Biology subjects

Simon, A. A.

Publications and source records attributed to Simon, A. A..

5 recordsLinked to original sources

bioRxiv2026

MS4A8B regulates Orai1-dependent Ca2+ influx to control motile cilia function in human nasal epithelial cells

Motile cilia demonstrate coordinated beating to propel fluids across epithelial tissues, and changes to their beating frequency are largely regulated by intracellular second messengers including Ca2+. In the airway epithelium, ciliary beating is essential to mucociliary clearance. Mucociliary clearance involves trapping inhaled pathogens and irritants in sticky mucus lining the airways for motile cilia to sweep away contaminated mucus, preventing infection and reducing general airway inflammation. Many chronic respiratory diseases, including chronic rhinosinusitis and asthma, are characterized by an acquired ciliary dysfunction. Despite the importance of Ca2+ signaling in cilia physiology, the identity and molecular mechanisms governing localized ciliary Ca2+ transport remain poorly understood. MS4A8B is an uncharacterized cilia-localized transmembrane protein. Other MS4A homologs have been indirectly linked to Ca2+ signaling via uncharacterized mechanisms. Using primary human nasal epithelial cells differentiated at air-liquid interface, we demonstrated that MS4A8B regulates motile cilia function. MS4A8B knockdown impairs ciliary beating and impacts cilia structure. Live-cell imaging combined with genetic analysis revealed that MS4A8B potentiates Orai1-mediated Ca2+ influx. Co-immunoprecipitation and FRET microscopy in ectopic expression systems demonstrated that MS4A8B interacts with Orai1 channels. Orai1 was further identified to reside in motile cilia of primary human nasal epithelial cells, allowing ciliary beat frequency to be stimulated by Orai1 agonists including arachidonic acid. MS4A8B functional coupling with Orai1 acts as an autonomous cilia signaling network. Targeting this compartmentalized signaling pathway offers a novel therapeutic approach to restore or enhance mucociliary clearance in airway diseases.

physiology↗

bioRxiv2026

Plantar flexor muscle size, composition and strength and the redistribution of joint work in gait of healthy older adults

BackgroundOlder adults exhibit hallmark changes in gait mechanics that may contribute to mobility loss. These changes are suggested to be precipitated by decreased mechanical output from the ankle plantar flexor muscles. However, the extent of age-related changes within individual triceps surae muscles and the consequences for plantar flexor specific torque (torque per unit muscle) and walking mechanics remains unclear. The study aims were to quantify age-related differences in triceps surae muscle cross-sectional area (CSA), fat fraction and peak absolute and specific plantar flexion torque, and to evaluate their relationships with positive hip and ankle work distribution during walking. MethodsEleven younger (36.2 +/-3.4 years, 5 female) and 12 older (73.3+/-2.8 years, 6 female) adults completed dynamometry, magnetic resonance fat-water imaging of the shank, and overground gait analysis at a prescribed (1.2 m/s) and fast walking speed. ResultsOlder adults had a smaller fat-free muscle CSA for the soleus and medial gastrocnemius, resulting in {bsim}25% smaller triceps surae CSA. There was no difference by age in fat fraction for any muscle. Peak plantar flexor torque was lower in older and proportional to the differences in cross-sectional area, thus specific torque did not differ by age. Older adults generated greater positive hip work at both speeds and less positive ankle work at the fast speed leading to a greater redistribution ratio compared to younger. However, no significant correlations were found between the redistribution ratio and triceps surae muscle morphology or plantar flexion absolute or specific torque in older adults. ConclusionsThese results suggest that triceps surae muscle morphology and function may not be a primary source of age-related changes in gait mechanics in healthy older adults.

physiology↗

bioRxiv2024

Identification of early-stage liver fibrosis by modifications in the interstitial space diffusive microenvironment using fluorescent single-walled carbon nanotubes

