Gastric Adenocarcinoma Predictive Long Intergenic Non-Coding RNA Promotes Tumor Occurrence and Progression in Non-Small Cell Lung Cancer via Regulation of the miR-661/eEF2K Signaling Pathway

Background/Aims: Long non-coding RNAs (lncRNAs) play vital roles in carcinogenesis as oncogenes or tumor suppressor genes. This study explored the biological function of lncRNA gastric adenocarcinoma predictive long intergenic non-coding RNA (GAPLINC) in human non-small cell lung cancer (NSCLC). Methods: GAPLINC expression in NSCLC specimens and cell lines was detected by qRT-PCR and Western blot. The effect of GAPLINC on cell proliferation was investigated using CCK8-assay, colony formation assay, and xenograft model. The effects of GAPLINC on apoptosis and cell cycle were determined using flow cytometry. The mechanism of GAPLINC involved in NSCLC was explored using Western blot, luciferase reporter assay, and RNA fluorescence in situ hybridization. Results: We found that GAPLINC expression was up-regulated in NSCLC tissues and cell lines. Overexpression of GAPLINC was associated with poor prognosis in patients with NSCLC. Silencing of GAPLINC significantly inhibited cell proliferation, promoted apoptosis, and induced cell cycle arrest in the G0/G1 phase. Results from xenograft transplantation showed that GAPLINC silencing inhibited the tumor growth in vivo. Interestingly, GAPLINC silencing decreased the expression of eukaryotic elongation factor-2 kinase (eEF2K) protein both in vivo and in vitro. Bioinformatic analysis and luciferase reporter confirmed that miR-661 targeted GAPLINC and eEF2K 3’-UTR and was negatively correlated with the expression of GAPLINC and eEF2K. Conclusion: Our findings indicate that GAPLINC promotes NSCLC tumorigenesis by regulating miR-661/eEF2K cascade and provide new insights for the pathogenesis underlying NSCLC and potential targets for therapeutic strategy

Defining the characteristics of interferon-alpha-stimulated human genes: insight from expression data and machine-learning

No abstract available

In silico prediction of HIV-1-host molecular interactions and their directionality

Human immunodeficiency virus type 1 (HIV-1) continues to be a major cause of disease and premature death. As with all viruses, HIV-1 exploits a host cell to replicate. Improving our understanding of the molecular interactions between virus and human host proteins is crucial for a mechanistic understanding of virus biology, infection and host antiviral activities. This knowledge will potentially permit the identification of host molecules for targeting by drugs with antiviral properties. Here, we propose a data-driven approach for the analysis and prediction of the HIV-1 interacting proteins (VIPs) with a focus on the directionality of the interaction: host-dependency versus antiviral factors. Using support vector machine learning models and features encompassing genetic, proteomic and network properties, our results reveal some significant differences between the VIPs and non-HIV-1 interacting human proteins (non-VIPs). As assessed by comparison with the HIV-1 infection pathway data in the Reactome database (sensitivity > 90%, threshold = 0.5), we demonstrate these models have good generalization properties. We find that the ‘direction’ of the HIV-1-host molecular interactions is also predictable due to different characteristics of ‘forward’/pro-viral versus ‘backward’/pro-host proteins. Additionally, we infer the previously unknown direction of the interactions between HIV-1 and 1351 human host proteins. A web server for performing predictions is available at http://hivpre.cvr.gla.ac.uk/

Appropriateness of Potential Evapotranspiration Models for Climate Change Impact Analysis in Yarlung Zangbo River Basin, China

Evapotranspiration (ET) is an important element in the water and energy cycle. Potential evapotranspiration (PET) is an important measurement of ET. Its accuracy has significant influence on agricultural water management, irrigation planning, and hydrological modelling. However, whether current PET models are applicable under climate change or not, is still a question. In this study, five frequently used PET models were chosen, including one combination model (the FAO Penman-Monteith model, FAO-PM), two temperature-based models (the Blaney-Criddle and the Hargreaves models) and two radiation-based models (the Makkink and the Priestley-Taylor models), to estimate their appropriateness in the historical and future periods under climate change impact on the Yarlung Zangbo river basin, China. Bias correction methods were not only applied to the temperature output of Global Climate Models (GCMs), but also for radiation, humidity, and wind speed. It was demonstrated that the results from the Blaney-Criddle and Makkink models provided better agreement with the PET obtained by the FAO-PM model in the historical period. In the future period, monthly PET estimated by all five models show positive trends. The changes of PET under RCP8.5 are much higher than under RCP2.6. The radiation-based models show better appropriateness than the temperature-based models in the future, as the root mean square error (RMSE) value of the former models is almost half of the latter models. The radiation-based models are recommended for use to estimate PET under climate change in the Yarlung Zangbo river basin

Evaluation of Remote Sensing-Based Evapotranspiration Datasets for Improving Hydrological Model Simulation in Humid Region of East China

