Applications of targeted proteomics in systems biology and translational medicine

Plasma Proteomic Assessment of Calcific Aortic Valve Disease in Older Adults

BACKGROUND

Calcific aortic valve disease (CAVD), and ensuing severe aortic stenosis (AS), is the foremost valvular disorder of aging, yet preventive therapies are lacking. A better understanding of the molecular underpinnings of aortic valve calcification (AVC) is necessary to develop pharmacologic interventions.

METHODS AND RESULTS

We undertook large-scale plasma proteomics in a cohort study of adults ≥65 years old, the CHS (Cardiovascular Health Study), to identify individual proteins associated with echocardiographic AVC and incident moderate/severe AS. Proteomics measurements were performed with the aptamer-based SomaLogic platform of ~5000 proteins. Significant proteins were validated in a second cohort, the AGES-RS (Age, Gene/Environment Susceptibility-Reykjavik Study), which assessed AVC and AS by computed tomography. The potential causal associations of replicated proteins were tested in 2-sample Mendelian randomization using identified cis protein quantitative trait loci in consortia having computed tomography-quantified AVC or AS as outcomes. Six proteins showed Bonferroni-corrected significant relationships with AVC in CHS. Three of these, CXCL-12 (C-X-C chemokine ligand 12), KLKB1 (kallikrein), and leptin, replicated in AGES-RS, of which the former 2 are novel. Only 1 protein, CXCL6, which showed a near-significant association with AS in the replication cohort, was significantly (positively) associated with incident AS. Mendelian randomization analysis was conducted for KLKB1, CXCL12, and CXCL6, which supported a causal relationship for higher KLKB1 with lower AVC (beta=-0.25, P=0.009).

CONCLUSIONS

This study of older adults newly identified and largely replicated associations of 3 circulating proteins with calcific aortic valve disease, of which the relationship of plasma KLKB1 may have a causal basis. Additional investigation is necessary to determine if KLKB1 could be harnessed for calcific aortic valve disease therapeutics.

ProPickML: Advancing Clinical Diagnostics with Automated Peak Picking in Label-Free Targeted Proteomics

In targeted proteomics utilizing Selected Reaction Monitoring (SRM), the precise detection of specific peptides within complex mixtures remains a significant challenge, particularly due to noise and interference in chromatograms. Existing methodologies, such as isotopic labeling and scoring algorithms, offer partial solutions but are constrained by high run times and elevated false discovery rates. To address these limitations, we have developed ProPickML a machine learning-based tool designed to accurately identify peptide peaks across diverse data sets, independent of the assumed presence of the peptide. This model was trained on a manually labeled data set and subsequently validated to assess its predictive accuracy. The results demonstrate that the model reliably identifies peptide peaks in the presence of noise, achieving a Matthews correlation coefficient (MCC) of 0.81 on an independent test data set, surpassing mProphet's MCC of 0.71. Implemented in R as ProPickML, this tool offers a competitive, cost-effective alternative to existing techniques, significantly reducing reliance on isotopic labeling and enhancing the accuracy of peptide identification in SRM workflows.

Optimizing SureQuant for Targeted Peptide Quantification: a Technical Comparison with PRM and SWATH-MS Methods

Bacterial infections are a major threat to human health worldwide. A better understanding of the properties and physiology of bacterial pathogens in human tissues is required to develop urgently needed novel control strategies. Mass spectrometry-based proteomics could yield such data, but identifying and quantifying scarce bacterial proteins against a preponderance of human proteins is challenging. Here, we explored the recently introduced SureQuant method for highly sensitive targeted mass spectrometry. Using a major human pathogen, the Gram-positive bacteria Staphylococcus aureus, as an example, we evaluated several parameters, including the number of targets and intensity thresholds, for optimal qualitative and quantitative protein analysis. By comparison, we found that SureQuant achieved the same quantitative performance as standard parallel reaction monitoring while allowing accurate and precise quantification of up to 400 targets. SureQuant also surpassed the sensitivity and quantification capabilities of global data-independent acquisition methods. Finally, to facilitate method development, we provide optimized MS parameters for the sensitive quantification of different peptide panel sizes. This study provides a foundation for the broader application of SureQuant in the analysis of clinical specimens containing trace amounts of bacterial proteins as well as other studies requiring ultrasensitive detection of low-abundant proteins.

Enhanced Sample Multiplexing-Based Targeted Proteomics with Intelligent Data Acquisition

Targeted proteomics has been playing an increasingly important role in hypothesis-driven protein research and clinical biomarker discovery. We previously created a workflow, Tomahto, to enable real-time targeted pathway proteomics assays using two-dimensional multiplexing technology. Coupled with the TMT 11-plex reagent, hundreds of proteins of interest from up to 11 samples can be targeted and accurately quantified in a single-shot experiment with remarkable sensitivity. However, room remains to further improve the sensitivity, accuracy, and throughput, especially for targeted studies demanding a high peptide-level success rate. Here, bearing in mind the goal to improve peptide-level targeting, we introduce several new functionalities in Tomahto, featuring the integration of gas-phase fractionation using the FAIMS device, an accompanying software program (TomahtoPrimer) to customize fragmentation for each peptide target, and support for higher multiplexing capacity with the latest TMTpro reagent. We demonstrate that adding these features to the Tomahto platform significantly improves overall success rate from 89% to 98% in a single 60 min targeted assay of 290 peptides across human cell lines, while boosting quantitative accuracy via reducing TMT reporter ion interference.

Delineating protein biomarkers for gastric cancers: A catalogue of mass spectrometry-based markers and assessment of their suitability for targeted proteomics applications

Gastric cancer (GC) is a global health concern. To facilitate improved management of GCs, protein biomarkers have been identified through mass spectrometry-based proteomics platforms. In order to exhibit clinical utility of such data, we congregated over 6800 differentially regulated proteins in GCs from proteomics studies and recorded the mass spectrometry platforms, association of the protein with infectious agents, protein identifiers, sample size and clinical characters of samples used with details on validation. Development of targeted proteomics methods is the cornerstone for pursuing these markers into clinical utility. Therefore, we developed Protein Biomarker Matrix for Gastric Cancer (PBMGC), a simple catalogue of robustness of each protein. This analysis yielded the identification of robust tissue, serum, and urine diagnostic and prognostic protein biomarker panels which can be further tested for their clinical utility. We also ascertained proteotypic tryptic peptides of 5631 proteins suitable for developing multiple reaction monitoring (MRM) assays. Extensive characterization of these peptides was carried out to record peptide ions, mass/charge and enhanced specific peptide features. With the vision of catering to proteomics researchers, the data generated through this analysis has been catalogued at Gastric Cancer Proteomics DataBase (GCPDB) (https://ciods.in/gcpdb/). Users can browse and download the data and improve GCPDB by submitting recently published data. SIGNIFICANCE: Mass spectrometry-based proteomics platforms have accumulated substantial data on proteins differentially regulated in gastric cancer (GC) clinical samples. The utility of such data in clinical applications is limited by search for suitable biomarker panels for assessment of GCs. We assembled over 6800 differentially regulated proteins in GCs from proteomics studies and recorded the corresponding details including mass spectrometry platforms, status on the association of the protein with infectious agents, protein identifiers from different databases, sample size and clinical characters of samples used in test and control conditions along with details on their validation. Towards the vision of utilizing these markers in clinical assays, Protein Biomarker Matrix for Gastric Cancer (PBMGC) was developed and clinically relevant multi-protein panels were identified. We also demonstrated identification and characterization of tryptic proteotypic tryptic peptides of 5631 proteins biomarkers of GCs which are suitable for development of MRM assays in a SCIEX QTRAP instrument. Aimed to caterproteomics researchers, the data generated through this analysis has been catalogued at Gastric Cancer Proteomics DataBase (GCPDB) (https://ciods.in/gcpdb/). The users can browse and download details on different markers and improve GCPDB by submitting recently published data. Such an analysis could lay a cornerstone for building more such resources or conduct such analysis in different clinical conditions to uptake and develop targeted proteomics as the method of choice for clinical applications.

