Data from human salivary proteome - A resource of potential biomarkers for oral cancer

Characterization of the internal working-life exposome using minimally and non-invasive sampling methods - a narrative review

During recent years, we are moving away from the 'one exposure, one disease'-approach in occupational settings and towards a more comprehensive approach, taking into account the totality of exposures during a life course by using an exposome approach. Taking an exposome approach however is accompanied by many challenges, one of which, for example, relates to the collection of biological samples. Methods used for sample collection in occupational exposome studies should ideally be minimally invasive, while at the same time sensitive, and enable meaningful repeated sampling in a large population and over a longer time period. This might be hampered in specific situations e.g., people working in remote areas, during pandemics or with flexible work hours. In these situations, using self-sampling techniques might offer a solution. Therefore, our aim was to identify existing self-sampling techniques and to evaluate the applicability of these techniques in an occupational exposome context by conducting a literature review. We here present an overview of current self-sampling methodologies used to characterize the internal exposome. In addition, the use of different biological matrices was evaluated and subdivided based on their level of invasiveness and applicability in an occupational exposome context. In conclusion, this review and the overview of self-sampling techniques presented herein can serve as a guide in the design of future (occupational) exposome studies while circumventing sample collection challenges associated with exposome studies.

Review Insights on Salivary Proteomics Biomarkers in Oral Cancer Detection and Diagnosis

Early detection is crucial for the treatment and prognosis of oral cancer, a potentially lethal condition. Tumor markers are abnormal biological byproducts produced by malignant cells that may be found and analyzed in a variety of bodily fluids, including saliva. Early detection and appropriate treatment can increase cure rates to 80-90% and considerably improve quality of life by reducing the need for costly, incapacitating medicines. Salivary diagnostics has drawn the interest of many researchers and has been proven to be an effective tool for both medication monitoring and the diagnosis of several systemic diseases. Since researchers are now searching for biomarkers in saliva, an accessible bodily fluid, for noninvasive diagnosis of oral cancer, measuring tumor markers in saliva is an interesting alternative to blood testing for early identification, post-treatment monitoring, and monitoring high-risk lesions. New molecular markers for oral cancer detection, treatment, and prognosis have been found as a result of developments in the fields of molecular biology and salivary proteomics. The numerous salivary tumor biomarkers and how they relate to oral cancer and pre-cancer are covered in this article. We are optimistic that salivary protein biomarkers may one day be discovered for the clinical detection of oral cancer because of the rapid advancement of proteomic technology.

Low-Abundance Protein Enrichment for Medical Applications: The Involvement of Combinatorial Peptide Library Technique

The discovery of low- and very low-abundance proteins in medical applications is considered a key success factor in various important domains. To reach this category of proteins, it is essential to adopt procedures consisting of the selective enrichment of species that are present at extremely low concentrations. In the past few years pathways towards this objective have been proposed. In this review, a general landscape of the enrichment technology situation is made first with the presentation and the use of combinatorial peptide libraries. Then, a description of this peculiar technology for the identification of early-stage biomarkers for well-known pathologies with concrete examples is given. In another field of medical applications, the determination of host cell protein traces potentially present in recombinant therapeutic proteins, such as antibodies, is discussed along with their potentially deleterious effects on the health of patients on the one hand, and on the stability of these biodrugs on the other hand. Various additional applications of medical interest are disclosed for biological fluids investigations where the target proteins are present at very low concentrations (e.g., protein allergens).

HBFP: a new repository for human body fluid proteome

Body fluid proteome has been intensively studied as a primary source for disease biomarker discovery. Using advanced proteomics technologies, early research success has resulted in increasingly accumulated proteins detected in different body fluids, among which many are promising biomarkers. However, despite a handful of small-scale and specific data resources, current research is clearly lacking effort compiling published body fluid proteins into a centralized and sustainable repository that can provide users with systematic analytic tools. In this study, we developed a new database of human body fluid proteome (HBFP) that focuses on experimentally validated proteome in 17 types of human body fluids. The current database archives 11 827 unique proteins reported by 164 scientific publications, with a maximal false discovery rate of 0.01 on both the peptide and protein levels since 2001, and enables users to query, analyze and download protein entries with respect to each body fluid. Three unique features of this new system include the following: (i) the protein annotation page includes detailed abundance information based on relative qualitative measures of peptides reported in the original references, (ii) a new score is calculated on each reported protein to indicate the discovery confidence and (iii) HBFP catalogs 7354 proteins with at least two non-nested uniquely mapping peptides of nine amino acids according to the Human Proteome Project Data Interpretation Guidelines, while the remaining 4473 proteins have more than two unique peptides without given sequence information. As an important resource for human protein secretome, we anticipate that this new HBFP database can be a powerful tool that facilitates research in clinical proteomics and biomarker discovery.

Database URL:https://bmbl.bmi.osumc.edu/HBFP/

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.

