Large-scale inference of protein tissue origin in gram-positive sepsis plasma using quantitative targeted proteomics

High-throughput mass spectrometry maps the sepsis plasma proteome and differences in patient response

Sepsis, the dysregulated host response to infection causing life-threatening organ dysfunction, is a global health challenge requiring better understanding of pathophysiology and new therapeutic approaches. Here, we applied high-throughput tandem mass spectrometry to delineate the plasma proteome for sepsis and comparator groups (noninfected critical illness, postoperative inflammation, and healthy volunteers) involving 2612 samples (from 1611 patients) and 4553 liquid chromatography-mass spectrometry analyses acquired through a single batch of continuous measurements, with a throughput of 100 samples per day. We show how this scale of data can delineate proteins, pathways, and coexpression modules in sepsis and be integrated with paired leukocyte transcriptomic data (837 samples from n = 649 patients). We mapped the plasma proteomic landscape of the host response in sepsis, including changes over time, and identified features relating to etiology, clinical phenotypes (including organ failures), and severity. This work reveals subphenotypes informative for sepsis response state, disease processes, and outcome; identifies potential biomarkers; and advances opportunities for a precision medicine approach to sepsis.

Quantification of Adaptive Immune Responses Against Protein-Binding Interfaces in the Streptococcal M1 Protein

Bacterial or viral antigens can contain subdominant protein regions that elicit weak antibody responses upon vaccination or infection although there is accumulating evidence that antibody responses against subdominant regions can enhance the protective immune response. One proposed mechanism for subdominant protein regions is the binding of host proteins that prevent antibody production against epitopes hidden within the protein binding interfaces. Here, we used affinity purification combined with quantitative mass spectrometry (AP-MS) to examine the level of competition between antigen-specific antibodies and host-pathogen protein interaction networks using the M1 protein from Streptococcus pyogenes as a model system. As most humans have circulating antibodies against the M1 protein, we first used AP-MS to show that the M1 protein interspecies protein network formed with human plasma proteins is largely conserved in naïve mice. Immunizing mice with the M1 protein generated a time-dependent increase of anti-M1 antibodies. AP-MS analysis comparing the composition of the M1-plasma protein network from naïve and immunized mice showed significant enrichment of 292 IgG peptides associated with 56 IgG chains in the immune mice. Despite the significant increase of bound IgGs, the levels of interacting plasma proteins were not significantly reduced in the immune mice. The results indicate that the antigen-specific polyclonal IgG against the M1 protein primarily targets epitopes outside the other plasma protein binding interfaces. In conclusion, this study demonstrates that AP-MS is a promising strategy to determine the relationship between antigen-specific antibodies and host-pathogen interaction networks that could be used to define subdominant protein regions of relevance for vaccine development.

HIV-2 mediated effects on target and bystander cells induce plasma proteome remodeling

Despite low or undetectable plasma viral load, people living with HIV-2 (PLWH2) typically progress toward AIDS. The driving forces behind HIV-2 disease progression and the role of viremia are still not known, but low-level replication in tissues is believed to play a role. To investigate the impact of viremic and aviremic HIV-2 infection on target and bystander cell pathology, we used data-independent acquisition mass spectrometry to determine plasma signatures of tissue and cell type engagement. Proteins derived from target and bystander cells in multiple tissues, such as the gastrointestinal tract and brain, were detected at elevated levels in plasma of PLWH2, compared with HIV negative controls. Moreover, viremic HIV-2 infection appeared to induce enhanced release of proteins from a broader range of tissues compared to aviremic HIV-2 infection. This study expands the knowledge on the link between plasma proteome remodeling and the pathological cell engagement in tissues during HIV-2 infection.

A pharmacoproteomic landscape of organotypic intervention responses in Gram-negative sepsis

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.

Multienzyme deep learning models improve peptide de novo sequencing by mass spectrometry proteomics

Generating and analyzing overlapping peptides through multienzymatic digestion is an efficient procedure for de novo protein using from bottom-up mass spectrometry (MS). Despite improved instrumentation and software, de novo MS data analysis remains challenging. In recent years, deep learning models have represented a performance breakthrough. Incorporating that technology into de novo protein sequencing workflows require machine-learning models capable of handling highly diverse MS data. In this study, we analyzed the requirements for assembling such generalizable deep learning models by systemcally varying the composition and size of the training set. We assessed the generated models' performances using two test sets composed of peptides originating from the multienzyme digestion of samples from various species. The peptide recall values on the test sets showed that the deep learning models generated from a collection of highly N- and C-termini diverse peptides generalized 76% more over the termini-restricted ones. Moreover, expanding the training set's size by adding peptides from the multienzymatic digestion with five proteases of several species samples led to a 2-3 fold generalizability gain. Furthermore, we tested the applicability of these multienzyme deep learning (MEM) models by fully de novo sequencing the heavy and light monomeric chains of five commercial antibodies (mAbs). MEMs extracted over 10000 matching and overlapped peptides across six different proteases mAb samples, achieving a 100% sequence coverage for 8 of the ten polypeptide chains. We foretell that the MEMs' proven improvements to de novo analysis will positively impact several applications, such as analyzing samples of high complexity, unknown nature, or the peptidomics field.

Tissue-Characteristic Expression of Mouse Proteome

The mouse is a valuable model organism for biomedical research. Here, we established a comprehensive spectral library and the data-independent acquisition-based quantitative proteome maps for 41 mouse organs, including some rarely reported organs such as the cornea, retina, and nine paired organs. The mouse spectral library contained 178,304 peptides from 12,320 proteins, including 1678 proteins not reported in previous mouse spectral libraries. Our data suggested that organs from the nervous system and immune system expressed the most distinct proteome compared with other organs. We also found characteristic protein expression of immune-privileged organs, which may help understanding possible immune rejection after organ transplantation. Each tissue type expressed characteristic high-abundance proteins related to its physiological functions. We also uncovered some tissue-specific proteins which have not been reported previously. The testis expressed highest number of tissue-specific proteins. By comparison of nine paired organs including kidneys, testes, and adrenal glands, we found left organs exhibited higher levels of antioxidant enzymes. We also observed expression asymmetry for proteins related to the apoptotic process, tumor suppression, and organ functions between the left and right sides. This study provides a comprehensive spectral library and a quantitative proteome resource for mouse studies.

