C1-Ten is a PTPase of nephrin, regulating podocyte hypertrophy through mTORC1 activation

Hypertrophy is a prominent feature of damaged podocytes in diabetic kidney disease (DKD). mTORC1 hyperactivation leads to podocyte hypertrophy, but the detailed mechanism of how mTORC1 activation occurs under pathological conditions is not completely known. Moreover, reduced nephrin tyrosine phosphorylation has been observed in podocytes under pathological conditions, but the molecular mechanism linking nephrin phosphorylation and pathology is unclear so far. In this study, we observed a significant increase in C1-Ten level in diabetic kidney and in high glucose-induced damaged podocytes. C1-Ten acts as a protein tyrosine phosphatase (PTPase) at the nephrin-PI3K binding site and renders PI3K for IRS-1, thereby activating mTORC1. Furthermore, C1-Ten causes podocyte hypertrophy and proteinuria by increasing mTORC1 activity in vitro and in vivo. These findings demonstrate the relationship between nephrin dephosphorylation and the mTORC1 pathway, mediated by C1-Ten PTPase activity. We suggest that C1-Ten contributes to the pathogenesis of DKD by inducing podocyte hypertrophy under high glucose conditions.111Ysciescopu

Noise reduction in functional near-infrared spectroscopy signals by independent component analysis

Functional near-infrared spectroscopy (fNIRS) is used to detect concentration changes of oxyhemoglobin and deoxy-hemoglobin in the human brain. The main difficulty entailed in the analysis of fNIRS signals is the fact that the hemodynamic response to a specific neuronal activation is contaminated by physiological and instrument noises, motion artifacts, and other interferences. This paper proposes independent component analysis (ICA) as a means of identifying the original hemodynamic response in the presence of noises. The original hemodynamic response was reconstructed using the primary independent component (IC) and other, less-weighting-coefficient ICs. In order to generate experimental brain stimuli, arithmetic tasks were administered to eight volunteer subjects. The t-value of the reconstructed hemodynamic response was improved by using the ICs found in the measured data. The best t-value out of 16 low-pass-filtered signals was 37, and that of the reconstructed one was 51. Also, the average t-value of the eight subjects' reconstructed signals was 40, whereas that of all of their low-pass-filtered signals was only 20. Overall, the results showed the applicability of the ICA-based method to noise-contamination reduction in brain mapping.open1

The State of Digital Media Data Research, 2023

DMD Report website: https://mddatacoop.org/dmd/The purpose of this report is to provide an account of digital media data (DMD) research practices and to highlight its ongoing challenges. We define DMD as data that are collected, extracted, gathered, or scraped from a web-based platform such as a website, social networking site, mobile application, or another virtual space. We break these practices and their challenges into three stages—collection, analysis, and sharing. We argue that continuing digital media data (DMD) research should be guided by four principles: collaboration, transparency, preparation, and consistency. 1. COLLABORATION: Working together on protocols for DMD research can occur across all stages of the research pipeline, including setting norms for data sharing, producing baseline research, and co-developing archives. 2. TRANSPARENCY: Make code and data accessible to other researchers, when possible. Open-source software development is especially helpful for advancing research in a transparent manner. Given the cost for collecting, storing, and analyzing DMD, we also encourage researchers to provide these details in their publications. 3. PREPARATION: Researchers should anticipate the risks or challenges to collecting, analyzing, and reporting on DMD. Emerging methods for data collection, such as donated data, provide a new mechanism for studying user-consented data. 4. CONSISTENCY: We end with this principle because it builds on the aforementioned three as, when researchers are collaborative, transparent and prepared, research approaches will be more consistent, allowing us to compare across studies and identify situational contexts that require nuanced protocol.This work has been generously supported by the Social Science Research Council.Journalis

News media use, talk networks, and anti-elitism across geographic location: evidence from Wisconsin

