Correction to: Identification of diagnostic biomarkers for relapsing-remitting multiple sclerosis in plasma by mass spectrometry-based proteomics.

Biomarkers in cancer: is ‘omices’ the way to go

In the absence of molecular biomarkers, the current diagnosis of multiple sclerosis is based on clinical assessment, neuroimaging, and detection of oligoclonal bands in cerebrospinal fluid. Early and rapid diagnosis using patient samples obtained by non-invasive methods would be a major advance in clinical management. We tested 5 different methods for preparation of the plasma proteome for liquid chromatography with tandem mass spectrometry (LC-MS/MS) analysis. These were as follows: (1) single-pot, solid-phase-enhanced sample preparation (SP3), (2) iST and (3) ENRICH-iST of raw plasma, (4) SP3 of highly abundant-proteins depleted plasma (DEPL-SP3), and (5) SP3 of plasma extracellular vesicles (EV-SP3). DEPL-SP3 and EV-SP3 sample preparation workflows yielded the highest numbers of quantified plasma proteins. Using both methods, we analyzed the plasma proteome of 15 relapsing-remitting multiple sclerosis (RRMS) patients and 5 healthy controls. We found 54 and 35 regulated plasma proteins with DEPL-SP3 and EV-SP3 workflows, respectively. Among them, von Villebrand factor (VWF) was identified as a potential RRMS diagnostic biomarker. The use of sample preparation workflows for LC-MS/MS analysis that describe both the soluble and EV plasma proteomes might increase the likelihood of identifying new and robust RRMS biomarkers.

BackgroundThe proteomic analysis of body fluids is a growing technology for the identification of protein biomarkers of disease. Given that Papanicolaou tests (Pap tests) are routinely performed on over 30 million women annually in the U.S. to screen for cervical cancer, we examined the residual Pap test fluid as a source of protein for analysis by mass spectrometry (MS). In the liquid-based Pap test, cervical cells are collected from the ectocervix and placed into an alcohol-based fixative prior to staining and pathologic examination. We hypothesized that proteins shed by cells of the female genital tract can be detected in the Pap test fixative by MS-based proteomic techniques. We examined the feasibility of using residual fluid from discarded Pap tests with cytologically “normal” results to optimize sample preparation for MS analysis. The protein composition of the cell-free Pap test fluid was determined by silver staining of sodium dodecyl sulfate -polyacrylamide gels, and the abundance of serum proteins was examined by Western immunoblot using an antibody against human serum albumin. Both pooled and individual samples were trypsin digested and analyzed by two-dimensional MS/MS. Proteins were identified by searching against the Human Uniprot database, and characterized for localization, function and relative abundance.ResultsThe average volume of the residual Pap test fluid was 1.5 ml and the average protein concentration was 0.14 mg/ml. By Western immunoblot we showed that the amount of albumin in each sample was significantly reduced compared to normal serum. By MS/MS, we identified 714 unique proteins in pooled Pap test samples and an average of 431 proteins in individual samples. About 40% of the proteins identified were extracellular or localized to the plasma membrane. Almost 20% of the proteins identified were involved in immunity and defense, characteristic of the healthy cervical-vaginal proteome. By merging the protein sets from the individual and pooled Pap test samples, we created a “Normal Pap test Core Proteome” consisting of 153 proteins.ConclusionsResidual Pap test fluid contains a sufficient amount of protein for analysis by MS and represents a valuable biospecimen source for the identification of protein biomarkers for gynecological diseases.

Identification of head and neck squamous cell carcinoma biomarker candidates through proteomic analysis of cancer cell secretome

The optimization of large-scale screening procedures of pathological specimens by genomic, proteomic and metabolic methods has drastically increased the bioanalytical capability for swiftly identifying novel biomarkers of inherited disorders, such as neuromuscular diseases. X-linked muscular dystrophy represents the most frequently inherited muscle disease and is characterized by primary abnormalities in the membrane cytoskeletal protein dystrophin. Mass spectrometry-based proteomics has been widely employed for the systematic analysis of dystrophin-deficient muscle tissues, using patient samples and animal models of dystrophinopathy. Both, gel-based methods and label-free mass spectrometric techniques have been applied in comparative analyses and established a large number of altered proteins that are associated with muscle contraction, energy metabolism, ion homeostasis, cellular signaling, the cytoskeleton, the extracellular matrix and the cellular stress response. Although these new indicators of muscular dystrophy have increased our general understanding of the molecular pathogenesis of dystrophinopathy, their application as new diagnostic or prognostic biomarkers would require the undesirable usage of invasive methodology. Hence, to reduce the need for diagnostic muscle biopsy procedures, more recent efforts have focused on the proteomic screening of suitable body fluids, such as plasma, serum or urine, for the identification of changed concentration levels of muscle-derived peptides, protein fragments or intact proteins. The occurrence of muscular dystrophy-related protein species in biofluids will be extremely helpful for the future development of cost-effective and non-invasive diagnostic procedures. Novel biomarker signatures of dystrophinopathies will be indispensible for the swift evaluation of innovative therapeutic approaches, such as exon skipping, codon-read-through or stem cell therapy.

