Proteomics: a new approach to the study of disease

Proteomic alterations in patients with atopic dermatitis

INTRODUCTION

Atopic Dermatitis (AD) is the most common inflammatory skin disease with a complex and multifactorial pathogenesis. The use of proteomics in understanding AD has yielded the discovery of novel biomarkers and may further expand therapeutic options.

AREAS COVERED

This review summarizes the most recent proteomic studies and the methodologies used in AD. It describes novel biomarkers that may monitor disease course and therapeutic response. The review also highlights skin and blood biomarkers characterizing different AD phenotypes and differentiates AD from other inflammatory skin disorders. A literature search was conducted by querying Scopus, Google Scholar, Pubmed/Medline, and Clinicaltrials.gov up to June 2023.

EXPERT OPINION

The integration of proteomics into research efforts in atopic dermatitis has broadened our understanding of the molecular profile of AD through the discovery of new biomarkers. In addition, proteomics may contribute to the development of targeted treatments ultimately improving personalized medicine. An increasing number of studies are utilizing proteomics to explore this heterogeneous disease.

Subcellular proteomics insights into Alzheimer's disease development

Alzheimer's disease (AD), one of the most common dementias, is a neurodegenerative disease characterized by cognitive impairment and decreased judgment function. The expected number of AD patient is increasing in the context of the world's advancing medical care and increasing human life expectancy. Since current molecular mechanism studies on AD pathogenesis are incomplete, there is no specific and effective therapeutic agent. Mass spectrometry (MS)-based unbiased proteomics studies provide an effective and comprehensive approach. Many advances have been made in the study of the mechanism, diagnostic markers, and drug targets of AD using proteomics. This paper focus on subcellular level studies, reviews studies using proteomics to study AD-associated mitochondrial dysfunction, synaptic, and myelin damage, the protein composition of amyloid plaques (APs) and neurofibrillary tangles (NFTs), changes in tissue extracellular vehicles (EVs) and exosome proteome, and the protein changes in ribosomes and lysosomes. The methods of sample separation and preparation and proteomic analysis as well as the main findings of these studies are involved. The results of these proteomics studies provide insights into the pathogenesis of AD and provide theoretical resource and direction for future research in AD, helping to identify new biomarkers and drugs targets for AD.

Recognition of Immunoreactive Proteins in Leishmania infantum Amastigote-Like and Promastigote Using Sera of Visceral Leishmaniasis Patients: a Preliminary Study

PURPOSE

Visceral leishmaniasis (VL) is a systemic and parasitic disease that is usually fatal if left untreated. VL is endemic in different parts of Iran and is caused mainly by Leishmania infantum. This study aimed to recognition immunoreactive proteins in amastigote-like and promastigote stages of L. infantum (Iranian strain) by antibodies present in the sera of VL patients.

METHODS

Total protein extract from amastigote-like and promastigote cells was separated by two-dimensional electrophoresis (2DE). To detect the immunoreactive proteins, 2DE immunoblotting method was performed using different pools of VL patients' sera.

RESULTS

Approximately 390 and 430 protein spots could be separated in 2DE profiles of L. infantum amastigote-like and promastigote stages, respectively. In immunoblotting method, approximately 295 and 135 immunoreactive proteins of amastigotes-like reacted with high antibody titer serum pool and low antibody titer serum pool, respectively. Approximately 120 and 85 immunoreactive proteins of promastigote extract were recognized using the high antibody titer sera pool and low antibody titer sera, respectively.

CONCLUSION

The present study has recognized a number of antigenic diversity proteins based on the molecular weight and pH in amastigote-like and promastigote stages of L. infantum. These results provide us a new concept for further analysis development in the field of diagnosis biomarkers and vaccine targets.

A Bioinformatics Approach to Mine the Microbial Proteomic Profile of COVID-19 Mass Spectrometry Data

Mass spectrometry (MS) is one of the key technologies used in proteomics. The majority of studies carried out using proteomics have focused on identifying proteins in biological samples such as human plasma to pin down prognostic or diagnostic biomarkers associated with particular conditions or diseases. This study aims to quantify microbial (viral and bacterial) proteins in healthy human plasma. MS data of healthy human plasma were searched against the complete proteomes of all available viruses and bacteria. With this baseline established, the same strategy was applied to characterize the metaproteomic profile of different SARS-CoV-2 disease stages in the plasma of patients. Two SARS-CoV-2 proteins were detected with a high confidence and could serve as the early markers of SARS-CoV-2 infection. The complete bacterial and viral protein content in SARS-CoV-2 samples was compared for the different disease stages. The number of viral proteins was found to increase significantly with the progression of the infection, at the expense of bacterial proteins. This strategy can be extended to aid in the development of early diagnostic tests for other infectious diseases based on the presence of microbial biomarkers in human plasma samples.

Metabolomic and Proteomic Analyses of Persistent Valvular Atrial Fibrillation and Non-Valvular Atrial Fibrillation

Atrial fibrillation (AF) is an abnormal heart rhythm related to an increased risk of heart failure, dementia, and stroke. The distinction between valvular and non-valvular AF remains a debate. In this study, proteomics and metabolomics were integrated to describe the dysregulated metabolites and proteins of AF patients relative to sinus rhythm (SR) patients. Totally 47 up-regulated and 41 down-regulated proteins in valvular AF, and 59 up-regulated and 149 down-regulated proteins in non-valvular AF were recognized in comparison to SR patients. Moreover, 58 up-regulated and 49 significantly down-regulated metabolites in valvular AF, and 47 up-regulated and 122 down-regulated metabolites in persistent non-valvular AF patients were identified in comparison to SR patients. Based on analysis of differential levels of metabolites and proteins, 15 up-regulated and 22 down-regulated proteins, and 13 up-regulated and 122 down-regulated metabolites in persistent non-valvular AF were identified relative to valvular AF. KEGG pathway enrichment analysis showed the altered proteins and metabolites were significantly related to multiple metabolic pathways, such as Glycolysis/Gluconeogenesis. Interestingly, the enrichment pathways related to non-valvular AF were obviously different from those in valvular AF. For example, valvular AF was significantly related to Glycolysis/Gluconeogenesis, but non-valvular AF was more related to Citrate cycle (TCA cycle). Correlation analysis between the differentially expressed proteins and metabolites was also performed. Several hub proteins with metabolites were identified in valvular AF and non-valvular AF. For example, Taurine, D-Threitol, L-Rhamnose, and DL-lactate played crucial roles in valvular AF, while Glycerol-3-phosphate dehydrogenase, Inorganic pyrophosphatase 2, Hydroxymethylglutaryl-CoAlyase, and Deoxyuridine 5-triphosphate nucleotidohydrolase were crucial in non-valvular AF. Then two hub networks were recognized as potential biomarkers, which can effectively distinguish valvular AF and non-valvular persistent AF from SR samples, with areas under curve of 0.75 and 0.707, respectively. Hence, these metabolites and proteins can be used as potential clinical molecular markers to discriminate two types of AF from SR samples. In summary, this study provides novel insights to understanding the mechanisms of AF progression and identifying novel biomarkers for prognosis of non-valvular AF and valvular AF by using metabolomics and proteomics analyses.

