ß-Neurexins Control Neural Circuits by Regulating Synaptic Endocannabinoid Signaling.

α- and ß-neurexins are presynaptic cell-adhesion molecules implicated in autism and schizophrenia. We find that, although ß-neurexins are expressed at much lower levels than α-neurexins, conditional knockout of ß-neurexins with continued expression of α-neurexins dramatically decreased neurotransmitter release at excitatory synapses in cultured cortical neurons. The ß-neurexin knockout phenotype was attenuated by CB1-receptor inhibition, which blocks presynaptic endocannabinoid signaling, or by 2-arachidonoylglycerol synthesis inhibition, which impairs postsynaptic endocannabinoid release. In synapses formed by CA1-region pyramidal neurons onto burst-firing subiculum neurons, presynaptic in vivo knockout of ß-neurexins aggravated endocannabinoid-mediated inhibition of synaptic transmission and blocked LTP; presynaptic CB1-receptor antagonists or postsynaptic 2-arachidonoylglycerol synthesis inhibition again reversed this block. Moreover, conditional knockout of ß-neurexins in CA1-region neurons impaired contextual fear memories. Thus, our data suggest that presynaptic ß-neurexins control synaptic strength in excitatory synapses by regulating postsynaptic 2-arachidonoylglycerol synthesis, revealing an unexpected role for ß-neurexins in the endocannabinoid-dependent regulation of neural circuits.

Neurexin-2 restricts synapse numbers and restrains the presynaptic release probability by an alternative splicing-dependent mechanism.

α- and ß-neurexins are extensively alternatively spliced, presynaptic cell-adhesion molecules that are thought to organize synapse assembly. However, recent data revealed that, in the hippocampus in vivo, the deletion of one neurexin isoform, Nrxn2, surprisingly increased excitatory synapse numbers and enhanced their presynaptic release probability, suggesting that Nrxn2 restricts, instead of enabling, synapse assembly. To delineate the synaptic function and mechanism of action of Nrxn2, we examined cultured hippocampal neurons as a reduced system. In heterologous synapse formation assays, different alternatively spliced Nrxn2ß isoforms robustly promoted synapse assembly similar to Nrxn1ß and Nrxn3ß, consistent with a general synaptogenic function of neurexins. Deletion of Nrxn2 from cultured hippocampal neurons, however, caused a significant increase in synapse density and release probability, replicating the in vivo data that suggested a synapse-restricting function. Rescue experiments revealed that two of the four Nrxn2ß splice variants (Nrxn2ß-SS4+/SS5- and Nrxn2ß-SS4+/SS5+) reversed the increase in synapse density in Nrxn2-deficient neurons, whereas only one of the four Nrxn2ß splice variants (Nrxn2ß-SS4+/SS5+) normalized the increase in release probability in Nrxn2-deficient neurons. Thus, a subset of Nrxn2 splice variants restricts synapse numbers and restrains their release probability in cultured neurons.

Trans-synaptic interaction of GluRdelta2 and Neurexin through Cbln1 mediates synapse formation in the cerebellum.

Elucidation of molecular mechanisms that regulate synapse formation is required for the understanding of neural wiring, higher brain functions, and mental disorders. Despite the wealth of in vitro information, fundamental questions about how glutamatergic synapses are formed in the mammalian brain remain unanswered. Glutamate receptor (GluR) delta2 is essential for cerebellar synapse formation in vivo. Here, we show that the N-terminal domain (NTD) of GluRdelta2 interacts with presynaptic neurexins (NRXNs) through cerebellin 1 precursor protein (Cbln1). The synaptogenic activity of GluRdelta2 is abolished in cerebellar primary cultures from Cbln1 knockout mice and is restored by recombinant Cbln1. Knockdown of NRXNs in cerebellar granule cells also hinders the synaptogenic activity of GluRdelta2. Both the NTD of GluRdelta2 and the extracellular domain of NRXN1beta suppressed the synaptogenic activity of Cbln1 in cerebellar primary cultures and in vivo. These results suggest that GluRdelta2 mediates cerebellar synapse formation by interacting with presynaptic NRXNs through Cbln1.