Exploring novel protein-based biomarkers for advancing breast cancer diagnosis: A review

This review provides a contemporary examination of the evolving landscape of breast cancer (BC) diagnosis, focusing on the pivotal role of novel protein-based biomarkers. The overview begins by elucidating the multifaceted nature of BC, exploring its prevalence, subtypes, and clinical complexities. A critical emphasis is placed on the transformative impact of proteomics, dissecting the proteome to unravel the molecular intricacies of BC. Navigating through various sources of samples crucial for biomarker investigations, the review underscores the significance of robust sample processing methods and their validation in ensuring reliable outcomes. The central theme of the review revolves around the identification and evaluation of novel protein-based biomarkers. Cutting-edge discoveries are summarised, shedding light on emerging biomarkers poised for clinical application. Nevertheless, the review candidly addresses the challenges inherent in biomarker discovery, including issues of standardisation, reproducibility, and the complex heterogeneity of BC. The future direction section envisions innovative strategies and technologies to overcome existing challenges. In conclusion, the review summarises the current state of BC biomarker research, offering insights into the intricacies of proteomic investigations. As precision medicine gains momentum, the integration of novel protein-based biomarkers emerges as a promising avenue for enhancing the accuracy and efficacy of BC diagnosis. This review serves as a compass for researchers and clinicians navigating the evolving landscape of BC biomarker discovery, guiding them toward transformative advancements in diagnostic precision and personalised patient care.

GLUT2 regulation of p38 MAPK isoform protein expression and p38 phosphorylation in male versus female rat hypothalamic primary astrocyte Cultures

Recent studies documented regulation of hypothalamic astrocyte mitogen-activated protein kinase (MAPK) pathways, including p38, by the plasma membrane glucose carrier/sensor glucose transporter-2 (GLUT2). Sex-specific GLUT2 control of p38 phosphorylation was observed, but effects on individual p38 family protein profiles were not investigated. Current research employed an established primary astrocyte culture model, gene knockdown tools, and selective primary antisera against p38-alpha, p38-beta, p38-gamma, and p38-delta isoforms to investigate whether GLUT2 governs expression of one or more of these variants in a glucose-dependent manner. Data show that GLUT2 inhibits baseline expression of each p38 protein in male cultures, yet stimulates p38-delta profiles without affecting other p38 proteins in female. Glucose starvation caused selective up-regulation of p38-delta profiles in male versus p38-alpha and -gamma proteins in female; these positive responses were amplified by GLUT2 siRNA pretreatment. GLUT2 opposes or enhances basal p38 phosphorylation in male versus female, respectively. GLUT2 siRNA pretreatment did not affect glucoprivic patterns of phospho-p38 protein expression in either sex. Outcomes document co-expression of the four principal p38 MAPK family proteins in hypothalamic astrocytes, and implicate GLUT2 in regulation of all (male) versus one (female) variant(s). Glucoprivation up-regulated expression of distinctive p38 isoforms in each sex; these stimulatory responses are evidently blunted by GLUT2. Glucoprivic-associated loss of GLUT2 gene silencing effects on p38 phosphorylation infers either that glucose status determines whether this sensor controls phosphorylation, or that decrements in screened glucose in each instance are of sufficient magnitude to abolish GLUT2 regulation of that function.

Proteomics and Its Current Application in Biomedical Area: Concise Review

Biomedical researchers tirelessly seek cutting-edge technologies to advance disease diagnosis, drug discovery, and therapeutic interventions, all aimed at enhancing human and animal well-being. Within this realm, proteomics stands out as a pivotal technology, focusing on extensive studies of protein composition, structure, function, and interactions. Proteomics, with its subdivisions of expression, structural, and functional proteomics, plays a crucial role in unraveling the complexities of biological systems. Various sophisticated techniques are employed in proteomics, including polyacrylamide gel electrophoresis, mass spectrometry analysis, NMR spectroscopy, protein microarray, X-ray crystallography, and Edman sequencing. These methods collectively contribute to the comprehensive understanding of proteins and their roles in health and disease. In the biomedical field, proteomics finds widespread application in cancer research and diagnosis, stem cell studies, and the diagnosis and research of both infectious and noninfectious diseases. In addition, it plays a pivotal role in drug discovery and the emerging frontier of personalized medicine. The versatility of proteomics allows researchers to delve into the intricacies of molecular mechanisms, paving the way for innovative therapeutic approaches. As infectious and noninfectious diseases continue to emerge and the field of biomedical research expands, the significance of proteomics becomes increasingly evident. Keeping abreast of the latest developments in proteomics applications becomes paramount for the development of therapeutics, translational research, and study of diverse diseases. This review aims to provide a comprehensive overview of proteomics, offering a concise outline of its current applications in the biomedical domain. By doing so, it seeks to contribute to the understanding and advancement of proteomics, emphasizing its pivotal role in shaping the future of biomedical research and therapeutic interventions.

Multiple reaction monitoring assays for large-scale quantitation of proteins from 20 mouse organs and tissues

Mouse is the mammalian model of choice to study human health and disease due to its size, ease of breeding and the natural occurrence of conditions mimicking human pathology. Here we design and validate multiple reaction monitoring mass spectrometry (MRM-MS) assays for quantitation of 2118 unique proteins in 20 murine tissues and organs. We provide open access to technical aspects of these assays to enable their implementation in other laboratories, and demonstrate their suitability for proteomic profiling in mice by measuring normal protein abundances in tissues from three mouse strains: C57BL/6NCrl, NOD/SCID, and BALB/cAnNCrl. Sex- and strain-specific differences in protein abundances are identified and described, and the measured values are freely accessible via our MouseQuaPro database: http://mousequapro.proteincentre.com . Together, this large library of quantitative MRM-MS assays established in mice and the measured baseline protein abundances represent an important resource for research involving mouse models.

Fast proteomics with dia-PASEF and analytical flow-rate chromatography

Increased throughput in proteomic experiments can improve accessibility of proteomic platforms, reduce costs, and facilitate new approaches in systems biology and biomedical research. Here we propose combination of analytical flow rate chromatography with ion mobility separation of peptide ions, data-independent acquisition, and data analysis with the DIA-NN software suite, to achieve high-quality proteomic experiments from limited sample amounts, at a throughput of up to 400 samples per day. For instance, when benchmarking our workflow using a 500-μL/min flow rate and 3-min chromatographic gradients, we report the quantification of 5211 proteins from 2 μg of a mammalian cell-line standard at high quantitative accuracy and precision. We further used this platform to analyze blood plasma samples from a cohort of COVID-19 inpatients, using a 3-min chromatographic gradient and alternating column regeneration on a dual pump system. The method delivered a comprehensive view of the COVID-19 plasma proteome, allowing classification of the patients according to disease severity and revealing plasma biomarker candidates.

