Manipulating plant-associated microbes to reduce disease or improve crop yields requires a thorough understanding of interactions within the phytobiome. Plants were sampled from a wheat/maize/soybean crop rotation site that implements four different crop management strategies. We analyzed the fungal and bacterial communities of leaves, stems, and roots of wheat throughout the growing season using 16S and fungal internal transcribed spacer 2 rRNA gene amplicon sequencing. The most prevalent operational taxonomic units (OTUs) were shared across all samples, although levels of the low-abundance OTUs varied. Endophytes were isolated from plants, and tested for antagonistic activity toward the wheat pathogen Fusarium graminearum. Antagonistic strains were assessed for plant protective activity in seedling assays. Our results suggest that microbial communities were strongly affected by plant organ and plant age, and may be influenced by management strategy.

1. Global climate change and shifting land-use are increasing plant stress due to abiotic factors such as drought, heat, salinity and cold, as well as via the intensifica

BackgroundOne of the most crucial steps in high-throughput sequence-based microbiome studies is the taxonomic assignment of sequences belonging to operational taxonomic units (OTUs). Without taxonomic classification, functional and biological information of microbial communities cannot be inferred or interpreted. The internal transcribed spacer (ITS) region of the ribosomal DNA is the conventional marker region for fungal community studies. While bioinformatics pipelines that cluster reads into OTUs have received much attention in the literature, less attention has been given to the taxonomic classification of these sequences, upon which biological inference is dependent.ResultsHere we compare how three common fungal OTU taxonomic assignment tools (RDP Classifier, UTAX, and SINTAX) handle ITS fungal sequence data. The classification power, defined as the proportion of assigned OTUs at a given taxonomic rank, varied among the classifiers. Classifiers were generally consistent (assignment of the same taxonomy to a given OTU) across datasets and ranks; a small number of OTUs were assigned unique classifications across programs. We developed CONSTAX (CONSensus TAXonomy), a Python tool that compares taxonomic classifications of the three programs and merges them into an improved consensus taxonomy. This tool also produces summary classification outputs that are useful for downstream analyses.ConclusionsOur results demonstrate that independent taxonomy assignment tools classify unique members of the fungal community, and greater classification power is realized by generating consensus taxonomy of available classifiers with CONSTAX.Electronic supplementary materialThe online version of this article (10.1186/s12859-017-1952-x) contains supplementary material, which is available to authorized users.

Tar spot is a devasting corn disease caused by the obligate fungal pathogen Phyllachora maydis. Since its initial identification in the United States in 2015, P. maydis has become an increasing threat to corn production. Despite this, P. maydis has remained largely understudied at the molecular level due to difficulties surrounding its obligate lifestyle. Here, we generated a significantly improved P. maydis nuclear and mitochondrial genome using a combination of long- and short-read technologies and also provide the first transcriptomic analysis of primary tar spot lesions. Our results show that P. maydis is deficient in inorganic nitrogen utilization, is likely heterothallic, and encodes for significantly more protein coding genes, including secreted enzymes and effectors, than previous determined. Furthermore, our expression analysis suggests that following primary tar spot lesion formation, P. maydis might reroute carbon flux away from DNA replication and cell division pathways and towards pathways previously implicated in having significant roles in pathogenicity, such as autophagy and secretion. Together, our results identified several highly expressed unique secreted factors that likely contribute to host recognition and subsequent infection, greatly increasing our knowledge of the biological capacity of P. maydis, which have much broader implications for mitigating tar spot of corn.

Microbiomes from maize and soybean were characterized in a long-term three-crop rotation research site, under four different land management strategies, to begin unraveling the effects of common farming practices on microbial communities. The fungal and bacterial communities of leaves, stems, and roots in host species were characterized across the growing season using amplicon sequencing and compared with the results of a similar study on wheat. Communities differed across hosts, and among plant growth stages and organs, and these effects were most pronounced in the bacterial communities of the wheat and maize phyllosphere. Roots consistently showed the highest number of bacterial OTUs compared to above-ground organs, whereas the alpha diversity of fungi was similar between above- and below-ground organs. Network analyses identified putatively influential members of the microbial communities of the three host plant species. The fungal taxa specific to roots, stems, or leaves were examined to determine if the specificity reflected their life histories based on previous studies. The analysis suggests that fungal spore traits are drivers of organ specificity in the fungal community. Identification of influential taxa in the microbial community and understanding how community structure of specific crop organs is formed, will provide a critical resource for manipulations of microbial communities. The ability to predict how organ specific communities are influenced by spore traits will enhance our ability to introduce them sustainably.