Quantitative label-free proteomics for discovery of biomarkers in cerebrospinal fluid: assessment of technical and inter-individual variation

Proteomics and Its Current Application in Biomedical Area: Concise Review

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

Exploration of key factors in Gingival Crevicular fluids from patients undergoing Periodontally Accelerated Osteogenic Orthodontics (PAOO) using proteome analysis

BACKGROUND

The aims of this study are to explore protein changes in gingival crevicular fluid at different time points after PAOO by proteomics method and to select significant bone metabolization-related biomarkers.

METHODS

This study included 10 adult patients experiencing PAOO. After orthodontic alignment and leveling, the maxillary anterior teeth were treated with PAOO, which is classified as the experimental area. The traditional orthodontic treatment was performed in the mandibular dentition as the control. Gingival crevicular fluid samples were collected at the following time points: the day before the PAOO (T1) and at 1 week, 2 weeks, 1 month, 2 months and 6 months after PAOO (T2, T3, T4, T5 and T6, respectively). The label-free quantitative proteomic assay was used to evaluate the gingival crevicular fluid in PAOO and control areas at time point T1, T2, and T4. Bioinformatics analysis was carried out to categorize proteins based on biological processes, cellular component and molecular function, which is in compliance with gene ontology (GO) standards. The changes of proteins were confirmed by ELISA.

RESULTS

A total of 134 proteins were selected by keywords (Osteoblast markers, Osteoclast markers, Osteoclastogenesis regulating genes and inflammatory marker). 33 of them were statistically different between groups, and 12 were related to bone metabolism. 5 proteins selected by label-free quantitative proteomics were KLF10, SYT7, APOA1, FBN1 and NOTCH1. KLF10 decreased after PAOO, hitting a trough at T4, and then leveled off. SYT7 increased after PAOO, reaching a peak at T3, and then stabilized until T6. APOA1 ascended to a peak at T4 after PAOO, and then remained stable until T6. The FBN1 rose after PAOO, reaching a peak at T4, and then went down slowly. NOTCH1 ascended rapidly in the first two weeks after PAOO and continued its slow growth trend.

CONCLUSION

In this study, protein changes in gingival crevicular fluid were detected by proteomics method, and significant bone metabolization-related proteins were selected. It is speculated that APOA1, FBN1, NOTCH1, SYT7 and KLF10 played key roles in regulating bone metabolic balance and in reversible osteopenia after PAOO, which might be involved in the accelerated tooth movement.

TRIAL REGISTRATION

This study was registered in the Chinese Clinical Trial Registry (Clinical trial registration number: ChiCTR-ONRC-13,004,129) (26/04/2013).

A single protein to multiple peptides: Investigation of protein-peptide correlations using targeted alpha-2-macroglobulin analysis

Recent advances in proteomics technologies have enabled the analysis of thousands of proteins in a high-throughput manner. Mass spectrometry (MS) based proteomics uses a peptide-centric approach where biological samples undergo specific proteolytic digestion and then only unique peptides are used for protein identification and quantification. Considering the fact that a single protein may have multiple unique peptides and a number of different forms, it becomes essential to understand dynamic protein-peptide relationships to ensure robust and reliable peptide-centric protein analysis. In this study, we investigated the correlation between protein concentration and corresponding unique peptide responses under a conventional proteolytic digestion condition. Protein-peptide correlation, digestion efficiency, matrix-effect, and concentration-effect were evaluated. Twelve unique peptides of alpha-2-macroglobulin (A2MG) were monitored using a targeted MS approach to acquire insights into protein-peptide dynamics. Although the peptide responses were reproducible between replicates, protein-peptide correlation was moderate in protein standards and low in complex matrices. The results suggest that reproducible peptide signal could be misleading in clinical studies and a peptide selection could dramatically change the outcome at protein level. This is the first study investigating quantitative protein-peptide correlations in biological samples using all unique peptides representing the same protein and opens a discussion on peptide-based proteomics.

Synaptic activity-dependent changes in the hippocampal palmitoylome

Dynamic protein S-palmitoylation is critical for neuronal function, development, and synaptic plasticity. Synaptic activity-dependent changes in palmitoylation have been reported for a small number of proteins. Here, we characterized the palmitoylome in the hippocampi of male mice before and after context-dependent fear conditioning. Of the 121 differentially palmitoylated proteins identified, just over half were synaptic proteins, whereas others were associated with metabolic functions, cytoskeletal organization, and signal transduction. The synapse-associated proteins generally exhibited increased palmitoylation after fear conditioning. In contrast, most of the proteins that exhibited decreased palmitoylation were associated with metabolic processes. Similar results were seen in cultured rat hippocampal neurons in response to chemically induced long-term potentiation. Furthermore, we found that the palmitoylation of one of the synaptic proteins, plasticity-related gene-1 (PRG-1), also known as lipid phosphate phosphatase-related protein type 4 (LPPR4), was important for synaptic activity-induced insertion of α-amino-3-hydroxy-5-methyl-4-isoxazolepropionic acid receptors (AMPARs) into the postsynaptic membrane. The findings identify proteins whose dynamic palmitoylation may regulate their role in synaptic plasticity, learning, and memory.

