germany

Reviewed by:Oliver Schmidt,

University of Bayreuth, GermanyPetri Penttinen,

Zhejiang Agriculture and ForestryUniversity, China

*Correspondence:Dagmar Woebken

woebken@microbial-ecology.net

†Present address:Roey Angel,

Soil and Water ResearchInfrastructure, Biology Centre CAS,

Ceské Budejovice, Czechia

Specialty section:This article was submitted to

Terrestrial Microbiology,a section of the journal

Frontiers in Microbiology

Received: 28 January 2018Accepted: 27 March 2018

Published: 30 April 2018

Citation:Angel R, Nepel M, Panhölzl C,

Schmidt H, Herbold CW, Eichorst SAand Woebken D (2018) Evaluation

of Primers Targeting the DiazotrophFunctional Gene and Development

of NifMAP – A Bioinformatics Pipelinefor Analyzing nifH Amplicon Data.

Front. Microbiol. 9:703.doi: 10.3389/fmicb.2018.00703

Evaluation of Primers Targeting theDiazotroph Functional Gene andDevelopment of NifMAP – ABioinformatics Pipeline for AnalyzingnifH Amplicon DataRoey Angel†, Maximilian Nepel, Christopher Panhölzl, Hannes Schmidt,Craig W. Herbold, Stephanie A. Eichorst and Dagmar Woebken*

Division of Microbial Ecology, Department of Microbiology and Ecosystem Science, Research Network ‘Chemistry meetsMicrobiology’, University of Vienna, Vienna, Austria

Diazotrophic microorganisms introduce biologically available nitrogen (N) to the globalN cycle through the activity of the nitrogenase enzyme. The genetically conserveddinitrogenase reductase (nifH) gene is phylogenetically distributed across four clusters(I–IV) and is widely used as a marker gene for N2 fixation, permitting investigators tostudy the genetic diversity of diazotrophs in nature and target potential participants inN2 fixation. To date there have been limited, standardized pipelines for analyzing thenifH functional gene, which is in stark contrast to the 16S rRNA gene. Here we presenta bioinformatics pipeline for processing nifH amplicon datasets – NifMAP (“NifH MiSeqIllumina Amplicon Analysis Pipeline”), which as a novel aspect uses Hidden-MarkovModels to filter out homologous genes to nifH. By using this pipeline, we evaluatedthe broadly inclusive primer pairs (Ueda19F–R6, IGK3–DVV, and F2–R6) that targetthe nifH gene. To evaluate any systematic biases, the nifH gene was amplified withthe aforementioned primer pairs in a diverse collection of environmental samples (soils,rhizosphere and roots samples, biological soil crusts and estuarine samples), in additionto a nifH mock community consisting of six phylogenetically diverse members. Wenoted that all primer pairs co-amplified nifH homologs to varying degrees; up to 90%of the amplicons were nifH homologs with IGK3–DVV in some samples (rhizosphereand roots from tall oat-grass). In regards to specificity, we observed some degree ofbias across the primer pairs. For example, primer pair F2–R6 discriminated againstcyanobacteria (amongst others), yet captured many sequences from subclusters IIIEand IIIL-N. These aforementioned subclusters were largely missing by the primer pairIGK3–DVV, which also tended to discriminate against Alphaproteobacteria, but amplifiedsequences within clusters IIIC (affiliated with Clostridia) and clusters IVB and IVC. Primerpair Ueda19F–R6 exhibited the least bias and successfully captured diazotrophs incluster I and subclusters IIIE, IIIL, IIIM, and IIIN, but tended to discriminate againstFirmicutes and subcluster IIIC. Taken together, our newly established bioinformaticspipeline, NifMAP, along with our systematic evaluations of nifH primer pairs permit morerobust, high-throughput investigations of diazotrophs in diverse environments.

Keywords: nitrogen fixation, primer evaluation, nifH gene, Illumina amplicon sequencing, NifMAP

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INTRODUCTION

Nitrogen gas (N2) fixing microorganisms (diazotrophs) are oneof the most ecologically important functional guilds on Earth,providing the primary natural source for nitrogen to ecosystemsthrough biological N2 fixation (BNF; Fowler et al., 2013).Isotopic evidence suggests that BNF has emerged as early asca. 3.2 Gyr ago (Stüeken et al., 2015). It is thought to haveevolved in an anaerobic archaeon and was later transferred toan aerobic bacterium (Boyd et al., 2015). Considering the timescale of the BNF evolution and the importance of N2 fixation,it is not surprising that the genetic potential to perform N2fixation (i.e., the nif genes) is found widely among differentbranches in the phylogenetic trees of archaea and bacteria. It isestimated that 6–15% of all sequenced microbial genomes harborthe minimum number of nif genes to provide them with thegenetic capacity to fix N2 (Dos Santos et al., 2012; Boyd et al.,2015), making diazotrophs one of the most diverse functionalguilds.

Among all known diazotrophs, N2 fixation is mediated bythe nitrogenase enzyme complex along with ca. 20 functionaland regulatory genes that are organized in several operonstermed together the Nif regulon (Rubio and Ludden, 2002).Of those genes, the nifH gene encoding for the dinitrogenasereductase is considered one of the most genetically conservedgenes in the regulon and has been traditionally used as a markergene for studying the genetic diversity of diazotrophs in nature(Zehr et al., 2003; Gaby and Buckley, 2011). In addition to thecanonical MoFe nitrogenase that is shared among all knowndiazotrophs, some diazotrophs possess in their genome one ofthe two alternative versions of nitrogenase, which use eitherVFe (Vnf ) or FeFe (Anf ) as metal cofactors (Raymond et al.,2004).

Based on phylogenetic analysis, the nifH genes typicallyform four clusters, termed clusters I–IV (Zehr et al., 2003;Raymond et al., 2004). Most nifH sequences fall into clusterI, which is composed almost entirely of sequences from thecanonical MoFe nitrogenase. This cluster typically consists ofsequences from Cyanobacteria, Frankia, Proteobacteria, andsome are affiliated with Clostridia, Bacilli, and Nitrospirae. Inaddition, cluster I contains some sequences of the alternativenitrogenase vnfH. Cluster II comprises nearly all sequences of thealternative vnfH nitrogenase as well as all the known sequencesof the second alternative nitrogenase, anfH. In addition, thenifH genes of some Archaea also fall into cluster II. ClusterIII consists mostly of nifH sequences of anaerobic bacteriaand archaea such as methanogens, spirochetes, sulfate reducers,non-sulfur purple bacteria, green sulfur bacteria, acetogens andClostridia. Cluster IV was considered until recently to onlycontain “uncharacterized” or “non-functional” nifH sequences,but in 2015 the first isolate of a diazotroph containing an activenitrogenase belonging to cluster IV was cultivated from a termitegut (Zheng et al., 2016). While it has been shown in several purecultures that most of the known cluster IV nitrogenases do notencode for a protein involved in N2 fixation (e.g., Staples et al.,2007), it is certainly possible that some of the environmentalsequences in this cluster do encode for active nitrogenases.

The dinitrogenase reductase gene is found in nearly everyenvironment studied so far (Gaby and Buckley, 2011), andenvironmental genetic surveys of the nifH gene have producedextensive datasets encompassing several tens of thousands ofunique sequences (Gaby and Buckley, 2014; Heller et al., 2014),and novel sequences continue to be discovered. Such PCR-basedsurveys continue to serve as an important tool for studying thediazotroph diversity and, if the nifH transcripts are targeted (viacDNA), can also reveal transcription patterns in the environment.Yet, the choice of primer pair can have a significant impacton the extent of diversity that is uncovered. Since the firstdegenerate primers were used to amplify environmental DNA(Kirshtein et al., 1991), several dozens of primer pairs targetingthe nifH gene have been designed to serve as group-specific orgeneral nifH primers. In 2012, Gaby and Buckley published anextensive review of all known primers for nifH, and evaluatedtheir performance in silico using a comprehensive databaseof nifH sequences (Gaby and Buckley, 2012). Through theiranalysis, several primer pairs were identified for their potentialto capture the largest diversity of nifH sequences and weretested for their ability to produce a PCR product from DNAfrom several diazotrophic strains and two soil samples (Gabyand Buckley, 2012). However, the extent to which these primersare able to capture nifH diversity in environmental samplesand their potential preferences of amplification have not beentested. Furthermore, the majority of the nifH sequences in publicdatabases have sequence data between positions 100 and 500bases (positions are relative to Azotobacter vinelandii), making itchallenging to perform in silico coverage estimates of primer pairsflanking this region.