Development and validation of multiple machine learning algorithms for the classification of G-protein-coupled receptors using molecular evolution model-based feature extraction strategy

Machine learning is one of the most potential ways to realize the function prediction of the incremental large-scale G-protein-coupled receptors (GPCR). Prior research reveals that the key to determining the overall classification accuracy of GPCR is extracting valuable features and filtering out redundancy. To achieve a more efficient classification model, we put the feature synonym problem into consideration and create a new method based on functional word clustering and integration. Through evaluating the evolution correlation between features using the transition scores in mature molecular substitution matrices, candidate features are clustered into synonym groups. Each group of the clustered features is then integrated and represented by a unique key functional word. These retained key functional words are used to form a feature knowledge base. The original GPCR sequences are then transferred into feature vectors based on a feature re-extraction strategy according to the features in the knowledge base before the training and testing stage. We create multiple machine learning models based on Naïve Bayesian (NB), random forest (RF), support vector machine (SVM), and multi-layer perceptron (MLP) algorithms. The established model is applied to classify two public data sets containing 8354 and 12,731 GPCRs, respectively. These models achieve significant performance in almost all evaluation criteria in comparison with state-of-the art. This work demonstrated the potential of the novel feature extraction strategy and provided an effective theoretical design for the hierarchical classification of GPCRs.

Platelet and Haemostasis are the Main Targets in Severe Cases of COVID-19 Infection; a System Biology Study

Introduction:

Many proteomics-based and bioinformatics-based efforts are made to detect the molecular mechanism of COVID-19 infection. Identification of the main protein targets and pathways of severe cases of COVID-19 infection is the aim of this study.

Methods:

Published differentially expressed proteins were screened and the significant proteins were investigated via protein-protein interaction network using Cytoscape software V. 3.7.2 and STRING database. The studied proteins were assessed via action map analysis to determine the relationship between individual proteins using CluePedia. The related biological terms were investigated using ClueGO and the terms were clustered and discussed.

Results:

Among the 35 queried proteins, six of them (FGA, FGB, FGG, and FGl1 plus TLN1 and THBS1) were identified as critical proteins. A total of 38 biological terms, clustered in 4 groups, were introduced as the affected terms. “Platelet degranulation” and “hereditary factor I deficiency disease” were introduced as the main class of the terms disturbed by COVID-19 virus.

Conclusion:

It can be concluded that platelet damage and disturbed haemostasis could be the main targets in severe cases of coronavirus infection. It is vital to follow patients’ condition by examining the introduced critical differentially expressed proteins (DEPs).

Isoform 1 of Fibrinogen Alpha Chain Precursor is a Potential Biomarker for Steroid-Induced Osteonecrosis of the Femoral Head

PURPOSE

Early diagnosis is crucial to increase the chances of conservation treatment for patients with steroid-induced osteonecrosis of the femoral head (SIONFH). This study aimed to identify serum peptides as potential biomarkers to diagnose SIONFH.

EXPERIMENTAL DESIGN

The serum proteome of 32 SIONFH patients and 24 healthy controls are analyzed using magnetic bead-based weak cation exchange (MB-WCX) and matrix-assisted laser desorption ionization-time of flight mass spectrometry (MALDI-TOF-MS). Next, candidate biomarkers are identified using liquid chromatography-electrospray ionization-tandem mass spectrometry (LC-ESI-MS/MS). Candidate biomarkers are then validated using ELISA and western blotting.

RESULTS

39 peaks are identified and the expression fold changes of seven peaks in the two groups are greater than 1.5. Three peaks (m/z: 1077.84 Da; m/z: 1061.78 Da; m/z: 1099.56 Da) tend to be upregulated, while four peaks (m/z: 3973.92 Da; m/z: 7766.53 Da; m/z: 3957.31 Da; m/z: 4212.02 Da) tend to be down-regulated in SIONFH patients. The peak for a 1077.84 Da peptide is identified as Isoform 1 of the Fibrinogen alpha chain precursor (FGA). ELISAs and western blot analyses reveal that the expression of FGA is significantly higher in SIONFH patients than healthy controls.

CONCLUSION AND CLINICAL RELEVANCE

FGA is overexpressed in SIONFH patients, and thus, is a novel potential biomarker for SIONFH.

Role of Personalized Nutrition in Chronic-Degenerative Diseases

Human nutrition is a branch of medicine based on foods biochemical interactions with the human body. The phenotypic transition from health to disease status can be attributed to changes in genes and/or protein expression. For this reason, a new discipline has been developed called “-omic science”. In this review, we analyzed the role of “-omics sciences” (nutrigenetics, nutrigenomics, proteomics and metabolomics) in the health status and as possible therapeutic tool in chronic degenerative diseases. In particular, we focused on the role of nutrigenetics and the relationship between eating habits, changes in the DNA sequence and the onset of nutrition-related diseases. Moreover, we examined nutrigenomics and the effect of nutrients on gene expression. We perused the role of proteomics and metabolomics in personalized nutrition. In this scenario, we analyzed also how dysbiosis of gut microbiota can influence the onset and progression of chronic degenerative diseases. Moreover, nutrients influencing and regulating gene activity, both directly and indirectly, paves the way for personalized nutrition that plays a key role in the prevention and treatment of chronic degenerative diseases.

