Misidentification of transthyretin and immunoglobulin variants by proteomics due to methyl lysine formation in formalin-fixed paraffin-embedded amyloid tissue

Dissecting the Molecular Features of Systemic Light Chain (AL) Amyloidosis: Contributions from Proteomics

Amyloidoses are characterized by aggregation of proteins into highly ordered amyloid fibrils, which deposit in the extracellular space of tissues, leading to organ dysfunction. In AL (amyloid light chain) amyloidosis, the most common form in Western countries, the amyloidogenic precursor is a misfolding-prone immunoglobulin light chain (LC), which, in the systemic form, is produced in excess by a plasma cell clone and transported to target organs though blood. Due to the primary role that proteins play in the pathogenesis of amyloidoses, mass spectrometry (MS)-based proteomic studies have gained an established position in the clinical management and research of these diseases. In AL amyloidosis, in particular, proteomics has provided important contributions for characterizing the precursor light chain, the composition of the amyloid deposits and the mechanisms of proteotoxicity in target organ cells and experimental models of disease. This review will provide an overview of the major achievements of proteomic studies in AL amyloidosis, with a presentation of the most recent acquisitions and a critical discussion of open issues and ongoing trends.

Clinical ApoA-IV amyloid is associated with fibrillogenic signal sequence

Apolipoprotein A‐IV amyloidosis is an uncommon form of the disease normally resulting in renal and cardiac dysfunction. ApoA‐IV amyloidosis was identified in 16 patients attending the National Amyloidosis Centre and in eight clinical samples received for histology review. Unexpectedly, proteomics identified the presence of ApoA‐IV signal sequence residues (p.18‐43 to p.20‐43) in 16/24 trypsin‐digested amyloid deposits but in only 1/266 non‐ApoA‐IV amyloid samples examined. These additional signal residues were also detected in the cardiac sample from the Swedish patient in which ApoA‐IV amyloid was first described, and in plasma from a single cardiac ApoA‐IV amyloidosis patient. The most common signal‐containing peptide observed in ApoA‐IV amyloid, p.20‐43, and to a far lesser extent the N‐terminal peptide, p.21‐43, were fibrillogenic in vitro at physiological pH, generating Congo red‐positive fibrils. The addition of a single signal‐derived alanine residue to the N‐terminus has resulted in markedly increased fibrillogenesis. If this effect translates to the mature circulating protein in vivo, then the presence of signal may result in preferential deposition as amyloid, perhaps acting as seed for the main circulating native form of the protein; it may also influence other ApoA‐IV‐associated pathologies. © 2021 The Authors. The Journal of Pathology published by John Wiley & Sons, Ltd. on behalf of The Pathological Society of Great Britain and Ireland.

Clinical Amyloid Typing by Proteomics: Performance Evaluation and Data Sharing Between Two Centres

Amyloidosis is a relatively rare human disease caused by the deposition of abnormal protein fibres in the extracellular space of various tissues, impairing their normal function. Proteomic analysis of patients’ biopsies, developed by Dogan and colleagues at the Mayo Clinic, has become crucial for clinical diagnosis and for identifying the amyloid type. Currently, the proteomic approach is routinely used at National Amyloidosis Centre (NAC, London, UK) and Istituto di Tecnologie Biomediche-Consiglio Nazionale delle Ricerche (ITB-CNR, Milan, Italy). Both centres are members of the European Proteomics Amyloid Network (EPAN), which was established with the aim of sharing and discussing best practice in the application of amyloid proteomics. One of the EPAN’s activities was to evaluate the quality and the confidence of the results achieved using different software and algorithms for protein identification. In this paper, we report the comparison of proteomics results obtained by sharing NAC proteomics data with the ITB-CNR centre. Mass spectrometric raw data were analysed using different software platforms including Mascot, Scaffold, Proteome Discoverer, Sequest and bespoke algorithms developed for an accurate and immediate amyloid protein identification. Our study showed a high concordance of the obtained results, suggesting a good accuracy of the different bioinformatics tools used in the respective centres. In conclusion, inter-centre data exchange is a worthwhile approach for testing and validating the performance of software platforms and the accuracy of results, and is particularly important where the proteomics data contribute to a clinical diagnosis.

Diagnostic amyloid proteomics: experience of the UK National Amyloidosis Centre

Systemic amyloidosis is a serious disease which is caused when normal circulating proteins misfold and aggregate extracellularly as insoluble fibrillary deposits throughout the body. This commonly results in cardiac, renal and neurological damage. The tissue target, progression and outcome of the disease depends on the type of protein forming the fibril deposit, and its correct identification is central to determining therapy. Proteomics is now used routinely in our centre to type amyloid; over the past 7 years we have examined over 2000 clinical samples. Proteomics results are linked directly to our patient database using a simple algorithm to automatically highlight the most likely amyloidogenic protein. Whilst the approach has proved very successful, we have encountered a number of challenges, including poor sample recovery, limited enzymatic digestion, the presence of multiple amyloidogenic proteins and the identification of pathogenic variants. Our proteomics procedures and approaches to resolving difficult issues are outlined.

