No abstract

Two major isoforms of theHere, Runx2-II expression was found to be specifically stimulated by BMP-2 treatment or by Dlx5 overexpression. In addition, BMP-2, Dlx5, and Runx2-II were found to be expressed in osteogenic fronts and parietal bones of the developing cranial vault and Runx2-I and Msx2 in the sutural mesenchyme. Furthermore, Runx2 P1 promoter activity was strongly stimulated by Dlx5 overexpression, whereas Runx2 P2 promoter activity was not. Runx2 P1 promoter deletion analysis indicated that the Dlx5-specific response is due to sequences between ؊756 and ؊342 bp of the P1 promoter, where three Dlx5-response elements are located. Dlx5 responsiveness to these elements was confirmed by gel mobility shift assay and site-directed mutagenesis. Moreover, Msx2 specifically suppressed the Runx2 P1 promoter, and the responsible region overlaps with that recognized by Dlx5. In summary, Dlx5 specifically transactivates the Runx2 P1 promoter, and its action on the P1 promoter is antagonized by Msx2.The Runt-related transcription factor Runx2 plays an essential role in osteoblast differentiation and bone mineralization (1, 2). Two major isoforms are expressed from the mouse Runx2 locus, and these isoforms are generated by different promoter usage. Runx2 type I (Runx2-I), 2 referred to as the Cbfa1/p56 isoform or PEBP2␣A, is a 513-amino acid protein that starts with the amino acid sequence MRIPV (3) and is derived from the proximal P2 promoter of the gene (4). More recently, upstream exons of the Runx2 gene that potentially encode the N termini of Runx2 isoforms expressed in osteoblasts have been identified (5, 6). These upstream exons contain a 5Ј-untranslated region and encode the N-terminal 19 amino acids of Runx2 type II (Runx2-II; also referred to as Cbfa1/p57 and OSF2), which starts with the sequence MASNSL (7). This isoform is expressed from the P1 or "bone-related" upstream promoter (8), and its expression is predominant in osteoblasts (9). The alternative promoter usage strongly implies that the expression pattern of each isoform differs temporally and/or spatially. Indeed, they exhibit distinct expression patterns during bone development (10, 11). Thus, it is natural to assume that these two promoters differently respond to different extracellular signals or their downstream transcription factors because these promoters have distinct transcription factor-binding sites.Runx2 plays a central role in the BMP-2-induced trans-differentiation of C2C12 cells at an early restriction point by diverting them from the myogenic pathway to the osteogenic pathway (12, 13). We found that the homeobox gene Dlx5 is an upstream target of BMP-2 signaling and that it plays a pivotal role in stimulating the downstream osteogenic master transcription factor Runx2. In turn, Runx2 acts simultaneously or sequentially to induce the expression of bone-specific genes that represent BMP-2-induced osteogenic trans-differentiation. In addition, it has also been suggested that Dlx5 is a critical target of the inhibitory action of transform...

With the continued development of artificial intelligence (AI) technology, research on interaction technology has become more popular. Facial expression recognition (FER) is an important type of visual information that can be used to understand a human's emotional situation. In particular, the importance of AI systems has recently increased due to advancements in research on AI systems applied to AI robots. In this paper, we propose a new scheme for FER system based on hierarchical deep learning. The feature extracted from the appearance feature-based network is fused with the geometric feature in a hierarchical structure. The appearance feature-based network extracts holistic features of the face using the preprocessed LBP image, whereas the geometric feature-based network learns the coordinate change of action units (AUs) landmark, which is a muscle that moves mainly when making facial expressions. The proposed method combines the result of the softmax function of two features by considering the error associated with the second highest emotion (Top-2) prediction result. In addition, we propose a technique to generate facial images with neutral emotion using the autoencoder technique. By this technique, we can extract the dynamic facial features between the neutral and emotional images without sequence data. We compare the proposed algorithm with the other recent algorithms for CK+ and JAFFE dataset, which are typically considered to be verified datasets in the facial expression recognition. The tenfold cross validation results show 96.46% of accuracy in the CK+ dataset and 91.27% of accuracy in the JAFFE dataset. When comparing with other methods, the result of the proposed hierarchical deep network structure shows up to about 3% of the accuracy improvement and 1.3% of average improvement in CK+ dataset, respectively. In JAFFE datasets, up to about 7% of the accuracy is enhanced, and the average improvement is verified by about 1.5%. INDEX TERMS Artificial intelligence (AI), facial expression recognition (FER), emotion recognition, deep learning, LBP feature, geometric feature, convolutional neural network (CNN).

