Construction and characterization of the Korean whole saliva proteome to determine ethnic differences in human saliva proteome

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/

Human OMICs and Computational Biology Research in Africa: Current Challenges and Prospects

Following the publication of the first human genome, OMICs research, including genomics, transcriptomics, proteomics, and metagenomics, has been on the rise. OMICs studies revealed the complex genetic diversity among human populations and challenged our understandings of genotype-phenotype correlations. Africa, being the cradle of the first modern humans, is distinguished by a large genetic diversity within its populations and rich ethnolinguistic history. However, the available human OMICs tools and databases are not representative of this diversity, therefore creating significant gaps in biomedical research. African scientists, students, and publics are among the key contributors to OMICs systems science. This expert review examines the pressing issues in human OMICs research, education, and development in Africa, as seen through a lens of computational biology, public health relevant technology innovation, critically-informed science governance, and how best to harness OMICs data to benefit health and societies in Africa and beyond. We underscore the disparities between North and Sub-Saharan Africa at different levels. A harmonized African ethnolinguistic classification would help address annotation challenges associated with population diversity. Finally, building on the existing strategic research initiatives, such as the H3Africa and H3ABioNet Consortia, we highly recommend addressing large-scale multidisciplinary research challenges, strengthening research collaborations and knowledge transfer, and enhancing the ability of African researchers to influence and shape national and international research, policy, and funding agendas. This article and analysis contribute to a deeper understanding of past and current challenges in the African OMICs innovation ecosystem, while also offering foresight on future innovation trajectories.

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.

Salivary concentrations of cytokines and other analytes in healthy children

BACKGROUND

Blood has been the usual biological fluid for measuring analytes, but there is mounting evidence that saliva may be also useful for detecting cytokines in a noninvasive way. Thus, in this study we aimed to determine concentration of cytokines and other analytes in saliva from a population of healthy children.

METHODS

We collected un-stimulated whole saliva samples from clinically healthy children, and concentration of 17 cytokines and 12 other analytes were measured in supernatants. All values were adjusted by albumin content and were log-transformed before multivariate statistical analysis.

RESULTS

We included 114 children (53.5% females) between 6.0 and 11.9 years old. The highest concentrations (medians, pg/µg albumin) were seen for visfatin (183.70) and adiponectin (162.26) and the lowest for IL-13 and IL-2 (~0.003). Albumin concentration was associated with age (r_S = 0.39, p < 0.001). In the multivariate analysis, five analytes (C peptide, ghrelin, GLP-1, glucagon, leptin) inversely correlated with age and positively with height-for-age. Age was also positively associated with PAI-1, while height-for-age was also positively associated with insulin and visfatin. Finally, BMI-for-age had a positive correlation with GM-CSF and insulin.

CONCLUSIONS

Herein, we provided concentration values for 29 analytes in saliva from healthy children that may be useful as preliminary reference framework in the clinical research setting.

Salivary protein candidates for biomarkers of oral disorders in alcohol and tobacco dependents

OBJECTIVES

To evaluate the oral condition of alcohol and tobacco dependents and identify salivary protein candidates for biomarkers of oral disorders.

SUBJECTS AND METHODS

Thirty-three male volunteers were evaluated for alcohol abuse rehabilitation; nine were selected for proteomic analysis. Intraoral examination was performed, and non-stimulated saliva was collected. Salivary proteins were extracted and processed for analysis. A list of proteins identified in saliva was generated from the database and manually revised, obtaining the total number of candidate biomarkers for oral disorders.

RESULTS

The mean age (n = 33) was 42.94 ± 8.61 years. Fourteen (42.4%) subjects presented with 23 oral mucosa changes, and 31 (94%) had dental plaque. A total of 282 proteins were found in saliva (n = 9), of which 26 were identified as candidates for biomarkers of oral disorders. After manual review, 21 proteins were selected. The highest number of candidates for biomarkers was associated with carcinoma of head and neck (n = 10), nasopharyngeal carcinoma (n = 6), and periodontal disease (n = 6).

CONCLUSION

Alcohol and tobacco dependents showed gingival inflammation, and less than half of them showed oral mucosa changes. Twenty-one protein candidates for biomarkers of oral disorders were identified in saliva. The two major oral disorders in number of candidates for biomarkers were head and neck cancer and periodontal disease.

Salivary proteome characterization of alcohol and tobacco dependents

BACKGROUND

Alcohol and substances found in tobacco may alter salivary flow and amount of saliva proteins. This study aimed to compare salivary proteins between alcohol dependent smokers and controls.

METHODS

This is a case-control study with men older than 18 years of age, matched by age. The alcohol-dependent group was composed by heavy smokers and alcohol consumers. Unstimulated whole saliva was collected from all subjects. Analysis of digested peptides was performed in mass spectrometer. Data were processed using ProteinLynx GlobalServer software. Results were obtained by searching theHomo sapiens database from the UniProt catalog. The search tool IBI-IMIM was used to identify candidate proteins for biomarkers.

RESULTS

Alcohol-dependent and control groups were composed of nine participants each, with mean age of 36.89 ± 2.57 and 35.78 ± 1.64 years, respectively. 404 salivary proteins were found in both groups; 282 in the alcohol-dependent. Among the 96 proteins presented in both groups, 32 were up-regulated in the alcohol dependents (i.e. "Hemoglobin subunit beta" and "Forkhead box protein P2" were up-regulated at least 10-fold), 23 were down-regulated (i.e. "Statherin" and "RNA-binding protein 25" were down-regulated at least 10-fold), and 41 presented similar expression in both groups. 71 proteins were candidates for biomarkers of disorders 58 presented in alcohol dependents' saliva. The most common disorders were neoplasms, genetic, cardiovascular, metabolic and glandular diseases.

CONCLUSIONS

Salivary protein profile undergoes strong changes in alcohol and tobacco dependents. 34% of salivary proteins present in alcohol and tobacco dependents were present in controls; 14.5% of them were expressed in similar quantity.

Salivary proteomics of healthy dogs: An in depth catalog

Objective

To provide an in-depth catalog of the salivary proteome and endogenous peptidome of healthy dogs, evaluate proteins and peptides with antimicrobial properties, and compare the most common salivary proteins and peptides between different breed phylogeny groups.

Methods

36 healthy dogs without evidence of periodontal disease representing four breed phylogeny groups, based upon single nucleotide polymorphism haplotypes (ancient, herding/sighthound, and two miscellaneous groups). Saliva collected from dogs was pooled by phylogeny group and analyzed using nanoscale liquid chromatography-tandem mass spectrometry. Resulting tandem mass spectra were compared to databases for identification of endogenous peptides and inferred proteins.

Results

2,491 proteins and endogenous peptides were found in the saliva of healthy dogs with no periodontal disease. All dog phylogeny groups’ saliva was rich in proteins and peptides with antimicrobial functions. The ancient breeds group was distinct in that it contained unique proteins and was missing many proteins and peptides present in the other groups.

Conclusions and clinical relevance

Using a sophisticated nanoscale liquid chromatography-tandem mass spectrometry, we were able to identify 10-fold more salivary proteins than previously reported in dogs. Seven of the top 10 most abundant proteins or peptides serve immune functions and many more with various antimicrobial mechanisms were found. This is the most comprehensive analysis of healthy canine saliva to date, and will provide the groundwork for future studies analyzing salivary proteins and endogenous peptides in disease states.