Screening diagnostic candidates from Leishmania infantum proteins for human visceral leishmaniasis using an immunoproteomics approach

There is no suitable vaccine against human visceral leishmaniasis (VL) and available drugs are toxic and/or present high cost. In this context, diagnostic tools should be improved for clinical management and epidemiological evaluation of disease. However, the variable sensitivity and/or specificity of the used antigens are limitations, showing the necessity to identify new molecules to be tested in a more sensitive and specific serology. In the present study, an immunoproteomics approach was performed in Leishmania infantum promastigotes and amastigotes employing sera samples from VL patients. Aiming to avoid undesired cross-reactivity in the serological assays, sera from Chagas disease patients and healthy subjects living in the endemic region of disease were also used in immunoblottings. The most reactive spots for VL samples were selected, and 29 and 21 proteins were identified in the promastigote and amastigote extracts, respectively. Two of them, endonuclease III and GTP-binding protein, were cloned, expressed, purified and tested in ELISA experiments against a large serological panel, and results showed high sensitivity and specificity values for the diagnosis of disease. In conclusion, the identified proteins could be considered in future studies as candidate antigens for the serodiagnosis of human VL.

In Search of Biomarkers for Pathogenesis and Control of Leishmaniasis by Global Analyses of Leishmania-Infected Macrophages

Leishmaniasis is a vector-borne, neglected tropical disease with a worldwide distribution that can present in a variety of clinical forms, depending on the parasite species and host genetic background. The pathogenesis of this disease remains far from being elucidated because the involvement of a complex immune response orchestrated by host cells significantly affects the clinical outcome. Among these cells, macrophages are the main host cells, produce cytokines and chemokines, thereby triggering events that contribute to the mediation of the host immune response and, subsequently, to the establishment of infection or, alternatively, disease control. There has been relatively limited commercial interest in developing new pharmaceutical compounds to treat leishmaniasis. Moreover, advances in the understanding of the underlying biology of Leishmania spp. have not translated into the development of effective new chemotherapeutic compounds. As a result, biomarkers as surrogate disease endpoints present several potential advantages to be used in the identification of targets capable of facilitating therapeutic interventions considered to ameliorate disease outcome. More recently, large-scale genomic and proteomic analyses have allowed the identification and characterization of the pathways involved in the infection process in both parasites and the host, and these analyses have been shown to be more effective than studying individual molecules to elucidate disease pathogenesis. RNA-seq and proteomics are large-scale approaches that characterize genes or proteins in a given cell line, tissue, or organism to provide a global and more integrated view of the myriad biological processes that occur within a cell than focusing on an individual gene or protein. Bioinformatics provides us with the means to computationally analyze and integrate the large volumes of data generated by high-throughput sequencing approaches. The integration of genomic expression and proteomic data offers a rich multi-dimensional analysis, despite the inherent technical and statistical challenges. We propose that these types of global analyses facilitate the identification, among a large number of genes and proteins, those that hold potential as biomarkers. The present review focuses on large-scale studies that have identified and evaluated relevant biomarkers in macrophages in response to Leishmania infection.

Understanding Leishmania parasites through proteomics and implications for the clinic

INTRODUCTION

Leishmania spp. are causative agents of leishmaniasis, a broad-spectrum neglected vector-borne disease. Genomic and transcriptional studies are not capable of solving intricate biological mysteries, leading to the emergence of proteomics, which can provide insights into the field of parasite biology and its interactions with the host. Areas covered: The combination of genomics and informatics with high throughput proteomics may improve our understanding of parasite biology and pathogenesis. This review analyses the roles of diverse proteomic technologies that facilitate our understanding of global protein profiles and definition of parasite development, survival, virulence and drug resistance mechanisms for disease intervention. Additionally, recent innovations in proteomics have provided insights concerning the drawbacks associated with conventional chemotherapeutic approaches and Leishmania biology, host-parasite interactions and the development of new therapeutic approaches. Expert commentary: With progressive breakthroughs in the foreseeable future, proteome profiles could provide target molecules for vaccine development and therapeutic intervention. Furthermore, proteomics, in combination with genomics and informatics, could facilitate the elimination of several diseases. Taken together, this review provides an outlook on developments in Leishmania proteomics and their clinical implications.

Classification of G-protein coupled receptors based on a rich generation of convolutional neural network, N-gram transformation and multiple sequence alignments

Sequence classification is crucial in predicting the function of newly discovered sequences. In recent years, the prediction of the incremental large-scale and diversity of sequences has heavily relied on the involvement of machine-learning algorithms. To improve prediction accuracy, these algorithms must confront the key challenge of extracting valuable features. In this work, we propose a feature-enhanced protein classification approach, considering the rich generation of multiple sequence alignment algorithms, N-gram probabilistic language model and the deep learning technique. The essence behind the proposed method is that if each group of sequences can be represented by one feature sequence, composed of homologous sites, there should be less loss when the sequence is rebuilt, when a more relevant sequence is added to the group. On the basis of this consideration, the prediction becomes whether a query sequence belonging to a group of sequences can be transferred to calculate the probability that the new feature sequence evolves from the original one. The proposed work focuses on the hierarchical classification of G-protein Coupled Receptors (GPCRs), which begins by extracting the feature sequences from the multiple sequence alignment results of the GPCRs sub-subfamilies. The N-gram model is then applied to construct the input vectors. Finally, these vectors are imported into a convolutional neural network to make a prediction. The experimental results elucidate that the proposed method provides significant performance improvements. The classification error rate of the proposed method is reduced by at least 4.67% (family level I) and 5.75% (family Level II), in comparison with the current state-of-the-art methods. The implementation program of the proposed work is freely available at: https://github.com/alanFchina/CNN .

