Cátia F. Marques

Accurate molecular typing of amyloid deposits is essential because morphologically similar deposits arise from clinically distinct precursor proteins that require different investigations and treatment. Mass spectrometry (MS)-based proteomics has become a reference approach for amyloid typing, but reliable diagnosis depends on the entire tissue-to-report workflow rather than on instrumentation alone. This review examines specimen selection and fixation, Congo red-guided enrichment and laser microdissection, protein extraction and digestion, LC–MS/MS acquisition, database searching, protein inference, precursor assignment, diagnostic ambiguity, quality control, reporting and clinical implementation. Established evidence supports bottom-up LC–MS/MS, particularly on enriched formalin-fixed paraffin-embedded tissue, for identifying common, hereditary, rare and unexpected amyloid precursors. However, inadequate sampling, low peptide recovery, incomplete sequence representation, competing amyloidogenic proteins and inappropriate interpretation can produce inconclusive or misleading results despite technically successful MS acquisition. Emerging strategies, including data-independent acquisition, targeted proteomics, variant-aware and patient-specific sequence analysis, spatial proteomics and machine-learning-supported interpretation, are promising but remain variably validated. The principal challenge is therefore shifting from demonstrating that MS can identify amyloid precursors to ensuring reproducible, transparent and clinically integrated typing across specimens, laboratories and health-care systems.

1,8-Dinitropyrene (1,8-DNP) is a diesel exhaust constituent classified as a possible human carcinogen (Group 2B) by the International Agency for Research on Cancer. Its mutagenic properties can be attributed in part through the formation of covalent DNA adducts that result from mononitroreduction (e.g., N-(deoxyguanosin-8-yl)-1-amino-8-nitropyrene). Recombinant aldo-keto reductases (AKRs) 1C1-1C3 catalyze the nitroreduction of 1,8-DNP, 1-nitropyrene, and 3-nitrobenzanthrone. Although AKR1C1-1C3 are induced by nuclear factor erythroid 2-related factor 2 (NRF2), the contribution of NRF2 toward the nitroreduction of 1,8-DNP in human lung cells is currently unknown. We used highly sensitive and specific in-cell fluorescence assays to examine the ability of human lung A549 and HBEC3-KT cells to metabolize 1,8-DNP to yield 1-amino-8-nitropyrene (1,8-ANP) and 1,8-DNP to yield 1,8-diaminopyrene (1,8-DAP) via mono- and bis-nitroreduction, respectively. A549 cells generated both 1,8-ANP and 1,8-DAP from 1,8-DNP. By contrast, HBEC3-KT cells formed 1,8-ANP, but essentially no 1,8-DAP, from 1,8-DNP. We used genetic and pharmacological approaches to investigate the dependence of 1,8-DNP nitroreduction on AKR1C1-1C3 and NRF2. A549 cells with homozygous NFE2L2/NRF2 knockout did not exhibit decreased 1,8-ANP formation but showed decreased 1,8-DAP formation, indicating that the second but not the first nitroreduction step was NRF2-dependent. Treatment of HBEC3-KT cells with NRF2 activators (R-sulforaphane (SFN) or 1-(2-cyano-3,12,28-trioxooleana-1,9(11)-dien-28-yl)-1H-imidazole (CDDO-Im) did not increase the mononitroreduction of 1,8-DNP to 1,8-ANP but increased the conversion of 1,8-ANP to 1,8-DAP consistent with the second step requiring inducible NRF2. AKR1C isoform specific inhibitors showed that these enzymes accounted for the majority of 1,8-ANP and 1,8-DAP formation in both cell lines. The ability of A549 NFE2L2/NRF2 knockout cells to still form 1,8-ANP coupled with their lack of AKR1C isoform expression indicated that a new nitroreductase was expressed as an adaptive response to NRF2 loss. We find that this nitroreductase is not NQO1, thioredoxin reductase, xanthine oxidase, or NADPH-P450 oxidoreductase.

