Sys-BodyFluid: a systematical database for human body fluid proteome research

SheddomeDB 2023: A Revision of an Ectodomain Shedding Database Based on a Comprehensive Literature Review and Online Resources

Ectodomain shedding of membrane proteins is a proteolytic event involved in several biological phenomena, including inflammation, development, diseases, and cancer progression. Though ectodomain shedding is a post-translational modification that plays an important role in cellular regulation, this biological phenomenon is seriously underannotated in public protein databases. Given the importance of the shedding events, we conducted a comprehensive literature review for membrane protein shedding and constructed the database, SheddomeDB in 2017. In response to user feedback, novel shedding findings, more associated biomedical events, and the advance in web technology, we revised SheddomeDB to a new version, SheddomeDB 2023. The revised SheddomeDB 2023 includes 481 protein entries across seven species; all the content was manually verified and curated. The content of SheddomeDB 2023 mainly came from a comprehensive literature survey by our newly developed semiautomated screening tool. We also integrated verified and updated cleavage and secretome information from other databases into the revision. In addition, SheddomeDB 2023 features a graphical presentation of cleavage information and a user-friendly interface for searching and browsing entries in the database. This revised comprehensive database of ectodomain shedding is expected to benefit biomedical researchers across different disciplines.

SecProCT: In Silico Prediction of Human Secretory Proteins Based on Capsule Network and Transformer

Identifying secretory proteins from blood, saliva or other body fluids has become an effective method of diagnosing diseases. Existing secretory protein prediction methods are mainly based on conventional machine learning algorithms and are highly dependent on the feature set from the protein. In this article, we propose a deep learning model based on the capsule network and transformer architecture, SecProCT, to predict secretory proteins using only amino acid sequences. The proposed model was validated using cross-validation and achieved 0.921 and 0.892 accuracy for predicting blood-secretory proteins and saliva-secretory proteins, respectively. Meanwhile, the proposed model was validated on an independent test set and achieved 0.917 and 0.905 accuracy for predicting blood-secretory proteins and saliva-secretory proteins, respectively, which are better than conventional machine learning methods and other deep learning methods for biological sequence analysis. The main contributions of this article are as follows: (1) a deep learning model based on a capsule network and transformer architecture is proposed for predicting secretory proteins. The results of this model are better than the those of existing conventional machine learning methods and deep learning methods for biological sequence analysis; (2) only amino acid sequences are used in the proposed model, which overcomes the high dependence of existing methods on the annotated protein features; (3) the proposed model can accurately predict most experimentally verified secretory proteins and cancer protein biomarkers in blood and saliva.

Influence of interaction between surface-modified magnetic nanoparticles with infectious biofilm components in artificial channel digging and biofilm eradication by antibiotics in vitro and in vivo

Magnetic targeting of antimicrobial-loaded magnetic nanoparticles to micrometer-sized infectious biofilms is challenging. Bacterial biofilms possess water channels that facilitate transport of nutrient and metabolic waste products, but are insufficient to allow deep penetration of antimicrobials and bacterial killing. Artificial channel digging in infectious biofilms involves magnetically propelling nanoparticles through a biofilm to dig additional channels to enhance antimicrobial penetration. This does not require precise targeting. However, it is not known whether interaction of magnetic nanoparticles with biofilm components impacts the efficacy of antibiotics after artificial channel digging. Here, we functionalized magnetic-iron-oxide-nanoparticles (MIONPs) with polydopamine (PDA) to modify their interaction with staphylococcal pathogens and extracellular-polymeric-substances (EPS) and relate the interaction with in vitro biofilm eradication by gentamicin after magnetic channel digging. PDA-modified MIONPs had less negative zeta potentials than unmodified MIONPs due to the presence of amino groups and accordingly more interaction with negatively charged staphylococcal cell surfaces than unmodified MIONPs. Neither unmodified nor PDA-modified MIONPs interacted with EPS. Concurrently, use of non-interacting unmodified MIONPs for artificial channel digging in in vitro grown staphylococcal biofilms enhanced the efficacy of gentamicin more than the use of interacting, PDA-modified MIONPs. In vivo experiments in mice using a sub-cutaneous infection model confirmed that non-interacting, unmodified MIONPs enhanced eradication by gentamicin of Staphylococcus aureus Xen36 biofilms about 10 fold. Combined with the high biocompatibility of magnetic nanoparticles, these results form an important step in understanding the mechanism of artificial channel digging in infectious biofilms for enhancing antibiotic efficacy in hard-to-treat infectious biofilms in patients.

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.

CapsNet-SSP: multilane capsule network for predicting human saliva-secretory proteins

Background

Compared with disease biomarkers in blood and urine, biomarkers in saliva have distinct advantages in clinical tests, as they can be conveniently examined through noninvasive sample collection. Therefore, identifying human saliva-secretory proteins and further detecting protein biomarkers in saliva have significant value in clinical medicine. There are only a few methods for predicting saliva-secretory proteins based on conventional machine learning algorithms, and all are highly dependent on annotated protein features. Unlike conventional machine learning algorithms, deep learning algorithms can automatically learn feature representations from input data and thus hold promise for predicting saliva-secretory proteins.

Results

We present a novel end-to-end deep learning model based on multilane capsule network (CapsNet) with differently sized convolution kernels to identify saliva-secretory proteins only from sequence information. The proposed model CapsNet-SSP outperforms existing methods based on conventional machine learning algorithms. Furthermore, the model performs better than other state-of-the-art deep learning architectures mostly used to analyze biological sequences. In addition, we further validate the effectiveness of CapsNet-SSP by comparison with human saliva-secretory proteins from existing studies and known salivary protein biomarkers of cancer.

Conclusions

The main contributions of this study are as follows: (1) an end-to-end model based on CapsNet is proposed to identify saliva-secretory proteins from the sequence information; (2) the proposed model achieves better performance and outperforms existing models; and (3) the saliva-secretory proteins predicted by our model are statistically significant compared with existing cancer biomarkers in saliva. In addition, a web server of CapsNet-SSP is developed for saliva-secretory protein identification, and it can be accessed at the following URL: http://www.csbg-jlu.info/CapsNet-SSP/. We believe that our model and web server will be useful for biomedical researchers who are interested in finding salivary protein biomarkers, especially when they have identified candidate proteins for analyzing diseased tissues near or distal to salivary glands using transcriptome or proteomics.

