A novel delta current method for transport stoichiometry estimation

BACKGROUND: The ion transport stoichiometry (q) of electrogenic transporters is an important determinant of their function. q can be determined by the reversal potential (E(rev)) if the transporter under study is the only electrogenic transport mechanism or a specific inhibitor is available. An alternative approach is to calculate delta reversal potential (ΔE(rev)) by altering the concentrations of the transported substrates. This approach is based on the hypothesis that the contributions of other channels and transporters on the membrane to E(rev) are additive. However, E(rev) is a complicated function of the sum of different conductances rather than being additive. RESULTS: We propose a new delta current (ΔI) method based on a simplified model for electrogenic secondary active transport by Heinz (Electrical Potentials in Biological Membrane Transport, 1981). ΔI is the difference between two currents obtained from altering the external concentration of a transported substrate thereby eliminating other currents without the need for a specific inhibitor. q is determined by the ratio of ΔI at two different membrane voltages (V(1) and V(2)) where q = 2RT/(F(V(2) –V(1)))ln(ΔI(2)/ΔI(1)) + 1. We tested this ΔI methodology in HEK-293 cells expressing the elctrogenic SLC4 sodium bicarbonate cotransporters NBCe2-C and NBCe1-A, the results were consistent with those obtained with the E(rev) inhibitor method. Furthermore, using computational simulations, we compared the estimates of q with the ΔE(rev) and ΔI methods. The results showed that the ΔE(rev) method introduces significant error when other channels or electrogenic transporters are present on the membrane and that the ΔI equation accurately calculates the stoichiometric ratio. CONCLUSIONS: We developed a ΔI method for estimating transport stoichiometry of electrogenic transporters based on the Heinz model. This model reduces to the conventional reversal potential method when the transporter under study is the only electrogenic transport process in the membrane. When there are other electrogenic transport pathways, ΔI method eliminates their contribution in estimating q. Computational simulations demonstrated that the ΔE(rev) method introduces significant error when other channels or electrogenic transporters are present and that the ΔI equation accurately calculates the stoichiometric ratio. This new ΔI method can be readily extended to the analysis of other electrogenic transporters in other tissues

Sustained Na⁺/H⁺ exchanger activation promotes gliotransmitter release from reactive hippocampal astrocytes following oxygen-glucose deprivation

Hypoxia ischemia (HI)-related brain injury is the major cause of long-term morbidity in neonates. One characteristic hallmark of neonatal HI is the development of reactive astrogliosis in the hippocampus. However, the impact of reactive astrogliosis in hippocampal damage after neonatal HI is not fully understood. In the current study, we investigated the role of Na +/H+ exchanger isoform 1 (NHE1) protein in mouse reactive hippocampal astrocyte function in an in vitro ischemia model (oxygen/glucose deprivation and reoxygenation, OGD/REOX). 2 h OGD significantly increased NHE1 protein expression and NHE1-mediated H+ efflux in hippocampal astrocytes. NHE1 activity remained stimulated during 1-5 h REOX and returned to the basal level at 24 h REOX. NHE1 activation in hippocampal astrocytes resulted in intracellular Na+ and Ca2+ overload. The latter was mediated by reversal of Na+/Ca2+ exchange. Hippocampal astrocytes also exhibited a robust release of gliotransmitters (glutamate and pro-inflammatory cytokines IL-6 and TNFα) during 1-24 h REOX. Interestingly, inhibition of NHE1 activity with its potent inhibitor HOE 642 not only reduced Na+ overload but also gliotransmitter release from hippocampal astrocytes. The noncompetitive excitatory amino acid transporter inhibitor TBOA showed a similar effect on blocking the glutamate release. Taken together, we concluded that NHE1 plays an essential role in maintaining H + homeostasis in hippocampal astrocytes. Over-stimulation of NHE1 activity following in vitro ischemia disrupts Na+ and Ca2+ homeostasis, which reduces Na+-dependent glutamate uptake and promotes release of glutamate and cytokines from reactive astrocytes. Therefore, blocking sustained NHE1 activation in reactive astrocytes may provide neuroprotection following HI. © 2014 Cengiz et al

Downscaling of AMSR-E soil moisture with MODIS products using machine learning approaches

Passive microwave remotely sensed soil moisture products, such as Advanced Microwave Scanning Radiometer on the Earth Observing System (AMSR-E) data, have been routinely used to monitor global soil moisture patterns. However, they are often limited in their ability to provide reliable spatial distribution data for soil moisture due to their coarse spatial resolutions. In this study, three machine learning approaches-random forest, boosted regression trees, and Cubist-were examined for the downscaling of AMSR-E soil moisture (25 9 25 km) data over two regions (South Korea and Australia) with different climatic characteristics using moderate resolution imaging spectroradiometer products (1 km), including surface albedo, land surface temperature (LST), Normalized Difference Vegetation Index, Enhanced Vegetation Index, Leaf Area Index, and evapotranspiration (ET). Results showed that the random forest approach was superior to the other machine learning models for downscaling AMSR-E soil moisture data in terms of the correlation coefficient [r = 0.71/0.84 (South Korea/Australia) for random forest, 0.75/0.77 for boosted regression trees, and 0.70/0.61 for Cubist] and root-mean-square error (RMSE = 0.049/0.057, 0.052/0.078, and 0.051/0.063, respectively) through cross-validation. The ET and LST were identified as the most influential among the six input parameters when estimating AMSR-E soil moisture for South Korea, while ET, albedo, and LST were very useful for Australia. In overall, the downscaled soil moisture with 1 km resolution yielded a higher correlation with in situ observations than the original AMSR-E soil moisture data. The latter appeared higher than the downscaled data in forested areas, possibly due to the overestimation of soil moisture by passive microwave sensors over forests, which implies that downscaling can mitigate such overestimation of soil moistureclos