Transsynaptic cerebellin 4-neogenin 1 signaling mediates LTP in the mouse dentate gyrus.

Five decades ago, long-term potentiation (LTP) of synaptic transmission was discovered at entorhinal cortex→dentate gyrus (EC→DG) synapses, but the molecular determinants of EC→DG LTP remain largely unknown. Here, we show that the presynaptic neurexin­ligand cerebellin-4 (Cbln4) is highly expressed in the entorhinal cortex and essential for LTP at EC→DG synapses, but dispensable for basal synaptic transmission at these synapses. Cbln4, when bound to cell-surface neurexins, forms transcellular complexes by interacting with postsynaptic DCC (deleted in colorectal cancer) or neogenin-1. DCC and neogenin-1 act as netrin and repulsive guidance molecule-a (RGMa) receptors that mediate axon guidance in the developing brain, but their binding to Cbln4 raised the possibility that they might additionally function in the mature brain as postsynaptic receptors for presynaptic neurexin/Cbln4 complexes, and that as such receptors, DCC or neogenin-1 might mediate EC→DG LTP that depends on Cbln4. Indeed, we observed that neogenin-1, but not DCC, is abundantly expressed in dentate gyrus granule cells, and that postsynaptic neogenin-1 deletions in dentate granule cells blocked EC→DG LTP, but again did not affect basal synaptic transmission similar to the presynaptic Cbln4 deletions. Thus, binding of presynaptic Cbln4 to postsynaptic neogenin-1 renders EC→DG synapses competent for LTP, but is not required for establishing these synapses or for otherwise enabling their function.

Photoprotective effects of Lithospermum erythrorhizon and Pueraria lobata extracts on UVB-induced photoaging: A study on skin barrier protection.

AIM: Lithospermum erythrorhizon and Pueraria lobata exhibit promising potential as cosmetic additives for mitigating skin barrier impairment induced by photoaging. Despite their potential, the precise mechanisms underlying their protective and ameliorative effects remain elusive. This study sought to assess the reparative properties of Lithospermum erythrorhizon and Pueraria lobata extracts (LP) on UVB-irradiated human skin keratinocytes (HaCaT cells) and explore the therapeutic potential of LP as a skin barrier protection agent. MATERIALS AND METHODS: Antioxidant activities were gauged through 2,2-diphenyl-1-picrylhydrazyl (DPPH), 2,2'-azino-bis(3-ethylbenzothiazoline-6-sulfonic acid) (ABTS), and reactive oxygen species (ROS) assays. The expression levels of skin barrier-related markers, encompassing metalloproteinases (MMPs) and hyaluronidase (HYAL) were scrutinized using enzyme-linked immunosorbent assay (ELISA), reverse transcriptase (RT)-PCR, and Western blotting, with a particular focus on the involvement of the transforming growth factor (TGF)-ß/Smad and nuclear factor-κB (NF-κB) signaling pathways. RESULTS: The study revealed that LP effectively scavenges free radicals, diminishes ROS production in a dose-dependent manner, and significantly attenuates UVB-induced expression of MMP-1 and MMP-3 through modulation of the hyaluronan synthase (HAS)2/HYAL1 signaling axis in UVB-irradiated HaCaT cells. Additionally, LP demonstrated enhanced TGF-ß signaling activation, fostering procollagen type I synthesis, and concurrently exhibited mitogen-activated protein kinases (MAPK)/NF-κB signaling inactivation, thereby mitigating pro-inflammatory cytokine release and alleviating UVB-induced cellular damage. CONCLUSION: In conclusion, the observed protective effects of LP on skin cellular constituents highlight its substantial biological potential for shielding against UVB-induced skin photoaging, positioning it as a promising candidate for both pharmaceutical and cosmetic applications.

Development of a neutralization monoclonal antibody with a broad neutralizing effect against SARS-CoV-2 variants.