Current trends in publicly available genetic databases

Analysis of human genetic data promises to uncover important disease targets. Genes known to cause or increase susceptibility for various diseases are being identified through analysis of genetic data, expression and metabolites. Future benefits to individuals are far-reaching, including improved gene therapy strategies, better drug development for disease treatment, pre-symptomatic disease intervention and risk susceptibility information. The rapid expansion of genetic databases has resulted in the emerging areas of genomics, transcriptomics, proteomics and metabolomics. The article presents a comprehensive overview of Internet databases, their trends over time and what 'omics' type they embody. With the completion of the human genome we are entering the postgenomic era. The use of microarrays and database software for genomic, transcriptomic, proteomic and metabolomic data for clinical assays and new diagnostic therapeutics will result in large, interlinked databases that will present unique issues of data management, standardization and information sharing.

Potential biomarkers associated with diabetic glomerulopathy through proteomics

Diabetic nephropathy (DN) is characterized by the development of progressive glomerulosclerotic lesions gradually leading to an increasing loss of functioning kidney parenchyma. Relatively little proteomic research of isolated glomeruli of experimental animal models has been done so far. Isolated glomerular proteomics is an innovative tool that potentially detects simultaneous expressions of glomeruli in diabetic pathological contexts. We compared the isolated glomerular profiles of rats with and without diabetes. The proteins in the aliquots of glomeruli were subjected to two-dimensional gel electrophoresis. The protein spots were matched and quantified using an imaging analysis system. The peptide mass fingerprints were identified by matrix-assisted laser desorption ionization time-of-flight mass spectrometry and a bioinformation search. We found that diabetes increased collagen type I and collagen type IV levels in diabetic glomeruli when compared to normal control group using Dynabeads. We found that rats with diabetes had significantly higher abundance of the Protein disulfide isomerase associated 3, Aspartoacylase-3,3-hydroxymethyl-3-methylglutaryl-Coenzyme A lyase, Lactamase beta 2 and Agmat protein. However, diabetic glomeruli in rats had significantly lower levels of the Regucalcin, rCG52140, Aldo-keto reductase family 1, Peroxiredoxin 1, and l-arginine: glycine amidinotransferase. These proteins of interest were reported to modulate disturbances in the homeostasis of endoplasmic reticulum stress, disturbance of inflammatory and fibrinogenic activities, impairing endothelial function, and dysregulation in the antioxidation capacity/oxidative stress in several tissue types under pathological contexts. Taken together, our high-throughput isolated glomerular proteomic findings indicated that multiple pathological reactions presumably occurred in DN.

Discriminating patients with early-stage pancreatic cancer or chronic pancreatitis using serum electrospray mass profiling

Blood tests are needed to aid in the early detection of pancreatic ductal adenocarcinoma (PDAC), and monitoring pancreatitis development into malignancy especially in high risk patients. This study exhibits efforts and progress toward developing such blood tests, using electrospray-mass spectrometry (MS) serum profiling to distinguish patients with early-stage PDAC or pancreatitis from each other and from controls. Identification of significant serum mass peak differences between these individuals was performed using t tests and "leave one out" cross validation. Serum mass peak distributions of control individuals were distinguished from those of patients with chronic pancreatitis or early-stage PDAC with P values <10(-15), and patients with chronic pancreatitis were distinguished from those of patients with early-stage PDAC with a P value <10(-12). Sera from 12 out of 12 patients with PDAC stages I, IIA and IIB were blindly validated from controls. Tandem MS/MS identified a cancer phenotype with elements of PDAC involved in early-stage PDAC/control discrimination. These studies indicate electrospray-MS mass profiling can detect serum changes in patients with pancreatitis or early-stage pancreatic cancer. Such technology has the potential to aid in early detection of pancreatic cancer, biomarker development, and in monitoring development of pancreatitis into PDAC.

Molecular mechanism of acute radiation enteritis revealed using proteomics and biological signaling network analysis in rats

BACKGROUND AND AIMS

Radiation enteritis (RE) has emerged as a significant complication that can progress to severe gastrointestinal disease and the mechanisms underlying its genesis remain poorly understood. The aim of this study was to identify temporal changes in protein expression potentially associated with acute inflammation and to elucidate the mechanism underlying radiation enteritis genesis.

METHODS

Male Sprague-Dawley rats were irradiated in the abdomen with a single dose of 10 Gy to establish an in vivo model of acute radiation enteritis. Two-dimensional fluorescence difference gel electrophoresis, matrix-assisted laser desorption/ionization time-of-flight spectrometer (MALDI-TOF) tandem mass spectrometry, and peptide mass fingerprinting were used to determine differentially expressed proteins between normal and inflamed intestinal mucosa. Additionally, differentially expressed proteins were evaluated by KO Based Annotation System to find the biological functions associated with acute radiation enteritis.

RESULTS

Intensity changes of 86 spots were detected with statistical significance (ratio ≥ 1.5 or ≤ 1.5, P < 0.05). Sixty one of the 86 spots were identified by MALDI-TOF/TOF tandem mass spectrometry. These radiation-induced proteins with biological functions showed that the FAS pathway and glycolysis signaling pathways were significantly altered using the KOBAS tool.

CONCLUSIONS

Our results reveal an underlying mechanism of radiation-induced acute enteritis, which may help clarify the pathogenesis of RE and point to potential targets for therapeutic interventions.

Proteomics analysis for finding serum markers of ovarian cancer

A combination of peptide ligand library beads (PLLB) and 1D gel liquid chromatography-mass spectrometry/mass spectrometry (1DGel-LC-MS/MS) was employed to analyze serum samples from patients with ovarian cancer and from healthy controls. Proteomic analysis identified 1200 serum proteins, among which 57 proteins were upregulated and 10 were downregulated in the sera from cancer patients. Retinol binding protein 4 (RBP4) is highly upregulated in the ovarian cancer serum samples. ELISA was employed to measure plasma concentrations of RBP4 in 80 samples from ovarian cancer patients, healthy individuals, myoma patients, and patients with benign ovarian tumor, respectively. The plasma concentrations of RBP4 ranging from 76.91 to 120.08 ng/mL with the mean value 89.13 ± 1.67 ng/mL in ovarian cancer patients are significantly higher than those in healthy individuals (10.85 ± 2.38 ng/mL). Results were further confirmed with immunohistochemistry, demonstrating that RBP4 expression levels in normal ovarian tissue were lower than those in ovarian cancer tissues. Our results suggested that RBP4 is a potential biomarker for diagnostic of screening ovarian cancer.

