Coordinated regulation of core and accessory genes in the multipartite genome of Sinorhizobium fredii

Abstract

Prokaryotes benefit from having accessory genes, but it is unclear how accessory genes can be linked with the core regulatory network when developing adaptations to new niches. Here we determined hierarchical core/accessory subsets in the multipartite pangenome (composed of genes from the chromosome, chromid and plasmids) of the soybean microsymbiont Sinorhizobium fredii by comparing twelve Sinorhizobium genomes. Transcriptomes of two S. fredii strains at mid-log and stationary growth phases and in symbiotic conditions were obtained. The average level of gene expression, variation of expression between different conditions, and gene connectivity within the co-expression network were positively correlated with the gene conservation level from strain-specific accessory genes to genus core. Condition-dependent transcriptomes exhibited adaptive transcriptional changes in pangenome subsets shared by the two strains, while strain-dependent transcriptomes were enriched with accessory genes on the chromid. Proportionally more chromid genes than plasmid genes were co-expressed with chromosomal genes, while plasmid genes had a higher within-replicon connectivity in expression than chromid ones. However, key nitrogen fixation genes on the symbiosis plasmid were characterized by high connectivity in both within- and between-replicon analyses. Among those genes with host-specific upregulation patterns, chromosomal znu and mdt operons, encoding a conserved high-affinity zinc transporter and an accessory multi-drug efflux system, respectively, were experimentally demonstrated to be involved in host-specific symbiotic adaptation. These findings highlight the importance of integrative regulation of hierarchical core/accessory components in the multipartite genome of bacteria during niche adaptation and in shaping the prokaryotic pangenome in the long run.

Metabolic Analyses of Nitrogen Fixation in the Soybean Microsymbiont Sinorhizobium fredii Using Constraint-Based Modeling

“…Most of these genes (84%) were located in the chromosome, while the others were present in the symbiosis plasmid, accessory plasmids, and chromid (Table S2). Coexpression patterns in the replicon of S. fredii strains have been observed under both free-living and symbiotic conditions (17).…”

“…To evaluate the capacity of CCBAU45436 to fix atmospheric nitrogen in the nodules of different soybean cultivars, a transcriptome data set was incorporated into the analysis. Raw transcriptome sequencing (RNA-seq) data of S. fredii CCBAU45436 bacteroid at the symbiotic stage within the nodules of cultivated soybean C08 and wild soybean W05 were retrieved from a previous publication for reanalysis (17). In brief, the clean reads in fastq files were mapped to the reference genomes of S. fredii CCBAU45436 using HISAT2 (default parameters) (93).…”

“…The S. fredii strain CCBAU45436 represents one of the dominant sublineages of S. fredii that nodulate soybeans in northern China (16). Recently, the complete genome sequence for CCBAU45436 was fully assembled (17). The CCBAU45436 multipartite genome consists of a chromosome (cSF45436, 4.16 Mb), a chromid (pSF45436b, 1.96 Mb), a symbiotic plasmid (pSF45436a, 0.42 Mb), and two accessory plasmids (pSF45436d, 0.20 Mb, and pSF45436e, 0.17 Mb).…”

“…The CCBAU45436 multipartite genome consists of a chromosome (cSF45436, 4.16 Mb), a chromid (pSF45436b, 1.96 Mb), a symbiotic plasmid (pSF45436a, 0.42 Mb), and two accessory plasmids (pSF45436d, 0.20 Mb, and pSF45436e, 0.17 Mb). Evidence suggests a distinct role for each of these forms of DNA in rhizobial adaptations to symbiotic conditions (17)(18)(19). CCBAU45436 is able to induce effective nodules on the roots of the wild soybean accession W05 (Glycine soja W05) and the cultivated soybean accession C08 (Glycine max C08).…”

Environment‐driven changes of mRNA and protein levels in Pseudomonas aeruginosa

“…Sequence parameters that are known to influence either translational efficiency, protein/mRNA stability and/or measured intensities of proteins were added to the model: codon usage (Supek et al, 2010;Hanson and Coller, 2018), Shine Dalgarno sequence strength (Osterman et al, 2013) (Supporting Information Fig. S2), gene type (Jiao et al, 2018) (core or accessory genome), gene length (Fernandes et al, 2017), position of the gene within the genome (Le et al, 2016) and the strength of inverted repeats (Iserentant and Fiers, 1980) were used for 3485 protein/mRNA pairs for which quantitative data of all seven conditions were available. Overall, the correlation coefficients of mRNA to protein levels improved from r = 0.65 to r = 0.77 ( Fig.…”