Vascular Proteome Responses Precede Organ Dysfunction in a Murine Model of Staphylococcus aureus Bacteremia

Vascular dysfunction and organ failure are two distinct, albeit highly interconnected, clinical outcomes linked to morbidity and mortality in human sepsis. The mechanisms driving vascular and parenchymal damage are dynamic and display significant molecular cross talk between organs and tissues. Therefore, assessing their individual contribution to disease progression is technically challenging. Here, we hypothesize that dysregulated vascular responses predispose the organism to organ failure. To address this hypothesis, we have evaluated four major organs in a murine model of Staphylococcus aureus sepsis by combining in vivo labeling of the endothelial cell surface proteome, data-independent acquisition (DIA) mass spectrometry, and an integrative computational pipeline. The data reveal, with unprecedented depth and throughput, that a septic insult evokes organ-specific proteome responses that are highly compartmentalized, synchronously coordinated, and significantly correlated with the progression of the disease. These responses include abundant vascular shedding, dysregulation of the intrinsic pathway of coagulation, compartmentalization of the acute phase response, and abundant upregulation of glycocalyx components. Vascular cell surface proteome changes were also found to precede bacterial invasion and leukocyte infiltration into the organs, as well as to precede changes in various well-established cellular and biochemical correlates of systemic coagulopathy and tissue dysfunction. Importantly, our data suggest a potential role for the vascular proteome as a determinant of the susceptibility of the organs to undergo failure during sepsis. IMPORTANCE Sepsis is a life-threatening response to infection that results in immune dysregulation, vascular dysfunction, and organ failure. New methods are needed for the identification of diagnostic and therapeutic targets. Here, we took a systems-wide approach using data-independent acquisition (DIA) mass spectrometry to track the progression of bacterial sepsis in the vasculature leading to organ failure. Using a murine model of S. aureus sepsis, we were able to quantify thousands of proteins across the plasma and parenchymal and vascular compartments of multiple organs in a time-resolved fashion. We showcase the profound proteome remodeling triggered by sepsis over time and across these compartments. Importantly, many vascular proteome alterations precede changes in traditional correlates of organ dysfunction, opening a molecular window for the discovery of early markers of sepsis progression.

Proteomic Approaches to Unravel Mechanisms of Antibiotic Resistance and Immune Evasion of Bacterial Pathogens

The profound effects of and distress caused by the global COVID-19 pandemic highlighted what has been known in the health sciences a long time ago: that bacteria, fungi, viruses, and parasites continue to present a major threat to human health. Infectious diseases remain the leading cause of death worldwide, with antibiotic resistance increasing exponentially due to a lack of new treatments. In addition to this, many pathogens share the common trait of having the ability to modulate, and escape from, the host immune response. The challenge in medical microbiology is to develop and apply new experimental approaches that allow for the identification of both the microbe and its drug susceptibility profile in a time-sensitive manner, as well as to elucidate their molecular mechanisms of survival and immunomodulation. Over the last three decades, proteomics has contributed to a better understanding of the underlying molecular mechanisms responsible for microbial drug resistance and pathogenicity. Proteomics has gained new momentum as a result of recent advances in mass spectrometry. Indeed, mass spectrometry-based biomedical research has been made possible thanks to technological advances in instrumentation capability and the continuous improvement of sample processing and workflows. For example, high-throughput applications such as SWATH or Trapped ion mobility enable the identification of thousands of proteins in a matter of minutes. This type of rapid, in-depth analysis, combined with other advanced, supportive applications such as data processing and artificial intelligence, presents a unique opportunity to translate knowledge-based findings into measurable impacts like new antimicrobial biomarkers and drug targets. In relation to the Research Topic “Proteomic Approaches to Unravel Mechanisms of Resistance and Immune Evasion of Bacterial Pathogens,” this review specifically seeks to highlight the synergies between the powerful fields of modern proteomics and microbiology, as well as bridging translational opportunities from biomedical research to clinical practice.

Seasonal shift of the gut microbiome synchronizes host peripheral circadian rhythm for physiological adaptation to a low-fat diet in the giant panda

Characteristics of the gut microbiome vary synchronously with changes in host diet. However, the underlying effects of these fluctuations remain unclear. Here, we performed fecal microbiota transplantation (FMT) of diet-specific feces from an endangered mammal (the giant panda) into a germ-free mouse model. We demonstrated that the butyrate-producing bacterium Clostridium butyricum was more abundant during shoot-eating season than during the leaf-eating season, congruent with the significant increase in host body mass. Following season-specific FMT, the microbiota of the mouse model resembled that of the donor, and mice transplanted with the microbiota from the shoot-eating season grew faster and stored more fat. Mechanistic investigations revealed that butyrate extended the upregulation of hepatic circadian gene Per2, subsequently increasing phospholipid biosynthesis. Validation experiments further confirmed this causal relationship. This study demonstrated that seasonal shifts in the gut microbiome affect growth performance, facilitating a deeper understanding of host-microbe interactions in wild mammals.

Leveraging homologies for cross-species plasma proteomics in ungulates using data-independent acquisition

The collection of blood plasma is minimally invasive, and the fluid is a rich source of proteins for biomarker studies in both humans and animals. Plasma protein analysis by mass spectrometry (MS) can be challenging, though modern data acquisition strategies, such as sequential window acquisition of all theoretical fragment ion spectra (SWATH), enable reproducible quantitation of hundreds of proteins in non-depleted plasma from humans and laboratory model animals. Although there is strong potential to enhance veterinary and translational research, SWATH-based plasma proteomics in non-laboratory animals is virtually non-existent. One limitation to date is the lack of comprehensively annotated genomes to aid protein identification. The current study established plasma peptide spectral repositories for sheep and cattle that enabled quantification of over 200 proteins in non-depleted plasma using SWATH approach. Moreover, bioinformatics pipeline was developed to leverage inter-species homologies to enhance the depth of baseline libraries and plasma protein quantification in bovids. Finally, the practical utility of using bovid libraries for SWATH data extraction in taxonomically related non-domestic ungulate species (giraffe) has been demonstrated. SIGNIFICANCE: Ability to quickly generate comprehensive spectral libraries is limiting the applicability of data-independent acquisition, such as SWATH, to study proteomes of non-laboratory animals. We describe an approach to obtain relatively shallow foundational plasma repositories from domestic ruminants and employ homology searches to increase the depth of data, which we subsequently extend to unsequenced ungulates using SWATH method. When applied to cross-species proteomics, the number of proteins quantified by our approach far exceeds what is traditionally used in plasma protein tests.

Evaluation of the Molecular Mechanisms of Sepsis Using Proteomics

Sepsis is a complex syndrome promoted by pathogenic and host factors; it is characterized by dysregulated host responses and multiple organ dysfunction, which can lead to death. However, its underlying molecular mechanisms remain unknown. Proteomics, as a biotechnology research area in the post-genomic era, paves the way for large-scale protein characterization. With the rapid development of proteomics technology, various approaches can be used to monitor proteome changes and identify differentially expressed proteins in sepsis, which may help to understand the pathophysiological process of sepsis. Although previous reports have summarized proteomics-related data on the diagnosis of sepsis and sepsis-related biomarkers, the present review aims to comprehensively summarize the available literature concerning “sepsis”, “proteomics”, “cecal ligation and puncture”, “lipopolysaccharide”, and “post-translational modifications” in relation to proteomics research to provide novel insights into the molecular mechanisms of sepsis.

Quantitative proteomic analysis of human peripheral nerves from subjects with type 2 diabetes

AIMS

Diabetic peripheral neuropathy (DPN) is a common and severe complication to type 2 diabetes. The pathogenesis of DPN is not fully known, but several pathways and gene polymorphisms contributing to DPN are described. DPN can be studied using nerve biopsies, but studies on the proteome of the nerve itself, and its surrounding tissue as a whole, are lacking. Studies on the posterior interosseous nerve (PIN) have proposed PIN a useful indicator of DPN.

METHODS

A quantitative mass spectrometry-based proteomics analysis was made of peripheral nerves from age- and gender-matched living human male tissue donors; nine type 2 diabetes subjects, with decreased sural nerve action potentials indicating DPN, and six controls without type 2 diabetes, with normal electrophysiology results.