MMP2 as an independent prognostic stratifier in oral cavity cancers

Background

Around 25% of oral cavity squamous cell carcinoma (OCSCC) are not controlled by the standard of care, but there is currently no validated biomarker to identify those patients. Our objective was to determine a robust biomarker for severe OCSCC, using a biology-driven strategy.

Patients and methods

Tumor and juxtatumor secretome were analyzed in a prospective discovery cohort of 37 OCSCC treated by primary surgery. Independent biomarker validation was performed by RTqPCR in a retrospective cohort of 145 patients with similar clinical features. An 18-gene signature (18 G) predictive of the response to PD-1 blockade was evaluated in the same cohort.

Results

Among 29 deregulated molecules identified in a secretome analysis, including chemokines, cytokines, growth factors, and molecules related to tumor growth and tissue remodeling, only soluble MMP2 was a prognostic biomarker. In our validation cohort, high levels of MMP2 and CD276, and low levels of CXCL10 and STAT1 mRNA were associated with poor prognosis in univariate analysis (Kaplan-Meier). MMP2 (p = .001) and extra-nodal extension (ENE) (p = .006) were independent biomarkers of disease-specific survival (DSS) in multivariate analysis and defined prognostic groups with 5-year DSS ranging from 36% (MMP2highENE+) to 88% (MMP2lowENE-). The expression of 18 G was similar in the different prognostic groups, suggesting comparable responsiveness to anti-PD-1.

Conclusion

High levels of MMP2 were an independent and validated prognostic biomarker, surpassing other molecules of a large panel of the tumor and immune-related processes, which may be used to select poor prognosis patients for intensified neoadjuvant or adjuvant regimens.

Bioinformatics for Renal and Urinary Proteomics: Call for Aggrandization

The clinical sampling of urine is noninvasive and unrestricted, whereby huge volumes can be easily obtained. This makes urine a valuable resource for the diagnoses of diseases. Urinary and renal proteomics have resulted in considerable progress in kidney-based disease diagnosis through biomarker discovery and treatment. This review summarizes the bioinformatics tools available for this area of proteomics and the milestones reached using these tools in clinical research. The scant research publications and the even more limited bioinformatic tool options available for urinary and renal proteomics are highlighted in this review. The need for more attention and input from bioinformaticians is highlighted, so that progressive achievements and releases can be made. With just a handful of existing tools for renal and urinary proteomic research available, this review identifies a gap worth targeting by protein chemists and bioinformaticians. The probable causes for the lack of enthusiasm in this area are also speculated upon in this review. This is the first review that consolidates the bioinformatics applications specifically for renal and urinary proteomics.

BoMiProt: A database of bovine milk proteins

Bovine milk has become an important biological fluid for proteomic research due to its nutritional and immunological benefits. To date, over 300 publications have reported changes in bovine milk protein composition based on seasons, lactation stages, breeds, health status and milk fractions while there are no reports on consolidation or overlap of data between studies. Thus, we have developed a literature-based, manually curated open online database of bovine milk proteome, BoMiProt (http://bomiprot.org), with over 3100 proteins from whey, fat globule membranes and exosomes. Each entry in the database is thoroughly cross-referenced including 397 proteins with well-defined information on protein function, biochemical properties, post-translational modifications and significance in milk from different publications. Of 397 proteins, over 199 have been reported with a structural gallery of homology models and crystal structures in the database. The proteome data can be retrieved using several search parameters such as protein name, accession IDs, FASTA sequence. Furthermore, the proteome data can be filtered based on milk fractions, post-translational modifications and/or structures. Taken together, BoMiProt represents an extensive compilation of bovine milk proteins from literature, providing a foundation for future studies to identify specific milk proteins which may be linked to mammary gland pathophysiology. BIOLOGICAL SIGNIFICANCE: Protein data identified from different previously published proteomic studies on bovine milk samples (21 publications) were gathered in the BoMiProt database. Unification of the identified proteins will give researchers an initial reference database on bovine milk proteome to understand the complexities of milk as a biological fluid. BoMiProt has a user-friendly interface with several useful features, including different search criteria for primary and secondary information of proteins along with cross-references to external databases. The database will provide insights into the existing literature and possible future directions to investigate further and improve the beneficial effects of bovine milk components and dairy products on human health.

Approaches to the discovery of non-invasive urinary biomarkers of prostate cancer

Prostate cancer (PCa) continues to be one of the most common cancers in men worldwide. Prostate specific antigen (PSA) measured in blood has been used for decades as an aid for physicians to detect the presence of prostate cancer. However, the PSA test has limited sensitivity and specificity, leading to unnecessary biopsies, overdiagnosis and overtreatment of patients. For these reasons, there is an urgent need for more accurate PCa biomarkers that can detect PCa with high sensitivity and specificity. Urine is a unique source of potential protein biomarkers that can be measured in a non-invasive way. This review comprehensively summarizes state of the art approaches used in the discovery and validation of urinary biomarkers for PCa. Numerous strategies are currently being used in the discovery of urinary biomarkers for prostate cancer including gel-based separation techniques, mass spectrometry, activity-based proteomic assays and software approaches. Antibody-based approaches remain preferred method for validation of candidate biomarkers with rapidly advancing multiplex immunoassays and MS-based targeted approaches. In the last decade, there has been a dramatic acceleration in the development of new techniques and approaches in the discovery of protein biomarkers for prostate cancer including computational, statistical and data mining methods. Many urinary-based protein biomarkers have been identified and have shown significant promise in initial studies. Examples of these potential biomarkers and the methods utilized in their discovery are also discussed in this review.

