Comprehensive Quantification of the Modified Proteome Reveals Oxidative Heart Damage in Mitochondrial Heteroplasmy

Bagwan, Navratan; Bonzón-Kulichenko, Elena; Lechuga‐Vieco, Ana Victoria; Michalakopoulos, Spiros; Trevisán-Herraz, Marco; Ezkurdia, Iakes; Rodrı́guez, José Manuel; Magni, Ricardo; Latorre‐Pellicer, Ana; Enríquez, José Antonio; Vázquez, Jesús

doi:10.1016/j.celrep.2018.05.080

Cited by 43 publications

(48 citation statements)

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“…Mitochondrial pathways are involved in many diseases, including malignant tumors, diabetes, Parkinson’s disease, Alzheimer’s disease, and cardiovascular disease [ 10 , 11 ]. Thus, mitochondrial proteomics has become a hotspot in the study of disease to discover new biomarkers and molecular targets for drug discovery and therapeutic intervention in recent years [ 12 ]. Our previous studies identified 5115 mitochondrial expressed proteins (mtEPs) and 70 statistically significant KEGG pathways based on 5115 mtEPs in human OCs [ 13 ], which provided the mitochondrial protein expression profiling and overall pathway profiling in human OC.…”

Section: Introductionmentioning

confidence: 99%

Signaling pathway network alterations in human ovarian cancers identified with quantitative mitochondrial proteomics

Zhan

2019

EPMA Journal

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Relevance Molecular network changes are the hallmark of the pathogenesis of ovarian cancers (OCs). Network-based biomarkers benefit for the effective treatment of OC. Purpose This study sought to identify key pathway–network alterations and network-based biomarkers for clarification of molecular mechanisms and treatment of OCs. Methods Ingenuity Pathway Analysis (IPA) platform was used to mine signaling pathway networks with 1198 human tissue mitochondrial differentially expressed proteins (mtDEPs) and compared those pathway network changes between OCs and controls. The mtDEPs in important cancer-related pathway systems were further validated with qRT-PCR and Western blot in OC cell models. Moreover, integrative analysis of mtDEPs and Cancer Genome Atlas (TCGA) data from 419 patients was used to identify hub molecules with molecular complex detection method. Hub molecule–based survival analysis and multiple multivariate regression analysis were used to identify survival-related hub molecules and hub molecule signature model. Results Pathway network analysis revealed 25 statistically significant networks, 192 canonical pathways, and 5 significant molecular/cellular function models. A total of 52 canonical pathways were activated or inhibited in cancer pathogenesis, including antigen presentation, mitochondrial dysfunction, GP6 signaling, EIF2 signaling, and glutathione-mediated detoxification. Of them, mtDEPs (TPM1, CALR, GSTP1, LYN, AKAP12, and CPT2) in those canonical pathway and molecular/cellular models were validated in OC cell models at the mRNA and protein levels. Moreover, 102 hub molecules were identified, and they were regulated by post-translational modifications and functioned in multiple biological processes. Of them, 62 hub molecules were individually significantly related to OC survival risk. Furthermore, multivariate regression analysis of 102 hub molecules identified significant seven hub molecule signature models (HIST1H2BK, ALB, RRAS2, HIBCH, EIF3E, RPS20, and RPL23A) to assess OC survival risks. Conclusion These findings provided the overall signaling pathway network profiling of human OCs; offered scientific data to discover pathway network-based cancer biomarkers for diagnosis, prognosis, and treatment of OCs; and clarify accurate molecular mechanisms and therapeutic targets. These findings benefit for the discovery of effective and reliable biomarkers based on pathway networks for OC predictive and personalized medicine. Electronic supplementary material The online version of this article (10.1007/s13167-019-00170-5) contains supplementary material, which is available to authorized users.

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Section: Introductionmentioning

confidence: 99%

Signaling pathway network alterations in human ovarian cancers identified with quantitative mitochondrial proteomics

Zhan

2019

EPMA Journal

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“…But also proteomic approaches suffer from the plethora of possible end products; allowing too many variable modifications leads to a combinatorial explosion of the search space and a concomitant loss of statistical power [122]. Novel search strategies have therefore been developed either based on dependent peptides [123] or wide precursor mass tolerance [124]. While these search strategies are better suited to deal with a large number of defined PTMs, they do not ameliorate the fundamental problem of undefined modifications such as random crosslinks between molecules.…”