A certain social-political geography recurs across European and North American societies: As post-industrialization and mechanization of agriculture have disrupted economies, rural and nonmetropolitan areas are aging and declining in population, leading to widening political and cultural gaps between metropolitan and rural communities. Yet political communication research tends to focus on national or cross-national levels, often emphasizing networked digital media and an implicitly global information order. We contend that geographic place still provides a powerful grounding for individuals’ lifeworld experiences, identities, and orientations to political communications and politics. Focusing on the U.S. state of Wisconsin, and presenting data gathered in 2018, this study demonstrates significant, though often small, differences between geographic locations in terms of their patterns of media consumption, political talk, and anti-elite attitudes. Importantly, television news continues to play a major role in citizens’ repertoires across locations, suggesting we must continue to pay attention to this broadcast medium. Residents of more metropolitan communities consume significantly more national and international news from prestige sources such as the New York Times, and their talk networks are more cleanly sorted by partisanship. Running against common stereotypes of news media use, residents of small towns and rural areas consume no more conservative media than other citizens, even without controlling for partisanship. Our theoretical model and empirical results call for further attention to the intersections of place and politics in understanding news consumption behaviors and the meanings citizens draw from media content

Antileishmanial High-Throughput Drug Screening Reveals Drug Candidates with New Scaffolds

Drugs currently available for leishmaniasis treatment often show parasite resistance, highly toxic side effects and prohibitive costs commonly incompatible with patients from the tropical endemic countries. In this sense, there is an urgent need for new drugs as a treatment solution for this neglected disease. Here we show the development and implementation of an automated high-throughput viability screening assay for the discovery of new drugs against Leishmania. Assay validation was done with Leishmania promastigote forms, including the screening of 4,000 compounds with known pharmacological properties. In an attempt to find new compounds with leishmanicidal properties, 26,500 structurally diverse chemical compounds were screened. A cut-off of 70% growth inhibition in the primary screening led to the identification of 567 active compounds. Cellular toxicity and selectivity were responsible for the exclusion of 78% of the pre-selected compounds. The activity of the remaining 124 compounds was confirmed against the intramacrophagic amastigote form of the parasite. In vitro microsomal stability and cytochrome P450 (CYP) inhibition of the two most active compounds from this screening effort were assessed to obtain preliminary information on their metabolism in the host. The HTS approach employed here resulted in the discovery of two new antileishmanial compounds, bringing promising candidates to the leishmaniasis drug discovery pipeline

Reducing LPS content in cockroach allergens increases pulmonary cytokine production without increasing inflammation: A randomized laboratory study

<p>Abstract</p> <p>Background</p> <p>Endotoxins are ubiquitously present in the environment and constitute a significant component of ambient air. These substances have been shown to modulate the allergic response, however a consensus has yet to be reached whether they attenuate or exacerbate asthmatic responses. The current investigation examined whether reducing the concentration of lipopolysaccharide (LPS) in a house dust extract (HDE) containing high concentrations of both cockroach allergens <abbrgrp><abbr bid="B1">1</abbr></abbrgrp> and LPS would attenuate asthma-like pulmonary inflammation.</p> <p>Methods</p> <p>Mice were sensitized with CRA and challenged with the intact HDE, containing 182 ng of LPS, or an LPS-reduced HDE containing 3 ng LPS, but an equivalent amount of CRA. Multiple parameters of asthma-like pulmonary inflammation were measured.</p> <p>Results</p> <p>Compared to HDE challenged mice, the LPS-reduced HDE challenged mice had significantly reduced TNFα levels in the bronchoalveolar lavage fluid. Plasma levels of IgE and IgG1 were significantly reduced, however no change in CRA-specific IgE was detected. In HDE mice, plasma IgG2a levels were similar to naïve mice, while LPS-reduced HDE mice had significantly greater concentrations. Reduced levels of LPS in the HDE did not decrease eosinophil or neutrophil recruitment into the alveolar space. Equivalent inflammatory cell recruitment occurred despite having generally higher pulmonary concentrations of eotaxins and CXC chemokines in the LPS-reduced HDE group. LPS-reduced HDE challenge induced significantly higher concentrations of IFNγ, and IL-5 and IL-13 in the BAL fluid, but did not decrease airways hyperresponsiveness or airway resistance to methacholine challenge. <it>Conclusion: </it>These data show that reduction of LPS levels in the HDE does not significantly protect against the severity of asthma-like pulmonary inflammation.</p

