Loss of sensory hair cells leads to deafness and balance deficiencies. In contrast to mammalian hair cells, zebrafish ear and lateral line hair cells regenerate from poorly characterized support cells. Equally ill-defined is the gene regulatory network underlying the progression of support cells to differentiated hair cells. scRNA-Seq of lateral line organs uncovered five different support cell types, including quiescent and activated stem cells. Ordering of support cells along a developmental trajectory identified self-renewing cells and genes required for hair cell differentiation. scRNA-Seq analyses of fgf3 mutants, in which hair cell regeneration is increased, demonstrates that Fgf and Notch signaling inhibit proliferation of support cells in parallel by inhibiting Wnt signaling. Our scRNA-Seq analyses set the foundation for mechanistic studies of sensory organ regeneration and is crucial for identifying factors to trigger hair cell production in mammals. The data is searchable and publicly accessible via a web-based interface.

MicroRNAs (miRNAs) are important post-transcriptional regulators of gene expression and are implicated in the etiology of several neuropsychiatric disorders, including substance use disorders (SUDs). Using in silico genome-wide sequence analyses, we identified miR-495 as a miRNA whose predicted targets are significantly enriched in the Knowledgebase of Addiction-Related Genes (ARG) database (KARG; http://karg.cbi.pku.edu.cn). This small non-coding RNA is also highly expressed within the nucleus accumbens (NAc), a pivotal brain region underlying reward and motivation. Using luciferase reporter assays, we found that miR-495 directly targeted the 3’UTRs of Bdnf, Camk2a, and Arc. Furthermore, we measured miR-495 expression in response to acute cocaine in mice and found that it is downregulated rapidly and selectively in the NAc, along with concomitant increases in ARG expression. Lentiviral-mediated miR-495 overexpression in the NAc shell (NAcsh) not only reversed these cocaine-induced effects, but also downregulated multiple ARG mRNAs in specific SUD-related biological pathways, including those that regulate synaptic plasticity. miR-495 expression was also downregulated in the NAcsh of rats following cocaine self-administration. Most importantly, we found that NAcsh miR-495 overexpression suppressed the motivation to self-administer and seek cocaine across progressive ratio, extinction, and reinstatement testing, but had no effect on food reinforcement, suggesting that miR-495 selectively affects addiction-related behaviors. Overall, our in silico search for post-transcriptional regulators identified miR-495 as a novel regulator of multiple ARGs that play a role in modulating motivation for cocaine.

Hypothesis: Menière's disease microRNA (miRNA) profiles are unique and are reflected in the perilymph and serum of patients.Background: Development of effective biomarkers for Menière's disease are needed. miRNAs are small RNA sequences that downregulate mRNA translation and play a significant role in a variety of disease states, ultimately making them a promising biomarker. miRNAs can be readily isolated from human inner ear perilymph and serum, and may exhibit disease-specific profiles.Methods: Perilymph sampling was performed in 10 patients undergoing surgery; 5 patients with Meniere's disease and 5 patients with otosclerosis serving as controls. miRNAs were isolated from the serum of 5 patients with bilateral Menière's disease and compared to 5 healthy age-matched controls. For evaluation of miRNAs an Agilent miRNA gene chip was used. Analysis of miRNA expression was carried out using Qlucore and Ingenuitey Pathway Analysis software. Promising miRNAs biomarkers were validated using qPCR.Results: In the perilymph of patients with Menière's disease, we identified 16 differentially expressed miRNAs that are predicted to regulate over 220 different cochlear genes. Six miRNAs are postulated to regulate aquaporin expression and twelve miRNAs are postulated to regulate a variety of inflammatory and autoimmune pathways. When comparing perilymph with serum samples, miRNA-1299 and−1270 were differentially expressed in both the perilymph and serum of Ménière's patients compared to controls. Further analysis using qPCR confirmed miRNA-1299 is downregulated over 3-fold in Meniere's disease serum samples compared to controls.Conclusions: Patients with Ménière's disease exhibit distinct miRNA expression profiles within both the perilymph and serum. The altered perilymph miRNAs identified can be linked to postulated Ménière's disease pathways and may serve as biomarkers. miRNA-1299 was validated to be downregulated in both the serum and perilymph of Menière's patients.

Objective Diagnosis and treatment of Ménière’s disease remains a significant challenge because of our inability to understand what is occurring on a molecular level. MicroRNA (miRNA) perilymph profiling is a safe methodology and may serve as a “liquid biopsy” equivalent. We used machine learning (ML) to evaluate miRNA expression profiles of various inner ear pathologies to predict diagnosis of Ménière’s disease. Study Design Prospective cohort study. Setting Tertiary academic hospital. Subjects and Methods Perilymph was collected during labyrinthectomy (Ménière’s disease, n = 5), stapedotomy (otosclerosis, n = 5), and cochlear implantation (sensorineural hearing loss [SNHL], n = 9). miRNA was isolated and analyzed with the Affymetrix miRNA 4.0 array. Various ML classification models were evaluated with an 80/20 train/test split and cross-validation. Permutation feature importance was performed to understand miRNAs that were critical to the classification models. Results In terms of miRNA profiles for conductive hearing loss versus Ménière’s, 4 models were able to differentiate and identify the 2 disease classes with 100% accuracy. The top-performing models used the same miRNAs in their decision classification model but with different weighted values. All candidate models for SNHL versus Ménière’s performed significantly worse, with the best models achieving 66% accuracy. Ménière’s models showed unique features distinct from SNHL. Conclusions We can use ML to build Ménière’s-specific prediction models using miRNA profile alone. However, ML models were less accurate in predicting SNHL from Ménière’s, likely from overlap of miRNA biomarkers. The power of this technique is that it identifies biomarkers without knowledge of the pathophysiology, potentially leading to identification of novel biomarkers and diagnostic tests.

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