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Integral membrane proteins are an important class of cellular proteins. These take part in key cellular processes such as signaling transducing receptors to transporters, many operating within the plasma membrane. More than half of the FDA-approved protein-targeting drugs operate via interaction with proteins that contain at least one membrane-spanning region, yet the characterization and study of their native interactions with therapeutic agents remains a significant challenge. This challenge is due in part to such proteins often being present in small quantities within a cell. Effective solubilization of membrane proteins is also problematic, with the detergents typically employed in solubilizing membranes leading to a loss of functional activity and key interacting partners. In recent years, alternative methods to extract membrane proteins within their native lipid environment have been investigated, with the aim of producing functional nanodiscs, maintaining protein–protein and protein–lipid interactions. A promising approach involves extracting membrane proteins in the form of styrene maleic acid lipid particles (SMALPs) that allow the retention of their native conformation. This extraction method offers many advantages for further protein analysis and allows the study of the protein interactions with other molecules, such as drugs. Here, we describe a protocol for efficient SMALP extraction of functionally active membrane protein complexes within nanodiscs. We showcase the method on the isolation of a low copy number plasma membrane receptor complex, the nicotinic acetylcholine receptor (nAChR), from adult Drosophila melanogaster heads. We demonstrate that these nanodiscs can be used to study native receptor–ligand interactions. This protocol can be applied across many biological scenarios to extract the native conformations of low copy number integral membrane proteins.

Global healthcare systems are challenged by the COVID-19 pandemic. There is a need to optimize allocation of treatment and resources in intensive care, as clinically established risk assessments such as SOFA and APACHE II scores show only limited performance for predicting the survival of severely ill COVID-19 patients. Additional tools are also needed to monitor treatment, including experimental therapies in clinical trials. Comprehensively capturing human physiology, we speculated that proteomics in combination with new data-driven analysis strategies could produce a new generation of prognostic discriminators. We studied two independent cohorts of patients with severe COVID-19 who required intensive care and invasive mechanical ventilation. SOFA score, Charlson comorbidity index, and APACHE II score showed limited performance in predicting the COVID-19 outcome. Instead, the quantification of 321 plasma protein groups at 349 timepoints in 50 critically ill patients receiving invasive mechanical ventilation revealed 14 proteins that showed trajectories different between survivors and non-survivors. A predictor trained on proteomic measurements obtained at the first time point at maximum treatment level (i.e. WHO grade 7), which was weeks before the outcome, achieved accurate classification of survivors (AUROC 0.81). We tested the established predictor on an independent validation cohort (AUROC 1.0). The majority of proteins with high relevance in the prediction model belong to the coagulation system and complement cascade. Our study demonstrates that plasma proteomics can give rise to prognostic predictors substantially outperforming current prognostic markers in intensive care. Author summary Healthcare systems around the world are struggling to accommodate high numbers of the most severely ill patients with COVID-19. Moreover, the pandemic creates a pressing need to accelerate clinical trials investigating potential new therapeutics. While various biomarkers can discriminate and predict the future course of disease for patients of different disease severity, prognosis remains difficult for patient groups with similar disease severity, e.g. patients requiring intensive care. Established risk assessments in intensive care medicine such as the SOFA or APACHE II show only limited reliability in predicting future disease outcomes for COVID-19. In this study we hypothesized that the plasma proteome, which reflects the complete set of proteins that are expressed by an organism and are present in the blood, and which is known to comprehensively capture the host response to COVID-19, can be leveraged to allow for prediction of survival in the most critically ill patients with COVID-19. Here, we found 14 proteins, which over time changed in opposite directions for patients who survive compared to patients who do not survive on intensive care. Using a machine learning model which combines the measurements of multiple proteins, we were able to accurately predict survival in critically ill patients with COVID-19 from single blood samples, weeks before the outcome, substantially outperforming established risk predictors.

The Drosophila shaggy gene (sgg, GSK-3) encodes multiple protein isoforms with serine/threonine kinase activity and is a key player in diverse developmental signalling pathways. Currently it is unclear whether different Sgg proteoforms are similarly involved in signalling or if different proteoforms have distinct functions. We used CRISPR/Cas9 genome engineering to tag eight different Sgg proteoform classes and determined their localization during embryonic development. We performed proteomic analysis of the two major proteoform classes and generated mutant lines for both of these for transcriptomic and phenotypic analysis. We uncovered distinct tissue-specific localization patterns for all of the tagged proteoforms we examined, most of which have not previously been characterised directly at the protein level, including one proteoform initiating with a non-standard codon. Collectively, this suggests complex developmentally regulated splicing of the sgg primary transcript. Further, affinity purification followed by mass spectrometric analyses indicate a different repertoire of interacting proteins for the two major proteoforms we examined, one with ubiquitous expression (Sgg-PB) and one with nervous system specific expression (Sgg-PA). Specific mutation of these proteoforms shows that Sgg-PB performs the well characterised maternal and zygotic segmentations functions of the sgg locus, while Sgg-PA mutants show adult lifespan and locomotor defects consistent with its nervous system localisation. Our findings provide new insights into the role of GSK-3 proteoforms and intriguing links with the GSK-3α and GSK-3β proteins encoded by independent vertebrate genes. Our analysis suggests that different proteoforms generated by alternative splicing are likely to perform distinct functions.

