Source Of Potential Biomarkers Research Articles

The Serum and Saliva Proteome of Dogs with Diabetes Mellitus.

Simple SummaryThe present study describes for the first time the differences in the serum and saliva proteomes between healthy dogs and dogs with diabetes mellitus by a high- throughput proteomic approach. More than 1000 proteins were identified, and 16 proteins in serum and 26 in saliva showed significant changes between both groups. Additionally, pathways that showed changes were discussed in order to improve the understanding of the pathophysiology of the disease and one protein in serum (haptoglobin) was successfully verified. The results of the present study could be a source of potential biomarkers for canine diabetes mellitus in saliva and serum and also contribute to increase the knowledge of the pathophysiology of the disease.This study aims to evaluate the changes in salivary and serum proteomes that occur in canine diabetes mellitus type-1 (DM) through a high-throughput quantitative proteomic analysis. The proteomes of 10 paired serum and saliva samples from healthy controls (HC group, n = 5) and dogs with untreated DM (DM group, n = 5) were analyzed using Tandem Mass Tags (TMT)-based proteomic approach. Additionally, 24 serum samples from healthy controls and untreated DM were used to validate haptoglobin in serum. The TMT analysis quantified 767 and 389 proteins in saliva and serum, respectively. Of those, 16 unique proteins in serum and 26 in saliva were differently represented between DM and HC groups. The verification of haptoglobin in serum was in concordance with the proteomic data. Our results pointed out changes in both saliva and serum proteomes that reflect different physiopathological changes in dogs with DM. Although some of the proteins identified here, such as malate dehydrogenase or glyceraldehyde-3-phosphate dehydrogenase, were previously related with DM in dogs, most of the proteins modulated in serum and saliva are described in canine DM for the first time and could be a source of potential biomarkers of the disease. Additionally, the molecular function, biological process, pathways and protein class of the differential proteins were revealed, which could improve the understanding of the disease’s pathological mechanisms.

Tear proteomic profile in three distinct ocular surface diseases: keratoconus, pterygium, and dry eye related to graft-versus-host disease

BackgroundDiseases of the anterior segment of the eye may present different mechanisms, intensity of symptoms, and impact on the patients’ quality of life and vision. The tear film is in direct contact with the ocular surface and cornea and can be easily accessed for sample collection, figuring as a promising source of potential biomarkers for diagnosis and treatment control. This study aimed to evaluate tear proteomic profile in 3 distinct ocular diseases: keratoconus (corneal ectasia), severe dry eye related to graft-versus-host-disease (tear film dysfunction and ocular inflammatory condition) and pterygium (conjunctival fibrovascular degenerative disease).MethodsTear samples were collected from patients of each condition and a control group. By using mass spectrometric analysis combined with statistics and bioinformatics tools, a detailed comparison of protein profile was performed.ResultsAfter Student’s t-test analyses comparing each condition to the control group, we found the following number of differentially expressed proteins: 7 in keratoconus group, 29 in pterygium group, and 79 in GVHD group. Following multivariate analyses, we also report potential candidates as biomarkers for each disease.ConclusionsWe demonstrated herein that mass spectrometry-based proteomics was able to indicate proteins that differentiate three distinct ocular conditions, which is a promising tool for the diagnosis of ocular diseases.

Meconium proteases and antiproteases as a potential source of biomarkers for the assessment of the intrauterine environment of the fetus

Background A protease–antiprotease balance is required to maintain the homeostasis of the intrauterine environment in which the fetus develops. Proteases and antiproteases accumulate in meconium exclusively during intrauterine life and are excreted after birth. Methods Proteomic analysis was used to investigate the protein composition in pooled 50 serial meconium portions from 10 neonates. The UniProt, BRENDA and MEROPS databases were the sources of information used to classify the meconium proteases and antiproteases among 946 proteins identified in meconium. Results A total of 265 enzymatic proteins and 33 protein inhibitors were identified in the meconium. The six main enzyme groups represented in the meconium were oxidoreductases (n = 44), transferases (n = 62), hydrolases (n = 137), lyases (n = 10), isomerases (n = 7) and ligases (n = 5). Six protease families were distinguished: serine (n = 28, 41.2% of all proteases), metallo (n = 23, 33.8%), cysteine (n = 10, 14.7%), aspartic (n = 4, 5.9%), theorine (n = 2, 2.9%) and mixed (n = 1, 1.5%) proteases. Conclusions The well-characterized meconium-based biomarker panel of proteases and their inhibitors may be a source of important information for use in diagnosing fetal disorders and predicting postnatal health and development. The differences in the composition and function between individual meconium proteases and antiproteases confirm their association with numerous metabolic processes characteristic of the intrauterine environment.

