Fibroblast bioenergetics to classify amyotrophic lateral sclerosis patients
Abstract Background The objective of this study was to investigate cellular bioenergetics in primary skin fibroblasts derived from patients with amyotrophic lateral sclerosis (ALS) and to determine if they can be used as classifiers for patient stratification. Methods We assembled a collection of un...
| Main Authors: | , , , , , , , , , , , , , |
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| Format: | Article |
| Language: | English |
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BMC
2017-10-01
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| Series: | Molecular Neurodegeneration |
| Subjects: | |
| Online Access: | http://link.springer.com/article/10.1186/s13024-017-0217-5 |
| _version_ | 1818036202706567168 |
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| author | Csaba Konrad Hibiki Kawamata Kirsten G. Bredvik Andrea J. Arreguin Steven A. Cajamarca Jonathan C. Hupf John M. Ravits Timothy M. Miller Nicholas J. Maragakis Chadwick M. Hales Jonathan D. Glass Steven Gross Hiroshi Mitsumoto Giovanni Manfredi |
| author_facet | Csaba Konrad Hibiki Kawamata Kirsten G. Bredvik Andrea J. Arreguin Steven A. Cajamarca Jonathan C. Hupf John M. Ravits Timothy M. Miller Nicholas J. Maragakis Chadwick M. Hales Jonathan D. Glass Steven Gross Hiroshi Mitsumoto Giovanni Manfredi |
| author_sort | Csaba Konrad |
| collection | DOAJ |
| description | Abstract Background The objective of this study was to investigate cellular bioenergetics in primary skin fibroblasts derived from patients with amyotrophic lateral sclerosis (ALS) and to determine if they can be used as classifiers for patient stratification. Methods We assembled a collection of unprecedented size of fibroblasts from patients with sporadic ALS (sALS, n = 171), primary lateral sclerosis (PLS, n = 34), ALS/PLS with C9orf72 mutations (n = 13), and healthy controls (n = 91). In search for novel ALS classifiers, we performed extensive studies of fibroblast bioenergetics, including mitochondrial membrane potential, respiration, glycolysis, and ATP content. Next, we developed a machine learning approach to determine whether fibroblast bioenergetic features could be used to stratify patients. Results Compared to controls, sALS and PLS fibroblasts had higher average mitochondrial membrane potential, respiration, and glycolysis, suggesting that they were in a hypermetabolic state. Only membrane potential was elevated in C9Orf72 lines. ATP steady state levels did not correlate with respiration and glycolysis in sALS and PLS lines. Based on bioenergetic profiles, a support vector machine (SVM) was trained to classify sALS and PLS with 99% specificity and 70% sensitivity. Conclusions sALS, PLS, and C9Orf72 fibroblasts share hypermetabolic features, while presenting differences of bioenergetics. The absence of correlation between energy metabolism activation and ATP levels in sALS and PLS fibroblasts suggests that in these cells hypermetabolism is a mechanism to adapt to energy dissipation. Results from SVM support the use of metabolic characteristics of ALS fibroblasts and multivariate analysis to develop classifiers for patient stratification. |
| first_indexed | 2024-12-10T07:07:12Z |
| format | Article |
| id | doaj.art-3d0d953bc6bb46cca8d20d3e2cd72eb2 |
| institution | Directory Open Access Journal |
| issn | 1750-1326 |
| language | English |
| last_indexed | 2024-12-10T07:07:12Z |
| publishDate | 2017-10-01 |
| publisher | BMC |
| record_format | Article |
| series | Molecular Neurodegeneration |
