RESUMEN
Deposition of misfolded α-synuclein (αsyn) in the enteric nervous system (ENS) is found in multiple neurodegenerative diseases. It is hypothesized that ENS synucleinopathy contributes to both the pathogenesis and non-motor morbidity in Parkinson's Disease (PD), but the cellular and molecular mechanisms that shape enteric histopathology and dysfunction are poorly understood. Here, we demonstrate that ENS-resident macrophages, which play a critical role in maintaining ENS homeostasis, initially respond to enteric neuronal αsyn pathology by upregulating machinery for complement-mediated engulfment. Pharmacologic depletion of ENS-macrophages or genetic deletion of C1q enhanced enteric neuropathology. Conversely, C1q deletion ameliorated gut dysfunction, indicating that complement partially mediates αsyn-induced gut dysfunction. Internalization of αsyn led to increased endo-lysosomal stress that resulted in macrophage exhaustion and temporally correlated with the progression of ENS pathology. These novel findings highlight the importance of enteric neuron-macrophage interactions in removing toxic protein aggregates that putatively shape the earliest stages of PD in the periphery.
RESUMEN
Proper normalization of RT-qPCR data is pivotal to the interpretation of results and accuracy of scientific conclusions. Though different approaches may be taken, normalization against multiple reference genes is now standard practice. Genes traditionally used and deemed constitutively expressed have demonstrated variability in expression under different experimental conditions, necessitating the proper validation of reference genes prior to utilization. Considering the wide use of fibroblasts in research and scientific applications, it is imperative that suitable reference genes for fibroblasts of different animal origins and conditions be elucidated. Previous studies on bovine fibroblasts have tested limited genes and/or samples. Herein, we present an extensive study investigating the expression stability of 16 candidate reference genes across 7 untreated bovine fibroblast cell lines subjected to controlled conditions. Data were analysed using various statistical tools and algorithms, including geNorm, NormFinder, BestKeeper, and RefFinder. A combined use of GUSB and RPL13A was determined to be the best approach for data normalization in untreated bovine fibroblasts.