Candidate proteomic biomarkers for non-alcoholic fatty liver disease (steatosis and non-alcoholic steatohepatitis) discovered with mass-spectrometry: a systematic review

Context: Non-alcoholic fatty liver disease (NAFLD) is characterized by lipid accumulation in the liver which is accompanied by a series of metabolic deregulations. There are sustained research efforts focusing upon biomarker discovery for NAFLD diagnosis and its prognosis in order investigate and follow-up patients as minimally invasive as possible.

Objective: The objective of this study is to critically review proteomic studies that used mass spectrometry techniques and summarize relevant proteomic NAFLD candidate biomarkers.

Methods: Medline and Embase databases were searched from inception to December 2014.

Results: A final number of 22 records were included that identified 251 candidate proteomic biomarkers. Thirty-three biomarkers were confirmed – 14 were found in liver samples, 21 in serum samples, and two from both serum and liver samples.

Conclusion: Some of the biomarkers identified have already been extensively studied regarding their diagnostic and prognostic capacity. However, there are also more potential biomarkers that still need to be addressed in future studies.     

Systemic sclerosis biomarkers discovered using mass-spectrometry-based proteomics: a systematic review

Abstract

Context: Systemic sclerosis (SSc) is an autoimmune disease with incompletely known physiopathology. There is a great challenge to predict its course and therapeutic response using biomarkers.

Objective: To critically review proteomic biomarkers discovered from biological specimens from systemic sclerosis patients using mass spectrometry technologies.

Methods: Medline and Embase databases were searched in February 2014.

Results: Out of the 199 records retrieved, a total of 20 records were included, identifying 116 candidate proteomic biomarkers.

Conclusion: Research in SSc proteomic biomarkers should focus on biomarker validation, as there are valuable mass-spectrometry proteomics studies in the literature.     

A catalogue of human saliva proteins identified by free flow electrophoresis-based peptide separation and tandem mass spectrometry

Human saliva has great potential for clinical disease diagnostics. Constructing a comprehensive catalogue of saliva proteins using proteomic approaches is a necessary first step to identifying potential protein biomarkers of disease. However, because of the challenge presented in cataloguing saliva proteins with widely varying abundance, new proteomic approaches are needed. To this end, we used a newly developed approach coupling peptide separation using free flow electrophoresis with linear ion trap tandem mass spectrometry to identify proteins in whole human saliva. We identified 437 proteins with high confidence (false positive rate below 1%), producing the largest catalogue of proteins from a single saliva sample to date and providing new information on the composition and potential diagnostic utility of this fluid. The statistically validated, transparently presented, and annotated dataset provides a model for presenting large scale proteomic data of this type, which should facilitate better dissemination and easier comparisons of proteomic datasets from future studies in saliva.     

Application of proteomics in the discovery of candidate protein biomarkers in a diabetes autoantibody standardization program sample subset

Novel biomarkers of type 1 diabetes must be identified and validated in initial, exploratory studies before they can be assessed in proficiency evaluations. Currently, untargeted "-omics" approaches are underutilized in profiling studies of clinical samples. This report describes the evaluation of capillary liquid chromatography (LC) coupled with mass spectrometry (MS) in a pilot proteomic analysis of human plasma and serum from a subset of control and type 1 diabetic individuals enrolled in the Diabetes Autoantibody Standardization Program, with the goal of identifying candidate biomarkers of type 1 diabetes. Initial high-resolution capillary LC-MS/MS experiments were performed to augment an existing plasma peptide database, while subsequent LC-FTICR studies identified quantitative differences in the abundance of plasma proteins. Analysis of LC-FTICR proteomic data identified five candidate protein biomarkers of type 1 diabetes. alpha-2-Glycoprotein 1 (zinc), corticosteroid-binding globulin, and lumican were 2-fold up-regulated in type 1 diabetic samples relative to control samples, whereas clusterin and serotransferrin were 2-fold up-regulated in control samples relative to type 1 diabetic samples. Observed perturbations in the levels of all five proteins are consistent with the metabolic aberrations found in type 1 diabetes. While the discovery of these candidate protein biomarkers of type 1 diabetes is encouraging, follow up studies are required for validation in a larger population of individuals and for determination of laboratory-defined sensitivity and specificity values using blinded samples.     

