To identify biomarker candidates associated with early IgA nephropathy (IgAN) and thin basement membrane nephropathy (TBMN), the most common causes presenting isolated hematuria in childhood, a proteomic approach of urinary exosomes from early IgAN and TBMN patients was introduced. The proteomic results from the patients were compared with a normal group to understand the pathophysiological processes associated with these diseases at the protein level. The urinary exosomes, which reflect pathophysiological processes, collected from three groups of young adults (early IgAN, TBMN, and normal) were trypsin-digested using a gel-assisted protocol, and quantified by label-free LC-MS/MS, using an MS(E) mode. A total of 1877 urinary exosome proteins, including cytoplasmic, membrane, and vesicle trafficking proteins, were identified. Among the differentially expressed proteins, four proteins (aminopeptidase N, vasorin precursor, α-1-antitrypsin, and ceruloplasmin) were selected as biomarker candidates to differentiate early IgAN from TBMN. We confirmed the protein levels of the four biomarker candidates by semi-quantitative immunoblot analysis in urinary exosomes independently prepared from other patients, including older adult groups. Further clinical studies are needed to investigate the diagnostic and prognostic value of these urinary markers for early IgAN and TBMN. Taken together, this study showed the possibility of identifying biomarker candidates for human urinary diseases using urinary exosomes and might help to understand the pathophysiology of early IgAN and TBMN at the protein level.

BackgroundPlatelet-rich plasma (PRP) has more concentrated platelets than normal plasma (approximately 150-400×103 cell/dL). Platelets excrete several growth factors and cytokines that are associated with the healing and regeneration process. However, even though PRP is widely used, the mechanism or actual effect is presently unclear. Therefore, this study was performed to investigate the levels of growth factors and platelet concentration rate.MethodsAutologous blood for preparing PRP was obtained from healthy subjects aged 25 to 35 years. The samples were divided into 4 experimental groups (inactivated whole blood, inactivated PRP, activated whole blood with thrombin and calcium chloride, and activated PRP). The platelet counts in the blood were analyzed and the growth factors were quantitatively measured. A statistical analysis was performed by using Dunn's multiple comparison test.ResultsIn the blood cell analysis, the platelet count of the PRP group was approximately 4.25 times higher than that of the whole blood group. In the quantitative analysis of growth factors, the platelet-derived growth factor (PDGF)-AB, PDGF-BB, and transforming growth factor-β of the inactivated and activated PRP groups were higher than those of the inactivated and activated whole blood groups (P<0.05).ConclusionsIn this study, the platelet count and the levels of PDGF-AB and PDGF-BB in the PRP were determined. Further, more research is required on the bioactivity level of the growth factors secreted during the process of PRP preparation and the potency of growth factors that can be exerted physiologically in vivo.

Post-traumatic stress disorder (PTSD) impacts many veterans and active duty soldiers, but diagnosis can be problematic due to biases in self-disclosure of symptoms, stigma within military populations, and limitations identifying those at risk. Prior studies suggest that PTSD may be a systemic illness, affecting not just the brain, but the entire body. Therefore, disease signals likely span multiple biological domains, including genes, proteins, cells, tissues, and organism-level physiological changes. Identification of these signals could aid in diagnostics, treatment decision-making, and risk evaluation. In the search for PTSD diagnostic biomarkers, we ascertained over one million molecular, cellular, physiological, and clinical features from three cohorts of male veterans. In a discovery cohort of 83 warzone-related PTSD cases and 82 warzone-exposed controls, we identified a set of 343 candidate biomarkers. These candidate biomarkers were selected from an integrated approach using (1) data-driven methods, including Support Vector Machine with Recursive Feature Elimination and other standard or published methodologies, and (2) hypothesis-driven approaches, using previous genetic studies for polygenic risk, or other PTSD-related literature. After reassessment of ~30% of these participants, we refined this set of markers from 343 to 28, based on their performance and ability to track changes in phenotype over time. The final diagnostic panel of 28 features was validated in an independent cohort (26 cases, 26 controls) with good performance (AUC = 0.80, 81% accuracy, 85% sensitivity, and 77% specificity). The identification and validation of this diverse diagnostic panel represents a powerful and novel approach to improve accuracy and reduce bias in diagnosing combat-related PTSD.

One third of humans are infected lifelong with the brain-dwelling, protozoan parasite, Toxoplasma gondii. Approximately fifteen million of these have congenital toxoplasmosis. Although neurobehavioral disease is associated with seropositivity, causality is unproven. To better understand what this parasite does to human brains, we performed a comprehensive systems analysis of the infected brain: We identified susceptibility genes for congenital toxoplasmosis in our cohort of infected humans and found these genes are expressed in human brain. Transcriptomic and quantitative proteomic analyses of infected human, primary, neuronal stem and monocytic cells revealed effects on neurodevelopment and plasticity in neural, immune, and endocrine networks. These findings were supported by identification of protein and miRNA biomarkers in sera of ill children reflecting brain damage and T. gondii infection. These data were deconvoluted using three systems biology approaches: “Orbital-deconvolution” elucidated upstream, regulatory pathways interconnecting human susceptibility genes, biomarkers, proteomes, and transcriptomes. “Cluster-deconvolution” revealed visual protein-protein interaction clusters involved in processes affecting brain functions and circuitry, including lipid metabolism, leukocyte migration and olfaction. Finally, “disease-deconvolution” identified associations between the parasite-brain interactions and epilepsy, movement disorders, Alzheimer’s disease, and cancer. This “reconstruction-deconvolution” logic provides templates of progenitor cells’ potentiating effects, and components affecting human brain parasitism and diseases.