RESULTS

A total of 2617 proteins were identified. Linear regression was used to discover which proteins were differentially expressed between type 2 diabetes and controls. Only soft signals were found. Therefore, clustering of the 500 most variable proteins was made to find clusters of similar proteins in type 2 diabetes subjects and healthy controls.

CONCLUSIONS

This feasibility study shows, for the first time, that the use of quantitative mass spectrometry enables quantification of proteins from nerve biopsies from subjects with and without type 2 diabetes, which may aid in finding biomarkers of importance to DPN development.

Streptococcus pyogenes Forms Serotype- and Local Environment-Dependent Interspecies Protein Complexes

Streptococcus pyogenes is known to cause both mucosal and systemic infections in humans. In this study, we used a combination of quantitative and structural mass spectrometry techniques to determine the composition and structure of the interaction network formed between human plasma proteins and the surfaces of different S. pyogenes serotypes. Quantitative network analysis revealed that S. pyogenes forms serotype-specific interaction networks that are highly dependent on the domain arrangement of the surface-attached M protein. Subsequent structural mass spectrometry analysis and computational modeling of one of the M proteins, M28, revealed that the network structure changes across different host microenvironments. We report that M28 binds secretory IgA via two separate binding sites with high affinity in saliva. During vascular leakage mimicked by increasing plasma concentrations in saliva, the binding of secretory IgA was replaced by the binding of monomeric IgA and C4b-binding protein (C4BP). This indicates that an upsurge of C4BP in the local microenvironment due to damage to the mucosal membrane drives the binding of C4BP and monomeric IgA to M28. These results suggest that S. pyogenes has evolved to form microenvironment-dependent host-pathogen protein complexes to combat human immune surveillance during both mucosal and systemic infections.

IMPORTANCEStreptococcus pyogenes (group A Streptococcus [GAS]), is a human-specific Gram-positive bacterium. Each year, the bacterium affects 700 million people globally, leading to 160,000 deaths. The clinical manifestations of S. pyogenes are diverse, ranging from mild and common infections like tonsillitis and impetigo to life-threatening systemic conditions such as sepsis and necrotizing fasciitis. S. pyogenes expresses multiple virulence factors on its surface to localize and initiate infections in humans. Among all these expressed virulence factors, the M protein is the most important antigen. In this study, we perform an in-depth characterization of the human protein interactions formed around one of the foremost human pathogens. This strategy allowed us to decipher the protein interaction networks around different S. pyogenes strains on a global scale and to compare and visualize how such interactions are mediated by M proteins.

Extending the Proteomic Characterization of Candida albicans Exposed to Stress and Apoptotic Inducers through Data-Independent Acquisition Mass Spectrometry

Candida albicans is a commensal fungus that causes systemic infections in immunosuppressed patients. In order to deal with the changing environment during commensalism or infection, C. albicans must reprogram its proteome. Characterizing the stress-induced changes in the proteome that C. albicans uses to survive should be very useful in the development of new antifungal drugs. We studied the C. albicans global proteome after exposure to hydrogen peroxide (H₂O₂) and acetic acid (AA), using a data-independent acquisition mass spectrometry (DIA-MS) strategy. More than 2,000 C. albicans proteins were quantified using an ion library previously constructed using data-dependent acquisition mass spectrometry (DDA-MS). C. albicans responded to treatment with H₂O₂ with an increase in the abundance of many proteins involved in the oxidative stress response, protein folding, and proteasome-dependent catabolism, which led to increased proteasome activity. The data revealed a previously unknown key role for Prn1, a protein similar to pirins, in the oxidative stress response. Treatment with AA resulted in a general decrease in the abundance of proteins involved in amino acid biosynthesis, protein folding, and rRNA processing. Almost all proteasome proteins declined, as did proteasome activity. Apoptosis was observed after treatment with H₂O₂ but not AA. A targeted proteomic study of 32 proteins related to apoptosis in yeast supported the results obtained by DIA-MS and allowed the creation of an efficient method to quantify relevant proteins after treatment with stressors (H₂O₂, AA, and amphotericin B). This approach also uncovered a main role for Oye32, an oxidoreductase, suggesting this protein as a possible apoptotic marker common to many stressors.

IMPORTANCE Fungal infections are a worldwide health problem, especially in immunocompromised patients and patients with chronic disorders. Invasive candidiasis, caused mainly by C. albicans, is among the most common fungal diseases. Despite the existence of treatments to combat candidiasis, the spectrum of drugs available is limited. For the discovery of new drug targets, it is essential to know the pathogen response to different stress conditions. Our study provides a global vision of proteomic remodeling in C. albicans after exposure to different agents, such as hydrogen peroxide, acetic acid, and amphotericin B, that can cause apoptotic cell death. These results revealed the significance of many proteins related to oxidative stress response and proteasome activity, among others. Of note, the discovery of Prn1 as a key protein in the defense against oxidative stress as well the increase in the abundance of Oye32 protein when apoptotic process occurred point them out as possible drug targets.

Endothelial Heparan Sulfate Mediates Hepatic Neutrophil Trafficking and Injury during Staphylococcus aureus Sepsis

Hepatic failure is an important risk factor for poor outcome in septic patients. Using a chemical tagging workflow and high-resolution mass spectrometry, we demonstrate that rapid proteome remodeling of the vascular surfaces precedes hepatic damage in a murine model of Staphylococcus aureus sepsis. These early changes include vascular deposition of neutrophil-derived proteins, shedding of vascular receptors, and altered levels of heparin/heparan sulfate-binding factors. Modification of endothelial heparan sulfate, a major component of the vascular glycocalyx, diminishes neutrophil trafficking to the liver and reduces hepatic coagulopathy and organ damage during the systemic inflammatory response to infection. Modifying endothelial heparan sulfate likewise reduces neutrophil trafficking in sterile hepatic injury, reflecting a more general role of heparan sulfate contribution to the modulation of leukocyte behavior during inflammation. IMPORTANCE Vascular glycocalyx remodeling is critical to sepsis pathology, but the glycocalyx components that contribute to this process remain poorly characterized. This article shows that during Staphylococcus aureus sepsis, the liver vascular glycocalyx undergoes dramatic changes in protein composition associated with neutrophilic activity and heparin/heparan sulfate binding, all before organ damage is detectable by standard circulating liver damage markers or histology. Targeted manipulation of endothelial heparan sulfate modulates S. aureus sepsis-induced hepatotoxicity by controlling the magnitude of neutrophilic infiltration into the liver in both nonsterile and sterile injury. These data identify an important vascular glycocalyx component that impacts hepatic failure during nonsterile and sterile injury.

Porous Inorganic Materials for Bioanalysis and Diagnostic Applications

Porous inorganic materials play an important role in adsorbing targeted analytes and supporting efficient reactions in analytical science. The detection performance relies on the structural properties of porous materials, considering the tunable pore size, shape, connectivity, etc. Herein, we first clarify the enhancement mechanisms of porous materials for bioanalysis, concerning the detection sensitivity and selectivity. The diagnostic applications of porous material-assisted platforms by coupling with various analytical techniques, including electrochemical sensing, optical spectrometry, and mass spectrometry, etc., are then reviewed. We foresee that advanced porous materials will bring far-reaching implications in bioanalysis toward real-case applications, especially as diagnostic assays in clinical settings.