ABSTRACT Productivity of bacterial cell factories is frequently compromised by stresses imposed by recombinant protein synthesis and carbon-to-product conversion, but little is known about these bioprocesses at a systems level. Production of the unnatural metabolite citramalate in Escherichia coli requires the expression of a single gene coding for citramalate synthase. Multiomic analyses of a fermentation producing 25 g liter−1 citramalate were undertaken to uncover the reasons for its productivity. Metabolite, transcript, protein, and lipid profiles of high-cell-density, fed-batch fermentations of E. coli expressing either citramalate synthase or an inactivated enzyme were similar. Both fermentations showed downregulation of flagellar genes and upregulation of chaperones IbpA and IbpB, indicating that these responses were due to recombinant protein synthesis and not citramalate production. Citramalate production did not perturb metabolite pools, except for an increased intracellular pyruvate pool. Gene expression changes in response to citramalate were limited; none of the general stress response regulons were activated. Modeling of transcription factor activities suggested that citramalate invoked a GadW-mediated acid response, and changes in GadY and RprA regulatory small RNA (sRNA) expression supported this. Although changes in membrane lipid composition were observed, none were unique to citramalate production. This systems analysis of the citramalate fermentation shows that E. coli has capacity to readily adjust to the redirection of resources toward recombinant protein and citramalate production, suggesting that it is an excellent chassis choice for manufacturing organic acids. IMPORTANCE Citramalate is an attractive biotechnology target because it is a precursor of methylmethacrylate, which is used to manufacture Perspex and other high-value products. Engineered E. coli strains are able to produce high titers of citramalate, despite having to express a foreign enzyme and tolerate the presence of a nonnative biochemical. A systems analysis of the citramalate fermentation was undertaken to uncover the reasons underpinning its productivity. This showed that E. coli readily adjusts to the redirection of metabolic resources toward recombinant protein and citramalate production and suggests that E. coli is an excellent chassis for manufacturing similar small, polar, foreign molecules.

The correct subcellular localization of proteins is vital for cellular function and the study of this process at the systems level will therefore enrich our understanding of the roles of proteins within the cell. Multiple methods are available for the study of protein subcellular localization, including fluorescence microscopy, organelle cataloging, proximity labeling methods, and whole-cell protein correlation profiling methods. We provide here a protocol for the systems-level study of the subcellular localization of the yeast proteome, using a version of hyperplexed Localization of Organelle Proteins by Isotope Tagging (hyperLOPIT) that has been optimized for use with Saccharomyces cerevisiae. The entire protocol encompasses cell culture, cell lysis by nitrogen cavitation, subcellular fractionation, monitoring of the fractionation using Western blotting, labeling of samples with TMT isobaric tags and mass spectrometric analysis. Also included is a brief explanation of downstream processing of the mass spectrometry data to produce a map of the spatial proteome. If required, the nitrogen cavitation lysis and Western blotting portions of the protocol may be performed independently of the mass spectrometry analysis. The protocol in its entirety, however, enables the unbiased, systems-level and high-resolution analysis of the localizations of thousands of proteins in parallel within a single experiment.

Two-dimensional polyacrylamide gel electrophoresis of the secreted proteins of Erwinia carotovora subsp. atroseptica revealed alow-abundance protein that was identified by mass spectrometry as a homologue of a Xanthomonas campestris avirulence protein with unknown function. The predicted Svx protein has an N-terminal signal sequence and zinc binding-region signature, and the mature protein is post-translationally modified. A 2D difference gel electrophoresis (DIGE) showed that the protein is secreted by the type II (out) secretion apparatus, which is also responsible for the secretion of the major known virulence factors, PelC and CelV. Transcription of the svx gene is under Nacyl- homoserine lactone-mediated quorum-sensing control. The svx gene was inactivated by transposon insertion. The mutant showed a decrease in virulence in potato plant assays, demonstrating a role for Svx in the pathogenicity of E. carotovora subsp. atroseptica. These results show that Svx is a previously unidentified virulence determinant which is secreted by the out machinery and is regulated by quorum sensing, two systems employed by several other virulence factors. Thus, the type II secretory machine is a conduit for virulence factors other than the main pectinnases and cellulase in E. carotovora subsp. atroseptica.