Conventional calibration methods used in hydrological modelling are based on runoff observations at the basin outlet. However, calibration with only runoff often produces reasonable runoff but poor results for other hydrological variables. Multi-variable calibration with both runoff and remote sensing-based evapotranspiration (ET) is developed naturally, due to the importance of ET and its data availability. This study compares two main calibration schemes: (1) calibration with only runoff (Scheme I) and (2) multi-variable calibration with both runoff and remote sensing-based ET (Scheme II). ET data are obtained from three remote sensing-based ET datasets, namely Penman–Monteith–Leuning (PML), FLUXCOM, and the Global Land Evaporation Amsterdam Model (GLEAM). The aforementioned calibration schemes are applied to calibrate the parameters of the Distributed Hydrology Soil Vegetation Model (DHSVM) through ε-dominance non-dominated sorted genetic algorithm II (ε-NSGAII). The results show that all three ET datasets have good performance for areal ET in the study area. The DHSVM model calibrated based on Scheme I produces acceptable performance in runoff simulation (Kling–Gupta Efficiency, KGE = 0.87), but not for ET simulation (KGE < 0.7). However, reasonable simulations can be achieved for both variables based on Scheme II. The KGE value of runoff simulation can reach 0.87(0.91), 0.72(0.85), and 0.75(0.86) in the calibration (validation) period based on Scheme II (PML), Scheme II (FLUXCOM), and Scheme II (GLEAM), respectively. Simultaneously, ET simulations are greatly improved both in the calibration and validation periods. Furthermore, incorporating ET data into all three Scheme II variants is able to improve the performance of extreme flow simulations (including extreme low flow and high flow). Based on the improvement of the three datasets in extreme flow simulations, PML can be utilized for multi-variable calibration in drought forecasting, and FLUXCOM and GLEAM are good choices for flood forecasting

Seasonal catchment memory of high mountain rivers in the Tibetan Plateau

Abstract Rivers originating in the Tibetan Plateau are crucial to the population in Asia. However, research about quantifying seasonal catchment memory of these rivers is still limited. Here, we propose a model able to accurately estimate terrestrial water storage change (TWSC), and characterize catchment memory processes and durations using the memory curve and the influence/domination time, respectively. By investigating eight representative basins of the region, we find that the seasonal catchment memory in precipitation-dominated basins is mainly controlled by precipitation, and that in non-precipitation-dominated basins is strongly influenced by temperature. We further uncover that in precipitation-dominated basins, longer influence time corresponds to longer domination time, with the influence/domination time of approximately six/four months during monsoon season. In addition, the long-term catchment memory is observed in non-precipitation-dominated basins. Quantifying catchment memory can identify efficient lead times for seasonal streamflow forecasts and water resource management

Genome-Wide Identification and Bioinformatics Analysis of Auxin Response Factor Genes in Highbush Blueberry

Auxin response factors (ARFs) are a transcription factor family that regulates the expression of auxin phase-responsive genes. Here, we performed a genome-wide investigation of the tetraploid blueberry (Vaccinium corymbosum cv. ‘Draper’) genome sequence. Physical and chemical properties, phylogenetic evolution, gene structure, conservative motifs, chromosome location, and cis-acting elements of blueberry ARF genes were comprehensively evaluated. A total of 70 blueberry ARF genes (VcARF) were found in its genome, which could be divided into six subfamilies. VcARF genes were unevenly distributed on 40 chromosomes and were observed to encode protein sequences ranging in length from 162 to 1117 amino acids. Their exon numbers range from 2 to 22. VcARF promoter regions contain multiple functional domains associated with light signaling, aerobic metabolism, plant hormones, stress, and cell cycle regulation. More family members of VcARF genes were discovered in blueberry than in previously studied plants, likely because of the occurrence of whole-genome duplication and/or tandem duplication. VcARF expression patterns were analyzed at different stages of fruit development, and VcARF3, VcARF4, VcARF14, VcARF37, and VcARF52 were observed to play important roles. VcARF3 and VcARF4 appeared to function as repressors, while VcARF14 acted as an essential factor in fruit firmness differences between firm and soft flesh cultivars

Endogenous Omega (n)-3 Fatty Acids in Fat-1 Mice Attenuated Depression-Like Behavior, Imbalance between Microglial M1 and M2 Phenotypes, and Dysfunction of Neurotrophins Induced by Lipopolysaccharide Administration

n-3 polyunsaturated fatty acids (PUFAs) have been reported to improve depression. However, PUFA purities, caloric content, and ratios in different diets may affect the results. By using Fat-1 mice which convert n-6 to n-3 PUFAs in the brain, this study further evaluated anti-depressant mechanisms of n-3 PUFAs in a lipopolysaccharide (LPS)-induced model. Adult male Fat-1 and wild-type (WT) mice were fed soybean oil diet for 8 weeks. Depression-like behaviors were measured 24 h after saline or LPS central administration. In WT littermates, LPS reduced sucrose intake, but increased immobility in forced-swimming and tail suspension tests. Microglial M1 phenotype CD11b expression and concentrations of interleukin (IL)-1&beta;, tumor necrosis factor (TNF)-&alpha;, and IL-17 were elevated, while M2 phenotype-related IL-4, IL-10, and transforming growth factor (TGF)-&beta;1 were decreased. LPS also reduced the expression of brain-derived neurotrophic factor (BDNF) and tyrosine receptor kinase B (Trk B), while increasing glial fibrillary acidic protein expression and pro-BDNF, p75, NO, and iNOS levels. In Fat-1 mice, LPS-induced behavioral changes were attenuated, which were associated with decreased pro-inflammatory cytokines and reversed changes in p75, NO, iNOS, and BDNF. Gas chromatography assay confirmed increased n-3 PUFA levels and n-3/n-6 ratios in the brains of Fat-1 mice. In conclusion, endogenous n-3 PUFAs may improve LPS-induced depression-like behavior through balancing M1 and M2-phenotypes and normalizing BDNF function