Global analysis of aging-related protein structural changes uncovers enzyme polymerization-based control of longevity

Paukštytė, Jurgita; Cabezas, Rosa María López; Feng, Yuehan; Kai, Tetsuya; Schnyder, Daniela; Elomaa, Ellinoora; Gregorová, Pavlína; Doudin, Matteo; Sarkka, Meeri; Sarameri, Jesse; Lippi, Alice; Vihinen, Helena; Juutila, Juhana; Nieminen, Anni I.; Törönen, Petri; Jokitalo, Eija; Kriško, Anita; Huiskonen, J.T.; Sarin, L. Peter; Hietakangas, Ville; Picotti, Paola; Barral, Yves; Saarikangas, Juha

doi:10.1101/2023.01.23.524173

Cited by 3 publications

(6 citation statements)

References 111 publications

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“…Interestingly, the stability of refolding chaperones increased in young mother cells compared to their daughter cells, was similar in young and middle-aged mother cells, and increased again in old mother cells. Occurrence of broad structural changes already in young mothers is consistent with a recent structural proteomics study 14 . These observations suggest that the primary differences in aggregate formation occur between mother and daughter cells (nucleation and asymmetric inheritance of aggregates) and later in life (possibly increased aggregation due to a decline in proteostasis).…”

Section: Resultssupporting

confidence: 88%

“…Protein stability is a function of protein structure, folding state, and interactions. Upon aging, protein stability is affected because proteins are exposed to changing environments and damage, undergo structural changes, and the machinery that maintains proteins soluble is changing [11][12][13][14] . Moreover, different posttranslational modifications are widely affected by age 7,15,16 .…”

Section: Introductionmentioning

confidence: 99%

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Multidimensional proteomics identifies molecular trajectories of cellular aging and rejuvenation

Leutert

Armstrong

Ollodart

et al. 2023

Preprint

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The declining capacity of cells to maintain a functional proteome is a major driver of cellular dysfunction and decreased fitness in aging. Here we assess the impact of aging on multiple proteome dimensions, which are reflective of function, across the replicative lifespan of Saccharomyces cerevisiae. We quantified protein abundance, protein turnover, protein thermal stability, and protein phosphorylation in mother yeast cells and their derived progeny at different ages. We find progressive and cumulative proteomic alterations that are reflective of dysregulation of complex assemblies, mitochondrial remodeling, post-translational activation of the AMPK/Snf1 energy sensor in mother cells, and an overall shift from biosynthetic to energy-metabolic processes. Our multidimensional proteomic study systematically corroborates previous findings of asymmetric segregation and daughter cell rejuvenation, and extends these concepts to protein complexes, protein phosphorylation, and activation of signaling pathways. Lastly, profiling age-dependent proteome changes in a caloric restriction model of yeast provided mechanistic insights into longevity, revealing minimal remodeling of energy-metabolic pathways, improved mitochondrial maintenance, ameliorated protein biogenesis, and decreased stress responses. Taken together, our study provides thousands of age-dependent molecular events that can be used to gain a holistic understanding of mechanisms of aging.

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Section: Resultssupporting

confidence: 88%

Section: Introductionmentioning

confidence: 99%

Multidimensional proteomics identifies molecular trajectories of cellular aging and rejuvenation

Leutert

Armstrong

Ollodart

et al. 2023

Preprint

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“…The middle level of merging (to a peptide) is the one that historically we have used, 18,23 as well as others. [8][9][10][20][21][22] The highest level of merging (to cut-site), to the best of our knowledge, is novel to this analysis workflow.…”

Section: Computational Sectionmentioning

confidence: 99%

“…Data- dependent acquisition (DDA) 8,18 and data-independent acquisition (DIA) 10,19 workflows have both been implemented. So far, it has been applied to probe a range of biological and biophysical questions at the proteome scale, such as metabolic rewiring in response to nutrients, 19 aging in yeast, 20 aging in rodents, 21 thermostability, 22 protein folding, 18,23 among others.…”

Section: Introductionmentioning

confidence: 99%

FLiPPR: A Processor for Limited Proteolysis (LiP) Mass Spectrometry Datasets Built on FragPipe

Manriquez-Sandoval,

Brewer,

Lule

et al. 2023

Preprint

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Here, we present FLiPPR, or FragPipe LiP (limited proteolysis) Processor, a tool that facilitates the analysis of data from limited proteolysis mass spectrometry (LiP-MS) experiments following primary search and quantification in FragPipe. LiP-MS has emerged as a method that can provide proteome-wide information on protein structure and has been applied to a range of biological and biophysical questions. Although LiP- MS can be carried out with standard laboratory reagents and mass spectrometers, analyzing the data can be slow and poses unique challenges compared to typical quantitative proteomics workflows. To address this, we leverage the fast, sensitive, and accurate search and label-free quantification algorithms in FragPipe and then process its output in FLiPPR. FLiPPR formalizes a specific data imputation heuristic that carefully uses missing data in LiP-MS experiments to report on the most significant structural changes. Moreover, FLiPPR introduces a new data merging scheme (from ions to cut-sites) and a protein-centric multiple hypothesis correction scheme, collectively enabling processed LiP-MS datasets to be more robust and less redundant. These improvements substantially strengthen statistical trends when previously published data are reanalyzed with the FragPipe/FLiPPR workflow. As a final feature, FLiPPR facilitates the collection of structural metadata to identify correlations between experiments and structural features. We hope that FLiPPR will lower the barrier for more users to adopt LiP-MS, standardize statistical procedures for LiP-MS data analysis, and systematize output to facilitate eventual larger-scale integration of LiP-MS data.

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“…protein interactions 17 , aging-related changes in yeast 18 , Caenorhabditis elegans 19 and mice 20 as well as the use of multi-dimensional protein-structural changes as a new class of disease biomarkers 21 . To quantify changes in the structural protein accessibility between conditions, lysates from all conditions undergo short (i.e.…”

mentioning

confidence: 99%

Enhancing the biological insight from limited proteolysis-coupled mass spectrometry

Nagel,

Grossbach,

Cappelletti

et al. 2024

Preprint

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Limited proteolysis combined with mass spectrometry (LiP-MS) facilitates probing structural changes on a proteome-wide scalein situ. Distinguishing the different signal contributions, such as changes in protein abundance, from protein abundance changes remains challenging. We propose a two-step approach, first removing unwanted variations from the LiP signal that are not caused by protein structural effects and subsequently inferring the effects of variables of interest on the remaining signal. Using LiP-MS data from three species we demonstrate that our framework provides a uniquely powerful approach for deconvolving LiP-MS signals and inferring protein structural changes.

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Global analysis of aging-related protein structural changes uncovers enzyme polymerization-based control of longevity

Cited by 3 publications

References 111 publications

Multidimensional proteomics identifies molecular trajectories of cellular aging and rejuvenation

Multidimensional proteomics identifies molecular trajectories of cellular aging and rejuvenation

FLiPPR: A Processor for Limited Proteolysis (LiP) Mass Spectrometry Datasets Built on FragPipe

Enhancing the biological insight from limited proteolysis-coupled mass spectrometry

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