Given the importance of exploring the diazotrophiccommunities in the environment and the need for bioinformaticspipelines for analysis of the nifH gene, we developed abioinformatics pipeline for processing nifH amplicon datasetsderived from the MiSeq Illumina sequencing platform – NifMAP(“NifH MiSeq Illumina Amplicon Analysis Pipeline”). Usingthis pipeline, our further goal of this study was to evaluate theperformance of general primer sets – selected based on theirhigh in silico coverage for amplifying nifH – via high-throughputsequencing of a mock community and across a diverse collectionof environmental samples. We discuss the performance ofthe tested primer pairs and provide a standard operatingprocedure for analyzing nifH genes using high-throughputsequencing.

MATERIALS AND METHODS

Primers Used in This StudyFour forward primers: Nh21F, Ueda19F, F2, and IGK3 andfour reverse primers: nifH1, nifH3, R6, and DVV were chosenfollowing the in silico analysis of Gaby and Buckley (2012)(Table 1). The forward primers Nh21F, Ueda19F, and F2 andthe reverse primers nifH1, nifH3, and R6 were tested in allnine combinations. In addition, the primer pair IGK3–DVV wastested, as it was suggested as the best performing primer pair inGaby and Buckley (2012).

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Samples Used in This StudyThirteen different samples were used in this study: soil samplesfrom (a) an Austrian beech forest (Rasche et al., 2011) and (b) anAustrian meadow (Angeringer and Karrer, 2008); (c) rhizosphereand (d) root of Arrhenatherum elatius (tall oat-grass) from anAustrian grassland site (Pötsch et al., 2013); (e) rhizosphere and(f) root of Oryza sativa (wetland rice, grown in the greenhouseon paddy soil from Vercelli, Italy); biological soil crusts (BSCs)from (g) a coastal, sub-arctic crust, Sweden, (h) a temperate crust,Germany, (i) a high Alpine crust, Austria, (j) a semiarid crust,Spain (Büdel et al., 2014) and (k) an arid crust, Israel (Angel andConrad, 2009); and two estuarine samples from (l) the Great Beltthat is separating the North Sea and the Baltic Sea and (m) theRoskilde Fjord, Denmark (Bentzon-Tilia et al., 2015).

DNA ExtractionWith the exception of the water samples, DNA was extracted from0.4 g of soil (or 0.2–0.4 g root and rhizosphere samples) usingmodification of a standard bead-beating protocol in the presenceof a CTAB buffer and phenol, according to a previously publishedphenol/chloroform-based extraction protocol (Angel, 2012).Following extraction, samples were purified using OneStepTM

PCR Inhibitor Removal Kit (Zymo, Irvine, CA, United States).DNA from the water samples were also obtained with aphenol/chloroform-based protocol, as described in Boströmet al. (2004). DNA was extracted from biological replicates: theAustrian beech forest (n = 6); Austrian meadow soil (n = 6);BSCs (n = 2/type); root and rhizosphere samples (n = 3/type);and estuarine samples (n = 2).

PCR Amplification and SequencingFor an initial pre-screening of the different nifH-primercombinations, DNA from the beech forest and meadow soils wereused for PCR amplification of the nifH gene fragment, and thePCR products were evaluated using agarose gel electrophoresis.Amplifications were performed in 25 µl volume using thefollowing mixture: 2.5 µl of 10× DreamTaq Green Buffer, 2 mM

MgCl2, 0.2 mM of each nucleotide dNTP mixture, 0.08 µg µl−1

of BSA, 0.625 U of DreamTaq Green DNA Polymerase (all fromThermo Fisher Scientific, Waltham, MA, United States) and0.8 µM of each primer (Thermo Fischer Scientific, Waltham,MA, United States) and 1 µl of DNA template. The primerswere designed to include a universal 16 bp head sequence attheir 5′ end for subsequent barcoding (Herbold et al., 2015).The following program was used for amplification: 94◦C for5 min followed by 35 cycles of 94◦C for 30 s, 52◦C for 45 s,and 72◦C for 30 s, and a single step of final elongation at72◦C for 10 min. Sequencing of amplified nifH genes was doneusing multiplexed barcoded amplicon sequencing on an IlluminaMiSeq platform (Illumina, San Diego, CA, United States), asdescribed previously (Herbold et al., 2015). First-step PCRamplifications were performed in triplicates of 25 µl each, usingthe mixture and conditions described above, except that PCRswere done in 25 cycles. Following PCR amplification, sampleswere purified using ZR-96 DNA Clean-up KitTM (Zymo, Irvine,CA, United States) and 3 µl from the purified sample wasused for a second PCR reaction, which was 50 µl in volumeand contained the following mixture: 5 µl of 10× DreamTaqGreen Buffer, 2 mM MgCl2, 0.2 mM of each nucleotide dNTPmixture, 0.08 µg µl−1 of BSA, 1.25 U of DreamTaq Green DNAPolymerase (all from Thermo Fisher Scientific, Waltham, MA,United States) and 0.4 µM of a barcode primer, which alsocontained a universal 16 bp head sequence at the 5′ end (Herboldet al., 2015). The following program was used for amplification:94◦C for 5 min followed by 10 cycles of 94◦C for 30 s, 52◦C for45 s, and 72◦C for 45 s, and a single step of final elongation at72◦C for 10 min. Following this barcoding PCR step, the sampleswere purified using ZR-96 DNA Clean-up KitTM (Zymo, Irvine,CA, United States), quantified using Quant-iTTM PicoGreen R©

dsDNA Assay Kit (Thermo Fisher Scientific, Waltham, MA,United States) on a Tecan Safire plate reader (Tecan, Männedorf,Switzerland) and pooled in equimolar amounts of 20× 109 copiesper sample. Library preparation and sequencing services wereprovided by Microsynth (Balgach, Switzerland). The library wasprepared by adaptor ligation and PCR using the TruSeq Nano

TABLE 1 | Summary of the nifH primers used in this study.

Primer name Direction1 Sequence (5′ to 3′) Position2 Reference Coverage (%) with mismatches3

0 1 2

Nh21F F GCI WTY TAY GGN AAR GG 19–35 Deslippe et al., 2005 81% 94% 98%

Ueda19F F GCI WTY TAY GGI AAR GGI GG 19–38 Ueda et al., 1995 81% 94% 98%

F2 F TGY GAY CCI AAI GCI GA 115–131 Marusina et al., 2001 87% 91% 98%

IGK3 F GCI WTH TAY GGI AAR GGI GGI ATH GGI AA 19–47 Ando et al., 2005 83% 96% 98%

nifH1 R ADN GCC ATC ATY TCN CC 406–476 Zehr and McReynolds, 1989 85% 93% 95%

nifH3 R ATR TTR TTN GCN GCR TA 478–494 Zani et al., 2000 89% 93% 96%

R6 R GCC ATC ATY TCI CCI GA 457–473 Marusina et al., 2001 90% 93% 96%

DVV R ATI GCR AAI CCI CCR CAI ACI ACR TC 388–413 Ando et al., 2005 96% 98% 99%

1Either forward (F) or reverse (R).2Position is relative to A. vinelandii nifH (GenBank ACCN# M20568).3Matching of the primers to nifH sequences in the “nifH_2014April04.arb” database (Heller et al., 2014; 41,231 sequences in the database). The number of sequencesin the database that extended into each respective primer matching region are: Nh21F (2156); Ueda19F (2156); F2 (14,529); IGK3 (2156); nifH1 (9788); nifH3 (5523); R6(13,673); and DVV (30,142). The percentages refer to the fraction of these aforementioned sequences covered by the primers with either 0, 1, or 2 mismatches.

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DNA Library Prep Kit (Illumina, Cat FC-121-4001) accordingto the TruSeq Nano protocol (Illumina, FC-121-4003), butexcluding the fragmentation step. Sequencing was performed ona MiSeq platform (Illumina, San Diego, CA, United States). TheMiSeq was run in the 2 × 300 cycle configuration using theMiSeq Reagent kit v3 (Illumina, San Diego, CA, United States).The raw sequence data were deposited into the NCBI ShortRead Archive under BioProject accession number PRJNA432667.