Endosomal-Lysosomal and Autophagy Pathway in Alzheimer's Disease: A Systematic Review and Meta-Analysis

BACKGROUND

The endosomal-lysosomal and autophagy (ELA) pathway may be implicated in the progression of Alzheimer's disease (AD); however, findings thus far have been inconsistent.

OBJECTIVE

To systematically summarize differences in endosomal-lysosomal and autophagy proteins in the cerebrospinal fluid (CSF) of people with AD and healthy controls (HC).

METHODS

Studies measuring CSF concentrations of relevant proteins in the ELA pathway in AD and healthy controls were included. Standardized mean differences (SMD) with 95% confidence intervals (CI) between AD and healthy controls in CSF concentrations of relevant proteins were meta-analyzed using random-effects models.

RESULTS

Of 2,471 unique studies, 43 studies were included in the systematic review and meta-analysis. Differences in ELA protein levels in the CSF between AD and healthy controls were observed, particularly in lysosomal membrane (LAMP-1: NAD/NHC = 348/381, SMD [95% CI] = 0.599 [0.268, 0.930], I2 = 72.8% ; LAMP-2: NAD/NHC = 401/510, SMD [95% CI] = 0.480 [0.134, 0.826], I2 = 78.7%) and intra-lysosomal proteins (GM2A: NAD/NHC = 390/420, SMD [95% CI] = 0.496 [0.039, 0.954], I2 = 87.7% ; CTSB: NAD/NHC = 485/443, SMD [95% CI] = 0.201 [0.029, 0.374], I2 = 28.5% ; CTSZ: NAD/NHC = 535/820, SMD [95% CI] = -0.160 [-0.305, -0.015], I2 = 24.0%) and in proteins involved in endocytosis (AP2B1:NAD/NHC = 171/205, SMD [95% CI] = 0.513 [0.259, 0.768], I2 = 27.4% ; FLOT1: NAD/NHC = 41/45, SMD [95% CI] = -0.489 [-0.919, -0.058], I2 <0.01). LC3B, an autophagy marker, also showed a difference (NAD/NHC = 70/59, SMD [95% CI] = 0.648 [0.180, 1.116], I2 = 38.3%)), but overall there was limited evidence suggesting differences in proteins involved in endosomal function and autophagy.

CONCLUSION

Dysregulation of proteins in the ELA pathway may play an important role in AD pathogenesis. Some proteins within this pathway may be potential biomarkers for AD.

Accurate Proteomewide Measurement of Methionine Oxidation in Aging Mouse Brains

The oxidation of methionine has emerged as an important post-translational modification of proteins. A number of studies have suggested that the oxidation of methionines in select proteins can have diverse impacts on cell physiology, ranging from detrimental effects on protein stability to functional roles in cell signaling. Despite its importance, the large-scale investigation of methionine oxidation in a complex matrix, such as the cellular proteome, has been hampered by technical limitations. We report a methodology, methionine oxidation by blocking (MobB), that allows for accurate and precise quantification of low levels of methionine oxidation typically observed in vivo. To demonstrate the utility of this methodology, we analyzed the brain tissues of young (6 m.o.) and old (20 m.o.) mice and identified over 280 novel sites for in vivo methionine oxidation. We further demonstrated that oxidation stoichiometries for specific methionine residues are highly consistent between individual animals and methionine sulfoxides are enriched in clusters of functionally related gene products including membrane and extracellular proteins. However, we did not detect significant changes in methionine oxidation in brains of old mice. Our results suggest that under normal conditions, methionine oxidation may be a biologically regulated process rather than a result of stochastic chemical damage.

VIQoR: a web service for visually supervised protein inference and protein quantification

MOTIVATION

In quantitative bottom-up mass spectrometry (MS)-based proteomics, the reliable estimation of protein concentration changes from peptide quantifications between different biological samples is essential. This estimation is not a single task but comprises the two processes of protein inference and protein abundance summarization. Furthermore, due to the high complexity of proteomics data and associated uncertainty about the performance of these processes, there is a demand for comprehensive visualization methods able to integrate protein with peptide quantitative data including their post-translational modifications. Hence, there is a lack of a suitable tool that provides post-identification quantitative analysis of proteins with simultaneous interactive visualization.

RESULTS

In this article, we present VIQoR, a user-friendly web service that accepts peptide quantitative data of both labeled and label-free experiments and accomplishes the crucial components protein inference and summarization and interactive visualization modules, including the novel VIQoR plot. We implemented two different parsimonious algorithms to solve the protein inference problem, while protein summarization is facilitated by a well-established factor analysis algorithm called fast-FARMS followed by a weighted average summarization function that minimizes the effect of missing values. In addition, summarization is optimized by the so-called Global Correlation Indicator (GCI). We test the tool on three publicly available ground truth datasets and demonstrate the ability of the protein inference algorithms to handle shared peptides. We furthermore show that GCI increases the accuracy of the quantitative analysis in datasets with replicated design.

