A calcium-activated, large conductance and non-selective cation channel in Paramecium cell

AbstractA non-selective cation channel was found in mutant Paramecium cells (K115). This cell had been selected as a resistant mutant in a high-K+ solution. In patch clamp studies of these cells in the inside-out configuration, this channel was activated by bath applications of elevated Ca2+ concentrations. The channels became very active when the Ca2+ concentration was above 3.2 μM. The channel was also activated by depolarization. The voltage dependency was steep upon depolarization, whereas upon hyperpolarization the channel activity barely changed. This channel had poor selectivity for monovalent alkali cations. Using the Goldman–Hodgkin–Katz equation for the reversal potential, the permeability ratios with respect to K+ for Na+, Rb+, Cs+ and Li+ were nearly 1. Although the permeability ratios were similar for each cation, the single channel conductances differed. The single channel conductances were 467 pS with K+ as the charge carrier, 406 pS with Na+, 397 pS with Rb+, 253 pS with Cs+ and 198 pS with Li+ upon depolarization in 100 mM cation solutions. A similar calcium-activated large conductance channel was observed in the wild-type (G3) Paramecium cells but was very rare

A Systems Genetics Approach Provides a Bridge from Discovered Genetic Variants to Biological Pathways in Rheumatoid Arthritis

Genome-wide association studies (GWAS) have yielded novel genetic loci underlying common diseases. We propose a systems genetics approach to utilize these discoveries for better understanding of the genetic architecture of rheumatoid arthritis (RA). Current evidence of genetic associations with RA was sought through PubMed and the NHGRI GWAS catalog. The associations of 15 single nucleotide polymorphisms and HLA-DRB1 alleles were confirmed in 1,287 cases and 1,500 controls of Japanese subjects. Among these, HLA-DRB1 alleles and eight SNPs showed significant associations and all but one of the variants had the same direction of effect as identified in the previous studies, indicating that the genetic risk factors underlying RA are shared across populations. By receiver operating characteristic curve analysis, the area under the curve (AUC) for the genetic risk score based on the selected variants was 68.4%. For seropositive RA patients only, the AUC improved to 70.9%, indicating good but suboptimal predictive ability. A simulation study shows that more than 200 additional loci with similar effect size as recent GWAS findings or 20 rare variants with intermediate effects are needed to achieve AUC = 80.0%. We performed the random walk with restart (RWR) algorithm to prioritize genes for future mapping studies. The performance of the algorithm was confirmed by leave-one-out cross-validation. The RWR algorithm pointed to ZAP70 in the first rank, in which mutation causes RA-like autoimmune arthritis in mice. By applying the hierarchical clustering method to a subnetwork comprising RA-associated genes and top-ranked genes by the RWR, we found three functional modules relevant to RA etiology: “leukocyte activation and differentiation”, “pattern-recognition receptor signaling pathway”, and “chemokines and their receptors”

New theory of effective work functions at metal/high-k dielectric interfaces : application to metal/high-k HfO2 and la2O 3 dielectric interfaces

We have constructed a universal theory of the work functions at metal/high-k HfO2 and La2O3 dielectric interfaces by introducing a new concept of generalized charge neutrality levels. Our theory systematically reproduces the experimentally observed work functions of various gate metals on Hf-based high-k dielectrics, including the hitherto unpredictable behaviors of the work functions of p-metals. Our new concept provides effective guiding principles to achieving near-bandedge work functions of gate metals. Moreover, we discuss the potential of the new high-k dielectrics of La2O3 based on this new concept

Decoding the diversity of killer immunoglobulin-like receptors by deep sequencing and a high-resolution imputation method

10.1016/j.xgen.2022.100101Cell Genomics23100101-10010