Neurodegenerative Disorders of the Eye and of the Brain: A Perspective on Their Fluid-Dynamical Connections and the Potential of Mechanism-Driven Modeling

Neurodegenerative disorders (NDD) such as Alzheimer's and Parkinson's diseases are significant causes of morbidity and mortality worldwide. The pathophysiology of NDD is still debated, and there is an urgent need to understand the mechanisms behind the onset and progression of these heterogenous diseases. The eye represents a unique window to the brain that can be easily assessed via non-invasive ocular imaging. As such, ocular measurements have been recently considered as potential sources of biomarkers for the early detection and management of NDD. However, the current use of ocular biomarkers in the clinical management of NDD patients is particularly challenging. Specifically, many ocular biomarkers are influenced by local and systemic factors that exhibit significant variation among individuals. In addition, there is a lack of methodology available for interpreting the outcomes of ocular examinations in NDD. Recently, mathematical modeling has emerged as an important tool capable of shedding light on the pathophysiology of multifactorial diseases and enhancing analysis and interpretation of clinical results. In this article, we review and discuss the clinical evidence of the relationship between NDD in the brain and in the eye and explore the potential use of mathematical modeling to facilitate NDD diagnosis and management based upon ocular biomarkers.

BIOM-57. PEDIATRIC TUMOR CLASSIFICATION THROUGH GENOME-WIDE METHYLATION PROFILING OF EXTRACELLULAR VESICLE DNA

Abstract BACKGROUND Genome-wide methylation profiling reliably classifies pediatric central nervous system (CNS) tumors. Extracellular vesicles (EVs) are released by pediatric CNS tumor cells (pCC) and contain high molecular weight tumor DNA, rendering EVs a potential biomarker source to identify tumor subgroups, stratify patients and monitor therapy by liquid biopsy. We investigated whether the DNA in pCC-derived EVs reflects genome-wide tumor methylation profiles and allows tumor subtype classification. METHODS DNA was isolated from EVs secreted by pediatric CNS tumor cells (pCC) as well as from the shortly cultured tumor cells and from the original tumor samples (n=4 patients). Pediatric Fibroblasts and EVs derived thereof were used as a non-tumorous control. EVs were classified by nanoparticle analysis (NTA), immunoblotting, imaging flow cytometry (IFCM and electron microscopy. Genome-wide DNA methylation profiling was performed using an 850k Illumina EPIC array and results were classified according to the DKFZ brain tumor classifier and further analysed by t-SNE and Copy number alteration analysis (CNA). RESULTS The size range of pCC-derived EVs was 120–150 nm, as measured by NTA. The majority of secreted EVs exhibited high expression of common EV markers (i.e. CD9, CD63 and CD81), as characterized by IFCM. Genome-wide DNA methylation profiling of pCC-derived EVs correctly identified the methylation class of the original tumor (i.e. pilocytic astrocytoma, medulloblastoma). In addition, t-SNE analysis and copy number alterations matched the pattern of the parental pCC and original tumor samples. CONCLUSION EV DNA faithfully reflects the tumor methylation class and copy number alterations present in the parental cells and the original tumor. Methylation profiling of circulating tumor EV DNA could become a useful tool to detect and classify pediatric CNS tumors.

Retinal imaging in Alzheimer's and neurodegenerative diseases.