AVAILABILITY AND IMPLEMENTATION

VIQoR is accessible at: http://computproteomics.bmb.sdu.dk/Apps/VIQoR/. The source code is available at: https://bitbucket.org/veitveit/viqor/.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Determining Plasma Protein Variation Parameters as a Prerequisite for Biomarker Studies-A TMT-Based LC-MSMS Proteome Investigation

Specific plasma proteins serve as valuable markers for various diseases and are in many cases routinely measured in clinical laboratories by fully automated systems. For safe diagnostics and monitoring using these markers, it is important to ensure an analytical quality in line with clinical needs. For this purpose, information on the analytical and the biological variation of the measured plasma protein, also in the context of the discovery and validation of novel, disease protein biomarkers, is important, particularly in relation to for sample size calculations in clinical studies. Nevertheless, information on the biological variation of the majority of medium-to-high abundant plasma proteins is largely absent. In this study, we hypothesized that it is possible to generate data on inter-individual biological variation in combination with analytical variation of several hundred abundant plasma proteins, by applying LC-MS/MS in combination with relative quantification using isobaric tagging (10-plex TMT-labeling) to plasma samples. Using this analytical proteomic approach, we analyzed 42 plasma samples prepared in doublets, and estimated the technical, inter-individual biological, and total variation of 265 of the most abundant proteins present in human plasma thereby creating the prerequisites for power analysis and sample size determination in future clinical proteomics studies. Our results demonstrated that only five samples per group may provide sufficient statistical power for most of the analyzed proteins if relative changes in abundances >1.5-fold are expected. Seventeen of the measured proteins are present in the European Federation of Clinical Chemistry and Laboratory Medicine (EFLM) Biological Variation Database, and demonstrated remarkably similar biological CV’s to the corresponding CV’s listed in the EFLM database suggesting that the generated proteomic determined variation knowledge is useful for large-scale determination of plasma protein variations.

Proteomics Landscape of Alzheimer's Disease

Alzheimer’s disease (AD) is the most prevalent form of dementia, and the numbers of AD patients are expected to increase as human life expectancy improves. Deposition of β-amyloid protein (Aβ) in the extracellular matrix and intracellular neurofibrillary tangles are molecular hallmarks of the disease. Since the precise pathophysiology of AD has not been elucidated yet, effective treatment is not available. Thus, understanding the disease pathology, as well as identification and development of valid biomarkers, is imperative for early diagnosis as well as for monitoring disease progression and therapeutic responses. Keeping this goal in mind several studies using quantitative proteomics platform have been carried out on both clinical specimens including the brain, cerebrospinal fluid (CSF), plasma and on animal models of AD. In this review, we summarize the mass spectrometry (MS)-based proteomics studies on AD and discuss the discovery as well as validation stages in brief to identify candidate biomarkers.

Human body-fluid proteome: quantitative profiling and computational prediction

Empowered by the advancement of high-throughput bio technologies, recent research on body-fluid proteomes has led to the discoveries of numerous novel disease biomarkers and therapeutic drugs. In the meantime, a tremendous progress in disclosing the body-fluid proteomes was made, resulting in a collection of over 15 000 different proteins detected in major human body fluids. However, common challenges remain with current proteomics technologies about how to effectively handle the large variety of protein modifications in those fluids. To this end, computational effort utilizing statistical and machine-learning approaches has shown early successes in identifying biomarker proteins in specific human diseases. In this article, we first summarized the experimental progresses using a combination of conventional and high-throughput technologies, along with the major discoveries, and focused on current research status of 16 types of body-fluid proteins. Next, the emerging computational work on protein prediction based on support vector machine, ranking algorithm, and protein-protein interaction network were also surveyed, followed by algorithm and application discussion. At last, we discuss additional critical concerns about these topics and close the review by providing future perspectives especially toward the realization of clinical disease biomarker discovery.

An Investigation into Proteomic Constituents of Cerebrospinal Fluid in Patients with Chronic Peripheral Neuropathic Pain Medicated with Opioids- a Pilot Study

The pharmacodynamics of opioids for chronic peripheral neuropathic pain are complex and likely extend beyond classical opioid receptor theory. Preclinical evidence of opioid modulation of central immune signalling has not been identified in vivo in humans. Examining the cerebrospinal fluid (CSF) of patients medicated with opioids is required to identify potential pharmacodynamic mechanisms. We compared CSF samples of chronic peripheral neuropathic pain patients receiving opioids (n = 7) versus chronic peripheral neuropathic pain patients not taking opioids (control group, n = 13). Baseline pain scores with demographics were recorded. Proteome analysis was performed using mass spectrometry and secreted neuropeptides were measured by enzyme-linked immunosorbent assay. Based on Gene Ontology analysis, proteins involved in the positive regulation of nervous system development and myeloid leukocyte activation were increased in patients taking opioids versus the control group. The largest decrease in protein expression in patients taking opioids were related to neutrophil mediated immunity. In addition, notably higher expression levels of neural proteins (85%) and receptors (80%) were detected in the opioid group compared to the control group. This study suggests modulation of CNS homeostasis, possibly attributable to opioids, thus highlighting potential mechanisms for the pharmacodynamics of opioids. We also provide new insights into the immunomodulatory functions of opioids in vivo.

