Exploring interactions of plant microbiomes

A plethora of microbial cells is present in every gram of soil, and microbes are found extensively in plant and animal tissues. The mechanisms governed by microorganisms in the regulation of physiological processes of their hosts have been extensively studied in the light of recent findings on microbiomes. In plants, the components of these microbiomes may form distinct communities, such as those inhabiting the plant rhizosphere, the endosphere and the phyllosphere. In each of these niches, the "microbial tissue" is established by, and responds to, specific selective pressures. Although there is no clear picture of the overall role of the plant microbiome, there is substantial evidence that these communities are involved in disease control, enhance nutrient acquisition, and affect stress tolerance. In this review, we first summarize features of microbial communities that compose the plant microbiome and further present a series of studies describing the underpinning factors that shape the phylogenetic and functional plant-associated communities. We advocate the idea that understanding the mechanisms by which plants select and interact with their microbiomes may have a direct effect on plant development and health, and further lead to the establishment of novel microbiome-driven strategies, that can cope with the development of a more sustainable agriculture

Multi-omics data integration reveals metabolome as the top predictor of the cervicovaginal microenvironment

Emerging evidence suggests that host-microbe interaction in the cervicovaginal microenvironment contributes to cervical carcinogenesis, yet dissecting these complex interactions is challenging. Herein, we performed an integrated analysis of multiple "omics"datasets to develop predictive models of the cervicovaginal microenvironment and identify characteristic features of vaginal microbiome, genital inflammation and disease status. Microbiomes, vaginal pH, immunoproteomes and metabolomes were measured in cervicovaginal specimens collected from a cohort (n = 72) of Arizonan women with or without cervical neoplasm. Multi-omics integration methods, including neural networks (mmvec) and Random Forest supervised learning, were utilized to explore potential interactions and develop predictive models. Our integrated analyses revealed that immune and cancer biomarker concentrations were reliably predicted by Random Forest regressors trained on microbial and metabolic features, suggesting close correspondence between the vaginal microbiome, metabolome, and genital inflammation involved in cervical carcinogenesis. Furthermore, we show that features of the microbiome and host microenvironment, including metabolites, microbial taxa, and immune biomarkers are predictive of genital inflammation status, but only weakly to moderately predictive of cervical neoplastic disease status. Different feature classes were important for prediction of different phenotypes. Lipids (e.g. sphingolipids and long-chain unsaturated fatty acids) were strong predictors of genital inflammation, whereas predictions of vaginal microbiota and vaginal pH relied mostly on alterations in amino acid metabolism. Finally, we identified key immune biomarkers associated with the vaginal microbiota composition and vaginal pH (MIF), as well as genital inflammation (IL-6, IL-10, MIP-1α). © 2022 Bokulich et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.Open access journalThis item from the UA Faculty Publications collection is made available by the University of Arizona with support from the University of Arizona Libraries. If you have questions, please contact us at [email protected]

Improved pipeline for reducing erroneous identification by 16S rRNA sequences using the Illumina MiSeq platform

The cost of DNA sequencing has decreased due to advancements in Next Generation Sequencing. The number of sequences obtained from the lllumina platform is large, use of this platform can reduce costs more than the 454 pyrosequencer. However, the Illumina platform has other challenges, including bioinformatics analysis of large numbers of sequences and the need to reduce erroneous nucleotides generated at the 3'-ends of the sequences. These erroneous sequences can lead to errors in analysis of microbial communities. Therefore, correction of these erroneous sequences is necessary for accurate taxonomic identification. Several studies that have used the Illumina platform to perform metagenomic analyses proposed curating pipelines to increase accuracy. In this study, we evaluated the likelihood of obtaining an erroneous microbial composition using the MiSeq 250 bp paired sequence platform and improved the pipeline to reduce erroneous identifications. We compared different sequencing conditions by varying the percentage of control phiX added, the concentration of the sequencing library, and the 16S rRNA gene target region using a mock community sample composed of known sequences. Our recommended method corrected erroneous nucleotides and improved identification accuracy. Overall, 99.5% of the total reads shared 95% similarity with the corresponding template sequences and 93.6% of the total reads shared over 97% similarity. This indicated that the MiSeq platform can be used to analyze microbial communities at the genus level with high accuracy. The improved analysis method recommended in this study can be applied to amplicon studies in various environments using high-throughput reads generated on the MiSeq platform.N