Construction of Hidden-Markov Modelsfor Filtering Non-nifH Reads and forAligning nifH SequencesNon-nifH reads were filtered out using four Hidden-MarkovModels (HMMs). One model (hmm_nuc_1160_nifH.hmm)was based on 1160 nucleotide sequences from thenifH_2014April04.arb database (Heller et al., 2014),with sequence data matching the amplification regionof Ueda19F–R6 primer pair (the longest ampliconin our test). Three other models (bchX.hmm, chlL-bchL.hmm, and Zehr_2014_1812genomes_nifH_AA.hmm)were based on amino acid sequences. The modelZehr_2014_1812genomes_nifH_AA.hmm was based on 1812aligned nifH amino acid sequences from sequenced genomes,obtained from the nifH_2014April04.arb database. The modelbchX.hmm included 79 amino acid sequences from the NCBIEntrez Protein Cluster chlorophyllide reductase iron proteinsubunit X (bchX, PCLA_924109; also useful in discriminatingagainst the chromosome partitioning protein (parA) sequences),and chlL-bchL.hmm included 250 amino acid sequences fromtwo light-independent protochlorophyllide reductase iron-sulfurATP-binding proteins (chlL, CHL00072 and bchL, PCLA_858313,PCLA_3385405). Because bchX and chlL-bchL sequences tend tobe more similar to nifH sequences than to each other, an attemptto generate a single HMM covering all three homologs butexcluding nifH was unsuccessful. The sequences for the HMMshmm_nuc_1160_nifH.hmm, bchX.hmm, and chlL-bchL.hmmwere aligned using MAFFT (V7.271; Katoh and Standley,2013), employing the L-INS-i algorithm, and then individualHMMs were built from each multiple sequence alignmentusing the hmmbuild in HMMER V3.1 (Mistry et al., 2013). Forconvenience, a single HMM database (nifH_chlL_bchX.hmm)was generated from the three amino-acid based HMMs usingcommand hmmpress in HMMER.

NifMAP – An Automated Pipeline forAnalyzing nifH Amplicon ReadsTo process the raw MiSeq amplicon reads, we devisedthe following pipeline (Figure 1): (1) Paired raw MiSeqreads were assembled into contigs using QIIME’sjoin_paired_ends.py (Caporaso et al., 2012). (2) The mergedcontigs were filtered against the nucleotide-based HMM(hmm_nuc_1160_nifH.hmm) using hmmsearch command inHMMER. All reads passing through this filtering step wereaccepted for the next step, irrespective of the model match score.(3) Sequences were chimera-checked and clustered using the

UPARSE pipeline (Edgar, 2013). First, contigs were dereplicatedwith the -derep_fullength command and singleton uniquesequences were removed. OTU centroids were then determinedwith the -cluster_otus command (using 3% radius). Abundancesof OTUs were determined by mapping the filtered contigs (priorto dereplication) to OTU centroids using the -usearch_globalcommand (at a 0.97% identity, hereafter referred to as OTU97).(4) Translation into amino acids and (potential) frameshiftcorrections were done using FrameBot (Wang et al., 2013)against the nifH protein reference set. (5) Homologous genesto the nifH gene (bchX, chlL, bchL, and parA) were filtered outagainst the HMM nifH_ChlL_bchX.hmm (see above) using thehmmscan command in HMMER. Only OTU representatives thatscored highest against the nifH model compared with the bchXand chlL-bchL models were retained. (6) OTU classification andphylogenetic placement: the remaining OTU representativeswere classified using BLASTP (Camacho et al., 2009) againstthe RefSeq database (Pruitt et al., 2005). In addition, OTUrepresentatives were aligned using hmmalign to the HMMZehr_2014_1812genomes_nifH_AA.hmm and assigned tophylogenetic clusters of nifH using Classification And RegressionTrees (CART; Frank et al., 2016). OTU representatives werealso placed on a base tree using the Evolutionary PlacementAlgorithm (EPA) implementation in RAxML (Stamatakis,2014). The base tree was generated as follows: (1) All entriescontaining amino acid sequence information for the nifH genein the nifH_2014April04.arb database were extracted. Theseincluded 1971 entries from genomes and 39,258 entries fromnon-genome sequencing sources. (2) All sequences shorterthan 133 AA were filtered out and the remaining sequenceswere dereplicated. (3) The remaining sequences were clusteredat a clustering threshold of 90% identity using CD-HIT (Fuet al., 2012). In addition, the sequences used for constructingthe bchX and chlL-bchL HMMs were clustered at a thresholdof 80% identity using CD-HIT and merged with the clusterednifH sequences. The combined dataset was then aligned usingMAFFT L-INS-i against the aligned collection of 1812 amino acidsequences of genomic origin used for constructing the HMMZehr_2014_1812genomes_nifH_AA.hmm, in order to maintainan alignment compatible with the nifH_2014April04.arbdatabase. Lastly, a bootstrapped maximum likelihood treebased on a CAT model was reconstructed using RAxML.For placing the OTU representatives on the base tree, thesequences were aligned using MAFFT against the alignmentused for constructing the base tree and then added to thebase tree using RAxML, employing the EPA. Steps 1 and3 of the pipeline are identical to the procedures describedin Herbold et al. (2015), while steps 2, 4, 5, and 6 are newfor this work. The HMMs, base tree and shell script forreproducing steps 2, 4, 5, and 6 are publicly available athttps://github.com/roey-angel/NifMAP.

Design and Testing of a nifH MockCommunityA nifH mock community was developed to estimate variationsin sequencing quality amongst runs and cross-contamination

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FIGURE 1 | Schematic overview of NifMAP – NifH MiSeq Illumina AmpliconAnalysis Pipeline. Pipeline begins with raw MiSeq reads from desiredsequencing facility (depicted in gray). Steps shaded in blue represent standardprocessing steps, while steps shaded in green represent steps specific forprocessing nifH sequences, introduced in this work.

within a run (Bokulich et al., 2016). This mock community wascomprised of DNA from the following six diazotrophic species:Anabaena torulosa (Carm.) Lagerh. (Cyanobacteria) AlgaeCollection Vienna (ASW 01028), Desulfosporosinus acidophilusstrain SJ4T (DSM22704) (Firmicutes) (Alazard et al., 2010),Kosakonia sacchari (Gammaproteobacteria) (in house strain, 16SrRNA gene sequence 99.89% identical (1105 bp) to the 16SrRNA gene sequence of K. sacchari SP1T (Chen et al., 2014),Mesorhizobium loti strain R7A (Alphaproteobacteria) (Kellyet al., 2014), Nostoc microscopicum Vaucher (Cyanobacteria)Algae Collection Vienna (ASW 01020), and Telmatospirillumsiberiense 26-4b1T (Alphaproteobacteria) (Hausmann et al.,2018). DNA was extracted using Qiagen DNeasy Blood andTissue Kit (Qiagen) according to the manufacturer’s instructionsexcept that 200 µl of culture was first transferred to LysingMatrix A tube and cells were disrupted using bead beating(30 s at 4 m s−1; FastPrep-24, MP Biomedicals, Santa Ana,CA, United States) after adding PBS and AL buffers. DNAwas used for PCR amplification of the nifH gene usingprimers Ueda19F–R6 as described above. PCR products werecloned using TOPO TA Cloning Kit (Thermo Fisher Scientific,Waltham, MA, United States). One clone containing nifHgene fragment from each strain was used for colony PCRamplification using primers M13, flanking the insert region(Thermo Fisher Scientific, Waltham, MA, United States). PCRwas performed with the following mixture: 5 µl of 10×DreamTaq Green Buffer, 2 mM MgCl2, 0.2 mM of eachnucleotide dNTP mixture, 0.05 µg µl−1 of BSA, 1.25 U ofDreamTaq Green DNA Polymerase (all from Thermo FisherScientific, Waltham, MA, United States) and 1 µM of eachprimer and the following conditions for amplification: 94◦Cfor 5 min followed by 30 cycles of 94◦C for 60 s, 55◦Cfor 60 s, and 72◦C for 60 s, and a single step of finalelongation at 72◦C for 10 min. Following amplification andpurification with ZR-96 DNA Clean-up KitTM (Zymo, Irvine,CA, United States), PCR products were quantified usingQuant-iTTM PicoGreen R© dsDNA Assay Kit (Thermo FisherScientific, Waltham, MA, United States) on a Tecan Safireplate reader (Tecan, Männedorf, Switzerland) and pooled inequal and also tiered proportions. These mixtures were used asPCR templates for MiSeq sequencing. Each mock communitymixture was amplified and sequenced in triplicate using primersUeda19F–R6, IGK3–DVV, and F2–R6 as described above.The information for generating the mock community can be

found at the Mockrobiota repository1 under mock-27 andmock-28.

