Background

Although MP20 is the second most highly expressed membrane protein in the lens its function remains an enigma. Putative functions for MP20 have recently been inferred from its assignment to the tetraspanin superfamily of integral membrane proteins. Members of this family have been shown to be involved in cellular proliferation, differentiation, migration, and adhesion. In this study, we show that MP20 associates with galectin-3, a known adhesion modulator.

Results

MP20 and galectin-3 co-localized in selected areas of the lens fiber cell plasma membrane. Individually, these proteins purified with apparent molecular masses of 60 kDa and 22 kDa, respectively. A 104 kDa complex was formed in vitro upon mixing the purified proteins. A 102 kDa complex of MP20 and galectin-3 could also be isolated from detergent-solubilized native fiber cell membranes. Binding between MP20 and galectin-3 was disrupted by lactose suggesting the lectin site was involved in the interaction.

Conclusions

MP20 adds to a growing list of ligands of galectin-3 and appears to be the first representative of the tetraspanin superfamily identified to possess this specificity.

The Mg2+-inhibited cation (MIC) current, believed to represent activity of TRPM7 channels, is found in lymphocytes and mast cells, cardiac and smooth muscle, and several other eukaryotic cell types. MIC current is activated during whole-cell dialysis with divalent-free internal solutions. Millimolar concentrations of intracellular Mg2+ (or other divalent metal cations) inhibit the channels in a voltage-independent manner. The nature of divalent inhibition and the mechanism of channel activation in an intact cell remain unknown. We show that the polyamines (spermine, spermidine, and putrescine) inhibit the MIC current, also in a voltage-independent manner, with a potency that parallels the number of charges. Neomycin and poly-lysine also potently inhibited MIC current in the absence of Mg2+. These same positively charged ions inhibited IRK1 current in parallel with MIC current, suggesting that they probably act by screening the head group phosphates on PIP2 and other membrane phospholipids. In agreement with this hypothesis, internal protons also inhibited MIC current. By contrast, tetramethylammonium, tetraethylammonium, and hexamethonium produced voltage-dependent block but no inhibition. We show that inhibition by internal polyvalent cations can be relieved by alkalinizing the cytosol using externally applied ammonium or by increasing pH in inside-out patches. Furthermore, in perforated-patch and cell-attached recordings, when intracellular Mg2+ is not depleted, endogenous MIC or recombinant TRPM7 currents are activated by cytosolic alkalinization and inhibited by acidification; and they can be reactivated by PIP2 following rundown in inside-out patches. We propose that MIC (TRPM7) channels are regulated by a charge screening mechanism and may function as sensors of intracellular pH.

Complex coevolutionary relationships among competitors, predators, and prey have shaped taxa diversity, life history strategies, and even the avian migratory patterns we see today. Consequently, accurate documentation of prey selection is often critical for understanding these ecological and evolutionary processes. Conventional diet study methods lack the ability to document the diet of inconspicuous or difficult-to-study predators, such as those with large home ranges and those that move vast distances over short amounts of time, leaving gaps in our knowledge of trophic interactions in many systems. Migratory raptors represent one such group of predators where detailed diet studies have been logistically challenging. To address knowledge gaps in the foraging ecology of migrant raptors and provide a broadly applicable tool for the study of enigmatic predators, we developed a minimally invasive method to collect dietary information by swabbing beaks and talons of raptors to collect trace prey DNA. Using previously published COI primers, we were able to isolate and reference gene sequences in an open-access barcode database to identify prey to species. This method creates a novel avenue to use trace molecular evidence to study prey selection of migrating raptors and will ultimately lead to a better understanding of raptor migration ecology. In addition, this technique has broad applicability and can be used with any wildlife species where even trace amounts of prey debris remain on the exterior of the predator after feeding.

Given the increasing use of cannabis in the US, there is an urgent need to better understand the drug's effects on central signaling mechanisms. Extracellular vesicles (EVs) have been identified as intercellular signaling mediators that contain a variety of cargo, including proteins. Here, we examined whether the main psychoactive component in cannabis, Δ9-tetrahydrocannabinol (THC), alters EV protein signaling dynamics in the brain. We first conducted in vitro studies, which found that THC activates signaling in choroid plexus epithelial cells, resulting in transcriptional upregulation of the cannabinoid 1 receptor and immediate early gene c-fos, in addition to the release of EVs containing RNA cargo. Next, male and female rats were examined for the effects of either acute or chronic exposure to aerosolized ('vaped') THC on circulating brain EVs. Cerebrospinal fluid was extracted from the brain, and EVs were isolated and processed with label-free quantitative proteomic analyses via high-resolution tandem mass spectrometry. Interestingly, circulating EV-localized proteins were differentially expressed based on acute or chronic THC exposure in a sex-specific manner. Taken together, these findings reveal that THC acts in the brain to modulate circulating EV signaling, thereby providing a novel understanding of how exogenous factors can regulate intercellular communication in the brain.

