Prediction of abundance of arthropods according to climate change scenario RCP 4.5 and 8.5 in South Korea

AbstractAbundance and diversity of arthropods were projected according to climate warming in South Korea. The taxa highly linked with temperature were selected for the projection. The values of abundance and richness were estimated using the mean values of abundance and richness in each temperature range. Temperature changes were based on the RCP (Representative Concentration Pathway) 4.5 and RCP 8.5, and the abundance and richness during two periods (2011 -2015, 2056 -2065) were projected. From these projected results, change of other common taxa (> 1% occurrence) were qualitatively predicted (i.e., decrease or increase). The projections showed that 45 of a total of 73 taxa will increase, 6 will change a little and 24 will decrease: the number of taxa that were expected to increase was two times more than the number of taxa that were expected to decrease. However, the overall abundance and diversity of arthropods were expected to decline as the temperature rises

Deleterious effects in reproduction and developmental immunity elicited by pulmonary iron oxide nanoparticles

With the extensive application of iron oxide nanoparticles (FeNPs), attention about their potential risks to human health is also rapidly raising, particularly in sensitive subgroups such as pregnant women and babies. In this study, we a single instilled intratracheally FeNPs (1, 2, and 4 mg/kg) to the male and female parent mice, mated, then assessed reproductive toxicity according to the modified OECD TG 421. During the pre-mating period (14 days), two female parent mice died at 4 mg/kg dose, and the body weight gain dose-dependently decreased in male and female parent mice exposed to FeNPs. Additionally, iron accumulation and the enhanced expression of MHC class II molecules were observed in the ovary and the testis of parent mice exposed to the highest dose of FeNPs, and the total sex ratio (male/female) of the offspring mice increased in the groups exposed to FeNPs. Following, we a single instilled intratracheally to their offspring mice with the same doses and evaluated the immunotoxic response on day 28. The increased mortality and significant hematological- and biochemical- changes were observed in offspring mice exposed at 4 mg/kg dose, especially in female mice. More interestingly, balance of the immune response was shifted to a different direction in male and female offspring mice. Taken together, we conclude that the NOAEL for reproductive and developmental toxicity of FeNPs may be lower than 2 mg/kg, and that female mice may show more sensitive response to FeNPs exposure than male mice. Furthermore, we suggest that further studies are necessary to identify causes of both the alteration in sex ratio of offspring mice and different immune response in male and female offspring mice.

Comprehensive Proteome Profiling of Platelet Identified a Protein Profile Predictive of Responses to An Antiplatelet Agent Sarpogrelate

Sarpogrelate is an antiplatelet agent widely used to treat arterial occlusive diseases. Evaluation of platelet aggregation is essential to monitor therapeutic effects of sarpogrelate. Currently, no molecular signatures are available to evaluate platelet aggregation. Here, we performed comprehensive proteome profiling of platelets collected from 18 subjects before and after sarpogrelate administration using LC-MS/MS analysis coupled with extensive fractionation. Of 5423 proteins detected, we identified 499 proteins affected by sarpogrelate and found that they strongly represented cellular processes related to platelet activation and aggregation, including cell activation, coagulation, and vesicle-mediated transports. Based on the network model of the proteins involved in these processes, we selected three proteins (cut-like homeobox 1; coagulation factor XIII, B polypeptide; and peptidylprolyl isomerase D) that reflect the platelet aggregation-related processes after confirming their alterations by sarpogrelate in independent samples using Western blotting. Our proteomic approach provided a protein profile predictive of therapeutic effects of sarpogrelate. © 2016 by The American Society for Biochemistry and Molecular Biology, Inc.1

Activation of AMP-activated protein kinase stimulates the nuclear localization of glyceraldehyde 3-phosphate dehydrogenase in human diploid fibroblasts

