Ambroxol for the Treatment of Patients With Parkinson Disease With and Without Glucocerebrosidase Gene Mutations: A Nonrandomized, Noncontrolled Trial

Importance: Mutations of the glucocerebrosidase gene, GBA1 (OMIM 606463), are the most important risk factor for Parkinson disease (PD). In vitro and in vivo studies have reported that ambroxol increases β-glucocerebrosidase (GCase) enzyme activity and reduces α-synuclein levels. These observations support a potential role for ambroxol therapy in modifying a relevant pathogenetic pathway in PD. Objective: To assess safety, tolerability, cerebrospinal fluid (CSF) penetration, and target engagement of ambroxol therapy with GCase in patients with PD with and without GBA1 mutations. / Interventions: An escalating dose of oral ambroxol to 1.26 g per day. Design, Setting, and Participants: This single-center open-label noncontrolled clinical trial was conducted between January 11, 2017, and April 25, 2018, at the Leonard Wolfson Experimental Neuroscience Centre, a dedicated clinical research facility and part of the University College London Queen Square Institute of Neurology in London, United Kingdom. Participants were recruited from established databases at the Royal Free London Hospital and National Hospital for Neurology and Neurosurgery in London. Twenty-four patients with moderate PD were evaluated for eligibility, and 23 entered the study. Of those, 18 patients completed the study; 1 patient was excluded (failed lumbar puncture), and 4 patients withdrew (predominantly lumbar puncture-related complications). All data analyses were performed from November 1 to December 14, 2018. / Main Outcomes and Measures: Primary outcomes at 186 days were the detection of ambroxol in the CSF and a change in CSF GCase activity. / Results: Of the 18 participants (15 men [83.3%]; mean [SD] age, 60.2 [9.7] years) who completed the study, 17 (8 with GBA1 mutations and 9 without GBA1 mutations) were included in the primary analysis. Between days 0 and 186, a 156-ng/mL increase in the level of ambroxol in CSF (lower 95% confidence limit, 129 ng/mL; P < .001) was observed. The CSF GCase activity decreased by 19% (0.059 nmol/mL per hour; 95% CI, -0.115 to -0.002; P = .04). The ambroxol therapy was well tolerated, with no serious adverse events. An increase of 50 pg/mL (13%) in the CSF α-synuclein concentration (95% CI, 14-87; P = .01) and an increase of 88 ng/mol (35%) in the CSF GCase protein levels (95% CI, 40-137; P = .002) were observed. Mean (SD) scores on part 3 of the Movement Disorders Society Unified Parkinson Disease Rating Scale decreased (ie, improved) by 6.8 (7.1) points (95% CI, -10.4 to -3.1; P = .001). These changes were observed in patients with and without GBA1 mutations. / Conclusions and Relevance: The study results suggest that ambroxol therapy was safe and well tolerated; CSF penetration and target engagement of ambroxol were achieved, and CSF α-synuclein levels were increased. Placebo-controlled clinical trials are needed to examine whether ambroxol therapy is associated with changes in the natural progression of PD. Trial Registration: ClinicalTrials.gov identifier: NCT02941822; EudraCT identifier: 2015-002571-24

Finding correspondence between metabolomic features in untargeted liquid chromatography-mass spectrometry metabolomics datasets.