In the last 20 years, research focused on developing retinal imaging as a source of potential biomarkers for Alzheimer's disease and other neurodegenerative diseases, has increased significantly. The Alzheimer's Association and the Alzheimer's & Dementia: Diagnosis, Assessment, Disease Monitoring editorial team (companion journal to Alzheimer's & Dementia) convened an interdisciplinary discussion in 2019 to identify a path to expedite the development of retinal biomarkers capable of identifying biological changes associated with AD, and for tracking progression of disease severity over time. As different retinal imaging modalities provide different types of structural and/or functional information, the discussion reflected on these modalities and their respective strengths and weaknesses. Discussion further focused on the importance of defining the context of use to help guide the development of retinal biomarkers. Moving from research to context of use, and ultimately to clinical evaluation, this article outlines ongoing retinal imaging research today in Alzheimer's and other brain diseases, including a discussion of future directions for this area of study.

167 VOLATILE ORGANIC COMPOUND PROFILING FOR DETECTION OF ESOPHAGEAL CANCER IN EXHALED BREATH

Abstract Endoscopic surveillance for Barrett’s esophagus (BE) is invasive but remains the standard modality for early diagnosis of esophageal adenocarcinoma (EAC) and intervention. Human breath contains an array of volatile organic compounds (VOC) that change in disease conditions. VOC detection provides a potential source of biomarkers for non-invasive, real-time identification of EAC. This study aimed to characterize a VOC-profile applicable to the detection of EAC and to provide pilot data to design a future validation trial. Methods Breath samples were collected in our endoscopy unit from BE, EAC, and control patients. Samples were collected in FlexFoil bags (SKC ltd) using previously standardized methods. Hydrogen, methane and other VOCs were quantified by QuinTron BreathTracker® and selected-ion flow tube mass spectrometry (SIFT-MS, Syft®) respectively. 250 reported cancer-related VOCs were selected for analysis. Non-parametric tests were used to identify candidate VOCs, and logistic regression analysis was then applied to determine the best predictors for EAC. Receiver Operating Characteristic (ROC) Curves were developed to determine the sensitivity and specificity of the model. Results 68 individuals were enrolled in the study (Controls, n = 37; BE, n = 21; EAC, n = 10). 8 VOCs were identified with significant concentration differences between the three groups: Trimethylbenzene (3 iso-forms), Dimethyl Sulfide, 4-isopropyl toluene, 1-butanol, trichloroethylene, hydrogen sulfide, methyl mercaptan, p-isopropenyl toluene. Logistic regression analysis of these 10 compounds demonstrated predictive probability of EAC from other groups with ROC curves calculating an area under the curve of 0.85. Conclusion Previous studies have supported the utility of VOCs in exhaled breath as non-invasive real-time tests for the identification of some other cancer types. This pilot study has identified potential VOCs which might identify individuals with EAC. A larger study will be needed to validate and confirm these findings.

Extracellular vesicles from human plasma and serum are carriers of extravesicular cargo-Implications for biomarker discovery.

Extracellular vesicles (EVs) in human blood are a potential source of biomarkers. To which extent anticoagulation affects their concentration, cellular origin and protein composition is largely unexplored. To study this, blood from 23 healthy subjects was collected in acid citrate dextrose (ACD), citrate or EDTA, or without anticoagulation to obtain serum. EVs were isolated by ultracentrifugation or by size-exclusion chromatography (SEC) for fluorescence-SEC. EVs were analyzed by micro flow cytometry, NTA, TEM, Western blot, and protein mass spectrometry. The plasma EV concentration was unaffected by anticoagulants, but serum contained more platelet EVs. The protein composition of plasma EVs differed between anticoagulants, and between plasma and serum. Comparison to other studies further revealed that the shared EV protein composition resembles the "protein corona" of synthetic nanoparticles incubated in plasma or serum. In conclusion, we have validated a higher concentration of platelet EVs in serum than plasma by contemporary EV methods. Anticoagulation should be carefully described (i) to enable study comparison, (ii) to utilize available sample cohorts, and (iii) when preparing/selecting biobank samples. Further, the similarity of the EV protein corona and that of nanoparticles implicates that EVs carry both intravesicular and extravesicular cargo, which will expand their applicability for biomarker discovery.

Exosomes in head and neck cancer: Roles, mechanisms and applications.

