ABSTRACT
Phaeochromocytomas and paragangliomas (PPGLs) are rare neuroendocrine tumours with a hereditary background in over one-third of patients. Mutations in succinate dehydrogenase (SDH) genes increase the risk for PPGLs and several other tumours. Mutations in subunit B (SDHB) in particular are a risk factor for metastatic disease, further highlighting the importance of identifying SDHx mutations for patient management. Genetic variants of unknown significance, where implications for the patient and family members are unclear, are a problem for interpretation. For such cases, reliable methods for evaluating protein functionality are required. Immunohistochemistry for SDHB (SDHB-IHC) is the method of choice but does not assess functionality at the enzymatic level. Liquid chromatography-mass spectrometry-based measurements of metabolite precursors and products of enzymatic reactions provide an alternative method. Here, we compare SDHB-IHC with metabolite profiling in 189 tumours from 187 PPGL patients. Besides evaluating succinate:fumarate ratios (SFRs), machine learning algorithms were developed to establish predictive models for interpreting metabolite data. Metabolite profiling showed higher diagnostic specificity compared to SDHB-IHC (99.2% versus 92.5%, p = 0.021), whereas sensitivity was comparable. Application of machine learning algorithms to metabolite profiles improved predictive ability over that of the SFR, in particular for hard-to-interpret cases of head and neck paragangliomas (AUC 0.9821 versus 0.9613, p = 0.044). Importantly, the combination of metabolite profiling with SDHB-IHC has complementary utility, as SDHB-IHC correctly classified all but one of the false negatives from metabolite profiling strategies, while metabolite profiling correctly classified all but one of the false negatives/positives from SDHB-IHC. From 186 tumours with confirmed status of SDHx variant pathogenicity, the combination of the two methods resulted in 185 correct predictions, highlighting the benefits of both strategies for patient management. © 2020 The Authors. The Journal of Pathology published by John Wiley & Sons Ltd on behalf of Pathological Society of Great Britain and Ireland.
Subject(s)
Adrenal Gland Neoplasms/diagnosis , Head and Neck Neoplasms/diagnosis , Machine Learning , Metabolomics , Paraganglioma/diagnostic imaging , Pheochromocytoma/diagnosis , Succinate Dehydrogenase/genetics , Adrenal Gland Neoplasms/genetics , Adrenal Gland Neoplasms/pathology , Cohort Studies , Head and Neck Neoplasms/genetics , Head and Neck Neoplasms/pathology , Humans , Immunohistochemistry , Mutation , Paraganglioma/genetics , Paraganglioma/pathology , Pheochromocytoma/genetics , Pheochromocytoma/pathologyABSTRACT
A novel esterase, PpEst, that hydrolyses the co-aromatic-aliphatic polyester poly(1,4-butylene adipate-co-terephthalate) (PBAT) was identified by proteomic screening of the Pseudomonas pseudoalcaligenes secretome. PpEst was induced by the presence of PBAT in the growth media and had predicted arylesterase (EC 3.1.1.2) activity. PpEst showed polyesterase activity on both whole and milled PBAT film releasing terephthalic acid and 4-(4-hydroxybutoxycarbonyl)benzoic acid while end product inhibition by 4-(4-hydroxybutoxycarbonyl)benzoic acid was observed. Modelling of an aromatic polyester mimicking oligomer into the PpEst active site indicated that the binding pocket could be big enough to accommodate large polymers. This is the first report of a PBAT degrading enzyme being identified by proteomic screening and shows that this approach can contribute to the discovery of new polymer hydrolysing enzymes. Moreover, these results indicate that arylesterases could be an interesting enzyme class for identifications of polyesterases.
Subject(s)
Bacterial Proteins/chemistry , Biodegradable Plastics/metabolism , Carboxylic Ester Hydrolases/chemistry , Polyesters/metabolism , Pseudomonas pseudoalcaligenes/enzymology , Bacterial Proteins/genetics , Bacterial Proteins/metabolism , Biodegradable Plastics/chemistry , Carboxylic Ester Hydrolases/genetics , Carboxylic Ester Hydrolases/metabolism , Catalytic Domain , Gene Expression , Models, Molecular , Phthalic Acids/chemistry , Phthalic Acids/metabolism , Polyesters/chemistry , Protein Binding , Proteomics , Pseudomonas pseudoalcaligenes/geneticsABSTRACT
Enzyme catalyzed processes are increasingly complementing chemical manufacturing as new enzymes are being discovered. Although, many industrially applied biocatalysts have been identified by functional screenings technological advances in the omics fields have created a different path to access novelty. Here we describe how omics technologies, especially proteomics and transcriptomics, can complement each other in the aim of finding new enzymatic functions. Special emphasis is laid on how mRNA sequencing Zcan improve proteomic experiments by allowing the generation of high quality protein sequence databases, which subsequently facilitates protein identification.
