scholarly journals Why? – Successful Pseudomonas aeruginosa clones with a focus on clone C

2020 ◽  
Vol 44 (6) ◽  
pp. 740-762
Author(s):  
Changhan Lee ◽  
Jens Klockgether ◽  
Sebastian Fischer ◽  
Janja Trcek ◽  
Burkhard Tümmler ◽  
...  
Keyword(s):  
Pseudomonas Aeruginosa ◽  
Core Genome ◽  
Genomic Island ◽  
Protein Quality ◽  
Temperature Tolerance ◽  
Protein Homeostasis ◽  
Gene Products ◽  
Accessory Genome ◽  
The Core ◽  
Conserved Core

ABSTRACT The environmental species Pseudomonas aeruginosa thrives in a variety of habitats. Within the epidemic population structure of P. aeruginosa, occassionally highly successful clones that are equally capable to succeed in the environment and the human host arise. Framed by a highly conserved core genome, individual members of successful clones are characterized by a high variability in their accessory genome. The abundance of successful clones might be funded in specific features of the core genome or, although not mutually exclusive, in the variability of the accessory genome. In clone C, one of the most predominant clones, the plasmid pKLC102 and the PACGI-1 genomic island are two ubiquitous accessory genetic elements. The conserved transmissible locus of protein quality control (TLPQC) at the border of PACGI-1 is a unique horizontally transferred compository element, which codes predominantly for stress-related cargo gene products such as involved in protein homeostasis. As a hallmark, most TLPQC xenologues possess a core genome equivalent. With elevated temperature tolerance as a characteristic of clone C strains, the unique P. aeruginosa and clone C specific disaggregase ClpG is a major contributor to tolerance. As other successful clones, such as PA14, do not encode the TLPQC locus, ubiquitous denominators of success, if existing, need to be identified.

scholarly journals Estimation of pathogenic potential of an environmental Pseudomonas aeruginosa isolate using comparative genomics

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Carola Berger ◽  
Christian Rückert ◽  
Jochen Blom ◽  
Korneel Rabaey ◽  
Jörn Kalinowski ◽  
...  
Keyword(s):  
Pseudomonas Aeruginosa ◽  
Genomic Island ◽  
Environmental Isolate ◽  
Genomic Islands ◽  
Published Data ◽  
Original Publication ◽  
Genomic Information ◽  
Pathogenic Potential ◽  
Accessory Genome ◽  
Highly Virulent

AbstractThe isolation and sequencing of new strains of Pseudomonas aeruginosa created an extensive dataset of closed genomes. Many of the publicly available genomes are only used in their original publication while additional in silico information, based on comparison to previously published genomes, is not being explored. In this study, we defined and investigated the genome of the environmental isolate P. aeruginosa KRP1 and compared it to more than 100 publicly available closed P. aeruginosa genomes. By using different genomic island prediction programs, we could identify a total of 17 genomic islands and 8 genomic islets, marking the majority of the accessory genome that covers ~ 12% of the total genome. Based on intra-strain comparisons, we are able to predict the pathogenic potential of this environmental isolate. It shares a substantial amount of genomic information with the highly virulent PSE9 and LESB58 strains. For both of these, the increased virulence has been directly linked to their accessory genome before. Hence, the integrated use of previously published data can help to minimize expensive and time consuming wetlab work to determine the pathogenetic potential.

scholarly journals A Genome-Based Model to Predict the Virulence of Pseudomonas aeruginosa Isolates

mBio ◽  
2020 ◽  
Vol 11 (4) ◽  
Author(s):  
Nathan B. Pincus ◽  
Egon A. Ozer ◽  
Jonathan P. Allen ◽  
Marcus Nguyen ◽  
James J. Davis ◽  
...  
Keyword(s):  
Machine Learning ◽  
Pseudomonas Aeruginosa ◽  
Core Genome ◽  
Learning Models ◽  
Single Nucleotide Variants ◽  
Bacterial Genomes ◽  
Content Type ◽  
Accessory Genome ◽  
A Genome ◽  
Machine Learning Models