Integration of multiple datasets can greatly enhance bioanalytical studies, for example, by increasing power to discover and validate biomarkers. In liquid chromatography-mass spectrometry (LC-MS) metabolomics, it is especially hard to combine untargeted datasets since the majority of metabolomic features are not annotated and thus cannot be matched by chemical identity. Typically, the information available for each feature is retention time (RT), mass-to-charge ratio (m/z), and feature intensity (FI). Pairs of features from the same metabolite in separate datasets can exhibit small but significant differences, making matching very challenging. Current methods to address this issue are too simple or rely on assumptions that cannot be met in all cases. We present a method to find feature correspondence between two similar LC-MS metabolomics experiments or batches using only the features' RT, m/z, and FI. We demonstrate the method on both real and synthetic datasets, using six orthogonal validation strategies to gauge the matching quality. In our main example, 4953 features were uniquely matched, of which 585 (96.8%) of 604 manually annotated features were correct. In a second example, 2324 features could be uniquely matched, with 79 (90.8%) out of 87 annotated features correctly matched. Most of the missed annotated matches are between features that behave very differently from modeled inter-dataset shifts of RT, MZ, and FI. In a third example with simulated data with 4755 features per dataset, 99.6% of the matches were correct. Finally, the results of matching three other dataset pairs using our method are compared with a published alternative method, metabCombiner, showing the advantages of our approach. The method can be applied using M2S (Match 2 Sets), a free, open-source MATLAB toolbox, available at https://github.com/rjdossan/M2S

Finding Correspondence between Metabolomic Features in Untargeted Liquid Chromatography-Mass Spectrometry Metabolomics Datasets

Integration of multiple datasets can greatly enhance bioanalytical studies, for example, by increasing power to discover and validate biomarkers. In liquid chromatography-mass spectrometry (LC-MS) metabolomics, it is especially hard to combine untargeted datasets since the majority of metabolomic features are not annotated and thus cannot be matched by chemical identity. Typically, the information available for each feature is retention time (RT), mass-to-charge ratio (m/z), and feature intensity (FI). Pairs of features from the same metabolite in separate datasets can exhibit small but significant differences, making matching very challenging. Current methods to address this issue are too simple or rely on assumptions that cannot be met in all cases. We present a method to find feature correspondence between two similar LC-MS metabolomics experiments or batches using only the features' RT, m/z, and FI. We demonstrate the method on both real and synthetic datasets, using six orthogonal validation strategies to gauge the matching quality. In our main example, 4953 features were uniquely matched, of which 585 (96.8%) of 604 manually annotated features were correct. In a second example, 2324 features could be uniquely matched, with 79 (90.8%) out of 87 annotated features correctly matched. Most of the missed annotated matches are between features that behave very differently from modeled inter-dataset shifts of RT, MZ, and FI. In a third example with simulated data with 4755 features per dataset, 99.6% of the matches were correct. Finally, the results of matching three other dataset pairs using our method are compared with a published alternative method, metabCombiner, showing the advantages of our approach. The method can be applied using M2S (Match 2 Sets), a free, open-source MATLAB toolbox, available at https://github.com/rjdossan/M2S

Operation Moonshot: rapid translation of a SARS-CoV-2 targeted peptide immunoaffinity liquid chromatography-tandem mass spectrometry test from research into routine clinical use

OBJECTIVES: During 2020, the UK's Department of Health and Social Care (DHSC) established the Moonshot programme to fund various diagnostic approaches for the detection of SARS-CoV-2, the pathogen behind the COVID-19 pandemic. Mass spectrometry was one of the technologies proposed to increase testing capacity. METHODS: Moonshot funded a multi-phase development programme, bringing together experts from academia, industry and the NHS to develop a state-of-the-art targeted protein assay utilising enrichment and liquid chromatography tandem mass spectrometry (LC-MS/MS) to capture and detect low levels of tryptic peptides derived from SARS-CoV-2 virus. The assay relies on detection of target peptides, ADETQALPQRK (ADE) and AYNVTQAFGR (AYN), derived from the nucleocapsid protein of SARS-CoV-2, measurement of which allowed the specific, sensitive, and robust detection of the virus from nasopharyngeal (NP) swabs. The diagnostic sensitivity and specificity of LC-MS/MS was compared with reverse transcription quantitative real-time polymerase chain reaction (RT-qPCR) via a prospective study. RESULTS: Analysis of NP swabs (n=361) with a median RT-qPCR quantification cycle (Cq) of 27 (range 16.7-39.1) demonstrated diagnostic sensitivity of 92.4% (87.4-95.5), specificity of 97.4% (94.0-98.9) and near total concordance with RT-qPCR (Cohen's Kappa 0.90). Excluding Cq>32 samples, sensitivity was 97.9% (94.1-99.3), specificity 97.4% (94.0-98.9) and Cohen's Kappa 0.95. CONCLUSIONS: This unique collaboration between academia, industry and the NHS enabled development, translation, and validation of a SARS-CoV-2 method in NP swabs to be achieved in 5 months. This pilot provides a model and pipeline for future accelerated development and implementation of LC-MS/MS protein/peptide assays into the routine clinical laboratory