Evaluation of Spin Columns for Human Plasma Depletion to Facilitate MS-Based Proteomics Analysis of Plasma

High abundant protein depletion is a common strategy applied to increase analytical depth in global plasma proteomics experiment setups. The standard strategies for depletion of the highest abundant proteins currently rely on multiple-use HPLC columns or multiple-use spin columns. Here we evaluate the performance of single-use spin columns for plasma depletion and show that the single-use spin reduces handling time by allowing parallelization and is easily adapted to a nonspecialized lab environment without reducing the high plasma proteome coverage and reproducibility. In addition, we evaluate the effect of viral heat inactivation on the plasma proteome, an additional step in the plasma preparation workflow that allows the sample preparation of SARS-Cov2-infected samples to be performed in a BSL3 laboratory, and report the advantage of performing the heat inactivation postdepletion. We further show the possibility of expanding the use of the depletion column cross-species to macaque plasma samples. In conclusion, we report that single-use spin columns for high abundant protein depletion meet the requirements for reproducibly in in-depth plasma proteomics and can be applied on a common animal model while also reducing the sample handling time.

Proteotyping of knockout mouse strains reveals sex- and strain-specific signatures in blood plasma

We proteotyped blood plasma from 30 mouse knockout strains and corresponding wild-type mice from the International Mouse Phenotyping Consortium. We used targeted proteomics with internal standards to quantify 375 proteins in 218 samples. Our results provide insights into the manifested effects of each gene knockout at the plasma proteome level. We first investigated possible contamination by erythrocytes during sample preparation and labeled, in one case, up to 11 differential proteins as erythrocyte originated. Second, we showed that differences in baseline protein abundance between female and male mice were evident in all mice, emphasizing the necessity to include both sexes in basic research, target discovery, and preclinical effect and safety studies. Next, we identified the protein signature of each gene knockout and performed functional analyses for all knockout strains. Further, to demonstrate how proteome analysis identifies the effect of gene deficiency beyond traditional phenotyping tests, we provide in-depth analysis of two strains, C8a-/- and Npc2+/-. The proteins encoded by these genes are well-characterized providing good validation of our method in homozygous and heterozygous knockout mice. Ig alpha chain C region, a poorly characterized protein, was among the differentiating proteins in C8a-/-. In Npc2+/- mice, where histopathology and traditional tests failed to differentiate heterozygous from wild-type mice, our data showed significant difference in various lysosomal storage disease-related proteins. Our results demonstrate how to combine absolute quantitative proteomics with mouse gene knockout strategies to systematically study the effect of protein absence. The approach used here for blood plasma is applicable to all tissue protein extracts.

Generation of a mouse SWATH-MS spectral library to quantify 10148 proteins involved in cell reprogramming

Murine models are amongst the most widely used systems to study biology and pathology. Targeted quantitative proteomic analysis is a relatively new tool to interrogate such systems. Recently the need for relative quantification on hundreds to thousands of samples has driven the development of Data Independent Acquisition methods. One such technique is SWATH-MS, which in the main requires prior acquisition of mass spectra to generate an assay reference library. In stem cell research, it has been shown pluripotency can be induced starting with a fibroblast population. In so doing major changes in expressed proteins is inevitable. Here we have created a reference library to underpin such studies. This is inclusive of an extensively documented script to enable replication of library generation from the raw data. The documented script facilitates reuse of data and adaptation of the library to novel applications. The resulting library provides deep coverage of the mouse proteome. The library covers 29519 proteins (53% of the proteome) of which 7435 (13%) are supported by a proteotypic peptide.

Label-Free Method Development for Hydroxyproline PTM Mapping in Human Plasma Proteome

Post-translational modifications (PTMs) impart structural heterogeneities that can alter plasma proteins' functions in various pathophysiological processes. However, the identification and mapping of PTMs in untargeted plasma proteomics is still a challenge due to the presence of diverse components in blood. Here, we report a label-free method for identifying and mapping hydroxylated proteins using tandem mass spectrometry (MS/MS) in the human plasma sample. Our untargeted proteomics approach led us to identify 676 de novo sequenced peptides in human plasma that correspond to 201 proteins, out of which 11 plasma proteins were found to be hydroxylated. Among these hydroxylated proteins, Immunoglobulin A1 (IgA1) heavy chain was found to be modified at residue 285 (Pro²⁸⁵ to Hyp²⁸⁵), which was further validated by MS/MS study. Molecular dynamics (MD) simulation analysis demonstrated that this proline hydroxylation in IgA1 caused both local and global structural changes. Overall, this study provides a comprehensive understanding of the protein profile containing Hyp PTMs in human plasma and shows the future perspective of identifying and discriminating Hyp PTM in the normal and the diseased proteomes.

Cerebrospinal fluid proteome maps detect pathogen-specific host response patterns in meningitis

Meningitis is a potentially life-threatening infection characterized by the inflammation of the leptomeningeal membranes. Many different viral and bacterial pathogens can cause meningitis, with differences in mortality rates, risk of developing neurological sequelae, and treatment options. Here, we constructed a compendium of digital cerebrospinal fluid (CSF) proteome maps to define pathogen-specific host response patterns in meningitis. The results revealed a drastic and pathogen-type specific influx of tissue-, cell-, and plasma proteins in the CSF, where, in particular, a large increase of neutrophil-derived proteins in the CSF correlated with acute bacterial meningitis. Additionally, both acute bacterial and viral meningitis result in marked reduction of brain-enriched proteins. Generation of a multiprotein LASSO regression model resulted in an 18-protein panel of cell- and tissue-associated proteins capable of classifying acute bacterial meningitis and viral meningitis. The same protein panel also enabled classification of tick-borne encephalitis, a subgroup of viral meningitis, with high sensitivity and specificity. The work provides insights into pathogen-specific host response patterns in CSF from different disease etiologies to support future classification of pathogen type based on host response patterns in meningitis.

Optimised data-independent acquisition strategy recaptures the classification of early-stage hepatocellular carcinoma based on data-dependent acquisition

Proteomics is increasingly used for exploring disease biomarkers and therapeutic targets. The data-independent acquisition (DIA) method collects all peptide signals in a sample, and provides a convenient way to archive disease-related molecular features for further exploration. In this study, we first established a high-coverage human hepatocellular carcinoma (HCC) spectral library containing 9393 protein groups, 119,903 peptides. Furthermore, we optimised the DIA method with respect to four key parameters: settings for mass spectrometry acquisition, gradient length, amount of sample loading, and length of analytical column. More than 6000 proteins from HepG2 cells could be stably quantified using the optimised one-shot DIA approach with a 2 h gradient time. One-shot DIA identified a similar number of proteins as did multi-fraction data-dependent acquisition (DDA) from the same group of HCC samples, but at a quarter of the total acquisition time. DIA data could recapture the classification results obtained from DDA data, thus paving the way for large-scale, multi-centre proteomics analysis of clinical samples. SIGNIFICANCE: The organ-specific spectral library for HCC and the optimised 2 h DIA approach met the urgent demands for large-scale quantitative proteomics analysis of HCC clinical samples. Compared with multi-fraction-DDA, the optimised one-shot DIA could reach a similar identification while consuming shorter acquisition time, thus making it possible to analyse thousands of clinical samples.

