An Integrated Quantitative Proteomics Workflow for Cancer Biomarker Discovery and Validation in Plasma

Plasma proteomics analysis reveals potential biomarkers for intracranial aneurysm formation and rupture

The aim of this study was to investigate the plasma proteome in individuals with intracranial aneurysms (IAs) and identify biomarkers associated with the formation and rupture of IAs. Proteomic profiles (N = 1069 proteins) were assayed in plasma (N = 120) collected from patients with ruptured and unruptured intracranial aneurysms (RIA and UIA), traumatic subarachnoid hemorrhage (tSAH), and healthy controls (HC) using tandem mass tag (TMT) labeling quantitative proteomics analysis. Gene ontology (GO) and pathway analysis revealed that these relevant proteins were involved in immune response and extracellular matrix organization pathways. Seven candidate biomarkers were verified by ELISA in a completely separate cohort for validation (N = 90). Among them, FN1, PON1, and SERPINA1 can be utilized as diagnosis biomarkers of IA, with a combined area under the ROC curve of 0.891. The sensitivity was 93.33%, specificity was 75.86%, and accuracy was 87.64%. PFN1, ApoA-1, and SERPINA1 can serve as independent risk factors for predicting aneurysm rupture. The combined prediction of aneurysm rupture yielded an area under the ROC curve of 0.954 with a sensitivity of 96.15%, specificity of 81.48%, and accuracy of 88.68%. This prediction model was more effective than PHASES score. In conclusion, high-throughput proteomics analysis with population validation was performed to assess blood-based protein expression characteristics. This revealed the potential mechanism of IA formation and rupture, facilitating the discovery of biomarkers. SIGNIFICANCE: Although the annual rupture rate of small unruptured aneurysms is believed to be minimal, studies have indicated that ruptured aneurysms typically have an average size of 6.28 mm, with 71.8% of them being <7 mm in diameter. Hence, evaluating the possibility of rupture in UIA and making a choice between aggressive treatment and conservative observation emerges as a significant challenge in the management of UIA. No biomarker or scoring system has been able to satisfactorily address this issue to date. It would be significant to develop biomarkers that could be used for early diagnosis of IA as well as for prediction of IA rupture. After TMT proteomics analysis and ELISA validation in independent populations, we found that FN1, PON1, and SERPINA1 can be utilized as diagnostic biomarkers for IA, and PFN1, ApoA-1, and SERPINA1 can serve as independent risk factors for predicting aneurysm rupture. Especially, when combined with ApoA-1, SERPINA1, and PFN1 for predicting IA rupture, the area under the curve (AUC) was 0.954 with a sensitivity of 96.15%, specificity of 81.48%, and accuracy of 88.68%. This prediction model was more effective than PHASES score.

Giant Virus Global Proteomics Innovation: Comparative Evaluation of In-Gel and In-Solution Digestion Methods

With their unusually large genome and particle sizes, giant viruses (GVs) defy the conventional definition of viruses. Although most GVs isolated infect unicellular protozoans, such as amoeba, studies in the last decade have established their much wider prevalence infecting most eukaryotic supergroups and some giant viral families with the potential to be human pathogens. Their complexity, almost autonomous life cycle, and enigmatic evolution necessitate the study of GVs. The accurate assessment of GV proteome is a veritable challenge. We have compared the coverage of global protein identification using different methods for GVs isolated in Mumbai, Mimivirus Bombay (MVB), Powai Lake Megavirus (PLMV), and Kurlavirus (KV), along with two previously studied GVs, Acanthamoeba polyphaga Mimivirus (APMV) and Marseillevirus (MV). Our study shows that the simultaneous use of in-gel and in-solution digestion methods can significantly increase the coverage of protein identification in the global proteome analysis of purified GV particles. Combining the two methods of analyses, we identified an additional 72 proteins in APMV and 114 in MV compared with what have been previously reported. Similarly, proteomes of MVB, PLMV, and KV were analyzed, and a total of 242 proteins in MVB, 287 proteins in PLMV, and 174 proteins in KV were identified. Our results suggest that a combined methodology of in-gel and in-solution methods is more efficient and opens up new avenues for innovation in global proteome analysis of GVs. Future planetary health research on GVs can benefit from consideration of a broader range of proteomics methodologies as illustrated by the present study.

