Sample Collection Method Bias Effects in Quantitative Phosphoproteomics

Proteome dynamics during transition from exponential to stationary phase under aerobic and anaerobic conditions in yeast

The yeast Saccharomyces cerevisiae is a widely used eukaryotic model organism and a promising cell factory for industry. However, despite decades of research, the regulation of its metabolism is not yet fully understood, and its complexity represents a major challenge for engineering and optimising biosynthetic routes. Recent studies have demonstrated the potential of resource and proteomic allocation data in enhancing models for metabolic processes. However, comprehensive and accurate proteome dynamics data that can be used for such approaches are still very limited. Therefore, we performed a quantitative proteome dynamics study to comprehensively cover the transition from exponential to stationary phase for both aerobically and anaerobically grown yeast cells. The combination of highly controlled reactor experiments, biological replicates and standardised sample preparation procedures ensured reproducibility and accuracy. Additionally, we selected the CEN.PK lineage for our experiments because of its relevance for both fundamental and applied research. Together with the prototrophic, standard haploid strain CEN.PK113-7D, we also investigated an engineered strain with genetic minimisation of the glycolytic pathway, resulting in the quantitative assessment of 54 proteomes. The anaerobic cultures showed remarkably less proteome-level changes compared to the aerobic cultures, during transition from the exponential to the stationary phase as a consequence of the lack of the diauxic shift in the absence of oxygen. These results support the notion that anaerobically growing cells lack resources to adequately adapt to starvation. This proteome dynamics study constitutes an important step towards better understanding of the impact of glucose exhaustion and oxygen on the complex proteome allocation process in yeast. Finally, the established proteome dynamics data provide a valuable resource for the development of resource allocation models as well as for metabolic engineering efforts.

A novel strategy to uncover specific GO terms/phosphorylation pathways in phosphoproteomic data in Arabidopsis thaliana

BACKGROUND

Proteins are the workforce of the cell and their phosphorylation status tailors specific responses efficiently. One of the main challenges of phosphoproteomic approaches is to deconvolute biological processes that specifically respond to an experimental query from a list of phosphoproteins. Comparison of the frequency distribution of GO (Gene Ontology) terms in a given phosphoproteome set with that observed in the genome reference set (GenRS) is the most widely used tool to infer biological significance. Yet, this comparison assumes that GO term distribution between the phosphoproteome and the genome are identical. However, this hypothesis has not been tested due to the lack of a comprehensive phosphoproteome database.

RESULTS

In this study, we test this hypothesis by constructing three phosphoproteome databases in Arabidopsis thaliana: one based in experimental data (ExpRS), another based in in silico phosphorylation protein prediction (PredRS) and a third that is the union of both (UnRS). Our results show that the three phosphoproteome reference sets show default enrichment of several GO terms compared to GenRS, indicating that GO term distribution in the phosphoproteomes does not match that of the genome. Moreover, these differences overshadow the identification of GO terms that are specifically enriched in a particular condition. To overcome this limitation, we present an additional comparison of the sample of interest with UnRS to uncover GO terms specifically enriched in a particular phosphoproteome experiment. Using this strategy, we found that mRNA splicing and cytoplasmic microtubule compounds are important processes specifically enriched in the phosphoproteome of dark-grown Arabidopsis seedlings.

CONCLUSIONS

This study provides a novel strategy to uncover GO specific terms in phosphoproteome data of Arabidopsis that could be applied to any other organism. We also highlight the importance of specific phosphorylation pathways that take place during dark-grown Arabidopsis development.

Improved phosphoproteomic analysis for phosphosignaling and active-kinome profiling in Matrigel-embedded spheroids and patient-derived organoids