Quasi-periodic eruptions (QPEs) are luminous bursts of soft X-rays from the nuclei of galaxies, repeating on timescales of hours to weeks1-5. The mechanism behind these rare systems is uncertain, but most theories involve accretion disks around supermassive black holes (SMBHs) undergoing instabilities6-8 or interacting with a stellar object in a close orbit9-11. It has been suggested that this disk could be created when the SMBH disrupts a passing star8,11, implying that many QPEs should be preceded by observable tidal disruption events (TDEs). Two known QPE sources show long-term decays in quiescent luminosity consistent with TDEs4,12 and two observed TDEs have exhibited X-ray flares consistent with individual eruptions13,14. TDEs and QPEs also occur preferentially in similar galaxies15. However, no confirmed repeating QPEs have been associated with a spectroscopically confirmed TDE or an optical TDE observed at peak brightness. Here we report the detection of nine X-ray QPEs with a mean recurrence time of approximately 48 h from AT2019qiz, a nearby and extensively studied optically selected TDE16. We detect and model the X-ray, ultraviolet (UV) and optical emission from the accretion disk and show that an orbiting body colliding with this disk provides a plausible explanation for the QPEs.

We present an analysis of DES17X1boj and DES16E2bjy, two peculiar transients discovered by the Dark Energy Survey (DES). They exhibit nearly identical double-peaked light curves that reach very different maximum luminosities (Mr = -15.4 and -17.9, respectively). The light-curve evolution of these events is highly atypical and has not been reported before. The transients are found in different host environments: DES17X1boj was found near the nucleus of a spiral galaxy, while DES16E2bjy is located in the outskirts of a passive red galaxy. Early photometric data are well fitted with a blackbody and the resulting moderate photospheric expansion velocities (1800 km s-1 for DES17X1boj and 4800 km s-1 for DES16E2bjy) suggest an explosive or eruptive origin. Additionally, a feature identified as high-velocity Ca ii absorption (v ≈ 9400 km s-1) in the near-peak spectrum of DES17X1boj may imply that it is a supernova. While similar light-curve evolution suggests a similar physical origin for these two transients, we are not able to identify or characterize the progenitors.

Recent research on disparate psychiatric disorders has implicated rare variants in genes involved in global gene regulation and chromatin modification, as well as many common variants located primarily in regulatory regions of the genome. Understanding precisely how these variants contribute to disease will require a deeper appreciation for the mechanisms of gene regulation in the developing and adult human brain. The PsychENCODE project aims to produce a public resource of multidimensional genomic data using tissue- and cell type–specific samples from approximately 1,000 phenotypically well-characterized, high-quality healthy and disease-affected human post-mortem brains, as well as functionally characterize disease-associated regulatory elements and variants in model systems. We are beginning with a focus on autism spectrum disorder, bipolar disorder and schizophrenia, and expect that this knowledge will apply to a wide variety of psychiatric disorders. This paper outlines the motivation and design of PsychENCODE.

We present the first cosmological parameter constraints using measurements of type Ia supernovae (SNe Ia) from the Dark Energy Survey Supernova Program (DES-SN). The analysis uses a subsample of 207 spectroscopically confirmed SNe Ia from the first three years of DES-SN, combined with a low-redshift sample of 122 SNe from the literature. Our "DES-SN3YR" result from these 329 SNe Ia is based on a series of companion analyses and improvements covering SN Ia discovery, spectroscopic selection, photometry, calibration, distance bias corrections, and evaluation of systematic uncertainties. For a flat ΛCDM model we find a matter density Ω m = 0.331 ± 0.038. For a flat wCDM model, and combining our SN Ia constraints with those from the cosmic microwave background (CMB), we find a dark energy equation of state w = -0.978 ± 0.059, and ω m = 0.321 ± 0.018. For a flat w 0 w a CDM model, and combining probes from SN Ia, CMB and baryon acoustic oscillations, we find w0 = -0.885 ± 0.114 and w a = -0.387 ± 0.430. These results are in agreement with a cosmological constant and with previous constraints using SNe Ia (Pantheon, JLA).

Most genetic risk for psychiatric disease lies in regulatory regions, implicating pathogenic dysregulation of gene expression and splicing. However, comprehensive assessments of transcriptomic organization in diseased brains are limited. In this work, we integrated genotypes and RNA sequencing in brain samples from 1695 individuals with autism spectrum disorder (ASD), schizophrenia, and bipolar disorder, as well as controls. More than 25% of the transcriptome exhibits differential splicing or expression, with isoform-level changes capturing the largest disease effects and genetic enrichments. Coexpression networks isolate disease-specific neuronal alterations, as well as microglial, astrocyte, and interferon-response modules defining previously unidentified neural-immune mechanisms. We integrated genetic and genomic data to perform a transcriptome-wide association study, prioritizing disease loci likely mediated by cis effects on brain expression. This transcriptome-wide characterization of the molecular pathology across three major psychiatric disorders provides a comprehensive resource for mechanistic insight and therapeutic development.

Despite progress in defining genetic risk for psychiatric disorders, their molecular mechanisms remain elusive. Addressing this, the PsychENCODE Consortium has generated a comprehensive online resource for the adult brain across 1866 individuals. The PsychENCODE resource contains ~79,000 brain-active enhancers, sets of Hi-C linkages, and topologically associating domains; single-cell expression profiles for many cell types; expression quantitative-trait loci (QTLs); and further QTLs associated with chromatin, splicing, and cell-type proportions. Integration shows that varying cell-type proportions largely account for the cross-population variation in expression (with >88% reconstruction accuracy). It also allows building of a gene regulatory network, linking genome-wide association study variants to genes (e.g., 321 for schizophrenia). We embed this network into an interpretable deep-learning model, which improves disease prediction by ~6-fold versus polygenic risk scores and identifies key genes and pathways in psychiatric disorders.