Distinguishing patients with stage I lung cancer versus control individuals using serum mass profiling

Serum mass profiling can discern physiological changes associated with specific disease states and their progression. Sera (86 total) from control individuals and patients with stage I nonsmall cell lung cancer or benign small pulmonary nodules were discriminated retrospectively by serum changes discerned by mass profiling. Control individuals were distinguished from patients with Stage I lung cancer or benign nodules with test sensitivities of 89% and 83%. Lung cancer patients versus those with benign nodules were distinguished with 80% sensitivity. This study exhibits progress toward a minimally-invasive aid in early detection of lung cancer and monitoring small pulmonary nodules for malignancy.

Mass profiling of serum to distinguish mice with pancreatic cancer induced by a transgenic Kras mutation

Mass spectrometry (MS) has the unique ability to profile, in an easily accessible body tissue (peripheral blood/serum,) the sizes and relative amounts of a wide variety of biomolecules in a single platform setting. Using electrospray ionization (ESI)-MS, we distinguished individual serum from wild-type control mice from serum of mice containing an oncogenic Kras mutation, which leads to development of pancreatic ductal adenocarcinoma (PDAC) similar to that observed in humans. Identification of differences in significant ESI-MS sera mass peaks between Kras-activated mice and control mice was performed using t tests and a "nested leave one out" cross-validation procedure. Peak distributions in serum of control mice from mice with Kras-mutant-dependent PDAC were distinguished from those of pancreatic intraepithelial neoplasia (PanIN) lesions (p = 0.00024). In addition, Kras mutant mice with PDAC were distinguished from Kras mutant mice with PanIN alone (p = 0.0057). Test specificity, a measure of the false positives, was greater for the control vs. Kras mutated mice, and the test sensitivity, a measure of false negatives, was greater for the PDAC vs. PanIN containing mice. Receiver-operating characteristic (ROC) curve discriminatory values were 0.85 for both comparisons. These studies indicate ESI-MS serum mass profiling can detect physiological changes associated with pancreatic cancer initiation and development in a GEM (genetic engineered mouse) model that mimics pancreatic cancer development in humans. Such technology has the potential to aid in early detection of pancreatic cancer and in developing therapeutic drug interventions.

Identification of altered plasma proteins by proteomic study in valvular heart diseases and the potential clinical significance

BACKGROUND

Little is known about genetic basis and proteomics in valvular heart disease (VHD) including rheumatic (RVD) and degenerative (DVD) valvular disease. The present proteomic study examined the hypothesis that certain proteins may be associated with the pathological changes in the plasma of VHD patients.

METHODS AND RESULTS

Differential protein analysis in the plasma identified 18 differentially expressed protein spots and 14 corresponding proteins or polypeptides by two-dimensional electrophoresis and mass spectrometry in 120 subjects. Two up-regulated (complement C4A and carbonic anhydrase 1) and three down-regulated proteins (serotransferrin, alpha-1-antichymotrypsin, and vitronectin) were validated by ELISA in enlarging samples. The plasma levels (n = 40 for each) of complement C4A in RVD (715.8±35.6 vs. 594.7±28.2 ng/ml, P = 0.009) and carbonic anhydrase 1 (237.70±15.7 vs. 184.7±10.8 U/L, P = 0.007) in DVD patients were significantly higher and that of serotransferrin (2.36±0.20 vs. 2.93±0.16 mg/ml, P = 0.025) and alpha-1-antichymotrypsin (370.0±13.7 vs. 413.0±11.6 µg/ml, P = 0.019) in RVD patients were significantly lower than those in controls. The plasma vitronectin level in both RVD (281.3±11.0 vs. 323.2±10.0 µg/ml, P = 0.006) and DVD (283.6±11.4 vs. 323.2±10.0 µg/ml, P = 0.011) was significantly lower than those in normal controls.

CONCLUSIONS

We have for the first time identified alterations of 14 differential proteins or polypeptides in the plasma of patients with various VHD. The elevation of plasma complement C4A in RVD and carbonic anhydrase 1 in DVD and the decrease of serotransferrin and alpha-1-antichymotrypsin in RVD patients may be useful biomarkers for these valvular diseases. The decreased plasma level of vitronectin - a protein related to the formation of valvular structure - in both RVD and DVD patients might indicate the possible genetic deficiency in these patients.

iTRAQ-based proteomics reveals novel biomarkers of osteoarthritis

Objective:

We performed comprehensive proteomic analyses of articular cartilage by using the isobaric tags for relative and absolute quantitation (iTRAQ) method, and searched for candidate biomarkers for osteoarthritis (OA).

Methods:

Articular cartilage was collected from patients with OA or femoral neck fracture for the control group. Molecular variations were detected by the iTRAQ method, and quantitative analyses were performed by western blot.

Results:

Using the iTRAQ method, we identified 76 proteins with different expression levels in OA patients and the control group. Among these proteins, we selected LECT2 (leukocyte cell-derived chemotaxin-2), BAALC (brain and acute leukemia, cytoplasmic), and PRDX6 (peroxiredoxin-6), which had not been reported as biomarkers for OA.

Conclusions:

Use of these proteins in combination with conventional OA biomarkers may better reflect the grade and prognosis of OA.

The impact of pre-analytical conditions on the serum proteome: heat-stabilization versus nitrogen storage

CONTEXT

Biological material reflecting the in vivo composition of markers provides a high potential for biomarker discovery.

OBJECTIVE

We compared the serum proteome following heat- and nitrogen-preservation, with and without subsequent storage at room temperature.

MATERIALS AND METHODS

Serum samples were collected, treated and analysed by two-dimensional gel electrophoresis. Protein spots were identified and confirmed by two mass spectrometry approaches (MALDI & ESI) and subjected to Ingenuity Pathway Analysis.

