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Guda, T.

Publications and source records attributed to Guda, T..

3 recordsLinked to original sources

Targeting Macrophage Connexin 43 Hemichannels Suppresses Synovial Inflammation and Osteoarthritis

Osteoarthritis (OA) is the most common chronic joint disease and a leading cause of disability worldwide, yet no disease-modifying therapies are available. Although OA has traditionally been viewed as a cartilage-centered degenerative disease, growing evidence suggests that synovial inflammation is a critical driver of disease progression. However, the upstream mechanisms and actionable targets that govern this inflammatory response remain poorly defined. Connexin 43 (Cx43), a key regulator of musculoskeletal homeostasis that forms both gap junctions and hemichannels (HCs), is upregulated in osteoarthritic cartilage and synovium, but the specific contribution of Cx43 channels remains unclear. Here, we show that selective inhibition of Cx43 HCs attenuates OA progression in a murine post-traumatic OA model. Using adeno-associated virus (AAV)-mediated gene deletion, we found that Cx43 knockdown in joint tissues reduced cartilage erosion, synovial hyperplasia, subchondral bone sclerosis, and OA-associated pain. To dissect HC-specific functions, we developed a monoclonal antibody, Cx43(M1), that blocks Cx43 HCs without affecting junctional communication. Cx43(M1) preserved cartilage integrity, suppressed synovial inflammation, and alleviated pain behaviors, even when treatment was initiated after established cartilage damage. Mechanistically, the antibody preferentially localized to synovial macrophages, where it inhibited HC activity, reduced ATP and PGE2 release, and suppressed pro-inflammatory gene expression. Macrophage-specific Cx43 deletion recapitulated these protective effects, and Cx43 HC blockade in primary human OA synovial macrophages similarly inhibited inflammatory and catabolic responses. These findings identify synovial macrophage Cx43 HCs as upstream drivers of OA and support a paradigm shift from a predominantly chondrocyte-centered view toward synovial macrophage directed intervention.

immunology↗

Machine learning of honey bee olfactory behavior identifies repellent odorants in free flying bees in the field

Preventing beneficial insects like honey bees (Apis mellifera) from contacting pesticides on crops using odorants could counter current pollinator declines. However, the discovery of behaviorally aversive odorants is impeded by the complexity of the honey bee olfactory system where >180 olfactory receptors detect volatiles and generate valence. To solve this systems-level challenge we generated a machine-learning model to predict aversive valence from chemical structure using published olfactory behavior data in honey bees. We refine the predictive model by generating species level behavioral data for honey bees and Drosophila on an initial set of novel predicted repellents. The improved second computational model was then used to screen a chemical space of >50 million compounds and identify >130 repellent candidates. Behavioral validation using honey bees in the laboratory show a high predictive success. Additional testing of the top seven candidates using freely foraging honey bees in a field assay confirmed strong repellency, thus predicting a high probability to repel foraging bees from pesticide-treated crops. Machine learning, with iterative testing and modeling therefore provides a powerful approach for rational discovery of aversive volatiles for control of insects for which limited data is available. SIGNIFICANCE STATEMENTWith honey bee populations declining partly due to pesticide exposure, we aimed to find smells that could keep bees away from pesticide-treated crops. We overcome challenges studying the complex bee olfactory system by developing an AI model trained on existing bee behavior data to predict chemicals bees would find aversive. The predictive model screened millions of compounds, identifying more than 130 potential repellents. Behavior testing in the lab and in field tests confirmed the effectiveness of the bee repellents. This method could lead to bee-safe pesticide formulations, potentially protecting pollinator populations while maintaining crop protection.

animal behavior and cognition↗

Circadian regulation of light-evoked attraction/avoidance in day- vs. night-biting mosquitoes

Mosquitoes pose widespread threats to humans and other animals as disease vectors. Day- vs. night-biting mosquitoes occupy distinct time-of-day niches and exhibit very different innate temporal attraction/avoidance behavioral responses to light, yet little is known about their circuit or molecular mechanisms. Day-biting diurnal mosquitoes Aedes aegypti are attracted to light during the day regardless of spectra. In contrast, night-biting nocturnal mosquitoes Anopheles coluzzii avoid short, but not long wavelength light. Attraction/avoidance behavioral responses to light in both species change with time-of-day and show distinct sex and circuit differences. The basis of diurnal versus nocturnal behavior is driven by clock timing, which cycle anti-phase between day-biting versus night-biting mosquito species. Disruption of the circadian molecular clock severely interferes with light-evoked attraction/avoidance behavior in mosquitoes. In summary, attraction/avoidance mosquito behaviors are circadian and light regulated, which may be applied towards species specific control of harmful mosquitoes.

neuroscience↗