GABA\u3csub\u3eB\u3c/sub\u3e Receptor Attenuation of GABA\u3csub\u3eA\u3c/sub\u3e Currents in Neurons of the Mammalian Central Nervous System

Ionotropic receptors are tightly regulated by second messenger systems and are often present along with their metabotropic counterparts on a neuron\u27s plasma membrane. This leads to the hypothesis that the two receptor subtypes can interact, and indeed this has been observed in excitatory glutamate and inhibitory GABA receptors. In both systems the metabotropic pathway augments the ionotropic receptor response. However, we have found that the metabotropic GABAB receptor can suppress the ionotropic GABAA receptor current, in both the in vitro mouse retina and in human amygdala membrane fractions. Expression of amygdala membrane microdomains in Xenopus oocytes by microtransplantation produced functional ionotropic and metabotropic GABA receptors. Most GABAA receptors had properties of α‐subunit containing receptors, with ~5% having ρ‐subunit properties. Only GABAA receptors with α‐subunit‐like properties were regulated by GABAB receptors. In mouse retinal ganglion cells, where only α‐subunit‐containing GABAA receptors are expressed, GABAB receptors suppressed GABAA receptor currents. This suppression was blocked by GABAB receptor antagonists, G‐protein inhibitors, and GABAB receptor antibodies. Based on the kinetic differences between metabotropic and ionotropic receptors, their interaction would suppress repeated, rapid GABAergic inhibition

Exposing new scalars hiding behind the Higgs boson

It is possible that there is another scalar hiding behind the known 125 GeV Higgs boson. If the hidden scalar exhibits a CP property different from the Higgs boson, it can be exposed in the di-Higgs production at the high-luminosity large hadron collider and future colliders.Comment: 6 pages, 3 figure

Destructive changes in the neuronal structure of the FVB/N mouse retina

© The Author(s), 2015. This article is distributed under the terms of the Creative Commons Attribution License. The definitive version was published in PLoS One 10 (2015): e0129719, doi: 10.1371/journal.pone.0129719.We applied a series of selective antibodies for labeling the various cell types in the mammalian retina. These were used to identify the progressive loss of neurons in the FVB/N mouse, a model of early onset retinal degeneration produced by a mutation in the pde6b gene. The immunocytochemical studies, together with electroretinogram (ERG) recordings, enabled us to examine the time course of the degenerative changes that extended from the photoreceptors to the ganglion cells at the proximal end of the retina. Our study indicates that photoreceptors in FVB/N undergo a rapid degeneration within three postnatal weeks, and that there is a concomitant loss of retinal neurons in the inner nuclear layer. Although the loss of rods was detected at an earlier age during which time M- and S-opsin molecules were translocated to the cone nuclei; by 6 months all cones had also degenerated. Neuronal remodeling was also seen in the second-order neurons with horizontal cells sprouting processes proximally and dendritic retraction in rod-driven bipolar cells. Interestingly, the morphology of cone-driven bipolar cells were affected less by the disease process. The cellular structure of inner retinal neurons, i.e., ChAT amacrine cells, ganglion cells, and melanopsin-positive ganglion cells did not exhibit any gross changes of cell densities and appeared to be relatively unaffected by the massive photoreceptor degeneration in the distal retina. However, Muller cell processes began to express GFAP at their endfeet at p14, and it climbed progressively to the cell’s distal ends by 6 months. Our study indicates that FVB/N mouse provides a useful model with which to assess possible intervention strategies to arrest photoreceptor death in related diseases.This study was supported by grants from the National Science Foundation (NSF, IOS-1021646, WS) and the National Eye Institute (NEI, EY 14161, WS)

Target SSR-Seq: A Novel SSR Genotyping Technology Associate With Perfect SSRs in Genetic Analysis of Cucumber Varieties

