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Previous studies have shown that the sugarcane microbiome harbors diverse plant growth promoting (PGP) microorganisms, including nitrogen-fixing bacteria, and the objective of this study was to design a genome-enabled approach to prioritize sugarcane associated nitrogen-fixing bacteria according to their potential as biofertilizers. Using a systematic high throughput approach, 22 pure cultures of nitrogen-fixing bacteria were isolated and tested for diazotrophic potential by PCR amplification of nitrogenase (nifH) genes, common molecular markers for nitrogen fixation capacity. Genome sequencing confirmed the presence of intact nitrogenasenifHgenes and operons in the genomes of 18 of the isolates. Isolate genomes also encoded operons for phosphate solubilization, siderophore production operons, and other PGP phenotypes.Klebsiella pneumoniaestrains comprised 14 of the 22 nitrogen-fixing isolates, and four others were members of closely related genera toKlebsiella. A computational phenotyping approach was developed to rapidly screen for strains that have high potential for nitrogen fixation and other PGP phenotypes while showing low risk for virulence and antibiotic resistance. The majority of sugarcane isolates were below a genotypic and phenotypic threshold, showing uniformly low predicted virulence and antibiotic resistance compared to clinical isolates. Six prioritized strains were experimentally evaluated for PGP phenotypes: nitrogen fixation, phosphate solubilization, and the production of siderophores, gibberellic acid and indole acetic acid. Results from the biochemical assays were consistent with the computational phenotype predictions for these isolates. Our results indicate that computational phenotyping is a promising tool for the assessment of benefits and risks associated with bacteria commonly detected in agricultural ecosystems.IMPORTANCEA genome-enabled approach was developed for the prioritization of native bacterial isolates with the potential to serve as biofertilizers for sugarcane fields in Colombia’s Cauca Valley. The approach is based on computational phenotyping, which entails predictions related to traits of interest based on bioinformatic analysis of whole genome sequences. Bioinformatic predictions of the presence of plant growth promoting traits were validated with experimental assays and more extensive genome comparisons, thereby demonstrating the utility of computational phenotyping for assessing the benefits and risks posed by bacterial isolates that can be used as biofertilizers. The quantitative approach to computational phenotyping developed here for the discovery of biofertilizers has the potential for use with a broad range of applications in environmental and industrial microbiology, food safety, water quality, and antibiotic resistance studies.

Volume 84, no. 4, e01512-17, 2018, [https://doi.org/10.1128/AEM.01512-17][1]. Page 14, Acknowledgments: The following data attribution should be added at the end of the first paragraph: “Data are publicly available through the Gulf of Mexico Research Initiative Information and Data Cooperative (

Members of the Klebsiella genus promote plant growth. We report here draft whole-genome sequences for 15 Klebsiella sp. isolates from sugarcane fields in the Cauca Valley of Colombia. The genomes of these isolates were characterized as part of a broader effort to evaluate their utility as endemic plant growth-promoting biofertilizers.

The dinitrogenase reductase gene ( nifH ) is the most widely established molecular marker for the study of nitrogen-fixing prokaryotes in nature. A large number of PCR primer sets have been developed for nifH amplification, and the effective deployment of these approaches should be guided by a rapid, easy-to-use analysis protocol. Bioinformatic analysis of marker gene sequences also requires considerable expertise. In this study, we advance the state of the art for nifH analysis by evaluating nifH primer set performance, developing an improved amplicon sequencing workflow, and implementing a user-friendly bioinformatics pipeline. The developed amplicon sequencing workflow is a three-stage PCR-based approach that uses established technologies for incorporating sample-specific barcode sequences and sequencing adapters. Based on our primer evaluation, we recommend the Ando primer set be used with a modified annealing temperature of 58°C, as this approach captured the largest diversity of nifH templates, including paralog cluster IV/V sequences. To improve nifH sequence analysis, we developed a computational pipeline which infers taxonomy and optionally filters out paralog sequences. In addition, we employed an empirical model to derive optimal operational taxonomic unit (OTU) cutoffs for the nifH gene at the species, genus, and family levels. A comprehensive workflow script named TaxADivA (TAXonomy Assignment and DIVersity Assessment) is provided to ease processing and analysis of nifH amplicons. Our approach is then validated through characterization of diazotroph communities across environmental gradients in beach sands impacted by the Deepwater Horizon oil spill in the Gulf of Mexico, in a peat moss-dominated wetland, and in various plant compartments of a sugarcane field. IMPORTANCE Nitrogen availability often limits ecosystem productivity, and nitrogen fixation, exclusive to prokaryotes, comprises a major source of nitrogen input that sustains food webs. The nifH gene, which codes for the iron protein of the nitrogenase enzyme, is the most widely established molecular marker for the study of nitrogen-fixing microorganisms (diazotrophs) in nature. In this study, a flexible sequencing/analysis pipeline, named TaxADivA, was developed for nifH amplicons produced by Illumina paired-end sequencing, and it enables an inference of taxonomy, performs clustering, and produces output in formats that may be used by programs that facilitate data exploration and analysis. Diazotroph diversity and community composition are linked to ecosystem functioning, and our results advance the phylogenetic characterization of diazotroph communities by providing empirically derived nifH similarity cutoffs for species, genus, and family levels. The utility of our pipeline is validated for diazotroph communities in a variety of ecosystems, including contaminated beach sands, peatland ecosystems, living plant tissues, and rhizosphere soil.