Diacylglycerol kinase (DGK) participates in regulating the intracellular concentrations of two bioactive lipids, diacylglycerol and phosphatidic acid. DGK (1, 128 kDa) is a type II isozyme containing a pleckstrin homology domain at the amino terminus. Here we identified another DGK isoform (2, 135 kDa) that shared the same sequence with DGK1 except for a sterile ␣ motif (SAM) domain added at the carboxyl terminus. The DGK1 mRNA was ubiquitously distributed in various tissues, whereas the DGK2 mRNA was detected only in testis, kidney, and colon. The expression of DGK2 was suppressed by glucocorticoid in contrast to the marked induction of DGK1. DGK2 was shown to form through its SAM domain homo-oligomers as well as hetero-oligomers with other SAM-containing DGKs (␦1 and ␦2). Interestingly, DGK1 and DGK2 were rapidly translocated from the cytoplasm to endosomes in response to stress stimuli. In this case, DGK1 was rapidly relocated back to the cytoplasm upon removal of stress stimuli, whereas DGK2 exhibited sustained endosomal association. The experiments using DGK mutants suggested that the oligomerization of DGK2 mediated by its SAM domain was largely responsible for its sustained endosomal localization. Similarly, the oligomerization of DGK2 was suggested to result in negative regulation of its catalytic activity. Taken together, alternative splicing of the human DGK gene generates at least two isoforms with distinct biochemical and cell biological properties responding to different cellular metabolic requirements.Upon cell stimulation by various hormones, growth factors, and other agonists, a variety of signaling lipids that regulate a wide range of biological output are liberated or synthesized. The cellular concentrations of such bioactive lipids must be strictly regulated by the action of metabolic enzymes. Diacylglycerol (DG) 1 kinase (DGK) phosphorylates DG to yield phosphatidic acid (PA) (1). By numerous studies, DG and PA have been well recognized as lipid second messengers. DG is known to be an activator of conventional and novel protein kinase Cs (PKCs), chimaerins, Unc-13, and Ras guanyl nucleotide-releasing protein (2-4), and PA has been reported to modulate the activities of phosphatidylinositol-4-phosphate kinase, Ras GTPase-activating protein, Raf-1 kinase, atypical PKC, and many other important enzymes (5, 6). DGK thus appears to participate in various physiological events through modulating the balance between two bioactive lipids, DG and PA, in microenvironments within the cells.It is now recognized that DGK represents a large enzyme family. The isoforms differ remarkably from each other with respect to their structures, the modes of tissue expression, and enzymological properties (7-10). To date, nine mammalian DGK isozymes (␣, , ␥, ␦, ⑀, , , , and ), containing in common two or three characteristic zinc finger structures and the catalytic region, are subdivided into five groups according to their structural features (7-10). Interestingly, the occurrence of alternative splicing was recently ...
Diacylglycerol kinase (DGK) plays an important role in signal transduction through modulating the balance between two signaling lipids, diacylglycerol and phosphatidic acid. DGK␦ (type II isozyme) contains a pleckstrin homology domain at the N terminus and a sterile ␣ motif domain at the C terminus. We identified another DGK␦ isoform (DGK␦2, 135 kDa) that shared the same sequence with DGK␦ previously cloned (DGK␦1, 130 kDa) except for the 52 residues N-terminally extended. Analysis of panels of human normal and tumor tissue cDNAs revealed that DGK␦2 was ubiquitously expressed in all normal and tumor tissues examined, whereas the transcript of DGK␦1 was detected only in ovary and spleen, and in a limited set of tumor-derived cells. The expression of DGK␦2 was induced by treating cells with epidermal growth factor and tumor-promoting phorbol ester. In contrast, the levels of mRNA and protein of DGK␦1 were suppressed by phorbol ester treatment. It thus becomes clear that the two DGK␦ isoforms are expressed under distinct regulatory mechanisms. DGK␦1 was translocated through its pleckstrin homology domain from the cytoplasm to the plasma membrane in response to phorbol ester stimulation, whereas DGK␦2 remained in the cytoplasm even after stimulation. Further experiments showed that the ␦2-specific N-terminal sequence blocks the phorbol ester-dependent translocation of this isoform. Co-immunoprecipitation analysis of differently tagged DGK␦1 and DGK␦2 proteins showed that they were able to form homo-as well as heterooligomers. Taken together, alternative splicing of the human DGK␦ gene generates at least two isoforms, differing in their expressions and regulatory functions.
Background:Tumour stroma has important roles in the development of colorectal cancer (CRC) metastasis. We aimed to clarify the roles of microRNAs (miRNAs) and their target genes in CRC stroma in the development of liver metastasis.Methods:Tumour stroma was isolated from formalin-fixed, paraffin-embedded tissues of primary CRCs with or without liver metastasis by laser capture microdissection, and miRNA expression was analysed using TaqMan miRNA arrays.Results:Hierarchical clustering classified 16 CRCs into two groups according to the existence of synchronous liver metastasis. Combinatory target prediction identified tenascin C as a predicted target of miR-198, one of the top 10 miRNAs downregulated in tumour stroma of CRCs with synchronous liver metastasis. Immunohistochemical analysis of tenascin C in 139 primary CRCs revealed that a high staining intensity was correlated with synchronous liver metastasis (P<0.001). Univariate and multivariate analyses revealed that the tenascin C staining intensity was an independent prognostic factor to predict postoperative overall survival (P=0.005; n=139) and liver metastasis-free survival (P=0.001; n=128).Conclusions:Alterations of miRNAs in CRC stroma appear to form a metastasis-permissive environment that can elevate tenascin C to promote liver metastasis. Tenascin C in primary CRC stroma has the potential to be a novel biomarker to predict postoperative prognosis.
It is demonstrated that cells can be classified by pattern recognition of the subcellular structure of non-stained live cells, and the pattern recognition was performed by machine learning. Human white blood cells and five types of cancer cell lines were imaged by quantitative phase microscopy, which provides morphological information without staining quantitatively in terms of optical thickness of cells. Subcellular features were then extracted from the obtained images as training data sets for the machine learning. The built classifier successfully classified WBCs from cell lines (area under ROC curve = 0.996). This label-free, non-cytotoxic cell classification based on the subcellular structure of QPM images has the potential to serve as an automated diagnosis of single cells.
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