Biology subjects

Lenfers Turnes, B.

Publications and source records attributed to Lenfers Turnes, B..

3 recordsLinked to original sources

bioRxiv2026

Preclinical validation of AAV9-TECPR2 gene therapy in a novel knock-in model of TECPR2-related disorder

TECPR2-related disorder is a rare, autosomal recessive neurodevelopmental and neurodegenerative disease characterized by early-onset motor dysfunction, sensory- and autonomic neuropathy, and progressive neurological decline with early mortality. Currently, there are no effective treatments for individuals affected by this debilitating condition. To advance our understanding of disease mechanisms and explore therapeutic strategies, we developed and then characterized a knock-in (KI) mouse model carrying the human TECPR2 c.1319delC frameshift mutation. TECPR2-KI mice exhibit a subset of disease-relevant phenotypes, most prominently abnormal gait, along with reduced body weight and altered tactile sensitivity. We additionally observe a reduction in acoustic startle responses, consistent with dysfunction of brainstem-associated sensorimotor pathways. Histopathological analyses reveal progressive accumulation of axonal spheroids in the dorsal column nuclei, together with abnormalities in autophagy-related markers, features previously reported in individuals with TECPR2-related disorder. To assess the therapeutic potential of gene replacement, we delivered TECPR2 via intracisternal infusion of AAV9/TECPR2 in neonatal KI mice. Gene therapy restored mechanosensory function, normalized gait and startle responses, maintain autophagic homeostasis, and partially reduced axonal pathology. These findings demonstrate that TECPR2-associated deficits are not only replicable in this new mouse model but are also amenable to postnatal intervention. Our study introduces a genetically accurate murine model of TECPR2 deficiency, identifies brainstem-associated phenotypes, and provides preliminary evidence supporting the feasibility of AAV9-mediated TECPR2 gene delivery, establishing a foundation for future translational research in a currently untreatable disease. One-Sentence Key MessageTECPR2 deficiency disrupts brainstem sensory-motor circuits, impairing autophagy and tactile, gait, and startle function and is prevented by neonatal AAV9.

neuroscience↗

bioRxiv2026

Colitis-induced visceral pain recruits central neurotensin neurons that modulate colonic sensitivity

Inflammatory bowel disease produces debilitating visceral pain that remains a major clinical challenge. Notably, many patients experience persistent pain even after the inflammation resolves, indicating a sustained sensitization of central neural circuits that drives enduring pain. The brainstem parabrachial nucleus integrates interoceptive signals from the gastrointestinal tract to elicit both pain perception and affective responses. Using activity-dependent mapping and an RNAscope assay, we identified a neurotensin (NT)-expressing neuronal population in the lateral PBN (PBNL) that is selectively activated during dextran sulfate sodium-induced colitis. In vivo neural activity recordings demonstrate that PBNL NT neurons encode colon-derived nociceptive signals in an intensity-dependent manner. Silencing these neurons attenuates colonic reflexes evoked by luminal distension and normalizes aberrant gastrointestinal transit and nociceptive licking behavior in colitic mice. Pharmacological blockade of NT signaling alleviates colitis-associated hypersensitivity. These findings identify a central neural population that encodes visceral inflammation and regulates peripheral organ function, and pinpoints neurotensin as a promising therapeutic target to treat colitis-induced visceral pain.

neuroscience↗

bioRxiv2024

ARBEL: A Machine Learning Tool with Light-Based Image Analysis for Automatic Classification of 3D Pain Behaviors

A detailed analysis of pain-related behaviors in rodents is essential for exploring both the mechanisms of pain and evaluating analgesic efficacy. With the advancement of pose-estimation tools, automatic single-camera video animal behavior pipelines are growing and integrating rapidly into quantitative behavioral research. However, current existing algorithms do not consider an animals body-part contact intensity with- and distance from- the surface, a critical nuance for measuring certain pain-related responses like paw withdrawals ( flinching) with high accuracy and interpretability. Quantifying these bouts demands a high degree of attention to body part movement and currently relies on laborious and subjective human visual assessment. Here, we introduce a supervised machine learning algorithm, ARBEL: Automated Recognition of Behavior Enhanced with Light, that utilizes a combination of pose estimation together with a novel light-based analysis of body part pressure and distance from the surface, to automatically score pain-related behaviors in freely moving mice in three dimensions. We show the utility and accuracy of this algorithm for capturing a range of pain-related behavioral bouts using a bottom-up animal behavior platform, and its application for robust drug-screening. It allows for rapid objective pain behavior scoring over extended periods with high precision. This open-source algorithm is adaptable for detecting diverse behaviors across species and experimental platforms.

neuroscience↗