Protective function of SLC30A10 induced via PERK-ATF4 pathway against 1-methyl-4-phenylpyridinium.

Solute carrier family 30 member 10 (SLC30A10) has been known as manganese transporter. It has been suggested that neurodegenerative diseases are related with cellular stress such as oxidative stress or endoplasmic reticulum (ER) stress. However, it remains unknown whether SLC30A10 is actually involved in several intracellular stress. We found that the level of Slc30a10 was increased in midbrain of 1-methyl-4-phenyl-1,2,3,6-tetrahydropyridine (MPTP)-treated mice. Therefore, we further investigated the role of SLC30A10 in the 1-methyl-4-phenylpiridium ion (MPP+)-induced intracellular stress, and the molecular mechanism underlying SLC30A10 induction by MPP+ treatment. In human neuroblastoma cell line (SH-SY5Y) treated with MPP+ (1 mM), the SLC30A10 mRNA level was significantly increased, and in addition, the expression of CHOP, which is known as one of ER stress markers, was significantly increased by MPP+. Interestingly, the level of SLC30A10 mRNA was significantly increased by tunicamycin as an ER stressor, suggesting that the induction of SLC30A10 by MPP+ was caused via ER stress. Considering that PKR-like endoplasmic reticulum kinase (PERK) pathway is activated under ER stress induced by MPP+, we investigated whether the expression of SLC30A10 is increased through ATF4, which is major transcription factor in PERK pathway. The increase of SLC30A10 expression in MPP+-treated cells was eliminated by ATF4 knockdown. And the protective role of SLC30A10 against MPP+-induced ER stress was confirmed by measuring cell viability in SLC30A10 knockdown cells. In conclusion, SLC30A10 is thought to have protective role for MPP+-induced toxicity via PERK-ATF4 pathway.

Protective roles of SLC30A3 against endoplasmic reticulum stress via ERK1/2 activation.

Endoplasmic reticulum (ER) stress has been thought to be involved to neurodegenerative diseases such as Alzheimer's disease (AD) or Amyotrophic lateral sclerosis (ALS). The previous studies have shown that SLC30A3 level is decreased in prefrontal cortex of AD patients. In addition, we have shown that level of zinc (Zn) is increased in cerebrospinal fluid and SLC30A3 level is decreased in spinal cord of ALS patients. It was thought that both SLC30A3 and ER stress could be related to the cause of AD and ALS, however the relationship between ER stress and SLC30A3 has not been elucidated. Therefore we investigated that the role of SLC30A3 against ER stress. The level of SLC30A3 mRNA was significantly increased by tunicamycin treatment in human neuroblastoma cell line (SH-SY5Y) and human embryonic kidney cell line (HEK293). Cell viability under tunicamycin treatment was significantly decreased in SLC30A3 knockdown cells by siRNA in comparison with negative control (NC) cells. Cleaved caspase-3 level was significantly increased in SLC30A3 knockdown cells, not in NC cells. These results showed that SLC30A3 has a protective role to ER stress-induced toxicities. The previous study has shown that SLC30A3 protect cells from oxidative stress in ERK1/2 signal dependent manner, thus we determined the activity of ERK1/2 in SLC30A3 knockdown cells under ER stress condition. The level of ERK1/2 phosphorylation was significantly increased by tunicamycin treatment in NC cells, not in SLC30A3 knockdown cells. The ERK1/2 pathway is thought to have an association with defensive effects of SLC30A3 on cellular stress such as ER stress. In conclusion, this study suggested that SLC30A3 is supposed to play a protective role against ER stress, which is related to ERK1/2 activation.

Capabilities of deep learning models on learning physical relationships: Case of rainfall-runoff modeling with LSTM.

This study investigates the relationships which deep learning methods can identify between the input and output data. As a case study, rainfall-runoff modeling in a snow-dominated watershed by means of a long short-term memory (LSTM) network is selected. Daily precipitation and mean air temperature were used as model input to estimate daily flow discharge. After model training and verification, two experimental simulations were conducted with hypothetical inputs instead of observed meteorological data to clarify the response of the trained model to the inputs. The first numerical experiment showed that even without input precipitation, the trained model generated flow discharge, particularly winter low flow and high flow during the snow melting period. The effects of warmer and colder conditions on the flow discharge were also replicated by the trained model without precipitation. Additionally, the model reflected only 17-39% of the total precipitation mass during the snow accumulation period in the total annual flow discharge, revealing a strong lack of water mass conservation. The results of this study indicated that a deep learning method may not properly learn the explicit physical relationships between input and target variables, although they are still capable of maintaining strong goodness-of-fit results.