Label-Free Quantitative Proteomics Identifies Novel Biomarkers for Distinguishing Tuberculosis Pleural Effusion from Malignant Pleural Effusion

PURPOSE

To identify potential protein biomarkers for distinguishing tuberculosis plural effusion (TBPE) from malignant plural effusion (MPE).

EXPERIMENTAL DESIGN

Five independent samples from each group (TBPE and MPE) are enrolled for label-free quantitative proteomics analyses. The differentially expressed proteins are validated by western blot and ELISA. Logistic regression analysis is used to obtain the optimal diagnostic model.

RESULTS

In total, 14 proteins with significant difference are identified between TBPE and MPE. Seven differentially expressed proteins are validated using western blot, and the expression patterns of these seven proteins are similar with those in proteomics analysis. Statistically significant differences in four proteins (AGP1, ORM2, C9, and SERPING1) are noted between TBPE and MPE in the training set (n = 230). Logistic regression analysis shows the combination of AGP1-ORM2-C9 presents a sensitivity of 73.0% (92/126) and specificity of 89.4% (93/104) in discriminating TBPE from MPE. Additional validation is performed to evaluate the diagnostic model in an independent blind testing set (n = 80), and yielded a sensitivity of 74.4% (32/43) and specificity of 91.9% (34/37) in discriminating TBPE from MPE.

CONCLUSION

The study uncovers the proteomic profiles of TBPE and MPE, and provides new potential diagnostic biomarkers for distinguishing TBPE from MPE.

Early Stage Alterations in CA1 Extracellular Region Proteins Indicate Dysregulation of IL6 and Iron Homeostasis in the 5XFAD Alzheimer's Disease Mouse Model

In recent years, an increasing number of research papers revealed that the compositional and volumetric alterations in the extracellular matrix are the consequences of aging and may be related to Alzheimer's disease (AD). In this study, we aimed to demonstrate the alterations in hippocampal extracellular fluid proteins in vivo using the 5XFAD mouse model. Samples were obtained from hippocampi of 5XFAD mice (n = 6) and their non-transgenic littermates by intracerebral push-pull perfusion technique at 3 months of age, representing the pre-pathological stage of the AD. Proteins in the hippocampal perfusates were analyzed by Ultra Performance Liquid Chromatography-Electrospray Ionization Quadrupole Time-of-Flight Mass Spectrometry (UPLC-ESI-qTOF-MS/MS). 178 proteins were identified and 19 proteins of them were found to be statistically significantly altered (p≤0.05, fold change ≥40%, unique peptide count ≥3) in the hippocampal CA1 extracellular fluid of the 5XFAD mouse model. Ingenuity pathway analysis of the protein expression results identified IL6 as an upstream regulator. The upregulation of IL6 was validated by immunohistochemical staining of the hippocampus and cortex of the 5XFAD mice prior to Aβ plaque formation. Furthermore, the iron level in the hippocampus was measured by inductively coupled plasma-mass spectrometry as IL6 is mentioned in several studies to take part in iron homeostasis and inflammation and found to be increased in 5XFAD mice hippocampus. Alterations in extracellular matrix proteins in addition to increasing amount of hippocampal IL6 and iron in the early stages of AD may reveal inflammation-mediated iron dyshomeostasis in the early stages of neurodegeneration.

Protein variability in cerebrospinal fluid and its possible implications for neurological protein biomarker research

Cerebrospinal fluid is investigated in biomarker studies for various neurological disorders of the central nervous system due to its proximity to the brain. Currently, only a limited number of biomarkers have been validated in independent studies. The high variability in the protein composition and protein abundance of cerebrospinal fluid between as well as within individuals might be an important reason for this phenomenon. To evaluate this possibility, we investigated the inter- and intraindividual variability in the cerebrospinal fluid proteome globally, with a specific focus on disease biomarkers described in the literature. Cerebrospinal fluid from a longitudinal study group including 12 healthy control subjects was analyzed by label-free quantification (LFQ) via LC-MS/MS. Data were quantified via MaxQuant. Then, the intra- and interindividual variability and the reference change value were calculated for every protein. We identified and quantified 791 proteins, and 216 of these proteins were abundant in all samples and were selected for further analysis. For these proteins, we found an interindividual coefficient of variation of up to 101.5% and an intraindividual coefficient of variation of up to 29.3%. Remarkably, these values were comparably high for both proteins that were published as disease biomarkers and other proteins. Our results support the hypothesis that natural variability greatly impacts cerebrospinal fluid protein biomarkers because high variability can lead to unreliable results. Thus, we suggest controlling the variability of each protein to distinguish between good and bad biomarker candidates, e.g., by utilizing reference change values to improve the process of evaluating potential biomarkers in future studies.