Non-clear cell renal cell carcinomas (non-ccRCCs) represent ~15-20% RCCs cases comprising nearly 20 different disease subtypes and a wide spectrum of clinical behavior from benign to highly aggressive course. Clinically, metastatic non-ccRCC patients, regardless of subtypes with distinct genomic aberrations, are all treated with the same standard of care therapies, underscoring the need for precision therapeutic strategies. Diagnostic challenges also exist as benign and malignant entities often display overlapping histomorphologies that current diagnostic cytokeratin markers cannot resolve. Therefore, identification of more reliable diagnostic and prognostic non-ccRCC biomarkers remains an unmet need in this field. As part of the Clinical Tumor Analysis Consortium (CPTAC), we performed integrative analysis of multi-omics data including genomic next generation sequencing-based whole exome, whole genome, RNAseq, snRNAseq and mass spectrometry-based proteomics, post translational modifications (glycosylation and phosphorylation) and metabolomic profiles generated by CPTAC. The composition of the kidney tumor cohort (n=151) included 103 ccRCC, 15 oncocytomas, 13 papillary RCC (PRCC), 11 other rare tumors and 8 unclassified RCCs. Our multi-omic analysis revealed both unique and shared molecular features of RCC subtypes. We characterized proteogenomic, PTM and glycoproteome impact of genome instability (GI), a feature that is associated with poor prognosis in both ccRCC and non-ccRCC and affects 10-15% of cases. These analyses identified new prognostic signatures, outlier targetable kinase expression patterns, kinase-substrate relationships and differential protein glycosylation events. Glycoproteome analysis also revealed variation in cell-type specific marker expression among RCC subtypes such as FUT8 (core-fucosyltransferase) associated protein glycosylation in PRCC. Integrative analysis of snRNA-seq data predicted diverse tumor cell-of-origin and stratified RCC subtype specific proteogenomic signatures. Differential expression analysis revealed several novel diagnostic makers including MAPRE3, GPNMB, PIGR, SOSTDC1. These biomarkers were validated by IHC and their addition to existing panels results in improved diagnostic specificity. Metabolic characterization revealed RCC subtype-specific differences and increased oncometabolite SAICAR in oncocytomas that may have functional significance. The valuable proteogenomic data resource we generated contains several rare tumor types that are hard to obtain for proteogenomic characterization at the scale described here, and will certainly aid in future pan-RCC studies. Citation Format: Ginny Xiaohe Li, Yi Hsiao, Lijun Chen, Rahul Mannan, Yuping Zhang, Francesca Petralia, Hanbyul Cho, Noshad Hosseini, Anna Calinawan, Yize Li, Shankara Anand, Aniket Dagar, Yifat Geffen, Felipe V. Leprevost, Anne Le, Sean Ponce, Michael Schnaubelt, Nataly Naser Al Deen, Wagma Caravan, Andrew Houston, Chandan Kumar-Sinha, Xiaoming Wang, Seema Chugh, Gilbert S. Omenn, Daniel W. Chan, Christopher Ricketts, Rohit Mehra, Arul Chinnaiyan, Li Ding, Marcin Cieslik, Hui Zhang, Saravana M. Dhanasekaran, Alexey I. Nesvizhskii. Comprehensive proteogenomic characterization of rare kidney tumors [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 3127.

