Differential requirement for NMDAR activity in SAP97 -mediated regulation of the number and strength of glutamatergic AMPAR-containing synapses

PSD-95-like, disc-large (DLG) family membrane-associated guanylate kinase proteins (PSD/DLG-MAGUKs) are essential for regulating synaptic AMPA receptor (AMPAR) function and activity-dependent trafficking of AMPARs. Using a molecular replacement strategy to replace endogenous PSD-95 with SAP97β, we show that the prototypic β-isoform of the PSD-MAGUKs, SAP97β, has distinct NMDA receptor (NMDAR)-dependent roles in regulating basic properties of AMPAR-containing synapses. SAP97β enhances the number of AMPAR-containing synapses in an NMDAR-dependent manner, whereas its effect on the size of unitary synaptic response is not fully dependent on NMDAR activity. These effects contrast with those of PSD-95α, which increases both the number of AMPAR-containing synapses and the size of unitary synaptic responses, with or without NMDAR activity. Our results suggest that SAP97β regulates synaptic AMPAR content by increasing surface expression of GluA1-containing AMPARs, whereas PSD-95α enhances synaptic AMPAR content presumably by increasing the synaptic scaffold capacity for synaptic AMPARs. Our approach delineates discrete effects of different PSD-MAGUKs on principal properties of glutamatergic synaptic transmission. Our results suggest that the molecular diversity of PSD-MAGUKs can provide rich molecular substrates for differential regulation of glutamatergic synapses in the brain.National Institutes of Health (U.S.) (Grant MH080310)National Institutes of Health (U.S.) (Transformative Research Award R01 GM104948-02

Human Skin, Oral, and Gut Microbiomes Predict Chronological Age.

Human gut microbiomes are known to change with age, yet the relative value of human microbiomes across the body as predictors of age, and prediction robustness across populations is unknown. In this study, we tested the ability of the oral, gut, and skin (hand and forehead) microbiomes to predict age in adults using random forest regression on data combined from multiple publicly available studies, evaluating the models in each cohort individually. Intriguingly, the skin microbiome provides the best prediction of age (mean ± standard deviation, 3.8 ± 0.45 years, versus 4.5 ± 0.14 years for the oral microbiome and 11.5 ± 0.12 years for the gut microbiome). This also agrees with forensic studies showing that the skin microbiome predicts postmortem interval better than microbiomes from other body sites. Age prediction models constructed from the hand microbiome generalized to the forehead and vice versa, across cohorts, and results from the gut microbiome generalized across multiple cohorts (United States, United Kingdom, and China). Interestingly, taxa enriched in young individuals (18 to 30 years) tend to be more abundant and more prevalent than taxa enriched in elderly individuals (>60 yrs), suggesting a model in which physiological aging occurs concomitantly with the loss of key taxa over a lifetime, enabling potential microbiome-targeted therapeutic strategies to prevent aging.IMPORTANCE Considerable evidence suggests that the gut microbiome changes with age or even accelerates aging in adults. Whether the age-related changes in the gut microbiome are more or less prominent than those for other body sites and whether predictions can be made about a person's age from a microbiome sample remain unknown. We therefore combined several large studies from different countries to determine which body site's microbiome could most accurately predict age. We found that the skin was the best, on average yielding predictions within 4 years of chronological age. This study sets the stage for future research on the role of the microbiome in accelerating or decelerating the aging process and in the susceptibility for age-related diseases

Cytological and transcript analyses reveal fat and lazy persister-like bacilli in tuberculous sputum

As nonreplicating tubercle bacilli are tolerant to the cidal action of antibiotics and resistant to multiple stresses, identification of this persister-like population of tubercle bacilli in sputum presents exciting and tractable new opportunities to investigate both responses to chemotherapy and the transmission of tuberculosis

Comparative Genomics Reveals Two Novel RNAi Factors in Trypanosoma brucei and Provides Insight into the Core Machinery