Glypican-1 may be a plasma biomarker for predicting the rupture of small intracranial aneurysms

Currently, there are no effective methods for predicting the rupture of asymptomatic small intracranial aneurysms (IA) (<7 mm). In this study the aim was to identify early warning biomarkers in peripheral plasma for predicting IA rupture. Four experimental groups were included: ruptured intracranial aneurysm (RIA), unruptured intracranial aneurysm (UIA), traumatic subarachnoid hemorrhage control (tSAHC), and healthy control (HC) groups. Plasma proteomics of these four groups were detected using iTRAQ combined LC-MS/MS. Differentially expressed proteins (DEPs) were identified in RIA, UIA, tSAHC compared with HC. Target proteins associated with aneurysm rupture were obtained by comparing the DEPs of the RIA and UIA groups after filtering out the DEPs of the tSAHC group. The plasma concentrations of target proteins were validated using enzyme-linked immunosorbent assay (ELISA). The iTRAQ analysis showed a significant increase in plasma GPC1 concentration in the RIA group compared to the UIA group, which was further validated among the IA patients. Logistic regression analysis identified GPC1 as an independent risk factor for predicting aneurysm rupture. The ROC curve indicated that the GPC1 plasma cut-off value for predicting aneurysms rupture was 4.99 ng/ml. GPC1 may be an early warning biomarker for predicting the rupture of small intracranial aneurysms. SIGNIFICANCE: The current management approach for asymptomatic small intracranial aneurysms (<7 mm) is limited to conservative observation and surgical intervention. However, the decision-making process regarding these options poses a dilemma due to weighing their respective advantages and disadvantages. Currently, there is a lack of effective diagnostic methods to predict the rupture of small aneurysms. Therefore, our aim is to identify early warning biomarkers in peripheral plasma that can serve as quantitative detection markers for predicting intracranial aneurysm rupture. In this study, four experimental populations were established: small ruptured intracranial aneurysm (sRIA) group, small unruptured intracranial aneurysm (sUIA) group, traumatic subarachnoid hemorrhage control (tSAHC) group, and healthy control (HC) group. The tSAH group was the control group of spontaneous subarachnoid hemorrhage caused by ruptured aneurysm. Compared with patients with UIA, aneurysm tissue and plasma GPC1 in patients with RIA is significantly higher, and GPC1 may be an early warning biomarker for predicting the rupture of intracranial small aneurysms.

Unveiling diagnostic and therapeutic strategies for cervical cancer: biomarker discovery through proteomics approaches and exploring the role of cervical cancer stem cells

Cervical cancer (CC) is a major global health problem and leading cause of cancer deaths among women worldwide. Early detection through screening programs has reduced mortality; however, screening compliance remains low. Identifying non-invasive biomarkers through proteomics for diagnosis and monitoring response to treatment could improve patient outcomes. Here we review recent proteomics studies which have uncovered biomarkers and potential drug targets for CC. Additionally, we explore into the role of cervical cancer stem cells and their potential implications in driving CC progression and therapy resistance. Although challenges remain, proteomics has the potential to revolutionize the field of cervical cancer research and improve patient outcomes.

Understanding COVID-19 progression with longitudinal peripheral blood mononuclear cell proteomics: Changes in the cellular proteome over time

The clinical presentation of COVID-19 is highly variable, and understanding the underlying biological processes is crucial. This study utilized a proteomic analysis to investigate dysregulated processes in the peripheral blood mononuclear cells of patients with COVID-19 compared to healthy volunteers. Samples were collected at different stages of the disease, including hospital admission, after 7 days of hospitalization, and 30 days after discharge. Metabolic pathway alterations and increased abundance of neutrophil-related proteins were observed in patients. Patients progressing to critical illness had significantly low-abundance proteins in the pentose phosphate and glycolysis pathways compared with those presenting clinical recovery. Important biological processes, such as fatty acid concentration and glucose metabolism disorder, remained altered even after 30 days of hospital discharge. Temporal proteomic changes revealed distinct pathways in critically ill and non-critically ill patients. Our study emphasizes the significance of longitudinal cellular proteomic studies in identifying disease progression-related pathways and persistent protein changes post-hospitalization.

In-depth proteome analysis of brain tissue from Ewsr1 knockout mouse by multiplexed isobaric tandem mass tag labeling

EWS RNA binding protein 1 (EWSR1) is a multifunctional protein whose epigenetic signatures contribute to the pathogenesis of various human diseases, such as neurodegenerative disorders, skin development, and tumorigenic processes. However, the specific cellular functions and physiological characteristics of EWSR1 remain unclear. In this study, we used quantitative mass spectrometry-based proteomics with tandem mass tag labeling to investigate the global proteome changes in brain tissue in Ewsr1 knockout and wild-type mice. From 9115 identified proteins, we selected 118 differentially expressed proteins, which is common to three quantitative data processing strategies including only protein level normalizations and spectrum-protein level normalization. Bioinformatics analysis of these common differentially expressed proteins revealed that proteins up-regulated in Ewsr1 knockout mouse are mostly related to the positive regulation of bone remodeling and inflammatory response. The down-regulated proteins were associated with the regulation of neurotransmitter levels or amino acid metabolic processes. Collectively, these findings provide insight into the physiological function and pathogenesis of EWSR1 on protein level. Better understanding of EWSR1 and its protein interactions will advance the field of clinical research into neuronal disorders. The mass spectrometry proteomics data have been deposited to the ProteomeXchange Consortium via the PRIDE partner repository with the dataset identifier PXD026994.