Neuroproteomics and microRNAs studies in multiple sclerosis: transforming research and clinical knowledge in biomarker research

Multiple sclerosis (MS) is a complex disease characterized by extensive phenotypic variability. Biomarkers to capture the different aspects of MS heterogeneity, and to help make a diagnosis and monitor disease progression, while providing insights into etiopathogenesis and response to treatment, are urgently needed. Omics technologies and research efforts with microRNAs have provide unparalleled opportunities for exploring altered protein profiles associated with molecular mechanisms of disease, substantially expanding the list of candidate biomarkers for MS. This review presents evidence from proteomic studies that have focused on identification of biomarkers released in biofluids as a result of the different pathophysiological processes of MS. Also discussed is the emerging role of miRNAs as complementary biomarkers related to cellular processes occurring in MS patients. Also provided is an overview of candidate biomarkers that have been proposed for elucidating pathophysiological processes and disease activity and for guiding clinical diagnosis and/or therapeutic interventions in MS.     

Clinical proteomics: towards early detection of cancers

A key challenge in clinical proteomic of cancer is the identification of biomarkers that would allow early detection, diagnosis and monitor progression of the disease to improve long-term survival of patients. Recent advances in proteomic instrumentation and computational methodologies offer unique chance to rapidly identify these new candidate markers or pattern of markers. The combination of retentate affinity chromatography and surfaced-enhanced laser desorption/ionization time-of-flight (SELDI-TOF) mass spectrometry is one of the most interesting new approaches for cancer diagnostic using proteomic profiling. This review aims to summarize the results of studies that have used this new technology method for the early diagnosis of human cancer. Despite promising results, the use of the proteomic profiling as a diagnostic tool brought some controversies and technical problems and still requires some efforts to be standardised and validated.     

Mapping cyclosporine-induced changes in protein secretion by renal cells using stable isotope labeling with amino acids in cell culture (SILAC)

Nephrotoxicity is an adverse event that strongly limits the use of the immunosuppressant cyclosporine in solid organ transplantation and the precise molecular mechanisms underlying this toxicity remain unclear. MS-based proteomic analysis of the secretome of HEK-293 renal cells exposed to cyclosporine was performed to identify changes in protein secretion, as a first step to discover potential biomarkers of such nephrotoxicity. To detect and quantify the perturbed proteins in the culture medium we used SILAC and nano-scale liquid chromatography followed by MALDI-TOF/TOF mass spectrometry. Among 106 proteins identified, 80 were quantified in both forward/reverse SILAC experiments and quantitative proteomic analysis revealed altered levels of expression for 24 secreted proteins. These included the down-regulation of a number of extracellular matrix/cell adhesion components, and the up-regulation of secreted cyclophilins A and B, macrophage inhibition factor and phosphatidylethanolamine-binding protein 1. These changes in protein secretion were not prevented by co-incubation with the antioxidant N-acetylcysteine, suggesting that they were not triggered by cyclosporine-induced oxidative stress. The results from the present study provide important new knowledge to gain insights into the molecular mechanisms of cyclosporine-related toxicity. Some of the proteins identified here should be tested as potential biomarkers of cyclosporine nephrotoxicity in subsequent clinical studies.     

A pipeline that integrates the discovery and verification of plasma protein biomarkers reveals candidate markers for cardiovascular disease

We developed a pipeline to integrate the proteomic technologies used from the discovery to the verification stages of plasma biomarker identification and applied it to identify early biomarkers of cardiac injury from the blood of patients undergoing a therapeutic, planned myocardial infarction (PMI) for treatment of hypertrophic cardiomyopathy. Sampling of blood directly from patient hearts before, during and after controlled myocardial injury ensured enrichment for candidate biomarkers and allowed patients to serve as their own biological controls. LC-MS/MS analyses detected 121 highly differentially expressed proteins, including previously credentialed markers of cardiovascular disease and >100 novel candidate biomarkers for myocardial infarction (MI). Accurate inclusion mass screening (AIMS) qualified a subset of the candidates based on highly specific, targeted detection in peripheral plasma, including some markers unlikely to have been identified without this step. Analyses of peripheral plasma from controls and patients with PMI or spontaneous MI by quantitative multiple reaction monitoring mass spectrometry or immunoassays suggest that the candidate biomarkers may be specific to MI. This study demonstrates that modern proteomic technologies, when coherently integrated, can yield novel cardiovascular biomarkers meriting further evaluation in large, heterogeneous cohorts.     

Schizophrenia proteomics: biomarkers on the path to laboratory medicine?