Proteomic analysis of cerebrospinal fluid from children with central nervous system tumors identifies candidate proteins relating to tumor metastatic spread

Central nervous system (CNS) tumors are the most common solid tumors in childhood. Since the sensitivity of combined cerebrospinal fluid (CSF) cytology and radiological neuroimaging in detecting meningeal metastases remains relatively low, we sought to characterize the CSF proteome of patients with CSF tumors to identify biomarkers predictive of metastatic spread. CSF samples from 27 children with brain tumors and 13 controls (extra-CNS non-Hodgkin lymphoma) were processed using core-shell hydrogel nanoparticles, and analyzed with reverse-phase liquid chromatography/electrospray tandem mass spectrometry (LC-MS/MS). Candidate proteins were identified with Fisher's exact test and/or a univariate logistic regression model. Reverse phase protein array (RPPA), Western blot (WB), and ELISA were used in the training set and in an independent set of CFS samples (60 cases, 14 controls) to validate our discovery findings. Among the 558 non-redundant proteins identified by LC-MS/MS, 147 were missing from the CSF database at http://www.biosino.org. Fourteen of the 26 final top-candidate proteins were chosen for validation with WB, RPPA and ELISA methods. Six proteins (type 1 collagen, insulin-like growth factor binding protein 4, procollagen C-endopeptidase enhancer 1, glial cell-line derived neurotrophic factor receptor α2, inter-alpha-trypsin inhibitor heavy chain 4, neural proliferation and differentiation control protein-1) revealed the ability to discriminate metastatic cases from controls. Combining a unique dataset of CSFs from pediatric CNS tumors with a novel enabling nanotechnology led us to identify CSF proteins potentially related to metastatic status.

Salivary proteome profiling of oral squamous cell carcinoma in a Hungarian population

Oral squamous cell carcinoma (OSCC) is the seventh most common malignancy and the ninth most frequent cause of cancer death in Europe. Within Europe, Hungary has one of the highest rates of OSCC incidence and mortality. Thus, there is an urgent need to improve early detection. Saliva, as a readily available body fluid, became an increasingly important substance for the detection of biomarkers for many diseases. Different research groups have identified salivary biomarkers specific for OSCC for different countries. In this study, saliva samples of Hungarian patients with OSCC were studied to discover disease‐specific and perhaps region‐specific biomarkers. LC‐mass spectrometry (MS)/MS analysis on a linear ion trap‐Orbitrap mass spectrometer was used for qualitative and quantitative salivary protein profiling. More than 500 proteins were identified from saliva by shotgun proteomics. The up‐ and downregulated proteins in the saliva of patients with OSCC highlighted the importance of protein–protein interaction networks involving the immune system and proteolysis in disease development. Two potential biomarkers from our shotgun analysis and a third candidate reported earlier by a Taiwanese group were further examined by ELISA on a larger reference set of samples. Resistin, a biomarker reported in Taiwan but not validated in our study, highlights the necessity of application of standardized analysis methods in different ethnic or geographical populations to identify biomarkers with sufficient specificity and sensitivity.

Current state of the art for enhancing urine biomarker discovery

INTRODUCTION

Urine is a highly desirable biospecimen for biomarker analysis because it can be collected recurrently by non-invasive techniques, in relatively large volumes. Urine contains cellular elements, biochemicals, and proteins derived from glomerular filtration of plasma, renal tubule excretion, and urogenital tract secretions that reflect, at a given time point, an individual's metabolic and pathophysiologic state.

AREAS COVERED

High-resolution mass spectrometry, coupled with state of the art fractionation systems are revealing the plethora of diagnostic/prognostic proteomic information existing within urinary exosomes, glycoproteins, and proteins. Affinity capture pre-processing techniques such as combinatorial peptide ligand libraries and biomarker harvesting hydrogel nanoparticles are enabling measurement/identification of previously undetectable urinary proteins. Expert commentary: Future challenges in the urinary proteomics field include a) defining either single or multiple, universally applicable data normalization methods for comparing results within and between individual patients/data sets, and b) defining expected urinary protein levels in healthy individuals.

Fluorescent binary ensemble with pattern recognition ability for identifying multiple metalloproteins with applications in serum and urine

A fluorescent binary ensemble with multiple-wavelength cross-reactivity functioning as a discriminative sensor to identify different metalloproteins in serum or urine solution.

Clusterin in the eye: An old dog with new tricks at the ocular surface

The multifunctional protein clusterin (CLU) was first described in 1983 as a secreted glycoprotein present in ram rete testis fluid that enhanced aggregation ('clustering') of a variety of cells in vitro. It was also independently discovered in a number of other systems. By the early 1990s, CLU was known under many names and its expression had been demonstrated throughout the body, including in the eye. Its homeostatic activities in proteostasis, cytoprotection, and anti-inflammation have been well documented, however its roles in health and disease are still not well understood. CLU is prominent at fluid-tissue interfaces, and in 1996 it was demonstrated to be the most highly expressed transcript in the human cornea, the protein product being localized to the apical layers of the mucosal epithelia of the cornea and conjunctiva. CLU protein is also present in human tears. Using a preclinical mouse model for desiccating stress that mimics human dry eye disease, the authors recently demonstrated that CLU prevents and ameliorates ocular surface barrier disruption by a remarkable sealing mechanism dependent on attainment of a critical all-or-none concentration in the tears. When the CLU level drops below the critical all-or-none threshold, the barrier becomes vulnerable to desiccating stress. CLU binds selectively to the ocular surface subjected to desiccating stress in vivo, and in vitro to LGALS3 (galectin-3), a key barrier component. Positioned in this way, CLU not only physically seals the ocular surface barrier, but it also protects the barrier cells and prevents further damage to barrier structure. CLU depletion from the ocular surface epithelia is seen in a variety of inflammatory conditions in humans and mice that lead to squamous metaplasia and a keratinized epithelium. This suggests that CLU might have a specific role in maintaining mucosal epithelial differentiation, an idea that can now be tested using the mouse model for desiccating stress. Most excitingly, the new findings suggest that CLU could serve as a novel biotherapeutic for dry eye disease.