RESULTS

We revealed 24 differentially expressed proteins (p ≤ 0.05) between nitrogen and heat preservation, and 87 between nitrogen and heat preservation with subsequent storage for 120 h at room-temperature. Mass spectrometry identified 25 polypeptides. Pathway analysis resulted in networks maintaining Cellular Assembly and Organization, Movement and Maintenance.

CONCLUSION

Heat-stabilization does not substantially change the short-term proteome composition of serum compared with nitrogen treatment. However, heat-stabilization alone seems insufficient for long-term sample preservation for serum samples. We identified transthyretin and apolipoprotein A-IV as sample quality markers.

MALDI-TOF mass spectrometry analysis of small molecular weight compounds (under 10 KDa) as biomarkers of rat hearts undergoing arecoline challenge

CONTEXT

Statistical and clinical reports indicate that betel nut chewing is strongly associated with progression of oral cancer because some ingredients in betel nuts are potential cancer promoters, especially arecoline. Early diagnosis for cancer biomarkers is the best strategy for prevention of cancer progression. Several methods are suggested for investigating cancer biomarkers. Among these methods, gel-based proteomics approach is the most powerful and recommended tool for investigating biomarkers due to its high-throughput. However, this proteomics approach is not suitable for screening biomarkers with molecular weight under 10 KDa because of the characteristics of gel electrophoresis.

OBJECTIVE

This study investigated biomarkers with molecular weight under 10 KDa in rats with arecoline challenge.

MATERIALS AND METHODS

The centrifuging vials with membrane (10 KDa molecular weight cut-off) played a crucial role in this study. After centrifuging, the filtrate (containing compounds with molecular weight under 10 KDa) was collected and spotted on a sample plate for MALDI-TOF mass spectrometry analysis.

RESULTS

Compared to control, three extra peaks (m/z values were 1553.1611, 1668.2097 and 1740.1832, respectively) were found in sera and two extra peaks were found in heart tissue samples (408.9719 and 524.9961, respectively). These small compounds should play important roles and may be potential biomarker candidates in rats with arecoline.

DISCUSSION AND CONCLUSIONS

This study successfully reports a mass-based method for investigating biomarker candidates with small molecular weight in different types of sample (including serum and tissue). In addition, this reported method is more time-efficient (1 working day) than gel-based proteomics approach (5~7 working days).

Discriminating experimental Listeria monocytogenes infections in mice using serum profiling

Serum profiling was used to distinguish mice infected with wild-type or mutant Listeria monocytogenes from noninfected control mice. Identifications of significant electrospray ionization mass spectrometry (ESI-MS) sera peak areas between Listeria-infected- and control mice were performed using t tests. ESI-MS cohort peak distributions differed from mice infected with wild-type or ∆actA Listeria versus control mice with p values of 0.00012 and 0.015, respectively. A "% wild-type Listeria peaks identified" assessment tool yielded values of 64 % for wild-type infection, 51 % for ∆actA infection, and 47 % for no infection. Receiver operator characteristic area discriminatory values were 0.97 (wild-type) and 0.82 (∆actA) versus controls. Predictive value measurements revealed overall test sensitivities of 88 % for wild-type infection and 63 % for ∆actA infection. These studies indicate that ESI-MS serum profiling holds promise for diagnosis of infection with intracellular pathogens such as Listeria and indicate that the technology could be useful in understanding the L. monocytogenes infection process.

Optimizing targeted cancer therapy: towards clinical application of systems biology approaches

In cancer, genetic and epigenetic alterations ultimately culminate in discordant activation of signal transduction pathways driving the malignant process. Pharmacological or biological inhibition of such pathways holds significant promise with respect to devising rational therapy for cancer. Thus, technical concepts pursuing robust characterization of kinase activity in tissue samples from cancer patients have been subject of investigation. In the present review we provide a comprehensive overview of these techniques and discuss their advantages and disadvantages for systems biology approaches to identify kinase targets in oncological disease. Recent advances in the development and application of array-based peptide-substrate kinase activity screens show great promise in overcoming the discrepancy between the evaluation of aberrant cell signaling in specific malignancies or even individual patients and the currently available ensemble of highly specific targeted treatment strategies. These developments have the potential to result in a more effective selection of kinase inhibitors and thus optimize mechanism-based patient-specific therapeutic strategies. Given the results from current research on the tumor kinome, generating network views on aberrant tumor cell signaling is critical to meet this challenge.

CM2 antigen, a potential novel molecule participating in glucuronide transport on rat hepatocyte canalicular membrane

The polarized molecules predominately distributing at hepatocyte canalicular surface play a vital role in disclosing the process of bile formation and etiopathogenisis of cholestatic live diseases. Therefore, it is important to find novel polarized molecules on hepatocyte canalicular membrane. In the present study, canalicular membrane vesicles (CMVs) isolated from rat hepatocyte by density gradient centrifugation were used as immunogens to produce hybridoma and 46 strains of monoclonal antibodies (mAb) against CMVs were obtained. With a series of morphological assay methods, including immunohistochemistry, immunofluorescence and immuno-electron microscope, the antigens recognized by canalicular mAb1 (CM1) and canalicular mAb2 (CM2) were confirmed to predominately distribute at hepatocyte canalicular membrane. Transport activity assay revealed that CM2 could inhibit ATP-dependent E217βG uptake of rat hepatocyte CMVs. Meanwhile, Western blotting analysis showed that the molecular mass of CM2 antigen was approximately 110kDa, which was much less than Mr 180kDa of multidrug resistance-associated protein 2 (MRP2) involved in glucuronide transport. These data indicated that CM2 antigen might be a potential novel molecule participating in glucuronide transport on the hepatocyte canalicular membrane.

Serum profiling to distinguish early- and late-stage ovarian cancer patients from disease-free individuals

Sera mass spectrometry (MS) peak differences were analyzed from 35 ovarian cancer patients and 16 disease-free individuals. "Leave one out" cross validation was used to assign "% cancer peaks" in control and ovarian cancer sera samples. Sera MS discriminated stage I/II and stage III/V ovarian cancer patients versus controls with ROC curve area values of 0.82 and 0.92. Test sensitivities for ovarian cancer stage I/II and III/V were 80% and 93% respectively. These results indicate that MS is useful for distinguishing sera from early-stage ovarian cancer patients, and has potential as a test for early detection of this disease.