Effects of pejus and pessimum zone salinity stress on gill proteome networks and energy homeostasis in Oreochromis mossambicus

Salinity tolerance in fish involves a suite of physiological changes, but a cohesive theory leading to a mechanistic understanding at the organismal level is lacking. To examine the potential of adapting energy homeostasis theory in the context of salinity stress in teleost fish, Oreochromis mossambicus were acclimated to hypersalinity at multiple rates and durations to determine salinity ranges of tolerance and resistance. Over 3000 proteins were quantified simultaneously to analyze molecular phenotypes associated with hypersalinity. A species- and tissue-specific data-independent acquisition (DIA) assay library of MSMS spectra was created. Protein networks representing complex molecular phenotypes associated with salinity acclimation were generated. O. mossambicus has a wide "zone of resistance" from 75 g/kg salinity to 120 g/kg. Crossing into the zone of resistance resulted in marked phenotypic changes including blood osmolality over 400 mOsm/kg, reduced body condition, and cessation of feeding. Protein networks impacted by hypersalinity consist of electron transport chain (ETC) proteins and specific osmoregulatory proteins. Cytoskeletal, cell adhesion, and extracellular matrix proteins are enriched in networks that are sensitive to the critical salinity threshold. These network analyses identify specific proteome changes that are associated with distinct zones described by energy homeostasis theory and distinguish them from general hypersalinity-induced proteome changes.

An overview of metabolomic and proteomic profiling in bipolar disorder and its clinical value

INTRODUCTION

Bipolar disorder (BD) is a complex psychiatric disease characterized by alternating mood episodes. As for any other psychiatric illness, currently there is no biochemical test that is able to support diagnosis or therapeutic decisions for BD. In this context, the discovery and validation of biomarkers are interesting strategies that can be achieved through proteomics and metabolomics.

AREAS COVERED

In this descriptive review, a literature search including original articles and systematic reviews published in the last decade was performed with the objective to discuss the results of BD proteomic and metabolomic profiling analyses and indicate proteins and metabolites (or metabolic pathways) with potential clinical value.

EXPERT OPINION

A large number of proteins and metabolites have been reported as potential BD biomarkers; however, most studies do not reach biomarker validation stages. An effort from the scientific community should be directed toward the validation of biomarkers and the development of simplified bioanalytical techniques or protocols to determine them in biological samples, in order to translate proteomic and metabolomic findings into clinical routine assays.

Targeted Proteomic Profiling Revealed Roles of Small GTPases during Osteogenic Differentiation

The small GTPase superfamily of proteins are crucial for numerous cellular processes, including early development. The roles of these proteins in osteogenic differentiation, however, remained poorly explored. In this study, we employed a high-throughput targeted proteomic method, relying on scheduled liquid chromatography-multiple-reaction monitoring (LC-MRM) coupled with synthetic stable isotope-labeled peptides, to interrogate systematically the temporal responses of the entire small GTPase proteome during the course of osteogenic differentiation of H9 human embryonic stem cells. Our results demonstrated that the method offers high quantification accuracy, reproducibility, and throughput. In addition, the quantification results revealed altered expression of a large number of small GTPases accompanied with osteogenic differentiation, especially those involved with autophagy. We also documented a previously unrecognized role of KRAS in osteogenesis, where it regulates the accumulation of extracellular matrix for mineralization through attenuating the activity of secreted matrix metalloproteinase 9 (MMP9). Together, this study represents a novel application of a state-of-the-art analytical method, i.e., targeted quantitative proteomics, for revealing the progressive reprogramming of the small GTPase proteome during osteogenic differentiation of human embryonic stem cells, and our results revealed KRAS as a new regulator for osteogenesis.

Optimised plasma sample preparation and LC-MS analysis to support large-scale proteomic analysis of clinical trial specimens: Application to the Fenofibrate Intervention and Event Lowering in Diabetes (FIELD) trial

PURPOSE

Robust, affordable plasma proteomic biomarker workflows are needed for large-scale clinical studies. We evaluated aspects of sample preparation to allow liquid chromatography-mass spectrometry (LC-MS) analysis of more than 1500 samples from the Fenofibrate Intervention and Event Lowering in Diabetes (FIELD) trial of adults with type 2 diabetes.

METHODS

Using LC-MS with data-independent acquisition we evaluated four variables: plasma protein depletion, EDTA or citrated anti-coagulant blood collection tubes, plasma lipid depletion strategies and plasma freeze-thaw cycles. Optimised methods were applied in a pilot study of FIELD participants.

RESULTS

LC-MS of undepleted plasma conducted over a 45 min gradient yielded 172 proteins after excluding immunoglobulin isoforms. Cibachrome-blue-based depletion yielded additional proteins but with cost and time expenses, while immunodepleting albumin and IgG provided few additional identifications. Only minor variations were associated with blood collection tube type, delipidation methods and freeze-thaw cycles. From 65 batches involving over 1500 injections, the median intra-batch quantitative differences in the top 100 proteins of the plasma external standard were less than 2%. Fenofibrate altered seven plasma proteins.

CONCLUSIONS AND CLINICAL RELEVANCE

A robust plasma handling and LC-MS proteomics workflow for abundant plasma proteins has been developed for large-scale biomarker studies that balance proteomic depth with time and resource costs.

FungiProteomeDB: a database for the molecular weight and isoelectric points of the fungal proteomes

Proteins' molecular weight (MW) and isoelectric point (pI) are crucial for their subcellular localization and subsequent function. These are also useful in 2D gel electrophoresis, liquid chromatography-mass spectrometry and X-ray protein crystallography. Moreover, visualizations like a virtual 2D proteome map of pI vs. MW are worthwhile to discuss the proteome diversity among different species. Although the genome sequence data of the fungi kingdom improved enormously, the proteomic details have been poorly elaborated. Therefore, we have calculated the MW and pI of the fungi proteins and reported them in, FungiProteomeDB, an online database (DB) https://vision4research.com/fungidb/. We analyzed the proteome of 685 fungal species that contain 7 127 141 protein sequences. The DB provides an easy-to-use and efficient interface for various search options, summary statistics and virtual 2D proteome map visualizations. The MW and pI of a protein can be obtained by searching the name of a protein, a keyword or a list of accession numbers. It also allows querying protein sequences. The DB will be helpful in hypothesis formulation and in various biotechnological applications. Database URL https://vision4research.com/fungidb/.

Combination of untargeted and targeted proteomics for secretome analysis of L-WRN cells

Organoid culture is a promising biomedical technology that requires specialized growth factors. Recently, a recombinant L-WRN cell line has been extensively used to generate conditioned medium (L-CM) for organoid culture. Nevertheless, methods for evaluating the stability of the L-WRN cells have been limited. In this study, a novel proteomics-based approach was developed to analyze the secretome of the cells. Serum-free L-CM was lyophilized, precipitated by trichloroacetic acid, and desalted prior to analysis by liquid chromatography-tandem mass spectrometry. Data-dependent acquisition (DDA) was conducted for the untargeted secretome profiling of the cells, and parallel reaction monitoring (PRM) was applied for the targeted quantification of the Wnt3A, R-spondin3, and noggin proteins (WRNs). This study also compared the performance of two types of PRM methods, namely MS1-independent PRM and MS1-dependent PRM, that can be executed on an Orbitrap instrument. The results showed that the growth of mouse intestinal organoids was closely related to the use of L-CM. The composition of L-CM could be markedly affected by the medium collection scheme. A total of 1725, 2302, and 2681 proteins were identified from the L-CM collected on day 5, day 9, and day 13, respectively. The MS1-independent PRM outperformed the MS1-dependent PRM and effectively quantified the WRNs with high repeatability and specificity. In conclusion, by integrating untargeted and targeted proteomics, this study develops a mass spectrometry-based method for the secretome analysis and quality control of the L-WRN cells. The methodology and findings of the present work will benefit future studies on organoids and secretomes.