Richness and Diversity EstimatesRichness was estimated using number of observed OTUs, whilediversity was estimated using the inverse Simpson metric:

1/D = 1/

R∑i=1

P2i

where R represents the number of OTUs in a sample and Piis the proportional abundance of each OTU. For each sample,both richness and diversity were estimated using a bootstrappedmethod by iterative subsampling (1000 iterations) to minimumread-depth (after dropping the lowest 15th percentile samples).

Quantitative PCR AssaysQuantitative PCR (qPCR) reactions were performed on a C1000Touch thermocycler equipped with a CFX96 Real Time Systemand the data were processed using CFX Manager software (allfrom Bio-Rad, Hercules, CA, United States). For all reactionplates, serially diluted standards containing known quantities ofDNA copies of the target gene (ranging between 4.2 × 101 and4.2 × 107) were used for establishing quantitative calibrationcurves. The standard was generated using a cloned fragment ofthe nifH gene from Didymococcus colitermitum TAV2 (ATCCBAA-2264; Wertz et al., 2012). A SYBR R© Green I-based assayfor nifH was established as follows: each reaction was 20 µlin volume and contained 10 µl of SYBR Green JumpStart TaqReadyMix (Bio-Rad, Hercules, CA, United States), 3 mM MgCl2,0.4 ng µl−1 BSA (Thermo Fisher Scientific, Waltham, MA,United States), 1.4 µM of each primer, and 2 µl of template.The program used was: 95◦C for 5 min, followed by 45 cyclesof 95◦C for 30 s, 52◦C for 45 s for annealing, 72◦C for 30 s forextension, and 78◦C for 10 s for signal acquisition. The reliabilityof quantification was evaluated using a melting curve from 55to 95◦C. A correction for the nifH copy numbers was done foreach sample separately by excluding the fraction of non-nifHsequences (i.e., the proportion of reads that were detected as non-nifH by the pipeline) from the initial values obtained from qPCR.Data were evaluated using ANOVA on log-transformed data.

RESULTS AND DISCUSSION

NifMAP – NifH MiSeq Illumina AmpliconAnalysis PipelineAs the majority of amplicon based diversity surveys employ the16S rRNA gene as a phylogenetic marker, there are standardizedanalysis pipelines and procedures (e.g., Caporaso et al., 2012;Edgar, 2013; Kozich et al., 2013). This is in stark contrast toamplicon-based surveys targeting functional genes, such as nifH.Some of the challenges limiting the development of standardizedpipelines for functional genes include co-amplification of non-target genes due to gene homology (Holmes et al., 1995);

1http://github.com/caporaso-lab/mockrobiota/

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TABLE 2 | Relative proportion of the expected and observed reads for an even and tiered nifH gene mock community for the different primer pairs.

Mock communitycomposition

Expected distribution of reads Observed distribution of reads

Ueda19F–R6 IGK3–DVV F2–R6

Members Even Tiered Even Tiered Even Tiered Even Tiered

(1) D. acidophilus strainSJ4T (Firmicutes,cluster IC)

16.7% 10% 33 ± 0.9% 23 ± 1.0% 48 ± 1.0% 31 ± 0.5% 0.5 ± 0.0% 0.0 ± 0.0%

(2) M. loti strain R7A(Alphaproteobacteria,cluster IJ)

16.7% 20% 16 ± 0.4% 21 ± 0.2% 18 ± 0.4% 25 ± 0.7% 90.7 ± 1.1% 93.1 ± 0.7%

(3) N. microscopicumVaucher ASW 0120(Cyanobacteria, cluster IB)

16.7% 40% 12 ± 0.4% 34 ± 0.4% 14 ± 0.4% 36 ± 0.8% 0.7 ± 0.2% 0.3 ± 0.1%

(4) A. torulosa (Carm.)Lagerh. ASW 01028(Cyanobacteria, cluster IB)

16.7% 5% 16 ± 0.9% 4 ± 0.4% 19 ± 1.4% 5 ± 0.8% 0.0 ± 0.0% 0.0 ± 0.0%

(5) K. sacchari(Gammaproteobacteria,cluster IG)

16.7% 20% 12 ± 0.2% 14 ± 0.5% 1 ± 0.1% 2 ± 0.3% 5.7 ± 0.3% 5.7 ± 0.5%

(6) T. siberiense strain26-4b1T(Alphaproteobacteria,cluster IJ)

16.7% 5% 11 ± 0.4% 4 ± 0.5% 1 ± 0.0% 0 ± 0.0% 0.9 ± 0.1% 0.2 ± 0.0%

amino acid-based analyses making sequencing error (generatinginsertion and deletion errors) more detrimental; and lackof classification methods or databases. Furthermore as everyfunctional gene is different, the methods oftentimes need to beadjusted in a gene-specific manner (Penton et al., 2013). Severalmethods have been proposed in the literature to tackle issues suchas alignment and filtering (Fish et al., 2013), correct conversion toamino acids (Wang et al., 2013) and classification (Dumont et al.,2014; Frank et al., 2016), yet complete pipelines for analyzingenvironmental functional genes are still lacking. Recently, Gabyet al. (2017) published a perl-based pipeline for OTU clusteringand inference of taxonomic affiliation through BLAST of nifHamplicon data; however, this pipeline did not employ the use ofHMMs to filter out homologous genes.

We developed a bioinformatics pipeline for processing nifHreads derived from the MiSeq Illumina sequencing platformcalled “NifH MiSeq Illumina Amplicon Analysis Pipeline” –NifMAP, which uses HMMs to filter out homologous genesand taxonomically classifies sequences using three differentapproaches. In the first step, the sequences are filteredagainst nucleic acids based HMMs to remove non-target, non-homologous sequences. Subsequently, sequences are clusteredbased on 97% identity to the centroid sequence, translatedinto amino acids, and corrected for possible frame shifts. TheOTU representatives are screened against specific HMMs inorder to filter out nifH-homolog sequences (namely bchX,chlL, bchL, or parA). The use of HMMs ensures a highlyspecific and sensitive method to detect and remove non-targetsequences that is orders of magnitude faster than filteringbased on taxonomic or phylogenetic affiliation. Finally, theremaining sequences are classified taxonomically to their closest

relatives and their phylogenetic cluster (Figure 1). We chosea combined classification approach using several independentmethods, providing information on both the closest taxonomicrelative of the queried sequence in addition to its placement ina phylogenetic cluster, because the two classification approachesmight occasionally disagree (Frank et al., 2016). Our pipeline wasevaluated with a nifH mock community and a diverse collectionof environmental samples (including roots and rhizospheresamples, BSCs and estuarine water samples).

Evaluating Coverage of nifH Primer PairCombinationsAcross numerous environments, the distribution of the nifH genehas been evaluated using various primer pair combinations (e.g.,Ueda et al., 1995; Farnelid et al., 2011; Collavino et al., 2014).Although there have been some in silico investigations evaluatingthe performance of these primer pairs along with suggestionsas to the best combination(s) (Gaby and Buckley, 2012), theseprimer pairs have yet to be thoroughly evaluated and tested inthe wet-lab, especially when considering samples from highlydiverse environments such as soils. To that end, we evaluatedthe performance of 10 different nifH primer combinations. Theprimer pairs consisted of all possible combinations of threeforward primers (Nh21F, Ueda19F, and F2) and three reverseprimers (nifH1, nifH3, and R6) (Table 1) as described previously(Gaby and Buckley, 2012), along with the combination IGK3and DVV. These primers were chosen due to their presumedhigh coverage (Table 1) and ability to generate amplicons inlengths that are suitable for MiSeq sequencing (200–500 bp).Extracted DNAs from beech forest and meadow soil were initially

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Angel et al. Evaluating nifH Primers and NifMAP

Ueda 19F-R6 IGK3-DVV F2-R6100%

75%

50%

25%

0%100%

75%

50%

25%

0%

Prop

. of O

TUs

Prop

. of r

eads

nifHNon-nifH

a hb c d e f g mi j k l

soil root orrhizosphere

BSCs aquatic

a hb c d e f g mi j k l

soil root orrhizosphere

BSCs aquatic

a hb c d e f g mi j k l

soil root orrhizosphere

BSCs aquatic

(A)