ABSTRACT Variation in the genome of Pseudomonas aeruginosa, an important pathogen, can have dramatic impacts on the bacterium’s ability to cause disease. We therefore asked whether it was possible to predict the virulence of P. aeruginosa isolates based on their genomic content. We applied a machine learning approach to a genetically and phenotypically diverse collection of 115 clinical P. aeruginosa isolates using genomic information and corresponding virulence phenotypes in a mouse model of bacteremia. We defined the accessory genome of these isolates through the presence or absence of accessory genomic elements (AGEs), sequences present in some strains but not others. Machine learning models trained using AGEs were predictive of virulence, with a mean nested cross-validation accuracy of 75% using the random forest algorithm. However, individual AGEs did not have a large influence on the algorithm’s performance, suggesting instead that virulence predictions are derived from a diffuse genomic signature. These results were validated with an independent test set of 25 P. aeruginosa isolates whose virulence was predicted with 72% accuracy. Machine learning models trained using core genome single-nucleotide variants and whole-genome k-mers also predicted virulence. Our findings are a proof of concept for the use of bacterial genomes to predict pathogenicity in P. aeruginosa and highlight the potential of this approach for predicting patient outcomes. IMPORTANCE Pseudomonas aeruginosa is a clinically important Gram-negative opportunistic pathogen. P. aeruginosa shows a large degree of genomic heterogeneity both through variation in sequences found throughout the species (core genome) and through the presence or absence of sequences in different isolates (accessory genome). P. aeruginosa isolates also differ markedly in their ability to cause disease. In this study, we used machine learning to predict the virulence level of P. aeruginosa isolates in a mouse bacteremia model based on genomic content. We show that both the accessory and core genomes are predictive of virulence. This study provides a machine learning framework to investigate relationships between bacterial genomes and complex phenotypes such as virulence.

scholarly journals Gene-gene relationships in an Escherichia coli accessory genome are linked to function and mobility

2021 ◽  
Vol 7 (9) ◽  
Author(s):  
Rebecca J. Hall ◽  
Fiona J. Whelan ◽  
Elizabeth A. Cummins ◽  
Christopher Connor ◽  
Alan McNally ◽  
...  
Keyword(s):  
Escherichia Coli ◽  
Complex Network ◽  
Type Species ◽  
Core Genome ◽  
Mobile Genetic Elements ◽  
Dynamic Nature ◽  
Content Type ◽  
Accessory Genome ◽  
The Core ◽  
Sequence Types

The pangenome contains all genes encoded by a species, with the core genome present in all strains and the accessory genome in only a subset. Coincident gene relationships are expected within the accessory genome, where the presence or absence of one gene is influenced by the presence or absence of another. Here, we analysed the accessory genome of an Escherichia coli pangenome consisting of 400 genomes from 20 sequence types to identify genes that display significant co-occurrence or avoidance patterns with one another. We present a complex network of genes that are either found together or that avoid one another more often than would be expected by chance, and show that these relationships vary by lineage. We demonstrate that genes co-occur by function, and that several highly connected gene relationships are linked to mobile genetic elements. We find that genes are more likely to co-occur with, rather than avoid, another gene in the accessory genome. This work furthers our understanding of the dynamic nature of prokaryote pangenomes and implicates both function and mobility as drivers of gene relationships.

scholarly journals PANINI: Pangenome Neighbor Identification for Bacterial Populations

2017 ◽  
Author(s):  
Khalil Abudahab ◽  
Joaquín M. Prada ◽  
Zhirong Yang ◽  
Stephen D. Bentley ◽  
Nicholas J. Croucher ◽  
...  
Keyword(s):  
Machine Learning ◽  
Core Genome ◽  
Population Genomics ◽  
Genomic Analysis ◽  
Diverse Populations ◽  
Data Set ◽  
Bacterial Populations ◽  
Accessory Genome ◽  
The Core ◽  
Link Type

ABSTRACTThe standard workhorse for genomic analysis of the evolution of bacterial populations is phylogenetic modelling of mutations in the core genome. However, in the current era of population genomics, a notable amount of information about evolutionary and transmission processes in diverse populations can be lost unless the accessory genome is also taken into consideration. Here we introduce PANINI, a computationally scalable method for identifying the neighbours for each isolate in a data set using unsupervised machine learning with stochastic neighbour embedding. PANINI is browser-based and integrates with the Microreact platform for rapid online visualisation and exploration of both core and accessory genome evolutionary signals together with relevant epidemiological, geographic, temporal and other metadata. Several case studies with single-and multi-clone pneumococcal populations are presented to demonstrate ability to identify biologically important signals from gene content data. PANINI is available at http://panini.wgsa.net/ and code at http://gitlab.com/cgps/panini