IQMMA: Efficient MS1 Intensity Extraction Pipeline Using Multiple Feature Detection Algorithms for DDA Proteomics

One of the key steps in data dependent acquisition (DDA) proteomics is detection of peptide isotopic clusters, also called "features", in MS1 spectra and matching them to MS/MS-based peptide identifications. A number of peptide feature detection tools became available in recent years, each relying on its own matching algorithm. Here, we provide an integrated solution, the intensity-based Quantitative Mix and Match Approach (IQMMA), which integrates a number of untargeted peptide feature detection algorithms and returns the most probable intensity values for the MS/MS-based identifications. IQMMA was tested using available proteomic data acquired for both well-characterized (ground truth) and real-world biological samples, including a mix of Yeast and E. coli digests spiked at different concentrations into the Human K562 digest used as a background, and a set of glioblastoma cell lines. Three open-source feature detection algorithms were integrated: Dinosaur, biosaur2, and OpenMS FeatureFinder. None of them was found optimal when applied individually to all the data sets employed in this work; however, their combined use in IQMMA improved efficiency of subsequent protein quantitation. The software implementing IQMMA is freely available at https://github.com/PostoenkoVI/IQMMA under Apache 2.0 license.

Mass spectrometry-based proteomics as an emerging tool in clinical laboratories

Mass spectrometry (MS)-based proteomics have been increasingly implemented in various disciplines of laboratory medicine to identify and quantify biomolecules in a variety of biological specimens. MS-based proteomics is continuously expanding and widely applied in biomarker discovery for early detection, prognosis and markers for treatment response prediction and monitoring. Furthermore, making these advanced tests more accessible and affordable will have the greatest healthcare benefit.This review article highlights the new paradigms MS-based clinical proteomics has created in microbiology laboratories, cancer research and diagnosis of metabolic disorders. The technique is preferred over conventional methods in disease detection and therapy monitoring for its combined advantages in multiplexing capacity, remarkable analytical specificity and sensitivity and low turnaround time.Despite the achievements in the development and adoption of a number of MS-based clinical proteomics practices, more are expected to undergo transition from bench to bedside in the near future. The review provides insights from early trials and recent progresses (mainly covering literature from the NCBI database) in the application of proteomics in clinical laboratories.

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.

Quantitative proteomics profiling of plasma from children with asthma

A lack of validated blood diagnostic markers presents an obstacle to asthma control. The present study sought to profile the plasma proteins of children with asthma and to determine potential biomarkers. Plasma samples from children in acute exacerbation (n = 4), in clinical remission (n = 4), and from healthy children (n = 4, control) were analyzed using a tandem mass tag (TMT)-labeling quantitative proteomics and the candidate biomarkers were validated using liquid chromatography-parallel reaction monitoring (PRM)/mass spectrometry (MS) with enzyme-linked immunosorbent assay (ELISA). We identified 347 proteins with differential expression between groups: 125 (50 upregulated, 75 downregulated) between acute exacerbation and control, 142 (72 upregulated, 70 downregulated) between clinical remission and control, and 55 (22 upregulated, 33 downregulated) between acute and remission groups (all between-group fold changes > 1.2; P < 0.05 by Student's t-test). Gene ontology analysis implicated differentially expressed proteins among children with asthma in immune response, the extracellular region, and protein binding. Further, KEGG pathway analysis of differentially expressed proteins identified complement and coagulation cascades and Staphylococcus aureus infection pathways as having the highest protein aggregation. Our analyses of protein interactions identified important node proteins, particularly KRT10. Among 11 differentially expressed proteins, seven proteins (IgHD, IgHG4, AACT, IgHA1, SAA, HBB, and HBA1) were verified through PRM/MS. Protein levels of AACT, IgA, SAA, and HBB were verified through ELISA and may be useful as biomarkers to identify individuals with asthma. In conclusion, our study presents a novel comprehensive analysis of changes in plasma proteins in children with asthma and identifies a panel for accessory diagnosis of pediatric asthma.

An overview of glioblastoma multiforme and temozolomide resistance: can LC-MS-based proteomics reveal the fundamental mechanism of temozolomide resistance?

Glioblastoma multiforme (GBM) is a primary type of lethal brain tumor. Over the last two decades, temozolomide (TMZ) has remained the primary chemotherapy for GBM. However, TMZ resistance in GBM constitutes an underlying factor contributing to high rates of mortality. Despite intense efforts to understand the mechanisms of therapeutic resistance, there is currently a poor understanding of the molecular processes of drug resistance. For TMZ, several mechanisms linked to therapeutic resistance have been proposed. In the past decade, significant progress in the field of mass spectrometry-based proteomics has been made. This review article discusses the molecular drivers of GBM, within the context of TMZ resistance with a particular emphasis on the potential benefits and insights of using global proteomic techniques.