Exosomes are extracellular vesicles with a diameter of 30-100nm that exist stably in various body fluids, including the blood, cerebrospinal fluid, urine, saliva, and serous cavity effusion. Compared with other vesicles, exosomes have a unique double-layer saclike structure and an endosomal origin. Depending on the physiological state of donor cells, there is heterogeneity in the bioactive substances present in exosomes, including proteins, RNA, DNA, lipids, and metabolites, providing a rich source of potential biomarkers for disease diagnosis. In head and neck cancer (HNC), exosomes have applications in disease diagnosis and prognosis based on liquid biopsy. Nonetheless, the functions and mechanisms of exosomes in HNC still remain unclear. Recent studies have elucidated the substances and signals transmitted between cancer cells using exosomes and have discussed tumor occurrence, development, metastasis, immune regulation, and drug resistance regulated by exosomes in HNC. In this review, we discuss these recent findings and highlight the significant role of exosomes in HNC, with a focus on the mechanism of tumor progression and the potential clinical value of exosomes as biomarkers and therapeutic targets.

Plasma extracellular vesicle proteins are associated with stress-induced myocardial ischemia in women presenting with chest pain

Diagnosing stable ischemic heart disease (IHD) is challenging, especially in females. Currently, no blood test is available. Plasma extracellular vesicles (EV) are emerging as potential biomarker source. We therefore aimed to identify stress induced ischemia due to stable IHD with plasma extracellular vesicle protein levels in chest pain patients. We analyzed 450 patients suspected for stable IHD who were referred for 82Rb PET/CT in the outpatient clinic. Blood samples were collected before PET/CT and plasma EVs were isolated in 3 plasma subfractions named: TEX, HDL, LDL. In total 6 proteins were quantified in each of these subfractions using immuno-bead assays. CD14 and CystatinC protein levels were independent significant predictors of stress-induced ischemia in the LDL and the HDL subfraction and SerpinC1 and SerpinG1 protein levels in the HDL fraction. Subgroup-analysis on sex revealed that these associations were completely attributed to the associations in women. None of the significant EV proteins remained significant in men. Plasma EV proteins levels are associated with the presence of stable IHD in females presenting with chest pain. This finding, if confirmed in larger cohort studies could be a crucial step in improving diagnostic assessment of women with suspected IHD.

Role of Extracellular Vesicles in the Pathophysiology, Diagnosis and Tracking of Non-Alcoholic Fatty Liver Disease.

Non-alcoholic fatty liver disease (NAFLD) is the most common chronic liver disease, affecting approximately one-third of the global population. Most affected individuals experience only simple steatosis—an accumulation of fat in the liver—but a proportion of these patients will progress to the more severe form of the disease, non-alcoholic steatohepatitis (NASH), which enhances the risk of cirrhosis and hepatocellular carcinoma. Diagnostic approaches to NAFLD are currently limited in accuracy and efficiency; and liver biopsy remains the only reliable way to confirm NASH. This technique, however, is highly invasive and poses risks to patients. Hence, there is an increasing demand for improved minimally invasive diagnostic tools for screening at-risk individuals and identifying patients with more severe disease as well as those likely to progress to such stages. Recently, extracellular vesicles (EVs)—small membrane-bound particles released by virtually all cell types into circulation—have emerged as a rich potential source of biomarkers that can reflect liver function and pathological processes in NAFLD. Of particular interest to the diagnosis and tracking of NAFLD is the potential to extract microRNAs miR-122 and miR-192 from EVs circulating in blood, particularly when using an isolation technique that selectively captures hepatocyte-derived EVs.

Identification of Human Secretome and Membrane Proteome-Based Cancer Biomarkers Utilizing Bioinformatics.

