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Plant–microbe symbioses such as the legume–rhizobium mutualism are vital in the web of ecological relationships within both natural and managed ecosystems, influencing primary productivity, crop yield, and ecosystem services. The outcome of these interactions for plant hosts varies quantitatively and can range from highly beneficial to even detrimental depending on natural genetic variation in microbial symbionts. Here, we take a systems genetics approach, harnessing the genetic diversity present in wild rhizobial populations to predict genes and molecular pathways crucial in determining partner quality, i.e., the benefits of symbiosis for legume hosts. We combine traits, dual-RNAseq of both partners from active nodules, pangenomics/pantranscriptomics, and Weighted Gene Co-expression Network Analysis (WGCNA) for a panel of 20Sinorhizobium melilotistrains that vary in symbiotic partner quality. We find that genetic variation in the nodule transcriptome predicts host plant biomass, and WGCNA reveals networks of genes in plants and rhizobia that are coexpressed and associated with high-quality symbiosis. Presence–absence variation of gene clusters on the symbiosis plasmid (pSymA), validated in planta, is associated with high or low-quality symbiosis and is found within important coexpression modules. Functionally our results point to management of oxidative stress, amino acid and carbohydrate transport, and NCR peptide signaling mechanisms in driving symbiotic outcomes. Our integrative approach highlights the complex genetic architecture of microbial partner quality and raises hypotheses about the genetic mechanisms and evolutionary dynamics of symbiosis. 

Stochastic nature of gene expression leads to the complex formation of the bacterial transcriptome and proteome. In contrast to typical transcriptome studies, we employ a near wild-type, Syn1.0, of the naturally genome-reducedMycoplasmas, and the dramatically further genome-reduced JCVI-syn3A thus avoiding additional contributions from many non-essential cellular functions. To aid in profiling the transcriptional landscape within these bacteria, we present a bioinformatic analysis of the genetic sequence motifs implicated in modulating the stochastic gene expression events, coupled with genome-wide short-read (Illumina) and long-read (Oxford Nanopore Technologies and Pacfic Biosciences) RNA sequencing. The bioinformatic analysis coupled with information from structural studies assigns strengths of the Shine-Dalgarno signatures and identifies both transcription initiation and termination sites, leading to predictions of RNA isoforms in Syn1.0 (and related organisms). The long-read and short-read RNA sequencing characterized the predicted transcriptional activity, and the long-read methods provide direct insight into the RNA isoform complexity within Syn1.0. Comparison of the RNA sequencing results with that of the bioinformatic analysis highlights the inability of bioinformatics alone to capture the results of bacterial transcription without including effects of RNA degradation. This study emphasizes the need for comparative analysis and potential dangers of genome reduction, exemplified through the discovery of altered gene expression patterns of JCVI-syn1.0 and JCVI-syn3A, achieved via the union of our transcriptome study with their proteomics data. Analysis of the transcriptomics data sets through a Jupyter notebook allows any genomic region to be easily examined. Table of Content Image