Salivary Diagnostics

Salivary diagnostics plays an important role in the early detection and prevention of many oral and systemic diseases in a fast and noninvasive way. Saliva collection is an easy, repeatable and inexpensive diagnostic source that can be used for both diagnosis and real-time monitoring of various human diseases. In the near future, many developed and validated salivary biomarkers have the potential to reach the clinical practice. Five diagnostic “omics” constituents of saliva include proteomics, transcriptomics, metabolomics, microbiomics and microRNAs. Based on them, the newly emerging technologies of salivary diagnostics are developed that include RNA-sequencing, point-of-care technologies and liquid biopsy. They have potential to enable screening, early detection, prognosis and monitoring of various human diseases. The recent developments broadened the salivary diagnostic approach from the oral cavity to the whole physiological system, thus toward personalized individual medicine applications

An analysis of protein patterns present in the saliva of diabetic patients using pairwise relationship and hierarchical clustering

Molecular diagnosis is based on the quantification of RNA, proteins, or metabolites whose concentration can be correlated to clinical situations. Usually, these molecules are not suitable for early diagnosis or to follow clinical evolution. Large-scale diagnosis using these types of molecules depends on cheap and preferably noninvasive strategies for screening. Saliva has been studied as a noninvasive, easily obtainable diagnosis fluid, and the presence of serum proteins in it enhances its use as a systemic health status monitoring tool. With a recently described automated capillary electrophoresis-based strategy that allows us to obtain a salivary total protein profile, it is possible to quantify and analyze patterns that may indicate disease presence or absence. The data of 19 persons with diabetes and 58 healthy donors obtained by capillary electrophoresis were transformed, treated, and grouped so that the structured values could be used to study individuals’ health state. After Pairwise Relationships and Hierarchical Clustering analysis were observed that amplitudes of protein peaks present in the saliva of these individuals could be used as differentiating parameters between healthy and unhealthy people. It indicates that these characteristics can serve as input for a future computational intelligence algorithm that will aid in the stratification of individuals that manifest changes in salivary proteins.info:eu-repo/semantics/acceptedVersio

Open Problems in Extracellular RNA Data Analysis: Insights From an ERCC Online Workshop.

We now know RNA can survive the harsh environment of biofluids when encapsulated in vesicles or by associating with lipoproteins or RNA binding proteins. These extracellular RNA (exRNA) play a role in intercellular signaling, serve as biomarkers of disease, and form the basis of new strategies for disease treatment. The Extracellular RNA Communication Consortium (ERCC) hosted a two-day online workshop (April 19-20, 2021) on the unique challenges of exRNA data analysis. The goal was to foster an open dialog about best practices and discuss open problems in the field, focusing initially on small exRNA sequencing data. Video recordings of workshop presentations and discussions are available (https://exRNA.org/exRNAdata2021-videos/). There were three target audiences: experimentalists who generate exRNA sequencing data, computational and data scientists who work with those groups to analyze their data, and experimental and data scientists new to the field. Here we summarize issues explored during the workshop, including progress on an effort to develop an exRNA data analysis challenge to engage the community in solving some of these open problems

Genetic architecture of heart mitochondrial proteome influencing cardiac hypertrophy.

Mitochondria play an important role in both normal heart function and disease etiology. We report analysis of common genetic variations contributing to mitochondrial and heart functions using an integrative proteomics approach in a panel of inbred mouse strains called the Hybrid Mouse Diversity Panel (HMDP). We performed a whole heart proteome study in the HMDP (72 strains, n=2-3 mice) and retrieved 848 mitochondrial proteins (quantified in ≥50 strains). High- resolution association mapping on their relative abundance levels revealed three trans-acting genetic loci on chromosomes (chr) 7, 13 and 17 that regulate distinct classes of mitochondrial proteins as well as cardiac hypertrophy. DAVID enrichment analyses of genes regulated by each of the loci revealed that the chr13 locus was highly enriched for complex-I proteins (24 proteins, P=2.2E-61), the chr17 locus for mitochondrial ribonucleoprotein complex (17 proteins, P=3.1E-25) and the chr7 locus for ubiquinone biosynthesis (3 proteins, P=6.9E-05). Follow-up high resolution regional mapping identified NDUFS4, LRPPRC and COQ7 as the candidate genes for chr13, chr17 and chr7 loci, respectively, and both experimental and statistical analyses supported their causal roles. Furthermore, a large cohort of Diversity Outbred mice was used to corroborate Lrpprc gene as a driver of mitochondrial DNA (mtDNA)-encoded gene regulation, and to show that the chr17 locus is specific to heart. Variations in all three loci were associated with heart mass in at least one of two independent heart stress models, namely, isoproterenol-induced heart failure and diet-induced obesity. These findings suggest that common variations in certain mitochondrial proteins can act in trans to influence tissue-specific mitochondrial functions and contribute to heart hypertrophy, eluci- dating mechanisms that may underlie genetic susceptibility to heart failure in human populations

Salivary exRNA biomarkers to detect gingivitis and monitor disease regression

AimThis study tests the hypothesis that salivary extracellular RNA (exRNA) biomarkers can be developed for gingivitis detection and monitoring disease regression.Materials and MethodsSalivary exRNA biomarker candidates were developed from a total of 100 gingivitis and nonâ gingivitis individuals using Affymetrix’s expression microarrays. The top 10 differentially expressed exRNAs were tested in a clinical cohort to determine whether the discovered salivary exRNA markers for gingivitis were associated with clinical gingivitis and disease regression. For this purpose, unstimulated saliva was collected from 30 randomly selected gingivitis subjects, the gingival and plaque indexes scores were taken at baseline, 3 and 6 weeks and salivary exRNAs were assayed by means of reverse transcription quantitative polymerase chain reaction.ResultsEight salivary exRNA biomarkers developed for gingivitis were statistically significantly changed over time, consistent with disease regression. A panel of four salivary exRNAs [SPRR1A, lncâ TET3â 2:1, FAM25A, CRCT1] can detect gingivitis with a clinical performance of 0.91 area under the curve, with 71% sensitivity and 100% specificity.ConclusionsThe clinical values of the developed salivary exRNA biomarkers are associated with gingivitis regression. They offer strong potential to be advanced for definitive validation and clinical laboratory development test.Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/144647/1/jcpe12930.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/144647/2/jcpe12930_am.pd

Performance of Salivary Extracellular RNA Biomarker Panels for Gastric Cancer Differs between Distinct Populations.

Gastric cancer (GC) has the fifth highest incidence among cancers and is the fourth leading cause of cancer-related death GC has predominantly a higher number of cases in certain ethnic groups such as the Korean population. GC found at an early stage is more treatable and has a higher survival rate as compared with GC found at a late stage. However, a diagnosis of GC is often delayed due to the lack of early symptoms and available screening programs in United States. Extracellular RNA (exRNA) is an emerging paradigm; exRNAs have the potential to serve as biomarkers in panels aimed at early detection of cancer. We previously reported the successful use of a panel of salivary exRNA for detecting GC in a high-prevalence Korean cohort, and that genetic changes reflected cancer-associated salivary exRNA changes. The current study is a case-control study of salivary exRNA biomarkers for detecting GC in an ethnically distinct U.S. cohort. A model constructed for the U.S. cohort combined demographic characteristics and salivary miRNA and mRNA biomarkers for GC and yielded an area under the receiver operating characteristic (ROC) curve (AUC) of 0.78. However, the constituents of this model differed from that constructed for the Korean cohort, thus, emphasizing the importance of population-specific biomarker development and validation