Dataset Information

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

ABSTRACT: 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.

SUBMITTER: Wang J

PROVIDER: S-EPMC3855806 | biostudies-literature | 2013

REPOSITORIES: biostudies-literature

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

Wang Jiaxin J Liang Yanchun Y Wang Yan Y Cui Juan J Liu Ming M Du Wei W Xu Ying Y

PloS one 20131112 11

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 ...[more]

PMID: 24324552

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Project description:Background: Alzheimer's disease (AD) is the major cause of dementia in population aged over 65 years, accounting up to 70% dementia cases. However, validated peripheral biomarkers for AD diagnosis are not available up to present. In this study, we adopted a new strategy of combination of computational prediction and experimental validation to identify blood protein biomarkers for AD. Methods: First, we collected tissue-based gene expression data of AD patients and healthy controls from GEO database. Second, we analyzed these data and identified differentially expressed genes for AD. Third, we applied a blood-secretory protein prediction program on these genes and predicted AD-related proteins in blood. Finally, we collected blood samples of AD patients and healthy controls to validate the potential AD biomarkers by using ELISA experiments and Western blot analyses. Results: A total of 2754 genes were identified to express differentially in brain tissues of AD, among which 296 genes were predicted to encode AD-related blood-secretory proteins. After careful analysis and literature survey on these predicted blood-secretory proteins, ten proteins were considered as potential AD biomarkers, five of which were experimentally verified with significant change in blood samples of AD vs. controls by ELISA, including GSN, BDNF, TIMP1, VLDLR, and APLP2. ROC analyses showed that VLDLR and TIMP1 had excellent performance in distinguishing AD patients from controls (area under the curve, AUC = 0.932 and 0.903, respectively). Further validation of VLDLR and TIMP1 by Western blot analyses has confirmed the results obtained in ELISA experiments. Conclusion: VLDLR and TIMP1 had better discriminative abilities between ADs and controls, and might serve as potential blood biomarkers for AD. To our knowledge, this is the first time to identify blood protein biomarkers for AD through combination of computational prediction and experimental validation. In addition, VLDLR was first reported here as potential blood protein biomarker for AD. Thus, our findings might provide important information for AD diagnosis and therapies.

Project description:Cardiac mechanical activity leads to periodic changes in the distribution of blood throughout the body, which causes micro-oscillations of the body's center of mass and can be measured by ballistocardiography (BCG). However, many of the BCG findings are based on parameters whose origins are poorly understood. Here, we generate simulated multidimensional BCG signals based on a more exhaustive and accurate computational model of blood circulation than previous attempts. This model consists in a closed loop 0D-1D multiscale representation of the human blood circulation. The 0D elements include the cardiac chambers, cardiac valves, arterioles, capillaries, venules, and veins, while the 1D elements include 55 systemic and 57 pulmonary arteries. The simulated multidimensional BCG signal is computed based on the distribution of blood in the different compartments and their anatomical position given by whole-body magnetic resonance angiography on a healthy young subject. We use this model to analyze the elements affecting the BCG signal on its different axes, allowing a better interpretation of clinical records. We also evaluate the impact of filtering and healthy aging on the BCG signal. The results offer a better view of the physiological meaning of BCG, as compared to previous models considering mainly the contribution of the aorta and focusing on longitudinal acceleration BCG. The shape of experimental BCG signals can be reproduced, and their amplitudes are in the range of experimental records. The contributions of the cardiac chambers and the pulmonary circulation are non-negligible, especially on the lateral and transversal components of the velocity BCG signal. The shapes and amplitudes of the BCG waveforms are changing with age, and we propose a scaling law to estimate the pulse wave velocity based on the time intervals between the peaks of the acceleration BCG signal. We also suggest new formulas to estimate the stroke volume and its changes based on the BCG signal expressed in terms of acceleration and kinetic energy.

Project description:IntroductionAlthough major depressive diroder (MDD) has brought huge burden and challenges to society globally, effective and accurate diagnoses and treatments remain inadequate. The pathogenesis that for women are more likely to suffer from depression than men needs to be excavated as well. The function of circRNAs in pathological process of depression has not been widely investigated. This study aims to explore potential diagnostic biomarker circRNA of female patients with MDD and to investigate its role in pathogenesis.MethodsFirst, an expression profile of circRNAs in the peripheral blood monocular cells of MDD patients and healthy peripherals were established based on high-throughput sequencing analysis. In addition, the top 10 differentially expressed circRNAs were quantified by quantitative real-time PCR to explore diagnostic biomarkers. To further investigate the function of biomarkers in the pathogenesis of MDD, bioinformatics analysis on downstream target genes of the biomarkers was carried out.ResultsThere is a mass of dysregulated circRNAs in PBMCs between female MDD patients and healthy controls. Among the top 10 differentially expressed circRNAs, hsa_circ_0126218 is more feasible as a diagnostic biomarker. The expression level of hsa_circ_0126218 displayed upregulation in patients with MDD and the area under the operating characteristic curve of hsa_circ_0126218 was 0.801 (95% CI 0.7226-0.8791, p < 0.0001). To explain the competing endogenous RNA role of hsa_circ_0126218 in the pathogenesis of female MDD, a hsa_circ_0126218-miRNA-mRNA network was established. Gene Ontology and Kyoto Encyclopedia of Genes and Genomes pathway enrichment analyses stated that some of the enriched pathways downstream of hsa_circ_0126218 are closely related to MDD. Moreover, we established a protein-protein network to further screen out the hub genes (PIK3CA, PTEN, MAPK1, CDC42, Lyn, YES1, EPHB2, SMAD2, STAT1, and ILK). The function of hsa_circ_0126218 was refined by constructing a verified circRNA-predicted miRNA-hub gene subnetwork.Conclusionhsa_circ_0126218 can be considered as a new female MDD biomarker, and the pathogenesis of female MDD by the downstream regulation of hsa_circ_0126218 has been predicted. These findings may help further improve the early detection, effective diagnosis, convenient monitoring of complications, precise treatment, and timely recurrence prevention of depression.

Dataset Information

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

Publications

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

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