Liquid chromatography-tandem mass spectrometry-based proteomics is a highly sensitive and effective tool to identify and quantify potential biomarkers in repeated dose toxicity studies using primary cell culture systems. In this respect, 8-plex isobaric tag for relative and absolute quantification labeling is the method of choice for relative quantification. After cell lysis and tryptic protein digestion, an individual isobaric tag is added to the amine groups of arginine and lysine. Then, up to eight differentially labeled samples are mixed and analyzed together in a mass spectrometry experiment. During peptide fragmentation in the mass spectrometer, the individual tag intensity of each identified peptide could be detected, reflecting the peptide intensities in the eight samples. The identified peptides are matched to their specific protein using specific search engines and finally to eight individual relative protein quantities. The two-dimensional fractionation of complex peptide mixtures minimizes the possibility of co-fragmentation of peptides from different origin in the mass spectrometer, which leads to a higher number of peptide search matches and therefore to better identification and quantification results.

The first corona-pandemic, coronavirus disease 2019 (COVID-19) caused a huge health crisis and incalculable damage worldwide. Knowledge of how to cure the disease is urgently needed. Emerging immune escaping mutants of the virus suggested that it may be potentially persistent in human society as a regular health threat as the flu virus. Therefore, it is imperative to identify appropriate biomarkers to indicate pathological and physiological states, and more importantly, clinic outcomes. Proteins are the performers of life functions, and their abundance and modification status can directly reflect the immune status. Protein glycosylation serves a great impact in modulating protein function. The use of both unmodified and glycosylated proteins as biomarkers has also been proved feasible in the studies of SARS, Zika virus, influenza, etc. In recent years, mass spectrometry-based glycoproteomics, as well as proteomics approaches, advanced significantly due to the evolution of mass spectrometry. We focus on the current development of the mass spectrometry-based strategy for COVID-19 biomarkers’ investigation. Potential application of glycoproteomics approaches and challenges in biomarkers identification are also discussed.

INTRODUCTION: Head and Neck Squamous Cell Carcinoma (HNSCC) is the sixth most common cancer worldwide with approximately 500,000 new cases diagnosed each year. Squamous cell carcinomas of the larynx (LSCC) and the hypopharynx (HSCC) are subtypes of HNSCC. The current diagnostic methods are not sensitive enough since pre-cancerous fields are often not visible to the naked eye during endoscopic examination and are difficult to detect even on histology. Discovery of novel biomarkers for HNSCC should lead to improved detection of HNSCC. Mass spectrometry-based proteomics methods have emerged as promising approaches for biomarker discovery. As one approach, mass-spectrometric identification of proteins shed or secreted from cancer cells can contribute to our understanding of tumour behaviour and to the identification of potential diagnostic biomarkers for HSCC and LSCC. EXPERIMENTAL DESIGN: In order to identify putative biomarkers for HNSCC detection, mass spectrometry-based proteomic profiling was performed on the conditioned media (i.e. secretome) of cancer cell lines of laryngeal and hypopharyngeal origin (UTSCC42a, UTSCC8, and FaDu). In addition, a human gene expression microarray was used to identify over-expressed genes in HNSCC cell lines in comparison to a control cell line. The protein expression data was integrated with gene expression microarray profiles and systematic bioinformatics data mining using publicly available resources (Human Protein Atlas and published proteomic/transcriptomic data) was used to prioritize the markers for validation. Subsequently, real-time quantitative PCR, Western Blotting, and immunohistochemistry (IHC), were performed to validate the over-expression of selected markers. RESULTS: Proteomic profiling of HNSCC cell lines resulted in 1850 protein identifications. By integrating the protein expression data with gene expression microarray profiles, we identified 90 putative protein biomarkers that were secreted or shed to the extracellular space and over-expressed in HNSCC cell lines, relative to controls. Subsequently, the over-expression of 5 markers was successfully validated at the transcriptional and translational levels using quantitative real-time PCR, Western Blotting, and IHC on the HNSCC cell lines, and xenograft tumour models. CONCLUSION: Secretome and transcriptome profiling of HNSCC cell lines enabled the identification of 90 putative HNSCC biomarkers for further validation, 5 of which were successfully validated in vitro. Several of these markers have been implicated in HNSCC, illustrating the robustness of our approach to biomarker discovery. Future validation steps will include examination of these proteins in primary HNSCC biopsies, and matching patient sera. Ultimately, identification of a panel of protein biomarkers in a biological fluid (e.g. serum) of HNSCC patients will allow the development of an effective diagnostic test for early diagnosis. Citation Format: {Authors}. {Abstract title} [abstract]. In: Proceedings of the 102nd Annual Meeting of the American Association for Cancer Research; 2011 Apr 2-6; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2011;71(8 Suppl):Abstract nr 5073. doi:10.1158/1538-7445.AM2011-5073

