Single-cell transcriptomics was used to profile cells of the normal murine middle ear. Clustering analysis of 6770 transcriptomes identified 17 cell clusters corresponding to distinct cell types: five epithelial, three stromal, three lymphocyte, two monocyte, two endothelial, one pericyte and one melanocyte cluster. Within some clusters, cell subtypes were identified. While many corresponded to those cell types known from prior studies, several novel types or subtypes were noted. The results indicate unexpected cellular diversity within the resting middle ear mucosa. The resolution of uncomplicated, acute, otitis media is too rapid for cognate immunity to play a major role. Thus innate immunity is likely responsible for normal recovery from middle ear infection. The need for rapid response to pathogens suggests that innate immune genes may be constitutively expressed by middle ear cells. We therefore assessed expression of innate immune genes across all cell types, to evaluate potential for rapid responses to middle ear infection. Resident monocytes/macrophages expressed the most such genes, including pathogen receptors, cytokines, chemokines and chemokine receptors. Other cell types displayed distinct innate immune gene profiles. Epithelial cells preferentially expressed pathogen receptors, bactericidal peptides and mucins. Stromal and endothelial cells expressed pathogen receptors. Pericytes expressed pro-inflammatory cytokines. Lymphocytes expressed chemokine receptors and antimicrobials. The results suggest that tissue monocytes, including macrophages, are the master regulators of the immediate middle ear response to infection, but that virtually all cell types act in concert to mount a defense against pathogens.

Kisspeptin controls reproduction by stimulating GnRH neurons via its receptor Kiss1r. Kiss1r is also expressed other brain areas and in peripheral tissues, suggesting additional non-reproductive roles. We recently determined that Kiss1r knockout (KO) mice develop an obese and diabetic phenotype. Here, we investigated whether Kiss1r KOs develop this metabolic phenotype due to alterations in the expression of metabolic genes involved in the appetite regulating system of the hypothalamus, including neuropeptide Y (Npy) and pro-opiomelanocortin (Pomc), as well as leptin receptor (Lepr), ghrelin receptor (Ghsr), and melanocortin receptor 3 and 4 (Mc3r, Mc4r). Body weights, leptin levels, and hypothalamic gene expression were measured in both gonad-intact and gonadectomised (GNX) mice at 8 and 20 weeks of age, fed either normal chow or a high-fat diet. We detected significant increases in Pomc expression in gonad-intact Kiss1r KOs at 8 weeks and 20 weeks, but no alterations in the other metabolic-related genes. However, the Pomc increases appeared to reflect genotype differences in circulating sex steroids, because GNX wildtype (WT) and Kiss1r KOs exhibited similar Pomc levels, along with similar Npy levels. The altered Pomc gene expression in gonad-intact Kiss1r KOs is consistent with previous reports of reduced food intake in these mice and may serve to increase the anorexigenic drive, perhaps compensating for the obese state. However, the surprising overall lack of changes in any of the hypothalamic metabolic genes in GNX KOs suggests that the aetiology of obesity in the absence of kisspeptin signalling may reflect peripheral rather than central metabolic impairments.

Highlights d Inflammation-dependent APOBEC3C C-to-T deaminase fuels human HSPC expansion d C-to-T mutagenesis increases during human MPN pre-LSC evolution d Inflammation-induced ADAR1p150 isoform expression promotes hyperediting in pre-LSCs d JAK2/STAT3 inhibition and shRNA ADAR1 knockdown prevent STAT3 isoform switching in LSCs

Schools are high-risk settings for SARS-CoV-2 transmission, but necessary for children's educational and social-emotional wellbeing. While wastewater monitoring has been implemented to mitigate outbreak risk in universities and residential settings, its effectiveness in community K-12 sites is unknown. We implemented a wastewater and surface monitoring system to detect SARS-CoV-2 in nine elementary schools in San Diego County. Ninety-three percent of identified cases were associated with either a positive wastewater or surface sample; 67% were associated with a positive wastewater sample, and 40% were associated with a positive surface sample. The techniques we utilized allowed for near-complete genomic sequencing of wastewater and surface samples. Passive environmental surveillance can complement approaches that require individual consent, particularly in communities with limited access and/or high rates of testing hesitancy.

Diagnosis of autism spectrum disorder (ASD) currently relies on behavioral observations because brain markers are unknown. Machine learning approaches can identify patterns in imaging data that predict diagnostic status, but most studies using functional connectivity MRI (fcMRI) data achieved only modest accuracies of 60-80%. We used conditional random forest (CRF), an ensemble learning technique protected against bias from feature correlation (which exists in fcMRI matrices). We selected 252 low-motion resting-state functional MRI scans from the Autism Brain Imaging Data Exchange, including 126 typically developing (TD) and 126 ASD participants, matched for age, nonverbal IQ, and head motion. A matrix of functional connectivities between 220 functionally defined regions of interest was used for diagnostic classification. In several runs, we achieved accuracies of 92-99% for classifiers with >300 features (most informative connections). Features, including pericentral somatosensory and motor regions, were disproportionately informative. Findings differed partially from a previous study in the same sample that used feature selection with random forest (which is biased by feature correlations). External validation in a smaller in-house data set, however, achieved only 67-71% accuracy. The large number of features in optimal models can be attributed to etiological heterogeneity under the clinical ASD umbrella. Lower accuracy in external validation is expected due to differences in unknown composition of ASD variants across samples. High accuracy in the main data set is unlikely due to noise overfitting, but rather indicates optimized characterization of a given cohort.