Literature DB >> 32878952

A Phylogeny-Informed Proteomics Approach for Species Identification within the Burkholderia cepacia Complex.

Honghui Wang1, Ousmane H Cissé1, Anthony F Suffredini2, John P Dekker3,4, Thomas Bolig1, Steven K Drake1, Yong Chen5, Jeffrey R Strich1, Jung-Ho Youn6, Uchenna Okoro1, Avi Z Rosenberg7,8, Junfeng Sun1, John J LiPuma9.   

Abstract

Ancestral genetic exchange between members of many important bacterial pathogen groups has resulted in phylogenetic relationships better described as networks than as bifurcating trees. In certain cases, these reticulated phylogenies have resulted in phenotypic and molecular overlap that challenges the construction of practical approaches for species identification in the clinical microbiology laboratory. Burkholderia cepacia complex (Bcc), a betaproteobacteria species group responsible for significant morbidity in persons with cystic fibrosis and chronic granulomatous disease, represents one such group where network-structured phylogeny has hampered the development of diagnostic methods for species-level discrimination. Here, we present a phylogeny-informed proteomics approach to facilitate diagnostic classification of pathogen groups with reticulated phylogenies, using Bcc as an example. Starting with a set of more than 800 Bcc and Burkholderia gladioli whole-genome assemblies, we constructed phylogenies with explicit representation of inferred interspecies recombination. Sixteen highly discriminatory peptides were chosen to distinguish B. cepacia, Burkholderia cenocepacia, Burkholderia multivorans, and B. gladioli and multiplexed into a single, rapid liquid chromatography-tandem mass spectrometry multiple reaction monitoring (LC-MS/MS MRM) assay. Testing of a blinded set of isolates containing these four Burkholderia species demonstrated 50/50 correct automatic negative calls (100% accuracy with a 95% confidence interval [CI] of 92.9 to 100%), and 70/70 correct automatic species-level positive identifications (100% accuracy with 95% CI 94.9 to 100%) after accounting for a single initial incorrect identification due to a preanalytic error, correctly identified on retesting. The approach to analysis described here is applicable to other pathogen groups for which development of diagnostic classification methods is complicated by interspecies recombination.

Entities:  

Keywords:  clinical microbiology; computational biology; genomics; mass spectrometry; network phylogeny; proteomics

Year:  2020        PMID: 32878952      PMCID: PMC7587091          DOI: 10.1128/JCM.01741-20

Source DB:  PubMed          Journal:  J Clin Microbiol        ISSN: 0095-1137            Impact factor:   5.948


  56 in total

1.  GeneMarkS: a self-training method for prediction of gene starts in microbial genomes. Implications for finding sequence motifs in regulatory regions.

Authors:  J Besemer; A Lomsadze; M Borodovsky
Journal:  Nucleic Acids Res       Date:  2001-06-15       Impact factor: 16.971

2.  Cd-hit: a fast program for clustering and comparing large sets of protein or nucleotide sequences.

Authors:  Weizhong Li; Adam Godzik
Journal:  Bioinformatics       Date:  2006-05-26       Impact factor: 6.937

3.  Phylogenetic super-networks from partial trees.

Authors:  Daniel H Huson; Tobias Dezulian; Tobias Klöpper; Mike A Steel
Journal:  IEEE/ACM Trans Comput Biol Bioinform       Date:  2004 Oct-Dec       Impact factor: 3.710

4.  Biocide susceptibility of the Burkholderia cepacia complex.

Authors:  Helen Rose; Adam Baldwin; Christopher G Dowson; Eshwar Mahenthiralingam
Journal:  J Antimicrob Chemother       Date:  2009-01-18       Impact factor: 5.790

5.  Expanded multilocus sequence typing for burkholderia species.

Authors:  Theodore Spilker; Adam Baldwin; Amy Bumford; Chris G Dowson; Eshwar Mahenthiralingam; John J LiPuma
Journal:  J Clin Microbiol       Date:  2009-06-03       Impact factor: 5.948

6.  A Novel Peptidomic Approach to Strain Typing of Clinical Acinetobacter baumannii Isolates Using Mass Spectrometry.

Authors:  Honghui Wang; Steven K Drake; Chen Yong; Marjan Gucek; Margaret Tropea; Avi Z Rosenberg; John P Dekker; Anthony F Suffredini
Journal:  Clin Chem       Date:  2016-04-26       Impact factor: 8.327

7.  Rapid Bacterial Identification, Resistance, Virulence and Type Profiling using Selected Reaction Monitoring Mass Spectrometry.

Authors:  Yannick Charretier; Olivier Dauwalder; Christine Franceschi; Elodie Degout-Charmette; Gilles Zambardi; Tiphaine Cecchini; Chloe Bardet; Xavier Lacoux; Philippe Dufour; Laurent Veron; Hervé Rostaing; Veronique Lanet; Tanguy Fortin; Corinne Beaulieu; Nadine Perrot; Dominique Dechaume; Sylvie Pons; Victoria Girard; Arnaud Salvador; Géraldine Durand; Frédéric Mallard; Alain Theretz; Patrick Broyer; Sonia Chatellier; Gaspard Gervasi; Marc Van Nuenen; Carolyn Ann Roitsch; Alex Van Belkum; Jérôme Lemoine; François Vandenesch; Jean-Philippe Charrier
Journal:  Sci Rep       Date:  2015-09-09       Impact factor: 4.379

8.  ETE 3: Reconstruction, Analysis, and Visualization of Phylogenomic Data.

Authors:  Jaime Huerta-Cepas; François Serra; Peer Bork
Journal:  Mol Biol Evol       Date:  2016-02-26       Impact factor: 16.240

9.  ASTRAL-II: coalescent-based species tree estimation with many hundreds of taxa and thousands of genes.

Authors:  Siavash Mirarab; Tandy Warnow
Journal:  Bioinformatics       Date:  2015-06-15       Impact factor: 6.937

10.  A Novel Approach to Helicobacter pylori Pan-Genome Analysis for Identification of Genomic Islands.

Authors:  Ikuo Uchiyama; Jacob Albritton; Masaki Fukuyo; Kenji K Kojima; Koji Yahara; Ichizo Kobayashi
Journal:  PLoS One       Date:  2016-08-09       Impact factor: 3.240

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  1 in total

Review 1.  Methodological tools to study species of the genus Burkholderia.

Authors:  Viola Camilla Scoffone; Gabriele Trespidi; Giulia Barbieri; Samuele Irudal; Aygun Israyilova; Silvia Buroni
Journal:  Appl Microbiol Biotechnol       Date:  2021-11-10       Impact factor: 4.813

  1 in total

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