Quantitative proteomics of patient fibroblasts reveal biomarkers and diagnostic signatures of mitochondrial disease
Sandrina P. Correia, Marco F. Moedas, Lucie S. Taylor, Karin Naess, Albert Z. Lim, Robert McFarland, Zuzanna Kazior, Anastasia Rumyantseva, Rolf Wibom, Martin Engvall, Helene Bruhn, Nicole Lesko, Ákos Végvári, Lukas Käll, Matthias Trost, Charlotte L. Alston, Christoph Freyer, Robert W. Taylor, Anna Wedell, Anna Wredenberg
Sandrina P. Correia, Marco F. Moedas, Lucie S. Taylor, Karin Naess, Albert Z. Lim, Robert McFarland, Zuzanna Kazior, Anastasia Rumyantseva, Rolf Wibom, Martin Engvall, Helene Bruhn, Nicole Lesko, Ákos Végvári, Lukas Käll, Matthias Trost, Charlotte L. Alston, Christoph Freyer, Robert W. Taylor, Anna Wedell, Anna Wredenberg
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Clinical Research and Public Health Metabolism

Quantitative proteomics of patient fibroblasts reveal biomarkers and diagnostic signatures of mitochondrial disease

Abstract

BACKGROUND Mitochondrial diseases belong to the group of inborn errors of metabolism (IEM), with a prevalence of 1 in 2,000–5,000 individuals. They are the most common form of IEM, but, despite advances in next-generation sequencing technologies, almost half of the patients are left genetically undiagnosed.METHODS We investigated a cohort of 61 patients with defined mitochondrial disease to improve diagnostics, identify biomarkers, and correlate metabolic pathways to specific disease groups. Clinical presentations were structured using human phenotype ontology terms, and mass spectrometry–based proteomics was performed on primary fibroblasts. Additionally, we integrated 6 patients carrying variants of uncertain significance (VUS) to test proteomics as a diagnostic expansion.RESULTS Proteomic profiles from patient samples could be classified according to their biochemical and genetic characteristics, with the expression of 5 proteins (GPX4, MORF4L1, MOXD1, MSRA, and TMED9) correlating with the disease cohort, thus acting as putative biomarkers. Pathway analysis showed a deregulation of inflammatory and mitochondrial stress responses. This included the upregulation of glycosphingolipid metabolism and mitochondrial protein import, as well as the downregulation of arachidonic acid metabolism. Furthermore, we could assign pathogenicity to a VUS in MRPS23 by demonstrating the loss of associated mitochondrial ribosome subunits.CONCLUSION We established mass spectrometry–based proteomics on patient fibroblasts as a viable and versatile tool for diagnosing patients with mitochondrial disease.FUNDING The NovoNordisk Foundation, Knut and Alice Wallenberg Foundation, Wellcome Centre for Mitochondrial Research, UK Medical Research Council, and the UK NHS Highly Specialised Service for Rare Mitochondrial Disorders of Adults and Children.

Authors

Sandrina P. Correia, Marco F. Moedas, Lucie S. Taylor, Karin Naess, Albert Z. Lim, Robert McFarland, Zuzanna Kazior, Anastasia Rumyantseva, Rolf Wibom, Martin Engvall, Helene Bruhn, Nicole Lesko, Ákos Végvári, Lukas Käll, Matthias Trost, Charlotte L. Alston, Christoph Freyer, Robert W. Taylor, Anna Wedell, Anna Wredenberg

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Figure 5

Proteomics analysis supports diagnosis of mitochondrial disease.

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Proteomics analysis supports diagnosis of mitochondrial disease. Proteom...
Proteomics data aids in the diagnosis of an unsolved case. (A) Expression levels of the potential biomarkers identified in DE analysis in the 6 VUS cases. Blue boxplots represent the control cohort while brown boxplots indicate the patient cohort. Values are normalized to median intensity of control cohort in Log2 scale (Normalized Log2FC). Labeled dots denote the VUS cases (genes): P07 (HTRA2), P18 (POLRMT), P19 (QRSL1), P41 (MRPS23), and P48 and P49 (COX20). (B) Mitochondrial ATP production rate (units/unit CS), was determined with the indicated substrate combinations. Boxplots represent the distribution of values for control individuals (n = 11, age 12–57 years) with the individual colored circle representing the value determined in the patient. Respiratory chain enzyme activities of complex I, complexes I + III, complex II, complexes II + III, and complex IV were determined in isolated mitochondria and adjusted to CS activity. Results are presented as percentage of mean control values with boxplots representing the distribution of values for control individuals (n = 15–42; age 5–70 years) and the individual colored circle representing the value determined in the patient. (C) BN-PAGE of mitochondria isolated from muscle from patient (P41) and a control individual. Enzyme complexes I–IV (CI–IV) were detected by Western blot with appropriate antibodies. (D) Western blot analysis of patient and control fibroblasts. HSC70 (heat shock protein family A[Hsp70] member 8) was used as loading control and protein signal was determined as described with appropriate antibodies. (E) Volcano plot of total proteomes for P41 (n = 1) versus controls (n = 17). Up or downregulated proteins (Log2FC > 2.5 or Log2FC < –2.5) are shown in red (increased) and blue (decreased), respectively. MRPS23 protein is labeled in orange. Average Log2 Intensities calculated from individual intensities of the entire dataset (patient and controls). (F) Volcano plot from E with mitochondrial ribosomal proteins emphasized in purple. (G) Volcano plot of GSEA analysis of P41 DE results, circle diameter denotes set size while color denotes annotation source (blue, GO:BP; red, Reactome; green, WikiPathways). Differentially enriched gene sets are labeled according to a normalized enrichment score (NES) > 1.3 or < –1.3 and a q < 0.05 (presented as –Log10 q value).