Salivary Proteomic Profiling Identifies Role of Neutrophil Extracellular Traps Formation in Pregnancy Gingivitis

Pregnancy gingivitis peaks during mid-pregnancy and resolves transiently towards the postpartum period. However, the role of maternal immune response in orchestrating gingival inflammation has not yet been fully understood. Hence, in this study, we examined the salivary protein profile during the three trimesters of pregnancy, in context to pregnancy gingivitis, employing iTRAQ-based quantitative proteomics. Unstimulated saliva was collected from 10 subjects in each trimester of pregnancy and postpartum period. Samples were analysed using iTRAQ analysis and ELISA and SEM was performed to validate results. Neutrophil mediated immune response was overrepresented in all three trimesters of pregnancy, despite the decrease in phagocytic responses during the second and third trimesters. ELISA showed a significantly higher Neutrophil Extracellular Traps (NETs) formation in the third trimester of pregnancy coinciding with the resolution of pregnancy gingivitis. The NETs-associated proteins (neutrophil elastase and myeloperoxidase) showed a positive correlation with estrogen hormones, which was also highest during the third trimester. Sex hormone-driven NETs formation could be the mainstay of defence that contributes to the remission of pregnancy gingivitis. This study has provided a new insight into the role of immune-modulation in pregnancy gingivitis, which will aid development of new therapeutics for managing pregnancy gingivitis in future.

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.

Recent advances in mass spectrometry based clinical proteomics: applications to cancer research

Cancer biomarkers have transformed current practices in the oncology clinic. Continued discovery and validation are crucial for improving early diagnosis, risk stratification, and monitoring patient response to treatment. Profiling of the tumour genome and transcriptome are now established tools for the discovery of novel biomarkers, but alterations in proteome expression are more likely to reflect changes in tumour pathophysiology. In the past, clinical diagnostics have strongly relied on antibody-based detection strategies, but these methods carry certain limitations. Mass spectrometry (MS) is a powerful method that enables increasingly comprehensive insights into changes of the proteome to advance personalized medicine. In this review, recent improvements in MS-based clinical proteomics are highlighted with a focus on oncology. We will provide a detailed overview of clinically relevant samples types, as well as, consideration for sample preparation methods, protein quantitation strategies, MS configurations, and data analysis pipelines currently available to researchers. Critical consideration of each step is necessary to address the pressing clinical questions that advance cancer patient diagnosis and prognosis. While the majority of studies focus on the discovery of clinically-relevant biomarkers, there is a growing demand for rigorous biomarker validation. These studies focus on high-throughput targeted MS assays and multi-centre studies with standardized protocols. Additionally, improvements in MS sensitivity are opening the door to new classes of tumour-specific proteoforms including post-translational modifications and variants originating from genomic aberrations. Overlaying proteomic data to complement genomic and transcriptomic datasets forges the growing field of proteogenomics, which shows great potential to improve our understanding of cancer biology. Overall, these advancements not only solidify MS-based clinical proteomics' integral position in cancer research, but also accelerate the shift towards becoming a regular component of routine analysis and clinical practice.

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.

Proteome Imaging: From Classic to Modern Mass Spectrometry-Based Molecular Histology

In order to overcome the limitations of classic imaging in Histology during the actually era of multiomics, the multi-color "molecular microscope" by its emerging "molecular pictures" offers quantitative and spatial information about thousands of molecular profiles without labeling of potential targets. Healthy and diseased human tissues, as well as those of diverse invertebrate and vertebrate animal models, including genetically engineered species and cultured cells, can be easily analyzed by histology-directed MALDI imaging mass spectrometry. The aims of this review are to discuss a range of proteomic information emerging from MALDI mass spectrometry imaging comparative to classic histology, histochemistry and immunohistochemistry, with applications in biology and medicine, concerning the detection and distribution of structural proteins and biological active molecules, such as antimicrobial peptides and proteins, allergens, neurotransmitters and hormones, enzymes, growth factors, toxins and others. The molecular imaging is very well suited for discovery and validation of candidate protein biomarkers in neuroproteomics, oncoproteomics, aging and age-related diseases, parasitoproteomics, forensic, and ecotoxicology. Additionally, in situ proteome imaging may help to elucidate the physiological and pathological mechanisms involved in developmental biology, reproductive research, amyloidogenesis, tumorigenesis, wound healing, neural network regeneration, matrix mineralization, apoptosis and oxidative stress, pain tolerance, cell cycle and transformation under oncogenic stress, tumor heterogeneity, behavior and aggressiveness, drugs bioaccumulation and biotransformation, organism's reaction against environmental penetrating xenobiotics, immune signaling, assessment of integrity and functionality of tissue barriers, behavioral biology, and molecular origins of diseases. MALDI MSI is certainly a valuable tool for personalized medicine and "Eco-Evo-Devo" integrative biology in the current context of global environmental challenges.

