Many endophytes and rhizobacteria associated with plants support the growth and health of their hosts. The vast majority of these potentially beneficial bacteria have yet to be characterized, in part because of the cost of identifying bacterial isolates. Matrix-assisted laser desorption-time of flight (MALDI-TOF) has enabled culturomic studies of host-associated microbiomes but analysis of mass spectra generated from plant-associated bacteria requires optimization. In this study, we aligned mass spectra generated from endophytes and rhizobacteria isolated from heritage and sweet varieties of Zea mays. Multiple iterations of alignment attempts identified a set of parameters that sorted 114 isolates into 60 coherent MALDI-TOF taxonomic units (MTUs). These MTUs corresponded to strains with practically identical (>99%) 16S rRNA gene sequences. Mass spectra were used to train a machine learning algorithm that classified 100% of the isolates into 60 MTUs. These MTUs provided >70% coverage of aerobic, heterotrophic bacteria readily cultured with nutrient rich media from the maize microbiome and allowed prediction of the total diversity recoverable with that particular cultivation method. Acidovorax sp., Pseudomonas sp. and Cellulosimicrobium sp. dominated the library generated from the rhizoplane. Relative to the sweet variety, the heritage variety c ontained a high number of MTUs. The ability to detect these differences in libraries, suggests a rapid and inexpensive method of describing the diversity of bacteria cultured from the endosphere and rhizosphere of maize.

Many endophytes and rhizobacteria associated with plants support the growth and health of their hosts. The vast majority of these potentially beneficial bacteria have yet to be cultured, in part because of the cost of identifying bacterial isolates. Matrix assisted laser desorption − time of flight (MALDI−TOF) has enabled high throughput culturomics studies of host-associated microbiomes but analysis of mass spectra generated from plant-associated bacteria requires optimization. In this study, we aligned mass spectra generated from endophytes and rhizobacteria isolated from heritage and sweet varieties of Zea mays. Multiple iterations of alignment attempts identified a set of parameters that sorted 114 isolates into 60 coherent MALDI-TOF taxonomic units (MTUs). These MTUs corresponded to strains with practically identical (> 99%) 16S rRNA gene sequences. Mass spectra were used to successfully train a machine learning algorithm that perfectly classified isolates. The 60 MTUs provided > 70 % coverage of aerobic, heterotrophic bacteria readily cultured with nutrient rich media from the maize microbiome and allowed prediction of the total diversity recoverable with that particular cultivation method. Acidovoraxis sp., Pseudomonas sp. and Cellulosimicrobium sp. dominated the library generated from the rhizoplane and the heritage variety contained a relatively high number of MTUs specific to that niche. This suggests a rapid, inexpensive method of describing the types of bacteria cultured from niches within maize microbiomes and the coverage achieved with a cultivation strategy.