Analysis of the core genome and pangenome of Clostridium butyricum

Genome ◽  
2021 ◽  
Vol 64 (1) ◽  
pp. 51-61
Author(s):  
Wei Zou ◽  
Guangbin Ye ◽  
Kaizheng Zhang ◽  
Haiquan Yang ◽  
Jiangang Yang
Keyword(s):  
Carbohydrate Metabolism ◽  
Anaerobic Bacterium ◽  
Biosynthetic Pathway ◽  
Core Genome ◽  
Mobile Genetic Elements ◽  
Clostridium Butyricum ◽  
Glycerol Metabolism ◽  
Accessory Genome ◽  
Future Studies ◽  
The Core

Clostridium butyricum is an anaerobic bacterium that inhabits broad niches. Clostridium butyricum is known for its production of butyrate, 1,3-propanediol, and hydrogen. This study aimed to present a comparative pangenome analysis of 24 strains isolated from different niches. We sequenced and annotated the genome of C. butyricum 3-3 isolated from the Chinese baijiu ecosystem. The pangenome of C. butyricum was open. The core genome, accessory genome, and strain-specific genes comprised 1011, 4543, and 1473 genes, respectively. In the core genome, Carbohydrate metabolism was the largest category, and genes in the biosynthetic pathway of butyrate and glycerol metabolism were conserved (in the core or soft-core genome). Furthermore, the 1,3-propanediol operon existed in 20 strains. In the accessory genome, numerous mobile genetic elements belonging to the Replication, recombination, and repair (L) category were identified. In addition, genome islands were identified in all 24 strains, ranging from 2 (strain KNU-L09) to 53 (strain SU1), and phage sequences were found in 17 of the 24 strains. This study provides an important genomic framework that could pave the way for the exploration of C. butyricum and future studies on the genetic diversification of C. butyricum.

scholarly journals The Accessory Genome of Pseudomonas aeruginosa

2010 ◽  
Vol 74 (4) ◽  
pp. 621-641 ◽  
Author(s):  
Vanderlene L. Kung ◽  
Egon A. Ozer ◽  
Alan R. Hauser
Keyword(s):  
Pseudomonas Aeruginosa ◽  
Core Genome ◽  
Rapid Progress ◽  
Functional Importance ◽  
Dynamic Nature ◽  
Interstrain Differences ◽  
Accessory Genome ◽  
Ecological Flexibility ◽  
Emerging Themes

SUMMARY Pseudomonas aeruginosa strains exhibit significant variability in pathogenicity and ecological flexibility. Such interstrain differences reflect the dynamic nature of the P. aeruginosa genome, which is composed of a relatively invariable “core genome” and a highly variable “accessory genome.” Here we review the major classes of genetic elements comprising the P. aeruginosa accessory genome and highlight emerging themes in the acquisition and functional importance of these elements. Although the precise phenotypes endowed by the majority of the P. aeruginosa accessory genome have yet to be determined, rapid progress is being made, and a clearer understanding of the role of the P. aeruginosa accessory genome in ecology and infection is emerging.

scholarly journals A Genome-based Model to Predict the Virulence of Pseudomonas aeruginosa Isolates

2020 ◽  
Author(s):  
Nathan B. Pincus ◽  
Egon A. Ozer ◽  
Jonathan P. Allen ◽  
Marcus Nguyen ◽  
James J. Davis ◽  
...  
Keyword(s):  
Machine Learning ◽  
Pseudomonas Aeruginosa ◽  
Core Genome ◽  
Large Degree ◽  
Learning Models ◽  
Single Nucleotide Variants ◽  
Bacterial Genomes ◽  
Accessory Genome ◽  
A Genome ◽  
Machine Learning Models