Targeted proteomics-derived biomarker profile develops a multi-protein classifier in liquid biopsies for early detection of esophageal squamous cell carcinoma from a population-based case-control study

BACKGROUND

Early diagnosis of esophageal squamous cell carcinoma (ESCC) remains a challenge due to the lack of specific blood biomarkers. We aimed to develop a serum multi-protein signature for the early detection of ESCC.

METHODS

We selected 70 healthy controls, 30 precancerous patients, 60 stage I patients, 70 stage II patients and 70 stage III/IV ESCC patients from a completed ESCC case-control study in a high-risk area of China. Olink Multiplex Oncology II targeted proteomics panel was used to simultaneously detect the levels of 92 cancer-related proteins in serum using proximity extension assay.

RESULTS

We found that 10 upregulated and 13 downregulated protein biomarkers in serum could distinguish the early-stage ESCC from healthy controls, which were validated by the significant dose-response relationships with ESCC pathological progression. Applying least absolute shrinkage and selection operator (LASSO) regression and backward elimination algorithm, ANXA1 (annexin A1), hK8 (kallikrein-8), hK14 (kallikrein-14), VIM (vimentin), and RSPO3 (R-spondin-3) were kept in the final model to discriminate early ESCC cases from healthy controls with an area under curve (AUC) of 0.936 (95% confidence interval: 0.899 ~ 0.973). The average accuracy rates of the five-protein classifier were 0.861 and 0.825 in training and test data by five-fold cross-validation.

CONCLUSIONS

Our study suggested that a combination of ANXA1, hK8, hK14, VIM and RSPO3 serum proteins could be considered as a potential tool for screening and early diagnosis of ESCC, especially with the establishment of a three-level hierarchical screening strategy for ESCC control.

New Approaches to Identify Sepsis Biomarkers: The Importance of Model and Sample Source for Mass Spectrometry

Septic shock is a systemic inflammatory response syndrome associated with circulatory failure leading to organ failure with a 40% mortality rate. Early diagnosis and prognosis of septic shock are necessary for specific and timely treatment. However, no predictive biomarker is available. In recent years, improvements in proteomics-based mass spectrometry have improved the detection of such biomarkers. This approach can be performed on different samples such as tissue or biological fluids. Working directly from human samples is complicated owing to interindividual variability. Indeed, patients are admitted at different stages of disease development and with signs of varying severity from one patient to another. All of these elements interfere with the identification of early, sensitive, and specific septic shock biomarkers. For these reasons, animal models of sepsis, although imperfect, are used to control the kinetics of the development of the pathology and to standardise experimentation, facilitating the identification of potential biomarkers. These elements underline the importance of the choice of animal model used and the sample to be studied during preclinical studies. The aim of this review is to discuss the relevance of different approaches to enable the identification of biomarkers that could indirectly be relevant to the clinical setting.

Comparison of the central human and mouse platelet signaling cascade by systems biological analysis

Background

Understanding the molecular mechanisms of platelet activation and aggregation is of high interest for basic and clinical hemostasis and thrombosis research. The central platelet protein interaction network is involved in major responses to exogenous factors. This is defined by systemsbiological pathway analysis as the central regulating signaling cascade of platelets (CC).

Results

The CC is systematically compared here between mouse and human and major differences were found. Genetic differences were analysed comparing orthologous human and mouse genes. We next analyzed different expression levels of mRNAs. Considering 4 mouse and 7 human high-quality proteome data sets, we identified then those major mRNA expression differences (81%) which were supported by proteome data. CC is conserved regarding genetic completeness, but we observed major differences in mRNA and protein levels between both species. Looking at central interactors, human PLCB2, MMP9, BDNF, ITPR3 and SLC25A6 (always Entrez notation) show absence in all murine datasets. CC interactors GNG12, PRKCE and ADCY9 occur only in mice. Looking at the common proteins, TLN1, CALM3, PRKCB, APP, SOD2 and TIMP1 are higher abundant in human, whereas RASGRP2, ITGB2, MYL9, EIF4EBP1, ADAM17, ARRB2, CD9 and ZYX are higher abundant in mouse. Pivotal kinase SRC shows different regulation on mRNA and protein level as well as ADP receptor P2RY12.

Conclusions

Our results highlight species-specific differences in platelet signaling and points of specific fine-tuning in human platelets as well as murine-specific signaling differences.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12864-020-07215-4.

Molecular Dynamics Simulation-assisted Ionic Liquid Screening for Deep Coverage Proteome Analysis

In-depth coverage of proteomic analysis could enhance our understanding to the mechanism of the protein functions. Unfortunately, many highly hydrophobic proteins and low-abundance proteins, which play critical roles in signaling networks, are easily lost during sample preparation, mainly attributed to the fact that very few extractants can simultaneously satisfy the requirements on strong solubilizing ability to membrane proteins and good enzyme compatibility. Thus, it is urgent to screen out ideal extractant from the huge compound libraries in a fast and effective way. Herein, by investigating the interior mechanism of extractants on the membrane proteins solubilization and trypsin compatibility, a molecular dynamics simulation system was established as complement to the experimental procedure to narrow down the scope of candidates for proteomics analysis. The simulation data shows that the van der Waals interaction between cation group of ionic liquid and membrane protein is the dominant factor in determining protein solubilization. In combination with the experimental data, 1-dodecyl-3-methylimidazolium chloride (C12Im-Cl) is on the shortlist for the suitable candidates from comprehensive aspects. Inspired by the advantages of C12Im-Cl, an ionic liquid-based filter-aided sample preparation (i-FASP) method was developed. Using this strategy, over 3,300 proteins were confidently identified from 103 HeLa cells (∼100 ng proteins) in a single run, an improvement of 53% over the conventional FASP method. Then the i-FASP method was further successfully applied to the label-free relative quantitation of human liver cancer and para-carcinoma tissues with obviously improved accuracy, reproducibility and coverage than the commonly used urea-based FASP method. The above results demonstrated that the i-FASP method could be performed as a versatile tool for the in-depth coverage proteomic analysis of biological samples.

A mouse SWATH-mass spectrometry reference spectral library enables deconvolution of species-specific proteomic alterations in human tumour xenografts

SWATH-mass spectrometry (MS) enables accurate and reproducible proteomic profiling in multiple model organisms including the mouse. Here, we present a comprehensive mouse reference spectral library (MouseRefSWATH) that permits quantification of up to 10,597 proteins (62.2% of the mouse proteome) by SWATH-MS. We exploit MouseRefSWATH to develop an analytical pipeline for species-specific deconvolution of proteomic alterations in human tumour xenografts (XenoSWATH). This method overcomes the challenge of high sequence similarity between mouse and human proteins, facilitating the study of host microenvironment-tumour interactions from 'bulk tumour' measurements. We apply the XenoSWATH pipeline to characterize an intraductal xenograft model of breast ductal carcinoma in situ and uncover complex regulation consistent with stromal reprogramming, where the modulation of cell migration pathways is not restricted to tumour cells but also operates in the mouse stroma upon progression to invasive disease. MouseRefSWATH and XenoSWATH open new opportunities for in-depth and reproducible proteomic assessment to address wide-ranging biological questions involving this important model organism.