Label-Free Quantitative Proteomics Identifies Novel Plasma Biomarkers for Distinguishing Pulmonary Tuberculosis and Latent Infection

The lack of effective differential diagnostic methods for active tuberculosis (TB) and latent infection (LTBI) is still an obstacle for TB control. Furthermore, the molecular mechanism behind the progression from LTBI to active TB has been not elucidated. Therefore, we performed label-free quantitative proteomics to identify plasma biomarkers for discriminating pulmonary TB (PTB) from LTBI. A total of 31 overlapping proteins with significant difference in expression level were identified in PTB patients (n = 15), compared with LTBI individuals (n = 15) and healthy controls (HCs, n = 15). Eight differentially expressed proteins were verified using western blot analysis, which was 100% consistent with the proteomics results. Statistically significant differences of six proteins were further validated in the PTB group compared with the LTBI and HC groups in the training set (n = 240), using ELISA. Classification and regression tree (CART) analysis was employed to determine the ideal protein combination for discriminating PTB from LTBI and HC. A diagnostic model consisting of alpha-1-antichymotrypsin (ACT), alpha-1-acid glycoprotein 1 (AGP1), and E-cadherin (CDH1) was established and presented a sensitivity of 81.2% (69/85) and a specificity of 95.2% (80/84) in discriminating PTB from LTBI, and a sensitivity of 81.2% (69/85) and a specificity of 90.1% (64/81) in discriminating PTB from HCs. Additional validation was performed by evaluating the diagnostic model in blind testing set (n = 113), which yielded a sensitivity of 75.0% (21/28) and specificity of 96.1% (25/26) in PTB vs. LTBI, 75.0% (21/28) and 92.3% (24/26) in PTB vs. HCs, and 75.0% (21/28) and 81.8% (27/33) in PTB vs. lung cancer (LC), respectively. This study obtained the plasma proteomic profiles of different M.TB infection statuses, which contribute to a better understanding of the pathogenesis involved in the transition from latent infection to TB activation and provide new potential diagnostic biomarkers for distinguishing PTB and LTBI.

On the Reproducibility of Label-Free Quantitative Cross-Linking/Mass Spectrometry

Quantitative cross-linking/mass spectrometry (QCLMS) is an emerging approach to study conformational changes of proteins and multi-subunit complexes. Distinguishing protein conformations requires reproducibly identifying and quantifying cross-linked peptides. Here we analyzed the variation between multiple cross-linking reactions using bis[sulfosuccinimidyl] suberate (BS³)-cross-linked human serum albumin (HSA) and evaluated how reproducible cross-linked peptides can be identified and quantified by LC-MS analysis. To make QCLMS accessible to a broader research community, we developed a workflow that integrates the established software tools MaxQuant for spectra preprocessing, Xi for cross-linked peptide identification, and finally Skyline for quantification (MS1 filtering). Out of the 221 unique residue pairs identified in our sample, 124 were subsequently quantified across 10 analyses with coefficient of variation (CV) values of 14% (injection replica) and 32% (reaction replica). Thus our results demonstrate that the reproducibility of QCLMS is in line with the reproducibility of general quantitative proteomics and we establish a robust workflow for MS1-based quantitation of cross-linked peptides. Graphical Abstract ᅟ.

Proteomic studies of cerebrospinal fluid biomarkers of Alzheimer's disease: an update

INTRODUCTION

Alzheimer's disease (AD) is a neurodegenerative disease affecting the brain. Today there are three cerebrospinal fluid (CSF) biomarkers, amyloid-β consisting of 42 amino acids (Aβ42), total-tau (t-tau) and phosphorylated-tau (p-tau), which combined have sensitivity and specificity figures around 80%. However, pathological studies have shown that comorbidity is a common feature in AD and that the three currently used CSF biomarkers do not optimally reflect the activity of the disease process. Thus, additional markers are needed. Areas covered: In the present review, we screened PubMed for articles published the last five years (2012-2017) for proteomic studies in CSF with the criteria that AD had to be included as one of the diagnostic groups. Based on inclusion criteria, 28 papers were included reporting in total 224 biomarker-data that were altered in AD compared to control. Both mass spectrometry and multi-panel immunoassays were considered as proteomic studies. Expert commentary: A large number of pilot studies have been reported but so far there is a lack of replicated findings and to date no CSF biomarker discovered in proteomic studies has reached the clinic to aid in the diagnostic work-up of patients with cognitive impairment.

A targeted proteomic multiplex CSF assay identifies increased malate dehydrogenase and other neurodegenerative biomarkers in individuals with Alzheimer's disease pathology

