P1.49 Mass spectrometry based clinical proteomics for biomarker discovery in Duchenne muscular dystrophy

2011 ◽  
Vol 21 (9-10) ◽  
pp. 656
Author(s):  
F.C. Martin ◽  
S. Oonk ◽  
P.A.C. ’t Hoen ◽  
V.D. Nadarajah ◽  
A. Chaouch ◽  
...  
2017 ◽  
Vol 12 (2) ◽  
pp. 1700071 ◽  
Author(s):  
Stephanie J. Carr ◽  
René P. Zahedi ◽  
Hanns Lochmüller ◽  
Andreas Roos

2015 ◽  
Vol 112 (23) ◽  
pp. 7153-7158 ◽  
Author(s):  
Yetrib Hathout ◽  
Edward Brody ◽  
Paula R. Clemens ◽  
Linda Cripe ◽  
Robert Kirk DeLisle ◽  
...  

Serum biomarkers in Duchenne muscular dystrophy (DMD) may provide deeper insights into disease pathogenesis, suggest new therapeutic approaches, serve as acute read-outs of drug effects, and be useful as surrogate outcome measures to predict later clinical benefit. In this study a large-scale biomarker discovery was performed on serum samples from patients with DMD and age-matched healthy volunteers using a modified aptamer-based proteomics technology. Levels of 1,125 proteins were quantified in serum samples from two independent DMD cohorts: cohort 1 (The Parent Project Muscular Dystrophy–Cincinnati Children’s Hospital Medical Center), 42 patients with DMD and 28 age-matched normal volunteers; and cohort 2 (The Cooperative International Neuromuscular Research Group, Duchenne Natural History Study), 51 patients with DMD and 17 age-matched normal volunteers. Forty-four proteins showed significant differences that were consistent in both cohorts when comparing DMD patients and healthy volunteers at a 1% false-discovery rate, a large number of significant protein changes for such a small study. These biomarkers can be classified by known cellular processes and by age-dependent changes in protein concentration. Our findings demonstrate both the utility of this unbiased biomarker discovery approach and suggest potential new diagnostic and therapeutic avenues for ameliorating the burden of DMD and, we hope, other rare and devastating diseases.


Author(s):  
Alexia Kakourou ◽  
Werner Vach ◽  
Simone Nicolardi ◽  
Yuri van der Burgt ◽  
Bart Mertens

AbstractMass spectrometry based clinical proteomics has emerged as a powerful tool for high-throughput protein profiling and biomarker discovery. Recent improvements in mass spectrometry technology have boosted the potential of proteomic studies in biomedical research. However, the complexity of the proteomic expression introduces new statistical challenges in summarizing and analyzing the acquired data. Statistical methods for optimally processing proteomic data are currently a growing field of research. In this paper we present simple, yet appropriate methods to preprocess, summarize and analyze high-throughput MALDI-FTICR mass spectrometry data, collected in a case-control fashion, while dealing with the statistical challenges that accompany such data. The known statistical properties of the isotopic distribution of the peptide molecules are used to preprocess the spectra and translate the proteomic expression into a condensed data set. Information on either the intensity level or the shape of the identified isotopic clusters is used to derive summary measures on which diagnostic rules for disease status allocation will be based. Results indicate that both the shape of the identified isotopic clusters and the overall intensity level carry information on the class outcome and can be used to predict the presence or absence of the disease.


ACS Omega ◽  
2020 ◽  
Vol 5 (41) ◽  
pp. 26504-26517
Author(s):  
Tchilabalo D. Alayi ◽  
Shefa M. Tawalbeh ◽  
Michael Ogundele ◽  
Holly R. Smith ◽  
Alison M. Samsel ◽  
...  

Author(s):  
Sylvain Lehmann ◽  
Pauline Poinot ◽  
Laurent Tiers ◽  
Christophe Junot ◽  
François Becher ◽  
...  

