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The failure of metabolic tissues to appropriately respond to insulin (“insulin resistance”) is an early marker in the pathogenesis of type 2 diabetes. Protein phosphorylation is central to the adipocyte insulin response, but how adipocyte signaling networks are dysregulated upon insulin resistance is unknown. Here we employ phosphoproteomics to delineate insulin signal transduction in adipocyte cells and adipose tissue. Across a range of insults causing insulin resistance, we observe a marked rewiring of the insulin signaling network. This includes both attenuated insulin-responsive phosphorylation, and the emergence of phosphorylation uniquely insulin-regulated in insulin resistance. Identifying dysregulated phosphosites common to multiple insults reveals subnetworks containing non-canonical regulators of insulin action, such as MARK2/3, and causal drivers of insulin resistance. The presence of several bona fide GSK3 substrates among these phosphosites led us to establish a pipeline for identifying context-specific kinase substrates, revealing widespread dysregulation of GSK3 signaling. Pharmacological inhibition of GSK3 partially reverses insulin resistance in cells and tissue explants. These data highlight that insulin resistance is a multi-nodal signaling defect that includes dysregulated MARK2/3 and GSK3 activity.

Yeast physiology is temporally regulated, this becomes apparent under nutrient-limited conditions and results in respiratory oscillations (YROs). YROs share features with circadian rhythms and interact with, but are independent of, the cell division cycle. Here, we show that YROs minimise energy expenditure by restricting protein synthesis until sufficient resources are stored, while maintaining osmotic homeostasis and protein quality control. Although nutrient supply is constant, cells sequester and store metabolic resources via increased transport, autophagy and biomolecular condensation. Replete stores trigger increased H+ export which stimulates TORC1 and liberates proteasomes, ribosomes, chaperones and metabolic enzymes from non-membrane bound compartments. This facilitates translational bursting, liquidation of storage carbohydrates, increased ATP turnover, and the export of osmolytes. We propose that dynamic regulation of ion transport and metabolic plasticity are required to maintain osmotic and protein homeostasis during remodelling of eukaryotic proteomes, and that bioenergetic constraints selected for temporal organisation that promotes oscillatory behaviour., Yeast exhibit oscillations that share features with circadian rhythms. The authors show that bioenergetic constraints promote oscillatory behaviour: resources are stored until supplies can support translational bursting, this is licensed by ion transport and release from membrane-less compartments.

Protein localisation and translocation between intracellular compartments underlie almost all physiological processes. The hyperLOPIT proteomics platform combines mass spectrometry with state-of-the-art machine learning to map the subcellular location of thousands of proteins simultaneously. We combine global proteome analysis with hyperLOPIT in a fully Bayesian framework to elucidate spatiotemporal proteomic changes during a lipopolysaccharide (LPS)-induced inflammatory response. We report a highly dynamic proteome in terms of both protein abundance and subcellular localisation, with alterations in the interferon response, endo-lysosomal system, plasma membrane reorganisation and cell migration. Proteins not previously associated with an LPS response were found to relocalise upon stimulation, the functional consequences of which are still unclear. By quantifying proteome-wide uncertainty through Bayesian modelling, a necessary role for protein relocalisation and the importance of taking a holistic overview of the LPS-driven immune response has been revealed. The data are showcased as an interactive application freely available for the scientific community., “Protein relocalisation plays a major role in the innate immune response but remains incompletely characterised. Here, the authors combine temporal proteomics with LOPIT, a spatial proteomic workflow, in a fully Bayesian framework to elucidate spatiotemporal proteomic changes during the LPS-induced immune response in THP-1 cells.

Telomeres are bound by dedicated protein complexes, like shelterin in mammals, which protect telomeres from DNA damage. In the nematodeCaenorhabditis elegans, a comprehensive understanding of the proteins interacting with the telomere sequence is lacking. Here, we harnessed a quantitative proteomics approach to screen for proteins binding toC. elegans telomeres, and identified TEBP-1 and TEBP-2, two paralogs that associate to telomeres in vitro and in vivo. TEBP-1 and TEBP-2 are expressed in the germline and during embryogenesis.tebp-1andtebp-2mutants display strikingly distinct phenotypes:tebp-1mutants have longer telomeres than wild-type animals, whiletebp-2mutants display shorter telomeres and a mortal germline, a phenotype characterized by transgenerational germline deterioration. Notably,tebp-1;tebp-2double mutant animals have synthetic sterility, with germlines showing signs of severe mitotic and meiotic arrest. TEBP-1 and TEBP-2 form a telomeric complex with the known single-stranded telomere-binding proteins POT-1, POT-2, and MRT-1. Furthermore, we find that POT-1 bridges the double- stranded binders TEBP-1 and TEBP-2, with the single-stranded binders POT-2 and MRT-1. These results describe the first telomere-binding complex inC. elegans, with TEBP-1 and TEBP-2, two double-stranded telomere binders required for fertility and that mediate opposite telomere dynamics.

Mass spectrometry (MS)-based ubiquitinomics provides system-level understanding of ubiquitin signaling. Here we present a scalable workflow for deep and precise in vivo ubiquitinome profiling, coupling an improved sample preparation protocol with data-independent acquisition (DIA)-MS and neural network-based data processing specifically optimized for ubiquitinomics. Compared to data-dependent acquisition (DDA), our method more than triples identification numbers to 70,000 ubiquitinated peptides in single MS runs, while significantly improving robustness and quantification precision. Upon inhibition of the oncology target USP7, we simultaneously record ubiquitination and consequent changes in abundance of more than 8,000 proteins at high temporal resolution. While ubiquitination of hundreds of proteins increases within minutes of USP7 inhibition, we find that only a small fraction of those are ever degraded, thereby dissecting the scope of USP7 action. Our method enables rapid mode-of-action profiling of candidate drugs targeting DUBs or ubiquitin ligases at high precision and throughput., Combining improved sample preparation, data-independent acquisition mass spectrometry and deep learning, the authors develop a workflow for more robust and precise quantitative ubiquitinome profiling. They use this method to characterize targets of the deubiquitinase USP7 and effects of USP7 inhibitors.

Protein localisation and translocation between intracellular compartments underlie almost all physiological processes. The hyperLOPIT proteomics platform combines mass spectrometry with state-of-the-art machine learning to map the subcellular location of thousands of proteins simultaneously. We combine global proteome analysis with hyperLOPIT in a fully Bayesian framework to elucidate spatiotemporal proteomic changes during a lipopolysaccharide (LPS)-induced inflammatory response. We report a highly dynamic proteome in terms of both protein abundance and subcellular localisation, with alterations in the interferon response, endo-lysosomal system, plasma membrane reorganisation and cell migration. Proteins not previously associated with an LPS response were found to relocalise upon stimulation, the functional consequences of which are still unclear. By quantifying proteome-wide uncertainty through Bayesian modelling, a necessary role for protein relocalisation and the importance of taking a holistic overview of the LPS-driven immune response has been revealed. The data are showcased as an interactive application freely available for the scientific community.