Simultaneous Quantification of Apolipoprotein C-III O-Glycoforms by Protein-MRM

Apolipoprotein C-III (APOC-III) regulates triglyceride levels, associated with a risk of cardiovascular disease. One gene generates several proteoforms, each with a different molecular mass and a unique function. Unlike peptide multiple reaction monitoring (MRM), protein-MRM without digestion is required to analyze clinically relevant individual proteoforms. We developed a protein-MRM method without digestion to individually quantify APOC-III proteoforms in human serum. We optimized the protein-MRM method following 60% acetonitrile extraction with C18 filtration. Bovine serum and myoglobin served as supporting cushions and the internal standard during sample preparation, respectively. Furthermore, we evaluated the LOD, lower limit of quantification, linearity, accuracy, and precision. Good correlation compared with turbidimetric immunoassay (TIA) and peptide-MRM was observed using 30 clinical sera. Individual APOC-III O-glycoforms were identified by top-down proteomics and simultaneously quantified using the protein-MRM method. The sum abundance of APOC-III proteoforms was significantly correlated with TIA and peptide-MRM. Our protein-MRM method provides an affordable and rapid quantification of potential disease-specific proteoforms. Precise quantification of each proteoform allows investigators to identify novel biological roles potentially related to cardiovascular disease or novel biomarkers. We expect our protein-oriented method to be more clinically useful than antibody-based immunoassays and peptide-oriented MRM analysis, especially for quantification of a biomarker proteoform with certain post-translational modifications.

Cancer-Associated Membrane Protein as Targeted Therapy for Bladder Cancer

Bladder cancer (BC) recurrence is one of the primary clinical problems encountered by patients following chemotherapy. However, the mechanisms underlying their resistance to chemotherapy remain unclear. Alteration in the pattern of membrane proteins (MPs) is thought to be associated with this recurrence outcome, often leading to cell dysfunction. Since MPs are found throughout the cell membrane, they have become the focus of attention for cancer diagnosis and treatment. Identifying specific and sensitive biomarkers for BC, therefore, requires a major collaborative effort. This review describes studies on membrane proteins as potential biomarkers to facilitate personalised medicine. It aims to introduce and discuss the types and significant functions of membrane proteins as potential biomarkers for future medicine. Other types of biomarkers such as DNA-, RNA- or metabolite-based biomarkers are not included in this review, but the focus is mainly on cell membrane surface protein-based biomarkers.

Reduction of multiple reaction monitoring protein target list using correlation analysis

High mass resolution mass spectrometry provides hundreds to thousands of protein identifications per sample, and quantification is typically performed using label-free quantification. However, the gold standard of quantitative proteomics is multiple reaction monitoring (MRM) using triple quadrupole mass spectrometers and stable isotope reference peptides. This raises the question how to reduce a large data set to a small one without losing essential information. Here we present the reduction of such a data set using correlation analysis of bovine dairy ingredients and derived products. We were able to explain the variance in the proteomics data set using only 9 proteins across all major dairy protein classes: caseins, whey, and milk fat globule membrane proteins. We term this method Trinity-MRM. The reproducibility of the protein extraction and Trinity-MRM methods was shown to be below 5% in independent experiments (multi-day single-user and single-day multi-user) using double cream. Further application of this reductionist approach might include screening of large sample cohorts for biologically interesting samples before analysis by high-resolution mass spectrometry or other omics methodologies.

Proteomics and Population Biology in the Cardiovascular Health Study (CHS): design of a study with mentored access and active data sharing

BACKGROUND

In the last decade, genomic studies have identified and replicated thousands of genetic associations with measures of health and disease and contributed to the understanding of the etiology of a variety of health conditions. Proteins are key biomarkers in clinical medicine and often drug-therapy targets. Like genomics, proteomics can advance our understanding of biology.

METHODS AND RESULTS

In the setting of the Cardiovascular Health Study (CHS), a cohort study of older adults, an aptamer-based method that has high sensitivity for low-abundance proteins was used to assay 4979 proteins in frozen, stored plasma from 3188 participants (61% women, mean age 74 years). CHS provides active support, including central analysis, for seven phenotype-specific working groups (WGs). Each CHS WG is led by one or two senior investigators and includes 10 to 20 early or mid-career scientists. In this setting of mentored access, the proteomic data and analytic methods are widely shared with the WGs and investigators so that they may evaluate associations between baseline levels of circulating proteins and the incidence of a variety of health outcomes in prospective cohort analyses. We describe the design of CHS, the CHS Proteomics Study, characteristics of participants, quality control measures, and structural characteristics of the data provided to CHS WGs. We additionally highlight plans for validation and replication of novel proteomic associations.

CONCLUSION

The CHS Proteomics Study offers an opportunity for collaborative data sharing to improve our understanding of the etiology of a variety of health conditions in older adults.

A TRUSTED targeted mass spectrometry assay for pan-herpesvirus protein detection

The presence and abundance of viral proteins within host cells are part of the essential signatures of the cellular stages of viral infections. However, methods that can comprehensively detect and quantify these proteins are still limited, particularly for viruses with large protein coding capacity. Here, we design and experimentally validate a mass spectrometry-based Targeted herpesviRUS proTEin Detection (TRUSTED) assay for monitoring human viruses representing the three Herpesviridae subfamilies—herpes simplex virus type 1, human cytomegalovirus (HCMV), and Kaposi sarcoma-associated herpesvirus. We demonstrate assay applicability for (1) capturing the temporal cascades of viral replication, (2) detecting proteins throughout a range of virus concentrations and in in vivo models of infection, (3) assessing the effects of clinical therapeutic agents and sirtuin-modulating compounds, (4) studies using different laboratory and clinical viral strains, and (5) discovering a role for carbamoyl phosphate synthetase 1 in supporting HCMV replication.

Herpesviruses encode many proteins, making it difficult to comprehensively monitor viral protein levels by traditional approaches. Kennedy et al. develop a set of targeted mass spectrometry-based assays for measuring herpesvirus protein levels spanning all virus subfamilies (α, β, and γ) and demonstrate their usefulness for a wide range of applications.

Obtaining Complete Human Proteomes

Proteins are the molecular effectors of the information encoded in the genome. Proteomics aims at understanding the molecular functions of proteins in their biological context. In contrast to transcriptomics and genomics, the study of proteomes provides deeper insight into the dynamic regulatory layers encoded at the protein level, such as posttranslational modifications, subcellular localization, cell signaling, and protein-protein interactions. Currently, mass spectrometry (MS)-based proteomics is the technology of choice for studying proteomes at a system-wide scale, contributing to clinical biomarker discovery and fundamental molecular biology. MS technologies are continuously being developed to fulfill the requirements of speed, resolution, and quantitative accuracy, enabling the acquisition of comprehensive proteomes. In this review, we present how MS technology and acquisition methods have evolved to meet the requirements of cutting-edge proteomics research, which is describing the human proteome and its dynamic posttranslational modifications with unprecedented depth. Finally, we provide a perspective on studying proteomes at single-cell resolution. Expected final online publication date for the Annual Review of Genomics and Human Genetics, Volume 23 is October 2022. Please see http://www.annualreviews.org/page/journal/pubdates for revised estimates.

Monitoring one-carbon metabolism by mass spectrometry to assess liver function and disease

Precision medicine promises to overcome the constraints of the traditional "one-for-all" healthcare approach through a clear understanding of the molecular features of a disease, allowing for innovative and tailored treatments. State-of-the-art proteomics has the potential to accurately explore the human proteome to identify, quantify, and characterize proteins associated with disease progression. There is a pressing need for informative biomarkers to diagnose liver disease early in its course to prevent severe disease for which no efficient treatment is yet available. Here, we propose the concept of a cellular pathway as a functional biomarker, whose monitorization may inform normal and pathological status. We have developed a standardized targeted selected-reaction monitoring assay to detect and quantify 13 enzymes of one-carbon metabolism (1CM). The assay is compliant with Clinical Proteomics Tumor Analysis Consortium (CPTAC) guidelines and has been included in the protein quantification assays that can be accessed through the assay portal at the CPTAC web page. To test the feasibility of the assay, we conducted a retrospective, proof-of-concept study on a collection of liver samples from healthy controls and from patients with cirrhosis or hepatocellular carcinoma (HCC). Our results indicate a significant reconfiguration of 1CM upon HCC development resulting from a process that can already be identified in cirrhosis. Our findings indicate that the systematic and integrated quantification of 1CM enzymes is a promising cell function-based biomarker for patient stratification, although further experiments with larger cohorts are needed to confirm these findings.

