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Dysregulated pathways and potential biomarkers in dermatomyositis plasma-derived extracellular vesicles

ABSTRACT: Extracellular vesicles (EVs) have been implicated in dermatomyositis pathogenesis, but their protein content is not well characterized. We collected EVs from plasma, isolated them using sequential ultracentrifugation and size-exclusion chromatography, and analyzed their content by mass spectrometry. Protein biomarkers that distinguish patients with DM from controls were assessed using the random forest algorithm. Out of these, select proteins were identified as highly potential disease biomarkers, and their abundance was confirmed in a separate cohort using ELISA. Thirty-five proteins were differentially expressed and 67 were uniquely detected in the patient cohort. The following proteins displayed the most significant differential expression and were validated in a separate cohort of patients: The antioxidant enzyme glutathione peroxidase 3 (GPX3), and two complement proteins, Ficolin-2 (FCN2) and SERPING1 were less abundant in patients' EVs. Surfactant protein B (SFTPB) was expressed almost exclusively in patients with lung disease, suggesting that it may be a marker for pulmonary involvement. In summary, we identified GPX3, FCN2 and SERPING1 as biomarkers in DM. Our data underline the importance of oxidative stress and complement regulation in DM. SFTPB is a potential biomarker for pulmonary involvement.

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ORGANISM(S): Homo Sapiens (human)

TISSUE(S): Extracellular Vesicle

SUBMITTER: Hossein Fazelinia

LAB HEAD: Victoria P. Werth, MD

PROVIDER: PXD060890 | Pride | 2025-07-10

REPOSITORIES: Pride

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Project description:Diabetic kidney disease is the leading cause of end-stage kidney disease worldwide; however, the integration of high-dimensional trans-omics data to predict this diabetic complication is rare. We develop artificial intelligence (AI)-assisted models using machine learning algorithms to identify a biomarker signature that predisposes high risk patients with diabetes mellitus (DM) to diabetic kidney disease based on clinical information, untargeted metabolomics, targeted lipidomics and genome-wide single nucleotide polymorphism (SNP) datasets. This involves 618 individuals who are split into training and testing cohorts of 557 and 61 subjects, respectively. Three models are developed. In model 1, the top 20 features selected by AI give an accuracy rate of 0.83 and an area under curve (AUC) of 0.89 when differentiating DM and non-DM individuals. In model 2, among DM patients, a biomarker signature of 10 AI-selected features give an accuracy rate of 0.70 and an AUC of 0.76 when identifying subjects at high risk of renal impairment. In model 3, among non-DM patients, a biomarker signature of 25 AI-selected features give an accuracy rate of 0.82 and an AUC of 0.76 when pinpointing subjects at high risk of chronic kidney disease. In addition, the performance of the three models is rigorously verified using an independent validation cohort. Intriguingly, analysis of the protein-protein interaction network of the genes containing the identified SNPs (RPTOR, CLPTM1L, ALDH1L1, LY6D, PCDH9, B3GNTL1, CDS1, ADCYAP and FAM53A) reveals that, at the molecular level, there seems to be interconnected factors that have an effect on the progression of renal impairment among DM patients. In conclusion, our findings reveal the potential of employing machine learning algorithms to augment traditional methods and our findings suggest what molecular mechanisms may underlie the complex interaction between DM and chronic kidney disease. Moreover, the development of our AI-assisted models will improve precision when diagnosing renal impairment in predisposed patients, both DM and non-DM. Finally, a large prospective cohort study is needed to validate the clinical utility and mechanistic implications of these biomarker signatures.

			Action	DRS
	20220429_145506_VW1651_dDIA.sne	Other
	checksum.txt	Txt
	q220421_VW1651_DIA_01.raw	Raw
	q220421_VW1651_DIA_02.raw	Raw
	q220421_VW1651_DIA_03.raw	Raw

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Dysregulated pathways and potential biomarkers in dermatomyositis plasma-derived extracellular vesicles

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