In addition to its well-known glycolytic activity, GAPDH displays multiple functions, such as nuclear RNA export, DNA replication and repair, and apoptotic cell death. This functional diversity depends on its intracellular localization. In this study, we explored the signal transduction pathways involved in the nuclear translocation of GAPDH using confocal laser scanning microscopy of immunostained human diploid fibroblasts (HDFs). GAPDH was present mainly in the cytoplasm when cultured with 10% FBS. Serum depletion by culturing cells in a serum-free medium (SFM) led to a gradual accumulation of GAPDH in the nucleus, and this nuclear accumulation was reversed by the re-addition of serum or growth factors, such as PDGF and lysophosphatidic acid. The nuclear export induced by the re-addition of serum or growth factors was prevented by LY 294002 and SH-5, inhibitors of phosphoinositide 3-kinase (PI3K) and Akt/protein kinase B, respectively, suggesting an involvement of the PI3K signaling pathway in the nuclear export of GAPDH. In addition, 5-aminoimidazole-4-carboxamide-1-beta-D-ribofuranoside (AICAR), an activator of AMP-activated protein kinase (AMPK), stimulated the nuclear translocation of GAPDH and prevented serum- and growth factor-induced GAPDH export. AMPK inhibition by compound C or AMPK depletion by siRNA treatment partially prevented SFM- and AICAR-induced nuclear translocation of GAPDH. Our data suggest that the nuclear translocation of GAPDH might be regulated by the PI3K signaling pathway acting mainly as a nuclear export signal and the AMPK signaling pathway acting as a nuclear import signal.Peairs A, 2009, CLIN EXP IMMUNOL, V156, P542, DOI 10.1111/j.1365-2249.2009.03924.xChen Z, 2009, CIRC RES, V104, P496, DOI 10.1161/CIRCRESAHA.108.187567Cao C, 2008, J BIOL CHEM, V283, P28897, DOI 10.1074/jbc.M804144200Li XX, 2008, ARTERIOSCL THROM VAS, V28, P1789, DOI 10.1161/ATVBAHA.108.172452Lombardi M, 2008, J CELL BIOL, V182, P327Sen N, 2008, NAT CELL BIOL, V10, P866, DOI 10.1038/ncb1747Kim HS, 2008, J BIOL CHEM, V283, P3731, DOI 10.1074/jbc.M704432200Du ZX, 2007, ENDOCRINOLOGY, V148, P4352, DOI 10.1210/en.2006-1511Harada N, 2007, J BIOL CHEM, V282, P22651, DOI 10.1074/jbc.M610724200Goirand F, 2007, J PHYSIOL-LONDON, V581, P1163, DOI 10.1113/jphysiol.2007.132589Barbini L, 2007, MOL CELL BIOCHEM, V300, P19, DOI 10.1007/s11010-006-9341-1Hurley RL, 2006, J BIOL CHEM, V281, P36662, DOI 10.1074/jbc.M606676200Hara MR, 2006, CELL MOL NEUROBIOL, V26, P527, DOI 10.1007/s10571-006-9011-6Tisdale EJ, 2006, J BIOL CHEM, V281, P8436, DOI 10.1074/jbc.M513031200Rattan R, 2005, J BIOL CHEM, V280, P39582, DOI 10.1074/jbc.M507443200Hara MR, 2005, NAT CELL BIOL, V7, P665, DOI 10.1038/ncb1268Sirover MA, 2005, J CELL BIOCHEM, V95, P45, DOI 10.1002/jcb.20399Jones RG, 2005, MOL CELL, V18, P283, DOI 10.1016/j.molcel.2005.03.027Tisdale EJ, 2004, J BIOL CHEM, V279, P54046, DOI 10.1074/jbc.M409472200Hardie DG, 2004, J CELL SCI, V117, P5479, DOI 10.1242/jcs.01540Li J, 2004, AM J PHYSIOL-ENDOC M, V287, pE834, DOI 10.1152/ajpendo.00234.2004Cooray S, 2004, J GEN VIROL, V85, P1065, DOI 10.1099/vir.0.1977-0Brown VM, 2004, J BIOL CHEM, V279, P5984, DOI 10.1074/jbc.M307071200Tisdale EJ, 2003, J BIOL CHEM, V278, P52524, DOI 10.1074/jbc.M309343200HAWLEY SA, 2003, J BIOL, V2, P28Schmitz HD, 2003, CELL BIOL INT, V27, P511, DOI 10.1011/S1065-6995(03)00096-9Tisdale EJ, 2002, J BIOL CHEM, V277, P3334, DOI 10.1074/jbc.M109744200Schmitz HD, 2001, EUR J CELL BIOL, V80, P419Dastoor Z, 2001, J CELL SCI, V114, P1643Yeo EJ, 2000, MOL CELLS, V10, P415Stein SC, 2000, BIOCHEM J, V345, P437Sirover MA, 1999, BBA-PROTEIN STRUCT M, V1432, P159Shashidharan P, 1999, NEUROREPORT, V10, P1149Rameh LE, 1999, J BIOL CHEM, V274, P8347Sawa A, 1997, P NATL ACAD SCI USA, V94, P11669Vincent MF, 1996, BIOCHEM PHARMACOL, V52, P999Reiss N, 1996, BIOCHEM MOL BIOL INT, V38, P711CORTON JM, 1995, EUR J BIOCHEM, V229, P558KAWAMOTO RM, 1986, BIOCHEMISTRY-US, V25, P657BOYCE ST, 1983, J INVEST DERMATOL S, V81, P33