AbstractClinical Proteomics biomarker discovery programs lead to the selection of putative new biomarkers of human pathologies. Following an initial discovery phase, validation of these candidates in larger populations is a major task that recently started relying upon the use of mass spectrometry approaches, especially in cases where classical immune-detection methods were lacking. Thanks to highly sensitive spectrometers, adapted measurement methods like selective reaction monitoring (SRM) and various pre-fractionation methods, the quantitative detection of protein/peptide biomarkers in low concentrations is now feasible from complex biological fluids. This possibility leads to the use of similar methodologies in clinical biology laboratories, within a new proteomic field that we shall name “Clinical Chemistry Proteomics” (CCP). Such evolution of Clinical Proteomics adds important constraints with regards to the in vitro diagnostic (IVD) application. As measured values of analytes will be used to diagnose, follow-up and adapt patient treatment on a routine basis; medical utility, robustness, reference materials and clinical feasibility are among the new issues of CCP to consider.


2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Ivana Dabaj ◽  
Justine Ferey ◽  
Florent Marguet ◽  
Vianney Gilard ◽  
Carole Basset ◽  
...  

AbstractDuchenne muscular dystrophy (DMD) is a common and severe X-linked myopathy, characterized by muscle degeneration due to altered or absent dystrophin. DMD has no effective cure, and the underlying molecular mechanisms remain incompletely understood. The aim of this study is to investigate the metabolic changes in DMD using mass spectrometry-based imaging. Nine human muscle biopsies from DMD patients and nine muscle biopsies from control individuals were subjected to untargeted MSI using matrix-assisted laser desorption/ionization Fourier-transform ion cyclotron resonance mass spectrometry. Both univariate and pattern recognition techniques have been used for data analysis. This study revealed significant changes in 34 keys metabolites. Seven metabolites were decreased in the Duchenne biopsies compared to control biopsies including adenosine triphosphate, and glycerophosphocholine. The other 27 metabolites were increased in the Duchenne biopsies, including sphingomyelin, phosphatidylcholines, phosphatidic acids and phosphatidylserines. Most of these dysregulated metabolites are tightly related to energy and phospholipid metabolism. This study revealed a deep metabolic remodelling in phospholipids and energy metabolism in DMD. This systems-based approach enabled exploring the metabolism in DMD in an unprecedented holistic and unbiased manner with hypothesis-free strategies.


Cytoskeleton ◽  
2010 ◽  
Vol 67 (12) ◽  
pp. 796-807 ◽  
Author(s):  
Christine Carag Krieger ◽  
Nishant Bhasin ◽  
Manorama Tewari ◽  
Andre E. X. Brown ◽  
Daniel Safer ◽  
...  

2020 ◽  
Vol 21 (18) ◽  
pp. 6830
Author(s):  
Maria Hernandez-Valladares ◽  
Øystein Bruserud ◽  
Frode Selheim

With the current reproducibility of proteome preparation workflows along with the speed and sensitivity of the mass spectrometers, the transition of the mass spectrometry (MS)-based proteomics technology from biomarker discovery to clinical implementation is under appraisal in the biomedicine community. Therefore, this technology might be implemented soon to detect well-known biomarkers in cancers and other diseases. Acute myeloid leukemia (AML) is an aggressive heterogeneous malignancy that requires intensive treatment to cure the patient. Leukemia relapse is still a major challenge even for patients who have favorable genetic abnormalities. MS-based proteomics could be of great help to both describe the proteome changes of individual patients and identify biomarkers that might encourage specific treatments or clinical strategies. Herein, we will review the advances and availability of the MS-based proteomics strategies that could already be used in clinical proteomics. However, the heterogeneity of complex diseases as AML requires consensus to recognize AML biomarkers and to establish MS-based workflows that allow their unbiased identification and quantification. Although our literature review appears promising towards the utilization of MS-based proteomics in clinical AML in a near future, major efforts are required to validate AML biomarkers and agree on clinically approved workflows.


2020 ◽  
Vol 6 (1) ◽  
Author(s):  
Jianqiang Wu ◽  
Jun Zhang ◽  
Jing Wei ◽  
Yuanli Zhao ◽  
Youhe Gao

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