Colon Adenocarcinoma Quantitative Proteomics Reveals Dysregulation in Key Cancer Signaling Pathways and a Candidate Protein Marker Panel

Colorectal cancer (CRC) is reportedly the second leading cause of cancer death worldwide. By the end of the decade, there will likely be more than one million fatalities worldwide from this cancer, with an estimated 2.2 million additional cases. We need new ways of thinking about cancer research. One approach is to deploy systems science using quantitative proteomics to obtain postgenomic and functional insights into cancer. The present study compares the tissue proteome of CRC (n = 10) with the matched peritumoral controls (n = 10) in samples obtained from the Indian subcontinent. When compared with the controls, a list of 22 substantially altered protein candidates was identified, which were associated with the growth, survival, and metastasis of the tumor. A list of the unique peptides from top significant proteins, including olfactomedin-4, alanyl aminopeptidase, and grancalcin was further validated using a parallel reaction monitoring-based targeted proteomics approach. In addition, biological pathway analysis showed perturbation in key biological processes, including dysregulation in purine metabolism, MYC targets in cancer, DNA repair, and replication, and leukocyte transendothelial migration, among others. The protein panel reported herein is also shown to be dysregulated in CRC and warrants further research toward understanding pathobiology, diagnostics, and therapeutics development in CRC.

Advancements in Oncoproteomics Technologies: Treading toward Translation into Clinical Practice

Proteomics continues to forge significant strides in the discovery of essential biological processes, uncovering valuable information on the identity, global protein abundance, protein modifications, proteoform levels, and signal transduction pathways. Cancer is a complicated and heterogeneous disease, and the onset and progression involve multiple dysregulated proteoforms and their downstream signaling pathways. These are modulated by various factors such as molecular, genetic, tissue, cellular, ethnic/racial, socioeconomic status, environmental, and demographic differences that vary with time. The knowledge of cancer has improved the treatment and clinical management; however, the survival rates have not increased significantly, and cancer remains a major cause of mortality. Oncoproteomics studies help to develop and validate proteomics technologies for routine application in clinical laboratories for (1) diagnostic and prognostic categorization of cancer, (2) real-time monitoring of treatment, (3) assessing drug efficacy and toxicity, (4) therapeutic modulations based on the changes with prognosis and drug resistance, and (5) personalized medication. Investigation of tumor-specific proteomic profiles in conjunction with healthy controls provides crucial information in mechanistic studies on tumorigenesis, metastasis, and drug resistance. This review provides an overview of proteomics technologies that assist the discovery of novel drug targets, biomarkers for early detection, surveillance, prognosis, drug monitoring, and tailoring therapy to the cancer patient. The information gained from such technologies has drastically improved cancer research. We further provide exemplars from recent oncoproteomics applications in the discovery of biomarkers in various cancers, drug discovery, and clinical treatment. Overall, the future of oncoproteomics holds enormous potential for translating technologies from the bench to the bedside.

A highly efficient protein corona-based proteomic analysis strategy for the discovery of pharmacodynamic biomarkers

The composition of serum is extremely complex, which complicates the discovery of new pharmacodynamic biomarkers via serum proteome for disease prediction and diagnosis. Recently, nanoparticles have been reported to efficiently reduce the proportion of high-abundance proteins and enrich low-abundance proteins in serum. Here, we synthesized a silica-coated iron oxide nanoparticle and developed a highly efficient and reproducible protein corona (PC)-based proteomic analysis strategy to improve the range of serum proteomic analysis. We identified 1,070 proteins with a median coefficient of variation of 12.56% using PC-based proteomic analysis, which was twice the number of proteins identified by direct digestion. There were also more biological processes enriched with these proteins. We applied this strategy to identify more pharmacodynamic biomarkers on collagen-induced arthritis (CIA) rat model treated with methotrexate (MTX). The bioinformatic results indicated that 485 differentially expressed proteins (DEPs) were found in CIA rats, of which 323 DEPs recovered to near normal levels after treatment with MTX. This strategy can not only help enhance our understanding of the mechanisms of disease and drug action through serum proteomics studies, but also provide more pharmacodynamic biomarkers for disease prediction, diagnosis, and treatment.

Ecological Monitoring and Omics: A Comprehensive Comparison of Workflows for Mass Spectrometry-Based Quantitative Proteomics of Fish (Labeo rohita) Liver Tissue

Introduction: The liver is highly sensitive to the environmental factors. Liver tissue, particularly from fish, is often used as a biological target in ecological monitoring, disease research, and stress response studies. Labeo rohita (rohu) is a fish with a significant role in the global aquaculture economy. Methods: Bottom-up proteomics relies on efficient sample preparation for performing mass spectrometric analysis of the liver tissue. Optimization of protein solubilization and digestion strategies is the key step to obtain reliable data for a successful proteomics experiment. Because the goal of extraction is to acquire the optimum protein quality and yield, the first step should be to choose an appropriate extraction method based on the type of sample. Solubilization buffers containing sodium dodecyl sulfate (SDS) or urea, and digestion methods such as filter-aided sample preparation (FASP), suspension trap (S-Trap) and in-solution are often used in proteomics but are in need of comparative evaluation with an eye to protocol optimization. Experiment: We applied two different solubilization buffers (one containing SDS, and other containing urea) and three digestion methods (FASP, S-Trap, and in-solution) to the proteomic analysis of the fish (L. rohita) liver tissue. Label-free quantification analysis was performed to analyze the similarities and differences in the results with each method. Gene ontology-based functional analysis was performed for the identified proteome across the experimental conditions to overview their protein classes, molecular functions, and biological processes. Results: SDS lysis followed by S-Trap digestion outperformed the other combinations of lysis and digestion in terms of higher protein coverage, consistency in the results and repeatability. Filter-based methods provided comparatively better results than in-solution digestion. Discussion: This protocol presents new insights on ways to optimize discovery and targeted proteomic analyses of liver tissue using the fish L. rohita as a case study. Other tissues can also be evaluated in the future drawing from the results in this study. This would help the scientific community with hypothesis-driven studies on topics ranging from basic biology to applied aquaculture research and ecological monitoring. This is particularly relevant in the current era of ecological crises and environmental pollution, where advances and optimization in research protocols can contribute to in-depth studies of ecosystems and planetary health.