Distinguishing non-small cell lung adenocarcinoma patients from squamous cell carcinoma patients and control individuals using serum profiling

Goals of this study were to analyze the ability of mass spectrometry serum profiling to distinguish non-small cell lung adenocarcinoma from squamous cell carcinoma patients and healthy controls. Sera were obtained from 19 adenocarcinoma patients, 24 squamous cell carcinoma patients, and 21 controls. Identifications of significant mass-to-charge ratio (m/z) peak differences between these groups were performed using t-tests. A "leave one out" cross-validation procedure yielded discriminatory lung adenocarcinoma versus squamous cell carcinoma p and ROC curve values of <.0001 and 0.92, respectively. Test sensitivity and specificity were 84% and 79%, respectively. This approach could aid in lung cancer diagnosis and sub-typing.

Development of a nano-liquid chromatography on chip tandem mass spectrometry method for high-sensitivity hepcidin quantitation

Microfluidic LC systems present undeniable advantages over classical LC in terms of sensitivity. Hepcidin, a peptide marker of clinical disorders linked to iron metabolism, was used as model to demonstrate peptide quantification potentialities of LC-chip coupled to a nanoelectrospray source ion trap mass spectrometer in an aqueous sample. First, stable isotope labelled hepcidin was chosen as internal standard and gradient as well as sample compositions were optimised using design of experiments as development tool. The method was then prevalidated using accuracy profiles in order to select the most appropriate response function and to confirm the ability of the technique to quantify low hepcidin concentration. A reliable and very sensitive quantitation method was finally obtained using this integrated microfluidic technology. Indeed, good results with respect to accuracy, trueness and precision were achieved, as well as a very low limit of quantitation (0.07 ng/ml). Method suitability of nano-LC on chip tandem mass spectrometry for hepcidin quantitation was also demonstrated in complex media such as human plasma.

Nanodevices in diagnostics

The real-time, personalized and highly sensitive early-stage diagnosis of disease remains an important challenge in modern medicine. With the ability to interact with matter at the nanoscale, the development of nanotechnology architectures and materials could potentially extend subcellular and molecular detection beyond the limits of conventional diagnostic modalities. At the very least, nanotechnology should be able to dramatically accelerate biomarker discovery, as well as facilitate disease monitoring, especially of maladies presenting a high degree of molecular and compositional heterogeneity. This article gives an overview of several of the most promising nanodevices and nanomaterials along with their applications in clinical practice. Significant work to adapt nanoscale materials and devices to clinical applications involving large interdisciplinary collaborations is already underway with the potential for nanotechnology to become an important enabling diagnostic technology.

Fusion of metabolomics and proteomics data for biomarkers discovery: case study on the experimental autoimmune encephalomyelitis

Background

Analysis of Cerebrospinal Fluid (CSF) samples holds great promise to diagnose neurological pathologies and gain insight into the molecular background of these pathologies. Proteomics and metabolomics methods provide invaluable information on the biomolecular content of CSF and thereby on the possible status of the central nervous system, including neurological pathologies. The combined information provides a more complete description of CSF content. Extracting the full combined information requires a combined analysis of different datasets i.e. fusion of the data.

Results

A novel fusion method is presented and applied to proteomics and metabolomics data from a pre-clinical model of multiple sclerosis: an Experimental Autoimmune Encephalomyelitis (EAE) model in rats. The method follows a mid-level fusion architecture. The relevant information is extracted per platform using extended canonical variates analysis. The results are subsequently merged in order to be analyzed jointly. We find that the combined proteome and metabolome data allow for the efficient and reliable discrimination between healthy, peripherally inflamed rats, and rats at the onset of the EAE. The predicted accuracy reaches 89% on a test set. The important variables (metabolites and proteins) in this model are known to be linked to EAE and/or multiple sclerosis.

Conclusions

Fusion of proteomics and metabolomics data is possible. The main issues of high-dimensionality and missing values are overcome. The outcome leads to higher accuracy in prediction and more exhaustive description of the disease profile. The biological interpretation of the involved variables validates our fusion approach.

Plasma proteome changes in cardiovascular disease patients: novel isoforms of apolipoprotein A1

Background

The aim of this proteomic study was to look for changes taking place in plasma proteomes of patients with acute myocardial infarction (AMI), unstable angina pectoris (UAP), and stable angina pectoris (SAP).

Methods

Depleted plasma proteins were separated by 2D SDS-PAGE (pI 4-7), and proteomes were compared using Progenesis SameSpots statistical software. Proteins were identified by nanoLC-MS/MS. Proteins were quantified using commercial kits. Apolipoprotein A1 was studied using 1D and 2D SDS-PAGE, together with western blotting.

Results

Reciprocal comparison revealed 46 unique, significantly different spots; proteins in 34 spots were successfully identified and corresponded to 38 different proteins. Discrete comparisons of patient groups showed 45, 41, and 8 significantly different spots when AMI, UAP, and SAP were compared with the control group. On the basis of our proteomic data, plasma levels of two of them, alpha-1 microglobulin and vitamin D-binding protein, were determined. The data, however, failed to prove the proteins to be suitable markers or risk factors in the studied groups. The plasma level and isoform representation of apolipoprotein A1 were also estimated. Using 1D and 2D SDS-PAGE, together with western blotting, we observed extra high-molecular weight apolipoprotein A1 fractions presented only in the patient groups, indicating that the novel high-molecular weight isoforms of apolipoprotein A1 may be potential new markers or possible risk factors of cardiovascular disease.

Conclusion

The reported data show plasma proteome changes in patients with AMI, UAP, and SAP. We propose some apolipoprotein A1 fractions as a possible new disease-associated marker of cardiovascular disorders.