Alzheimer's disease (AD) is the most common cause of dementia. Biomarkers are required to identify individuals in the preclinical phase, explain phenotypic diversity, measure progression and estimate prognosis. The development of assays to validate candidate biomarkers is costly and time-consuming. Targeted proteomics is an attractive means of quantifying novel proteins in cerebrospinal and other fluids, and has potential to help overcome this bottleneck in biomarker development. We used a previously validated multiplexed 10-min, targeted proteomic assay to assess 54 candidate cerebrospinal fluid (CSF) biomarkers in two independent cohorts comprising individuals with neurodegenerative dementias and healthy controls. Individuals were classified as 'AD' or 'non-AD' on the basis of their CSF T-tau and amyloid Aβ1-42 profile measured using enzyme-linked immunosorbent assay; biomarkers of interest were compared using univariate and multivariate analyses. In all, 35/31 individuals in Cohort 1 and 46/36 in Cohort 2 fulfilled criteria for AD/non-AD profile CSF, respectively. After adjustment for multiple comparisons, five proteins were elevated significantly in AD CSF compared with non-AD CSF in both cohorts: malate dehydrogenase; total APOE; chitinase-3-like protein 1 (YKL-40); osteopontin and cystatin C. In an independent multivariate orthogonal projection to latent structures discriminant analysis (OPLS-DA), these proteins were also identified as major contributors to the separation between AD and non-AD in both cohorts. Independent of CSF Aβ1-42 and tau, a combination of these biomarkers differentiated AD and non-AD with an area under curve (AUC)=0.88. This targeted proteomic multiple reaction monitoring (MRM)-based assay can simultaneously and rapidly measure multiple candidate CSF biomarkers. Applying this technique to AD we demonstrate differences in proteins involved in glucose metabolism and neuroinflammation that collectively have potential clinical diagnostic utility.

Inorganic Arsenic-Related Changes in the Stromal Tumor Microenvironment in a Prostate Cancer Cell-Conditioned Media Model

BACKGROUND

The tumor microenvironment plays an important role in the progression of cancer by mediating stromal-epithelial paracrine signaling, which can aberrantly modulate cellular proliferation and tumorigenesis. Exposure to environmental toxicants, such as inorganic arsenic (iAs), has also been implicated in the progression of prostate cancer.

OBJECTIVE

The role of iAs exposure in stromal signaling in the tumor microenvironment has been largely unexplored. Our objective was to elucidate molecular mechanisms of iAs-induced changes to stromal signaling by an enriched prostate tumor microenvironment cell population, adipose-derived mesenchymal stem/stromal cells (ASCs).

RESULTS

ASC-conditioned media (CM) collected after 1 week of iAs exposure increased prostate cancer cell viability, whereas CM from ASCs that received no iAs exposure decreased cell viability. Cytokine array analysis suggested changes to cytokine signaling associated with iAs exposure. Subsequent proteomic analysis suggested a concentration-dependent alteration to the HMOX1/THBS1/TGFβ signaling pathway by iAs. These results were validated by quantitative reverse transcriptase-polymerase chain reaction (RT-PCR) and Western blotting, confirming a concentration-dependent increase in HMOX1 and a decrease in THBS1 expression in ASC following iAs exposure. Subsequently, we used a TGFβ pathway reporter construct to confirm a decrease in stromal TGFβ signaling in ASC following iAs exposure.

CONCLUSIONS

Our results suggest a concentration-dependent alteration of stromal signaling: specifically, attenuation of stromal-mediated TGFβ signaling following exposure to iAs. Our results indicate iAs may enhance prostate cancer cell viability through a previously unreported stromal-based mechanism. These findings indicate that the stroma may mediate the effects of iAs in tumor progression, which may have future therapeutic implications.

CITATION

Shearer JJ, Wold EA, Umbaugh CS, Lichti CF, Nilsson CL, Figueiredo ML. 2016. Inorganic arsenic-related changes in the stromal tumor microenvironment in a prostate cancer cell-conditioned media model. Environ Health Perspect 124:1009-1015; http://dx.doi.org/10.1289/ehp.1510090.

Cell Autonomous and Nonautonomous Function of CUL4B in Mouse Spermatogenesis

CUL4B ubiquitin ligase belongs to the cullin-RING ubiquitin ligase family. Although sharing many sequence and structural similarities, CUL4B plays distinct roles in spermatogenesis from its homologous protein CUL4A. We previously reported that genetic ablation ofCul4ain mice led to male infertility because of aberrant meiotic progression. In the present study, we generated Cul4bgerm cell-specific conditional knock-out (Cul4b(Vasa)),as well asCul4bglobal knock-out (Cul4b(Sox2)) mouse, to investigate its roles in spermatogenesis. Germ cell-specific deletion of Cul4bled to male infertility, despite normal testicular morphology and comparable numbers of spermatozoa. Notably, significantly impaired sperm mobility caused by reduced mitochondrial activity and glycolysis level were observed in the majority of the mutant spermatozoa, manifested by low, if any, sperm ATP production. Furthermore,Cul4b(Vasa)spermatozoa exhibited defective arrangement of axonemal microtubules and flagella outer dense fibers. Our mass spectrometry analysis identified INSL6 as a novel CUL4B substrate in male germ cells, evidenced by its direct polyubiquination and degradation by CUL4B E3 ligase. Nevertheless,Cul4bglobal knock-out males lost their germ cells in an age-dependent manner, implying failure of maintaining the spermatogonial stem cell niche in somatic cells. Taken together, our results show that CUL4B is indispensable to spermatogenesis, and it functions cell autonomously in male germ cells to ensure spermatozoa motility, whereas it functions non-cell-autonomously in somatic cells to maintain spermatogonial stemness. Thus, CUL4B links two distinct spermatogenetic processes to a single E3 ligase, highlighting the significance of ubiquitin modification during spermatogenesis.