Clinical Proteomics for Meningioma: An Integrated Workflow for Quantitative Proteomics and Biomarker Validation in Formalin-Fixed Paraffin-Embedded Tissue Samples

Clinical proteomics is a rapidly emerging frontier in laboratory medicine. High-throughput proteomic investigations of biopsy tissues provide mechanistic insights into complex human diseases. For large-scale proteomics, formalin-fixed and paraffin-embedded (FFPE) tissue samples offer a viable alternative to fresh-frozen (FF) tissues that have restricted availability. In this context, meningioma is one of the most common primary brain tumors where innovation in diagnostics and therapeutic targets can benefit from clinical proteomics. We present here an integrated workflow for quantitative proteomics and biomarker validation of meningioma FFPE tissues. Applying label-free quantitative (LFQ) proteomics, we reproducibly (Pearson's correlation: 0.84-0.91) obtained an in-depth proteome coverage (nearly 4000 proteins per sample) from 120 min gradient of single unfractionated mass spectrometry run. Furthermore, building upon LFQ data and literature curated set of meningioma-associated proteins, we validated VIM, AHNAK, and CLU from FFPE tissues using selected reaction monitoring (SRM) assay and compared its performance with FF tissues. This study illustrates how knowledge from label-free proteomics can be integrated for selecting peptides for targeted validation and suggests that FFPE tissues are comparable to FF tissues for SRM assays. This quantitative clinical proteomics workflow is scalable for large-scale clinical diagnostics studies in the future, for example, utilizing the global repository of FFPE tissues in meningioma and possibly in other cancers.

Internal Standard Triggered-Parallel Reaction Monitoring Mass Spectrometry Enables Multiplexed Quantification of Candidate Biomarkers in Plasma

Despite advances in proteomic technologies, clinical translation of plasma biomarkers remains low, partly due to a major bottleneck between the discovery of candidate biomarkers and costly clinical validation studies. Due to a dearth of multiplexable assays, generally only a few candidate biomarkers are tested, and the validation success rate is accordingly low. Previously, mass spectrometry-based approaches have been used to fill this gap but feature poor quantitative performance and were generally limited to hundreds of proteins. Here, we demonstrate the capability of an internal standard triggered-parallel reaction monitoring (IS-PRM) assay to greatly expand the numbers of candidates that can be tested with improved quantitative performance. The assay couples immunodepletion and fractionation with IS-PRM and was developed and implemented in human plasma to quantify 5176 peptides representing 1314 breast cancer biomarker candidates. Characterization of the IS-PRM assay demonstrated the precision (median % CV of 7.7%), linearity (median R² > 0.999 over 4 orders of magnitude), and sensitivity (median LLOQ < 1 fmol, approximately) to enable rank-ordering of candidate biomarkers for validation studies. Using three plasma pools from breast cancer patients and three control pools, 893 proteins were quantified, of which 162 candidate biomarkers were verified in at least one of the cancer pools and 22 were verified in all three cancer pools. The assay greatly expands capabilities for quantification of large numbers of proteins and is well suited for prioritization of viable candidate biomarkers.

Bottom-Up Approach to the Discovery of Clinically Relevant Biomarker Genes: The Case of Colorectal Cancer

Traditional approaches to genome-wide marker discovery often follow a common top-down strategy, where a large scale ‘omics’ investigation is followed by the analysis of functional pathways involved, to narrow down the list of identified putative biomarkers, and to deconvolute gene expression networks, or to obtain an insight into genetic alterations observed in cancer. We set out to investigate whether a reverse approach would allow full or partial reconstruction of the transcriptional programs and biological pathways specific to a given cancer and whether the full or substantially expanded list of putative markers could thus be identified by starting with the partial knowledge of a few disease-specific markers. To this end, we used 10 well-documented differentially expressed markers of colorectal cancer (CRC), analyzed their transcription factor networks and biological pathways, and predicted the existence of 193 new putative markers. Incredibly, the use of a validation marker set of 10 other completely different known CRC markers and the same procedure resulted in a very similar set of 143 predicted markers. Of these, 138 were identical to those found using the training set, confirming our main hypothesis that a much-expanded set of disease markers can be predicted by starting with just a small subset of validated markers. Further to this, we validated the expression of 42 out of 138 top-ranked predicted markers experimentally using qPCR in surgically removed CRC tissues. We showed that 41 out of 42 mRNAs tested have significantly altered levels of mRNA expression in surgically excised CRC tissues. Of the markers tested, 36 have been reported to be associated with aspects of CRC in the past, whilst only limited published evidence exists for another three genes (BCL2, PDGFRB and TSC2), and no published evidence directly linking genes to CRC was found for CCNA1, SHC1 and TGFB3. Whilst we used CRC to test and validate our marker discovery strategy, the reported procedures apply more generally to cancer marker discovery.