Over two million Americans are afflicted with schizophrenia, a debilitating mental health disorder with a unique symptomatic and epidemiological profile. Genomics studies have hinted towards candidate schizophrenia susceptibility chromosomal loci and genes. Modern proteomic tools, particularly mass spectrometry and expression scanning, aim to identify both pathogenic-revealing and diagnostically significant biomarkers. Only a few studies on basic proteomics have been conducted for psychiatric disorders relative to the plethora of cancer specific experiments. One such proteomic utility enables the discovery of proteins and biological marker fingerprinting profiling techniques (SELDI-TOF-MS), and then subjects them to tandem mass spectrometric fragmentation and de novo protein sequencing (MALDI-TOF/TOF-MS) for the accurate identification and characterization of the proteins. Such utilities can explain the pathogenesis of neuro-psychiatric disease, provide more objective testing methods, and further demonstrate a biological basis to mental illness. Although clinical proteomics in schizophrenia have yet to reveal a biomarker with diagnostic specificity, methods that better characterize the disorder using endophenotypes can advance findings. Schizophrenia biomarkers could potentially revolutionize its psychopharmacology, changing it into a more hypothesis and genomic/proteomic-driven science.     

Identification of differentially accumulating pistil proteins associated with self‐incompatibility of non‐heading Chinese cabbage

Non‐heading Chinese cabbage (Brassica campestris L. ssp. chinensis Makino), an important vegetable crop in China, exhibits a typical sporophytic self‐incompatibility (SI) system. To better understand the mechanism of SI response and identify potential candidate proteins involved in the SI system of this vegetable crop, the proteomic approach was taken to identify differential accumulating pistil proteins. Pistils were collected at 0 h and 2 h after self‐pollination at anthesis in self‐incompatible and compatible lines of non‐heading Chinese cabbage, and total proteins were extracted and separated by two‐dimensional gel electrophoresis (2‐DE). A total of 25 protein spots that displayed differential abundance were identified by matrix‐assisted laser desorption/ionisation‐time of flight mass spectrometry (MALDI–TOF/TOF MS) and peptide mass fingerprinting (PMF). Among them, 22 protein spots were confidently established. The mRNA levels of the corresponding genes were detected by quantitative RT‐PCR. The 22 identified protein spots are involved in energy metabolism (four), protein biosynthesis (three), photosynthesis (six), stress response and defence (five), and protein degradation (four). Among these potential candidate proteins, UDP‐sugar pyrophosphorylase could be involved in sucrose degradation to influence pollen germination and growth. Glutathione S–transferases could be involved in pollen maturation, and affect pollen fertility. Senescence‐associated cysteine protease, which is related to programmed cell death, could be mainly related to self pollen recognition of non‐heading Chinese cabbage. The study will contribute to further investigations of molecular mechanism of sporophytic SI in Brassicaceae.     

Clinical proteomics and mass spectrometry profiling for cancer detection

A key challenge in the clinical proteomics of cancer is the identification of biomarkers that would enable early detection, diagnosis and monitoring of disease progression to improve long-term survival of patients. Recent advances in proteomic instrumentation and computational methodologies offer a unique chance to rapidly identify these new candidate markers or pattern of markers. The combination of retentate affinity chromatography and mass spectrometry is one of the most interesting new approaches for cancer diagnostics using proteomic profiling. This review presents two technologies in this field, surface-enhanced laser desorption/ionization time-of-flight and Clinprot?, and aims to summarize the results of studies obtained with the first of them for the early diagnosis of human cancer. Despite promising results, the use of the proteomic profiling as a diagnostic tool brought some controversies and technical problems, and still requires some efforts to be standardized and validated.     

Clinical proteomics and mass spectrometry profiling for cancer detection

A key challenge in the clinical proteomics of cancer is the identification of biomarkers that would enable early detection, diagnosis and monitoring of disease progression to improve long-term survival of patients. Recent advances in proteomic instrumentation and computational methodologies offer a unique chance to rapidly identify these new candidate markers or pattern of markers. The combination of retentate affinity chromatography and mass spectrometry is one of the most interesting new approaches for cancer diagnostics using proteomic profiling. This review presents two technologies in this field, surface-enhanced laser desorption/ionization time-of-flight and Clinprot, and aims to summarize the results of studies obtained with the first of them for the early diagnosis of human cancer. Despite promising results, the use of the proteomic profiling as a diagnostic tool brought some controversies and technical problems, and still requires some efforts to be standardized and validated.     

