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Streptococcus pyogenes can cause invasive disease with high mortality despite adequate antibiotic treatments. To address this unmet need, we have previously generated an opsonic IgG1 monoclonal antibody, Ab25, targeting the bacterial M protein. Here, we engineer the IgG2-4 subclasses of Ab25. Despite having reduced binding, the IgG3 version promotes stronger phagocytosis of bacteria. Using atomic simulations, we show that IgG3’s Fc tail has extensive movement in 3D space due to its extended hinge region, possibly facilitating interactions with immune cells. We replaced the hinge of IgG1 with four different IgG3-hinge segment subclasses, IgGhxx. Hinge-engineering does not diminish binding as with IgG3 but enhances opsonic function, where a 47 amino acid hinge is comparable to IgG3 in function. IgGh47 shows improved protection against S. pyogenes in a systemic infection mouse model, suggesting that IgGh47 has promise as a preclinical therapeutic candidate. Importantly, the enhanced opsonic function of IgGh47 is generalizable to diverse S. pyogenes strains from clinical isolates. We generated IgGh47 versions of anti-SARS-CoV-2 mAbs to broaden the biological applicability, and these also exhibit strongly enhanced opsonic function compared to the IgG1 subclass. The improved function of the IgGh47 subclass in two distant biological systems provides new insights into antibody function.Here, the authors elongated the hinge structure of IgG1 monoclonal antibodies. The modified IgG1-IgG3 hybrid subclass showed enhanced Fc-mediated function compared to IgG1 in two distinct biological systems, Streptococcus pyogenes and SARS-CoV-2. [ABSTRACT FROM AUTHOR]

The incorporation of machine learning methods into proteomics workflows improves the identification of disease-relevant biomarkers and biological pathways. However, machine learning models, such as deep neural networks, typically suffer from lack of interpretability. Here, we present a deep learning approach to combine biological pathway analysis and biomarker identification to increase the interpretability of proteomics experiments. Our approach integrates a priori knowledge of the relationships between proteins and biological pathways and biological processes into sparse neural networks to create biologically informed neural networks. We employ these networks to differentiate between clinical subphenotypes of septic acute kidney injury and COVID-19, as well as acute respiratory distress syndrome of different aetiologies. To gain biological insight into the complex syndromes, we utilize feature attribution-methods to introspect the networks for the identification of proteins and pathways important for distinguishing between subtypes. The algorithms are implemented in a freely available open source Python-package (https://github.com/InfectionMedicineProteomics/BINN). Deep neural networks hold significant promise in capturing the complexity of biological systems. However, they suffer from a lack of interpretability. Here, authors present a generalizable method for developing, interpreting, and visualizing biologically informed neural networks for proteomics data. [ABSTRACT FROM AUTHOR]

Sepsis is the major cause of mortality across intensive care units globally, yet details of accompanying pathological molecular events remain unclear. This knowledge gap has resulted in ineffective biomarker development and suboptimal treatment regimens to prevent and manage organ dysfunction/damage. Here, we used pharmacoproteomics to score time-dependent treatment impact in a murine Escherichia coli sepsis model after administering beta-lactam antibiotic meropenem (Mem) and/or the immunomodulatory glucocorticoid methylprednisolone (Gcc). Three distinct proteome response patterns were identified, which depended on the underlying proteotype for each organ. Gcc enhanced some positive proteome responses of Mem, including superior reduction of the inflammatory response in kidneys and partial restoration of sepsis-induced metabolic dysfunction. Mem introduced sepsis-independent perturbations in the mitochondrial proteome that Gcc counteracted. We provide a strategy for the quantitative and organotypic assessment of treatment effects of candidate therapies in relationship to dosing, timing, and potential synergistic intervention combinations during sepsis. Sepsis can cause organ damage through disparate immunological and metabolic processes. Here the authors demonstrate a proteomics-based scoring strategy for quantifying quantitative and organotypic changes in relationship to dosing, timing, and potential synergistic intervention combinations during sepsis. [ABSTRACT FROM AUTHOR]

Data independent acquisition mass spectrometry (DIA-MS) has recently emerged as an important method for the identification of blood-based biomarkers. However, the large search space required to identify novel biomarkers from the plasma proteome can introduce a high rate of false positives that compromise the accuracy of false discovery rates (FDR) using existing validation methods. We developed a generalized precursor scoring (GPS) method trained on 2.75 million precursors that can confidently control FDR while increasing the number of identified proteins in DIA-MS independent of the search space. We demonstrate how GPS can generalize to new data, increase protein identification rates, and increase the overall quantitative accuracy. Finally, we apply GPS to the identification of blood-based biomarkers and identify a panel of proteins that are highly accurate in discriminating between subphenotypes of septic acute kidney injury from undepleted plasma to showcase the utility of GPS in discovery DIA-MS proteomics. A generalized precursor scoring method increases protein identification rates and overall quantitative accuracy in data independent acquisition mass spectrometry proteomics and can generalize to new data. [ABSTRACT FROM AUTHOR]