Hyperandrogenism is associated with polycystic ovary syndrome (PCOS), acne, and alopecia. In PCOS, subcutaneous fat has been shown to contribute to hyperandrogenism through increased testosterone (T) production which is accompanied by an increase in the intra-adipose 5α-dihydrotestosterone (5α-DHT):T ratio. However, whether 5α-DHT is produced in isolated adipocytes is uncertain. Here we investigated the ability of subcutaneous human adipocytes to synthesize and inactivate 5α-DHT in a model of subcutaneous white adipocytes, Simpson-Golabi-Behmel syndrome (SGBS) cells, and primary adipocytes. We quantified the transcripts of genes involved in the biosynthesis of 5α-DHT (AKR1C3, SRD5A1, SRD5A2, and HSD17B6) and the inactivation of 5α-DHT (AKR1C1 and AKR1C2). We found that genes that inactivate 5α-DHT were more abundantly transcribed than genes that biosynthesize 5α-DHT. This trend was reflected by radioisotope tracing. We developed a radiochromatographic method involving high-performance liquid chromatography and in-line detection of radioactive analytes with precision and accuracy within the 15% tolerance allowable by the US Food and Drug Administration criteria for analytical assays. The lower limit of detection and quantification for 5α-DHT was 3.4 pg and 15 pg, respectively. The formation of 5α-DHT was barely detectable when starting with either 10 nM T or 3α-androstanediol (3α-diol). Conversely, 5α-DHT was rapidly metabolized to 3α-diol but not 3β-diol. 3α-Diol was the major metabolite despite comparable levels of AKR1C1 and AKR1C2 transcripts. The same result was observed in both cell lines. Our data reveal that adipocytes do not biosynthesize 5α-DHT from testosterone. By contrast, 5α-DHT is rapidly metabolized by AKR1C2 in subcutaneous adipocytes.

Untargeted metabolomics approaches require complex samples containing the endogenous metabolites of a biological system. Here, we describe a set of protocols that can be applied to various types of samples, including prokaryotic and eukaryotic cells, as well as animal and human samples. Following a single extraction step, samples are analysed using different chromatographic conditions coupled to high-resolution mass spectrometry. Quantification of metabolite changes between samples is performed without internal standards, using peak areas from extracted ion chromatograms for statistical analysis. Bioinformatics annotation of the results allows a pathway- and process-oriented analysis across biological sample conditions, allowing a complete pathway interrogation.

Background and purpose: Montelukast, an antagonist of the cysteinyl leukotrienes receptor 1, has been proposed for repurposing for the treatment of neurodegenerative disorders, including Alzheimer’s disease. Clinical trials are ongoing but the mechanisms supporting this repurposing are still poorly understood. Experimental Approach: Taking advantage of proteomics datasets deposited in public repositories, data from mouse brain and a neuronal chicken model exposed to the drug were reinterpreted in view of the repurposing proposal. Key Results: Montelukast increases the levels of presenilin 1, nicastrin, neprilysin, and insulin-degrading enzyme, all of which are involved in the amyloid aggregation and clearance processes. Hexokinase 1, malate and isocitrate dehydrogenate enzymes, from central metabolism pathways, are also affected. Conclusions and Implications: The data suggest that montelukast is a modulator of the amyloid clearance process, favouring the removal of aggregates and counterbalancing the overall amyloidogenic process. Montelukast also acts on energy supply pathways, compensating the ageing-associated decrease of the basal cell metabolism. Taken together, these actions of montelukast clearly support its repurposing as a candidate for Alzheimer’s disease management.

Increasing environmental distress is associated with a growing asthma incidence; no treatments are available but montelukast (MTK)—an antagonist of the cysteinyl leukotrienes receptor 1—is widely used in the management of symptoms among adults and children. Recently, new molecular targets have been identified and MTK has been proposed for repurposing in other therapeutic applications, with several ongoing clinical trials. The proposed applications include neuroinflammation control, which could be explored in some neurodegenerative disorders, such as Alzheimer’s and Parkinson’s diseases (AD and PD). However, this drug has been associated with an increasing number of reported neuropsychiatric adverse drug reactions (ADRs). Besides, and despite being on the market since 1998, MTK metabolism is still poorly understood and the mechanisms underlying neuropsychiatric ADRs remain unknown. We review the role of MTK as a modulator of leukotriene pathways and systematize the current knowledge about MTK metabolism. Known toxic effects of MTK are discussed, and repurposing applications are presented comprehensively, with a focus on AD and PD.