Predicting Mechanical Complications After Adult Spinal Deformity Operation Using a Machine Learning Based on Modified Global Alignment and Proportion Scoring With Body Mass Index and Bone Mineral Density

Objective This study aimed to create an ideal machine learning model to predict mechanical complications in adult spinal deformity (ASD) surgery based on GAPB (modified global alignment and proportion scoring with body mass index and bone mineral density) factors. Methods Between January 2009 and December 2018, 238 consecutive patients with ASD, who received at least 4-level fusions and were followed-up for ≥2 years, were included in the study. The data were stratified into training (n=167, 70%) and test (n=71, 30%) sets and input to machine learning algorithms, including logistic regression, random forest gradient boosting system, and deep neural network. Results Body mass index, bone mineral density, the relative pelvic version score, the relative lumbar lordosis score, and the relative sagittal alignment score of the global alignment and proportion score were significantly different in the training and test sets (p<0.05) between the complication and no complication groups. In the training set, the area under receiver operating characteristics (AUROCs) for logistic regression, gradient boosting, random forest, and deep neural network were 0.871 (0.817–0.925), 0.942 (0.911–0.974), 1.000 (1.000–1.000), and 0.947 (0.915–0.980), respectively, and the accuracies were 0.784 (0.722–0.847), 0.868 (0.817–0.920), 1.000 (1.000–1.000), and 0.856 (0.803–0.909), respectively. In the test set, the AUROCs were 0.785 (0.678–0.893), 0.808 (0.702–0.914), 0.810 (0.710–0.910), and 0.730 (0.610–0.850), respectively, and the accuracies were 0.732 (0.629–0.835), 0.718 (0.614–0.823), 0.732 (0.629–0.835), and 0.620 (0.507–0.733), respectively. The random forest achieved the best predictive performance on the training and test dataset. Conclusion This study created a comprehensive model to predict mechanical complications after ASD surgery. The best prediction accuracy was 73.2% for predicting mechanical complications after ASD surgery. This information can be used to prevent mechanical complications during ASD surgery

The genome sequence of Xanthomonas oryzae pathovar oryzae KACC10331, the bacterial blight pathogen of rice

The nucleotide sequence was determined for the genome of Xanthomonas oryzae pathovar oryzae (Xoo) KACC10331, a bacterium that causes bacterial blight in rice (Oryza sativa L.). The genome is comprised of a single, 4 941 439 bp, circular chromosome that is G + C rich (63.7%). The genome includes 4637 open reading frames (ORFs) of which 3340 (72.0%) could be assigned putative function. Orthologs for 80% of the predicted Xoo genes were found in the previously reported X.axonopodis pv. citri (Xac) and X.campestris pv. campestris (Xcc) genomes, but 245 genes apparently specific to Xoo were identified. Xoo genes likely to be associated with pathogenesis include eight with similarity to Xanthomonas avirulence (avr) genes, a set of hypersensitive reaction and pathogenicity (hrp) genes, genes for exopolysaccharide production, and genes encoding extracellular plant cell wall-degrading enzymes. The presence of these genes provides insights into the interactions of this pathogen with its gramineous host