Proteomic Dynamics of Breast Cancer Cell Lines Identifies Potential Therapeutic Protein Targets.

Treatment and relevant targets for breast cancer (BC) remain limited, especially for triple-negative BC (TNBC). We identified 6091 proteins of 76 human BC cell lines using data-independent acquisition (DIA). Integrating our proteomic findings with prior multi-omics datasets, we found that including proteomics data improved drug sensitivity predictions and provided insights into the mechanisms of action. We subsequently profiled the proteomic changes in nine cell lines (five TNBC and four non-TNBC) treated with EGFR/AKT/mTOR inhibitors. In TNBC, metabolism pathways were dysregulated after EGFR/mTOR inhibitor treatment, while RNA modification and cell cycle pathways were affected by AKT inhibitor. This systematic multi-omics and in-depth analysis of the proteome of BC cells can help prioritize potential therapeutic targets and provide insights into adaptive resistance in TNBC.

Reproducible Determination of High-Density Lipoprotein Proteotypes.

High-density lipoprotein (HDL) is a heterogeneous mixture of blood-circulating multimolecular particles containing many different proteins, lipids, and RNAs. Recent advancements in mass spectrometry-based proteotype analysis show promise for the analysis of proteoforms across large patient cohorts. In order to create the required spectral libraries enabling these data-independent acquisition (DIA) strategies, HDL was isolated from the plasma of more than 300 patients with a multiplicity of physiological HDL states. HDL proteome spectral libraries consisting of 296 protein groups and more than 786 peptidoforms were established, and the performance of the DIA strategy was benchmarked for the detection of HDL proteotype differences between healthy individuals and a cohort of patients suffering from diabetes mellitus type 2 and/or coronary heart disease. Bioinformatic interrogation of the data using the generated spectral libraries showed that the DIA approach enabled robust HDL proteotype determination. HDL peptidoform analysis enabled by using spectral libraries allowed for the identification of post-translational modifications, such as in APOA1, which could affect HDL functionality. From a technical point of view, data analysis further shows that protein and peptide quantities are currently more discriminative between different HDL proteotypes than peptidoforms without further enrichment. Together, DIA-based HDL proteotyping enables the robust digitization of HDL proteotypes as a basis for the analysis of larger clinical cohorts.

Elucidation of host-virus surfaceome interactions using spatial proteotyping.

The cellular surfaceome and its residing extracellularly exposed proteins are involved in a multitude of molecular signaling processes across the viral infection cycle. Successful viral propagation, including viral entry, immune evasion, virion release and viral spread rely on dynamic molecular interactions with the surfaceome. Decoding of these viral-host surfaceome interactions using advanced technologies enabled the discovery of fundamental new functional insights into cellular and viral biology. In this review, we highlight recently developed experimental strategies, with a focus on spatial proteotyping technologies, aiding in the rational design of theranostic strategies to combat viral infections.

A Proteogenomic Resource Enabling Integrated Analysis of Listeria Genotype-Proteotype-Phenotype Relationships.

Listeria monocytogenes is an opportunistic foodborne pathogen responsible for listeriosis, a potentially fatal foodborne disease. Many different Listeria strains and serotypes exist, but a proteogenomic resource that bridges the gap in our molecular understanding of the relationships between the Listeria genotypes and phenotypes via proteotypes is still missing. Here, we devised a next-generation proteogenomics strategy that enables the community to rapidly proteotype Listeria strains and relate this information back to the genotype. Based on sequencing and de novo assembly of the two most commonly used Listeria model strains, EGD-e and ScottA, we established two comprehensive Listeria proteogenomic databases. A genome comparison established core- and strain-specific genes potentially responsible for virulence differences. Next, we established a DIA/SWATH-based proteotyping strategy, including a new and robust sample preparation workflow, that enables the reproducible, sensitive, and relative quantitative measurement of Listeria proteotypes. This reusable and publicly available DIA/SWATH library covers 70% of open reading frames of Listeria and represents the most extensive spectral library for Listeria proteotype analysis to date. We used these two new resources to investigate the Listeria proteotype in states mimicking the upper gastrointestinal passage. Exposure of Listeria to bile salts at 37 °C, which simulates conditions encountered in the duodenum, showed significant proteotype perturbations including an increase of FlaA, the structural protein of flagella. Given that Listeria is known to lose its flagella above 30 °C, this was an unexpected finding. The formation of flagella, which might have implications on infectivity, was validated by parallel reaction monitoring and light and scanning electron microscopy. flaA transcript levels did not change significantly upon exposure to bile salts at 37 °C, suggesting regulation at the post-transcriptional level. Together, these analyses provide a comprehensive proteogenomic resource and toolbox for the Listeria community enabling the analysis of Listeria genotype-proteotype-phenotype relationships.

Genetics' Piece of the PI: Inferring the Origin of Complex Traits and Diseases from Proteome-Wide Protein-Protein Interaction Dynamics.

How do common and rare genetic polymorphisms contribute to quantitative traits or disease risk and progression? Multiple human traits have been extensively characterized at the genomic level, revealing their complex genetic architecture. However, it is difficult to resolve the mechanisms by which specific variants contribute to a phenotype. Recently, analyses of variant effects on molecular traits have uncovered intermediate mechanisms that link sequence variation to phenotypic changes. Yet, these methods only capture a fraction of genetic contributions to phenotype. Here, in reviewing the field, it is proposed that complex traits can be understood by characterizing the dynamics of biochemical networks within living cells, and that the effects of genetic variation can be captured on these networks by using protein-protein interaction (PPI) methodologies. This synergy between PPI methodologies and the genetics of complex traits opens new avenues to investigate the molecular etiology of human diseases and to facilitate their prevention or treatment.

Advancing translational research and precision medicine with targeted proteomics.

Remarkable advances in quantitative mass spectrometry have shifted the focus of proteomics from the characterization of protein expression profiles to detailed investigations on the spatial and temporal organization of the proteome. Demands for precision therapy and personalized medicine are challenged by heterogeneity in the larger population, which have led to drawbacks in biomarker performance and therapeutic efficacy. The consistent adaptation of the cellular proteome in response to distinctive signals defines a phenotype. Acquisition of quantitative multi-layered omics data on multiple individuals over defined time scales has made it possible to establish means to probe the extent to which the genome, transcriptome and environment influence the variability of the proteome in given conditions, over time. Comprehensive, reproducible datasets generated with contemporary quantitative, massively parallel, targeted proteomic approaches offer as yet untapped benefits for biomarker discovery, development, and validation. The objective of this review is to recapitulate on advances in targeted proteomics approaches for quantifying the cellular proteome and to address ways to incorporate these data towards improving present day methodologies for biomarker evaluation and precision medicine. SIGNIFICANCE: Advances in quantitative mass spectrometry have shifted the focus of proteomics from the characterization of protein expression profiles to detailed investigations on the spatial and temporal organization of the proteome. This review expounds on avenues through which targeted proteomic methodologies can be constructively implemented in translational research and precision medicine to overcome existing challenges that hinder the success of protein biomarkers in clinics, and to develop precise therapeutics for future applications.