Melanoma accounts for the majority of all skin cancer-related deaths and only 1/3rd of melanoma patients with distal metastasis survive beyond five years. However, current therapies including BRAF/MEK targeted therapies or immunotherapies only benefit a subset of melanoma patients due to the emergence of intrinsic or extrinsic resistance mechanisms. Effective treatment of melanoma will thus require new and more effective therapeutic agents. Towards the goal of identifying new therapeutic agents, we conducted an unbiased, druggable epigenetic drug screen using a library of 32 epigenetic inhibitors obtained from the Structural Genome Consortium that targets proteins encoding for epigenetic regulators. This chemical genetic screening identified TP-472, which targets bromodomain-7/9, as the strongest inhibitor of melanoma growth in both short- and long-term survival assays and in mouse models of melanoma tumor growth. Mechanistically, using a transcriptome-wide mRNA sequencing profile we identified TP-472 treatment downregulates genes encoding various extracellular matrix (ECM) proteins, including integrins, collagens, and fibronectins. Reactome-based functional pathway analyses revealed that many of the ECM proteins are involved in extracellular matrix interactions required for cancer cell growth and proliferation. TP-472 treatment also upregulated several pro-apoptotic genes that can inhibit melanoma growth. Collectively, our results identify BRD7/9 inhibitor TP-472 as a potentially useful therapeutic agent for melanoma therapy.

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Berberine (BBR), a plant alkaloid, is known for its therapeutic properties of anticancer, cardioprotective, antidiabetic, hypolipidemic, neuroprotective, and hepatoprotective activities. The present study was to determine the molecular mechanism of BBR’s pharmacological activity in human monocytic (THP-1) cells induced by arachidonic acid (AA) or lipopolysaccharide (LPS). The effect of BBR on AA/LPS activated proinflammatory markers including TNF-α, MCP-1, IL-8 and COX-2 was measured by ELISA or quantitative real-time PCR. Furthermore, the effect of BBR on LPS-induced NF-κB translocation was determined by immunoblotting and confocal microscopy. AA/ LPS-induced TNF-α, MCP-1, IL-6, IL-8, and COX-2 markers were markedly attenuated by BBR treatment in THP-1 cells by inhibiting NF-κB translocation into the nucleus. Molecular modeling studies suggested the direct interaction of BBR to IKKα at its ligand binding site, which led to the inhibition of the LPS-induced NF-κB translocation to the nucleus. Thus, the present study demonstrated the anti-inflammatory potential of BBR via NF-κB in activated monocytes, whose interplay is key in health and in the pathophysiology of atherosclerotic development in blood vessel walls. The present study findings suggest that BBR has the potential for treating various chronic inflammatory disorders.

The Chongming white goat (CM) is an indigenous goat breed exhibits unique traits that are adapted to the local environment and artificial selection. By performing wholegenome re-sequencing, we generated 14-20× coverage sequences from 10 domestic goat breeds to explore the genomic characteristics and selection signatures of the CM breed. We identified a total of 23,508,551 single-nucleotide polymorphisms (SNPs) and 2,830,800 insertion-deletion mutations (indels) after read mapping and variant calling. We further specifically identified 1.2% SNPs (271,713) and 0.9% indels (24,843) unique to the CM breed in comparison with the other nine goat breeds. Missense (SIFT < 0.05), frameshift, splice-site, start-loss, stop-loss, and stop-gain variants were identified in 183 protein-coding genes of the CM breed. Of the 183, 36 genes, including AP4E1, FSHR, COL11A2, and DYSF, are involved in phenotype ontology terms related to the nervous system, short stature, and skeletal muscle morphology. Moreover, based on genomewide F ST and pooled heterozygosity (Hp) calculation, we further identified selection signature genes between the CM and the other nine goat breeds. These genes are significantly associated with the nervous system (C2CD3, DNAJB13, UCP2, ZMYND11, CEP126, SCAPER, and TSHR), growth (UCP2, UCP3, TSHR, FGFR1, ERLIN2, and ZNF703), and coat color (KITLG, ASIP, AHCY, RALY, and MC1R). Our results suggest that the CM breed may be differentiated from other goat breeds in terms of nervous system owing to natural or artificial selection. The whole-genome analysis provides an improved understanding of genetic diversity and trait exploration for this indigenous goat breed.

The digital medical images are stored in large databases for easy accessibility and Content based image retrieval (CBIR) is used to retrieve diagnostic cases similar to the query medical image. Image compression condense the amount of data required to represent an image, it reduces the storage and transmission requirements. The medical image retrieval problem for compressed images is studied in this paper. The proposed method integrates image retrieval to retrieve diagnostic cases similar to the query medical image and image compression techniques to minimize the bandwidth utilization. Haar wavelet is used for image compression without losses. Edge and texture features are extracted from the medical compressed medical images using Sobel edge detector and Gabor transforms respectively. The classification accuracy of retrieval is evaluated using Naïve Bayes and Support Vector Machine.

The connectivity of a protein and its structure is related to its functional properties. Many experimental approaches have been employed for the identification of Diabetes Mellitus (DM) associated candidate genes. Therefore, it is of interest to use var ious graph centrality measures integrated with the genes associated with the human Diabetes Mellitus network for the identification of potential targets. We used 2728 genes known to cause Diabetes Mellitus from Jensenlab (Novo Nordisk Foundation Center for Protein Research, Denmark) for this analysis. A protein-protein interaction network was further constructed using a tool Centralities in Biological Networks (CentiBiN) with 1020 nodes after eliminating the duplicates, parallel edges, self -loop edges and unknown Human Protein Reference Database (HPRD) IDS. We used fourteen centralities measures which are useful in identifying the structural characteristic of individuals in the network. The results of the centrality measures are highly correlated. Thus, we identified genes that are critically associated with DM. We further report the top ten genes of all fourteen centrality measures for further consideration as targets for DM.

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With the enormous growth in the Internet and network, data security has become an inevitable concern for any organization. From antecedent security has attracted considerable attention from network researchers. In this perspective many possible fields of endeavour come to mind with many cryptographic algorithms in a broader way, each is highly worthy and lengthy. As society is moving towards digital information age we necessitate highly standard algorithms which compute faster when data size is of wide range or scope. On survey, numerous sequential approaches carried out by symmetric key algorithms on 128 bits as block size are ascertained to be highly in securable and resulting at a low speed. As in the course the commodities are immensely parallelized on multi core processors to solve computational problems, in accordance with, propound parallel symmetric key based algorithms to encrypt/decrypt large data for secure conveyance. The algorithm is aimed to prevail by considering 64 character (512 bits) plain text data, processed 16 characters separately by applying parallelism and finally combine each 16 character cipher data to form 64 character cipher text. The round function employed in the algorithm is very complex, on which improves efficacy.