Mitochondrial proteomics with siRNA knockdown to reveal ACAT1 and MDH2 in the development of doxorubicin‐resistant uterine cancer

Mitochondria are key organelles in mammary cells in responsible for a number of cellular functions including cell survival and energy metabolism. Moreover, mitochondria are one of the major targets under doxorubicin treatment. In this study, low‐abundant mitochondrial proteins were enriched for proteomic analysis with the state‐of‐the‐art two‐dimensional differential gel electrophoresis (2D‐DIGE) and matrix‐assistant laser desorption ionization time‐of‐flight mass spectrometry (MALDI‐TOF MS) strategy to compare and identify the mitochondrial protein profiling changes in response to the development of doxorubicin resistance in human uterine cancer cells. The mitochondrial proteomic results demonstrate more than fifteen hundred protein features were resolved from the equal amount pooled of three purified mitochondrial proteins and 101 differentially expressed spots were identified. In which, 39 out of these 101 identified proteins belong to mitochondrial proteins. Mitochondrial proteins such as acetyl‐CoA acetyltransferase (ACAT1) and malate dehydrogenase (MDH2) have not been reported with the roles on the formation of doxorubicin resistance in our knowledge. Further studies have used RNA interference and cell viability analysis to evidence the essential roles of ACAT1 and MDH2 on their potency in the formation of doxorubicin resistance through increased cell viability and decreased cell apoptosis during doxorubicin treatment. To sum up, our current mitochondrial proteomic approaches allowed us to identify numerous proteins, including ACAT1 and MDH2, involved in various drug‐resistance‐forming mechanisms. Our results provide potential diagnostic markers and therapeutic candidates for the treatment of doxorubicin‐resistant uterine cancer.     

Identification of protein fragments as pattern features in MALDI-MS analyses of serum

The use of matrix-assisted laser desorption ionization mass spectrometry (MALDI-MS) to acquire spectral profiles has become a common approach to detect proteomic biomarkers of disease. MALDI-MS signals may represent both intact proteins as well as proteolysis products. Liquid chromatography-tandem mass spectrometry (LC-MS/MS) analysis can tentatively identify the corresponding proteins Here, we describe the application of a data analysis utility called FragMint, which combines MALDI-MS spectral data with LC-MS/MS based protein identifications to generate candidate protein fragments consistent with both types of data. This approach was used to identify protein fragments corresponding to spectral signals in MALDI-MS analyses of unfractionated human serum. The serum also was analyzed by one-dimensional SDS-PAGE and bands corresponding to the MALDI-MS signal masses were excised and subjected to in-gel digestion and LC-MS/MS analysis. Database searches mapped all of the identified peptides to abundant blood proteins larger than the observed MALDI-MS signals. FragMint identified fragments of these proteins that contained the MS/MS identified sequences and were consistent with the observed MALDI-MS signals. This approach should be generally applicable to identify protein species corresponding to MALDI-MS signals.     

Targeted mass spectrometry approaches for protein biomarker verification

The search for protein biomarkers has been a highly pursued topic in the proteomics community in the last decade. This relentless search is due to the constant need for validated biomarkers that could facilitate disease risk stratification, disease diagnosis, prognosis, monitoring as well as drug development, which ultimately would improve our quality of life. The recent development of proteomic technologies including the advancement of mass spectrometers with high sensitivity and speed has greatly advanced the discovery of potential biomarkers. One of the bottlenecks lies in the development of well-established verification assays to screen the biomarker candidates identified in the discovery stage. Recently, absolute quantitation using multiple-reaction monitoring mass spectrometry (MRM-MS) in combination with isotope-labeled internal standards has been extensively investigated as a tool for high-throughput protein biomarker verification. In this review, we describe and discuss recent developments and applications of MRM-MS methods for biomarker verification.     