Insights on Proteomics-Driven Body Fluid-Based Biomarkers of Cervical Cancer

Cervical cancer is one of the top malignancies in women around the globe, which still holds its place despite being preventable at early stages. Gynecological conditions, even maladies like cervical cancer, still experience scrutiny from society owing to prevalent taboo and invasive screening methods, especially in developing economies. Additionally, current diagnoses lack specificity and sensitivity, which prolong diagnosis until it is too late. Advances in omics-based technologies aid in discovering differential multi-omics profiles between healthy individuals and cancer patients, which could be utilized for the discovery of body fluid-based biomarkers. Body fluids are a promising potential alternative for early disease detection and counteracting the problems of invasiveness while also serving as a pool of potential biomarkers. In this review, we will provide details of the body fluids-based biomarkers that have been reported in cervical cancer. Here, we have presented our perspective on proteomics for global biomarker discovery by addressing several pertinent problems, including the challenges that are confronted in cervical cancer. Further, we also used bioinformatic methods to undertake a meta-analysis of significantly up-regulated biomolecular profiles in CVF from cervical cancer patients. Our analysis deciphered alterations in the biological pathways in CVF such as immune response, glycolytic processes, regulation of cell death, regulation of structural size, protein polymerization disease, and other pathways that can cumulatively contribute to cervical cancer malignancy. We believe, more extensive research on such biomarkers, will speed up the road to early identification and prevention of cervical cancer in the near future.

Mass spectrometry and proteome analysis to identify SARS-CoV-2 protein from COVID-19 patient swab samples

With new emerging SARS-CoV-2 strains and their increased pathogenicity, diagnosis has become more challenging. Molecular diagnosis often involves the use of nasopharyngeal swabs and subsequent real-time PCR-based tests. Although this test is the gold standard, it has several limitations; therefore, more complementary assays are required. This protocol describes how to identify SARS-CoV-2 protein from patients' nasopharyngeal swab samples. We first introduce the approach of label-free quantitative proteomics. We then detail target verification by triple quadrupole mass spectrometry (MS)-based targeted proteomics.

For complete details on the use and execution of this profile, please refer to Bankar et al. (2021).

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A protocol for identification of SARS-CoV-2 protein from patients' swab samples

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Sequential steps involved in proteomic sample preparation are elaborated

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Detailed procedure for MS-based targeted proteomic verification is presented

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A detailed presentation of workflow for label-free and targeted data analyses

Developing a Targeted Quantitative Strategy for Sulfoxide-Containing MS-Cleavable Cross-Linked Peptides to Probe Conformational Dynamics of Protein Complexes

In recent years, cross-linking mass spectrometry (XL-MS) has made enormous strides as a technology for probing protein-protein interactions (PPIs) and elucidating architectures of multisubunit assemblies. To define conformational and interaction dynamics of protein complexes under different physiological conditions, various quantitative cross-linking mass spectrometry (QXL-MS) strategies based on stable isotope labeling have been developed. These QXL-MS approaches have effectively allowed comparative analysis of cross-links to determine their relative abundance changes at global scales. Although successful, it remains challenging to consistently obtain quantitative measurements on low-abundant cross-links. Therefore, targeted QXL-MS is needed to enable MS "Western" analysis of cross-links to enhance sensitivity and reliability in quantitation. To this end, we have established a robust parallel reaction monitoring (PRM)-based targeted QXL-MS platform using sulfoxide-containing MS-cleavable cross-linker disuccinimidyl sulfoxide (DSSO), permitting label-free comparative analysis of selected cross-links across multiple samples. In addition, we have applied this methodology to study phosphorylation-dependent conformational dynamics of the human 26S proteasome. The PRM-based targeted QXL-MS analytical platform described here is applicable for all sulfoxide-containing MS-cleavable cross-linkers and can be directly adopted for comparative studies of protein-protein interactions in various cellular contexts.