BACKGROUND: The emergence of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) variants has challenged the effectiveness of current therapeutic regimens. Here, we aimed to develop a potent SARS-CoV-2 antibody with broad neutralizing effect by screening a scFv library with the spike protein receptor-binding domain (RBD) via phage display. METHODS: SKAI-DS84 was identified through phage display, and we performed pseudovirus neutralization assays, authentic virus neutralization assays, and in vivo neutralization efficacy evaluations. Furthermore, surface plasmon resonance (SPR) analysis was conducted to assess the physical characteristics of the antibody, including binding kinetics and measure its affinity for variant RBDs. RESULTS: The selected clones were converted to human IgG, and among them, SKAI-DS84 was selected for further analyses based on its binding affinity with the variant RBDs. Using pseudoviruses, we confirmed that SKAI-DS84 was strongly neutralizing against wild-type, B.1.617.2, B.1.1.529, and subvariants of SARS-CoV-2. We also tested the neutralizing effect of SKAI-DS84 on authentic viruses, in vivo and observed a reduction in viral replication and improved lung pathology. We performed binding and epitope mapping experiments to understand the mechanisms underlying neutralization and identified quaternary epitopes formed by the interaction between RBDs as the target of SKAI-DS84. CONCLUSIONS: We identified, produced, and tested the neutralizing effect of SKAI-DS84 antibody. Our results highlight that SKAI-DS84 could be a potential neutralizing antibody against SARS-CoV-2 and its variants.

DNA lattice growth with single, double, and triple double-crossover boundaries by stepwise self-assembly.

Construction of various nanostructures with nanometre-scale precision through various DNA building blocks depends upon self-assembly, base-pair complementarity and sequence programmability. During annealing, unit tiles are formed by the complementarity of base pairs in each strand. Enhancement of growth of target lattices is expected if seed lattices (i.e. boundaries for growth of target lattices) are initially present in a test tube during annealing. Although most processes for annealing DNA nanostructures adopt a one-step high temperature method, multi-step annealing provides certain advantages such as reusability of unit tiles and tuneability of lattice formation. We can construct target lattices effectively (through multi-step annealing) and efficiently (via boundaries) by multi-step annealing and combining boundaries. Here, we construct efficient boundaries made of single, double, and triple double-crossover DNA tiles for growth of DNA lattices. Two unit double-crossover DNA tile-based lattices and copy-logic implemented algorithmic lattices were introduced to test the growth of target lattices on boundaries. We used multi-step annealing to tune the formation of DNA crystals during fabrication of DNA crystals comprised of boundaries and target lattices. The formation of target DNA lattices was visualized using atomic force microscopy (AFM). The borders between boundaries and lattices in a single crystal were clearly differentiable from AFM images. Our method provides way to construct various types of lattices in a single crystal, which might generate various patterns and enhance the information capacity in a given crystal.

AC-motif: a DNA motif containing adenine and cytosine repeat plays a role in gene regulation.

I-motif or C4 is a four-stranded DNA structure with a protonated cytosine:cytosine base pair (C+:C) found in cytosine-rich sequences. We have found that oligodeoxynucleotides containing adenine and cytosine repeats form a stable secondary structure at a physiological pH with magnesium ion, which is similar to i-motif structure, and have named this structure 'adenine:cytosine-motif (AC-motif)'. AC-motif contains C+:C base pairs intercalated with putative A+:C base pairs between protonated adenine and cytosine. By investigation of the AC-motif present in the CDKL3 promoter (AC-motifCDKL3), one of AC-motifs found in the genome, we confirmed that AC-motifCDKL3 has a key role in regulating CDKL3 gene expression in response to magnesium. This is further supported by confirming that genome-edited mutant cell lines, lacking the AC-motif formation, lost this regulation effect. Our results verify that adenine-cytosine repeats commonly present in the genome can form a stable non-canonical secondary structure with a non-Watson-Crick base pair and have regulatory roles in cells, which expand non-canonical DNA repertoires.

Rethinking Breast Cancer Diagnosis through Deep Learning Based Image Recognition.