Proteomic identification of potential cancer markers in human urine using subtractive analysis

Urine is an ideal medium in which to focus diagnostic cancer research due to the non-invasive nature and ease of sampling. Many large-scale proteomic studies have shown that urine is unexpectedly complex. We hypothesised that novel diagnostic cancer biomarkers could be discovered using a comparative proteomic analysis of pre-existing data. We assembled a database of 100 published datasets of 5,620 urinary proteins, as well as 46 datasets of 8,620 non-redundant proteins derived from kidney and blood proteome analyses. The data were then used to either subtract or compare molecules from a novel urinary proteome profiling dataset that we generated. We identified 1,161 unique proteins in samples from either cancer-bearing or healthy subjects. Subtractive analysis yielded a subset of 44 proteins that were found uniquely in urine from cancer patients, 30 of which were linked previously to cancer. In conclusion, this approach is useful in discovering novel biomarkers in tissues where unrelated profiling data is available. Only a limited disease-specific novel dataset is required to define new targets or substantiate previous findings. We have shared this discovery platform in the form of our Large Scale Screening Resource database, accessible through the Proteomic Analysis DataBase portal (www.PADB.org).

Ratiometric fluorescence sensor arrays based on quantum dots for detection of proteins

Optical cross-reactive sensor arrays have recently been demonstrated as a powerful tool for high-throughput protein analysis.

Integrated data mining of transcriptomic and proteomic datasets to predict the secretome of adipose tissue and muscle in ruminants

Adipose tissue and muscle are endocrine organs releasing signalling and mediator proteins termed adipokines and myokines. The identification of the complete set of proteins secreted by adipose tissue and muscle is a challenge to understand the molecular cross-talk between these tissues and to reveal potential targets to control body or muscle composition and metabolism.

Human Urine Proteomics: Analytical Techniques and Clinical Applications in Renal Diseases

Urine has been in the center of attention among scientists of clinical proteomics in the past decade, because it is valuable source of proteins and peptides with a relative stable composition and easy to collect in large and repeated quantities with a noninvasive procedure. In this review, we discuss technical aspects of urinary proteomics in detail, including sample preparation, proteomic technologies, and their advantage and disadvantages. Several recent experiments are presented which applied urinary proteome for biomarker discovery in renal diseases including diabetic nephropathy, immunoglobulin A (IgA) nephropathy, focal segmental glomerulosclerosis, lupus nephritis, membranous nephropathy, and acute kidney injury. In addition, several available databases in urinary proteomics are also briefly introduced.

Comparative Tissue Proteomics of Microdissected Specimens Reveals Novel Candidate Biomarkers of Bladder Cancer

More than 380,000 new cases of bladder cancer are diagnosed worldwide, accounting for ∼150,200 deaths each year. To discover potential biomarkers of bladder cancer, we employed a strategy combining laser microdissection, isobaric tags for relative and absolute quantitation labeling, and liquid chromatography-tandem MS (LC-MS/MS) analysis to profile proteomic changes in fresh-frozen bladder tumor specimens. Cellular proteins from four pairs of surgically resected primary bladder cancer tumor and adjacent nontumorous tissue were extracted for use in two batches of isobaric tags for relative and absolute quantitation experiments, which identified a total of 3220 proteins. A DAVID (database for annotation, visualization and integrated discovery) analysis of dysregulated proteins revealed that the three top-ranking biological processes were extracellular matrix organization, extracellular structure organization, and oxidation-reduction. Biological processes including response to organic substances, response to metal ions, and response to inorganic substances were highlighted by up-expressed proteins in bladder cancer. Seven differentially expressed proteins were selected as potential bladder cancer biomarkers for further verification. Immunohistochemical analyses showed significantly elevated levels of three proteins-SLC3A2, STMN1, and TAGLN2-in tumor cells compared with noncancerous bladder epithelial cells, and suggested that TAGLN2 could be a useful tumor tissue marker for diagnosis (AUC = 0.999) and evaluating lymph node metastasis in bladder cancer patients. ELISA results revealed significantly increased urinary levels of both STMN1 and TAGLN2 in bladder cancer subgroups compared with control groups. In comparisons with age-matched hernia urine specimens, urinary TAGLN2 in bladder cancer samples showed the largest fold change (7.13-fold), with an area-under-the-curve value of 0.70 (p < 0.001, n = 205). Overall, TAGLN2 showed the most significant overexpression in individual bladder cancer tissues and urine specimens, and thus represents a potential biomarker for noninvasive screening for bladder cancer. Our findings highlight the value of bladder tissue proteome in providing valuable information for future validation studies of potential biomarkers in urothelial carcinoma.

A computational method for prediction of saliva-secretory proteins and its application to identification of head and neck cancer biomarkers for salivary diagnosis

Human saliva is rich in proteins, which have been used for disease detection such as oral diseases and systematic diseases. In this paper, we present a computational method for predicting secretory proteins in human saliva based on two sets of human proteins from published literatures and public databases. One set contains known proteins which can be secreted into saliva, and the other contains the proteins that are deemed to be not extracellular secretion. The protein features with discerning power between two sets were firstly gathered. Then a classifier was trained based on the identified features to predict whether a protein was saliva-secretory one or not. The average values of the sensitivity, specificity, precision, accuracy, and Matthews correlation coefficient value by 10-fold cross validation repeated 100 times were 80.67%, 90.56%, 90.09%, 85.53%, and 0.7168, respectively. These results indicated that our selected features are informative. We applied the classifier for prediction saliva-secretory proteins out of all human proteins, if a known biomarker was likely to enter into saliva, and the potential salivary biomarkers for head and neck squamous cell carcinoma. We also compared the top 1000 proteins predicted by computational methods in different kind of fluids. This work provided a useful tool for effectively identifying the salivary biomarkers for various human diseases and facilitate the development of salivary diagnosis.

Cerebrospinal fluid proteomics in multiple sclerosis

Multiple sclerosis (MS) is an immune mediated chronic inflammatory disease of the central nervous system usually initiated during young adulthood, affecting approximately 2.5 million people worldwide. There is currently no cure for MS, but disease modifying treatment has become increasingly more effective, especially when started in the first phase of the disease. The disease course and prognosis are often unpredictable and it can be challenging to determine an early diagnosis. The detection of novel biomarkers to understand more of the disease mechanism, facilitate early diagnosis, predict disease progression, and find treatment targets would be very attractive. Over the last decade there has been an increasing effort toward finding such biomarker candidates. One promising strategy has been to use state-of-the-art quantitative proteomics approaches to compare the cerebrospinal fluid (CSF) proteome between MS and control patients or between different subgroups of MS. In this review we summarize and discuss the status of CSF proteomics in MS, including the latest findings with a focus on the last five years. This article is part of a Special Issue entitled: Neuroproteomics: Applications in Neuroscience and Neurology.