Prognostic significance of head-and-neck cancer biomarkers previously discovered and identified using iTRAQ-labeling and multidimensional liquid chromatography-tandem mass spectrometry

Diagnostic oncoproteomics is an emerging field; at present, studies on evaluation of prognostic utility of potential biomarkers identified using proteomic techniques are limited. Analysis with isobaric mass tags (iTRAQ) by multidimensional liquid chromatography-mass spectrometry (LC-MS/MS) to identify proteins that are differentially expressed in human head-and-neck/oral squamous cell carcinomas (HNOSCCs) versus noncancerous head-and-neck tissues has led to the discovery, identification, and verification of consistently increased expression of a panel of proteins, including stratifin (14-3-3sigma) and YWHAZ (14-3-3zeta), that may serve as potential cancer biomarkers. Herein, we describe the prognostic utility of these two candidate biomarkers for head-and-neck/oral squamous cell carcinoma (HNOSCC). To determine the clinical significance of stratifin and YWHAZ in head-and-neck tumorigenesis, the expressions of these two proteins were analyzed in HNOSCCs (51 cases) and nonmalignant tissues (39 cases) using immunohistochemistry. Significant increase in stratifin expression was observed in the HNOSCCs as compared to the nonmalignant mucosa [p=0.003, Odd's Ratio (OR)=3.8, 95% CI=1.6-9.2]. Kaplan-Meier survival analysis reveals correlation of stratifin overexpression with reduced disease-free survival of HNOSCC patients (p=0.06). The most intriguing finding is the significant decrease in median disease-free survival (13 months) in HNOSCC patients showing overexpression of both stratifin and YWHAZ proteins, as compared to patients that did not show overexpression of these proteins (median disease-free survival=38 months, p=0.019), underscoring their utility as adverse prognosticators for HNOSCCs. Co-immunoprecipitation assays show the formation of stratifin-YWHAZ heterodimers in HNOSCC cells and tissue samples, and interactions with NFkappaB, beta-catenin, and Bcl-2 proteins. These results suggest the involvement of these proteins in the development of head-and-neck cancer and their association with adverse disease outcome.     

Rapid discovery and identification of a tissue-specific tumor biomarker from 39 human cancer cell lines using the SELDI ProteinChip platform

Useful biomarkers are needed for early detection of cancers. To demonstrate the potential diagnostic usefulness of a new proteomic technology, we performed Expression Difference Mapping analysis on 39 cancer cell lines from 9 different tissues using ProteinChip technology. A protein biomarker candidate of 12kDa was found in colon cancer cells. We then optimized the purification conditions for this biomarker by utilizing Retentate Chromatography mass spectrometry (RC-MS). The optimized purification conditions developed "on-chip" were directly transferred to conventional chromatography to purify the biomarker, which was identified as prothymosin-alpha by ProteinChip time-of-flight mass spectrometry (TOF MS) and ProteinChip-Tandem MS systems. The relative expression level of prothymosin-alpha between colon cancer cells and normal colon mucosal cells was evaluated on the same ProteinChip platform. Prothymosin-alpha expression in colon cancer cells was clearly higher than in normal colon cells. These results indicate that prothymosin-alpha could be a potential biomarker for colon cancer, and that the ProteinChip platform could perform the whole process of biomarker discovery from screening to evaluation of the identified marker.     

Large-scale proteomic analysis of membrane proteins

Proteomic analysis of membrane proteins is a promising approach for the identification of novel drug targets and/or disease biomarkers. Despite notable technological developments, obstacles related to extraction and solublization of membrane proteins are encountered. A critical discussion of the different preparative methods of membrane proteins is offered in relation to downstream proteomic applications, mainly gel-based analyses and mass spectrometry. Frequently, unknown proteins are identified by high-throughput profiling of membrane proteins. In search for novel membrane proteins, analysis of protein sequences using computational tools is performed to predict the presence of transmembrane domains. This review also presents these bioinformatic tools with the human proteome as a case study. Along with technological innovations, advancements in the areas of sample preparation and computational prediction of membrane proteins will lead to exciting discoveries.     

Clinical application of urinary proteomics/peptidomics

The technology platforms for proteome analysis have advanced considerably over the last few years. Driven by these advancements in technology, the number of studies on the analysis of the proteome/peptidome, with the aim of defining clinically relevant biomarkers, has substantially risen. Urine has become an increasingly relevant target for clinically oriented proteome analysis; the first clinical trials based on urinary proteomics have been initiated, and studies including several hundred patients have been published. In this article, we summarize the relevant technical aspects in biomarkers discovery and the course from biomarker discovery or ‘potential’ biomarkers to those that have been validated and are clinically important. We discuss experimental design based on the statistics calculated to produce a clinically important end point. We present several examples of proteomic studies that have defined urinary biomarkers for clinical applications, focusing on capillary electrophoresis coupled to mass spectrometry as a technology. Finally, current challenges and considerations for future studies will be discussed.