Generation of a murine SWATH-MS spectral library to quantify more than 11,000 proteins

Targeted SWATH-MS data analysis is critically dependent on the spectral library. Comprehensive spectral libraries of human or several other organisms have been published, but the extensive spectral library for mouse, a widely used model organism is not available. Here, we present a large murine spectral library covering more than 11,000 proteins and 240,000 proteotypic peptides, which included proteins derived from 9 murine tissue samples and one murine L929 cell line. This resource supports the quantification of 67% of all murine proteins annotated by UniProtKB/Swiss-Prot. Furthermore, we applied the spectral library to SWATH-MS data from murine tissue samples. Data are available via SWATHAtlas (PASS01441).

Measurement(s)	Mouse Protein • mass spectrum • spectral library
Technology Type(s)	mass spectrometry • combined ms-ms + spectral library search
Sample Characteristic - Organism	Mus musculus

Machine-accessible metadata file describing the reported data: 10.6084/m9.figshare.11968230

Reproducibility, Specificity and Accuracy of Relative Quantification Using Spectral Library-based Data-independent Acquisition

Currently data-dependent acquisition (DDA) is the method of choice for mass spectrometry-based proteomics discovery experiments, but data-independent acquisition (DIA) is steadily becoming more important. One of the most important requirements to perform a DIA analysis is the availability of suitable spectral libraries for peptide identification and quantification. Several studies were performed addressing the evaluation of spectral library performance for protein identification in DIA measurements. But so far only few experiments estimate the effect of these libraries on the quantitative level.In this work we created a gold standard spike-in sample set with known contents and ratios of proteins in a complex protein matrix that allowed a detailed comparison of DIA quantification data obtained with different spectral library approaches. We used in-house generated sample-specific spectral libraries created using varying sample preparation approaches and repeated DDA measurement. In addition, two different search engines were tested for protein identification from DDA data and subsequent library generation. In total, eight different spectral libraries were generated, and the quantification results compared with a library free method, as well as a default DDA analysis. Not only the number of identifications on peptide and protein level in the spectral libraries and the corresponding DIA analysis results was inspected, but also the number of expected and identified differentially abundant protein groups and their ratios.We found, that while libraries of prefractionated samples were generally larger, there was no significant increase in DIA identifications compared with repetitive non-fractionated measurements. Furthermore, we show that the accuracy of the quantification is strongly dependent on the applied spectral library and whether the quantification is based on peptide or protein level. Overall, the reproducibility and accuracy of DIA quantification is superior to DDA in all applied approaches.Data has been deposited to the ProteomeXchange repository with identifiers PXD012986, PXD012987, PXD012988 and PXD014956.

Proteomic atlas of organ vasculopathies triggered by Staphylococcus aureus sepsis

Sepsis is a life-threatening condition triggered by a dysregulated host response to microbial infection resulting in vascular dysfunction, organ failure and death. Here we provide a semi-quantitative atlas of the murine vascular cell-surface proteome at the organ level, and how it changes during sepsis. Using in vivo chemical labeling and high-resolution mass spectrometry, we demonstrate the presence of a vascular proteome that is perfusable and shared across multiple organs. This proteome is enriched in membrane-anchored proteins, including multiple regulators of endothelial barrier functions and innate immunity. Further, we automated our workflows and applied them to a murine model of methicillin-resistant Staphylococcus aureus (MRSA) sepsis to unravel changes during systemic inflammatory responses. We provide an organ-specific atlas of both systemic and local changes of the vascular proteome triggered by sepsis. Collectively, the data indicates that MRSA-sepsis triggers extensive proteome remodeling of the vascular cell surfaces, in a tissue-specific manner.

Vascular surfaces are rapidly remodeled during systemic inflammatory responses and sepsis. Here, the authors combine in vivo biotinylation and high-resolution mass spectrometry to characterize organ-level changes of the murine vascular cell surface proteome induced by MRSA sepsis.

Proteomic Analysis of Infants Undergoing Cardiopulmonary Bypass Using Contemporary Ontological Tools

BACKGROUND

Cardiopulmonary bypass (CPB) is essential for the repair of many congenital cardiac defects in infants but is associated with significant derangements in hemostasis and systemic inflammation. As a result, hemorrhagic complications and thrombosis are major challenges in the management of children requiring CPB or extracorporeal membrane oxygenation. Conventional clinical laboratory tests capture individual hemostatic derangements (low platelets, elevated fibrinogen) but fail to describe the complex, overlapping interactions among the various components of coagulation, including cellular interactions, contact activation, fibrinolysis, and inflammation. Given recent advances in analytic tools for identifying protein-protein interactions in the plasma proteome, we hypothesized that an unbiased proteomic analysis would help identify networks of interacting proteins for further investigation in pediatric CPB.

MATERIALS AND METHODS

Infants up to 1 y of age were enrolled. Plasma samples were collected at 0, 1, 4, and 24 h after CPB. Mass spectrometry was used to identify proteins undergoing changes in concentration after CPB, and STRING and ToppGene tools were used to identify biological networks. Two-dimensional difference gel electrophoresis identified changes in protein concentrations. Inflammatory markers were assessed by enzyme-linked immunosorbent assay at the same time points.

RESULTS

Ten infants with cardiac anomalies requiring surgery and CPB were enrolled; no major complications were recorded (median age, 127.5 d; interquartile range, 181.25 d). Using two-dimensional difference gel electrophoresis, >1400 individual protein spots were observed, and 89 proteins demonstrated change in concentration >30% with P < 0.02 when comparing 1, 4, or 24 h to baseline. Among protein spots with significant changes in concentration after CPB, 29 were identified with mass spectrometry (33%). In our interrogation of functional associations among these differentially expressed proteins, our results were dominated by the acute phase response, coagulation, and cell signaling functional categories. Among cytokines analyzed by enzyme-linked immunosorbent assay, IL-2, IL-8, and IL-10 were elevated at 4 h but normalized by 24 h, whereas IL-6 was persistently elevated.

CONCLUSIONS

Infants manifest a robust response to CPB that includes overlapping, complex pathways. Further investigation of interactions among immune, coagulation, and cell signaling systems may lead to novel therapeutics or biomarkers useful in the management of infants requiring CPB.

The Generation of a Comprehensive Spectral Library for the Analysis of the Guinea Pig Proteome by SWATH-MS

Advances in liquid chromatography‐mass spectrometry have facilitated the incorporation of proteomic studies to many biology experimental workflows. Data‐independent acquisition platforms, such as sequential window acquisition of all theoretical mass spectra (SWATH‐MS), offer several advantages for label‐free quantitative assessment of complex proteomes over data‐dependent acquisition (DDA) approaches. However, SWATH data interpretation requires spectral libraries as a detailed reference resource. The guinea pig (Cavia porcellus) is an excellent experimental model for translation to many aspects of human physiology and disease, yet there is limited experimental information regarding its proteome. To overcome this knowledge gap, a comprehensive spectral library of the guinea pig proteome is generated. Homogenates and tryptic digests are prepared from 16 tissues and subjected to >200 DDA runs. Analysis of >250 000 peptide‐spectrum matches resulted in a library of 73 594 peptides from 7666 proteins. Library validation is provided by i) analyzing externally derived SWATH files (https://doi.org/10.1016/j.jprot.2018.03.023) and comparing peptide intensity quantifications; ii) merging of externally derived data to the base library. This furnishes the research community with a comprehensive proteomic resource that will facilitate future molecular‐phenotypic studies using (re‐engaging) the guinea pig as an experimental model of relevance to human biology. The spectral library and raw data are freely accessible in the MassIVE repository (MSV000083199).