Cellular secreted proteins (secretome), together with cellular membrane proteins, collectively referred to as secretory and membrane proteins (SMPs) are a large potential source of biomarkers as they can be used to indicate cell types and conditions. SMPs have been shown to be ideal candidates for several clinically approved drug regimens including for cancer. This study aimed at performing a functional analysis of SMPs within different cancer subtypes to provide great clinical targets for potential prognostic, diagnostic and the therapeutics use. Using an innovative majority decision-based algorithm and transcriptomic data spanning 5 cancer types and over 3000 samples, we quantified the relative difference in SMPs gene expression compared to normal adjacent tissue. A detailed deep data mining analysis revealed a consistent group of downregulated SMP isoforms, enriched in hematopoietic cell lineages (HCL), in multiple cancer types. HCL-associated genes were frequently downregulated in successive cancer stages and high expression was associated with good patient prognosis. In addition, we suggest a potential mechanism by which cancer cells suppress HCL signaling by reducing the expression of immune-related genes. Our data identified potential biomarkers for the cancer immunotherapy. We conclude that our approach may be applicable for the delineation of other types of cancer and illuminate specific targets for therapeutics and diagnostics.

Ocular biomarkers for cognitive impairment in nonagenarians; a prospective cross-sectional study

BackgroundOcular imaging receives much attention as a source of potential biomarkers for dementia. In the present study, we analyze these ocular biomarkers in cognitively impaired and healthy participants in a population aged over 90 years (= nonagenarian), and elucidate the effects of age on these biomarkers.MethodsFor this prospective cross-sectional study, we included individuals from the EMIF-AD 90+ study, consisting of a cognitively healthy (N = 67) and cognitively impaired group (N = 33), and the EMIF-AD PreclinAD study, consisting of cognitively healthy controls aged ≥60 (N = 198). Participants underwent Optical Coherence Tomography (OCT) and fundus photography of both eyes. OCT was used to asses total and individual inner retinal layer thickness in the macular region (Early Treatment Diabetic Retinopathy Study circles) as well as peripapillary retinal nerve fiber layer thickness, fundus images were analyzed with Singapore I Vessel Assessment to obtain 7 retinal vascular parameters. Values for both eyes were averaged. Differences in ocular biomarkers between the 2 nonagenarian groups were analyzed using linear regression, differences between the individual nonagenarian groups and controls were analyzed using generalized estimating equations.ResultsOcular biomarkers did not differ between the healthy and cognitively impaired nonagenarian groups. 19 out of 22 ocular biomarkers assessed in this study differed between either nonagenarian group and the younger controls.ConclusionThe ocular biomarkers assessed in this study were not associated with cognitive impairment in nonagenarians, making their use as a screening tool for dementing disorders in this group limited. However, ocular biomarkers were significantly associated with chronological age, which were very similar to those ascribed to occur in Alzheimer’s Disease.

Changes in Serum and Salivary Proteins in Canine Mammary Tumors.

Simple SummaryThe present study describes for the first time the differences in the serum and saliva proteomes between healthy bitches and bitches with mammary tumors using a high-throughput proteomic approach. More than 1000 proteins were identified and 35 in serum and 49 in saliva were significantly modulated. Additionally, their related pathways were discussed in order to improve understanding of the pathophysiology of the disease and one protein, serum albumin, was further validated. The results of the present study could be a source of potential biomarkers for canine mammary tumors in saliva and serum and increase the knowledge on the pathophysiology of the disease.The aim of this study was to evaluate changes in serum and saliva proteomes in canine mammary tumors (CMT) using a high-throughput quantitative proteomic analysis in order to potentially discover possible biomarkers of this disease. Proteomes of paired serum and saliva samples from healthy controls (HC group, n = 5) and bitches with CMT (CMT group, n = 5) were analysed using a Tandem Mass Tags-based approach. Twenty-five dogs were used to validate serum albumin as a candidate biomarker in an independent sample set. The proteomic analysis quantified 379 and 730 proteins in serum and saliva, respectively. Of those, 35 proteins in serum and 49 in saliva were differentially represented. The verification of albumin in serum was in concordance with the proteomic data, showing lower levels in CMT when compared to the HC group. Some of the modulated proteins found in the present study such as haptoglobin or S100A4 have been related to CMT or human breast cancer previously, while others such as kallikrein-1 and immunoglobulin gamma-heavy chains A and D are described here for the first time. Our results indicate that saliva and serum proteomes can reflect physiopathological changes that occur in CMT in dogs and can be a potential source of biomarkers of the disease.