(B)

FIGURE 2 | Performance of the nifH primer pairs in amplifying nifH and non-nifH (homologous) genes across environmental samples based on proportion of reads(A) and OTUs (B). The environmental samples include: (a) beech forest soil; (b) meadow soil; (c) rhizosphere and (d) root samples of Arrhenatherum elatius; (e)rhizosphere and (f) root samples of Oryza sativa; (g) coastal, sub-arctic biological soil crust (BSC); (h) temperate BSC; (i) high alpine BSC; (j) semiarid BSC; (k) aridBSC; estuarine samples from the (l) Great Belt and (m) Roskilde Fjord. More details on the samples can be found in the Section “Materials and Methods.” Reads orOTUs classified as homologous genes (such as bchX, chlL, bchL, and parA) are summarized as “non-nifH.”

used in a pilot study to evaluate these combinations. The initialcriterion was generation of a correctly sized PCR fragment.Only three primer pairs produced PCR fragments of the correctsize: F2–R6 (358 bp), Ueda19F–R6 (394 bp), and IGK3–DVV(454 bp) without unspecific bands (data not shown). Primer pairsF2–R6 and IGK3–DVV have been used in the past for analyzingdiazotrophic communities (Gaby and Buckley, 2012), while thiswas the first time that the Ueda19F–R6 combination was used.

Assessing nifH Primer Pair CoverageUsing a Mock CommunityIn order to assess potential primer biases of these three primercombinations, their performance was tested on constructednifH mock communities containing six phylogenetically diversemembers. Mock communities, i.e., a defined mixture of knownmicrobial strains, have become a standard tool for benchmarkingdifferent aspects of high-throughput sequencing techniques, forexample, base calling error, variation between indecent runs andcross contamination (Schloss et al., 2011; Bokulich et al., 2016;Singer et al., 2016). In addition, mock communities can be usedto assess amplification bias of different primers. Until now nearlyall established mock communities were designed for 16S rRNAgene surveys, while there is a growing need to develop mockcommunities for other (functional) genes.

Two mock communities were generated using six clonescontaining nifH gene fragments of diazotrophic cultures of evenand tiered proportions (Table 2). Prior to pooling the clones,each insert was confirmed to perfectly match the used primers

based on Sanger sequencing (data not shown). The nifH genesin each mock community were amplified and sequenced intriplicate using all three primers pairs, in order to assess theability of the primers to reconstruct the diazotrophic communitystructure. Across all three primer pairs, sequencing produced664–7883 reads per sample after quality filtering, which wereclassified into 6 (primers Ueda19F–R6, and F2–R6) and 7 (IGK3–DVV) OTUs. OTUs 1–6 were confirmed to match the mockcommunity members (Table 2), while OTU 7 was a contaminantidentified as a Geobacter sp. and comprised of a single readin two mock community replicates amplified using the primerpair IGK3–DVV (data not shown). Sequencing exhibited a highdegree of reproducibility among replicates as indicated by thelow standard errors (Table 2), but showed deviations fromthe expected read distribution in the even and tiered mockcommunities. Using primers F2–R6, over 90% of the reads wereaffiliated to M. loti and about 6% were affiliated to K. sacchari,while other members of the mock community (including twocyanobacteria) were nearly not detected. This is despite perfectin silico primer matching to the nifH sequences of the mockcommunity members.

In contrast, all mock community members could be recoveredusing primers Ueda19F–R6 and IGK3–DVV. The bias from theexpected distribution (even and tiered) was greater in the IGK3–DVV primers compared to the Ueda19F–R6 primers (Table 2).Specifically, D. acidophilus was consistently overrepresented inboth the even and tiered mock communities for both primerpairs, while K. sacchari and T. siberiense were underrepresented.D. acidophilus, K. sacchari, and T. siberiense were particularly

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biased against using the primer pair IGK3–DVV. Biases inthe composition of microbial communities resulting fromdifferential amplification of templates by PCR especially whenusing degenerate primer pairs, are well documented throughhigh-throughput sequencing of 16S rRNA gene as well asfunctional gene mock communities (e.g., Caporaso et al.,2011; Schloss et al., 2011; Pinto and Raskin, 2012; Pelikanet al., 2016). As in 16S rRNA gene amplicon sequencing(Schloss et al., 2011), our results therefore stress the needfor including mock community samples that are specific tothe target gene of interest in sequencing experiments forassessing the extent of primer biases. Furthermore, when mockcommunities are sequenced along with environmental sampleson one sequencing run, it allows verifying the reproducibility ofdifferent runs.

Assessing nifH Primer Pair PerformanceUsing Environmental SamplesThe specificities of the primer pairs Ueda19F–R6, IGK3–DVV,and F2–R6 were further evaluated by amplicon sequencing ofenvironmental samples. In addition to the above-mentionedtemperate soil samples, we extended our investigation to a diversecollection of terrestrial samples (such as BSCs, rhizosphere androot samples) along with two estuarine water samples. A totalof 424,234 raw Illumina paired-end reads were generated acrossthese primer pairs: Ueda19F–R6 (156,476 reads), IGK3–DVV(202,300 reads), and F2–R6 (65,458 reads). The raw readswere processed through our automated pipeline NifMAP (seesection “Materials and Methods” and Figure 1). After contigassembly, the merged reads were filtered using the nucleicacids based HMM, which filtered out different proportionsof reads depending on the used primer combinations. Mostreads were removed in the F2–R6 primer pair (ca. 7.5%)followed by Ueda19F–R6 (ca. 2.0%) and IGK3–DVV (ca. 0.3%).Investigating these non-nifH sequences using BLASTN againstthe Nr database gave no significant matches. The remaining

reads were clustered into OTUs with the IGK3–DVV primerpair generating the most reads (89,453) that were clustered into884 OTU97, followed by Ueda19F–R6 (76,897 reads; 528 OTU97)and F2–R6 (45808 reads; 763 OTU97). OTU representativeswere converted to amino acid sequences, but in no case didwe observe that frame shift correction was needed. This isin line with previous reports showing a particularly low indelerror rate for Illumina MiSeq technology (Schirmer et al., 2015).Next, filtering of the OTU sequence representative via HMMsof homologous genes to nifH showed that all primer pairs andsamples produced reads, and consequently OTUs, which werenot classified as nifH, but rather as these homologous genes.Most of these homologs were classified as chlL-bchL, followedby bchX, and only a minority of sequences were classified asparA.

While the amplification of homologous genes was observedin all three primer pairs, striking differences amongst theprimer pairs were seen in the proportion of reads and OTUsclassified as homologs rather than as nifH (Figure 2). Acrossmany environmental samples, the IGK3–DVV combinationexhibited the highest proportion of non-specific productswith on average 48% non-nifH OTUs and up to 92% non-nifH reads (Figure 2, green bars). The primer pairs F2–R6and the new tested combination, Ueda19F–R6, amplified lessnon-nifH sequences across many of the investigated samples,with an average of 7.5 and 13% for primers Ueda19F–R6and F2–R6, respectively. Our analysis shows not only largedifferences in the extent of non-specific products amongstthe different primer pairs (P < 0.01), but also amongstenvironmental samples when the same primer pair was used(P = 0.02). The most striking difference was detectableamongst the samples of rice and tall oat-grass. Using primersIGK3–DVV, nearly all reads and OTUs in the rice sampleswere classified as nifH-related (Figure 2, samples “e, f ”),while in the tall oat-grass samples only around 10% ofthem were identified as nifH-related (Figure 2, samples “c,

FIGURE 3 | Taxonomic classification of nifH sequences based on a BLASTP search for the different primer pairs and environmental samples. Colors of the heatmapindicate the mean relative abundance of the taxonomic classes from each sample. The environmental samples include: (a) beech forest soil; (b) meadow soil; (c)rhizosphere and (d) root samples of Arrhenatherum elatius; (e) rhizosphere and (f) root samples of Oryza sativa; (g) coastal, sub-arctic biological soil crust (BSC); (h)temperate BSC; (i) high alpine BSC; (j) semiarid BSC; (k) arid BSC; estuarine samples from the (l) Great Belt and (m) Roskilde Fjord.