Hydrogen-Deuterium Exchange and Hydroxyl Radical Footprinting for Mapping Hydrophobic Interactions of Human Bromodomain with a Small Molecule Inhibitor

Mass spectrometry (MS)-based protein footprinting, a valuable structural tool in mapping protein-ligand interaction, has been extensively applied to protein-protein complexes, showing success in mapping large interfaces. Here, we utilized an integrated footprinting strategy incorporating both hydrogen-deuterium exchange (HDX) and hydroxyl radical footprinting (i.e., fast photochemical oxidation of proteins (FPOP)) for molecular-level characterization of the interaction of human bromodomain-containing protein 4 (BRD4) with a hydrophobic benzodiazepine inhibitor. HDX does not provide strong evidence for the location of the binding interface, possibly because the shielding of solvent by the small molecule is not large. Instead, HDX suggests that BRD4 appears to be stabilized by showing a modest decrease in dynamics caused by binding. In contrast, FPOP points to a critical binding region in the hydrophobic cavity, also identified by crystallography, and, therefore, exhibits higher sensitivity than HDX in mapping the interaction of BRD4 with compound 1. In the absence or under low concentrations of the radical scavenger, FPOP modifications on Met residues show significant differences that reflect the minor change in protein conformation. This problem can be avoided by using a sufficient amount of proper scavenger, as suggested by the FPOP kinetics directed by a dosimeter of the hydroxyl radical.

The complementary role of histology and proteomics for diagnosis and typing of systemic amyloidosis

The tissue diagnosis of amyloidosis and confirmation of fibril protein type, which are crucial for clinical management, have traditionally relied on Congo red (CR) staining followed by immunohistochemistry (IHC) using fibril protein specific antibodies. However, amyloid IHC is qualitative, non-standardised, requires operator expertise, and not infrequently fails to produce definitive results. More recently, laser dissection mass spectrometry (LDMS) has been developed as an alternative method to characterise amyloid in tissue sections. We sought to compare these techniques in a real world setting. During 2017, we performed LDMS on 640 formalin-fixed biopsies containing amyloid (CR+ve) comprising all 320 cases that could not be typed by IHC (IHC-ve) and 320 randomly selected CR+ve samples that had been typed (IHC+ve). In addition, we studied 60 biopsies from patients in whom there was a strong suspicion of amyloidosis, but in whom histology was non-diagnostic (CR-ve). Comprehensive clinical assessments were conducted in 532 (76%) of cases. Among the 640 CR+ve samples, 602 (94%) contained ≥2 of 3 amyloid signature proteins (ASPs) on LDMS (ASP+ve) supporting the presence of amyloid. A total of 49 of the 60 CR-ve samples were ASP-ve; 7 of 11 that were ASP+ve were glomerular. The amyloid fibril protein was identified by LDMS in 255 of 320 (80%) of the IHC-ve samples and in a total of 545 of 640 (85%) cases overall. The LDMS and IHC techniques yielded discordant results in only 7 of 320 (2%) cases. CR histology and LDMS are corroborative for diagnosis of amyloid, but LDMS is superior to IHC for confirming amyloid type.

Proteomic Analysis for the Diagnosis of Fibrinogen Aα-chain Amyloidosis

Introduction

Hereditary fibrinogen Aα-chain (AFib) amyloidosis is a relatively uncommon renal disease associated with a small number of pathogenic fibrinogen Aα (FibA) variants; wild-type FibA normally does not result in amyloid deposition. Proteomics is now routinely used to identify the amyloid type in clinical samples, and we report here our algorithm for identification of FibA in amyloid.

Methods

Proteomics data from 1001 Congo red–positive patient samples were examined using the Mascot search engine to interrogate the Swiss-Prot database and generate protein identity scores. An algorithm was applied to identify FibA as the amyloid protein based on Mascot scores. FibA variants were identified by appending the known amyloidogenic variant sequences to the Swiss-Prot database.

Results

AFib amyloid was identified by proteomics in 64 renal samples based on the Mascot scores relative to other amyloid proteins, the presence of a pathogenic variant, and coverage of the p.449-621 sequence. Contamination by blood could be excluded from a comparison of the FibA score with that of the fibrinogen β and γ chains. The proteomics results were consistent with the clinical diagnosis. Four additional renal samples did not fulfill all the criteria using the algorithm but were adjudged as AFib amyloid based on a full assessment of the clinical and biochemical results.

Conclusion

AFib amyloid can be identified reliably in glomerular amyloid by proteomics using a score-based algorithm. Proteomics data should be used as a guide to AFib diagnosis, with the results considered together with all available clinical and laboratory information.