Genome-Wide TSS Distribution in Three Related Clostridia with Normalized Capp-Switch Sequencing

Hocq, Rémi; Jagtap, Surabhi; Boutard, Magali; Tolonen, Andrew C.; Duval, Laurent; Pirayre, Aurélie; Ferreira, Nicolas Lopes; Wasels, François

doi:10.1128/spectrum.02288-21

Cited by 1 publication

(4 citation statements)

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“…TSSs further upstream of the CDS were classified as secondary TSS. Secondary TSSs are hypothesized to regulate gene expression in specific conditions where polymerases bind to alternative sigma factors ( 21 ). Internal TSSs are located inside a CDS on the sense strand.…”

Section: Resultsmentioning

confidence: 99%

“…We performed ortholog clustering of protein sequences across the three BV genomes using PIRATE v.1.0.4 ( 63 ). We selected a 50% amino acid sequence similarity threshold to compare the conservation and divergence of orthologs as previously described ( 21 ). TSS data sets from ANNOgesic and ortholog clustering from PIRATE were analyzed in Python v.3.10.…”

Section: Methodsmentioning

confidence: 99%

“…In particular, dRNA-seq has been used to identify TSSs in plant symbionts such as Bacillus amyloliquefaciens ( 18 ) and Sinorhizobium meliloti ( 19 ). Biotechnologically relevant bacteria have also been analyzed by dRNA-seq, and the results may be exploited to optimize transcriptional regulation of metabolic pathways ( 20 , 21 ). For example, the distance of the TSSs relative to RNA polymerase binding sites or the sequence identity of the 5′ UTR has been used to modulate gene expression in synthetic circuits.…”

Section: Introductionmentioning

confidence: 99%

“…Previous work provided a comprehensive approach for genome-wide TSS identification by performing manual curation of TSSs and comparing algorithm results to previous manually annotated TSSs across the genome of Helicobacter pylori under five different growth conditions ( 24 ). Previous work in Clostridium species ( 21 ) also incorporated optimization by accounting for expression bias from highly expressed genes by incorporating RNA-seq from the same libraries in order to normalize their TSS data set. These results are a reminder that algorithms, especially in genomics, have limited accuracy without performing parameter optimization, then validation on separate subsets of manually annotated data can lead to an inaccurate representation of biological phenomena.…”

Section: Introductionmentioning

confidence: 99%

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Transcriptome architecture of the three main lineages of agrobacteria

et al. 2023

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Agrobacteria are a diverse, polyphyletic group of prokaryotes with multipartite genomes capable of transferring DNA into the genomes of host plants, making them an essential tool in plant biotechnology. Despite their utility in plant transformation, genome-wide transcriptional regulation is not well understood across the three main lineages of agrobacteria. Transcription start sites (TSSs) are a necessary component of gene expression and regulation. In this study, we used differential RNA-seq and a TSS identification algorithm optimized on manually annotated TSS, then validated with existing TSS to identify thousands of TSS with nucleotide resolution for representatives of each lineage. We extend upon the 356 TSSs previously reported in Agrobacterium fabrum C58 by identifying 1,916 TSSs. In addition, we completed genomes and phenotyping of Rhizobium rhizogenes C16/80 and Allorhizobium vitis T60/94, identifying 2,650 and 2,432 TSSs, respectively. Parameter optimization was crucial for an accurate, high-resolution view of genome and transcriptional dynamics, highlighting the importance of algorithm optimization in genome-wide TSS identification and genomics at large. The optimized algorithm reduced the number of TSSs identified internal and antisense to the coding sequence on average by 90.5% and 91.9%, respectively. Comparison of TSS conservation between orthologs of the three lineages revealed differences in cell cycle regulation of ctrA as well as divergence of transcriptional regulation of chemotaxis-related genes when grown in conditions that simulate the plant environment. These results provide a framework to elucidate the mechanistic basis and evolution of pathology across the three main lineages of agrobacteria. IMPORTANCE Transcription start sites (TSSs) are fundamental for understanding gene expression and regulation. Agrobacteria, a group of prokaryotes with the ability to transfer DNA into the genomes of host plants, are widely used in plant biotechnology. However, the genome-wide transcriptional regulation of agrobacteria is not well understood, especially in less-studied lineages. Differential RNA-seq and an optimized algorithm enabled identification of thousands of TSSs with nucleotide resolution for representatives of each lineage. The results of this study provide a framework for elucidating the mechanistic basis and evolution of pathology across the three main lineages of agrobacteria. The optimized algorithm also highlights the importance of parameter optimization in genome-wide TSS identification and genomics at large.

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Section: Resultsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%