In-depth characterization of the cerebrospinal fluid (CSF) proteome displayed through the CSF proteome resource (CSF-PR)

In this study, the human cerebrospinal fluid (CSF) proteome was mapped using three different strategies prior to Orbitrap LC-MS/MS analysis: SDS-PAGE and mixed mode reversed phase-anion exchange for mapping the global CSF proteome, and hydrazide-based glycopeptide capture for mapping glycopeptides. A maximal protein set of 3081 proteins (28,811 peptide sequences) was identified, of which 520 were identified as glycoproteins from the glycopeptide enrichment strategy, including 1121 glycopeptides and their glycosylation sites. To our knowledge, this is the largest number of identified proteins and glycopeptides reported for CSF, including 417 glycosylation sites not previously reported. From parallel plasma samples, we identified 1050 proteins (9739 peptide sequences). An overlap of 877 proteins was found between the two body fluids, whereas 2204 proteins were identified only in CSF and 173 only in plasma. All mapping results are freely available via the new CSF Proteome Resource (http://probe.uib.no/csf-pr), which can be used to navigate the CSF proteome and help guide the selection of signature peptides in targeted quantitative proteomics.

Proteomic analysis of the asthmatic airway

Proteomic investigations in general utilize varied technologies for sample preparation, separations, quantification, protein identification, and biological rationalization. Their applications range from pure discovery and mechanistic studies to biomarker discovery/verification/validation. In each specific case, the analytical strategy to be implemented is tailored to the type of sample that serves as the target of the investigations. Proteomic investigations take into consideration sample complexity, the cellular heterogeneity (particularly from tissues), the potential dynamic range of the protein and peptide abundance within the sample, the likelihood of posttranslational modifications (PTM), and other important factors that might influence the final output of the study. We describe the sample types typically used for proteomic investigations into the biology of asthma and review the most recent related publications with special attention to those that deal with the unique airway samples such as bronchoalveolar lavage fluids (BALF), epithelial lining fluid and cells (ELF), induced sputum (IS), and exhaled breath condensate (EBC). Finally, we describe the newest proteomics approaches to sample preparation of the unique airway samples, BALF and IS.

The human salivary proteome is radiation responsive

In the event of a nuclear incident in a heavily populated area, the surge in demand for medical evaluation will likely overwhelm our emergency care system, compromising our ability to care for victims with life-threatening injuries or exposures. Therefore, there exists a need for a rapidly deployable biological assay for radiation exposure that can be performed in the field by individuals with little to no medical training. Saliva is an attractive biofluid for this purpose, due to the relative ease of its collection and the wide array of biomolecules it contains. To determine whether the human salivary proteome is responsive to ionizing radiation exposure, we characterized the abundances of salivary proteins in humans before and after total body irradiation. Using an assay panel targeting 90 analytes (growth factors, chemokines and cytokines), we identified proteins that were significantly radiation responsive in human saliva. The responses of three proteins (monocyte chemo-attractant protein 1, interleukin 8 and intercellular adhesion molecule 1) were confirmed using independent immunoassay platforms and then verified and further characterized in 130 saliva samples from a completely independent set of 38 patients undergoing total body irradiation. The results demonstrate the potential for detecting radiation exposure based on analysis of human saliva.

Identification and characterization of proteins isolated from microvesicles derived from human lung cancer pleural effusions

Microvesicles (MVs, also known as exosomes, ectosomes, microparticles) are released by various cancer cells, including lung, colorectal, and prostate carcinoma cells. MVs released from tumor cells and other sources accumulate in the circulation and in pleural effusion. Although recent studies have shown that MVs play multiple roles in tumor progression, the potential pathological roles of MV in pleural effusion, and their protein composition, are still unknown. In this study, we report the first global proteomic analysis of highly purified MVs derived from human nonsmall cell lung cancer (NSCLC) pleural effusion. Using nano-LC-MS/MS following 1D SDS-PAGE separation, we identified a total of 912 MV proteins with high confidence. Three independent experiments on three patients showed that MV proteins from PE were distinct from MV obtained from other malignancies. Bioinformatics analyses of the MS data identified pathologically relevant proteins and potential diagnostic makers for NSCLC, including lung-enriched surface antigens and proteins related to epidermal growth factor receptor signaling. These findings provide new insight into the diverse functions of MVs in cancer progression and will aid in the development of novel diagnostic tools for NSCLC.

Genomic convergence and network analysis approach to identify candidate genes in Alzheimer's disease

Background

Alzheimer’s disease (AD) is one of the leading genetically complex and heterogeneous disorder that is influenced by both genetic and environmental factors. The underlying risk factors remain largely unclear for this heterogeneous disorder. In recent years, high throughput methodologies, such as genome-wide linkage analysis (GWL), genome-wide association (GWA) studies, and genome-wide expression profiling (GWE), have led to the identification of several candidate genes associated with AD. However, due to lack of consistency within their findings, an integrative approach is warranted. Here, we have designed a rank based gene prioritization approach involving convergent analysis of multi-dimensional data and protein-protein interaction (PPI) network modelling.

Results

Our approach employs integration of three different AD datasets- GWL,GWA and GWE to identify overlapping candidate genes ranked using a novel cumulative rank score (S_R) based method followed by prioritization using clusters derived from PPI network. S_R for each gene is calculated by addition of rank assigned to individual gene based on either p value or score in three datasets. This analysis yielded 108 plausible AD genes. Network modelling by creating PPI using proteins encoded by these genes and their direct interactors resulted in a layered network of 640 proteins. Clustering of these proteins further helped us in identifying 6 significant clusters with 7 proteins (EGFR, ACTB, CDC2, IRAK1, APOE, ABCA1 and AMPH) forming the central hub nodes. Functional annotation of 108 genes revealed their role in several biological activities such as neurogenesis, regulation of MAP kinase activity, response to calcium ion, endocytosis paralleling the AD specific attributes. Finally, 3 potential biochemical biomarkers were found from the overlap of 108 AD proteins with proteins from CSF and plasma proteome. EGFR and ACTB were found to be the two most significant AD risk genes.

