Npas4 regulates IQSEC3 expression in hippocampal somatostatin interneurons to mediate anxiety-like behavior

Activity-dependent GABAergic synapse plasticity is important for normal brain functions, but the underlying molecular mechanisms remain incompletely understood. Here, we show that Npas4 (neuronal PAS-domain protein 4) transcriptionally regulates the expression of IQSEC3, a GABAergic synapse-specific guanine nucleotide-exchange factor for ADP-ribosylation factor (ARF-GEF) that directly interacts with gephyrin. Neuronal activation by an enriched environment induces Npas4-mediated upregulation of IQSEC3 protein specifically in CA1 stratum oriens layer somatostatin (SST)-expressing GABAergic interneurons. SST+ interneuron-specific knockout (KO) of Npas4 compromises synaptic transmission in these GABAergic interneurons, increases neuronal activity in CA1 pyramidal neurons, and reduces anxiety behavior, all of which are normalized by the expression of wild-type IQSEC3, but not a dominant-negative ARF-GEF-inactive mutant, in SST+ interneurons of Npas4-KO mice. Our results suggest that IQSEC3 is a key GABAergic synapse component that is directed by Npas4 and ARF activity, specifically in SST+ interneurons, to orchestrate excitation-to-inhibition balance and control anxiety-like behavior.1

Crosstalk among Set1 complex subunits involved in H2B ubiquitylation-dependent H3K4 methylation.

Nucleic Acids Research. Volume 46, Issue 21, 2018, Pages 11129-11143. Authors: Jongcheol Jeon, Robert K. McGinty, Thomas W. Muir (Tom W Muir), Jung-Ae Kim, Jaehoon Kim.H2B ubiquitylation (H2Bub)-dependent H3K4 methylation is mediated by the multisubunit Set1 complex (Set1C) in yeast, but precisely how Set1C subunits contribute to this histone modification remains unclear. Here, using reconstituted Set1Cs and recombinant H2Bub chromatin, we identified Set1C subunits and domains involved in the H2Bub-dependent H3K4 methylation process, showing that the Spp1 PHDL domain, in conjunction with the Set1 n-SET domain, interacts with Swd1/Swd3 and that this interaction is essential for H2Bub-dependent H3K4 methylation. Importantly, Set1C containing an Spp1-Swd1 fusion bypasses the requirement for H2Bub for H3K4 methylation, suggesting that the role of H2Bub is to induce allosteric rearrangements of the subunit-interaction network within the active site of Set1C that are necessary for methylation activity. Moreover, the interaction between the Set1 N-terminal region and Swd1 renders the Spp1-lacking Set1C competent for H2Bub-dependent H3K4 methylation. Collectively, our results suggest that H2Bub induces conformational changes in Set1C that support H3K4 methylation activity

Replacing the internal standard to estimate micropollutants using deep and machine learning

Similar to the worldwide proliferation of urbanization, micropollutants have been involved in aquatic and ecological environmental systems. These pollutants have the propensity to wreak havoc on human health and the ecological system; hence, it is important to persistently monitor micropollutants in the environment. Micropollutants are commonly quantified via target analysis using high resolution mass spectrometry and the stable isotope labeled (SIL) standard. However, the cost-intensiveness of this standard presents a major obstacle in measuring micropollutants. This study resolved this problem by developing data-driven models, including deep learning (DL) and machine learning (ML), to estimate the concentration of micropollutants without resorting to the SIL standard. Our study hypothesized that natural organic matter (NOM) could replace internal standards if there was a specific mass spectrum (MS) subset, including NOM information, which correlated with an SIL standard peak. Therefore, we analyzed the MS to find the specific MS subsets for replacing the SIL standard peak. Thirty-five alternative MS subsets were determined for applying DL and ML as input data. Thereafter, we trained four different DL models, namely, ResNet101, GoogLeNet, VGG16, and Inception v3, as well as three different ML models, i.e., random forest (RF), support vector machine (SVM), and artificial neural network (ANN). A total of 680 MS data were used for the model training to estimate five different micropollutants, namely Sulpiride, Metformin, and Benzotriazole. Among the DL models, ResNet 101 exhibited the highest model performance, showing that the average validation R 2 and MSE were 0.84 and 0.26 ng/L, respectively, while RF was the best in the ML models, manifesting R 2 and MSE values of 0.69 and 0.58 ng/L. The trained models showed accurate training and validation results for the estimation of the five micropollutant concentrations. Therefore, this study demonstrates that the suggested analysis has a potential for alternative micropollutant measurement that has rapid and economic vantages. (C) 2020 Elsevier Ltd. All rights reserved