Lack of anti-TNF drugs levels in fistula tissue - a reason for nonresponse in Crohn's perianal fistulating disease?

INTRODUCTION

Anti-TNF therapy is recommended as treatment for patients with Crohn´s perianal fistulas. However, a significant proportion of patients have a sub-optimal response to anti-TNF therapy. Higher serum levels of anti-TNF agents have been associated with improved outcomes in perianal Crohn's disease. Currently, it is unknown whether anti-TNF agent levels can be detected in tissue from fistula tracts themselves and whether this is associated with response.

AIMS AND METHODS

We undertook a pilot study to measure fistula tissue levels of anti-TNF medication (infliximab and adalimumab). We used a previously validated targeted proteomic technique, employing ultraperformance liquid chromatography-mass spectrometry, to detect/quantify anti-TNF drugs. Biopsies were obtained from fistula tracts of patients with Crohn's disease on maintenance treatment; with idiopathic (cryptoglandular) fistula tissues used as negative controls as well as positive controls (by spiking the latter tissues with anti-TNF drugs).

RESULTS

Tissue was sampled from the fistula tracts of seven patients with Crohn's perianal disease (five patients were on adalimumab and two patients were on infliximab). The anti-TNF drugs, infliximab and adalimumab, were not detected in fistula samples from any of the Crohn's patients despite detection in 'spiked' positive control samples.

CONCLUSION

Absence of detection of the anti-TNF drugs in fistula tissue raises the question on the role of tissue penetrance of anti-TNF drugs in response to therapy. Further work is required in a larger number of patients to validate the findings observed and investigate if any correlation exists between tissue and serum levels of anti-TNF and clinical outcome.

SUMMARY

Predicting response in Crohn's fistula patients on biologic therapy is difficult with no reliable biomarkers. This pilot study uses targeted proteomics to investigate the potential role of tissue drug levels in acting as a biomarker of treatment response.

Maximizing Depth of PTM Coverage: Generating Robust MS Datasets for Computational Prediction Modeling

Post-translational modifications (PTMs) regulate complex biological processes through the modulation of protein activity, stability, and localization. Insights into the specific modification type and localization within a protein sequence can help ascertain functional significance. Computational models are increasingly demonstrated to offer a low-cost, high-throughput method for comprehensive PTM predictions. Algorithms are optimized using existing experimental PTM data, thus accurate prediction performance relies on the creation of robust datasets. Herein, advancements in mass spectrometry-based proteomics technologies to maximize PTM coverage are reviewed. Further, requisite experimental validation approaches for PTM predictions are explored to ensure that follow-up mechanistic studies are focused on accurate modification sites.

Application of Skyline for Analysis of Protein-Protein Interactions In Vivo

Quantitative and qualitative analyses of cell protein composition using liquid chromatography/tandem mass spectrometry are now standard techniques in biological and clinical research. However, the quantitative analysis of protein–protein interactions (PPIs) in cells is also important since these interactions are the bases of many processes, such as the cell cycle and signaling pathways. This paper describes the application of Skyline software for the identification and quantification of the biotinylated form of the biotin acceptor peptide (BAP) tag, which is a marker of in vivo PPIs. The tag was used in the Proximity Utilizing Biotinylation (PUB) method, which is based on the co-expression of BAP-X and BirA-Y in mammalian cells, where X or Y are interacting proteins of interest. A high level of biotinylation was detected in the model experiments where X and Y were pluripotency transcription factors Sox2 and Oct4, or heterochromatin protein HP1γ. MRM data processed by Skyline were normalized and recalculated. Ratios of biotinylation levels in experiment versus controls were 86 ± 6 (3 h biotinylation time) and 71 ± 5 (9 h biotinylation time) for BAP-Sox2 + BirA-Oct4 and 32 ± 3 (4 h biotinylation time) for BAP-HP1γ + BirA-HP1γ experiments. Skyline can also be applied for the analysis and identification of PPIs from shotgun proteomics data downloaded from publicly available datasets and repositories.

An Introduction to Advanced Targeted Acquisition Methods

Targeted proteomics via selected reaction monitoring (SRM) or parallel reaction monitoring (PRM) enables fast and sensitive detection of a preselected set of target peptides. However, the number of peptides that can be monitored in conventional targeting methods is usually rather small. Recently, a series of methods has been described that employ intelligent acquisition strategies to increase the efficiency of mass spectrometers to detect target peptides. These methods are based on one of two strategies. First, retention time adjustment-based methods enable intelligent scheduling of target peptide retention times. These include Picky, iRT, as well as spike-in free real-time adjustment methods such as MaxQuant.Live. Second, in spike-in triggered acquisition methods such as SureQuant, Pseudo-PRM, TOMAHAQ, and Scout-MRM, targeted scans are initiated by abundant labeled synthetic peptides added to samples before the run. Both strategies enable the mass spectrometer to better focus data acquisition time on target peptides. This either enables more sensitive detection or a higher number of targets per run. Here, we provide an overview of available advanced targeting methods and highlight their intrinsic strengths and weaknesses and compatibility with specific experimental setups. Our goal is to provide a basic introduction to advanced targeting methods for people starting to work in this field.

Nonlinear effects of environmental salinity on the gill transcriptome versus proteome of Oreochromis niloticus modulate epithelial cell turnover

A data-independent acquisition (DIA) assay library for targeted quantitation of thousands of Oreochromis niloticus gill proteins using a label- and gel-free workflow was generated and used to compare protein and mRNA abundances. This approach generated complimentary rather than redundant data for 1899 unique genes in gills of tilapia acclimated to freshwater and brackish water. Functional enrichment analyses identified mitochondrial energy metabolism, serine protease and immunity-related functions, and cytoskeleton/ extracellular matrix organization as major processes controlled by salinity in O. niloticus gills. Non-linearity in salinity-dependent transcriptome versus proteome regulation was revealed for specific functional groups of genes. The relationship was more linear for other molecular functions/ cellular processes, suggesting that the salinity-dependent regulation of O. niloticus gill function relies on post-transcriptional mechanisms for some functions/ processes more than others. This integrative systems biology approach can be adopted for other tissues and organisms to study cellular dynamics for many changing ecological contexts.

Ultra-fast proteomics with Scanning SWATH

Accurate quantification of the proteome remains challenging for large sample series and longitudinal experiments. We report a data-independent acquisition method, Scanning SWATH, that accelerates mass spectrometric (MS) duty cycles, yielding quantitative proteomes in combination with short gradients and high-flow (800 µl min-1) chromatography. Exploiting a continuous movement of the precursor isolation window to assign precursor masses to tandem mass spectrometry (MS/MS) fragment traces, Scanning SWATH increases precursor identifications by ~70% compared to conventional data-independent acquisition (DIA) methods on 0.5-5-min chromatographic gradients. We demonstrate the application of ultra-fast proteomics in drug mode-of-action screening and plasma proteomics. Scanning SWATH proteomes capture the mode of action of fungistatic azoles and statins. Moreover, we confirm 43 and identify 11 new plasma proteome biomarkers of COVID-19 severity, advancing patient classification and biomarker discovery. Thus, our results demonstrate a substantial acceleration and increased depth in fast proteomic experiments that facilitate proteomic drug screens and clinical studies.