Conclusions

With the assumption that common genetic signals obtained from different methodological platforms might serve as robust AD risk markers than candidates identified using single dimension approach, here we demonstrated an integrated genomic convergence approach for disease candidate gene prioritization from heterogeneous data sources linked to AD.

Electronic supplementary material

The online version of this article (doi:10.1186/1471-2164-15-199) contains supplementary material, which is available to authorized users.

Plasma Proteome Database as a resource for proteomics research: 2014 update

Plasma Proteome Database (PPD; http://www.plasmaproteomedatabase.org/) was initially described in the year 2005 as a part of Human Proteome Organization's (HUPO's) pilot initiative on Human Plasma Proteome Project. Since then, improvements in proteomic technologies and increased throughput have led to identification of a large number of novel plasma proteins. To keep up with this increase in data, we have significantly enriched the proteomic information in PPD. This database currently contains information on 10,546 proteins detected in serum/plasma of which 3784 have been reported in two or more studies. The latest version of the database also incorporates mass spectrometry-derived data including experimentally verified proteotypic peptides used for multiple reaction monitoring assays. Other novel features include published plasma/serum concentrations for 1278 proteins along with a separate category of plasma-derived extracellular vesicle proteins. As plasma proteins have become a major thrust in the field of biomarkers, we have enabled a batch-based query designated Plasma Proteome Explorer, which will permit the users in screening a list of proteins or peptides against known plasma proteins to assess novelty of their data set. We believe that PPD will facilitate both clinical and basic research by serving as a comprehensive reference of plasma proteins in humans and accelerate biomarker discovery and translation efforts.

Computational prediction of human salivary proteins from blood circulation and application to diagnostic biomarker identification

Proteins can move from blood circulation into salivary glands through active transportation, passive diffusion or ultrafiltration, some of which are then released into saliva and hence can potentially serve as biomarkers for diseases if accurately identified. We present a novel computational method for predicting salivary proteins that come from circulation. The basis for the prediction is a set of physiochemical and sequence features we found to be discerning between human proteins known to be movable from circulation to saliva and proteins deemed to be not in saliva. A classifier was trained based on these features using a support-vector machine to predict protein secretion into saliva. The classifier achieved 88.56% average recall and 90.76% average precision in 10-fold cross-validation on the training data, indicating that the selected features are informative. Considering the possibility that our negative training data may not be highly reliable (i.e., proteins predicted to be not in saliva), we have also trained a ranking method, aiming to rank the known salivary proteins from circulation as the highest among the proteins in the general background, based on the same features. This prediction capability can be used to predict potential biomarker proteins for specific human diseases when coupled with the information of differentially expressed proteins in diseased versus healthy control tissues and a prediction capability for blood-secretory proteins. Using such integrated information, we predicted 31 candidate biomarker proteins in saliva for breast cancer.

The Human Urinary Proteome Fingerprint Database UPdb

The use of human urine as a diagnostic tool has many advantages, such as ease of sample acquisition and noninvasiveness. However, the discovery of novel biomarkers, as well as biomarker patterns, in urine is hindered mainly by a lack of comparable datasets. To fill this gap, we assembled a new urinary fingerprint database. Here, we report the establishment of a human urinary proteomic fingerprint database using urine from 200 individuals analysed by SELDI-TOF (surface enhanced laser desorption ionisation-time of flight) mass spectrometry (MS) on several chip surfaces (SEND, HP50, NP20, Q10, CM10, and IMAC30). The database currently lists 2490 unique peaks/ion species from 1172 nonredundant SELDI analyses in the mass range of 1500 to 150000. All unprocessed mass spectrometric scans are available as ".xml" data files. Additionally, 1384 peaks were included from external studies using CE (capillary electrophoresis)-MS, MALDI (matrix assisted laser desorption/ionisation), and CE-MALDI hybrids. We propose to use this platform as a global resource to share and exchange primary data derived from MS analyses in urinary research.

Functional analysis of human cancer-associated genes and their association with the testes and epididymis

Human cancer-associated UniGene sets (NCBI GeneBank) provide a platform for identifying differentially-expressed genes in human cancers. The present study identified and characterized a set of human cancer-associated genes using the Digital Differential Display (DDD) and functional analysis tools. A total of 1,904 genes were differentially expressed in 15 cancer types, including genes that had been previously shown to be specific in certain human cancers. A total of 274 genes were uniquely expressed in certain cancer types, including 37 genes that were highly expressed in the human testes and epididymis. These genes mainly functioned as ribosomal proteins, enzymes, receptors, secretory proteins and cell adhesion molecules. The most common domains that were encoded by the cancer-associated genes were those of cytochrome P450 CYP2D6, serpin and apolipoprotein A-I. A further gene ontology (GO) enrichment analysis revealed seven major functional clusters, which corresponded to the enriched pathways involved in cancer. The present study provides a source of cancer-associated genes and their functions. The results provide new insights into cancer biology and the involvement of highly-expressed epididymal genes in cancer biomarkers.

Urban legends series: Sjögren's syndrome

Sjögren's syndrome (SjS) is one of the most common autoimmune rheumatic diseases, clinically characterized by xerostomia and keratoconjunctivitis sicca. We investigated the following controversial topics: (i) Do we have reliable ways of assessing saliva production? (ii) How important are the quantity and quality of saliva? (iii) Are only anti-SSA/Ro and anti-SSB/La relevant for the diagnosis of SjS? (iv) Are the American-European Consensus criteria (AECC) the best way to diagnose SjS? Results from literature searches suggested the following: (i) Despite the fact that numerous tests are available to assess salivation rates, direct comparisons among them are scarce with little evidence to suggest one best test. (ii) Recent developments highlight the importance of investigating the composition of saliva. However, more research is needed to standardize the methods of analysis and collection and refine the quality of the accumulating data. (iii) In addition to anti-Ro/La autoantibodies, anti α-fodrin IgA and anti-MR3 autoantibodies seem to be promising diagnostic markers of SjS, but more studies are warranted to test their sensitivity and specificity. (iv) AECC are classification, not diagnostic criteria. Moreover, recent innovations have not been incorporated into these criteria. Consequently, treatment directed to patients diagnosed using the AECC might exclude a significant proportion of patients with SjS.