A fingerprint of plasma proteome alteration after local tissue damage induced by Bothrops leucurus snake venom in mice

Bothrops spp. is responsible for about 70% of snakebites in Brazil, causing a diverse and complex pathophysiological condition. Bothrops leucurus is the main species of medical relevance found in the Atlantic coast in the Brazilian Northeast region. The pathophysiological effects involved B. leucurus snakebite as well as the organism's reaction in response to this envenoming, it has not been explored yet. Thus, edema was induced in mice paw using 1.2, 2.5, and 5.0 μg of B. leucurus venom, the percentage of edema was measured 30 min after injection and the blood plasma was collected and analyzed by shotgun proteomic strategy. We identified 80 common plasma proteins with differential abundance among the experimental groups and we can understand the early aspects of this snake envenomation, regardless of the suggestive severity of an ophidian accident. The results showed B. leucurus venom triggers a thromboinflammation scenario where family's proteins of the Serpins, Apolipoproteins, Complement factors and Component subunits, Cathepsins, Kinases, Oxidoreductases, Proteases inhibitors, Proteases, Collagens, Growth factors are related to inflammation, complement and coagulation systems, modulators platelets and neutrophils, lipid and retinoid metabolism, oxidative stress and tissue repair. Our findings set precedents for future studies in the area of early diagnosis and/or treatment of snakebites. SIGNIFICANCE: The physiopathological effects that the snake venoms can cause have been investigated through classical and reductionist tools, which allowed, so far, the identification of action mechanisms of individual components associated with specific tissue damage. The currently incomplete limitations of this knowledge must be expanded through new approaches, such as proteomics, which may represent a big leap in understanding the venom-modulated pathological process. The exploration of the complete protein set that suffer modifications by the simultaneous action of multiple toxins, provides a map of the establishment of physiopathological phenotypes, which favors the identification of multiple toxin targets, that may or may not act in synergy, as well as favoring the discovery of biomarkers and therapeutic targets for manifestations that are not neutralized by the antivenom.

Recent advances in mass-spectrometry based proteomics software, tools and databases

The field of proteomics immensely depends on data generation and data analysis which are thoroughly supported by software and databases. There has been a massive advancement in mass spectrometry-based proteomics over the last 10 years which has compelled the scientific community to upgrade or develop algorithms, tools, and repository databases in the field of proteomics. Several standalone software, and comprehensive databases have aided the establishment of integrated omics pipeline and meta-analysis workflow which has contributed to understand the disease pathobiology, biomarker discovery and predicting new therapeutic modalities. For shotgun proteomics where Data Dependent Acquisition is performed, several user-friendly software are developed that can analyse the pre-processed data to provide mechanistic insights of the disease. Likewise, in Data Independent Acquisition, pipelines are emerged which can accomplish the task from building the spectral library to identify the therapeutic targets. Furthermore, in the age of big data analysis the implications of machine learning and cloud computing are appending robustness, rapidness and in-depth proteomics data analysis. The current review talks about the recent advancement, and development of software, tools, and database in the field of mass-spectrometry based proteomics.

Vitamin C Differentially Impacts the Serum Proteome Profile in Female and Male Mice

A suboptimal blood vitamin C (ascorbate) level increases the risk of several chronic diseases. However, the detection of hypovitaminosis C is not a simple task, as ascorbate is unstable in blood samples. In this study, we examined the serum proteome of mice lacking the gulonolactone oxidase (Gulo) required for the ascorbate biosynthesis. Gulo^-/- mice were supplemented with different concentrations of ascorbate in drinking water, and serum was collected to identify proteins correlating with serum ascorbate levels using an unbiased label-free liquid chromatography-tandem mass spectrometry global quantitative proteomic approach. Parallel reaction monitoring was performed to validate the correlations. We uncovered that the serum proteome profiles differ significantly between male and female mice. Also, unlike Gulo^-/- males, a four-week ascorbate treatment did not entirely re-establish the serum proteome profile of ascorbate-deficient Gulo^-/- females to the optimal profile exhibited by Gulo^-/- females that never experienced an ascorbate deficiency. Finally, the serum proteins involved in retinoid metabolism, cholesterol, and lipid transport were similarly affected by ascorbate levels in males and females. In contrast, the proteins regulating serum peptidases and the protein of the acute phase response were different between males and females. These proteins are potential biomarkers correlating with blood ascorbate levels and require further study in standard clinical settings. The complete proteomics data set generated in this study has been deposited to the public repository ProteomeXchange with the data set identifier: PXD027019.

HBFP: a new repository for human body fluid proteome

Body fluid proteome has been intensively studied as a primary source for disease biomarker discovery. Using advanced proteomics technologies, early research success has resulted in increasingly accumulated proteins detected in different body fluids, among which many are promising biomarkers. However, despite a handful of small-scale and specific data resources, current research is clearly lacking effort compiling published body fluid proteins into a centralized and sustainable repository that can provide users with systematic analytic tools. In this study, we developed a new database of human body fluid proteome (HBFP) that focuses on experimentally validated proteome in 17 types of human body fluids. The current database archives 11 827 unique proteins reported by 164 scientific publications, with a maximal false discovery rate of 0.01 on both the peptide and protein levels since 2001, and enables users to query, analyze and download protein entries with respect to each body fluid. Three unique features of this new system include the following: (i) the protein annotation page includes detailed abundance information based on relative qualitative measures of peptides reported in the original references, (ii) a new score is calculated on each reported protein to indicate the discovery confidence and (iii) HBFP catalogs 7354 proteins with at least two non-nested uniquely mapping peptides of nine amino acids according to the Human Proteome Project Data Interpretation Guidelines, while the remaining 4473 proteins have more than two unique peptides without given sequence information. As an important resource for human protein secretome, we anticipate that this new HBFP database can be a powerful tool that facilitates research in clinical proteomics and biomarker discovery.