The introduction ten years ago of RNA interference (RNAi) as a tool for molecular exploration in Trypanosoma brucei has led to a surge in our understanding of the pathogenesis and biology of this human parasite. In particular, a genome-wide RNAi screen has recently been combined with next-generation Illumina sequencing to expose catalogues of genes associated with loss of fitness in distinct developmental stages. At present, this technology is restricted to RNAi-positive protozoan parasites, which excludes T. cruzi, Leishmania major, and Plasmodium falciparum. Therefore, elucidating the mechanism of RNAi and identifying the essential components of the pathway is fundamental for improving RNAi efficiency in T. brucei and for transferring the RNAi tool to RNAi-deficient pathogens. Here we used comparative genomics of RNAi-positive and -negative trypanosomatid protozoans to identify the repertoire of factors in T. brucei. In addition to the previously characterized Argonaute 1 (AGO1) protein and the cytoplasmic and nuclear Dicers, TbDCL1 and TbDCL2, respectively, we identified the RNA Interference Factors 4 and 5 (TbRIF4 and TbRIF5). TbRIF4 is a 3′-5′ exonuclease of the DnaQ superfamily and plays a critical role in the conversion of duplex siRNAs to the single-stranded form, thus generating a TbAGO1-siRNA complex required for target-specific cleavage. TbRIF5 is essential for cytoplasmic RNAi and appears to act as a TbDCL1 cofactor. The availability of the core RNAi machinery in T. brucei provides a platform to gain mechanistic insights in this ancient eukaryote and to identify the minimal set of components required to reconstitute RNAi in RNAi-deficient parasites

Interpretation of inverted photocurrent transients in organic lead halide perovskite solar cells: proof of the field screening by mobile ions and determination of the space charge layer widths

In Methyl Ammonium Lead Iodide (MAPI) perovskite solar cells, screening of the built-in field by mobile ions has been proposed as part of the cause of the large hysteresis observed in the current/voltage scans in many cells. We show that photocurrent transients measured immediately (e.g. 100 μs) after a voltage step can provide direct evidence that this field screening exists. Just after a step to forward bias, the photocurrent transients are reversed in sign (i.e. inverted), and the magnitude of the inverted transients can be used to find an upper bound on the width of the space charge layers adjacent to the electrodes. This in turn provides a lower bound on the mobile charge concentration, which we find to be ≳1 × 1017 cm−3. Using a new photocurrent transient experiment, we show that the space charge layer thickness remains approximately constant as a function of bias, as expected for mobile ions in a solid electrolyte. We also discuss additional characteristics of the inverted photocurrent transients that imply either an unusually stable deep trapping, or a photo effect on the mobile ion conductivity

Genomic, Pathway Network, and Immunologic Features Distinguishing Squamous Carcinomas

This integrated, multiplatform PanCancer Atlas study co-mapped and identified distinguishing molecular features of squamous cell carcinomas (SCCs) from five sites associated with smokin

Spatial Organization and Molecular Correlation of Tumor-Infiltrating Lymphocytes Using Deep Learning on Pathology Images

Beyond sample curation and basic pathologic characterization, the digitized H&E-stained images of TCGA samples remain underutilized. To highlight this resource, we present mappings of tumorinfiltrating lymphocytes (TILs) based on H&E images from 13 TCGA tumor types. These TIL maps are derived through computational staining using a convolutional neural network trained to classify patches of images. Affinity propagation revealed local spatial structure in TIL patterns and correlation with overall survival. TIL map structural patterns were grouped using standard histopathological parameters. These patterns are enriched in particular T cell subpopulations derived from molecular measures. TIL densities and spatial structure were differentially enriched among tumor types, immune subtypes, and tumor molecular subtypes, implying that spatial infiltrate state could reflect particular tumor cell aberration states. Obtaining spatial lymphocytic patterns linked to the rich genomic characterization of TCGA samples demonstrates one use for the TCGA image archives with insights into the tumor-immune microenvironment