Whole-genome sequencing improves MS-based proteotyping of clinically-relevant bacteria

Francisco Salvà Serra

7th Congress of European Microbiologists

Valencia, Spain

12th July 2017

Department of Infectious DiseasesSahlgrenska AcademyUniversity of Gothenburg

MicrobiologyDepartment of Biology

University of the Balearic Islands

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Era of antibiotics

Overuse & missuse of antibiotics:

Review on Antimicrobial Resistance. Antimicrobial Resistance: Tackling a Crisis for the Health and Wealth of Nations. 2014

Annual deaths attributable to antibiotic resistance

Economic impact: 100 trillions (100 · 1012) USD

Rapid and better diagnostics of infectious diseases

Less antibiotic resistance

Reduce overuse & misuse

CRITICAL POINT

Database must have:• Species diversity (comprehensive)• Correct classifications

‘Proteotyping’: characterization, identification and diagnostics of microorganisms with MS-based proteomics.

Peptides

Genomes DB

All complete genomes from the NCBI

Reference Sequence Database (RefSeq)

Taxonomic

assignment

Expressed

proteins

LC-MS/MS

PROBLEM 1 - Insufficient and biased number of genomes

S. mitis

S. pneumoniae (major human pathogen)

Members of theStreptococcus mitis group

A single strain does not cover the whole repertoire of genes of a species!

PROBLEM 1 - Insufficient and biased number of genomes

Figure source: Soucy et al., 2015

We need several strains per species!

PROBLEM 2 – Misclassified genomes

Beaz-Hidalgo et al. (2015)Aeromonas:

Gomila et al. (2015)Pseudomonas:

PROBLEM 2 – Misclassified genomes

< 93% 93 - 96% ≥ 96%

Streptococcus pneumoniae 323 0 0 323

Streptococcus mitis 40 18 22 0

Streptococcus australis 0 0 0 0

Streptococcus cristatus 2 0 1 1

Streptococcus infantis 4 3 1 0

Streptococcus oligofermentans 7 7 0 0

Streptococcus oralis 11 7 4 0

Streptococcus parasanguinis 9 1 6 2

Streptococcus peroris 0 0 0 0

Streptococcus pseudopneumoniae 6 0 0 6

Streptococcus sanguinis 23 2 16 5

Streptococcus sinensis 0 0 0 0

Total 425 38 50 337

100% 8.94% 11.76% 79.29%

Total number of

genome sequences

ANIb

Organism

PROBLEM 2 – Misclassified genomes

< 93% 93 - 96% ≥ 96%

Streptococcus pneumoniae

Streptococcus mitis 40 18 22 0

Streptococcus australis 0 0 0 0

Streptococcus cristatus 2 0 1 1

Streptococcus infantis 4 3 1 0

Streptococcus oligofermentans 7 7 0 0

Streptococcus oralis 11 7 4 0

Streptococcus parasanguinis 9 1 6 2

Streptococcus peroris 0 0 0 0

Streptococcus pseudopneumoniae 6 0 0 6

Streptococcus sanguinis 23 2 16 5

Streptococcus sinensis 0 0 0 0

Total 102 38 50 14

100% 37.25% 49.02% 13.73%

Organism

Total number of

genome sequences

ANIb

Consequences PROBLEM 1 Insufficient and biased number of genomes

S. mitisS. pseudopneumoniae

S. pneumoniae

REALITY

S. mitisS. pseudopneumoniae

S. pneumoniae

DATABASE

Species poorly represented or not present: Loss of peptide hits

Consequences of PROBLEM 2 – Missclassified genomes

S. mitisS. pneumoniae

What happens if a genome of S. mitis is missclassified as S. pneumoniae?

Many peptides that in reality are discriminative for S. mitis, willnow be classified as “shared” with S. pneumoniae

Solution

Genome DB

Additional genomes from

GenBank

In-house whole-genome

sequencing

Taxonomical verification

(ANIb, MLSA, core-genome)

Improvement

2015-02 2016-09

Organism Before addition of reference genomes After addition of reference genomes

Streptococcus pneumoniae 27 (0) 31(1)

Streptococcus pseudopneumoniae 1 (0) 6 (1)Streptococcus mitis 1 (0) 30 (1)

Type strains included

Results of proteotyping:species discriminatory (species unique) peptide matches

Matches to correct species

Matches to other species

356 291 244

BEFORE

223 327 381

AFTER

> 700 public genome

sequences analysed

> 100 in-house sequenced

genomes

More comprehensive

database

Discriminatory peptides

(pathogen biomarkers)

Higher accuracyHigher sensitivity:

105 → 103 - 102 cells/ml

Results

Goal of proteotyping

Proteotyping analysis

IDExpressed antibiotic resistance

Expressed virulence

factors

PROTEOTYPINGDirect analysis of clinical samples – Benefits and features

Proteotyping depends on having a high quality

genome database

Conclusions

Several genomes of each species are necessary

More effort should be put on sequencing more

species diversity, including type strains!

Databases must be curated!

Hedvig Engström Jakobsson, Roger Karlsson, Lucia Gonzales Siles, Daniel

Jaén Luchoro, Shora Yazdan, Beatriz Piñeiro, Omar AL-Bayati, Sebastian

Feine, Edward Moore

Acknowledgements

Susann Skovbjerg, Per Sikora, Erika Tång Hallbäck, Christina Åhrén,

Nahid Karami, Liselott Svensson & ”the CCUG ladies”

Margarita Gomila, Antonio Busquets, Francisco Aliaga Lozano, Antoni

Bennasar Figueras

Anna Johnning, Erik Kristiansson

Fredrik Boulund, Kaisa Thorell, Lars Engstrand

Anders Karlsson

¡Muchas gracias!Questions?

Supplementaryslides

Core-genome-based tree of theStreptococcus mitis group

Taxonomical verification

LC-MS/MS

Match peptides to

sequences

(BLAT)Taxonomic

assignment /

antibiotic

resistance genes

Match

Mass spectra to

peptides

(X!Tandem)

GENOME DBNCBI Taxonomy

DBPEPTIDE DB

ANIb

(JSpecies)

Core genome-based

cluster analysis

Genome sources

All complete genomes from RefSeq

Additional genomes from GenBank

In-house sequenced CCUG strains

Trypsin digestion of proteins

‘Proteotyping’ – Workflow (Open approach)

AMR genes

Proteotyping pipeline (TCUP, Boulund et al. 2017)

LC-MS/MSSearch of biomarkers (lists of 50

peptides)

Trypsin digestion of proteins

‘Proteotyping’ – Workflow (Targeted approach)

Advantages• From 105 a 103 – 102 cells/ml

Disadvantages• Peptide biomarker lists needs to be created.• Do not detect other peptides

GOOD BIOMARKER• Species-specific• Present in all the strains• Always expressed and detected

http://nanoxisconsulting.com/services-2/lpi-technology-2.html

LPI FlowCell

Minimal ancestry principle

Genus

Species

Family

Different species (< 93%) Same species (> 96%)(Rosselló-Móra and Amann, 2015)

Undefined (93 – 96%)

> 96% = Same species93 – 96% = Species boundary< 93% = Different species

ANI value between two genomes

Average Nucleotide Identity