Metabolite and lipoprotein profiles reveal sex-related oxidative stress imbalance in de novo drug-naive Parkinson's disease patients

Parkinson’s disease (PD) is the neurological disorder showing the greatest rise in prevalence from 1990 to 2016. Despite clinical definition criteria and a tremendous effort to develop objective biomarkers, precise diagnosis of PD is still unavailable at early stage. In recent years, an increasing number of studies have used omic methods to unveil the molecular basis of PD, providing a detailed characterization of potentially pathological alterations in various biological specimens. Metabolomics could provide useful insights to deepen our knowledge of PD aetiopathogenesis, to identify signatures that distinguish groups of patients and uncover responsive biomarkers of PD that may be significant in early detection and in tracking the disease progression and drug treatment efficacy. The present work is the first large metabolomic study based on nuclear magnetic resonance (NMR) with an independent validation cohort aiming at the serum characterization of de novo drug-naive PD patients. Here, NMR is applied to sera from large training and independent validation cohorts of German subjects. Multivariate and univariate approaches are used to infer metabolic differences that characterize the metabolite and the lipoprotein profiles of newly diagnosed de novo drug-naive PD patients also in relation to the biological sex of the subjects in the study, evidencing a more pronounced fingerprint of the pathology in male patients. The presence of a validation cohort allowed us to confirm altered levels of acetone and cholesterol in male PD patients. By comparing the metabolites and lipoproteins levels among de novo drug-naive PD patients, age- and sex-matched healthy controls, and a group of advanced PD patients, we detected several descriptors of stronger oxidative stress

Early downregulation of hsa-miR-144-3p in serum from drug-naïve Parkinson’s disease patients

Advanced age represents one of the major risk factors for Parkinson’s Disease. Recent biomedical studies posit a role for microRNAs, also known to be remodelled during ageing. However, the relationship between microRNA remodelling and ageing in Parkinson’s Disease, has not been fully elucidated. Therefore, the aim of the present study is to unravel the relevance of microRNAs as biomarkers of Parkinson’s Disease within the ageing framework. We employed Next Generation Sequencing to profile serum microRNAs from samples informative for Parkinson’s Disease (recently diagnosed, drug-naïve) and healthy ageing (centenarians) plus healthy controls, age-matched with Parkinson’s Disease patients. Potential microRNA candidates markers, emerging from the combination of differential expression and network analyses, were further validated in an independent cohort including both drug-naïve and advanced Parkinson’s Disease patients, and healthy siblings of Parkinson’s Disease patients at higher genetic risk for developing the disease. While we did not find evidences of microRNAs co-regulated in Parkinson’s Disease and ageing, we report that hsa-miR-144-3p is consistently down-regulated in early Parkinson’s Disease patients. Moreover, interestingly, functional analysis revealed that hsa-miR-144-3p is involved in the regulation of coagulation, a process known to be altered in Parkinson’s Disease. Our results consistently show the down-regulation of hsa-mir144-3p in early Parkinson’s Disease, robustly confirmed across a variety of analytical and experimental analyses. These promising results ask for further research to unveil the functional details of the involvement of hsa-mir144-3p in Parkinson’s Disease

‘The long tail of Covid-19’ - The detection of a prolonged inflammatory response after a SARS-CoV-2 infection in asymptomatic and mildly affected patients [version 2; peer review: 2 approved]

