Optimization of a Top-Down Proteomics Platform for Closely Related Pathogenic Bacterial Discrimination

Dupré, Mathieu; Duchateau, Magalie; Malosse, Christian; Lima, Diogo Borges; Calvaresi, Valeria; Podglajen, Isabelle; Clermont, Dominique; Rey, Martial; Chamot‐Rooke, Julia

doi:10.1021/acs.jproteome.0c00351

Cited by 30 publications

(47 citation statements)

References 39 publications

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“…1,2 ). Proteoforms have high phenotypic heterogeneity in different biological systems, and proteoform level information can provide important insights into biochemical function or disease phenotypes [3][4][5][6][7] . TDP as the method of the choice to study proteoforms has thus become important for many biomedical applications 8,9 .…”

Section: Mainmentioning

confidence: 99%

FLASHIda: Intelligent data acquisition for top-down proteomics that doubles proteoform level identification count

Jeong

Babović

Gorshkov

et al. 2021

Preprint

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Top-down proteomics (TDP) has gained a lot of interest in biomedical application for detailed analysis and structural characterization of proteoforms. Data-dependent acquisition (DDA) of intact proteins is non-trivial due to the diversity and complex signal of proteoforms. Dedicated acquisition methods thus have the potential to greatly improve TDP. We present FLASHIda, an intelligent online data acquisition algorithm for TDP that ensures the real-time selection of high-quality precursors of diverse proteoforms. FLASHIda combines fast charge deconvolution algorithms and machine learning-based quality assessment for optimal precursor selection. In analysis in E. coli lysates, FLASHIda increased the number of unique proteoform level identifications from 800 to 1,500, or generated a near-identical number of identifications in ⅓ of instrument time when compared to standard DDA mode. Furthermore, FLASHIda enabled sensitive mapping of post translational modifications and detection of chemical adducts. As an extension module to the instrument, FLASHIda can be readily adopted for TDP studies of complex samples to enhance proteoform identification rates.

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Section: Mainmentioning

confidence: 99%

FLASHIda: Intelligent data acquisition for top-down proteomics that doubles proteoform level identification count

Jeong

Babović

Gorshkov

et al. 2021

Preprint

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“…Recent work by Chamot-Rooke and co-workers demonstrated identification of >200 proteins and >500 proteoforms by LC TDP. 18 …”

Section: Introductionmentioning

confidence: 99%

Liquid Extraction Surface Analysis Mass Spectrometry of ESKAPE Pathogens

Havlíková

May

Styles

et al. 2021

J. Am. Soc. Mass Spectrom.

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The ESKAPE pathogens ( Enterococcus faecium , Staphylococcus aureus , Klebsiella pneumoniae , Acinetobacter baumannii , Pseudomonas aeruginosa , and Enterobacter cloacae ) represent clinically important bacterial species that are responsible for most hospital-acquired drug-resistant infections; hence, the need for rapid identification is of high importance. Previous work has demonstrated the suitability of liquid extraction surface analysis mass spectrometry (LESA MS) for the direct analysis of colonies of two of the ESKAPE pathogens ( Staphylococcus aureus and Pseudomonas aeruginosa ) growing on agar. Here, we apply LESA MS to the remaining four ESKAPE species ( E. faecium E745, K. pneumoniae KP257, A. baumannii AYE, and E. cloacae S11) as well as E. faecalis V583 (a close relative of E. faecium ) and a clinical isolate of A. baumannii AC02 using an optimized solvent sampling system. In each case, top-down LESA MS/MS was employed for protein identification. In total, 24 proteins were identified from 37 MS/MS spectra by searching against protein databases for the individual species. The MS/MS spectra for the identified proteins were subsequently searched against multiple databases from multiple species in an automated data analysis workflow with a view to determining the accuracy of identification of unknowns. Out of 24 proteins, 19 were correctly assigned at the protein and species level, corresponding to an identification success rate of 79%.

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“…The latter, however, is challenging because of the absence of the sequence under study in the databases usually employed in mass spectrometry (MS)-based proteomics 39 . In order to investigate the link between light chain sequence and aggregation behaviour, we have developed a complete de novo protein sequencing workflow based on a combination of top-down 40 and bottom-up proteomics with specific data analysis for patient-derived light chains. The details of this workflow are published separately 41 .…”

Section: Introductionmentioning

confidence: 99%

Universal amyloidogenicity of patient-derived immunoglobulin light chains

Sternke-Hoffmann

Pauly

Norrild

et al. 2021

Preprint

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The deposition of immunoglobulin light chains (IgLCs) in the form of amorphous aggregates or amyloid fibrils in different tissues of patients can lead to severe and potentially fatal organ damage, requiring transplantation in some cases. There has been great interest in recent years to elucidate the origin of the very different in vivo solubilities of IgLCs, as well as the molecular determinants that drive either the formation of ordered amyloid fibrils or disordered amorphous aggregates. It is commonly thought that the reason of this differential aggregation behaviour is to be found in the amino acid sequences of the respective IgLCs, i.e. that some sequences display higher intrinsic tendencies to form amyloid fibrils. Here we perform in depth Thermodynamic and Aggregation Fingerprinting (ThAgg-Fip) of 9 multiple myeloma patient-derived IgLCs, the amino acid sequences of all of which we have solved by de novo protein sequencing with mass spectrometry. The latter technique was also used for one IgLc from a patient with AL amyloidosis. We find that all samples also contain proteases that fragment the proteins under physiologically relevant mildly acidic pH conditions, leading to amyloid fibril formation in all cases. Our results suggest that while every pathogenic IgLC has a unique ThAgg fingerprint, all sequences have comparable amyloidogenic potential. Therefore, extrinsic factors, in particular presence of, and susceptibility to, proteolytic cleavage is likely to be a strong determinant of in vivo aggregation behaviour. The important conclusion, which is corroborated by systematic analysis of our sequences, as well as many sequences of IgLCs from amyloidosis patients reported in the literature, challenges the current paradigm of the link between sequence and amyloid fibril formation of pathogenic light chains.

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Optimization of a Top-Down Proteomics Platform for Closely Related Pathogenic Bacterial Discrimination

Cited by 30 publications

References 39 publications

FLASHIda: Intelligent data acquisition for top-down proteomics that doubles proteoform level identification count

FLASHIda: Intelligent data acquisition for top-down proteomics that doubles proteoform level identification count

Liquid Extraction Surface Analysis Mass Spectrometry of ESKAPE Pathogens

Universal amyloidogenicity of patient-derived immunoglobulin light chains

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