Autophagy accounts for approximately one-third of mitochondrial protein turnover and is protein selective.

The destruction of mitochondria through macroautophagy (autophagy) has been recognised as a major route of mitochondrial protein degradation since its discovery more than 50 years ago, but fundamental questions remain unanswered. First, how much mitochondrial protein turnover occurs through auto-phagy? Mitochondrial proteins are also degraded by nonautophagic mechanisms, and the proportion of mitochondrial protein turnover that occurs through autophagy is still unknown. Second, does auto-phagy degrade mitochondrial proteins uniformly or selectively? Autophagy was originally thought to degrade all mitochondrial proteins at the same rate, but recent work suggests that mitochondrial autophagy may be protein selective. To investigate these questions, we used a proteomics-based approach in the fruit fly Drosophila melanogaster, comparing mitochondrial protein turnover rates in autophagy-deficient Atg7 mutants and controls. We found that ~35% of mitochondrial protein turnover occurred via autophagy. Similar analyses using parkin mutants revealed that parkin-dependent mitophagy accounted for ~25% of mitochondrial protein turnover, suggesting that most mitochondrial autophagy specifically eliminates dysfunctional mitochondria. We also found that our results were incompatible with uniform autophagic turnover of mitochondrial proteins and consistent with protein-selective autophagy. In particular, the autophagic turnover rates of individual mitochondrial proteins varied widely, and only a small amount of the variation could be attributed to tissue differences in mitochondrial composition and autophagy rate. Furthermore, analyses comparing autophagy-deficient and control human fibroblasts revealed diverse autophagy-dependent turnover rates even in homogeneous cells. In summary, our work indicates that autophagy acts selectively on mitochondrial proteins, and that most mitochondrial protein turnover occurs through non-autophagic processes. Abbreviations:Atg5: Autophagy-related 5 (Drosophila); ATG5: autophagy related 5 (human); Atg7: Autophagy-related 7 (Drosophila); ATG7: autophagy related 7 (human); DNA: deoxyribonucleic acid; ER: endoplasmic reticulum; GFP: green fluorescent protein; MS: mass spectrometry; park: parkin (Drosophila); Pink1: PTEN-induced putative kinase 1 (Drosophila); PINK1: PTEN-induced kinase 1 (human); PRKN: parkin RBR E3 ubiquitin protein ligase (human); RNA: ribonucleic acid; SD: standard deviation; Ub: ubiquitin/ubiquitinated; WT: wild-type; YME1L: YME1 like ATPase (Drosophila); YME1L1: YME1 like 1 ATPase (human).

Panorama: a targeted proteomics knowledge base.

Panorama is a web application for storing, sharing, analyzing, and reusing targeted assays created and refined with Skyline,1 an increasingly popular Windows client software tool for targeted proteomics experiments. Panorama allows laboratories to store and organize curated results contained in Skyline documents with fine-grained permissions, which facilitates distributed collaboration and secure sharing of published and unpublished data via a web-browser interface. It is fully integrated with the Skyline workflow and supports publishing a document directly to a Panorama server from the Skyline user interface. Panorama captures the complete Skyline document information content in a relational database schema. Curated results published to Panorama can be aggregated and exported as chromatogram libraries. These libraries can be used in Skyline to pick optimal targets in new experiments and to validate peak identification of target peptides. Panorama is open-source and freely available. It is distributed as part of LabKey Server,2 an open source biomedical research data management system. Laboratories and organizations can set up Panorama locally by downloading and installing the software on their own servers. They can also request freely hosted projects on https://panoramaweb.org , a Panorama server maintained by the Department of Genome Sciences at the University of Washington.

Maximizing peptide identification events in proteomic workflows using data-dependent acquisition (DDA).

Current analytical strategies for collecting proteomic data using data-dependent acquisition (DDA) are limited by the low analytical reproducibility of the method. Proteomic discovery efforts that exploit the benefits of DDA, such as providing peptide sequence information, but that enable improved analytical reproducibility, represent an ideal scenario for maximizing measureable peptide identifications in "shotgun"-type proteomic studies. Therefore, we propose an analytical workflow combining DDA with retention time aligned extracted ion chromatogram (XIC) areas obtained from high mass accuracy MS1 data acquired in parallel. We applied this workflow to the analyses of sample matrixes prepared from mouse blood plasma and brain tissues and observed increases in peptide detection of up to 30.5% due to the comparison of peptide MS1 XIC areas following retention time alignment of co-identified peptides. Furthermore, we show that the approach is quantitative using peptide standards diluted into a complex matrix. These data revealed that peptide MS1 XIC areas provide linear response of over three orders of magnitude down to low femtomole (fmol) levels. These findings argue that augmenting "shotgun" proteomic workflows with retention time alignment of peptide identifications and comparative analyses of corresponding peptide MS1 XIC areas improve the analytical performance of global proteomic discovery methods using DDA.