ABSTRACTVariation in the genome of Pseudomonas aeruginosa, an important pathogen, can have dramatic impacts on the bacterium’s ability to cause disease. We therefore asked whether it was possible to predict the virulence of P. aeruginosa isolates based upon their genomic content. We applied a machine learning approach to a genetically and phenotypically diverse collection of 115 clinical P. aeruginosa isolates using genomic information and corresponding virulence phenotypes in a mouse model of bacteremia. We defined the accessory genome of these isolates through the presence or absence of accessory genomic elements (AGEs), sequences present in some strains but not others. Machine learning models trained using AGEs were predictive of virulence, with a mean nested cross-validation accuracy of 75% using the random forest algorithm. However, individual AGEs did not have a large influence on the algorithm’s performance, suggesting instead that the virulence prediction derives from a diffuse genomic signature. These results were validated with an independent test set of 25 P. aeruginosa isolates whose virulence was predicted with 72% accuracy. Machine learning models trained using core genome single nucleotide variants and whole genome k-mers also predicted virulence. Our findings are a proof of concept for the use of bacterial genomes to predict pathogenicity in P. aeruginosa and highlight the potential of this approach for predicting patient outcomes.IMPORTANCEPseudomonas aeruginosa is a clinically important gram-negative opportunistic pathogen. As a species, P. aeruginosa has a large degree of heterogeneity both through variation in sequences found throughout the species (core genome) and the presence or absence of sequences in different isolates (accessory genome). P. aeruginosa isolates also differ markedly in their ability to cause disease. In this study, we used machine learning to predict the virulence level of P. aeruginosa isolates in a mouse bacteremia model based on genomic content. We show that both the accessory and core genome are predictive of virulence. This study provides a machine learning framework to investigate relationships between bacterial genomes and complex phenotypes such as virulence.

scholarly journals Evolutionary responses to codon usage of horizontally transferred genes in Pseudomonas aeruginosa

2020 ◽  
Author(s):  
Martijn Callens ◽  
Celine Scornavacca ◽  
Stéphanie Bedhomme
Keyword(s):  
Pseudomonas Aeruginosa ◽  
Codon Usage ◽  
Core Genome ◽  
Trna Genes ◽  
Compensatory Evolution ◽  
Synonymous Mutations ◽  
The Core ◽  
Selective Retention ◽  
Fitness Benefits ◽  
Core Genes

AbstractProkaryote genome evolution is characterized by the frequent gain of genes through horizontal gene transfer (HGT). For a gene, being horizontally transferred can represent a strong change in its genomic and physiological context. If the codon usage of a transferred gene deviates from that of the receiving organism, the fitness benefits it provides can be reduced due to a mismatch with the expression machinery. Consequently, transferred genes with a deviating codon usage can be selected against or elicit evolutionary responses that enhance their integration. In this study, a comparative genomics approach was used to investigate evolutionary responses after the horizontal transfer of genes with diverse degrees of codon usage mismatch in Pseudomonas aeruginosa. Selection on codon usage of genes acquired through HGT was observed, with the overall codon usage converging towards that of the core genome over evolutionary time. This pattern seemed to be mainly driven by selective retention of transferred genes with an initial codon usage similar to that of the core genes. Gene amelioration, through the accumulation of synonymous mutations after HGT, did not seem to systematically affect transferred genes. Additionally, variation in the copy number of tRNA genes was often associated with the acquisition of genes for which the observed variation could enhance their expression. This provides evidence that compensatory evolution might be an important mechanism for the integration of horizontally transferred genes.

scholarly journals Horizontal Transmission of Stress Resistance Genes Shape the Ecology of Beta- and Gamma-Proteobacteria

2021 ◽  
Vol 12 ◽  
Author(s):  
Shady Mansour Kamal ◽  
David J. Simpson ◽  
Zhiying Wang ◽  
Michael Gänzle ◽  
Ute Römling
Keyword(s):  
Core Genome ◽  
Protein Quality ◽  
Current Knowledge ◽  
Horizontal Transmission ◽  
Gene Products ◽  
Gamma Proteobacteria ◽  
Environmental Bacterium ◽  
Counter Selection ◽  
Distinct Sequence ◽  
Shock Proteins

The transmissible locus of stress tolerance (tLST) is found mainly in beta- and gamma-Proteobacteria and confers tolerance to elevated temperature, pressure, and chlorine. This genomic island, previously referred to as transmissible locus of protein quality control or locus of heat resistance likely originates from an environmental bacterium thriving in extreme habitats, but has been widely transmitted by lateral gene transfer. Although highly conserved, the gene content on the island is subject to evolution and gene products such as small heat shock proteins are present in several functionally distinct sequence variants. A number of these genes are xenologs of core genome genes with the gene products to widen the substrate spectrum and to be highly (complementary) expressed thus their functionality to become dominant over core genome genes. In this review, we will present current knowledge of the function of core tLST genes and discuss current knowledge on selection and counter-selection processes that favor maintenance of the tLST island, with frequent acquisition of gene products involved in cyclic di-GMP signaling, in different habitats from the environment to animals and plants, processed animal and plant products, man-made environments, and subsequently humans.

Sign in / Sign up

Export Citation Format

Share Document