6 research outputs found
Norepinephrine transporter variant A457P knock-in mice display key features of human postural orthostatic tachycardia syndrome
SUMMARY
Postural orthostatic tachycardia syndrome (POTS) is a common autonomic disorder of largely unknown etiology that presents with sustained tachycardia on standing, syncope and elevated norepinephrine spillover. Some individuals with POTS experience anxiety, depression and cognitive dysfunction. Previously, we identified a mutation, A457P, in the norepinephrine (NE; also known as noradrenaline) transporter (NET; encoded by SLC6A2) in POTS patients. NET is expressed at presynaptic sites in NE neurons and plays a crucial role in regulating NE signaling and homeostasis through NE reuptake into noradrenergic nerve terminals. Our in vitro studies demonstrate that A457P reduces both NET surface trafficking and NE transport and exerts a dominant-negative impact on wild-type NET proteins. Here we report the generation and characterization of NET A457P mice, demonstrating the ability of A457P to drive the POTS phenotype and behaviors that are consistent with reported comorbidities. Mice carrying one A457P allele (NET+/P) exhibited reduced brain and sympathetic NE transport levels compared with wild-type (NET+/+) mice, whereas transport activity in mice carrying two A457P alleles (NETP/P) was nearly abolished. NET+/P and NETP/P mice exhibited elevations in plasma and urine NE levels, reduced 3,4-dihydroxyphenylglycol (DHPG), and reduced DHPG:NE ratios, consistent with a decrease in sympathetic nerve terminal NE reuptake. Radiotelemetry in unanesthetized mice revealed tachycardia in NET+/P mice without a change in blood pressure or baroreceptor sensitivity, consistent with studies of human NET A457P carriers. NET+/P mice also demonstrated behavioral changes consistent with CNS NET dysfunction. Our findings support that NET dysfunction is sufficient to produce a POTS phenotype and introduces the first genetic model suitable for more detailed mechanistic studies of the disorder and its comorbidities
Walk before you run: feasibility challenges and lessons learned from the PROCLAIM Study, a multicenter randomized controlled trial of misoprostol for prevention of recurrent C. difficile during COVID-19
We analyzed our challenging experience with a randomized controlled trial of misoprostol for prevention of recurrent C. difficile. Despite careful prescreening and thoughtful protocol modifications to facilitate enrollment, we closed the study early after enrolling just 7 participants over 3 years. We share lessons learned, noting the importance of feasibility studies, inclusion of biomarker outcomes, and dissemination of such findings to inform future research design and implementation successes
Human and Machine Intelligence Together Drive Drug Repurposing in Rare Diseases
Repurposing is an increasingly attractive method within the field of drug development for its efficiency at identifying new therapeutic opportunities among approved drugs at greatly reduced cost and time of more traditional methods. Repurposing has generated significant interest in the realm of rare disease treatment as an innovative strategy for finding ways to manage these complex conditions. The selection of which agents should be tested in which conditions is currently informed by both human and machine discovery, yet the appropriate balance between these approaches, including the role of artificial intelligence (AI), remains a significant topic of discussion in drug discovery for rare diseases and other conditions. Our drug repurposing team at Vanderbilt University Medical Center synergizes machine learning techniques like phenome-wide association study-a powerful regression method for generating hypotheses about new indications for an approved drug-with the knowledge and creativity of scientific, legal, and clinical domain experts. While our computational approaches generate drug repurposing hits with a high probability of success in a clinical trial, human knowledge remains essential for the hypothesis creation, interpretation, "go-no go" decisions with which machines continue to struggle. Here, we reflect on our experience synergizing AI and human knowledge toward realizable patient outcomes, providing case studies from our portfolio that inform how we balance human knowledge and machine intelligence for drug repurposing in rare disease