Dataset on the comparative proteomic profiling of mouse saliva and serum from wild type versus the dystrophic mdx-4cv mouse model of dystrophinopathy

The comparative proteomic data presented in this article provide supporting information to the related research article "Proteomic identification of elevated saliva kallikrein levels in the mdx-4cv mouse model of Duchenne muscular dystrophy " (Murphy et al., 2018). Here we provide additional datasets on the comparative proteomic analysis of saliva and serum proteins and the mass spectrometric identification of kallikrein isoform Klk-1 in wild type versus mdx-4cv saliva specimens. The data article presents the systematic identification of the assessable saliva proteome and the differential presence of proteins in saliva versus serum samples. Representative mass spectrometric scans of unique peptides that were employed to identify the kallikrein isoform Klk-1 in wild type versus mdx-4cv saliva specimens are provided. The dataset contains typical saliva-associated marker proteins, including alpha-amylase and albumin, as well as distinct isoforms of cystatin, serpin, kallikrein, cathepsin, glutathione transferase, carbonic anhydrase, mucin, pyruvate kinase, and aldolase.

Variability Assessment of 90 Salivary Proteins in Intraday and Interday Samples from Healthy Donors by Multiple Reaction Monitoring-Mass Spectrometry

PURPOSE

Saliva is an attractive sample source for the biomarker-based testing of several diseases, especially oral cancer. Here, we sought to apply multiplexed LC-MRM-MS to precisely quantify 90 disease-related proteins and assess their intra- and interindividual variability in saliva samples from healthy donors.

EXPERIMENTAL DESIGN

We developed two multiplexed LC-MRM-MS assays for 122 surrogate peptides representing a set of disease-related proteins. Saliva samples were collected from 10 healthy volunteers at three different time points (Day 1 morning and afternoon, and Day 2 morning). Each sample was spiked with a constant amount of a ¹⁵ N-labeled protein and analyzed by MRM-MS in triplicate. Quantitative results from LC-MRM-MS were calculated by single-point quantification with reference to a known amount of internal standard (heavy peptide).

RESULTS

The CVs for assay reproducibility and technical variation were 13 and 11%, respectively. The average concentrations of the 99 successfully quantified proteins ranged from 0.28 ± 0.58 ng mL^-1 for profilin-2 (PFN2) to 8.55 ±8.96 μg mL^-1 for calprotectin (S100A8). For the 90 proteins detectable in >50% of samples, the average CVs for intraday, interday, intraindividual, and interindividual samples were 38%, 43%, 45%, and 69%, respectively. The fluctuations of most target proteins in individual subjects were found to be within ± twofold.

CONCLUSIONS AND CLINICAL RELEVANCE

Our study elucidated the intra- and interindividual variability of 90 disease-related proteins in saliva samples from healthy donors. The findings may facilitate the further development of salivary biomarkers for oral and systemic diseases.

A novel multiplex detection array revealed systemic complement activation in oral squamous cell carcinoma

Oral squamous cell carcinoma (OSCC) is one of the most common tumors within the oral cavity. Early diagnosis and prognosis tools are urgently needed. This study aimed to investigate the activation of the complement system in OSCC patients as potential biomarker. Therefore, an innovative complement activation array was developed. Characterized antibodies detecting the complement activation specific epitopes C3a, C5a and sC5b-9 along with control antibodies were implemented into a suspension bead array. Human serum from a healthy (n = 46) and OSCC patient (n = 57) cohort were used to investigate the role of complement activation in oral tumor progression. The novel multiplex assay detected C3a, C5a and sC5b-9 from a minimal sample volume of human tears, aqueous humor and blood samples. Limits of detection were 0.04 ng/mL for C3a, 0.03 ng/mL for C5a and 18.9 ng/mL for sC5b-9, respectively. Biological cut-off levels guaranteed specific detections from serum. The mean serum concentration of a healthy control cohort was 680 ng/mL C3a, 70 ng/mL C5a and 2247 ng/mL sC5b-9, respectively. The assay showed an intra-assay precision of 2.9-6.4% and an inter-assay precision of 9.2-18.2%. Increased systemic C5a (p < 0.0001) and sC5b-9 (p = 0.01) concentrations in OSCC patients were determined using the validated multiplex complement assay. Higher C5a concentrations correlated with tumor differentiation and OSCC extension state. Systemic sC5b-9 determination provided a novel biomarker for infiltrating tumor growth and C3a levels were associated with local tumor spreading. Our study suggests that systemic complement activation levels in OSCC patients may be useful to assess disease progression.

Proteomic and N-glycoproteomic quantification reveal aberrant changes in the human saliva of oral ulcer patients

Human whole saliva is a vital body fluid for studying the physiology and pathology of the oral cavity. As a powerful technique for biomarker discovery, MS-based proteomic strategies have been introduced for saliva analysis and identified hundreds of proteins and N-glycosylation sites. However, there is still a lack of quantitative analysis, which is necessary for biomarker screening and biological research. In this study, we establish an integrated workflow by the combination of stable isotope dimethyl labeling, HILIC enrichment, and high resolution MS for both quantification of the global proteome and N-glycoproteome of human saliva from oral ulcer patients. With the help of advanced bioinformatics, we comprehensively studied oral ulcers at both protein and glycoprotein scales. Bioinformatics analyses revealed that starch digestion and protein degradation activities are inhibited while the immune response is promoted in oral ulcer saliva.