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Exosomes are membrane vesicles that are secreted by cells upon fusion of multivesicular bodies with the plasma membrane. Exosomal proteomics has emerged as a powerful approach to understand the molecular composition of exosomes and has potential to accelerate biomarker discovery. Different proteomic analysis methods have been previously employed to establish several exosome protein databases. In this study, TFE solution phase digestion was compared with in-gel digestion and found to yield similar results. Proteomic analysis of urinary exosomes was performed by multidimensional protein identification technology (MudPIT) after TFE digestion. 3280 proteins were identified from nine human urine samples with 31% overlap among nine samples. Gene ontology (GO) analysis, coupled with detection of all of the members of ESCRT machinery complex, supports the multivesicular origin of these particles. These results significantly expand the existing database of urinary exosome proteins. Our results also indicate that more than 1000 proteins can be detected from exosomes prepared from as little as 25 mL of urine. This study provides the largest set of proteins present in human urinary exosome proteomes, provides a valuable reference for future studies, and provides methods that can be applied to exosomal proteomic analysis from other tissue sources.

To quantify and contextualize the risk for coronavirus disease 2019 (COVID-19)related hospitalization and illness severity in type 1 diabetes. RESEARCH DESIGN AND METHODS We conducted a prospective cohort study to identify case subjects with COVID-19 across a regional health care network of 137 service locations. Using an electronic health record query, chart review, and patient contact, we identified clinical factors influencing illness severity. RESULTS We identified COVID-19 in 6,138, 40, and 273 patients without diabetes and with type 1 and type 2 diabetes, respectively. Compared with not having diabetes, people with type 1 diabetes had adjusted odds ratios of 3.90 (95% CI 1.75-8.69) for hospitalization and 3.35 (95% CI 1.53-7.33) for greater illness severity, which was similar to risk in type 2 diabetes. Among patients with type 1 diabetes, glycosylated hemoglobin (HbA 1c), hypertension, race, recent diabetic ketoacidosis, health insurance status, and less diabetes technology use were significantly associated with illness severity. CONCLUSIONS Diabetes status, both type 1 and type 2, independently increases the adverse impacts of COVID-19. Potentially modifiable factors (e.g., HbA 1c) had significant but modest impact compared with comparatively static factors (e.g., race and insurance) in type 1 diabetes, indicating an urgent and continued need to mitigate severe acute respiratory syndrome coronavirus 2 infection risk in this community. The medical community currently lacks sufficient data to adequately mitigate the impact of the novel coronavirus disease 2019 (COVID-19) in the type 1 diabetes community. At present, our knowledge is largely extrapolated from recent retrospective analyses of hospitalized patients (1-5), which have strongly suggested "diabetes" increases risk for COVID-19 morbidity and mortality. These studies did not, however, distinguish between type 1 diabetes and type 2 diabetesdtwo pathophysiologically distinct conditions. Although reports of COVID-19 in type 1 diabetes are emerging, the scope of these investigations to date has been limited by including only hospitalized

Somatic mutations have been identified in aldosterone-producing adenomas (APAs) in genes that include KCNJ5 , ATP1A1 , ATP2B3 , and CACNA1D . Based on independent studies, there appears to be racial differences in the prevalence of somatic KCNJ5 mutations, particularly between East Asians and Europeans. Despite the high cardiovascular disease mortality of blacks, there have been no studies focusing on somatic mutations in APAs in this population. In the present study, we investigated genetic characteristics of APAs in blacks using a CYP11B2 (aldosterone synthase) immunohistochemistry-guided next-generation sequencing approach. The adrenal glands with adrenocortical adenomas from 79 black patients with primary aldosteronism were studied. Seventy-three tumors from 69 adrenal glands were confirmed to be APAs by CYP11B2 immunohistochemistry. Sixty-five of 73 APAs (89%) had somatic mutations in aldosterone-driver genes. Somatic CACNA1D mutations were the most prevalent genetic alteration (42%), followed by KCNJ5 (34%), ATP1A1 (8%), and ATP2B3 mutations (4%). CACNA1D mutations were more often observed in APAs from males than those from females (55% versus 29%, P =0.033), whereas KCNJ5 mutations were more prevalent in APAs from females compared with those from males (57% versus 13%, P <0.001). No somatic mutations in aldosterone-driver genes were identified in tumors without CYP11B2 expression. In conclusion, 89% of APAs in blacks harbor aldosterone-driving mutations, and unlike Europeans and East Asians, the most frequently mutated aldosterone-driver gene was CACNA1D . Determination of racial differences in the prevalence of aldosterone-driver gene mutations may facilitate the development of personalized medicines for patients with primary aldosteronism.

Characterization of exosomal cargo is of significant interest because this cargo can provide clues to exosome biogenesis, targeting, and cellular effects and may be a source of biomarkers for disease diagnosis, prognosis and response to treatment. With recent improvements in proteomics technologies, both qualitative and quantitative characterization of exosomal proteins is possible. Here we provide a brief review of exosome proteomics studies and provide detailed protocols for global qualitative, global quantitative, and targeted quantitative analysis of exosomal proteins. In addition, we provide an example application of a standard global quantitative analysis followed by validation via a targeted quantitative analysis of urine exosome samples from human patients. Advantages and limitations of each method are discussed as well as future directions for exosome proteomics analysis.