Biology subjects

Rogers, M. A.

Publications and source records attributed to Rogers, M. A..

4 recordsLinked to original sources

bioRxiv2021

PDGFR dimer-specific activation, trafficking and downstream signaling dynamics

Signaling through the platelet-derived growth factor receptors (PDGFRs) plays a critical role in multiple cellular processes during development. The two PDGFRs, PDGFR and PDGFR{beta}, dimerize to form homodimers and/or heterodimers. Here, we overcome previous limitations in studying PDGFR dimer-specific dynamics by generating cell lines stably expressing C-terminal fusions of each PDGFR with bimolecular fluorescence complementation (BiFC) fragments corresponding to the N-terminal or C-terminal regions of the Venus fluorescent protein. We find that PDGFR{beta} receptors homodimerize more quickly than PDGFR receptors in response to PDGF ligand, with increased levels of autophosphorylation. Further, we demonstrate that PDGFR homodimers are trafficked and degraded more quickly, while PDGFR{beta} homodimers are more likely to be recycled back to the cell membrane. We show that PDGFR{beta} homodimer activation results in a greater amplitude of phospho-ERK1/2 and phospho-AKT signaling, as well as increased proliferation and migration. Collectively, our findings provide significant insight into how biological specificity is introduced to generate unique responses downstream of PDGFR engagement. SummaryThe authors utilize a novel bimolecular fluorescence complementation approach to investigate PDGFR homodimer-specific dynamics. They uncover differences in the timing and extent of receptor dimerization, activation and trafficking, which lead to changes in downstream signaling and cellular activity.

cell biology↗

bioRxiv2020

Acat1 gene KO restores TGN cholesterol deficiency in mutant NPC1 cells and expands mutant Npc1 mouse lifespan

Niemann-Pick type C (NPC) is a neurological disorder with no cure. NPC proteins deliver cholesterol from endosomes to other compartments including trans-Golgi network (TGN) and endoplasmic reticulum (ER). Acyl-coenzyme A:cholesterol acyltransferase 1 (ACAT1) is a resident ER enzyme that converts cholesterol to cholesteryl esters for storage. Here, we report the surprising finding that in a mutant Npc1 mice, Acat1-deficiency delayed the onset of weight loss and declining motor skill, prolonged lifespan, delayed Purkinje neuron death, and improved hepatosplenic pathology. Furthermore, syntaxin 6, a cholesterol-binding t-SNARE normally localized to TGN, is mislocalized in mutant NPC cells. However, upon ACAT1 inhibition this mislocalization is corrected, and increase the level of a few proteins further downstream. Our results imply that ACAT1 inhibition diverts a cholesterol storage pool in a way that replenished the low cholesterol level in NPC-deficient TGN. Taking together, we identify ACAT1 inhibition as a potential therapeutic target for NPC treatment.

neuroscience↗

bioRxiv2020

Conserved and Divergent Modulation of Calcification in Atherosclerosis and Aortic Valve Disease by Tissue Extracellular Vesicles

BackgroundFewer than 50% of patients develop calcification of both atherosclerotic plaques and aortic valves, implying differential pathogenesis. While circulating extracellular vesicles (EVs) act as biomarkers of cardiovascular diseases, tissue-entrapped EVs associate with early mineralization, but their contents, function, and contributions to disease remain unknown. ResultsGlobal proteomics of human carotid artery endarterectomies and calcified aortic valves from a total of 27 donors/patients revealed significant over-representation of proteins with vesicle-associated pathways/ontologies common to both diseases. We exploited enzymatic digestion, serial (ultra)centrifugation and OptiPrep density-gradient separation to isolate EV populations from diseased arteries and valves. Mass spectrometry found 22 EV marker proteins to be highly enriched in the four least-dense OptiPrep fractions while extracellular matrix proteins predominated in denser fractions, as confirmed by CD63 immunogold electron microscopy and nanoparticle tracking analysis. Proteomics and miRNA-sequencing of OptiPrep-enriched tissue EVs quantified 1,104 proteins and 123 miR cargoes linked to 5,182 target genes. Pathway networks of proteins and miR targets common to artery and valve tissue EVs revealed a shared regulation of Rho GTPase and MAPK intracellular signaling cascades. 179 proteins and 5 miRs were significantly altered between artery and valve EVs; multi-omics integration determined that EVs differentially modulated cellular contraction and p53-mediated transcriptional regulation in diseased vascular vs. valvular tissue. ConclusionsOur findings delineate a strategy to isolate, purify, and study protein and RNA cargoes from EVs entrapped in fibrocalcific tissues. Multi-omics and network approaches implicated tissue-resident EVs in human cardiovascular disease.

systems biology↗

bioRxiv2017

The effect of treatment on the microbiota of patients diagnosed with colonic lesions

BackgroundColorectal cancer is a worldwide health problem. Despite growing evidence that members of the gut microbiota can drive tumorigenesis, little is known about what happens to it after treatment for an adenoma or carcinoma. This study tested the hypothesis that treatment for adenoma or carcinoma alters the abundance of bacterial populations associated with disease to those associated with a normal colon. We tested this hypothesis by sequencing the 16S rRNA genes in the feces of 67 individuals before and after treatment for adenoma (N = 22), advanced adenoma (N = 19), and carcinoma (N = 26).\n\nResultsThere were small changes to the bacterial community associated with adenoma or advanced adenoma and large changes associated with carcinoma. The communities from patients with carcinomas changed significantly more than those with adenoma following treatment (P-value < 0.001). Although treatment was associated with intrapersonal changes, the change in the abundance of individual OTUs in response to treatment was not consistent within diagnosis groups (P-value > 0.05). Because the distribution of OTUs across patients and diagnosis groups was irregular, we used the Random Forest machine learning algorithm to identify groups of OTUs that could be used to classify pre and post-treatment samples for each of the diagnosis groups. Although the three models could differentiate between the pre and post-treatment samples, there was little overlap between the OTUs that were indicative of each treatment. To determine whether individuals who underwent treatment were more likely to have OTUs associated with normal colons we used a larger cohort that contained individuals with normal colons and those with adenomas, advanced adenomas, and carcinomas. We again built Random Forest models and measured the change in the positive probability of having one of the three diagnoses to assess whether the post-treatment samples received the same classification as the pre-treatment samples. Samples from patients who had carcinomas changed towards a microbial milieu that resembles the normal colon after treatment (P-value < 0.05). Finally, we were unable to detect any significant differences in the microbiota of individuals treated with surgery alone and those treated with chemotherapy or chemotherapy and radiation (P-value > 0.05).\n\nConclusionsBy better understanding the response of the microbiota to treatment for adenomas and carcinomas, it is likely that biomarkers will eventually be validated that can be used to quantify the risk of recurrence and the likelihood of survival. Although it was difficult to identify significant differences between pre and post-treatment samples from patients with adenoma and advanced adenoma, this was not the case for carcinomas. Not only were there large changes in pre versus post-treatment samples for those with carcinoma, but these changes were towards a more normal microbiota.

microbiology↗