During liver fibrosis, recurrent hepatic injuries lead to accumulation of collagen and other extracellular matrix components in the interstitial space, ultimately disrupting liver functions. Early stages of liver fibrosis may be reversible, but opportunities for diagnosis at these stages are currently limited. Here, we show that the alterations of the interstitial space associated with fibrosis can be probed by tracking individual fluorescent single-walled carbon nanotubes (SWCNTs) diffusing in that space. In a mouse model of early liver fibrosis, we find that nanotubes generally explore elongated areas, whose length decrease as the disease progresses even in regions where histopathological examination does not reveal fibrosis yet. Furthermore, this decrease in nanotube mobility is a purely geometrical effect, as the instantaneous nanotube diffusivity stays unmodified. This work establishes the promise of SWCNTs both for the diagnosis of liver fibrosis at an early stage, and for more in-depth studies of the biophysical effects of the disease.

cancer biology↗

bioRxiv2018

Identifying neural predictors of response to cortical or striatal deep brain stimulation in a rodent model of alcohol drinking: Towards developing individualized therapies for alcohol use disorders

Individuals differ in their vulnerability to develop alcohol dependence that are determined by innate and environmental factors. The corticostriatal circuit is heavily involved in the development of alcohol dependence and may contain neural information regarding vulnerability to drink excessively. In the current experiment, we hypothesized that we could characterize high and low alcohol-drinking rats (HD and LD, respectively) based on corticostriatal oscillations, and that these subgroups would differentially respond to corticostriatal brain stimulation. Rats were trained to drink 10% alcohol in a limited access paradigm. In separate sessions, local field potentials (LFPs) were recorded from the nucleus accumbens shell (NAcSh) and medial prefrontal cortex (mPFC) of male Sprague-Dawley rats (n=13). Based on training alcohol consumption levels, we classified rats using a median split as HD or LD. Then, using machine-learning, we built predictive models to classify rats as HD or LD by corticostriatal LFPs and compared the model performance from real data to the performance of models built on data permutations. Additionally, we explored the impact of NAcSh or mPFC stimulation on alcohol consumption in HD vs. LD. Corticostriatal LFPs were able predict HD vs. LD group classification with greater accuracy than expected by chance (>80% accuracy). Additionally, NAcSh stimulation significantly reduced alcohol consumption in HD, but not LD (p<0.05), while mPFC stimulation did not alter drinking behavior in either HD or LD (p>0.05). These data collectively show that the corticostriatal circuit is differentially involved in regulating alcohol intake in HD vs. LD rats, and suggests that corticostriatal activity may have the potential to predict a vulnerability to develop alcohol dependence in a clinical population.

neuroscience↗

bioRxiv2018

Machine learning based classification of deep brain stimulation outcomes in a rat model of binge eating using ventral striatal oscillations.

Neuromodulation-based interventions continue to be evaluated across an array of appetitive disorders but broader implementation of these approaches remains limited due to variable treatment outcomes. We hypothesize that individual variation in treatment outcomes may be linked to differences in the networks underlying these disorders. Here, Sprague-Dawley rats received deep brain stimulation separately within each nucleus accumbens (NAc) sub-region (core and shell) using a within-animal crossover design in a rat model of binge eating. Significant reductions in binge size were observed with stimulation of either target but with significant variation in effectiveness across individuals. When features of local field potentials (LFPs) recorded from the NAc were used as predictors of the pre-defined stimulation outcomes (response or non-response) from each rat using a machine-learning approach (lasso), stimulation outcomes could be predicted with greater accuracy than expected by chance (effect sizes: core = 1.13, shell = 1.05). Further, these LFP features could be used to identify the best stimulation target for each animal (core vs. shell) with an effect size = 0.96. These data suggest that individual differences in underlying network activity may contribute to the variable outcomes of circuit based interventions and that measures of network activity have the potential to individually guide the selection of an optimal stimulation target and improve overall treatment response rates.

neuroscience↗