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FIGURE 4 | Taxonomic classification of nifH sequences based on CART for the different primer pairs and environmental samples. Colors of the heatmap indicate themean relative abundance of the subclusters in each sample. The environmental samples include: (a) beech forest soil; (b) meadow soil; (c) rhizosphere and (d) rootsamples of Arrhenatherum elatius; (e) rhizosphere and (f) root samples of Oryza sativa; (g) coastal, sub-arctic biological soil crust (BSC); (h) temperate BSC; (i) highalpine BSC; (j) semiarid BSC; (k) arid BSC; estuarine samples from the (l) Great Belt and (m) Roskilde Fjord.

d”). Similar trends can be seen for the crust and watersamples, though to a lesser extent. The problem of co-amplification of non-target homologous or non-homologousgenes during PCR is well documented in the literature,particularly when amplifying functional genes (Holmes et al.,1995). Unfortunately, this phenomenon can erroneously inflatediversity estimates and population size when left unnoticed.Our analysis clearly shows that different primer pairs can havestriking differences in amplification specificities and that thisshould be taken into consideration when designing a geneticsurvey.

Comparing Alpha-Diversity MetricsAcross the Primer PairsWe calculated the number of observed OTUs (richness estimate)and the inverse Simpson metric (diversity estimate) from eachsample type and with the three different primer pairs. Onaverage, similar numbers of nifH OTUs per sample were detected

using each of the three primer pairs Ueda19F–R6 (49 ± 3);IGK3–DVV (39 ± 3); and F2–R6 (49 ± 4), while the inverseSimpson index was somewhat lower using primers Ueda19F–R6(9.5 ± 0.5) compared to the other two primer pairs, IGK3–DVV (14.0 ± 1.5) and F2–R6 (14.0 ± 1.8) (SupplementaryFigure S1) indicating no clear advantage of one primer pairin capturing higher richness. A general agreement amongst theprimer pairs was seen across the different sample types regardingrichness and diversity patters. For example, the beech forest andmeadow soils harbored richer and more diverse communitiesthan the crust and water samples. Nevertheless, there were somedifferences amongst the primer pairs with regards to richnessand diversity estimates. Most notably primer pair F2–R6 yieldedhigher richness and diversity estimates for rice rhizosphere androot samples (Supplementary Figure S1, samples “e, f ”), butlower for many of the crust samples (Supplementary Figure S1,samples “g–k”), compared to primer pairs IGK3–DVV andUeda19F–R6.

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ClusterIII

bchX

, chl

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40.50.

60.70.