Identification of two abundant Aerococcus urinae cell wall-anchored proteins

Aerococcus urinae is an emerging pathogen that causes urinary tract infections, bacteremia and infective endocarditis. The mechanisms through which A. urinae cause infection are largely unknown. The aims of this study were to describe the surface proteome of A. urinae and to analyse A. urinae genomes in search for genes encoding surface proteins. Two proteins, denoted Aerococcal surface protein (Asp) 1 and 2, were through the use of mass spectrometry based proteomics found to quantitatively dominate the aerococcal surface. The presence of these proteins on the surface was also shown using ELISA with serum from rabbits immunized with the recombinant Asp. These proteins had a signal sequence in the amino-terminal end and a cell wall-sorting region in the carboxy-terminal end, which contained an LPATG-motif, a hydrophobic domain and a positively charged tail. Twenty-three additional A. urinae genomes were sequenced using Illumina HiSeq technology. Six different variants of asp genes were found (denoted asp1-6). All isolates had either one or two of these asp-genes located in a conserved locus, designated Locus encoding Aerococcal Surface Proteins (LASP). The 25 genomes had in median 13 genes encoding LPXTG-proteins (range 6-24). For other Gram-positive bacteria, cell wall-anchored surface proteins with an LPXTG-motif play a key role for virulence. Thus, it will be of great interest to explore the function of the Asp proteins of A. urinae to establish a better understanding of the molecular mechanisms by which A. urinae cause disease.

A quantitative Streptococcus pyogenes-human protein-protein interaction map reveals localization of opsonizing antibodies

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.

Computational Health Engineering Applied to Model Infectious Diseases and Antimicrobial Resistance Spread

Infectious diseases are the primary cause of mortality worldwide. The dangers of infectious disease are compounded with antimicrobial resistance, which remains the greatest concern for human health. Although novel approaches are under investigation, the World Health Organization predicts that by 2050, septicaemia caused by antimicrobial resistant bacteria could result in 10 million deaths per year. One of the main challenges in medical microbiology is to develop novel experimental approaches, which enable a better understanding of bacterial infections and antimicrobial resistance. After the introduction of whole genome sequencing, there was a great improvement in bacterial detection and identification, which also enabled the characterization of virulence factors and antimicrobial resistance genes. Today, the use of in silico experiments jointly with computational and machine learning offer an in depth understanding of systems biology, allowing us to use this knowledge for the prevention, prediction, and control of infectious disease. Herein, the aim of this review is to discuss the latest advances in human health engineering and their applicability in the control of infectious diseases. An in-depth knowledge of host–pathogen–protein interactions, combined with a better understanding of a host’s immune response and bacterial fitness, are key determinants for halting infectious diseases and antimicrobial resistance dissemination.

Streptococcus pyogenes evades adaptive immunity through specific IgG glycan hydrolysis

EndoS from Streptococcus pyogenes hydrolyzes the functionally important glycan on the Fc portion of IgG during infections in humans. In mice with IgG mediated immunity against the M1 protein on the bacteria, EndoS is a virulence factor.

Streptococcus pyogenes (Group A streptococcus; GAS) is a human pathogen causing diseases from uncomplicated tonsillitis to life-threatening invasive infections. GAS secretes EndoS, an endoglycosidase that specifically cleaves the conserved N-glycan on IgG antibodies. In vitro, removal of this glycan impairs IgG effector functions, but its relevance to GAS infection in vivo is unclear. Using targeted mass spectrometry, we characterized the effects of EndoS on host IgG glycosylation during the course of infections in humans. Substantial IgG glycan hydrolysis occurred at the site of infection and systemically in the severe cases. We demonstrated decreased resistance to phagocytic killing of GAS lacking EndoS in vitro and decreased virulence in a mouse model of invasive infection. This is the first described example of specific bacterial IgG glycan hydrolysis during infection and thereby verifies the hypothesis that EndoS modifies antibodies in vivo. This mechanisms of immune evasion could have implications for treatment of severe GAS infections and for future efforts at vaccine development.

Multi-Omics Strategies Uncover Host-Pathogen Interactions

With the success of the Human Genome Project, large-scale systemic projects became a reality that enabled rapid development of the systems biology field. Systems biology approaches to host-pathogen interactions have been instrumental in the discovery of some specifics of Gram-negative bacterial recognition, host signal transduction, and immune tolerance. However, further research, particularly using multi-omics approaches, is essential to untangle the genetic, immunologic, (post)transcriptional, (post)translational, and metabolic mechanisms underlying progression from infection to clearance of microbes. The key to understanding host-pathogen interactions lies in acquiring, analyzing, and modeling multimodal data obtained through integrative multi-omics experiments. In this article, we will discuss how multi-omics analyses are adding to our understanding of the molecular basis of host-pathogen interactions and systemic maladaptive immune response of the host to microbes and microbial products.

Proteomic Analysis of Human Plasma during Intermittent Fasting

Intermittent fasting (IF) increases lifespan and decreases metabolic disease phenotypes and cancer risk in model organisms, but the health benefits of IF in humans are less clear. Human plasma derived from clinical trials is one of the most difficult sample sets to analyze using mass spectrometry-based proteomics due to the extensive sample preparation required and the need to process many samples to achieve statistical significance. Here, we describe an optimized and accessible device (Spin96) to accommodate up to 96 StageTips, a widely used sample preparation medium enabling efficient and consistent processing of samples prior to LC-MS/MS. We have applied this device to the analysis of human plasma from a clinical trial of IF. In this longitudinal study employing 8-weeks IF, we identified significant abundance differences induced by the IF intervention, including increased apolipoprotein A4 (APOA4) and decreased apolipoprotein C2 (APOC2) and C3 (APOC3). These changes correlated with a significant decrease in plasma triglycerides after the IF intervention. Given that these proteins have a role in regulating apolipoprotein particle metabolism, we propose that IF had a positive effect on lipid metabolism through modulation of HDL particle size and function. In addition, we applied a novel human protein variant database to detect common protein variants across the participants. We show that consistent detection of clinically relevant peptides derived from both alleles of many proteins is possible, including some that are associated with human metabolic phenotypes. Together, these findings illustrate the power of accessible workflows for proteomics analysis of clinical samples to yield significant biological insight.

Generation of a zebrafish SWATH-MS spectral library to quantify 10,000 proteins

Sequential window acquisition of all theoretical mass spectra (SWATH-MS) requires a spectral library to extract quantitative measurements from the mass spectrometry data acquired in data-independent acquisition mode (DIA). Large combined spectral libraries containing SWATH assays have been generated for humans and several other organisms, but so far no publicly available library exists for measuring the proteome of zebrafish, a rapidly emerging model system in biomedical research. Here, we present a large zebrafish SWATH spectral library to measure the abundance of 104,185 proteotypic peptides from 10,405 proteins. The library includes proteins expressed in 9 different zebrafish tissues (brain, eye, heart, intestine, liver, muscle, ovary, spleen, and testis) and provides an important new resource to quantify 40% of the protein-coding zebrafish genes. We employ this resource to quantify the proteome across brain, muscle, and liver and characterize divergent expression levels of paralogous proteins in different tissues. Data are available via ProteomeXchange (PXD010876, PXD010869) and SWATHAtlas (PASS01237).