Neurodegeneration and Alzheimer's disease (AD). What Can Proteomics Tell Us About the Alzheimer's Brain?

Neurodegenerative diseases, such as Alzheimer's diseases (AD), are becoming more prevalent as the population ages. However, the mechanisms that lead to synapse destabilization and neuron death remain elusive. The advent of proteomics has allowed for high-throughput screening methods to search for biomarkers that could lead to early diagnosis and treatment and to identify alterations in the cellular proteome that could provide insight into disease etiology and possible treatment avenues. In this review, we have concentrated mainly on the findings that are related to how and whether proteomics studies have contributed to two aspects of AD research, the development of biomarkers for clinical diagnostics, and the recognition of proteins that can help elucidate the pathways leading to AD brain pathology. As a result of these studies, several candidate cerebrospinal fluid biomarkers are now available for further validation in different AD cohorts. Studies in AD brain and AD transgenic models support the notion that oxidative damage results in the alterations of metabolic enzymes and that mitochondrial dysfunction is central to AD neuropathology.

Label-Free LC-MS/MS Proteomic Analysis of Cerebrospinal Fluid Identifies Protein/Pathway Alterations and Candidate Biomarkers for Amyotrophic Lateral Sclerosis

Analysis of the cerebrospinal fluid (CSF) proteome has proven valuable to the study of neurodegenerative disorders. To identify new protein/pathway alterations and candidate biomarkers for amyotrophic lateral sclerosis (ALS), we performed comparative proteomic profiling of CSF from sporadic ALS (sALS), healthy control (HC), and other neurological disease (OND) subjects using label-free liquid chromatography-tandem mass spectrometry (LC-MS/MS). A total of 1712 CSF proteins were detected and relatively quantified by spectral counting. Levels of several proteins with diverse biological functions were significantly altered in sALS samples. Enrichment analysis was used to link these alterations to biological pathways, which were predominantly related to inflammation, neuronal activity, and extracellular matrix regulation. We then used our CSF proteomic profiles to create a support vector machines classifier capable of discriminating training set ALS from non-ALS (HC and OND) samples. Four classifier proteins, WD repeat-containing protein 63, amyloid-like protein 1, SPARC-like protein 1, and cell adhesion molecule 3, were identified by feature selection and externally validated. The resultant classifier distinguished ALS from non-ALS samples with 83% sensitivity and 100% specificity in an independent test set. Collectively, our results illustrate the utility of CSF proteomic profiling for identifying ALS protein/pathway alterations and candidate disease biomarkers.

A Proteomic Analysis of Individual and Gender Variations in Normal Human Urine and Cerebrospinal Fluid Using iTRAQ Quantification

Urine and cerebrospinal fluid (CSF) are two important biofluids used for disease biomarker discovery. For differential proteomic analysis, it is essential to evaluate individual and gender variations. In this study, we characterized urinary and CSF proteomes of 14 healthy volunteers with regard to individual and gender variations using 2DLC-MS/MS analysis and 8-plex iTRAQ quantification. A total of 968/512 urinary/CSF proteins were identified, with 406/280 quantified in all individuals. The median inter-individual coefficients of variation (CVs) were 0.262 and 0.183 for urinary and CSF proteomes, respectively. Cluster analysis showed that male and female urinary proteomes exhibited different patterns, though CSF proteome showed no remarkable gender differences. In comparison with CSF proteome, urinary proteome showed higher individual variation. Further analysis revealed that individual variation was not correlated with protein abundance. The minimum sample size for proteomic analysis with a 2-fold change was 10 (4/5 for males/females using iTRAQ quantification) for urinary or 8 for CSF proteome. Intracellular proteins leaked from exfoliative cells tended to have higher CVs, and extracellular proteins secreted from urinary tract or originating from plasma tended to have lower CVs. The above results might be beneficial for differential proteomic analysis and biomarker discovery.

Comparative Proteomics for the Evaluation of Protein Expression and Modifications in Neurodegenerative Diseases

Together with hypothesis-driven approaches, high-throughput differential proteomic analysis performed primarily not only in human cerebrospinal fluid and serum but also on protein content of other tissues (blood cells, muscles, peripheral nerves, etc.) has been used in the last years to investigate neurodegenerative diseases. Even if the goal for these analyses was mainly the discovery of neurodegenerative disorders biomarkers, the characterization of specific posttranslational modifications (PTMs) and the differential protein expression resulted in being very informative to better define the pathological mechanisms. In this chapter are presented and discussed the positive aspects and challenges of the outcomes of some of our investigations on neurological and neurodegenerative disease, in order to highlight the important role of protein PTMs studies in proteomics-based approaches.