Comparative and targeted proteomic analyses of urinary microparticles from bladder cancer and hernia patients

Bladder cancer is a common urologic cancer whose incidence continues to rise annually. Urinary microparticles are an attractive material for noninvasive bladder cancer biomarker discovery. In this study, we applied isotopic dimethylation labeling coupled with liquid chromatography-tandem mass spectrometry (LC-MS/MS) to discover bladder cancer biomarkers in urinary microparticles isolated from hernia (control) and bladder cancer patients. This approach identified 2964 proteins based on more than two distinct peptides, of which 2058 had not previously been reported as constituents of human urine exosomes/microparticles. A total of 107 differentially expressed proteins were identified as candidate biomarkers. Differences in the concentrations of 29 proteins (41 signature peptides) were precisely quantified by LC-MRM/MS in 48 urine samples of bladder cancer, hernia, and urinary tract infection/hematuria. Concentrations of 24 proteins changed significantly (p<0.05) between bladder cancer (n=28) and hernia (n=12), with area-under-the-curve values ranging from 0.702 to 0.896. Finally, we quantified tumor-associated calcium-signal transducer 2 (TACSTD2) in raw urine specimens (n=221) using a commercial ELISA and confirmed its potential value for diagnosis of bladder cancer. Our study reveals a strong association of TACSTD2 with bladder cancer and highlights the potential of human urinary microparticles in the noninvasive diagnosis of bladder cancer.

Differential proteomic analysis of pathway biomarkers in human breast cancer by integrated bioinformatics

The aim of this study was to better understand the altered functional modules in breast cancer at pathway and network levels. An integrated bioinformatics analysis of differentially expressed proteins in human breast cancer was performed. Breast cancer protein profiles were constructed by data mining proteins in literature and public databases, including 1031 proteins with 153 secretory and 69 cell surface proteins. An experimental investigation was performed by two-dimensional electrophoresis, and 4 proteins were further validated by western blotting. Enriched bioinformatics functions were clustered. This study may be used as a reference in further studies to help identify the underlying biological interactions associated with breast cancer and discover potential cancer targets.

The scientific exploration of saliva in the post-proteomic era: from database back to basic function

The proteome of human saliva can be considered as being essentially completed. Diagnostic markers for a number of diseases have been identified among salivary proteins and peptides, taking advantage of saliva as an easy-to-obtain biological fluid. Yet, the majority of disease markers identified so far are serum components and not intrinsic proteins produced by the salivary glands. Furthermore, despite the fact that saliva is essential for protecting the oral integuments and dentition, little progress has been made in finding risk predictors in the salivary proteome for dental caries or periodontal disease. Since salivary proteins, and in particular the attached glycans, play an important role in interactions with the microbial world, the salivary glycoproteome and other post-translational modifications of salivary proteins need to be studied. Risk markers for microbial diseases, including dental caries, are likely to be discovered among the highly glycosylated major protein species in saliva. This review will attempt to raise new ideas and also point to under-researched areas that may hold promise for future applicability in oral diagnostics and prediction of oral disease.

The human salivary proteome: a critical overview of the results obtained by different proteomic platforms

The development of new separation techniques and different mass spectrometry instrumental devices, as well as the great availability of specific reactants, offers ample choice to scientists for carrying out high-throughput proteomic studies and being competitive in the field today. However, the different options available often do not provide comparable results, which can be linked to factors such as the strategy adopted, the nature of the sample and the instrumental availability. In this critical review, the results obtained so far in the study of human saliva by different proteomic approaches will be compared and discussed.

From the salivary proteome to the OralOme: comprehensive molecular oral biology

OBJECTIVES

There have been several efforts to identify the protein components of saliva. Some of these studies were conducted in healthy individuals and other in individuals with different oral and systemic disorders. However, a resource compiling and reviewing all of the proteins identified in proteomic studies is still lacking. The aim of this project is to develop such a resource.

DESIGN

The proteins identified by proteomic studies were compiled and all information concerning them was manually curated according to "IPI History search" and UniProt. Proteins were classified according to gene ontology using PANTHER. The involvement of each protein in disease was scrutinized using DAVID and a classification into protein disease classes was performed.

RESULTS

This survey of proteins in the oral cavity lead to the identification of 3397 non-redundant proteins, 605 altered in pathological conditions and 51 present only in disease. These proteins originate from different sources: 3115 from saliva, 990 from oral mucosa and 1929 from plasma. All protein sources contribute with different numbers and types of proteins to identical functions. However, each source produces specific proteins. Examples of the use of this proteomics database of saliva included the analysis of the changes in the proteome associated with periodontitis and a survey of systemic disease potential biomarkers in saliva.

CONCLUSION

The database generated with this work and the information therein stands as a resource for investigators/clinicians studying the oral biology, searching for molecular disease markers, developing diagnostic and prognostic tests, and contributing to the discovery of new therapeutic agents.

The discovery of putative urine markers for the specific detection of prostate tumor by integrative mining of public genomic profiles

Urine has emerged as an attractive biofluid for the noninvasive detection of prostate cancer (PCa). There is a strong imperative to discover candidate urinary markers for the clinical diagnosis and prognosis of PCa. The rising flood of various omics profiles presents immense opportunities for the identification of prospective biomarkers. Here we present a simple and efficient strategy to derive candidate urine markers for prostate tumor by mining cancer genomic profiles from public databases. Prostate, bladder and kidney are three major tissues from which cellular matters could be released into urine. To identify urinary markers specific for PCa, upregulated entities that might be shed in exosomes of bladder cancer and kidney cancer are first excluded. Through the ontology-based filtering and further assessment, a reduced list of 19 entities encoding urinary proteins was derived as putative PCa markers. Among them, we have found 10 entities closely associated with the process of tumor cell growth and development by pathway enrichment analysis. Further, using the 10 entities as seeds, we have constructed a protein-protein interaction (PPI) subnetwork and suggested a few urine markers as preferred prognostic markers to monitor the invasion and progression of PCa. Our approach is amenable to discover and prioritize potential markers present in a variety of body fluids for a spectrum of human diseases.