Database URL:https://bmbl.bmi.osumc.edu/HBFP/

Targeted liquid chromatography-tandem mass spectrometry analysis of proteins: Basic principles, applications, and perspectives

Liquid chromatography coupled to tandem mass spectrometry (LC-MS/MS) is now the main analytical method for the identification and quantification of peptides and proteins in biological samples. In modern research, identification of biomarkers and their quantitative comparison between samples are becoming increasingly important for discovery, validation, and monitoring. Such data can be obtained following specific signals after fragmentation of peptides using multiple reaction monitoring (MRM) and parallel reaction monitoring (PRM) methods, with high specificity, accuracy, and reproducibility. In addition, these methods allow measurement of the amount of post-translationally modified forms and isoforms of proteins. This review article describes the basic principles of MRM assays, guidelines for sample preparation, recent advanced MRM-based strategies, applications and illustrative perspectives of MRM/PRM methods in clinical research and molecular biology.

Proteomics, Personalized Medicine and Cancer

As of 2020 the human genome and proteome are both at >90% completion based on high stringency analyses. This has been largely achieved by major technological advances over the last 20 years and has enlarged our understanding of human health and disease, including cancer, and is supporting the current trend towards personalized/precision medicine. This is due to improved screening, novel therapeutic approaches and an increased understanding of underlying cancer biology. However, cancer is a complex, heterogeneous disease modulated by genetic, molecular, cellular, tissue, population, environmental and socioeconomic factors, which evolve with time. In spite of recent advances in treatment that have resulted in improved patient outcomes, prognosis is still poor for many patients with certain cancers (e.g., mesothelioma, pancreatic and brain cancer) with a high death rate associated with late diagnosis. In this review we overview key hallmarks of cancer (e.g., autophagy, the role of redox signaling), current unmet clinical needs, the requirement for sensitive and specific biomarkers for early detection, surveillance, prognosis and drug monitoring, the role of the microbiome and the goals of personalized/precision medicine, discussing how emerging omics technologies can further inform on these areas. Exemplars from recent onco-proteogenomic-related publications will be given. Finally, we will address future perspectives, not only from the standpoint of perceived advances in treatment, but also from the hurdles that have to be overcome.

Clinical Assay for the Early Detection of Colorectal Cancer Using Mass Spectrometric Wheat Germ Agglutinin Multiple Reaction Monitoring

Simple Summary

Colorectal cancer (CRC) is currently the third leading cause of cancer death worldwide. Early diagnosis of CRC is important for increasing the opportunity for treatment and receiving a good prognosis. The aim of our study was to develop a detection method that combined wheat germ agglutinin (WGA) chromatography with mass spectrometry (MS) for early detection of CRC. Further, machine learning algorithms and logistic regression were applied to combine multiple biomarkers we discovered. We validated in a population of 286 plasma samples the diagnostic performance of peptides corresponding to WGA-captured protein and its combination, which received a sensitivity of 84.5% and a specificity of 97.5% in the diagnoses of CRC. Proteomic biomarkers combined with algorithms can provide a powerful tool for discriminating patients with CRC and health controls (HCs). Measurements of WGA-captured PF4, ITIH4, and APOE with MS are then useful for early detection of CRC. Additionally, our study revealed the potential of applying lectin chromatography with MS for disease diagnosis.

Abstract

Colorectal cancer (CRC) is currently the third leading cause of cancer-related mortality in the world. U.S. Food and Drug Administration-approved circulating tumor markers, including carcinoembryonic antigen, carbohydrate antigen (CA) 19-9 and CA125 were used as prognostic biomarkers of CRC that attributed to low sensitivity in diagnosis of CRC. Therefore, our purpose is to develop a novel strategy for novel clinical biomarkers for early CRC diagnosis. We used mass spectrometry (MS) methods such as nanoLC-MS/MS, targeted LC-MS/MS, and stable isotope-labeled multiple reaction monitoring (MRM) MS coupled to test machine learning algorithms and logistic regression to analyze plasma samples from patients with early-stage CRC, late-stage CRC, and healthy controls (HCs). On the basis of our methods, 356 peptides were identified, 6 differential expressed peptides were verified, and finally three peptides corresponding wheat germ agglutinin (WGA)-captured proteins were semi-quantitated in 286 plasma samples (80 HCs and 206 CRCs). The novel peptide biomarkers combination of PF4^54–62, ITIH4^429–438, and APOE^198–207 achieved sensitivity 84.5%, specificity 97.5% and an AUC of 0.96 in CRC diagnosis. In conclusion, our study demonstrated that WGA-captured plasma PF4^54–62, ITIH4^429–438, and APOE^198–207 levels in combination may serve as highly effective early diagnostic biomarkers for patients with CRC.