Erratum for Beskrovnaya et al., “Comparative Genomics Identified a Genetic Locus in Plant-Associated
            <i>Pseudomonas</i>
            spp. That Is Necessary for Induced Systemic Susceptibility”

Beskrovnaya, Polina; Melnyk, Ryan A.; Liu, Zhexian; Liu, Yang; Higgins, M.A.; Song, Yi; Ryan, Katherine S.; Haney, Cara H.

doi:10.1128/mbio.01906-20

Cited by 4 publications

(7 citation statements)

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“…1A ). However, unlike many traits that show phylogenetic signatures within the genus Pseudomonas ( 16 , 17 , 23 , 37 ), we were unable to identify components of the Pseudomonas accessory genome that correlate with protection. As a result, this work implicates variation in regulation of components of the core genome in protection against an opportunistic pathogen.…”

Section: Discussionmentioning

confidence: 59%

“…A number of phenotypes within Pseudomonas , ranging from plant-beneficial traits ( 16 , 17 ), to virulence toward plants and animals ( 16 , 23 ), can be attributed to the gain and loss of components of the Pseudomonas accessory genome. To identify genes that may correlate with protection, we used PyParanoid, a previously described comparative genomics platform ( 16 ), to identify genes that were unique to protective strains.…”

Section: Resultsmentioning

confidence: 99%

“…Comparative genomics was performed using the PyParanoid pipeline described previously ( 16 , 17 ). Briefly, the PyParanoid pipeline was previously used to create a database using 3886 Pseudomonas genomes, identifying 24066 homologous protein families (“gene groups”) that covered 94.2% of the generated Pseudomonas pangenome.…”

Section: Methodsmentioning

confidence: 99%

“…Phylogenetic trees were generated using the PyParanoid pipeline and FastTree 2 as described previously ( 16 , 17 ). Briefly, an alignment of 122 single-copy genes conserved within the Pseudomonas genus was created previously.…”

Section: Methodsmentioning

confidence: 99%

“…The species complex P. fluorescens is genetically and functionally diverse, despite being over 97% identical based on 16S rRNA gene sequence ( 16 , 17 ). Even within closely related P. fluorescens strains, host-associated lifestyles can differ.…”

Section: Introductionmentioning

confidence: 99%

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Commensal Pseudomonas fluorescens Strains Protect Arabidopsis from Closely Related Pseudomonas Pathogens in a Colonization-Dependent Manner

Wang

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et al. 2022

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Section: Discussionmentioning

confidence: 59%

Section: Resultsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Commensal Pseudomonas fluorescens Strains Protect Arabidopsis from Closely Related Pseudomonas Pathogens in a Colonization-Dependent Manner

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Wiesmann

Melnyk

et al. 2022

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Taxonomic Description and Complete Genome Sequencing of Pseudomonas silvicola sp. nov. Isolated from Cunninghamia laceolata

Tian

Zhang

Yang

et al. 2023

Forests

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The Pseudomonas strain T1-3-2T isolated from the cone of Cunninghamia laceolata exhibited growth-promoting and antifungal activity. Strain T1-3-2T was characterized by a polyphasic taxonomy and complete genome sequencing analysis to explore its taxonomic position and biocontrol potentials fully. The results revealed that strain T1-3-2T shares a high degree of similarity with Pseudomonas eucalypticola and is distinct from any known Pseudomonas species. The G + C content was 61.65%, and the difference was greater than 1 compared to “P. eucalypticola”. Additionally, values of the average nucleotide identity blast (ANIb), average nucleotide identity MUMmer (ANIm), and DNA-DNA hybridization (DDH) between T1-3-2T and its closest known related species, “P. eucalypticola”, were below the thresholds necessary for species delineation. Furthermore, the T1-3-2T strain exhibited the distinctions with the multiple polar flagella and the specific quinone system with MK8 compared with that of “P. eucalypticola”. Collectively, these findings affirm the designation of strain T1-3-2T as a new Pseudomonas species, proposed to be named Pseudomonas silvicola, with T1-3-2T as the type strain. Genomic analyses revealed strain T1-3-2T contains three circular DNA contigs, including a 7,613,303 bp chromosome and two plasmids (952,764 bp and 84,880 bp). Bioinformatics analyses further offered potential insight into the molecular mechanisms whereby this strain can promote plant growth and control disease, revealing encoded genes related to antibiotic and secondary metabolite production, the uptake and biosynthesis of siderophores, and pyoverdine biosynthesis. These genomic data offer a valuable foundation for future efforts to apply the T1-3-2T strain in research contexts.

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Navigating and expanding the roadmap of natural product genome mining tools

Biermann¹,

Wenski²,

Helfrich³

2022

Beilstein J. Org. Chem.

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Natural products are structurally highly diverse and exhibit a wide array of biological activities. As a result, they serve as an important source of new drug leads. Traditionally, natural products have been discovered by bioactivity-guided fractionation. The advent of genome sequencing technology has resulted in the introduction of an alternative approach towards novel natural product scaffolds: Genome mining. Genome mining is an in-silico natural product discovery strategy in which sequenced genomes are analyzed for the potential of the associated organism to produce natural products. Seemingly universal biosynthetic principles have been deciphered for most natural product classes that are used to detect natural product biosynthetic gene clusters using pathway-encoded conserved key enzymes, domains, or motifs as bait. Several generations of highly sophisticated tools have been developed for the biosynthetic rule-based identification of natural product gene clusters. Apart from these hard-coded algorithms, multiple tools that use machine learning-based approaches have been designed to complement the existing genome mining tool set and focus on natural product gene clusters that lack genes with conserved signature sequences. In this perspective, we take a closer look at state-of-the-art genome mining tools that are based on either hard-coded rules or machine learning algorithms, with an emphasis on the confidence of their predictions and potential to identify non-canonical natural product biosynthetic gene clusters. We highlight the genome mining pipelines' current strengths and limitations by contrasting their advantages and disadvantages. Moreover, we introduce two indirect biosynthetic gene cluster identification strategies that complement current workflows. The combination of all genome mining approaches will pave the way towards a more comprehensive understanding of the full biosynthetic repertoire encoded in microbial genome sequences.

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Erratum for Beskrovnaya et al., “Comparative Genomics Identified a Genetic Locus in Plant-Associated Pseudomonas spp. That Is Necessary for Induced Systemic Susceptibility”

Abstract: In Fig. 3B, the labels for the 3rd and 4th data sets previously labeled ΔspeE2ϩpSpeE2 and Δclusterϩvector, respectively, were swapped. The correctly labeled figure appears below. There are no corresponding changes to the text or figure legend.

Cited by 4 publications

References 0 publications

Commensal Pseudomonas fluorescens Strains Protect Arabidopsis from Closely Related Pseudomonas Pathogens in a Colonization-Dependent Manner

Commensal Pseudomonas fluorescens Strains Protect Arabidopsis from Closely Related Pseudomonas Pathogens in a Colonization-Dependent Manner

Taxonomic Description and Complete Genome Sequencing of Pseudomonas silvicola sp. nov. Isolated from Cunninghamia laceolata

Navigating and expanding the roadmap of natural product genome mining tools

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