Discovery of Q203, a potent clinical candidate for the treatment of tuberculosis

New therapeutic strategies are needed to combat the tuberculosis pandemic and the spread of multidrug-resistant (MDR) and extensively drug-resistant (XDR) forms of the disease, which remain a serious public health challenge worldwide1, 2. The most urgent clinical need is to discover potent agents capable of reducing the duration of MDR and XDR tuberculosis therapy with a success rate comparable to that of current therapies for drug-susceptible tuberculosis. The last decade has seen the discovery of new agent classes for the management of tuberculosis3, 4, 5, several of which are currently in clinical trials6, 7, 8. However, given the high attrition rate of drug candidates during clinical development and the emergence of drug resistance, the discovery of additional clinical candidates is clearly needed. Here, we report on a promising class of imidazopyridine amide (IPA) compounds that block Mycobacterium tuberculosis growth by targeting the respiratory cytochrome bc1 complex. The optimized IPA compound Q203 inhibited the growth of MDR and XDR M. tuberculosis clinical isolates in culture broth medium in the low nanomolar range and was efficacious in a mouse model of tuberculosis at a dose less than 1 mg per kg body weight, which highlights the potency of this compound. In addition, Q203 displays pharmacokinetic and safety profiles compatible with once-daily dosing. Together, our data indicate that Q203 is a promising new clinical candidate for the treatment of tuberculosis

RNAseq analysis of bronchial epithelial cells to identify COPD-associated genes and SNPs

Abstract Background There is a need for more powerful methods to identify low-effect SNPs that contribute to hereditary COPD pathogenesis. We hypothesized that SNPs contributing to COPD risk through cis-regulatory effects are enriched in genes comprised by bronchial epithelial cell (BEC) expression patterns associated with COPD. Methods To test this hypothesis, normal BEC specimens were obtained by bronchoscopy from 60 subjects: 30 subjects with COPD defined by spirometry (FEV1/FVC < 0.7, FEV1% < 80%), and 30 non-COPD controls. Targeted next generation sequencing was used to measure total and allele-specific expression of 35 genes in genome maintenance (GM) genes pathways linked to COPD pathogenesis, including seven TP53 and CEBP transcription factor family members. Shrinkage linear discriminant analysis (SLDA) was used to identify COPD-classification models. COPD GWAS were queried for putative cis-regulatory SNPs in the targeted genes. Results On a network basis, TP53 and CEBP transcription factor pathway gene pair network connections, including key DNA repair gene ERCC5, were significantly different in COPD subjects (e.g., Wilcoxon rank sum test for closeness, p-value = 5.0E-11). ERCC5 SNP rs4150275 association with chronic bronchitis was identified in a set of Lung Health Study (LHS) COPD GWAS SNPs restricted to those in putative regulatory regions within the targeted genes, and this association was validated in the COPDgene non-hispanic white (NHW) GWAS. ERCC5 SNP rs4150275 is linked (D’ = 1) to ERCC5 SNP rs17655 which displayed differential allelic expression (DAE) in BEC and is an expression quantitative trait locus (eQTL) in lung tissue (p = 3.2E-7). SNPs in linkage (D’ = 1) with rs17655 were predicted to alter miRNA binding (rs873601). A classifier model that comprised gene features CAT, CEBPG, GPX1, KEAP1, TP73, and XPA had pooled 10-fold cross-validation receiver operator characteristic area under the curve of 75.4% (95% CI: 66.3%–89.3%). The prevalence of DAE was higher than expected (p = 0.0023) in the classifier genes. Conclusions GM genes comprised by COPD-associated BEC expression patterns were enriched for SNPs with cis-regulatory function, including a putative cis-rSNP in ERCC5 that was associated with COPD risk. These findings support additional total and allele-specific expression analysis of gene pathways with high prior likelihood for involvement in COPD pathogenesis.https://deepblue.lib.umich.edu/bitstream/2027.42/142723/1/12890_2018_Article_603.pd

AI is a viable alternative to high throughput screening: a 318-target study

: High throughput screening (HTS) is routinely used to identify bioactive small molecules. This requires physical compounds, which limits coverage of accessible chemical space. Computational approaches combined with vast on-demand chemical libraries can access far greater chemical space, provided that the predictive accuracy is sufficient to identify useful molecules. Through the largest and most diverse virtual HTS campaign reported to date, comprising 318 individual projects, we demonstrate that our AtomNet® convolutional neural network successfully finds novel hits across every major therapeutic area and protein class. We address historical limitations of computational screening by demonstrating success for target proteins without known binders, high-quality X-ray crystal structures, or manual cherry-picking of compounds. We show that the molecules selected by the AtomNet® model are novel drug-like scaffolds rather than minor modifications to known bioactive compounds. Our empirical results suggest that computational methods can substantially replace HTS as the first step of small-molecule drug discovery