Hirak Das (Wuerzburg / DE), Ruth Carmichael (Exeter / GB), Serhii Chornyi (Amsterdam / NL), Sven Spielhaupter (Wuerzburg / DE), Claudio Costa (Leuven / BE), Ann-Kathrin Richard (Wuerzburg / DE), Wignand W. D. Mühlhäuser (Berlin / DE), Julian Bender (Wuerzburg / DE), Ann-Kathrin Staudt (Wuerzburg / DE), Bettina Knapp (Freiburg / DE), Silke Oeljeklaus (Wuerzburg / DE), Alexander Buchberger (Wuerzburg / DE), Marc Fransen (Leuven / BE), Hans Waterham (Amsterdam / NL), Michael Schrader (Exeter / GB), Bettina Warscheid (Wuerzburg / DE)
Objectives:
Peroxisomes perform vital metabolic functions, such as β-oxidation of fatty acids or synthesis of lipids, plasmalogens and bile acid. In addition to the detoxification of hydrogen peroxide, they also play role in regulating immune responses and cellular signalling. Importantly, they share metabolic pathways and form membrane contact sites with other organelles. Deficiencies in peroxisome biogenesis or function are linked to severe, often lethal human diseases (e.g. Zellweger spectrum disorders). To fully understand peroxisomal functions and their ways of communication and metabolite exchange, knowledge of the complete peroxisomal proteome is crucial. However, due to small size of peroxisomes, low abundance, membrane contact sites and multi-localized protein (MLP) content, defining the proteome is challenging. Since previous spatial proteomics studies did not properly address peroxisomes, we aim at establishing a multifaceted high-resolution spatial proteomics and data analysis pipeline tailored to human peroxisomes.
Methods:
We established a workflow for the separation of peroxisomes from human cells (HEK293) employing subcellular and density gradient fractionation. Using label-free LC-MS, we profiled the distribution of organelles across all fractions using organellar marker proteins and peroxisomal MLPs. Through high-resolution protein correlation profiling, we identify known and new peroxisomal proteins. To validate peroxisomal candidates, we employed a new SILAC-based comparative spatial proteomics strategy and fluorescence microscopy. We performed multi-modal integration of our dataset with auxiliary data from other spatial/interaction proteomics datasets and single protein studies to boost confidence of inference for our candidates, score them using an ensemble machine learning framework, and catalogue all proteins to make for the first time a comprehensive inventory of human peroxisomal proteins, which we termed PeroCarta.
Results:
We profiled more than 8,800 proteins and identified most known peroxisomal proteins and novel candidates. Based on orthogonal validation and integrated datasets from other studies, we compile a first draft of the human peroxisomal proteome with 242 proteins as peroxisomal of which 216 are identified in our study and includes 82 new candidates. Our data confirm that peroxisomal proteins are mainly MLPs, with the bulk of candidates shared with mitochondria or the cytosol. Selected candidates including potential contact-site/tethering proteins have been investigated, which provide new insight into peroxisome functions in a cellular context.
Conclusion:
Taken together, results from our multifaceted spatial proteomics study represent a milestone towards defining the human peroxisomal proteome including MLPs. Our newly established PeroCarta, which includes all known and new peroxisomal proteins, will serve as point of reference for future studies of human peroxisomes in health and disease.