Computational Proteomics with Jupyter and Python

Proteomics based on mass spectrometry produces complex data in large quantities. The need for flexible computational pipelines, in the context of big data, in proteomics and other areas of science, has prompted the development of computational platforms and libraries that facilitate data analysis and data processing. In this respect, Python appears to be one of the winners among programming languages in terms of popularity and development. This chapter shows how to perform basic tasks using Python and dedicated libraries in a Jupyter framework: from basic search result summarizations to the creation of MS1 chromatograms.

Improvement in Classification Performance Based on Target Vector Modification for All-Transfer Deep Learning

This paper proposes a target vector modification method for the all-transfer deep learning (ATDL) method. Deep neural networks (DNNs) have been used widely in many applications; however, the DNN has been known to be problematic when large amounts of training data are not available. Transfer learning can provide a solution to this problem. Previous methods regularize all layers, including the output layer, by estimating the relation vectors, which are then used instead of one-hot target vectors of the target domain. These vectors are estimated by averaging the target domain data of each target domain label in the output space. This method improves the classification performance, but it does not consider the relation between the relation vectors. From this point of view, we propose a relation vector modification based on constrained pairwise repulsive forces. High pairwise repulsive forces provide large distances between the relation vectors. In addition, the risk of divergence is mitigated by the constraint based on distributions of the output vectors of the target domain data. We apply our method to two simulation experiments and a disease classification using two-dimensional electrophoresis images. The experimental results show that reusing all layers through our estimation method is effective, especially for a significantly small number of the target domain data.

Human serum albumin alters specific genes that can play a role in survival and persistence in Acinetobacter baumannii

In the past few decades Acinetobacter baumannii has emerged as a notorious nosocomial pathogen because of its ability to acquire genetic material and persist in extreme environments. Recently, human serum albumin (HSA) was shown to significantly increase natural transformation frequency in A. baumannii. This observation led us to perform transcriptomic analysis of strain A118 under HSA induction to identify genes that are altered by HSA. Our results revealed the statistically significant differential expression of 296 protein-coding genes, including those associated with motility, biofilm formation, metabolism, efflux pumps, capsule synthesis, and transcriptional regulation. Phenotypic analysis of these traits showed an increase in surface-associated motility, a decrease in biofilm formation, reduced activity of a citric acid cycle associated enzyme, and increased survival associated with zinc availability. Furthermore, the expression of genes known to play a role in pathogenicity and antibiotic resistance were altered. These genes included those associated with RND-type efflux pumps, the type VI secretion system, iron acquisition/metabolism, and ß-lactam resistance. Together, these results illustrate how human products, in particular HSA, may play a significant role in both survival and persistence of A. baumannii.

Paving the way for precision medicine v2.0 in intensive care by profiling necroinflammation in biofluids

Current clinical diagnosis is typically based on a combination of approaches including clinical examination of the patient, clinical experience, physiologic and/or genetic parameters, high-tech diagnostic medical imaging, and an extended list of laboratory values mostly determined in biofluids such as blood and urine. One could consider this as precision medicine v1.0. However, recent advances in technology and better understanding of molecular mechanisms underlying disease will allow us to better characterize patients in the future. These improvements will enable us to distinguish patients who have similar clinical presentations but different cellular and molecular responses. Treatments will be able to be chosen more “precisely”, resulting in more appropriate therapy, precision medicine v2.0. In this review, we will reflect on the potential added value of recent advances in technology and a better molecular understanding of necrosis and inflammation for improving diagnosis and treatment of critically ill patients. We give a brief overview on the mutual interplay between necrosis and inflammation, which are two crucial detrimental factors in organ and/or systemic dysfunction. One of the challenges for the future will thus be the cellular and molecular profiling of necroinflammation in biofluids. The huge amount of data generated by profiling biomolecules and single cells through, for example, different omic-approaches is needed for data mining methods to allow patient-clustering and identify novel biomarkers. The real-time monitoring of biomarkers will allow continuous (re)evaluation of treatment strategies using machine learning models. Ultimately, we may be able to offer precision therapies specifically designed to target the molecular set-up of an individual patient, as has begun to be done in cancer therapeutics.

Critical care mostly implies life-threatening situations involving systemic infection, inflammation and necrosis. Biofluids are an easily accessible source of liquid biopsies that can be used to monitor the evolution of the patient’s critical illness. The cellular and molecular profiling of necrosis and inflammation in biofluids using cutting-edge technologies such as realtime immunodiagnostics, next-generation sequencing and mass spectrometry will pave the way for precision medicine v2.0 in critical care. This is needed for data mining approaches to allow patientclustering, identify novel biomarkers and develop novel intervention strategies controlling necrosis and inflammation. The real-time monitoring of biomarkers will allow continued (re)evaluation of treatment strategies using machine learning models.

Streptococcal inhibitor of complement (SIC) modulates fibrinolysis and enhances bacterial survival within fibrin clots

Some strains of the bacterial pathogen Streptococcus pyogenes secrete protein SIC (streptococcal inhibitor of complement), including strains of the clinically relevant M1 serotype. SIC neutralizes the effect of a number of antimicrobial proteins/peptides and interferes with the function of the host complement system. Previous studies have shown that some S. pyogenes proteins bind and modulate coagulation and fibrinolysis factors, raising the possibility that SIC also may interfere with the activity of these factors. Here we show that SIC interacts with both human thrombin and plasminogen, key components of coagulation and fibrinolysis. We found that during clot formation, SIC binds fibrin through its central region and that SIC inhibits fibrinolysis by interacting with plasminogen. Flow cytometry results indicated that SIC and plasminogen bind simultaneously to S. pyogenes bacteria, and fluorescence microscopy revealed co-localization of the two proteins at the bacterial surface. As a consequence, SIC-expressing bacteria entrapped in clots inhibit fibrinolysis, leading to delayed bacterial escape from the clots as compared with mutant bacteria lacking SIC. Moreover, within the clots SIC-expressing bacteria were protected against killing. In an animal model of subcutaneous infection, SIC-expressing bacteria exhibited a delayed systemic spread. These results demonstrate that the bacterial protein SIC interferes with coagulation and fibrinolysis and thereby enhances bacterial survival, a finding that has significant implications for S. pyogenes virulence.

Sepsis Diagnostics in the Era of "Omics" Technologies

Sepsis is a multifactorial clinical syndrome with an extremely dynamic clinical course and with high diverse clinical phenotype. Early diagnosis is crucial for the final clinical outcome. Previous studies have not identified a biomarker for the diagnosis of sepsis which would have sufficient sensitivity and specificity. Identification of the infectious agents or the use of molecular biology, next gene sequencing, has not brought significant benefit for the patient in terms of early diagnosis. Therefore, we are currently searching for biomarkers, through "omics" technologies with sufficient diagnostic specificity and sensitivity, able to predict the clinical course of the disease and the patient response to therapy. Current progress in the use of systems biology technologies brings us hope that by using big data from clinical trials such biomarkers will be found.