Nonalcoholic fatty liver disease is a hepatic condition characterized by excessive fat accumulation in the liver with advanced stage nonalcoholic steatohepatitis (NASH), potentially leading to liver fibrosis, cirrhosis, and cancer. Currently, the identification and classification of NASH require invasive liver biopsy, which has certain limitations. Mass spectrometry-based proteomics can detect crucial proteins and pathways implicated in NASH development and progression. We collected the liver and serum samples from choline-deficient, L-amino acid-defined high-fat diet fed NASH C57BL/6J mice and human serum samples to examine proteomic alterations and identify early biomarkers for NASH diagnosis. In-depth targeted multiple reaction monitoring scanning and immunoblotting assays were used to verify the biomarker candidates from mouse liver and serum samples, and enzyme-linked immunosorbent assay (ELISA) was employed to analyze human serum samples. The multiple reaction monitoring analysis of NASH liver revealed 50 proteins with altered expression (21 upregulated and 29 downregulated) that are involved in biological processes such as detoxification, fibrosis, inflammation, and fatty acid metabolism. Ingenuity pathway analysis identified impaired protein synthesis, cellular stress and defense, cellular processes and communication, and metabolism in NASH mouse liver. Immunoblotting analysis confirmed that the expression of proteins associated with fatty acid metabolism (Aldo B and Fasn) and urea cycle (Arg1, Cps1, and Otc) was altered in the mouse liver and serum. Further analysis on human serum samples using ELISA confirmed the increased expression of multiple proteins, including Aldo B, Asl, and Lgals3, demonstrating values of 0.917, 0.979, and 0.965 of area under the curve in NASH diagnosis. These findings offer valuable insights into the molecular mechanisms of NASH and possible diagnostic biomarkers for early detection.

Hemorrhagic stroke (HS) is a significant cause of mortality that requires rapid diagnosis and prompt medical attention. A time-efficient diagnostic test to assist in the early classification of patients with stroke would be of great value. The aims here were to (a) select "brain-specific" proteins using a bioinformatics approach, (b) develop selected reaction monitoring (SRM) assays for candidate proteins, and (c) quantify these proteins in cerebrospinal fluid (CSF). "The Human Protein Atlas" and the "Peptide Atlas" were used to select proteins specifically and abundantly expressed in brain tissue, excluding high-abundance plasma proteins. Protein extracts from brain tissue were used for SRM assay development of proteins of interest. The levels of 68 "brain-specific" proteins were measured by SRM in 36 age-matched patients, including individuals with HS (n = 15), ischemic stroke (n = 11), and controls (n = 10). Additionally, S100B was measured using an electrochemoluminometric immunoassay. CSF levels of S100B and eight of the "brain-specific" proteins (NSE, GFAP, α-Inx, MBP, MT3, NFM, β-Syn, and γ-Syn) were increased in a subset of samples from HS patients, especially in those individuals with intraventricular hemorrhage and poor outcome. Seven of these proteins (S100B, NSE, GFAP, α-Inx, MBP, NFM, and β-Syn) showed significant differences between patients with and without brain hemorrhage. Novel biomarkers of brain injury (α-Inx, NFM, and β-Syn) were identified in the CSF of patients with HS. Investigating the role of these proteins in blood with more sensitive methods is warranted.

Motor neuron disease is a major group of inherited or spontaneous disorders that are associated with muscular atrophy. Recently, muscle preparations from the genetic wobbler mouse model of primary motor neuronopathy have been analyzed by mass spectrometry-based proteomics. The progressive degeneration of individual motor neurons was shown to cause complex alterations in the concentration or isoform expression pattern of muscle proteins involved in the excitation-contraction-relaxation cycle, the cytoskeleton, ion handling, cellular signaling, the stress response and energy metabolism. In this article, we compare the panel of potential new muscle-associated biomarkers that have been obtained by two different, but complementary, bioanalytical approaches, i.e. label-free mass spectrometric analysis versus fluorescence two-dimensional difference-in gel electrophoresis. The complex disease-associated changes in the muscle proteome are considerably different to the more unilateral skeletal muscle transitions observed in experimentally denervated fibers or disuse-related muscular atrophy. The apparent subtype-specific vulnerability of neuromuscular synapses and compensatory mechanisms of fiber type shifting in motor neuron disease is discussed, and contrasted to other forms of muscular atrophy.