Mass spectrometry of peptides and proteins from human blood

It is difficult to convey the accelerating rate and growing importance of mass spectrometry applications to human blood proteins and peptides. Mass spectrometry can rapidly detect and identify the ionizable peptides from the proteins in a simple mixture and reveal many of their post-translational modifications. However, blood is a complex mixture that may contain many proteins first expressed in cells and tissues. The complete analysis of blood proteins is a daunting task that will rely on a wide range of disciplines from physics, chemistry, biochemistry, genetics, electromagnetic instrumentation, mathematics and computation. Therefore the comprehensive discovery and analysis of blood proteins will rank among the great technical challenges and require the cumulative sum of many of mankind's scientific achievements together. A variety of methods have been used to fractionate, analyze and identify proteins from blood, each yielding a small piece of the whole and throwing the great size of the task into sharp relief. The approaches attempted to date clearly indicate that enumerating the proteins and peptides of blood can be accomplished. There is no doubt that the mass spectrometry of blood will be crucial to the discovery and analysis of proteins, enzyme activities, and post-translational processes that underlay the mechanisms of disease. At present both discovery and quantification of proteins from blood are commonly reaching sensitivities of ∼1 ng/mL.

A tool for biomarker discovery in the urinary proteome: a manually curated human and animal urine protein biomarker database

Urine is an important source of biomarkers. A single proteomics assay can identify hundreds of differentially expressed proteins between disease and control samples; however, the ability to select biomarker candidates with the most promise for further validation study remains difficult. A bioinformatics tool that allows accurate and convenient comparison of all of the existing related studies can markedly aid the development of this area. In this study, we constructed the Urinary Protein Biomarker (UPB) database to collect existing studies of urinary protein biomarkers from published literature. To ensure the quality of data collection, all literature was manually curated. The website (http://122.70.220.102/biomarker) allows users to browse the database by disease categories and search by protein IDs in bulk. Researchers can easily determine whether a biomarker candidate has already been identified by another group for the same disease or for other diseases, which allows for the confidence and disease specificity of their biomarker candidate to be evaluated. Additionally, the pathophysiological processes of the diseases can be studied using our database with the hypothesis that diseases that share biomarkers may have the same pathophysiological processes. Because of the natural relationship between urinary proteins and the urinary system, this database may be especially suitable for studying the pathogenesis of urological diseases. Currently, the database contains 553 and 275 records compiled from 174 and 31 publications of human and animal studies, respectively. We found that biomarkers identified by different proteomic methods had a poor overlap with each other. The differences between sample preparation and separation methods, mass spectrometers, and data analysis algorithms may be influencing factors. Biomarkers identified from animal models also overlapped poorly with those from human samples, but the overlap rate was not lower than that of human proteomics studies. Therefore, it is not clear how well the animal models mimic human diseases.

Bioinformatics approaches in the discovery and understanding of reproduction-related biomarkers

The emerging field of bioinformatics in proteomics is introducing new algorithms in order to handle large and heterogeneous datasets and improve the knowledge-discovery process. Management systems, software construction and application, database population and leverage, as well as computed prediction, have crafted bioinformatics into a valuable tool for basic research. Human reproduction is one of many fields proteomics has been extensively studying over the last decade, accumulating complex experimental data at a rate far exceeding the ability to assimilate them. Transformation of the rapidly proliferating quantities of experimental information into a usable form in order to facilitate their analysis is a challenging task. On this track, bioinformatics, an essential part of proteomics research, aspires to amend inquiries into a better manipulated, a better handled and a better understood form so as to enhance existing knowledge expansion.

Prediction of body fluids where proteins are secreted into based on protein interaction network

Determining the body fluids where secreted proteins can be secreted into is important for protein function annotation and disease biomarker discovery. In this study, we developed a network-based method to predict which kind of body fluids human proteins can be secreted into. For a newly constructed benchmark dataset that consists of 529 human-secreted proteins, the prediction accuracy for the most possible body fluid location predicted by our method via the jackknife test was 79.02%, significantly higher than the success rate by a random guess (29.36%). The likelihood that the predicted body fluids of the first four orders contain all the true body fluids where the proteins can be secreted into is 62.94%. Our method was further demonstrated with two independent datasets: one contains 57 proteins that can be secreted into blood; while the other contains 61 proteins that can be secreted into plasma/serum and were possible biomarkers associated with various cancers. For the 57 proteins in first dataset, 55 were correctly predicted as blood-secrete proteins. For the 61 proteins in the second dataset, 58 were predicted to be most possible in plasma/serum. These encouraging results indicate that the network-based prediction method is quite promising. It is anticipated that the method will benefit the relevant areas for both basic research and drug development.

Preliminary use of differential scanning calorimetry of cerebrospinal fluid for the diagnosis of glioblastoma multiforme

Thermal stability signatures of complex molecule interaction in biological fluids can be measured using a new approach called differential scanning calorimetry (DSC). The thermal stability of plasma proteome has been described previously as a method of producing a disease-specific "signature," termed thermogram, in several neoplastic and autoimmune diseases. We describe the preliminary use of DSC performed on cerebrospinal fluid (CSF) as a diagnostic tool for the identification of patients with glioblastoma multiforme (GBM). Samples of CSF from nine patients with confirmed GBM were evaluated using DSC, and the thermogram signatures evaluated. These thermograms were compared with thermograms of CSF taken from patients with non-neoplastic conditions such as head trauma, hydrocephalus, or CSF leak. Further analysis was also performed on CSF from patients who had non-GBM neoplastic conditions such as carcinomatosis meningitis or central nervous system lymphoma or leukemia. The DSC thermograms of CSF of the patients with GBM were significantly different when compared with other neoplastic and non-neoplastic cases. The melting temperature of the major transition was shifted by 5°C, which makes it easily distinguishable from control cases. Our results are very preliminary, but it appears that the DSC of CSF has potential utility in diagnostics and monitoring disease progression in GBM patients.