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0

PlrSpec2

WP 007206408-1

ArbSvalb

Ueda19FR6 OTU 257

DelProt3F2R6 OTU 319

F2R6 OTU 68

IGK3DVV OTU 605

F2R6 OTU 139

IGK3

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61

UncB5916

Uni

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Ueda19FR6 OTU 236

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UncM2717

Ueda19FR6 OTU 42

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166

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70

IGK3

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137

8

IGK3DVV OTU 1068

F2R6 OTU 318

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Ueda19FR6 OTU 8

Ueda19FR

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398

F2R6 OTU 250

AnaCyli4

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26

F2R6 OTU 308

Ueda19FR6 OTU 87

F2R6 O

TU 93

Ueda19FR6 OTU 205

Ueda19FR6 OTU 509

Ued

a19F

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165

Ueda19FR6 OTU 351

Ueda19FR6 OTU 6

IGK3DVV OTU 135

Ueda19FR6 OTU 187

IGK3DVV OTU 384

Ueda19FR6 OTU 151

IGK3

DVV

OTU

787

IGK3DVV OTU 788

CloKlu17

IGK3

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Ueda19FR6 OTU 184

F2R6 OTU 291

IGK3DVV OTU 1226

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IGK3DVV OTU 99

F2R6 OTU 202

452

UTO

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IGK3DVV OTU 1290

F2R6 OTU 111

Ueda19FR6 OTU 435

Unc1

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IGK3DVV OTU 560

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3

UncMi753

F2R6 OTU 239

MetLacus

WP 007814214-1

Uni

Bac5

0

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TU 784

UncN1551

Ueda19FR6 OTU 47

IGK3DVV OTU 168

Uni

Nit9

4

AcnLong4

F2R6 O

TU 329

IGK3DVV OTU 544

IGK3DVV OTU 512

UncM2055

Unc

N16

28

UncM

1464

Ueda19FR6 OTU 191

IGK3DVV OTU 677

643UT

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Ueda19FR6 OTU 313

IGK3DVV O

TU 1566

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8

Ueda19FR6 OTU 15

Ueda1

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TU 203

UncPr

180

IGK3

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OTU

228

IGK3DVV OTU 1083

UncNos29

IGK3DVV OTU 943

UniNi155IG

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VV O

TU 1

075

F2R

6 O

TU 3

9

F2R6 O

TU 16

2

F2R6

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62

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1309

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VV OTU

321

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Ueda19FR

6 OTU

247

Ueda1

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TU 38

2

425UT

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6 O

TU 1

85

IGK3

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TU 1

499

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Ueda19FR6 OTU 202

AnrColih

IGK3D

VV OTU

766

IGK3D

VV OTU

73

F2R6 OTU 288

IGK3DVV OTU 1247

F2R6 O

TU 31

Ueda

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TU 1

63

Ueda19FR6 OTU 493

IGK3DVV OTU 233

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525

Ueda19FR6 OTU 432

Unc

N16

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27

UncN1648

WP 019012955-1

WP 017672669-1

IGK3DVV OTU 1314

WP 008296332-1

IGK3DVV OTU 15

IGK3D

VV OTU

996

F2R6 O

TU 84

Ueda19FR

6 OTU

292

IGK3DVV OTU 386

443

UTO

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IGK3DVV OTU 41

UncN

1539

WP 003605362-1

F2R6 OTU 374

IGK3

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OTU

756

Ueda

19FR

6 OTU

196

Ueda19FR6 OTU 73

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77

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307

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IGK3DVV OTU 1404

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234

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563

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F2R6 OTU 138

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2070

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224

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813UT

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Ueda19FR6 OTU 71Uncu1116

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45

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Ueda19FR6 OTU 272

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442

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869

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IGK3DVV OTU 273

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243

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IGK3DVV OTU 750

IGK3

DVV

OTU

577

Ueda19FR6 OTU 39

WP 012475625-1

F2R

6 O

TU 3

43

Ueda19FR6 OTU 463

UniNi125

WP 009021548-1

F2R6 O

TU 377

IGK3D

VV OTU

1153

UncB5

670

UniNi139

IGK3DVV O

TU 928

IGK3DVV OTU 287

Ued

a19F

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OTU

140

CloSpe17

Uni

Bac5

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Ueda19FR6 OTU 300

MnbEve

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Ueda19FR6 OTU 458

IGK3DVV OTU 1462

F2R6 OTU 174Ueda19FR6 OTU 85

Ued

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OTU

66

IGK3D

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88

Ueda19FR6 OTU 90

Ueda19FR6 OTU 341

F2R6 OTU 16

Unc

B576

1

95UT

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WP 008931734-1

IGK3DVV O

TU 1200

UncMi90

6

Ueda19FR6 OTU 296

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Spec

6

F2R6 OTU 281

Ueda19FR6 OTU 242

IGK3D

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IGK3DVV OTU 1622

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211UT

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TU 350

F2R6 O

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Ueda19FR6 OTU 442

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TU 128

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WP 007196589-1

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VV OTU

960

F2R6 OTU 275

Ueda19FR6 OTU 229

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59UT

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TU 181

CloLjun4

IGK3DVV O

TU 673

F2R6 OTU 148

IGK3DVV OTU 504

Ueda19FR6 OTU 335

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OTU

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6

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IGK3DVV OTU 456

WP 018411937-1

Ueda19FR

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340

Ueda19FR6 OTU 366

F2R6 OTU 65

Ueda19FR6 OTU 221

Ueda19FR6 OTU 72

IGK3D

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457

IGK3DVV OTU 330

F2R6 O

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Ueda19FR6 OTU 498

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Ueda19FR6 OTU 387

IGK3

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Unc

1545

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IGK3DVV OTU 460

IGK3DVV OTU 1288

F2R6 O

TU 142

Ueda19FR6 OTU 115

F2R6 OTU 286

Ueda19FR6 OTU 348

CorAkaji

IGK3DVV O

TU 1359

IGK3DVV O

TU 1281

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IGK3DVV O

TU 810

Ueda19FR

6 OTU

420

F2R

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TU 2

74

IGK3D

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IGK3DVV OTU 910

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F2R6 O

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IGK3DVV OTU 617

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Ueda19FR6 OTU 405

IGK3DVV OTU 502

IGK3DVV OTU 396

Ueda19FR6 OTU 33

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Ueda19FR6 OTU 93

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IGK3

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Ueda19FR6 OTU 388

451UT

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IGK3DVV OTU 1263

IGK3DVV OTU 1277

WP 008028560-1

Uncu1028Ueda19FR6 OTU 125

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WP 015281842-1

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Ueda19FR6 OTU 288

F2R6 OTU 64

F2R6 O

TU 339

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IGK3DVV OTU 128

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UncPlanc

IGK3DVV OTU 3

Unc10685

Ueda

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TU 2

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F2R6 OTU 71

IGK3DVV OTU 109

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IGK3DVV OTU 700

HdrThermIG

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4621UT

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768

IGK3DVV OTU 20

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F2R6 O

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9

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Ueda19FR6 OTU 248

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CntSpec7

IGK3DVV OTU 1450

Ued

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OTU

28

PlrSpeci

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FlaPlau2

WP 007067496-1

AlkO

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l

F2R6 OTU 313

IGK3DVV O

TU 85

F2R6 O

TU 294

IGK3DVV OTU 892

IGK3DVV OTU 1480

F2R6 O

TU 118

Ueda19FR

6 OTU

350

F2R6 OTU 356

IGK3DVV O

TU 1107

Ueda19FR

6 OTU

510

Ueda19FR6 OTU 508

Ueda19FR6 OTU 246

UniNi164

UncM

1688

MhnAeoli

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OTU

116

IGK3DVV OTU 1475

WP 002735653-1

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IGK3DVV OTU 757

IGK3

DVV

OTU

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610

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1592

IGK3DVV OTU 969

IGK3DVV OTU 1108

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IGK3DVV OTU 1515

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F2R6 OTU 92

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440

Ueda19FR6 OTU 316

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IGK3DVV OTU 475

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Ueda19FR6 OTU 481

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6

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Cluster ICluster IICluster IIICluster IVbchX, chlL, bchLparA

Primer pairsUeda19F-R6IGK3-DVVF2-R6

nifH & non-nifH clusters

Relative abundance (log)

FIGURE 5 | Unrooted RAxML-EPA tree of nifH deduced amino acid sequences (with at least 133 positions). The nifH and non-nifH (homologous genes) clusters arerepresented in different colors. Representative sequences from the “nifH_2014April04.arb” database (Heller et al., 2014) are depicted in black. The phylogeneticplacements of OTU representatives from this study for the three primer pairs are depicted at the terminal nodes: sequences obtained with primer pair Ueda19F–R6are shown in red, IGK3–DVV-derived sequences in blue, and F2–R6-derived sequences in yellow. Outermost bars represent the relative abundance [log(x% + 1)] ofeach OTU.

Taxonomic Description of theDiazotrophic Community Using MultipleClassification Approaches Across thePrimer PairsThe classification of nifH genes is not an easy task due toa lack of taxonomic affiliation to functional gene sequences(Gaby and Buckley, 2011), poor reflection of the nifH phylogenyto the 16S rRNA gene phylogeny (Zehr et al., 2003), andinsufficient information in k-mer frequencies to differentiateamongst the subclusters (Frank et al., 2016) as was also observed

with pmoA sequences (Dumont et al., 2014). As such, wepropose that a combined classification approach, using severalindependent methods might be more beneficial for characterizingnifH sequence datasets. Several approaches have been describedin the literature for rapid, accurate and biologically meaningfulclassification of functional gene sequences in general and nifH inparticular, including nearest neighbor identification in pairwisealignment using FrameBot (Wang et al., 2013), lowest commonancestor parsing of BLAST bit scores using MEGAN (Husonet al., 2007; Dumont et al., 2014), or CART (Frank et al., 2016).Here, we used a combined approach in our pipeline and describe

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the taxonomic affiliation of the sequences using closest relativevia BLASTP, nifH cluster and subcluster affiliation using CART(Frank et al., 2016), and the EPA (Berger et al., 2011) forphylogenetic tree generation.

A representative from each OTU was classified using thebest matching hit in BLASTP (Figure 3), CART (Figure 4),and the EPA for phylogeny (Figure 5). The BLASTP classifiessequences based on their closest relative. This classificationyielded an overall agreement amongst the three primerpairs, with mainly members of the Alphaproteobacteria,Betaproteobacteria, Deltaproteobacteria, Cyanobacteria, andFirmicutes populating the diazotrophs across the environments(Figure 3). However, the relative abundance of these taxonomicgroups varied amongst the investigated primer pairs. Forexample, Cyanobacteria were underrepresented by primer pairF2–R6 in comparison to the other two primer pairs (inconcordance with the performance of primers F2–R6 amplifyingthe mock community). Furthermore, the relative abundance ofAlphaproteobacteria derived in PCRs with primer pair IGK3–DVV was lower in some samples than in PCRs with the othertwo primer combinations, and Firmicutes were comparativelyunderrepresented by primer pair Ueda19F–R6 in some samples(Figure 3). Some taxa (such as the Green Sulfur Bacteria)were completely missing with the IGK3–DVV primer pair,while Euryarchaeota were missing in the Ueda19F–R6 primerpair and at low relative abundance with primer pair F2–R6(Figure 3).

The CART classification assigns sequences to four clusters ofnifH, termed clusters I–IV with subclusters (Zehr et al., 2003;Raymond et al., 2004). The majority of the sequences amongstall primer pairs were classified into cluster I based on theCART classification (Figure 4). This cluster is known to reflect16S rRNA-based phylogeny relatively well (Zehr et al., 2003)and in many environments is the largest and most ecologicallyimportant diazotroph cluster (Gaby and Buckley, 2011). Withincluster I, differences in the relative abundances of certain definedclusters can be observed, some of which are in agreement withthe BLASTP results. For instance, the relative abundance ofsequences in cluster IB (affiliated with Cyanobacteria) derivedfrom PCRs with primer pair F2–R6 was lower than from theother two primer combinations. Furthermore, less sequencesassigned to cluster IJ and IK (containing Alphaproteobacteria)were derived from primer pair IGK3–DVV than in the other twoprimer pairs (Figure 4). The same trend was observed when thesequences were analyzed via BLASTP.

As expected, only a minority of the sequences wereaffiliated with clusters II, III, and IV. All three primer pairsamplified sequences from the same subclusters in clusters II–IV, with some exceptions in clusters III and IV, indicatingpreferential amplifications of subclusters amongst these primersets (Figure 4). Most notably the IGK3–DVV primer pairamplified many sequences from clusters IIIC (affiliated withClostridia) as well as from clusters IVB and IVC. In contrast,primer pairs Ueda19F–R6 and F2–R6 amplified very fewsequences from clusters IIIC, IVB and IVC. A close examinationof the sequence region matching the R6 primer showed thatthe primer has indeed two mismatches at the 3′ end of several

a hb c d e f g mi j k lsoil root or

rhizosphereBSCs aquatic

Avg.

nifH

cop

ies n

g DN

A-1 +/

- stn

d. e

rror

104

102

100

IGK3-DVVF2-R6

Ueda 19F-R6

FIGURE 6 | Average nifH copies per ng DNA ± standard error acrossenvironmental samples based on quantitative PCR using the different primerpairs. The environmental samples include: (a) beech forest soil; (b) meadowsoil; (c) rhizosphere and (d) root samples of Arrhenatherum elatius; (e)rhizosphere and (f) root samples of Oryza sativa; (g) coastal, sub-arcticbiological soil crust (BSC); (h) temperate BSC; (i) high alpine BSC; (j) semiaridBSC; (k) arid BSC; estuarine samples from the (l) Great Belt and (m) RoskildeFjord. Numbers of copies were corrected to exclude non-nifH genes that wereco-amplified using information obtained from amplicon sequencing of thesamples with the specific primer pairs.

Clostridia sequences affiliated with cluster IIIC (with residues CTinstead of GC).