An Integrated Meta-Analysis of Secretome and Proteome Identify Potential Biomarkers of Pancreatic Ductal Adenocarcinoma.

Pancreatic ductal adenocarcinoma (PDAC) is extremely aggressive, has an unfavorable prognosis, and there are no biomarkers for early detection of the disease or identification of individuals at high risk for morbidity or mortality. The cellular and molecular complexity of PDAC leads to inconsistences in clinical validations of many proteins that have been evaluated as prognostic biomarkers of the disease. The tumor secretome, a potential source of biomarkers in PDAC, plays a crucial role in cell proliferation and metastasis, as well as in resistance to treatments, which together contribute to a worse clinical outcome. The massive amount of proteomic data from pancreatic cancer that has been generated from previous studies can be integrated and explored to uncover secreted proteins relevant to the diagnosis and prognosis of the disease. The present study aimed to perform an integrated meta-analysis of PDAC proteome and secretome public data to identify potential biomarkers of the disease. Our meta-analysis combined mass spectrometry data obtained from two systematic reviews of the pancreatic cancer literature, which independently selected 20 studies of the secretome and 35 of the proteome. Next, we predicted the secreted proteins using seven in silico tools or databases, which identified 39 secreted proteins shared between the secretome and proteome data. Notably, the expression of 31 genes of these secretome-related proteins was upregulated in PDAC samples from The Cancer Genome Atlas (TCGA) when compared to control samples from TCGA and The Genotype-Tissue Expression (GTEx). The prognostic value of these 39 secreted proteins in predicting survival outcome was confirmed using gene expression data from four PDAC datasets (validation set). The gene expression of these secreted proteins was able to distinguish high- and low-survival patients in nine additional tumor types from TCGA, demonstrating that deregulation of these secreted proteins may also contribute to the prognosis in multiple cancers types. Finally, we compared the prognostic value of the identified secreted proteins in PDAC biomarkers studies from the literature. This analysis revealed that our gene signature performed equally well or better than the signatures from these previous studies. In conclusion, our integrated meta-analysis of PDAC proteome and secretome identified 39 secreted proteins as potential biomarkers, and the tumor gene expression profile of these proteins in patients with PDAC is associated with worse overall survival.

Edgetic perturbation signatures represent known and novel cancer biomarkers

Isoform switching is a recently characterized hallmark of cancer, and often translates to the loss or gain of domains mediating protein interactions and thus, the re-wiring of the interactome. Recent computational tools leverage domain-domain interaction data to resolve the condition-specific interaction networks from RNA-Seq data accounting for the domain content of the primary transcripts expressed. Here, we used The Cancer Genome Atlas RNA-Seq datasets to generate 642 patient-specific pairs of interactomes corresponding to both the tumor and the healthy tissues across 13 cancer types. The comparison of these interactomes provided a list of patient-specific edgetic perturbations of the interactomes associated with the cancerous state. We found that among the identified perturbations, select sets are robustly shared between patients at the multi-cancer, cancer-specific and cancer sub-type specific levels. Interestingly, the majority of the alterations do not directly involve significantly mutated genes, nevertheless, they strongly correlate with patient survival. The findings (available at EdgeExplorer: “http://webclu.bio.wzw.tum.de/EdgeExplorer”) are a new source of potential biomarkers for classifying cancer types and the proteins we identified are potential anti-cancer therapy targets.

Early changes in the urine proteome in a rat liver tumour model.

BackgroundUrine, as a potential biomarker source among body fluids, can accumulate many early changes in the body due to the lack of mechanisms to maintain a homeostatic state. This study aims to detect early changes in the urinary proteome in a rat liver tumour model.MethodsThe tumour model was established with the Walker-256 carcinosarcoma cell line (W256). Urinary proteins at days 3, 5, 7 and 11 were profiled by liquid chromatography coupled with tandem mass spectrometry (LC-MS/MS). Compared with controls, differential proteins were selected. Associations of differential proteins with cancer were retrieved.ResultsAt days 3, 5, 7 and 11, five, fifteen, eleven and twelve differential proteins were identified, respectively. Some of the differential proteins were reported to be associated with liver cancer. This differential urinary protein pattern was different from the patterns in W256 subcutaneous, lung metastasis and intracerebral tumour models.ConclusionsThis study demonstrates that (1) early changes in urinary proteins can be found in the rat liver tumour model; (2) urinary proteins can be used to differentiate the same tumour cells grown in different organs.