| spelling | doaj.art-3d0d953bc6bb46cca8d20d3e2cd72eb22022-12-22T01:58:09ZengBMCMolecular Neurodegeneration1750-13262017-10-0112111210.1186/s13024-017-0217-5Fibroblast bioenergetics to classify amyotrophic lateral sclerosis patientsCsaba Konrad0Hibiki Kawamata1Kirsten G. Bredvik2Andrea J. Arreguin3Steven A. Cajamarca4Jonathan C. Hupf5John M. Ravits6Timothy M. Miller7Nicholas J. Maragakis8Chadwick M. Hales9Jonathan D. Glass10Steven Gross11Hiroshi Mitsumoto12Giovanni Manfredi13Feil Family Brain and Mind Research Institute, Weill Cornell MedicineFeil Family Brain and Mind Research Institute, Weill Cornell MedicineFeil Family Brain and Mind Research Institute, Weill Cornell MedicineFeil Family Brain and Mind Research Institute, Weill Cornell MedicineFeil Family Brain and Mind Research Institute, Weill Cornell MedicineDepartment of Neurology, Columbia UniversityDepartment of Neuroscience, University of California San DiegoDepartment of Neurology, Washington University School of MedicineDepartment of Neurology, Johns Hopkins University School of MedicineDepartment of Neurology, Emory School of MedicineDepartment of Neurology, Emory School of MedicineDepartment of Pharmacology, Weill Cornell MedicineDepartment of Neurology, Columbia UniversityFeil Family Brain and Mind Research Institute, Weill Cornell MedicineAbstract Background The objective of this study was to investigate cellular bioenergetics in primary skin fibroblasts derived from patients with amyotrophic lateral sclerosis (ALS) and to determine if they can be used as classifiers for patient stratification. Methods We assembled a collection of unprecedented size of fibroblasts from patients with sporadic ALS (sALS, n = 171), primary lateral sclerosis (PLS, n = 34), ALS/PLS with C9orf72 mutations (n = 13), and healthy controls (n = 91). In search for novel ALS classifiers, we performed extensive studies of fibroblast bioenergetics, including mitochondrial membrane potential, respiration, glycolysis, and ATP content. Next, we developed a machine learning approach to determine whether fibroblast bioenergetic features could be used to stratify patients. Results Compared to controls, sALS and PLS fibroblasts had higher average mitochondrial membrane potential, respiration, and glycolysis, suggesting that they were in a hypermetabolic state. Only membrane potential was elevated in C9Orf72 lines. ATP steady state levels did not correlate with respiration and glycolysis in sALS and PLS lines. Based on bioenergetic profiles, a support vector machine (SVM) was trained to classify sALS and PLS with 99% specificity and 70% sensitivity. Conclusions sALS, PLS, and C9Orf72 fibroblasts share hypermetabolic features, while presenting differences of bioenergetics. The absence of correlation between energy metabolism activation and ATP levels in sALS and PLS fibroblasts suggests that in these cells hypermetabolism is a mechanism to adapt to energy dissipation. Results from SVM support the use of metabolic characteristics of ALS fibroblasts and multivariate analysis to develop classifiers for patient stratification.http://link.springer.com/article/10.1186/s13024-017-0217-5BioenergeticsMitochondriaALSFibroblastsPLSMachine learning |
| spellingShingle | Csaba Konrad Hibiki Kawamata Kirsten G. Bredvik Andrea J. Arreguin Steven A. Cajamarca Jonathan C. Hupf John M. Ravits Timothy M. Miller Nicholas J. Maragakis Chadwick M. Hales Jonathan D. Glass Steven Gross Hiroshi Mitsumoto Giovanni Manfredi Fibroblast bioenergetics to classify amyotrophic lateral sclerosis patients Molecular Neurodegeneration Bioenergetics Mitochondria ALS Fibroblasts PLS Machine learning |
| title | Fibroblast bioenergetics to classify amyotrophic lateral sclerosis patients |
| title_full | Fibroblast bioenergetics to classify amyotrophic lateral sclerosis patients |
| title_fullStr | Fibroblast bioenergetics to classify amyotrophic lateral sclerosis patients |
| title_full_unstemmed | Fibroblast bioenergetics to classify amyotrophic lateral sclerosis patients |
| title_short | Fibroblast bioenergetics to classify amyotrophic lateral sclerosis patients |
| title_sort | fibroblast bioenergetics to classify amyotrophic lateral sclerosis patients |
| topic | Bioenergetics Mitochondria ALS Fibroblasts PLS Machine learning |
| url | http://link.springer.com/article/10.1186/s13024-017-0217-5 |
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