Simple sequence repeats (SSR) – also known as microsatellites – have been used extensively in genetic analysis, fine mapping, quantitative trait locus (QTL) mapping, as well as marker-assisted selection (MAS) breeding and other techniques. Despite a plethora of studies reporting that perfect SSRs with stable motifs and flanking sequences are more efficient for genetic research, the lack of a high throughput technology for SSR genotyping has limited their use as genetic targets in many crops. In this study, we developed a technology called Target SSR-seq that combined the multiplexed amplification of perfect SSRs with high throughput sequencing. This method can genotype plenty of SSR loci in hundreds of samples with highly accurate results, due to the substantial coverage afforded by high throughput sequencing. We also detected 844 perfect SSRs based on 182 resequencing datasets in cucumber, of which 91 SSRs were selected for Target SSR-seq. Finally, 122 SSRs, including 31 SSRs for varieties identification, were used to genotype 382 key cucumber varieties readily available in Chinese markets using our Target SSR-seq method. Libraries of PCR products were constructed and then sequenced on the Illumina HiSeq X Ten platform. Bioinformatics analysis revealed that 111 filtered SSRs were accurately genotyped with an average coverage of 1289× at an extremely low cost; furthermore, 398 alleles were observed in 382 cucumber cultivars. Genetic analysis identified four populations: northern China type, southern China type, European type, and Xishuangbanna type. Moreover, we acquired a set of 16 core SSRs for the identification of 382 cucumber varieties, of which 42 were isolated as backbone cucumber varieties. This study demonstrated that Target SSR-seq is a novel and efficient method for genetic research

BPLLDA: Predicting lncRNA-Disease Associations Based on Simple Paths With Limited Lengths in a Heterogeneous Network

In recent years, it has been increasingly clear that long noncoding RNAs (lncRNAs) play critical roles in many biological processes associated with human diseases. Inferring potential lncRNA-disease associations is essential to reveal the secrets behind diseases, develop novel drugs, and optimize personalized treatments. However, biological experiments to validate lncRNA-disease associations are very time-consuming and costly. Thus, it is critical to develop effective computational models. In this study, we have proposed a method called BPLLDA to predict lncRNA-disease associations based on paths of fixed lengths in a heterogeneous lncRNA-disease association network. Specifically, BPLLDA first constructs a heterogeneous lncRNA-disease network by integrating the lncRNA-disease association network, the lncRNA functional similarity network, and the disease semantic similarity network. It then infers the probability of an lncRNA-disease association based on paths connecting them and their lengths in the network. Compared to existing methods, BPLLDA has a few advantages, including not demanding negative samples and the ability to predict associations related to novel lncRNAs or novel diseases. BPLLDA was applied to a canonical lncRNA-disease association database called LncRNADisease, together with two popular methods LRLSLDA and GrwLDA. The leave-one-out cross-validation areas under the receiver operating characteristic curve of BPLLDA are 0.87117, 0.82403, and 0.78528, respectively, for predicting overall associations, associations related to novel lncRNAs, and associations related to novel diseases, higher than those of the two compared methods. In addition, cervical cancer, glioma, and non-small-cell lung cancer were selected as case studies, for which the predicted top five lncRNA-disease associations were verified by recently published literature. In summary, BPLLDA exhibits good performances in predicting novel lncRNA-disease associations and associations related to novel lncRNAs and diseases. It may contribute to the understanding of lncRNA-associated diseases like certain cancers

Transcriptome Profile Analysis Reveals that <i>CsTCP14</i> Induces Susceptibility to Foliage Diseases in Cucumber

Foliage diseases are prevalent in cucumber production and cause serious yield reduction across the world. Identifying resistance or susceptible genes under foliage-disease stress is essential for breeding resistant varieties, of which leaf-specific expressed susceptible genes are extremely important but rarely studied in crops. This study performed an in-depth mining of public transcriptome data both in different cucumber tissues and under downy mildew (DM) inoculation, and found that the expression of leaf-specific expressed transcription factor CsTCP14 was significantly increased after treatment with DM, as well as being upregulated under stress from another foliage disease, watermelon mosaic virus (WMV), in susceptible cucumbers. Furthermore, the Pearson correlation analysis identified genome-wide co-expressed defense genes with CsTCP14. A potential target CsNBS-LRR gene, Csa6M344280.1, was obtained as obviously reduced and was negatively correlated with the expression of the susceptible gene CsTCP14. Moreover, the interaction experiments of electrophoretic mobility shift assay (EMSA) and yeast one-hybrid assay (Y1H) were successfully executed to prove that CsTCP14 could transcriptionally repress the expression of the CsNBS-LRR gene, Csa6M344280.1, which resulted in inducing susceptibility to foliage diseases in cucumber. As such, we constructed a draft model showing that the leaf-specific expressed gene CsTCP14 was negatively regulating the defense gene Csa6M344280.1 to induce susceptibility to foliage diseases in cucumber. Therefore, this study explored key susceptible genes in response to foliage diseases based on a comprehensive analysis of public transcriptome data and provided an opportunity to breed new varieties that can resist foliage diseases in cucumber, as well as in other crops