This paper explored techniques for diagnosing breast cancer using deep learning based medical image recognition. X-ray (Mammography) images, ultrasound images, and histopathology images are used to improve the accuracy of the process by diagnosing breast cancer classification and by inferring their affected location. For this goal, the image recognition application strategies for the maximal diagnosis accuracy in each medical image data are investigated in terms of various image classification (VGGNet19, ResNet50, DenseNet121, EfficietNet v2), image segmentation (UNet, ResUNet++, DeepLab v3), and related loss functions (binary cross entropy, dice Loss, Tversky loss), and data augmentation. As a result of evaluations through the presented methods, when using filter-based data augmentation, ResNet50 showed the best performance in image classification, and UNet showed the best performance in both X-ray image and ultrasound image as image segmentation. When applying the proposed image recognition strategies for the maximal diagnosis accuracy in each medical image data, the accuracy can be improved by 33.3% in image segmentation in X-ray images, 29.9% in image segmentation in ultrasound images, and 22.8% in image classification in histopathology images.

Optimal Configuration of Multi-Task Learning for Autonomous Driving.

For autonomous driving, it is imperative to perform various high-computation image recognition tasks with high accuracy, utilizing diverse sensors to perceive the surrounding environment. Specifically, cameras are used to perform lane detection, object detection, and segmentation, and, in the absence of lidar, tasks extend to inferring 3D information through depth estimation, 3D object detection, 3D reconstruction, and SLAM. However, accurately processing all these image recognition operations in real-time for autonomous driving under constrained hardware conditions is practically unfeasible. In this study, considering the characteristics of image recognition tasks performed by these sensors and the given hardware conditions, we investigated MTL (multi-task learning), which enables parallel execution of various image recognition tasks to maximize their processing speed, accuracy, and memory efficiency. Particularly, this study analyzes the combinations of image recognition tasks for autonomous driving and proposes the MDO (multi-task decision and optimization) algorithm, consisting of three steps, as a means for optimization. In the initial step, a MTS (multi-task set) is selected to minimize overall latency while meeting minimum accuracy requirements. Subsequently, additional training of the shared backbone and individual subnets is conducted to enhance accuracy with the predefined MTS. Finally, both the shared backbone and each subnet undergo compression while maintaining the already secured accuracy and latency performance. The experimental results indicate that integrated accuracy performance is critically important in the configuration and optimization of MTL, and this integrated accuracy is determined by the ITC (inter-task correlation). The MDO algorithm was designed to consider these characteristics and construct multi-task sets with tasks that exhibit high ITC. Furthermore, the implementation of the proposed MDO algorithm, coupled with additional SSL (semi-supervised learning) based training, resulted in a significant performance enhancement. This advancement manifested as approximately a 12% increase in object detection mAP performance, a 15% improvement in lane detection accuracy, and a 27% reduction in latency, surpassing the results of previous three-task learning techniques like YOLOP and HybridNet.

A Method of Deep Learning Model Optimization for Image Classification on Edge Device.

Due to the recent increasing utilization of deep learning models on edge devices, the industry demand for Deep Learning Model Optimization (DLMO) is also increasing. This paper derives a usage strategy of DLMO based on the performance evaluation through light convolution, quantization, pruning techniques and knowledge distillation, known to be excellent in reducing memory size and operation delay with a minimal accuracy drop. Through experiments regarding image classification, we derive possible and optimal strategies to apply deep learning into Internet of Things (IoT) or tiny embedded devices. In particular, strategies for DLMO technology most suitable for each on-device Artificial Intelligence (AI) service are proposed in terms of performance factors. In this paper, we suggest a possible solution of the most rational algorithm under very limited resource environments by utilizing mature deep learning methodologies.

Determinants of replication protein A subunit interactions revealed using a phosphomimetic peptide.

