Streamline Proteomic Approach for Characterizing Protein−Protein Interaction Network in a RAD52 Protein Complex

Large-scale identification of protein-protein interactions (PPIs) in functional complexes represents an efficient route to elucidate the regulatory rules of cellular functions. While many methods have been developed to identify the PPIs associated with particular target/bait protein in complexes, little information is available about the interaction relationships among all components in a complex. Here, we have established a strategy of integrating proteomic identification of complex components with mammalian two-hybrid screening of their binary relationships to achieve information content of both breadth (i.e., identifying all potential interacting partners of the protein of interest) and depth (i.e., detailed mapping of the physical interactions of a subset of the identified and functionally related proteins) in characterizing protein complexes. In the initial phase of quantitative proteomic analysis of this streamline, the proteins that specifically complex with the target/bait protein were pulled down by immunoprecipitation and identified by mass spectrometry (MS)-based “dual-tagging” quantitative proteomic approach. In the second phase of in-depth characterizations of binary relationships, the physical interactions of a subset of functionally closely related complex components are mapped by mammalian two-hybrid assay. The screening for binary relationships of complex components not only serves as a validation of the first phase of proteomic identification, but also further deepens the understanding of the protein complex of interest. With this streamlined approach, we studied the protein complexes that are associated with a DNA recombination protein RAD52. In the initial phase, multiple proteins both known and unknown to interact with RAD52 were identified by the “dual-tagging” proteomic method. In the second phase, a complex protein-protein interaction network, which may play important roles in coordinating the activity of DNA repair with that of cell division, was defined by the mammalian two-hybrid assay

Hormone-Dependent Expression of a Steroidogenic Acute Regulatory Protein Natural Antisense Transcript in MA-10 Mouse Tumor Leydig Cells

Cholesterol transport is essential for many physiological processes, including steroidogenesis. In steroidogenic cells hormone-induced cholesterol transport is controlled by a protein complex that includes steroidogenic acute regulatory protein (StAR). Star is expressed as 3.5-, 2.8-, and 1.6-kb transcripts that differ only in their 3′-untranslated regions. Because these transcripts share the same promoter, mRNA stability may be involved in their differential regulation and expression. Recently, the identification of natural antisense transcripts (NATs) has added another level of regulation to eukaryotic gene expression. Here we identified a new NAT that is complementary to the spliced Star mRNA sequence. Using 5′ and 3′ RACE, strand-specific RT-PCR, and ribonuclease protection assays, we demonstrated that Star NAT is expressed in MA-10 Leydig cells and steroidogenic murine tissues. Furthermore, we established that human chorionic gonadotropin stimulates Star NAT expression via cAMP. Our results show that sense-antisense Star RNAs may be coordinately regulated since they are co-expressed in MA-10 cells. Overexpression of Star NAT had a differential effect on the expression of the different Star sense transcripts following cAMP stimulation. Meanwhile, the levels of StAR protein and progesterone production were downregulated in the presence of Star NAT. Our data identify antisense transcription as an additional mechanism involved in the regulation of steroid biosynthesis

Mitochondrial TSPO Deficiency Triggers Retrograde Signaling in MA-10 Mouse Tumor Leydig Cells

The mitochondrial translocator protein (TSPO) has been shown to bind cholesterol with high affinity and is involved in mediating its availability for steroidogenesis. We recently reported that targeted Tspo gene deletion in MA-10 mouse tumor Leydig cells resulted in reduced cAMP-stimulated steroid formation and significant reduction in the mitochondrial membrane potential (ΔΨm) compared to control cells. We hypothesized that ΔΨm reduction in the absence of TSPO probably reflects the dysregulation and/or maintenance failure of some basic mitochondrial function(s). To explore the consequences of TSPO depletion via CRISPR-Cas9-mediated deletion (indel) mutation in MA-10 cells, we assessed the transcriptome changes in TSPO-mutant versus wild-type (Wt) cells using RNA-seq. Gene expression profiles were validated using real-time PCR. We report herein that there are significant changes in nuclear gene expression in Tspo mutant versus Wt cells. The identified transcriptome changes were mapped to several signaling pathways including the regulation of membrane potential, calcium signaling, extracellular matrix, and phagocytosis. This is a retrograde signaling pathway from the mitochondria to the nucleus and is probably the result of changes in expression of several transcription factors, including key members of the NF-κB pathway. In conclusion, TSPO regulates nuclear gene expression through intracellular signaling. This is the first evidence of a compensatory response to the loss of TSPO with transcriptome changes at the cellular level

