Estrogen receptor-α (ERα), -β (ERβ) and progestin receptor (PR) immunoreactivities are localized to extranuclear sites in the rat hippocampal formation. Since rats and mice respond differently to estradiol treatment at a cellular level, the present study examined the distribution of ovarian hormone receptors in the dorsal hippocampal formation of mice. For this, antibodies to ERα, ERβ, and PR were localized by light and electron immunomicroscopy in male and female mice across the estrous cycle. Light microscopic examination of the mouse hippocampal formation showed sparse nuclear ERα–, and PR-immunoreactivity (-ir) most prominent in the CA1 region and diffuse ERβ-ir primarily in the CA1 pyramidal cell layer as well as in a few interneurons. Ultrastructural analysis additionally revealed discrete extranuclear ERα-, ERβ- and PR-ir in neuronal and glial profiles throughout the hippocampal formation. While extranuclear profiles were detected in all animal groups examined, the amount and types of profiles varied with sex and estrous cycle phase. ERα-ir was highest in diestrus females, particularly in dendritic spines, axons and glia. Similarly, ERβ-ir was highest in estrus and diestrus females, mainly in dendritic spines and glia. Conversely, PR-ir was highest during proestrus, and mostly in axons. Except for very low levels of extranuclear ERβ-ir in mossy fiber terminals in mice, the labeling patterns in the mice for all three antibodies were similar to the ultrastructural labeling found previously in rats, suggesting that regulation of these receptors is well conserved across the two species.

These observations provide novel insights into the molecular phenotype and biologic functions of the human club cell population and identify basal cells as the human progenitor cells for club cells.

No abstract

Idiopathic multicentric Castleman disease (iMCD) is a poorly understood hematologic disorder involving cytokine-induced polyclonal lymphoproliferation, systemic inflammation, and potentially fatal multiorgan failure. Although the etiology of iMCD is unknown, interleukin-6 (IL-6) is an established disease driver in approximately one-third of patients. Anti–IL-6 therapy, siltuximab, is the only US Food and Drug Administration–approved treatment. Few options exist for siltuximab nonresponders, and no validated tests are available to predict likelihood of response. We procured and analyzed the largest-to-date cohort of iMCD samples, which enabled classification of iMCD into disease categories, discovery of siltuximab response biomarkers, and identification of therapeutic targets for siltuximab nonresponders. Proteomic quantification of 1178 analytes was performed on serum of 88 iMCD patients, 60 patients with clinico-pathologically overlapping diseases (human herpesvirus-8–associated MCD, N = 20; Hodgkin lymphoma, N = 20; rheumatoid arthritis, N = 20), and 42 healthy controls. Unsupervised clustering revealed iMCD patients have heterogeneous serum proteomes that did not cluster with clinico-pathologically overlapping diseases. Clustering of iMCD patients identified a novel subgroup with superior response to siltuximab, which was validated using a 7-analyte panel (apolipoprotein E, amphiregulin, serum amyloid P-component, inactivated complement C3b, immunoglobulin E, IL-6, erythropoietin) in an independent cohort. Enrichment analyses and immunohistochemistry identified Janus kinase (JAK)/signal transducer and activator of transcription 3 signaling as a candidate therapeutic target that could potentially be targeted with JAK inhibitors in siltuximab nonresponders. Our discoveries demonstrate the potential for accelerating discoveries for rare diseases through multistakeholder collaboration.

As genetic datasets increase in size, the fraction of samples with one or more close relatives increases rapidly, resulting in sets of mutually related individuals. We present DRUID-Deep Relatedness Utilizing Identity by Descent-a method that works by inferring the identical by descent (IBD) sharing profile of an ungenotyped ancestor of a set of close relatives. Using this IBD profile, DRUID infers relatedness between these unobserved ancestors and more distant relatives, thereby combining information from multiple samples to remove one or more generations between the deep relationships to be identified. DRUID constructs sets of close relatives by detecting full siblings and also uses a novel approach to identify the aunts/uncles of two or more siblings, recovering 95.4% of real aunts/uncles with zero false positives. We used DRUID to infer relatedness among individuals in both real and simulated data by applying it to close relatives consisting of full siblings or siblings and their aunts/uncles. Compared to PADRE, DRUID correctly infers up to 10% more relatives when using data from two sets of distantly related siblings, and 10-30% more relatives given two sets of siblings and their aunts/uncles. DRUID frequently infers relationships either correctly or within one degree of the truth, with PADRE classifying 43-58% of tenth degree relatives in this way compared to 78-96% using DRUID.

As genetic datasets increase in size, the fraction of samples with one or more close relatives grows rapidly, resulting in sets of mutually related individuals. We present DRUID-deep relatedness utilizing identity by descent-a method that works by inferring the identical-by-descent (IBD) sharing profile of an ungenotyped ancestor of a set of close relatives. Using this IBD profile, DRUID infers relatedness between unobserved ancestors and more distant relatives, thereby combining information from multiple samples to remove one or more generations between the deep relationships to be identified. DRUID constructs sets of close relatives by detecting full siblings and also uses an approach to identify the aunts/uncles of two or more siblings, recovering 92.2% of real aunts/uncles with zero false positives. In real and simulated data, DRUID correctly infers up to 10.5% more relatives than PADRE when using data from two sets of distantly related siblings, and 10.7%-31.3% more relatives given two sets of siblings and their aunts/uncles. DRUID frequently infers relationships either correctly or within one degree of the truth, with PADRE classifying 43.3%-58.3% of tenth degree relatives in this way compared to 79.6%-96.7% using DRUID.

No abstract

PM exposure contributes to significant dysregulation of the SAE transcriptome of smokers, linking pollution and airway epithelial biology in the risk of development of respiratory disease in susceptible individuals.