Serum discrimination of early-stage lung cancer patients using electrospray-ionization mass spectrometry

The goal of this study was to evaluate the usefulness of electrospray ionization-mass spectrometry (ESI-MS) technology to distinguish sera of early-stage lung cancer patients from control individuals. ESI-MS m/z (mass divided by charge) data were generated from sera of 43 non-small cell lung cancer patients (pathological stages I and II) and 21 control individuals. Identifications of m/z peak area significances between cancer and control ESI-MS sera spectra were performed using t-tests. A "leave one out" cross validation procedure, which mimics blinded sera analysis and corrects for "over-fitting" of data, yielded discriminatory cancer versus control distribution p value and ROC curve area value of <0.001 and 0.87, respectively. Analysis without the "leave one out" cross validation procedure yielded a ROC curve area of 0.99 for discrimination of sera from lung cancer patients versus control individuals. Predictive value measurements revealed overall test efficiency and sensitivity for distinguishing sera from lung cancer patients from controls (using "leave one out" cross validation) of 80% and 84%, respectively. ESI-MS serum analysis between control individuals and lung cancer patients who smoked or did not smoke had p values in ranges indicating that smoking effects are not pronounced in our analysis. These studies indicate that ESI-MS analyses of sera from early stage non-small cell lung cancer patients were helpful in distinguishing these patients from control individuals.

Label-free proteomics reveals decreased expression of CD18 and AKNA in peripheral CD4+ T cells from patients with Vogt-Koyanagi-Harada syndrome

Vogt-Koyanagi-Harada (VKH) syndrome is a systemic autoimmune disease. CD4⁺ T cells have been shown to be involved in autoimmune diseases including VKH syndrome. To screen aberrantly expressed membrane proteins in CD4⁺ T cell from patients with active VKH syndrome, blood samples were taken from five patients with active VKH syndrome and five healthy individuals. A label-free quantitative proteomic strategy was used to identify the differently expressed proteins between the two groups. The results revealed that the expression of 102 peptides was significantly altered (p<0.05) between two groups and matched amino acid sequences of proteins deposited in the international protein index (ipi.HUMAN.v3.36.fasta). The identified peptides corresponded to 64 proteins, in which 30 showed more than a 1.5-fold difference between the two groups. The decreased expression of CD18 and AKNA transcription factor (AKNA), both being three-fold lower than controls in expression identified by the label-free method, was further confirmed in an additional group of five active VKH patients and six normal individuals using the Western blot technique. A significantly decreased expression of CD18 and AKNA suggests a role for both proteins in the pathogenesis of this syndrome.

Down-regulation of Prdx6 contributes to DNA vaccine induced vitiligo in mice

DNA vaccines are widely used against infectious agents for their ability to induce both humoral and cellular immune responses. However, safety concerns regarding autoimmune responses to DNA vaccines, particularly to certain plasmids, should not be neglected. In this study, we serendipitously found that mice inoculated with pcDNA3-ANXB1 (pcDNA3-b1) developed autoimmunity, which did not happen in pVAX-ANXB1 (pVAX-b1) inoculated mice. We also employed proteomics approaches to investigate the distinction between the two groups of DNA vaccine immunized mice. Five different proteins with three-fold or greater changes were separated and identified by two-dimensional electrophoresis. Our study verified the safety of the DNA vaccine and unveiled the underlying potential molecular mechanism of DNA vaccine delivery.

αB-crystallin is elevated in highly infiltrative apoptosis-resistant glioblastoma cells

We have previously established two distinct glioma phenotypes by serial xenotransplantation of human glioblastoma (GBM) biopsies in nude rats. These tumors undergo a gradual transition from a highly invasive nonangiogenic to a less-invasive angiogenic phenotype. In a protein screen to identify molecular markers associated with the infiltrative phenotype, we identified α-basic-crystallin (αBc), a small heat-shock protein with cytoprotective properties. Its increased expression in the infiltrative phenotype was validated by immunohistochemistry and Western blots, confirming its identity to be tumor-derived and not from the host. Stereotactic human GBM biopsies taken from MRI-defined areas verified stronger αBc expression in the infiltrative edge compared to the tumor core. Cell migration assays and immunofluorescence staining showed αBc to be expressed by migrating cells in vitro. To determine αBc function, we altered its expression levels. αBc siRNA depletion caused a loss of migrating tumor cells from biopsy spheroids and delayed monolayer wound closure. In contrast, glioma cell migration in a Boyden chamber assay was unaffected by either αBc knockdown or overexpression, indicating that αBc is not functionally linked to the cell migration machinery. However, after siRNA αBc depletion, a significant sensitization of cells to various apoptotic inducers was observed (actinomycin, tumor necrosis factor α, and TNF-related apoptosis-inducing ligand [TRAIL]). In conclusion, αBc is overexpressed by highly migratory glioma cells where it plays a functional role in apoptosis resistance.

A review of current applications of mass spectrometry for neuroproteomics in epilepsy

The brain is unquestionably the most fascinating organ, and the hippocampus is crucial in memory storage and retrieval and plays an important role in stress response. In temporal lobe epilepsy (TLE), the seizure origin typically involves the hippocampal formation. Despite tremendous progress, current knowledge falls short of being able to explain its function. An emerging approach toward an improved understanding of the complex molecular mechanisms that underlie functions of the brain and hippocampus is neuroproteomics. Mass spectrometry has been widely used to analyze biological samples, and has evolved into an indispensable tool for proteomics research. In this review, we present a general overview of the application of mass spectrometry in proteomics, summarize neuroproteomics and systems biology-based discovery of protein biomarkers for epilepsy, discuss the methodology needed to explore the epileptic hippocampus proteome, and also focus on applications of ingenuity pathway analysis (IPA) in disease research. This neuroproteomics survey presents a framework for large-scale protein research in epilepsy that can be applied for immediate epileptic biomarker discovery and the far-reaching systems biology understanding of the protein regulatory networks. Ultimately, knowledge attained through neuroproteomics could lead to clinical diagnostics and therapeutics to lessen the burden of epilepsy on society.

Can proteomics lead to the discovery of real biomarkers for HCC?

The development of proteomics technologies has lead to a great deal of effort being focused on the identification of biomarkers for cancers. Although many papers have reported candidate biomarkers for hepatocellular carcinomas (HCCs) in particular, so far none of these candidate biomarkers have been used either for diagnosis or therapy intreating patients. The question remains: Can proteomics identify real biomarkers for HCCs?