Section: Summary and Discussionmentioning

confidence: 99%

Irreversible oxidative post-translational modifications in heart disease

Tomin

Schittmayer

Honeder

et al. 2019

Expert Review of Proteomics

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Introduction: Development of specific biomarkers aiding early diagnosis of heart failure is an ongoing challenge. Biomarkers commonly used in clinical routine usually act as readouts of an already existing acute condition rather than disease initiation. Functional decline of cardiac muscle is greatly aggravated by increased oxidative stress and damage of proteins. Oxidative post-translational modifications occur already at early stages of tissue damage and are thus regarded as potential up-coming disease markers. Areas covered: Clinical practice regarding commonly used biomarkers for heart disease is briefly summarized. The types of oxidative post-translational modification in cardiac pathologies are discussed with a special focus on available quantitative techniques and characteristics of individual modifications with regard to their stability and analytical accessibility. As irreversible oxidative modifications trigger protein degradation pathways or cause protein aggregation, both influencing biomarker abundance, a chapter is dedicated to their regulation in the heart.

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“…Alternatively, it has also been shown that the presence of heteroplasmy, in genotypes which are healthy when present at 100%, can induce fitness disadvantages (Acton et al 2007;Sharpley et al 2012;Bagwan et al 2018). In cases where heteroplasmy itself is disadvantageous, especially in later life where such mutations may have already accumulated, accelerating heteroplasmy variance increase to achieve fixation of a species could be advantageous.…”

Section: Control Strategies Against Mutant Expansionsmentioning

confidence: 99%

Mitochondrial Network State Scales mtDNA Genetic Dynamics

et al. 2019

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Mitochondrial DNA (mtDNA) mutations cause severe congenital diseases but may also be associated with healthy aging. mtDNA is stochastically replicated and degraded, and exists within organelles which undergo dynamic fusion and fission. The role of the resulting mitochondrial networks in the time evolution of the cellular proportion of mutated mtDNA molecules (heteroplasmy), and cell-to-cell variability in heteroplasmy (heteroplasmy variance), remains incompletely understood. Heteroplasmy variance is particularly important since it modulates the number of pathological cells in a tissue. Here, we provide the first wide-reaching theoretical framework which bridges mitochondrial network and genetic states. We show that, under a range of conditions, the (genetic) rate of increase in heteroplasmy variance and de novo mutation are proportionally modulated by the (physical) fraction of unfused mitochondria, independently of the absolute fission-fusion rate. In the context of selective fusion, we show that intermediate fusion:fission ratios are optimal for the clearance of mtDNA mutants. Our findings imply that modulating network state, mitophagy rate, and copy number to slow down heteroplasmy dynamics when mean heteroplasmy is low could have therapeutic advantages for mitochondrial disease and healthy aging. KEYWORDS mitochondrial DNA; mitochondrial networks; heteroplasmy variance; cellular noise M ITOCHONDRIAL DNA (mtDNA) encodes elements of the respiratory system vital for cellular function. Mutation of mtDNA is one of several leading hypotheses for the cause of normal aging (López-Otín et al. 2013; Kauppila et al. 2017), as well as underlying a number of heritable mtDNArelated diseases (Schon et al. 2012). Cells typically contain hundreds, or thousands, of copies of mtDNA per cell: each molecule encodes crucial components of the electron transport chain, which generates energy for the cell in the form of ATP. Consequently, the mitochondrial phenotype of a single cell is determined, in part, by its fluctuating population of mtDNA molecules (Wallace and Chalkia 2013; Stewart and Chinnery 2015; Aryaman et al. 2019; Johnston 2019). The broad biomedical implications of mtDNA mutation, combined with the countable nature of mtDNAs and the stochastic nature of their dynamics, offer the opportunity for mathematical understanding to provide important insights into human health and disease (Aryaman et al. 2019). An important observation in mitochondrial physiology is the threshold effect, whereby cells may often tolerate relatively high levels of mtDNA mutation until the fraction of mutated mtDNAs (termed heteroplasmy) exceeds a certain critical value where a pathological phenotype occurs (Rossignol et al. 2003; Picard et al. 2014; Stewart and Chinnery 2015; Aryaman et al. 2017). Fluctuations within individual cells mean that the fraction of mutant mtDNAs per cell is not constant within a tissue (Figure 1A), but follows a probability distribution which changes with time

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Comprehensive Quantification of the Modified Proteome Reveals Oxidative Heart Damage in Mitochondrial Heteroplasmy

Cited by 43 publications

References 45 publications

Signaling pathway network alterations in human ovarian cancers identified with quantitative mitochondrial proteomics

Signaling pathway network alterations in human ovarian cancers identified with quantitative mitochondrial proteomics

Irreversible oxidative post-translational modifications in heart disease

Mitochondrial Network State Scales mtDNA Genetic Dynamics

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