ARG2
Arginase-2, mitochondrial precursor (EC 3.5.3.1) (Arginase II) (Kidney-type arginase) (Non-hepatic arginase) (Type II arginase)
Publications[править]
Expression profiles revealed miR-1299 downregulation concomitant with arginase-2 (ARG2) upregulation in hyperpigmented skin of melasma patients. Opposite regulation of tyrosinase and PMEL17 by miR-1299 and inverse relationship between miR-1299 and ARG2 expression denoted a role of miR-1299 in pigmentation with ARG2 as a miR-1299 target. ARG2 overexpression or knock-down in keratinocytes, the main source of ARG2 in epidermis, positively regulated tyrosinase and PMEL17 protein levels, but not mRNA levels or melanosome transfer. ARG2 overexpression in keratinocytes reduced autophagy equivalent to 3-MA, an autophagy inhibitor which also increased tyrosinase and PMEL17 protein levels, whereas ARG2 knock-down induced opposite results. Autophagy inducer rapamycin reduced ARG2-increased tyrosinase and PMEL17 protein levels. Also, autophagy was reduced in late passage-induced senescent keratinocytes showing ARG2 upregulation. ARG2, but not 3-MA, stimulated keratinocyte senescence. These results suggest that ARG2 reduces autophagy in keratinocytes by stimulating cellular senescence, resulting in skin pigmentation by reducing degradation of transferred melanosomes.
MeSH Terms
- Adult
- Arginase
- Autophagy
- Base Sequence
- Cellular Senescence
- Female
- Gene Expression Regulation
- Gene Knockdown Techniques
- Humans
- Melanins
- Melanosis
- Melanosomes
- MicroRNAs
- Middle Aged
- Skin Pigmentation
Keywords
- ARG2
- MiR-1299
- autophagy inhibition
- keratinocyte senescence
- melanosome degradation
- skin pigmentation
High-throughput expression profiling enables the global study of gene activities. Genes with positively correlated expression profiles are likely to encode functionally related proteins. However, all biological processes are interlocked, and each protein may play multiple cellular roles. Thus the coexpression of any two functionally related genes may depend on the constantly varying, yet often-unknown cellular state. To initiate a systematic study on this issue, a theory of coexpression dynamics is presented. This theory is used to rationalize a strategy of conducting a genome-wide search for the most critical cellular players that may affect the coexpression pattern of any two genes. In one example, using a yeast data set, our method reveals how the enzymes associated with the urea cycle are expressed to ensure proper mass flow of the involved metabolites. The correlation between ARG2 and CAR2 is found to change from positive to negative as the expression level of CPA2 increases. This delicate interplay in correlation signifies a remarkable control on the influx and efflux of ornithine and reflects well the intrinsic cellular demand for arginine. In addition to the urea cycle, our examples include SCH9 and CYR1 (both implicated in a recent longevity study), cytochrome c1 (mitochondrial electron transport), calmodulin (main calcium-binding protein), PFK1 and PFK2 (glycolysis), and two genes, ECM1 and YNL101W, the functions of which are newly revealed. The complexity in computation is eased by a new result from mathematical statistics.
MeSH Terms
- Calmodulin
- Electron Transport
- Gene Expression Profiling
- Genome, Fungal
- Glycolysis
- Longevity
- Oligonucleotide Array Sequence Analysis
- Saccharomyces cerevisiae
- Urea