StrainR2 accurately deconvolutes strain-level abundances in synthetic microbial communities.

Background: Synthetic microbial communities offer an opportunity to conduct reductionist research in tractable model systems. However, deriving abundances of highly related strains within these communities is currently unreliable. 16S rRNA gene sequencing does not resolve abundance at the strain level, standard methods for analysis of shotgun metagenomic sequencing do not account for ambiguous mapping between closely related strains, and other methods such as quantitative PCR (qPCR) scale poorly and are resource prohibitive for complex communities. We present StrainR2, which utilizes shotgun metagenomic sequencing paired with a k-mer-based normalization strategy to provide high accuracy strain-level abundances for all members of a synthetic community, provided their genomes. Results: Both in silico, and using sequencing data derived from gnotobiotic mice colonized with a synthetic fecal microbiota, StrainR2 resolves strain abundances with greater accuracy than other tools utilizing shotgun metagenomic sequencing reads and can resolve complex mixtures of highly related strains. Through experimental validation and benchmarking, we demonstrate that StrainR2's accuracy is comparable to that of qPCR on a subset of strains resolved using absolute quantification. Further, it is capable of scaling to communities of hundreds of strains and efficiently utilizes memory being capable of running both on personal computers and high-performance computing nodes. Conclusions: Using shotgun metagenomic sequencing reads is a viable method for determining accurate strain-level abundances in synthetic communities using StrainR2.

Comparative Genomics Identifies Conserved and Variable TAL Effectors in African Strains of the Cotton Pathogen Xanthomonas citri pv. malvacearum.

Strains of Xanthomonas citri pv. malvacearum cause bacterial blight of cotton, a potentially serious threat to cotton production worldwide, including in sub-Saharan countries. Development of disease symptoms, such as water soaking, has been linked to the activity of a class of type 3 effectors, called transcription activator-like (TAL) effectors, which induce susceptibility genes in the host's cells. To gain further insight into the global diversity of the pathogen, to elucidate their repertoires of TAL effector genes, and to better understand the evolution of these genes in the cotton-pathogenic xanthomonads, we sequenced the genomes of three African strains of X. citri pv. malvacearum using nanopore technology. We show that the cotton-pathogenic pathovar of X. citri is a monophyletic lineage containing at least three distinct genetic subclades, which appear to be mirrored by their repertoires of TAL effectors. We observed an atypical level of TAL effector gene pseudogenization, which might be related to resistance genes that are deployed to control the disease. Our work thus contributes to a better understanding of the conservation and importance of TAL effectors in the interaction with the host plant, which can inform strategies for improving resistance against bacterial blight in cotton.