Biology subjects

Merkel, P. A.

Publications and source records attributed to Merkel, P. A..

3 recordsLinked to original sources

bioRxiv2019

Preferential Binding of Anti-Neutrophil Cytoplasmic Antibodies to an Unexpected Epitope of a Chimeric Proteinase 3 Mutant

ObjectiveProteinase 3 (PR3) is the major antigen for anti-neutrophil cytoplasmic antibodies (ANCAs) in the systemic autoimmune vasculitis, granulomatosis with polyangiitis (GPA). PR3 anti-neutrophil cytoplasmic antibodies (PR3-ANCAs) recognize different epitopes on PR3. We aimed to study the effect of mutations on PR3 antigenicity. MethodsThe recombinant PR3 variants, iPR3 which is clinically used to detect PR3-ANCAs and iHm5 which contains three point mutations in Epitope 1 and 5 generated for epitope mapping studies, immunoassays and serum samples from patients enrolled in ANCA-associated vasculitis (AAV) clinical trials were used to screen the differential PR3-ANCA binding. Selective binding was determined by inhibition experiments. ResultsRather than a reduced binding of PR3-ANCAs to iHm5, we found substantially increased binding of the majority of PR3-ANCAs to iHm5 compared with iPR3. A monoclonal ANCA (moANCA518) from a patient with GPA was found to selectively bind to iHm5 within the mutation-free Epitope 3 and distant from the point mutations of iHm5 contained in Epitope 1 and 5. Binding of iPR3 to monoclonal antibody MCPR3-2 also induced recognition by moANCA518. ConclusionThe preferential binding of PR3-ANCAs from patients like the selective binding of moANCA518 to iHm5 is conferred by increased antigenicity of Epitope 3 on iHm5. This can also be induced on iPR3 when it is captured by monoclonal antibody MCPR-2. This previously unrecognized characteristic of PR3-ANCA interactions with its target antigen has implications for studying antibody-mediated autoimmune diseases, understanding of variable performance characteristics of immunoassays and design of potential novel treatment approaches.

immunology↗

bioRxiv2018

Inflammatory and JAK-STAT Pathways as Shared Molecular Targets for ANCA-Associated Vasculitis and Nephrotic Syndrome

BackgroundGlomerular diseases of the kidney are presently differentiated, diagnosed and treated according to conventional clinical or structural features. While etiologically diverse, these diseases share common clinical features including but not limited to reduced glomerular filtration rate, increased serum creatinine and proteinuria suggesting shared pathogenic mechanisms across diseases. Renal biopsies from patients with nephrotic syndrome (NS) or ANCA-associated vasculitis (AAV) were evaluated for molecular signals cutting across conventional disease categories as candidates for therapeutic targets.\n\nMethodsRenal biopsies were obtained from patients with NS (minimal change disease, focal segmental glomerulosclerosis, or membranous nephropathy) (n=187) or AAV (granulomatosis with polyangiitis or microscopic polyangiitis) (n=80) from the Nephrotic Syndrome Study Network (NEPTUNE) and the European Renal cDNA Bank. Transcriptional profiles were assessed for shared disease mechanisms.\n\nResultsIn the discovery cohort, 10-25% transcripts were differentially regulated versus healthy controls in both NS and AAV, >500 transcripts were shared across diseases. The majority of shared transcripts (60-77%) were validated in independent samples. Therapeutically targetable networks were identified, including inflammatory JAK-STAT signaling. STAT1 eQTLs were identified and STAT1 expression associated with GFR-based outcome. A transcriptional STAT1 activity score was generated from STAT1-regulated target genes which correlated with CXCL10 (p<0.001), a JAK-STAT biomarker, predictors of CKD progression, interstitial fibrosis (r=0.41, p<0.001), and urinary EGF (r=-0.51, p<0.001).\n\nConclusionAAV and NS caused from histopathologically distinct disease categories share common intra-renal molecular pathways cutting across conventional disease classifications. This approach provides a starting point for de novo drug development, and repurposing efforts in rare kidney diseases.

systems biology↗

bioRxiv2018

MultiPLIER: a transfer learning framework reveals systemic features of rare autoimmune disease

Unsupervised machine learning methods provide a promising means to analyze and interpret large datasets. However, most gene expression datasets generated by individual researchers remain too small to fully benefit from these methods. In the case of rare diseases, there may be too few cases available, even when multiple studies are combined. We trained a Pathway Level Information ExtractoR (PLIER) model using on a large public data compendium comprised of multiple experiments, tissues, and biological conditions. We then transferred the model to small rare disease datasets in an approach we term MultiPLIER. Models constructed from large, diverse public data i) included features that aligned well to important biological factors; ii) were more comprehensive than those constructed from individual datasets or conditions; iii) transferred to rare disease datasets where the models describe biological processes related to disease severity more effectively than models trained on specifically those datasets.

bioinformatics↗