The Set1 N-terminal domain and Swd2 interact with RNA polymerase II CTD to recruit COMPASS

International audienceMethylation of histone H3 lysine 4 (H3K4) by Set1/COMPASS occurs co-transcriptionally, and is important for gene regulation. Set1/COMPASS associates with the RNA polymerase II C-terminal domain (CTD) to establish proper levels and distribution of H3K4 methylations. However, details of CTD association remain unclear. Here we report that the Set1 N-terminal region and the COMPASS subunit Swd2, which interact with each other, are both needed for efficient CTD binding in Saccharomyces cerevisiae. Moreover, a single point mutation in Swd2 that affects its interaction with Set1 also impairs COMPASS recruitment to chromatin and H3K4 methylation. A CTD interaction domain (CID) from the protein Nrd1 can partially substitute for the Set1 N-terminal region to restore CTD interactions and histone methylation. However, even when Set1/COMPASS is recruited via the Nrd1 CID, histone H2B ubiquitylation is still required for efficient H3K4 methylation, indicating that H2Bub acts after the initial recruitment of COMPASS to chromatin

A novel method for micropollutant quantification using deep learning and multi-objective optimization

Micropollutants (MPs) released into aquatic ecosystems have adverse effects on public health. Hence, monitoring and managing MPs in aquatic systems are imperative. MPs can be quantified by high-resolution mass spectrometry (HRMS) with stable isotope-labeled (SIL) standards. However, high cost of SIL solutions is a significant issue. This study aims to develop a rapid and cost-effective analytical approach to estimate MP concentrations in aquatic systems based on deep learning (DL) and multi-objective optimization. We hypothesized that internal standards could quantify the MP concentrations other than the target substance. Our approach considered the precision of intra-/inter-day repeatability and natural organic matter information to reduce instrumental error and matrix effect. We selected standard solutions to estimate the concentrations of 18 MPs. Among the optimal DL models, DarkNet-53 using nine standard solutions yielded the highest performance, while ResNet-50 yielded the lowest. Overall, this study demonstrated the capability of DL models for estimating MP concentrations

Nickelocene as an Air- and Moisture-Tolerant Precatalyst in the Regioselective Synthesis of Multisubstituted Pyridines

Ni(COD)(2) -catalyzed cycloaddition reactions to access pyridines have been extensively studied. However, this catalyst typically requires drying procedures and inert-atmosphere techniques for the reactions. Herein, we report operationally simple nickel(0) catalysis to access substituted pyridines from various nitriles and 1,6-diynes without the aid of air-free techniques. The Ni-Xantphos-based catalytic manifold is tolerant to air, moisture, and heat while promoting the [2 + 2 + 2] cycloaddition reactions with high reaction yields and broad substrate scope. In addition, we disclose that not only the steric effect but also the frontier molecular orbital interactions can play a critical role in determining the regiochemical outcome of nickel-catalyzed [2 + 2 + 2] cycloaddition for the synthesis of substituted pyridines

Binding to RNA regulates Set1 function

International audienceThe Set1 family of histone H3 lysine 4 (H3K4) methyltransferases is highly conserved from yeast to human. Here we show that the Set1 complex (Set1C) directly binds RNA in vitro through the regions that comprise the double RNA recognition motifs (dRRM) and N-SET domain within Set1 and its subunit Spp1. To investigate the functional relevance of RNA binding, we performed UV RNA crosslinking (CRAC) for Set1 and RNA polymerase II in parallel with ChIP-seq experiments. Set1 binds nascent transcripts through its dRRM. RNA binding is important to define the appropriate topology of Set1C distribution along transcription units and correlates with the efficient deposition of the H3K4me3 mark. In addition, we uncovered that Set1 binds to different classes of RNAs to levels that largely exceed the levels of binding to the general population of transcripts, suggesting the Set1 persists on these RNAs after transcription. This class includes RNAs derived from SET1, Ty1 retrotransposons, specific transcription factors genes and snRNAs (small nuclear RNAs). We propose that Set1 modulates adaptive responses, as exemplified by the post-transcriptional inhibition of Ty1 retrotransposition