Replication protein A (RPA) is a eukaryotic ssDNA-binding protein and contains three subunits: RPA70, RPA32, and RPA14. Phosphorylation of the N-terminal region of the RPA32 subunit plays an essential role in DNA metabolism in processes such as replication and damage response. Phosphorylated RPA32 (pRPA32) binds to RPA70 and possibly regulates the transient RPA70-Bloom syndrome helicase (BLM) interaction to inhibit DNA resection. However, the structural details and determinants of the phosphorylated RPA32-RPA70 interaction are still unknown. In this study, we provide molecular details of the interaction between RPA70 and a mimic of phosphorylated RPA32 (pmRPA32) using fluorescence polarization and NMR analysis. We show that the N-terminal domain of RPA70 (RPA70N) specifically participates in pmRPA32 binding, whereas the unphosphorylated RPA32 does not bind to RPA70N. Our NMR data revealed that RPA70N binds pmRPA32 using a basic cleft region. We also show that at least 6 negatively charged residues of pmRPA32 are required for RPA70N binding. By introducing alanine mutations into hydrophobic positions of pmRPA32, we found potential points of contact between RPA70N and the N-terminal half of pmRPA32. We used this information to guide docking simulations that suggest the orientation of pmRPA32 in complex with RPA70N. Our study demonstrates detailed features of the domain-domain interaction between RPA70 and RPA32 upon phosphorylation. This result provides insight into how phosphorylation tunes transient bindings between RPA and its partners in DNA resection.

Loop-mediated fluorescent probes for selective discrimination of parallel and antiparallel G-Quadruplexes.

Herein we report simple pyridinium (1-3) and quinolinium (4) salts for the selective recognition of G-quadruplexes (G4s). Among them, the probe 1, interestingly, selectively discriminated parallel (c-KIT-1, c-KIT-2, c-MYC) G4s from anti-parallel/hybrid (22AG, HRAS-1, BOM-17, TBA) G4s at pH 7.2, through a switch on response in the far-red window. Significant changes in the absorption (broad 575 nm â†’ sharp 505 nm) and emission of probe 1 at 620 nm, attributed to selective interaction with parallel G4s, resulted in complete disaggregation-induced monomer emission. Symmetrical push/pull molecular confinements across the styryl units in probe 1 enhanced the intramolecular charge transfer (ICT) by restricting the free rotation of CC units in the presence of sterically less hindered and highly accessible G4 surface/bottom tetrads in the parallel G4s, which is relatively lower extent in antiparallel/hybrid G4s. We confirm that the disaggregation of probe 1 was very effective in the presence of parallel G4-forming ODNs, due to the presence of highly available free surface area, resulting in additional π-stacking interactions. The selective sensing capabilities of probe 1 were analyzed using UV-Vis spectroscopy, fluorescence spectroscopy, molecular dynamics (MD)-based simulation studies, and 1H NMR spectroscopy. This study should afford insights for the future design of selective compounds targeting parallel G4s.

Evaluation of a Concrete Slab Track with Debonding at the Interface between Track Concrete Layer and Hydraulically Stabilized Base Course Using Multi-Channel Impact-Echo Testing.

Multi-channel Impact-echo (IE) testing was used to evaluate debonding defects at the interface between track concrete layer, TCL, and hydraulically stabilized base course, HSB, in a real scale mockup model of concrete slab tracks for Korea high-speed railway (KHSR) system. The mockup model includes three debonding defects that were fabricated by inserting three 400 mm by 400 mm (length and width) thin plastic foam boards with three different thicknesses of 5 mm, 10 mm, and 15 mm, before casting concrete in TCL. Multi-channel IE signals obtained over solid concrete and debonding defects were reduced to three critical IE testing parameters (the velocity of concrete, peak frequency, and Q factor). Bilinear classification models were used to evaluate the individual and a combination of the characteristic parameters. It was demonstrated that the best evaluation performance was obtained by using average peak frequency or the combination of average peak frequency and average Q factor, obtained by eight accelerometers in the multi-channel IE device. The results and discussion in this study would improve the understanding of characteristics of multiple IE testing parameters in concrete slab tracks and provide a fundamental basis to develop an effective prediction model of non-destructive evaluation for debonding defects at the interface between TCL and HSB in concrete slab tracks.

Semantic Cardiac Segmentation in Chest CT Images Using K-Means Clustering and the Mathematical Morphology Method.