A mean-field approach for modeling the propagation of perturbations in biochemical reaction networks

Often, the time evolution of a biochemical reaction network is crucial for determining the effects of combining multiple pharmaceuticals. Here we illustrate a mathematical framework for modeling the dominant temporal behaviour of a complicated molecular pathway or biochemical reaction network in response to an arbitrary perturbation, such as resulting from the administration of a therapeutic agent. The method enables the determination of the temporal evolution of a target protein as the perturbation propagates through its regulatory network. The mathematical approach is particularly useful when the experimental data that is available for characterizing or parameterizing the regulatory network is limited or incomplete. To illustrate the method, we consider the examples of the regulatory networks for the target proteins c-Myc and Chop, which play an important role in venetoclax resistance in acute myeloid leukemia. First we show how the networks that regulate each target protein can be reduced to a mean-field model by identifying the distinct effects that groups of proteins in the regulatory network have on the target protein. Then we show how limited protein-level data can be used to further simplify the mean-field model to pinpoint the dominant effects of the network perturbation on the target protein. This enables a further reduction in the number of parameters in the model. The result is an ordinary differential equation model that captures the temporal evolution of the expression of a target protein when one or more proteins in its regulatory network have been perturbed. Finally, we show how the dominant effects predicted by the mathematical model agree with RNA sequencing data for the regulatory proteins comprising the molecular network, despite the model not having a priori knowledge of this data. Thus, while the approach gives a simplified model for the expression of the target protein, it allows for the interpretation of the effects of the perturbation on the regulatory network itself. This method can be easily extended to sets of target proteins to model components of a larger systems biology model, and provides an approach for partially integrating RNA sequencing data and protein expression data. Moreover, it is a general approach that can be used to study drug effects on specific protein(s) in any disease or condition.

A data-independent acquisition (DIA) assay library for quantitation of environmental effects on the kidney proteome of Oreochromis niloticus

Interactions of organisms with their environment are complex and environmental regulation at different levels of biological organization is often nonlinear. Therefore, the genotype to phenotype continuum requires study at multiple levels of organization. While studies of transcriptome regulation are now common for many species, quantitative studies of environmental effects on proteomes are needed. Here we report the generation of a data-independent acquisition (DIA) assay library that enables simultaneous targeted proteomics of thousands of Oreochromis niloticus kidney proteins using a label- and gel-free workflow that is well suited for ecologically relevant field samples. We demonstrate the usefulness of this DIA assay library by discerning environmental effects on the kidney proteome of O. niloticus. Moreover, we demonstrate that the DIA assay library approach generates data that are complimentary rather than redundant to transcriptomic data. Transcript and protein abundance differences in kidneys of tilapia acclimated to freshwater and brackish water (25 g/kg) were correlated for 2114 unique genes. A high degree of non-linearity in salinity-dependent regulation of transcriptomes and proteomes was revealed suggesting that the regulation of O. niloticus renal function by environmental salinity relies heavily on post-transcriptional mechanisms. The application of functional enrichment analyses using STRING and KEGG to DIA assay data sets is demonstrated by identifying myo-inositol metabolism, antioxidant and xenobiotic functions, and signalling mechanisms as key elements controlled by salinity in tilapia kidneys. The DIA assay library resource presented here can be adopted for other tissues and other organisms to study proteome dynamics during changing ecological contexts.

DCAF11 Supports Targeted Protein Degradation by Electrophilic Proteolysis-Targeting Chimeras

Ligand-induced protein degradation has emerged as a compelling approach to promote the targeted elimination of proteins from cells by directing these proteins to the ubiquitin-proteasome machinery. So far, only a limited number of E3 ligases have been found to support ligand-induced protein degradation, reflecting a dearth of E3-binding compounds for proteolysis-targeting chimera (PROTAC) design. Here, we describe a functional screening strategy performed with a focused library of candidate electrophilic PROTACs to discover bifunctional compounds that degrade proteins in human cells by covalently engaging E3 ligases. Mechanistic studies revealed that the electrophilic PROTACs act through modifying specific cysteines in DCAF11, a poorly characterized E3 ligase substrate adaptor. We further show that DCAF11-directed electrophilic PROTACs can degrade multiple endogenous proteins, including FBKP12 and the androgen receptor, in human prostate cancer cells. Our findings designate DCAF11 as an E3 ligase capable of supporting ligand-induced protein degradation via electrophilic PROTACs.

Benchmarking mass spectrometry based proteomics algorithms using a simulated database

Protein sequencing algorithms process data from a variety of instruments that has been generated under diverse experimental conditions. Currently there is no way to predict the accuracy of an algorithm for a given data set. Most of the published algorithms and associated software has been evaluated on limited number of experimental data sets. However, these performance evaluations do not cover the complete search space the algorithmand the software might encounter in real-world. To this end, we present a database of simulated spectra that can be used to benchmark any spectra to peptide search engine. We demonstrate the usability of this database by bench marking two popular peptide sequencing engines. We show wide variation in the accuracy of peptide deductions and a complete quality profile of a given algorithm can be useful for practitioners and algorithm developers. All benchmarking data is available at https://users.cs.fiu.edu/~fsaeed/Benchmark.html.

Proteomics Complementation of the Rat Uterotrophic Assay for Estrogenic Endocrine Disruptors: A Roadmap of Advancing High Resolution Mass Spectrometry-Based Shotgun Survey to Targeted Biomarker Quantifications

The widely used rat uterotrophic assay to assess known and potential estrogenic compounds only considers uterine weight gain as endpoint measurement. To complement this method with an advanced technology that reveals molecular targets, we analyzed changes in protein expression using label-free quantitative proteomics by nanoflow liquid chromatography coupled with high-resolution mass spectrometry and tandem mass spectrometry from uterine protein extracts of ovariectomized rats after daily 17β-estradiol exposure for five days in comparison with those of vehicle-treated control animals. Our discovery-driven study revealed 165 uterine proteins significantly regulated by estrogen treatment and mapped by pathway analyses. Estrogen-regulated proteins represented cell death, survival and development, cellular growth and proliferation, and protein synthesis as top molecular and cellular functions, and a network found with the presence of nuclear estrogen receptor(s) as a prominent molecular node confirmed the relevance of our findings to hormone-associated events. An exploratory application of targeted proteomics to bisphenol A as a well-known example of an estrogenic endocrine disruptor is also presented. Overall, the results of this study have demonstrated the power of combining untargeted and targeted quantitative proteomic strategies to identify and verify candidate molecular markers for the evaluation of endocrine-disrupting chemicals to complement a conventional bioassay.

Finding new edges: systems approaches to MTOR signaling

Cells have evolved highly intertwined kinase networks to finely tune cellular homeostasis to the environment. The network converging on the mechanistic target of rapamycin (MTOR) kinase constitutes a central hub that integrates metabolic signals and adapts cellular metabolism and functions to nutritional changes and stress. Feedforward and feedback loops, crosstalks and a plethora of modulators finely balance MTOR-driven anabolic and catabolic processes. This complexity renders it difficult — if not impossible — to intuitively decipher signaling dynamics and network topology. Over the last two decades, systems approaches have emerged as powerful tools to simulate signaling network dynamics and responses. In this review, we discuss the contribution of systems studies to the discovery of novel edges and modulators in the MTOR network in healthy cells and in disease.