Exosomes as potential sources of biomarkers in colorectal cancer.

Colorectal cancer (CRC), a common malignancy, is among the leading causes of cancer-related deaths worldwide. Developing novel biomarkers is an important public health strategy to effectively reduce the mortality of this disease. Recent studies have found that exosomes may be important sources of biomarkers in CRC. Exosomes are nanometer-sized membrane vesicles (30-200nm) secreted by normal or cancer cells, which participate in intercellular communication by transporting RNAs and proteins. Accumulating evidence has shown that some differentially expressed RNAs and proteins in exosomes play key roles in the initiation and development of CRC and are potential candidates for malignancy detection. Accordingly, exploring the correlation between these exosomes and CRC may be beneficial for the development of novel biomarkers in this disease. Here, we summarize the important roles of exosomes as biomarkers in CRC diagnosis, as well as the application in the metastasis, chemoresistance, and recrudescence of CRC. In particular, we discuss the prospects and limitations of exosomes as tumor markers.

Roles for Exosome in Various Kidney Diseases and Disorders.

Exosome is a nanoscale vesicle with a size range of 30–100 nm. It is secreted from cell to extracellular space by exocytosis after fusion of multivesicular body (MVB) (formed by endocytic vesicles) with plasma membrane. Exosome plays several important roles in cellular homeostasis and intercellular communications. During the last two decades, exosome has acquired a wide attention to explore its additional roles in various aspects of cell biology and function in several organ systems. For the kidney, several lines of evidence have demonstrated 1that exosome is involved in the renal physiology and pathogenic mechanisms of various kidney diseases/disorders. This article summarizes roles of the exosome as the potential source of biomarkers, pathogenic molecules, and therapeutic biologics that have been extensively investigated in many kidney diseases/disorders, including lupus nephritis (LN), other glomerular diseases, acute kidney injury (AKI), diabetic nephropathy (DN), as well as in the process of renal fibrosis and chronic kidney disease (CKD) progression, in addition to polycystic kidney disease (PKD), kidney transplantation, and renal cell carcinoma (RCC). Moreover, the most recent evidence has shown its emerging role in kidney stone disease (or nephrolithiasis), involving inflammasome activation and inflammatory cascade frequently found in kidney stone pathogenesis.

Diagnostic potential of gut microbiota in Parkinson’s disease

Background. Nowadays many efforts are taken in searching for Parkinson’s disease biomarkers, especially for an early recognition of the disease. The gut microbiota is one of the potential sources of biomarkers, changes in the composition of which in PD are actively studied.The aim of this study is to identify microbiota biomarkers in the Parkinson’s disease with an estimated accuracy of the diagnostics, including differential diagnostics, relative to other neurological diseases for patients of the Russian population.Material and methods. One hundred ninety-two metagenomics profiles from patients with Parkinson’s disease (n = 93), people with other neurological diagnoses (n = 33), and healthy controls (n = 66) were included in this study. These profiles were obtained with amplicon sequencing of bacterial 16S rRNA genes. Classifying models were made using the naive Bayes classifier, the artificial neural network, support vector machine, generalized linear model, and partial least squares regression.As a result we established that an optimal classification by the composition of the gut microbiota on the validation sample (sensitivity 91.30%, specificity 91.67% at 91.49% accuracy) amid patients was demonstrated with a naive Bayes classifier using the representation of the following genera as predictors: Christensenella, Methanobrevibacter, Leuconostoc, Enterococcus, Catabacter, Desulfovibrio, Sphingomonas, Yokenella, Atopobium, Fusicatenibacter, Cloacibacillus, Bulleidia, Acetanaerobacterium, and Staphylococcus.Conclusions. Information of the gut microbiota taxonomic composition may be used in differential diagnosis of Parkinson’s disease.