Global proteomics and pathway analysis of pressure-overload-induced heart failure and its attenuation by mitochondrial-targeted peptides.

BACKGROUND: We investigated the protective effects of mitochondrial-targeted antioxidant and protective peptides, Szeto-Schiller (SS) 31 and SS20, on cardiac function, proteomic remodeling, and signaling pathways. METHODS AND RESULTS: We applied an improved label-free shotgun proteomics approach to evaluate the global proteomics changes in transverse aortic constriction (TAC)-induced heart failure and the associated signaling pathway changes using ingenuity pathway analysis. We found that 538 proteins significantly changed after TAC, which mapped to 53 pathways. The top pathways were in the categories of actin cytoskeleton, mitochondrial function, intermediate metabolism, glycolysis/gluconeogenesis, and citrate cycle. Concomitant treatment with SS31 ameliorated the congestive heart failure phenotypes and mitochondrial damage induced by TAC, in parallel with global attenuation of mitochondrial proteome changes, with an average of 84% protection of mitochondrial and 69% of nonmitochondrial protein changes. This included significant amelioration of all the ingenuity pathway analysis noted above. SS20 had only modest effects on heart failure and this tracked with only partial attenuation of global proteomics changes; furthermore, actin cytoskeleton pathways were significantly protected in SS20, whereas mitochondrial and metabolic pathways essentially were not. CONCLUSIONS: This study elucidates the signaling pathways significantly changed in pressure-overload-induced heart failure. The global attenuation of TAC-induced proteomic alterations by the mitochondrial-targeted peptide SS31 suggests that perturbed mitochondrial function may be an upstream signal to many of the pathway alterations in TAC and supports the potential clinical application of mitochondrial-targeted peptide drugs for the treatment heart failure.

Integrative phenomics reveals insight into the structure of phenotypic diversity in budding yeast.

To better understand the quantitative characteristics and structure of phenotypic diversity, we measured over 14,000 transcript, protein, metabolite, and morphological traits in 22 genetically diverse strains of Saccharomyces cerevisiae. More than 50% of all measured traits varied significantly across strains [false discovery rate (FDR) = 5%]. The structure of phenotypic correlations is complex, with 85% of all traits significantly correlated with at least one other phenotype (median = 6, maximum = 328). We show how high-dimensional molecular phenomics data sets can be leveraged to accurately predict phenotypic variation between strains, often with greater precision than afforded by DNA sequence information alone. These results provide new insights into the spectrum and structure of phenotypic diversity and the characteristics influencing the ability to accurately predict phenotypes.

The PINK1-Parkin pathway promotes both mitophagy and selective respiratory chain turnover in vivo.

The accumulation of damaged mitochondria has been proposed as a key factor in aging and the pathogenesis of many common age-related diseases, including Parkinson disease (PD). Recently, in vitro studies of the PD-related proteins Parkin and PINK1 have found that these factors act in a common pathway to promote the selective autophagic degradation of damaged mitochondria (mitophagy). However, whether Parkin and PINK1 promote mitophagy under normal physiological conditions in vivo is unknown. To address this question, we used a proteomic approach in Drosophila to compare the rates of mitochondrial protein turnover in parkin mutants, PINK1 mutants, and control flies. We found that parkin null mutants showed a significant overall slowing of mitochondrial protein turnover, similar to but less severe than the slowing seen in autophagy-deficient Atg7 mutants, consistent with the model that Parkin acts upstream of Atg7 to promote mitophagy. By contrast, the turnover of many mitochondrial respiratory chain (RC) subunits showed greater impairment in parkin than Atg7 mutants, and RC turnover was also selectively impaired in PINK1 mutants. Our findings show that the PINK1-Parkin pathway promotes mitophagy in vivo and, unexpectedly, also promotes selective turnover of mitochondrial RC subunits. Failure to degrade damaged RC proteins could account for the RC deficits seen in many PD patients and may play an important role in PD pathogenesis.

Topograph, a software platform for precursor enrichment corrected global protein turnover measurements.

Defects in protein turnover have been implicated in a broad range of diseases, but current proteomics methods of measuring protein turnover are limited by the software tools available. Conventional methods require indirect approaches to differentiate newly synthesized protein when synthesized from partially labeled precursor pools. To address this, we have developed Topograph, a software platform which calculates the fraction of peptides that are from newly synthesized proteins and their turnover rates. A unique feature of Topograph is the ability to calculate amino acid precursor pool enrichment levels which allows for accurate calculations when the precursor pool is not fully labeled, and the approach used by Topograph is applicable regardless of the stable isotope label used. We validate the Topograph algorithms using data acquired from a mouse labeling experiment and demonstrate the influence that precursor pool corrections can have on protein turnover measurements.