Open Access

The mammalian target of rapamycin and the integrated stress response are central cellular hubs regulating translation upon stress. The precise proteins and pathway specificity of translation targets of these pathways remained largely unclear. We recently described a new method for quantitative translation proteomics and found that both pathways control translation of the same sets of proteins.

Here, we present a peptide-based linear mixed models tool—PBLMM, a standalone desktop application for differential expression analysis of proteomics data. We also provide a Python package that allows streamlined data analysis workflows implementing the PBLMM algorithm. PBLMM is easy to use without scripting experience and calculates differential expression by peptide-based linear mixed regression models. We show that peptide-based models outperform classical methods of statistical inference of differentially expressed proteins. In addition, PBLMM exhibits superior statistical power in situations of low effect size and/or low sample size. Taken together our tool provides an easy-to-use, high-statistical-power method to infer differentially expressed proteins from proteomics data.

Correct cellular function is ensured by a complex network of proteins and enzymes, regulating protein synthesis and degradation. This protein network, maintaining the so-called protein homeostasis, regulates those processes on multiple levels, producing new or degrading old proteins to cope with changing intra- and extracellular environments. Disturbance of this tightly regulated machinery can have severe effects on the cell and can lead to a variety of pathologies on organism level. Diseases including cancer, neurodegeneration and infections are associated with causative or consequent alterations in protein homeostasis. To understand the pathologies of these diseases, it is therefore critical to examine how perturbations of protein homeostasis affect cellular pathways and physiology. In the recent years, analysis of protein homeostasis networks has resulted in the development of novel therapeutic approaches. However, for many factors it remains unclear how the cell is affected, if they are disturbed. Protein synthesis and degradation represent immediate responses of the cell to changes and need to be studied in the right timeframe, making them difficult to access by common methodology. In this work we developed a new mass spectrometry (MS) based method to study protein synthesis and degradation on a system-wide scale. Multiplexed enhanced protein dynamic (mePROD) MS was developed, overcoming these limitations by special sample mixing and novel data analysis protocols. MePROD thereby enables the measurement of rapid and transient (e.g. minutes) changes in protein synthesis of thousands of proteins. During responses of the cell to stressors (e.g. protein misfolding, oxidation or infection), two major pathways regulate the protein synthesis: the Integrated Stress Response (ISR) and mammalian target of rapamycin (mTOR). Both pathways have been connected with various diseases in the past and are common therapy targets. Although both pathways target protein synthesis in stress responses, the set of targets regulated by these pathways was believed to differ. Through the new mePROD MS method we could measure a comprehensive comparison of both pathways for the first time, revealing comparable system-wide patterns of regulation between the two pathways. This changed the current view on the regulation elicited by these pathways and furthermore represents a useful resource for the whole field of research. We could further develop the mePROD method and decrease MS measurement time needed to obtain an in-depth dataset. Through implementation of logic based instrument methods, it was possible to enhance the number of measured proteins by approximately three-fold within the same measurement time. The dynamics of protein synthesis and degradation are frequently modulated by pathogens infecting the cell to promote pathogen replication. At the same time, the cell counteracts the infection by modulating protein dynamics as well. To develop useful therapy approaches to fight infections, it therefore is necessary to understand the complex changes within the host cell during infections on a system-wide scale. In 2019, a novel coronavirus spread around the world, causing a world-wide health-crisis. To better understand this novel virus and its infection of the host cell we conducted a study applying the mePROD methodology and classical proteomics to characterize the dynamic changes during the infection course in vitro. We discovered that the infection remodeled a diverse set of host cell pathways (e.g. mRNA splicing, glycolysis, DNA synthesis and protein homeostasis) and thereby showed possible targets for antiviral therapy. By targeted inhibition of these pathways, we could observe that these pathways indeed are necessary for SARS-CoV-2 replication and their inhibition could reduce viral load in the cells. Another experimental approach focused on the dynamic changes of protein modification, namely phosphorylation, after infection with SARS-CoV-2. Here, we could show the very important participation of growth factor signaling pathways in viral proliferation. Both studies together revealed critical pathways that are needed for the viral proliferation and hence are promising candidates for further therapies. Subsequent targeting of these pathways by either already approved drugs (Ribavirin and Sorafenib) or drugs in clinical trials (2-deoxyglucose, Pladienolide-B, NMS-873, Pictilisib, Omipalisib, RO5126766 and Lonafarnib) could block viral replication in vitro and suggests important clinical approaches targeting SARS-COV-2 infection.

Cell-free therapy using extracellular vesicles (EVs) from adipose-derived mesenchymal stromal/stem cells (ASCs) seems to be a safe and effective therapeutic option to support tissue and organ regeneration. The application of EVs requires particles with a maximum regenerative capability and hypoxic culture conditions as an in vitro preconditioning regimen has been shown to alter the molecular composition of released EVs. Nevertheless, the EV cargo after hypoxic preconditioning has not yet been comprehensively examined. The aim of the present study was the characterization of EVs from hypoxic preconditioned ASCs. We investigated the EV proteome and their effects on renal tubular epithelial cells in vitro. While no effect of hypoxia was observed on the number of released EVs and their protein content, the cargo of the proteins was altered. Proteomic analysis showed 41 increased or decreased proteins, 11 in a statistically significant manner. Furthermore, the uptake of EVs in epithelial cells and a positive effect on oxidative stress in vitro were observed. In conclusion, culture of ASCs under hypoxic conditions was demonstrated to be a promising in vitro preconditioning regimen, which alters the protein cargo and increases the anti-oxidative potential of EVs. These properties may provide new potential therapeutic options for regenerative medicine.

The measurement of protein dynamics by proteomics to study cell remodeling has seen increased attention over the last years. This development is largely driven by a number of technological advances in proteomics methods. Pulsed stable isotope labeling in cell culture (SILAC) combined with tandem mass tag (TMT) labeling has evolved as a gold standard for profiling protein synthesis and degradation. While the experimental setup is similar to typical proteomics experiments, the data analysis proves more difficult: After peptide identification through search engines, data extraction requires either custom scripted pipelines or tedious manual table manipulations to extract the TMT-labeled heavy and light peaks of interest. To overcome this limitation, which deters researchers from using protein dynamic proteomics, we developed a user-friendly, browser-based application that allows easy and reproducible data analysis without the need for scripting experience. In addition, we provide a python package that can be implemented in established data analysis pipelines. We anticipate that this tool will ease data analysis and spark further research aimed at monitoring protein translation and degradation by proteomics.