Many attempts have been made to reproduce the three-dimensional (3D) cancer behavior. For that purpose, Matrigel, an extracellular matrix from Engelbreth-Holm-Swarm mouse sarcoma cell, is widely used in 3D cancer models such as scaffold-based spheroids and patient-derived organoids. However, severe ion suppression caused by contaminants from Matrigel hampers large-scale phosphoproteomics. In the present study, we successfully performed global phosphoproteomics from Matrigel-embedded spheroids and organoids. Using acetone precipitations of tryptic peptides, we identified more than 20,000 class 1 phosphosites from HCT116 spheroids. Bioinformatic analysis revealed that phosphoproteomic status are significantly affected by the method used for the recovery from the Matrigel, i.e., Dispase or Cell Recovery Solution. Furthermore, we observed the activation of several phosphosignalings only in spheroids and not in adherent cells which are coincident with previous study using 3D culture. Finally, we demonstrated that our protocol enabled us to identify more than 20,000 and nearly 3,000 class 1 phosphosites from 1.4 mg and 150 μg of patient-derived organoid, respectively. Additionally, we were able to quantify phosphosites with high reproducibility (r = 0.93 to 0.95). Our phosphoproteomics protocol is useful for analyzing the phosphosignalings of 3D cancer behavior and would be applied for precision medicine with patient-derived organoids.

Dissection of Human Retina and RPE-Choroid for Proteomic Analysis

The human retina is composed of the sensory neuroretina and the underlying retinal pigmented epithelium (RPE), which is firmly complexed to the vascular choroid layer. Different regions of the retina are anatomically and molecularly distinct, facilitating unique functions and demonstrating differential susceptibility to disease. Proteomic analysis of each of these regions and layers can provide vital insights into the molecular process of many diseases, including Age-Related Macular Degeneration (AMD), diabetes mellitus, and glaucoma. However, separation of retinal regions and layers is essential before quantitative proteomic analysis can be accomplished. Here, we describe a method for dissection and collection of the foveal, macular, and peripheral retinal regions and underlying RPE-choroid complex, involving regional punch biopsies and manual removal of tissue layers from a human eye.One-dimensional SDS-PAGE as well as downstream proteomic analysis, such as liquid chromatography-tandem mass spectrometry (LC-MS/MS), can be used to identify proteins in each dissected retinal layer, revealing molecular biomarkers for retinal disease.

Machine Learning of Global Phosphoproteomic Profiles Enables Discrimination of Direct versus Indirect Kinase Substrates

Mass spectrometry allows quantification of tens of thousands of phosphorylation sites from minute amounts of cellular material. Despite this wealth of information, our understanding of phosphorylation-based signaling is limited, in part because it is not possible to deconvolute substrate phosphorylation that is directly mediated by a particular kinase versus phosphorylation that is mediated by downstream kinases. Here, we describe a framework for assignment of direct in vivo kinase substrates using a combination of selective chemical inhibition, quantitative phosphoproteomics, and machine learning techniques. Our workflow allows classification of phosphorylation events following inhibition of an analog-sensitive kinase into kinase-independent effects of the inhibitor, direct effects on cognate substrates, and indirect effects mediated by downstream kinases or phosphatases. We applied this method to identify many direct targets of Cdc28 and Snf1 kinases in the budding yeast Saccharomyces cerevisiae Global phosphoproteome analysis of acute time-series demonstrated that dephosphorylation of direct kinase substrates occurs more rapidly compared with indirect substrates, both after inhibitor treatment and under a physiological nutrient shift in wt cells. Mutagenesis experiments revealed a high proportion of functionally relevant phosphorylation sites on Snf1 targets. For example, Snf1 itself was inhibited through autophosphorylation on Ser391 and new phosphosites were discovered that modulate the activity of the Reg1 regulatory subunit of the Glc7 phosphatase and the Gal83 β-subunit of SNF1 complex. This methodology applies to any kinase for which a functional analog sensitive version can be constructed to facilitate the dissection of the global phosphorylation network.

Recent findings and technological advances in phosphoproteomics for cells and tissues

Site-specific phosphorylation is a fast and reversible covalent post-translational modification that is tightly regulated in cells. The cellular machinery of enzymes that write, erase and read these modifications (kinases, phosphatases and phospho-binding proteins) is frequently deregulated in different diseases, including cancer. Large-scale studies of phosphoproteins – termed phosphoproteomics – strongly rely on the use of high-performance mass spectrometric instrumentation. This powerful technology has been applied to study a great number of phosphorylation-based phenotypes. Nevertheless, many technical and biological challenges have to be overcome to identify biologically relevant phosphorylation sites in cells and tissues. This review describes different technological strategies to identify and quantify phosphorylation sites with high accuracy, without significant loss of analysis speed and reproducibility in tissues and cells. Moreover, computational tools for analysis, integration and biological interpretation of phosphorylation events are discussed.