Ipsilateral breast tumor recurrence (IBTR) is a clinically important event, where an isolated in-breast recurrence is a potentially curable event but associated with an increased risk of distant metastasis and breast cancer death. It remains unclear if IBTRs are associated with molecular changes that can be explored as a resource for precision medicine strategies. Here, we employed proteogenomics to analyze a cohort of 27 primary breast cancers and their matched IBTRs to define proteogenomic determinants of molecular tumor evolution. Our analyses revealed a relationship between hormonal receptors status and proliferation levels resulting in the gain of somatic mutations and copy number. This in turn re-programmed the transcriptome and proteome towards a highly replicating and genomically unstable IBTRs, possibly enhanced by APOBEC3B. In order to investigate the origins of IBTRs, a second analysis that included primaries with no recurrence pinpointed proliferation and immune infiltration as predictive of IBTR. In conclusion, our study shows that breast tumors evolve into different IBTRs depending on hormonal status and proliferation and that immune cell infiltration and Ki-67 are significantly elevated in primary tumors that develop IBTR. These results can serve as a starting point to explore markers to predict IBTR formation and stratify patients for adjuvant therapy. A proteogenomic analysis of matching primary breast cancer and ipsilateral breast tumor recurrence (IBTR) samples provides insight into the development of IBTR and may be useful in identifying biomarkers for IBTR formation. [ABSTRACT FROM AUTHOR]

A central challenge in infection medicine is to determine the structure and function of host–pathogen protein–protein interactions to understand how these interactions facilitate bacterial adhesion, dissemination and survival. In this review, we focus on proteomics, electron cryo-microscopy and structural modeling to showcase instances where affinity-purification (AP) and cross-linking (XL) mass spectrometry (MS) has advanced our understanding of host–pathogen interactions. We highlight cases where XL-MS in combination with structural modeling has provided insight into the quaternary structure of interspecies protein complexes. We further exemplify how electron cryo-tomography has been used to visualize bacterial–human interactions during attachment and infection. Lastly, we discuss how AP-MS, XL-MS and electron cryo-microscopy and -tomography together with structural modeling approaches can be used in future studies to broaden our knowledge regarding the function, dynamics and evolution of such interactions. This knowledge will be of relevance for future drug and vaccine development programs. [ABSTRACT FROM AUTHOR]

Consistent detection and quantification of protein post-translational modifications (PTMs) across sample cohorts is a prerequisite for functional analysis of biological processes. Data-independent acquisition (DIA) is a bottom-up mass spectrometry approach that provides complete information on precursor and fragment ions. However, owing to the convoluted structure of DIA data sets, confident, systematic identification and quantification of peptidoforms has remained challenging. Here, we present inference of peptidoforms (IPF), a fully automated algorithm that uses spectral libraries to query, validate and quantify peptidoforms in DIA data sets. The method was developed on data acquired by the DIA method SWATH-MS and benchmarked using a synthetic phosphopeptide reference data set and phosphopeptide-enriched samples. IPF reduced false site-localization by more than sevenfold compared with previous approaches, while recovering 85.4% of the true signals. Using IPF, we quantified peptidoforms in DIA data acquired from >200 samples of blood plasma of a human twin cohort and assessed the contribution of heritable, environmental and longitudinal effects on their PTMs. [ABSTRACT FROM AUTHOR]

A letter to the editor is presented related to the analysis of data independent acquisition of mass spectrometry data by the proteomics software OpenSWATH.

An amendment to this paper has been published and can be accessed via a link at the top of the paper. [ABSTRACT FROM AUTHOR]

A fundamental challenge in medical microbiology is to characterize the dynamic protein–protein interaction networks formed at the host–pathogen interface. Here, we generate a quantitative interaction map between the significant human pathogen, Streptococcus pyogenes, and proteins from human saliva and plasma obtained via complementary affinity-purification and bacterial-surface centered enrichment strategies and quantitative mass spectrometry. Perturbation of the network using immunoglobulin protease cleavage, mixtures of different concentrations of saliva and plasma, and different S. pyogenes serotypes and their isogenic mutants, reveals how changing microenvironments alter the interconnectivity of the interaction map. The importance of host immunoglobulins for the interaction with human complement proteins is demonstrated and potential protective epitopes of importance for phagocytosis of S. pyogenes cells are localized. The interaction map confirms several previously described protein–protein interactions; however, it also reveals a multitude of additional interactions, with possible implications for host–pathogen interactions involving other bacterial species. Characterizing host-pathogen protein interactions can help elucidate the molecular basis of bacterial infections. Here, the authors use an integrative proteomics approach to generate a quantitative map of protein interactions between Streptococcus pyogenes and human saliva and plasma. [ABSTRACT FROM AUTHOR]

The understanding of complex biological systems is still hampered by limited knowledge of biologically relevant quaternary protein structures. Here, we demonstrate quaternary structure determination in biological samples using a combination of chemical cross-linking, high-resolution mass spectrometry and high-accuracy protein structure modeling. This approach, termed targeted cross-linking mass spectrometry (TX-MS), relies on computational structural models to score sets of targeted cross-linked peptide signals acquired using a combination of mass spectrometry acquisition techniques. We demonstrate the utility of TX-MS by creating a high-resolution quaternary model of a 1.8 MDa protein complex composed of a pathogen surface protein and ten human plasma proteins. The model is based on a dense network of cross-link distance constraints obtained directly in a mixture of human plasma and live bacteria. These results demonstrate that TX-MS can increase the applicability of flexible backbone docking algorithms to large protein complexes by providing rich cross-link distance information from complex biological samples. Protein structure determination in complex biological samples is still challenging. Here, the authors develop a computational modeling-guided cross-linking mass spectrometry method, obtaining a high-resolution model of a 1.8 MDa protein assembly from cross-links detected in a mixture of human plasma and bacteria. [ABSTRACT FROM AUTHOR]