No abstract

A hallmark of cancer cells is the metabolic switch from oxidative phosphorylation (OXPHOS) to glycolysis, a phenomenon referred to as the ‘Warburg effect’, which is also observed in primed human pluripotent stem cells (hPSCs). Here, we report that downregulation of SIRT2 and upregulation of SIRT1 is a molecular signature of primed hPSCs and that SIRT2 critically regulates metabolic reprogramming during induced pluripotency by targeting glycolytic enzymes including aldolase, glyceraldehyde-3-phosphate dehydrogenase, phosphoglycerate kinase, and enolase. Remarkably, knockdown of SIRT2 in human fibroblasts resulted in significantly decreased OXPHOS and increased glycolysis. In addition, we found that miR-200c-5p specifically targets SIRT2, downregulating its expression. Furthermore, SIRT2 overexpression in hPSCs significantly affected energy metabolism, altering stem cell functions such as pluripotent differentiation properties. Taken together, our results identify the miR-200c–SIRT2 axis as a key regulator of metabolic reprogramming (Warburg-like effect), via regulation of glycolytic enzymes, during human induced pluripotency and pluripotent stem cell function.

Small cell lung cancer (SCLC) is an aggressive type of lung cancer, and the detection of SCLCs at an early stage is necessary for successful therapy and for improving cancer survival rates. Fucosylation is one of the most common glycosylation-based modifications. Increased levels of fucosylation have been reported in a number of pathological conditions, including cancers. In this study, we aimed to identify and validate the aberrant and selective fucosylated glycoproteins in the sera of patients with SCLC. Fucosylated glycoproteins were enriched by the Aleuria aurantia lectin column after serum albumin and IgG depletion. In a narrowed down and comparative data analysis of both label-free proteomics and isobaric peptide-tagging chemistry iTRAQ approaches, the fucosylated glycoproteins were identified as up- or down-regulated in the sera of limited disease and extensive disease stage patients with SCLC. Verification was performed by multiple reaction monitoring-mass spectrometry to select reliable markers. Four fucosylated proteins, APCS, C9, SERPINA4, and PON1, were selected and subsequently validated by hybrid A. aurantia lectin ELISA (HLE) and Western blotting. Compared with Western blotting, the HLE analysis of these four proteins produced more optimal diagnostic values for SCLC. The PON1 protein levels were significantly reduced in the sera of patients with SCLC, whereas the fucosylation levels of PON1 were significantly increased. Fucosylated PON1 exhibited an area under curve of 0.91 for the extensive disease stage by HLE, whereas the PON1 protein levels produced an area under curve of 0.82 by Western blot. The glycan structural analysis of PON1 by MS/MS identified a biantennary fucosylated glycan modification consisting of a core + 2HexNAc + 1Fuc at increased levels in the sera of patients with SCLC. In addition, the PON1 levels were decreased in the sera of the Lewis lung carcinoma lung cancer mouse model that we examined. Our data suggest that fucosylated protein biomarkers, such as PON1, and their fucosylation levels and patterns can serve as diagnostic and prognostic serological markers for SCLC.

Runx2 is a key transcription factor in osteoblast differentiation, and its activity is regulated by fibroblast growth factors (FGFs). Craniosynostosis, characterized by premature suture closure, results from mutations that generate constitutively active FGF receptors (FGFRs). We previously showed that FGF/FGFR-activated protein kinase C (PKC) is involved in the expression and activity of Runx2. Activated PKCdelta physically interacts with Runx2 in FGF2-stimulated MC3T3-E1 preosteoblastic cells. Immunopurified Runx2 protein reacted with PKCdelta kinase, and a phosphorylated 1460-Da peptide fragment (amino acids 241-252, 1380-Da) derived from Runx2 was also detected in MS analysis. Computer analysis predicted that Ser247 in this Runx2 can be a possible phosphorylation site by PKCdelta. We also showed that Runx2 activity after FGF stimulation correlates with the presence of the Runx2 Ser247 residue. The S247A (Ser --> Ala) mutation confers decreased transcriptional activity on a Runx2-responsive promoter after FGF treatment.

The tyrosine kinase inhibitor sorafenib is the only therapeutic agent approved for the treatment of advanced hepatocellular carcinoma (HCC), but acquired resistance to sorafenib is high. Here, we report metabolic reprogramming in sorafenib-resistant HCC and identify a regulatory molecule, peroxisome proliferator-activated receptor-δ (PPARδ), as a potential therapeutic target. Sorafenib-resistant HCC cells showed markedly higher glutamine metabolism and reductive glutamine carboxylation, which was accompanied by increased glucose-derived pentose phosphate pathway and glutamine-derived lipid biosynthetic pathways and resistance to oxidative stress. These glutamine-dependent metabolic alterations were attributed to PPARδ, which was upregulated in sorafenib-resistant HCC cells and human HCC tissues. Furthermore, PPARδ contributed to increased proliferative capacity and redox homeostasis in sorafenib-resistant HCC cells. Accordingly, inhibiting PPARδ activity reversed compensatory metabolic reprogramming in sorafenib-resistant HCC cells and sensitized them to sorafenib. Therefore, targeting compensatory metabolic reprogramming of glutamine metabolism in sorafenib-resistant HCC by inhibiting PPARδ constitutes a potential therapeutic strategy for overcoming sorafenib-resistance in HCC. This study provides novel insight into the mechanism underlying sorafenib resistance and a potential therapeutic strategy targeting PPARδ in advanced hepatocellular carcinoma. .