‘Long Covid’, or medical complications associated with post SARS-CoV2 infection, is a significant post-viral complication that is being more and more commonly reported in patients. Therefore, there is an increasing need to understand the disease mechanisms, identify drug targets and inflammatory processes associated with a SARS-CoV-2 infection. To address this need, we created a targeted mass spectrometry based multiplexed panel of 96 immune response associated proteins. We applied the multiplex assay to a cohort of serum samples from asymptomatic and moderately affected patients. All patients had tested positive for a SARS-CoV-2 infection by PCR and were determined to be subsequently positive for antibodies. Even 40- 60 days post-viral infection, we observed a significant remaining inflammatory response in all patients. Proteins that were still affected were associated with the anti-inflammatory response and mitochondrial stress. This indicates that biochemical and inflammatory pathways within the body can remain perturbed long after SARS-CoV-2 infections have subsided even in asymptomatic and moderately affected patients

'The long tail of Covid-19' - The detection of a prolonged inflammatory response after a SARS-CoV-2 infection in asymptomatic and mildly affected patients [version 1; peer review: awaiting peer review]

‘Long Covid’, or medical complications associated with post SARS-CoV-2 infection, is a significant post-viral complication that is being more and more commonly reported in patients. Therefore, there is an increasing need to understand the disease mechanisms, identify drug targets and inflammatory processes associated with a SARS-CoV-2 infection. To address this need, we created a targeted mass spectrometry based multiplexed panel of 96 immune response associated proteins. We applied the multiplex assay to a cohort of serum samples from asymptomatic and moderately affected patients. All patients had tested positive for a SARS-CoV-2 infection by PCR and were determined to be subsequently positive for antibodies. Even 40-60 days post-viral infection, we observed a significant remaining inflammatory response in all patients. Proteins that were still affected were associated with the anti-inflammatory response and mitochondrial stress. This indicates that biochemical and inflammatory pathways within the body can remain perturbed long after SARS-CoV-2 infections have subsided even in asymptomatic and moderately affected patients

Rapid, proteomic urine assay for monitoring progressive organ disease in Fabry disease

BACKGROUND: Fabry disease is a progressive multisystemic disease, which affects the kidney and cardiovascular systems. Various treatments exist but decisions on how and when to treat are contentious. The current marker for monitoring treatment is plasma globotriaosylsphingosine (lyso-Gb3), but it is not informative about the underlying and developing disease pathology. METHODS: We have created a urine proteomic assay containing a panel of biomarkers designed to measure disease-related pathology which include the inflammatory system, lysosome, heart, kidney, endothelium and cardiovascular system. Using a targeted proteomic-based approach, a series of 40 proteins for organ systems affected in Fabry disease were multiplexed into a single 10 min multiple reaction monitoring Liquid Chromatography Tandem Mass Spectrometry (LC-MS/MS) assay and using only 1 mL of urine. RESULTS: Six urinary proteins were elevated in the early-stage/asymptomatic Fabry group compared with controls including albumin, uromodulin, α1-antitrypsin, glycogen phosphorylase brain form, endothelial protein receptor C and intracellular adhesion molecule 1. Albumin demonstrated an increase in urine and could indicate presymptomatic disease. The only protein elevated in the early-stage/asymptomatic patients that continued to increase with progressive multiorgan involvement was glycogen phosphorylase brain form. Podocalyxin, fibroblast growth factor 23, cubulin and Alpha-1-Microglobulin/Bikunin Precursor (AMBP) were elevated only in disease groups involving kidney disease. Nephrin, a podocyte-specific protein, was elevated in all symptomatic groups. Prosaposin was increased in all symptomatic groups and showed greater specificity (p<0.025-0.0002) according to disease severity. CONCLUSION: This work indicates that protein biomarkers could be helpful and used in conjunction with plasma lyso-Gb3 for monitoring of therapy or disease progression in patients with Fabry disease