Whole cardiac segmentation in chest CT images is important to identify functional abnormalities that occur in cardiovascular diseases, such as coronary artery disease (CAD) detection. However, manual efforts are time-consuming and labor intensive. Additionally, labeling the ground truth for cardiac segmentation requires the extensive manual annotation of images by the radiologist. Due to the difficulty in obtaining the annotated data and the required expertise as an annotator, an unsupervised approach is proposed. In this paper, we introduce a semantic whole-heart segmentation combining K-Means clustering as a threshold criterion of the mean-thresholding method and mathematical morphology method as a threshold shifting enhancer. The experiment was conducted on 500 subjects in two cases: (1) 56 slices per volume containing full heart scans, and (2) 30 slices per volume containing about half of the top of heart scans before the liver appears. In both cases, the results showed an average silhouette score of the K-Means method of 0.4130. Additionally, the experiment on 56 slices per volume achieved an overall accuracy (OA) and mean intersection over union (mIoU) of 34.90% and 41.26%, respectively, while the performance for the first 30 slices per volume achieved an OA and mIoU of 55.10% and 71.46%, respectively.

Deep-Learning-Based Coronary Artery Calcium Detection from CT Image.

One of the most common methods for diagnosing coronary artery disease is the use of the coronary artery calcium score CT. However, the current diagnostic method using the coronary artery calcium score CT requires a considerable time, because the radiologist must manually check the CT images one-by-one, and check the exact range. In this paper, three CNN models are applied for 1200 normal cardiovascular CT images, and 1200 CT images in which calcium is present in the cardiovascular system. We conduct the experimental test by classifying the CT image data into the original coronary artery calcium score CT images containing the entire rib cage, the cardiac segmented images that cut out only the heart region, and cardiac cropped images that are created by using the cardiac images that are segmented into nine sub-parts and enlarged. As a result of the experimental test to determine the presence of calcium in a given CT image using Inception Resnet v2, VGG, and Resnet 50 models, the highest accuracy of 98.52% was obtained when cardiac cropped image data was applied using the Resnet 50 model. Therefore, in this paper, it is expected that through further research, both the simple presence of calcium and the automation of the calcium analysis score for each coronary artery calcium score CT will become possible.

Seeded Ising Model and Distributed Biometric Template Storage and Matching.

It is known that a variant of Ising model, called Seeded Ising Model, can be used to recover the information content of a biometric template from a fraction of information therein. The method consists in reconstructing the whole template, which is called the intruder template in this paper, using only a small portion of the given template, a partial template. This reconstruction method may pose a security threat to the integrity of a biometric identity management system. In this paper, based on the Seeded Ising Model, we present a systematic analysis of the possible security breach and its probability of accepting the intruder templates as genuine. Detailed statistical experiments on the intruder match rate are also conducted under various scenarios. In particular, we study (1) how best a template is divided into several small pieces called partial templates, each of which is to be stored in a separate silo; (2) how to do the matching by comparing partial templates in the locked-up silos, and letting only the results of these intra-silo comparisons be sent to the central tallying server for final scoring without requiring the whole templates in one location at any time.

Carbonic anhydrase-related protein CA10 is an evolutionarily conserved pan-neurexin ligand.

Establishment, specification, and validation of synaptic connections are thought to be mediated by interactions between pre- and postsynaptic cell-adhesion molecules. Arguably, the best-characterized transsynaptic interactions are formed by presynaptic neurexins, which bind to diverse postsynaptic ligands. In a proteomic screen of neurexin-1 (Nrxn1) complexes immunoisolated from mouse brain, we identified carbonic anhydrase-related proteins CA10 and CA11, two homologous, secreted glycoproteins of unknown function that are predominantly expressed in brain. We found that CA10 directly binds in a cis configuration to a conserved membrane-proximal, extracellular sequence of α- and ß-neurexins. The CA10-neurexin complex is stable and stoichiometric, and results in formation of intermolecular disulfide bonds between conserved cysteine residues in neurexins and CA10. CA10 promotes surface expression of α- and ß-neurexins, suggesting that CA10 may form a complex with neurexins in the secretory pathway that facilitates surface transport of neurexins. Moreover, we observed that the Nrxn1 gene expresses from an internal 3' promoter a third isoform, Nrxn1γ, that lacks all Nrxn1 extracellular domains except for the membrane-proximal sequences and that also tightly binds to CA10. Our data expand the understanding of neurexin-based transsynaptic interaction networks by providing further insight into the interactions nucleated by neurexins at the synapse.