Development and Multiple Validation of the Protein Multi-marker Panel for Diagnosis of Pancreatic Cancer

PURPOSE

To develop and validate a protein-based, multi-marker panel that provides superior pancreatic ductal adenocarcinoma (PDAC) detection abilities with sufficient diagnostic performance.

EXPERIMENTAL DESIGN

A total of 959 plasma samples from patients at multiple medical centers were used. To construct an optimal, diagnostic, multi-marker panel, we applied data preprocessing procedure to biomarker candidates. The multi-marker panel was developed using a training set comprised of 261 PDAC cases and 290 controls. Subsequent evaluations were performed in a validation set comprised of 65 PDAC cases and 72 controls. Further validation was performed in an independent set comprised of 75 PDAC cases and 47 controls.

RESULTS

A multi-marker panel containing 14 proteins was developed. The multi-marker panel achieved AUCs of 0.977 and 0.953 for the training set and validation set, respectively. In an independent validation set, the multi-marker panel yielded an AUC of 0.928. The diagnostic performance of the multi-marker panel showed significant improvements compared with carbohydrate antigen (CA) 19-9 alone (training set AUC = 0.977 vs. 0.872, P < 0.001; validation set AUC = 0.953 vs. 0.832, P < 0.01; independent validation set AUC = 0.928 vs. 0.771, P < 0.001). When the multi-marker panel and CA 19-9 were combined, the diagnostic performance of the combined panel was improved for all sets.

CONCLUSIONS

This multi-marker panel and the combined panel showed statistically significant improvements in diagnostic performance compared with CA 19-9 alone and has the potential to complement CA 19-9 as a diagnostic marker in clinical practice.

Metabolic changes in the brain and blood of rats following acoustic trauma, tinnitus and hyperacusis

It has been increasingly recognized that tinnitus is likely to be generated by complex network changes. Acoustic trauma that causes tinnitus induces significant changes in multiple metabolic pathways in the brain. However, it is not clear whether those metabolic changes in the brain could also be reflected in blood samples and whether metabolic changes could discriminate acoustic trauma, hyperacusis and tinnitus. We analyzed brain and serum metabolic changes in rats following acoustic trauma or a sham procedure using metabolomics. Hearing levels were recorded before and after acoustic trauma and behavioral measures to quantify tinnitus and hyperacusis were conducted at 4 weeks following acoustic trauma. Tissues from 11 different brain regions and serum samples were collected at about 3 months following acoustic trauma. Among the acoustic trauma animals, eight exhibited hyperacusis-like behavior and three exhibited tinnitus-like behavior. Using Gas chromatography-mass spectrometry and multivariate statistical analysis, significant metabolic changes were found in acoustic trauma animals in both the brain and serum samples with a number of metabolic pathways significantly perturbated. Furthermore, metabolic changes in the serum were able to differentiate sham from acoustic trauma animals, as well as sham from hyperacusis animals, with high accuracy. Our results suggest that serum metabolic profiling in combination with machine learning analysis may be a promising approach for identifying biomarkers for acoustic trauma, hyperacusis and potentially, tinnitus.

Systems-based proteomics to resolve the biology of Alzheimer's disease beyond amyloid and tau

The repeated failures of amyloid-targeting therapies have challenged our narrow understanding of Alzheimer's disease (AD) pathogenesis and inspired wide-ranging investigations into the underlying mechanisms of disease. Increasing evidence indicates that AD develops from an intricate web of biochemical and cellular processes that extend far beyond amyloid and tau accumulation. This growing recognition surrounding the diversity of AD pathophysiology underscores the need for holistic systems-based approaches to explore AD pathogenesis. Here we describe how network-based proteomics has emerged as a powerful tool and how its application to the AD brain has provided an informative framework for the complex protein pathophysiology underlying the disease. Furthermore, we outline how the AD brain network proteome can be leveraged to advance additional scientific and translational efforts, including the discovery of novel protein biomarkers of disease.

High-Throughput Large-Scale Targeted Proteomics Assays for Quantifying Pathway Proteins in Pseudomonas putida KT2440

Targeted proteomics is a mass spectrometry-based protein quantification technique with high sensitivity, accuracy, and reproducibility. As a key component in the multi-omics toolbox of systems biology, targeted liquid chromatography-selected reaction monitoring (LC-SRM) measurements are critical for enzyme and pathway identification and design in metabolic engineering. To fulfill the increasing need for analyzing large sample sets with faster turnaround time in systems biology, high-throughput LC-SRM is greatly needed. Even though nanoflow LC-SRM has better sensitivity, it lacks the speed offered by microflow LC-SRM. Recent advancements in mass spectrometry instrumentation significantly enhance the scan speed and sensitivity of LC-SRM, thereby creating opportunities for applying the high speed of microflow LC-SRM without losing peptide multiplexing power or sacrificing sensitivity. Here, we studied the performance of microflow LC-SRM relative to nanoflow LC-SRM by monitoring 339 peptides representing 132 enzymes in Pseudomonas putida KT2440 grown on various carbon sources. The results from the two LC-SRM platforms are highly correlated. In addition, the response curve study of 248 peptides demonstrates that microflow LC-SRM has comparable sensitivity for the majority of detected peptides and better mass spectrometry signal and chromatography stability than nanoflow LC-SRM.

piNET: a versatile web platform for downstream analysis and visualization of proteomics data

Rapid progress in proteomics and large-scale profiling of biological systems at the protein level necessitates the continued development of efficient computational tools for the analysis and interpretation of proteomics data. Here, we present the piNET server that facilitates integrated annotation, analysis and visualization of quantitative proteomics data, with emphasis on PTM networks and integration with the LINCS library of chemical and genetic perturbation signatures in order to provide further mechanistic and functional insights. The primary input for the server consists of a set of peptides or proteins, optionally with PTM sites, and their corresponding abundance values. Several interconnected workflows can be used to generate: (i) interactive graphs and tables providing comprehensive annotation and mapping between peptides and proteins with PTM sites; (ii) high resolution and interactive visualization for enzyme-substrate networks, including kinases and their phospho-peptide targets; (iii) mapping and visualization of LINCS signature connectivity for chemical inhibitors or genetic knockdown of enzymes upstream of their target PTM sites. piNET has been built using a modular Spring-Boot JAVA platform as a fast, versatile and easy to use tool. The Apache Lucene indexing is used for fast mapping of peptides into UniProt entries for the human, mouse and other commonly used model organism proteomes. PTM-centric network analyses combine PhosphoSitePlus, iPTMnet and SIGNOR databases of validated enzyme-substrate relationships, for kinase networks augmented by DeepPhos predictions and sequence-based mapping of PhosphoSitePlus consensus motifs. Concordant LINCS signatures are mapped using iLINCS. For each workflow, a RESTful API counterpart can be used to generate the results programmatically in the json format. The server is available at http://pinet-server.org, and it is free and open to all users without login requirement.

Mass Spectrometry Techniques: Principles and Practices for Quantitative Proteomics

In the current omics-age of research, major developments have been made in technologies that attempt to survey the entire repertoire of genes, transcripts, proteins, and metabolites present within a cell. While genomics has led to a dramatic increase in our understanding of such things as disease morphology and how organisms respond to medications, it is critical to obtain information at the proteome level since proteins carry out most of the functions within the cell. The primary tool for obtaining proteome-wide information on proteins within the cell is mass spectrometry (MS). While it has historically been associated with the protein identification, developments over the past couple of decades have made MS a robust technology for protein quantitation as well. Identifying quantitative changes in proteomes is complicated by its dynamic nature and the inability of any technique to guarantee complete coverage of every protein within a proteome sample. Fortunately, the combined development of sample preparation and MS methods have made it capable of quantitatively comparing many thousands of proteins obtained from cells and organisms.