Image Shadow Removal Using End-to-End Deep Convolutional Neural Networks

Image degradation caused by shadows is likely to cause technological issues in image segmentation and target recognition. In view of the existing shadow removal methods, there are problems such as small and trivial shadow processing, the scarcity of end-to-end automatic methods, the neglecting of light, and high-level semantic information such as materials. An end-to-end deep convolutional neural network is proposed to further improve the image shadow removal effect. The network mainly consists of two network models, an encoder–decoder network and a small refinement network. The former predicts the alpha shadow scale factor, and the latter refines to obtain sharper edge information. In addition, a new image database (remove shadow database, RSDB) is constructed; and qualitative and quantitative evaluations are made on databases such as UIUC, UCF and newly-created databases (RSDB) with various real images. Using the peak signal-to-noise ratio (PSNR) and the structural similarity (SSIM) for quantitative analysis, the algorithm has a big improvement on the PSNR and the SSIM as opposed to other methods. In terms of qualitative comparisons, the network shadow has a clearer and shadow-free image that is consistent with the original image color and texture, and the detail processing effect is much better. The experimental results show that the proposed algorithm is superior to other algorithms, and it is more robust in subjective vision and objective quantization

Multifocus Image Fusion Using Wavelet-Domain-Based Deep CNN

Multifocus image fusion is the merging of images of the same scene and having multiple different foci into one all-focus image. Most existing fusion algorithms extract high-frequency information by designing local filters and then adopt different fusion rules to obtain the fused images. In this paper, a wavelet is used for multiscale decomposition of the source and fusion images to obtain high-frequency and low-frequency images. To obtain clearer and complete fusion images, this paper uses a deep convolutional neural network to learn the direct mapping between the high-frequency and low-frequency images of the source and fusion images. In this paper, high-frequency and low-frequency images are used to train two convolutional networks to encode the high-frequency and low-frequency images of the source and fusion images. The experimental results show that the method proposed in this paper can obtain a satisfactory fusion image, which is superior to that obtained by some advanced image fusion algorithms in terms of both visual and objective evaluations

Image Sand Style Painting Algorithm

Abstract: With the development of computer technology and the improvement of people’s living level, the use of computers to deal with the photos is more and more popular. How to combine the computer and image perfectly will directly affect the quality of our image. Research of this article is how to manage the image by computer to desertification, that processing the image into desertification style. The result of traditional desertification method is not ideal, this article based on the matrix low-rank decomposition to processing image that decompose the matrix corresponding from the image, deal with the high order matrix into low order matrix and then reduce reduction into high order matrix, which simplifies the processing step and obtained good results

Image Inpainting Algorithm Based on Low-Rank Approximation and Texture Direction

Existing image inpainting algorithm based on low-rank matrix approximation cannot be suitable for complex, large-scale, damaged texture image. An inpainting algorithm based on low-rank approximation and texture direction is proposed in the paper. At first, we decompose the image using low-rank approximation method. Then the area to be repaired is interpolated by level set algorithm, and we can reconstruct a new image by the boundary values of level set. In order to obtain a better restoration effect, we make iteration for low-rank decomposition and level set interpolation. Taking into account the impact of texture direction, we segment the texture and make low-rank decomposition at texture direction. Experimental results show that the new algorithm is suitable for texture recovery and maintaining the overall consistency of the structure, which can be used to repair large-scale damaged image

Evolutionary Origin of the Mitochondrial Cholesterol Transport Machinery Reveals a Universal Mechanism of Steroid Hormone Biosynthesis in Animals

<div><p>Steroidogenesis begins with the transport of cholesterol from intracellular stores into mitochondria <i>via</i> a series of protein-protein interactions involving cytosolic and mitochondrial proteins located at both the outer and inner mitochondrial membranes. In adrenal glands and gonads, this process is accelerated by hormones, leading to the production of high levels of steroids that control tissue development and function. A hormone-induced multiprotein complex, the transduceosome, was recently identified, and is composed of cytosolic and outer mitochondrial membrane proteins that control the rate of cholesterol entry into the outer mitochondrial membrane. More recent studies unveiled the steroidogenic metabolon, a bioactive, multimeric protein complex that spans the outer-inner mitochondrial membranes and is responsible for hormone-induced import, segregation, targeting, and metabolism of cholesterol by cytochrome P450 family 11 subfamily A polypeptide 1 (CYP11A1) in the inner mitochondrial membrane. The availability of genome information allowed us to systematically explore the evolutionary origin of the proteins involved in the mitochondrial cholesterol transport machinery (transduceosome, steroidogenic metabolon, and signaling proteins), trace the original archetype, and predict their biological functions by molecular phylogenetic and functional divergence analyses, protein homology modeling and molecular docking. Although most members of these complexes have a history of gene duplication and functional divergence during evolution, phylogenomic analysis revealed that all vertebrates have the same functional complex members, suggesting a common mechanism in the first step of steroidogenesis. An archetype of the complex was found in invertebrates. The data presented herein suggest that the cholesterol transport machinery is responsible for steroidogenesis among all vertebrates and is evolutionarily conserved throughout the entire animal kingdom.</p> </div