Transdifferentiation of bovine epithelial towards adipocytes in the presence of myoepithelium.

Orchastric changes in the mammary glands are vital, especially during the lactaction. The secretary epithelial cells together with the supporting myoepithelial and stromal cells function cordially to secrete milk.Increase in the number of luminal epithelial cells and a decrease in adipocytes are visible during lactation, whereas the reverse happens in the involution. However, an early involution occurs if the epithelial transdifferente towards adipocytes in lactation period. We aimed to inhibit the adipocyte transdifferentiation of luminal cells by restraining the PPARγ pathway. Linolenic acid (LA) and thiazolidinediones (TZDs) induced adipogenesis in mammary epithelial cells were conducted in monolayer, mixed culture as well as in transwell plate co-culture with mammary myoepithelial cells.Co-culture with myoepithelial cells showed higher adipogenic gene expression in epithelial cells under LA+TZDs treatment. Increase in the expressions of PPARγ, C/EBPα and vimentin in both mRNA as well as protein levels were observed.Whereas,BADGE treatment blocked LA+TZDs induced adipogenesis, as it could not show a significant rise in adipose related markers. Although comparative results were found in both mixed culture and monolayer conditions, co-culture technic found to work better than the others. In summary, antagonizing PPARγ pathway in the presence of myoepithelial cells can significantly reduce the adipogenisis in epithelial cells, suggestingtherapeutic inhibition of PPARγ can be considered to counter early involution or excessive adipogenesis in mammary epithelium in animals.

Presynaptic Neuronal Pentraxin Receptor Organizes Excitatory and Inhibitory Synapses.

Three neuronal pentraxins are expressed in brain, the membrane-bound "neuronal pentraxin receptor" (NPR) and the secreted proteins NP1 and NARP (i.e., NP2). Neuronal pentraxins bind to AMPARs at excitatory synapses and play important, well-documented roles in the activity-dependent regulation of neural circuits via this binding activity. However, it is unknown whether neuronal pentraxins perform roles in synapses beyond modulating postsynaptic AMPAR-dependent plasticity, and whether they may even act in inhibitory synapses. Here, we show that NPR expressed in non-neuronal cells potently induces formation of both excitatory and inhibitory postsynaptic specializations in cocultured hippocampal neurons. Knockdown of NPR in hippocampal neurons, conversely, dramatically decreased assembly and function of both excitatory and inhibitory postsynaptic specializations. Overexpression of NPR rescued the NPR knockdown phenotype but did not in itself change synapse numbers or properties. However, the NPR knockdown decreased the levels of NARP, whereas NPR overexpression produced a dramatic increase in the levels of NP1 and NARP, suggesting that NPR recruits and stabilizes NP1 and NARP on the presynaptic plasma membrane. Mechanistically, NPR acted in excitatory synapse assembly by binding to the N-terminal domain of AMPARs; antagonists of AMPA and GABA receptors selectively inhibited NPR-induced heterologous excitatory and inhibitory synapse assembly, respectively, but did not affect neurexin-1ß-induced synapse assembly as a control. Our data suggest that neuronal pentraxins act as signaling complexes that function as general trans-synaptic organizers of both excitatory and inhibitory synapses by a mechanism that depends, at least in part, on the activity of the neurotransmitter receptors at these synapses. SIGNIFICANCE STATEMENT: Neuronal pentraxins comprise three neuronal proteins, neuronal pentraxin receptor (NPR) which is a type-II transmembrane protein on the neuronal surface, and secreted neuronal pentraxin-1 and NARP. The general functions of neuronal pentraxins at synapses have not been explored, except for their basic AMPAR binding properties. Here, we examined the functional role of NPR at synapses because it is the only neuronal pentraxin that is anchored to the neuronal cell-surface membrane. We find that NPR is a potent inducer of both excitatory and inhibitory heterologous synapses, and that knockdown of NPR in cultured neurons decreases the density of both excitatory and inhibitory synapses. Our data suggest that NPR performs a general, previously unrecognized function as a universal organizer of synapses.