Proteomics of Osmoregulatory Responses in Threespine Stickleback Gills

The gill proteome of threespine sticklebacks (Gasterosteus aculeatus) differs greatly in populations that inhabit diverse environments characterized by different temperature, salinity, food availability, parasites, and other parameters. To assess the contribution of a specific environmental parameter to such differences it is necessary to isolate its effects from those of other parameters. In this study the effect of environmental salinity on the gill proteome of G. aculeatus was isolated in controlled mesocosm experiments. Salinity-dependent changes in the gill proteome were analyzed by Liquid chromatography/Tandem mass spectrometry data-independent acquisition (DIA) and Skyline. Relative abundances of 1691 proteins representing the molecular phenotype of stickleback gills were quantified using previously developed MSMS spectral and assay libraries in combination with DIA quantitative proteomics. Non-directional stress responses were distinguished from osmoregulatory protein abundance changes by their consistent occurrence during both hypo- and hyper-osmotic salinity stress in six separate mesocosm experiments. If the abundance of a protein was consistently regulated in opposite directions by hyper- versus hypo-osmotic salinity stress, then it was considered an osmoregulatory protein. In contrast, if protein abundance was consistently increased irrespective of whether salinity was increased or decreased, then it was considered a non-directional response protein. KEGG pathway analysis revealed that the salivary secretion, inositol phosphate metabolism, valine, leucine, and isoleucine degradation, citrate cycle, oxidative phosphorylation, and corresponding endocrine and extracellular signaling pathways contain most of the osmoregulatory gill proteins whose abundance is directly proportional to environmental salinity. Most proteins that were inversely correlated with salinity map to KEGG pathways that represent proteostasis, immunity, and related intracellular signaling processes. Non-directional stress response proteins represent fatty and amino acid degradation, purine metabolism, focal adhesion, mRNA surveillance, phagosome, endocytosis, and associated intracellular signaling KEGG pathways. These results demonstrate that G. aculeatus responds to salinity changes by adjusting osmoregulatory mechanisms that are distinct from transient non-directional stress responses to control compatible osmolyte synthesis, transepithelial ion transport, and oxidative energy metabolism. Furthermore, this study establishes salinity as a key factor for causing the regulation of numerous proteins and KEGG pathways with established functions in proteostasis, immunity, and tissue remodeling. We conclude that the corresponding osmoregulatory gill proteins and KEGG pathways represent molecular phenotypes that promote transepithelial ion transport, cellular osmoregulation, and gill epithelial remodeling to adjust gill function to environmental salinity.

Evaluation and minimization of nonspecific tryptic cleavages in proteomic sample preparation

High specificity of trypsin is a prerequisite for accurate identification and quantification of proteins in shotgun proteomics. It is important to minimize nonspecific enzymatic cleavages during proteomic sample preparation.

METHODS

In this study, protein extraction and trypsin digestion conditions were extensively evaluated using the less-complex Escherichia coli lysates to improve the sensitivity of detecting low-abundance nonspecific peptides by liquid chromatography/tandem mass spectrometry.

RESULTS

Trypsin digestion buffers and digestion times were proved to have a significant effect on nonspecific cleavages. The triethylammonium bicarbonate buffer induces significantly lower nonspecific cleavages than the other two buffers, but a freshly prepared urea solution does not induce more than sodium dodecyl sulfate. Because prolonged trypsin digestion resulted in a considerable number of nonspecific cleavages, an optimized 2-h protocol was developed with 45.2% less semispecific tryptic peptides but 18.5% more unmodified peptides identified than the commonly used 16-h protocol.

CONCLUSIONS

The significant decrease in nonspecific cleavages and artificial modifications improves the accuracy of protein quantification and the identification of low-abundance proteins, and it is especially useful for studying protein posttranslational modifications. For trypsin digestion, the proposed 2-h protocol can potentially be a replacement for the traditional 16-h protocol.

Absolute quantification of urinary neutrophil gelatinase-associated lipocalin by ultra-high-performance liquid chromatography/tandem mass spectrometry and the diagnostic efficacy of acute kidney injury

RATIONALE

To establish an absolute quantification method for neutrophil gelatinase-associated lipocalin (NGAL) by ultra-high-performance liquid chromatography tandem positive ion electrospray ionization mass spectrometry (UHPLC/MS/MS) and evaluate its diagnostic efficacy for acute kidney injury (AKI).

METHODS

Three target peptides of NGA were prescreened by Skyline software, and two of them could be detected in tryptic peptides of NGAL recombinant protein and human urinary NGAL (uNGAL). Peptide (WYVVGLAGNAILR) was then selected as surrogate peptide. The corresponding isotope-labeled peptide as the internal standard was next synthesized. Quantification of uNGAL was based on equations of linear regression, and method validation was then conducted. The diagnostic efficacy of uNGAL for AKI was also evaluated using receiver operating characteristic curve (ROC) analysis. Lastly, the UHPLC/MS/MS and the particle-enhanced turbidimetric immunoassay (PETIA) methods for uNGAL quantification were compared.

RESULTS

For the y⁹ and y¹⁰ product ions, the linear regression equations were y = 2.5519x-4.6955 (R² = 0.994, P<.01) and y = 2.4619x-4.3 (R² =0.993, P<.01), respectively, and both of the linear ranges were from 0.5 to 15 mg/L. The limits of detection and quantification were 0.037 mg/L and 0.081 mg/L, respectively. The recoveries were from 97.32% to 107.28% at different uNGAL levels, and the within- and between-day CVs for uNGAL quantification were from 0.22% to 7.65% and from 0.66% to 5.97%, respectively. The carryover rates of uNGAL were in the range of 0.70%-0.99%. The area under the ROC curve (AUC) of uNGAL was 0.96 (P<0.01), and the sensitivity and specificity of uNGAL for AKI diagnosis were 90.0% and 92.5%, respectively. In addition, the UHPLC/MS/MS and PETIA methods showed good agreement for uNGAL quantification (y = 0.7112x-0.0139, P = 0.34).

CONCLUSIONS

The UHPLC/MS/MS method for uNGAL quantification has a wide linear range, high sensitivity, precision, and recovery, and low carryover rates, and uNGAL detected by this method had high sensitivity and specificity for AKI diagnosis.

Multiplexed MRM-based protein quantification of putative prognostic biomarkers for chronic kidney disease progression in plasma

Current diagnostic measures for Chronic Kidney Disease (CKD) include detection of reduced estimated glomerular filtration rate (eGFR) and albuminuria, which have suboptimal accuracies in predicting disease progression. The disease complexity and heterogeneity underscore the need for multiplex quantification of different markers. The goal of this study was to determine the association of six previously reported CKD-associated plasma proteins [B2M (Beta-2-microglobulin), SERPINF1 (Pigment epithelium-derived factor), AMBP (Protein AMBP), LYZ (Lysozyme C), HBB (Hemoglobin subunit beta) and IGHA1 (Immunoglobulin heavy constant alpha 1)], as measured in a multiplex format, with kidney function, and outcome. Antibody-free, multiple reaction monitoring mass spectrometry (MRM) assays were developed, characterized for their analytical performance, and used for the analysis of 72 plasma samples from a patient cohort with longitudinal follow-up. The MRM significantly correlated (Rho = 0.5–0.9) with results from respective ELISA. Five proteins [AMBP, B2M, LYZ, HBB and SERPINF1] were significantly associated with eGFR, with the three former also associated with unfavorable outcome. The combination of these markers provided stronger associations with outcome (p < 0.0001) compared to individual markers. Collectively, our study describes a multiplex assay for absolute quantification and verification analysis of previously described putative CKD prognostic markers, laying the groundwork for further use in prospective validation studies.