Scarcity of high-quality organs, suboptimal organ quality assessment, unsatisfactory pre-implantation procedures, and poor long-term organ and patient survival are the main challenges currently faced by the solid organ transplant (SOT) field. New biomarkers for assessing graft quality pre-implantation, detecting, and predicting graft injury, rejection, dysfunction, and survival are critical to provide clinicians with invaluable prediction tools and guidance for personalized patients' treatment. Additionally, new therapeutic targets are also needed to reduce injury and rejection and improve transplant outcomes. Proteins, which underlie phenotypes, are ideal candidate biomarkers of health and disease statuses and therapeutic targets. A protein can exist in different molecular forms, called proteoforms. As the function of a protein depends on its exact composition, proteoforms can offer a more accurate basis for connection to complex phenotypes than protein from which they derive. Mass spectrometry-based proteomics has been largely used in SOT research for identification of candidate biomarkers and therapeutic intervention targets by so-called "bottom-up" proteomics (BUP). However, such BUP approaches analyze small peptides in lieu of intact proteins and provide incomplete information on the exact molecular composition of the proteins of interest. In contrast, "Top-down" proteomics (TDP), which analyze intact proteins retaining proteoform-level information, have been only recently adopted in transplantation studies and already led to the identification of promising proteoforms as biomarkers for organ rejection and dysfunction. We anticipate that the use of top-down strategies in combination with new technological advancements in single-cell and spatial proteomics could drive future breakthroughs in biomarker and therapeutic target discovery in SOT.

Streptococcus pneumoniae (pneumococcus) is a prominent cause of bacterial pneumonia, meningitis, and septicemia, causing high morbidity and high mortality, particularly in children and the elderly. In this study, proteomics- and genomics-based approaches were used for the identification of pneumococcal protein and peptide biomarkers of S. pneumoniae for diagnostics and prospective targets for treatment. Through a pan-genome analysis, 11 S. pneumoniae strains, demonstrating genetic variation within the species, were selected for proteomic characterization. Mass spectrometry-based proteomics, in combination with bacterial surface-shaving, were used to study the cell-surface proteome of S. pneumoniae. The data obtained from three biological replicates per strain were analyzed to identify and rank the proteins and peptides according to their presence in the strains, as well as their presence in all available S. pneumoniae proteomes (8,892) archived in public databases. Several highly ranked proteins have been described as "species-specific" for S. pneumoniae and as surface-associated virulence factors or demonstrate highly antigenic properties. Proteins (34) previously not recognized as S. pneumoniae-specific were proposed to be novel biomarkers, demonstrating high degrees of prevalence in all analyzed proteomes, with little or no sequence similarities to closely related species but common among the genetically diverse strains included in this study.

Head and neck squamous cell carcinomas (HNSCC) can arise from the oral cavity, oropharynx, larynx or hypopharynx, and is the sixth leading cancer by incidence worldwide. The 5-year survival rate of HNSCC patients remains static at 40-60%. Hence, biomarkers which can improve detection of HNSCC or early recurrences should improve clinical outcome. Mass spectrometry-based proteomics methods have emerged as promising approaches for biomarker discovery. As one approach, mass-spectrometric identification of proteins shed or secreted from cancer cells can contribute to the identification of potential biomarkers for HNSCC and our understanding of tumor behavior. In the current study, mass spectrometry-based proteomic profiling was performed on the conditioned media (i.e. secretome) of head and neck cancer (HNC) cell lines (FaDu, UTSCC8 and UTSCC42a) in addition to gene expression microarrays to identify over-expressed transcripts in the HNSCC cells in comparison to a normal control cell line. This integrated data set was systematically mined using publicly available resources (Human Protein Atlas and published proteomic/transcriptomic data) to prioritize putative candidates for validation. Subsequently, quantitative real-time PCR (qRT-PCR), Western blotting, immunohistochemistry (IHC), and ELISAs were performed to verify selected markers. Our integrated analyses identified 90 putative protein biomarkers that were secreted or shed to the extracellular space and over-expressed in HNSCC cell lines, relative to controls. Subsequently, the over-expression of five markers was verified in vitro at the transcriptional and translational levels using qRT-PCR and Western blotting, respectively. IHC-based validation conducted in two independent cohorts comprising of 40 and 39 HNSCC biopsies revealed that high tumor expression of PLAU, IGFBP7, MMP14 and THBS1 were associated with inferior disease-free survival, and increased risk of disease progression or relapse. Furthermore, as demonstrated using ELISAs, circulating levels of PLAU and IGFBP7 were significantly higher in the plasma of HNSCC patients compared with healthy individuals.