Update on the core and developing cerebrospinal fluid biomarkers for Alzheimer disease

Alzheimer disease (AD) is a complex neurodegenerative disorder, whose prevalence will dramatically rise by 2050. Despite numerous clinical trials investigating this disease, there is still no effective treatment. Many trials showed negative or inconclusive results, possibly because they recruited only patients with severe disease, who had not undergone disease-modifying therapies in preclinical stages of AD before severe degeneration occurred. Detection of AD in asymptomatic at risk individuals (and a few presymptomatic individuals who carry an autosomal dominant monogenic AD mutation) remains impractical in many of clinical situations and is possible only with reliable biomarkers. In addition to early diagnosis of AD, biomarkers should serve for monitoring disease progression and response to therapy. To date, the most promising biomarkers are cerebrospinal fluid (CSF) and neuroimaging biomarkers. Core CSF biomarkers (amyloid β_1-42, total tau, and phosphorylated tau) showed a high diagnostic accuracy but were still unreliable for preclinical detection of AD. Hence, there is an urgent need for detection and validation of novel CSF biomarkers that would enable early diagnosis of AD in asymptomatic individuals. This article reviews recent research advances on biomarkers for AD, focusing mainly on the CSF biomarkers. In addition to core CSF biomarkers, the potential usefulness of novel CSF biomarkers is discussed.

Determination of variation parameters as a crucial step in designing TMT-based clinical proteomics experiments

In quantitative shotgun proteomic analyses by liquid chromatography and mass spectrometry, a rigid study design is necessary in order to obtain statistically relevant results. Hypothesis testing, sample size calculation and power estimation are fundamental concepts that require consideration upon designing an experiment. For this reason, the reproducibility and variability of the proteomic platform needs to be assessed. In this study, we evaluate the technical (sample preparation), labeling (isobaric labels), and total (biological + technical + labeling + experimental) variability and reproducibility of a workflow that employs a shotgun LC-MS/MS approach in combination with TMT peptide labeling for the quantification of peripheral blood mononuclear cell (PBMC) proteome. We illustrate that the variability induced by TMT labeling is small when compared to the technical variation. The latter is also responsible for a substantial part of the total variation. Prior knowledge about the experimental variability allows for a correct design, a prerequisite for the detection of biologically significant disease-specific differential proteins in clinical proteomics experiments.

Mass spectrometric analysis of rat cerebrospinal fluid proteins following exposure to the neurotoxicant carbonyl sulfide

RATIONALE

Using a proteomic-based approach we have investigated possible altered expression of a range of cerebral spinal fluid (CSF) proteins following exposure to the neurotoxicant carbonyl sulfide (COS). CSF is ideal for the investigation of markers of brain injury or disease since it is secreted from several central nervous system structures and changes in the CSF composition may reflect brain insult and many pathological processes.

METHODS

Animals were placed in exposure chambers and were exposed to 0 ppm or 500 ppm COS for 1, 2 or 3 days, 6 h per day. After the last inhalation exposure, 50-70 μL CSF sample was obtained by lumbar puncture. CSF samples were analyzed by electrospray ionization mass spectrometry (ESI-MS) on either a Premier quadrupole time-of-flight (QTOF) or an Agilent 6340 ion trap and by matrix-assisted laser desorption/ionization (MALDI)-MS on a 4800 MALDI-TOF/TOF analyzer.

RESULTS

The dynamic range of abundance of the identified proteins spanned over more than three orders of magnitude. The four most abundant proteins identified (albumin, cystatin C, serotransferrin, transthyretin) are major proteins that are present in both CSF and blood at high levels but the fifth most abundant protein identified (prostaglandin H2D isomerase) is the second most abundant protein in human CSF and is secreted and synthesized in the rat central nervous system. No significant differences were observed between COS-treated CSF samples and the control CSF samples because of blood contamination.

CONCLUSIONS

Quantitative MS protein analyses of rat CSF is limited by the low sample volumes that can practicably be obtained from rats and the low protein concentrations in rat CSF. Results of this work suggest a clear need for CSF collection that would minimize blood contamination. Published in 2014. This article is a U.S. Government work and is in the public domain in the USA.

Systematic assessment of survey scan and MS2-based abundance strategies for label-free quantitative proteomics using high-resolution MS data

Survey-scan-based label-free method have shown no compelling benefit over fragment ion (MS2)-based approaches when low-resolution mass spectrometry (MS) was used, the growing prevalence of high-resolution analyzers may have changed the game. This necessitates an updated, comparative investigation of these approaches for data acquired by high-resolution MS. Here, we compared survey scan-based (ion current, IC) and MS2-based abundance features including spectral-count (SpC) and MS2 total-ion-current (MS2-TIC), for quantitative analysis using various high-resolution LC/MS data sets. Key discoveries include: (i) study with seven different biological data sets revealed only IC achieved high reproducibility for lower-abundance proteins; (ii) evaluation with 5-replicate analyses of a yeast sample showed IC provided much higher quantitative precision and lower missing data; (iii) IC, SpC, and MS2-TIC all showed good quantitative linearity (R² > 0.99) over a >1000-fold concentration range; (iv) both MS2-TIC and IC showed good linear response to various protein loading amounts but not SpC; (v) quantification using a well-characterized CPTAC data set showed that IC exhibited markedly higher quantitative accuracy, higher sensitivity, and lower false-positives/false-negatives than both SpC and MS2-TIC. Therefore, IC achieved an overall superior performance than the MS2-based strategies in terms of reproducibility, missing data, quantitative dynamic range, quantitative accuracy, and biomarker discovery.