The phylogenetic placement of OTU representatives suggeststhat the sequences are in clusters I–IV (Figure 5). It showeda nearly identical coverage of clusters I and II by all threeprimer pairs, thus indicating that all three primer pairs coverequally well the diazotrophic diversity in these clusters. Althoughall three primer pairs captured sequences from cluster III,discrepancies within subclusters were noted, especially with theprimer pair IGK3–DVV as compared to F2–R6 and Ueda19F–R6. While primers IGK3–DVV captured many sequences insubcluster IIIC, primers F2–R6 and Ueda19F–R6 captured manyfrom subclusters IIIE, IIIL, IIIM, and IIIN, which were largelymissed out by primers IGK3–DVV. Cluster IV sequences werenearly only captured by primer pair IGK3–DVV. However, theecological relevance of cluster IV members is still debatable,as until recently it was considered to be populated only bygenes encoding for functions unrelated to N2 fixation (Raymondet al., 2004; Zheng et al., 2016). Moreover this cluster includesseveral nifH homologs, which might be more abundant than nifHin certain environments. Attempts to use primers that capturesequences from this cluster could inflate datasets with non-targetgenes.

Suitability of nifH Primers for qPCRAssaysThe primer pairs F2–R6, IGK3–DVV, and Ueda19F–R6 werealso evaluated for their suitability in qPCR assays. Sinceusing degenerate primers incurs a potential template-dependedquantification bias, we increased the primer concentration inthese reactions to 1.4 µM. This has been recently shown to

Frontiers in Microbiology | www.frontiersin.org 11 April 2018 | Volume 9 | Article 703

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Angel et al. Evaluating nifH Primers and NifMAP

minimize the effect of this specific bias (Gaby and Buckley,2017) and has increased efficiency in our assays (data notshown). High assay efficiencies across all three primer pairs –Ueda19F–R6 (97.4%), IGK3–DVV (92.5%), and F2–R6 (90.5%) –were attained (Supplementary Figure S2), illustrating that theseprimer sets worked almost equally well and therefore can beused for reliable quantification of nifH genes and transcripts.In a proof-of-principle experiment, we quantified nifH genes inthe environmental samples used in this study across all threeprimer pairs. A caveat when using such quantitative assays isthat qPCR works under the underlying assumption that allquantified templates belong to the target gene. As we havedemonstrated above, this is not the case for many types ofsamples when using general nifH-targeting primers. Therefore,we corrected for the proportion of nifH sequences from thetotal reads based on the sequencing results (Figure 6). Amongstthese diverse sample types, the numbers of nifH copies perng DNA ranged between 1.2 × 101 and 1.8 × 105 with ageometric average of 1.3 × 103. Interestingly, no single primerpair gave consistently higher or lower estimations compared toother primer pairs (P = 0.5, ANOVA test), further reiteratingthat all three primer sets worked equally well. Yet, significantdifferences appeared to be sample-type dependent (P < 0.01,ANOVA test). The estimated nifH copies per ng DNA werecongruent within a given sample amongst the three primer pairs,considering the precision limitations of qPCR (two- to threefolddifference between samples; Hospodsky et al., 2010). However,some exceptions to that observation were notable. For instance,primer pairs F2–R6 and Ueda19F–R6 estimated similar copynumbers in estuarine water samples (Figure 6, samples “l, m”)and the arid crust sample (Figure 6, sample “k”), while primerpair IGK3–DVV estimated an order of magnitude more copies.Furthermore, primers Ueda19F–R6 and IGK3–DVV measuredsimilar copy numbers in the temperate and semiarid BSCs(Figure 6, samples “h, j”) while primers F2–R6 measured anorder of magnitude less copies. This latter observation could beexplained by the fact that primer pair F2–R6 has the tendencyto discriminate against cyanobacteria, which would result ina decreased copy number in these cyanobacteria-dominatedsamples.

CONCLUSION

We tested the nifH-targeting primer combinations Ueda19F–R6,IGK3–DVV, and F2–R6 for their performance in genetic surveysof diazotrophs, thereby elaborating on the in silico analysis ofGaby and Buckley (2012). All primer pairs had a propensity toco-amplify homologs of the nifH gene at varying proportions,which is common in many studies of functional gene diversity.Most severe, primer combination IGK3–DVV had the largesttendency to co-amplify these sequences (which are most closelyrelated to cluster IV), with some samples yielding up to ca.90% homolog sequences. Using a pipeline such as our newlyestablished NifMAP, that permits the detection of non-specificco-amplified reads via specifically designed HMMs, proved to bea useful strategy in filtering out such reads.

When analyzing the specificity of these primers throughthe use of a mock community along with a diverse collectionof environmental samples, we observed some degree of biasamongst the primer pairs. As such, care should be taken whenchoosing primer pairs for diazotrophic diversity investigations.More specifically, primer pair F2–R6 has a propensity todiscriminate against cyanobacteria (amongst others) in our mockcommunities and environmental samples, yet captured manysequences from subclusters IIIE, IIIL, IIIM, and IIIN. Theseaforementioned subclusters were largely missing by the primerpair IGK3–DVV, which also tended to discriminate againstthe Alphaproteobacteria, but amplified many sequences withinclusters IIIC (affiliated with Clostridia) and clusters IVB andIVC. Primer pair Ueda19F–R6 exhibited the least bias basedon our mock community analysis and successfully captureddiazotrophs in cluster I as well as in subclusters IIIE, IIIL,IIIM, and IIIN, but discriminated against Firmicutes (basedon BLASTP analysis) and subcluster IIIC. Thus, depending onthe investigated environmental sample and the aforementionedprimer performance, one should choose the appropriate primercombination. Apart from providing useful analysis protocolsand standards for the study of environmental diazotrophs, ourwork highlights some of the important pitfalls and caveats ofstudying nifH, and potentially other functional genes in theenvironment. Furthermore, these primer pairs can be usedin targeted functional investigations in tandem with activitymeasurements of N2 fixation (Angel et al., 2018) to betterunderstand the ecophysiology of diazotrophs in the environmentand could potentially be used to identify target groups fordownstream single-cell analyses (Eichorst et al., 2015; Woebkenet al., 2015). We hope this knowledge, along with our newlydevelop pipeline (NifMAP) will aide investigators to better detectand capture diazotrophs in their respective environment.

AUTHOR CONTRIBUTIONS

RA, SE, and DW designed the study and participated in writing.RA, CP, and HS extracted DNA and performed the PCRs.MN and HS constructed the mock community. RA, MN, andCH established and tested the analysis pipeline, NifMAP. RAanalyzed the data.

FUNDING

This work was supported by an Austrian Science Fund (FWF)project grant (P25700-B20 to DW and SE). SE was supportedby the Austrian Science Fund (FWF) project grant (P26392-B20 to DW and SE). CP and HS were supported by an ERCStarting grant (Grant Agreement No: 636928 to DW) fromthe European Research Council (ERC) under the EuropeanUnion’s Horizon 2020 Research and Innovation Program. HSwas supported by a Marie Curie Intra European Fellowshipwithin the 7th European Community Framework Program(Grant Agreement No: 628361). MN was funded by a fellowshipfrom the Austrian Academy of Sciences (ÖAW). RA was alsosupported by the Ministry of Education, Youth and Sports

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of the Czech Republic – MEYS (projects LM2015075 andEF16_013/0001782).

ACKNOWLEDGMENTS

The authors are grateful to Lasse Riemann for providing estuarinesamples, Gerhard Karrer for granting access to the samplingsite at Lainzer Tiergarten, Osnat Gillor and Burkhard Büdel forassistance in obtaining biological soil crust samples, Erich M.Pötsch for his assistance with sampling Arrhenatherum elatiusplants at AREC Raumberg-Gumpenstein, Michael Schagerl forproviding a culture of A. torulosa and N. microscopicum,

Stefanie Wienkoop for providing a culture of M. loti, BelaHausmann for providing gDNA of Desulfosporosinus acidophilusand Telmatospirillum siberiense, David Seki for his assistance withthe nifH mock community and culture of K. sacchari, and FlorianHöggerl for his assistance in preparing samples for ampliconsequencing.

SUPPLEMENTARY MATERIAL

The Supplementary Material for this article can be foundonline at: https://www.frontiersin.org/articles/10.3389/fmicb.2018.00703/full#supplementary-material

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Conflict of Interest Statement: The authors declare that the research wasconducted in the absence of any commercial or financial relationships that couldbe construed as a potential conflict of interest.

Copyright © 2018 Angel, Nepel, Panhölzl, Schmidt, Herbold, Eichorst and Woebken.This is an open-access article distributed under the terms of the Creative CommonsAttribution License (CC BY). The use, distribution or reproduction in other forumsis permitted, provided the original author(s) and the copyright owner are creditedand that the original publication in this journal is cited, in accordance with acceptedacademic practice. No use, distribution or reproduction is permitted which does notcomply with these terms.

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