AOP-Anchored Transcriptome Analysis Catalogue Accelerates the Discovery of Environmental Toxicants in Zebrafish.

Current toxicity screening approaches to evaluate the vast number of environmental chemicals that require assessment are hampered due to their significant costs, time requirements, and reliance on live animal testing. The aim of the present study was to develop an adverse outcome pathway (AOP)-anchored transcriptome analysis (AATA) catalogue to expedite the discovery of environmental toxicants. 437 AOPs from the AOPwiki (https://aopwiki.org/) and 2280 transcriptomics data sets from NCBI Gene Expression Omnibus (GEO) and EMBL-EBI ArrayExpress (AE) repositories were comprehensively reviewed and analyzed. By using the differentially expressed molecular key event (mKE) genes as connection nodes, we created a large-scale environmental substance─target gene (mKE)─predicted adverse outcomes (SGAs) network that included 78 substances, 1099 genes, and 354 adverse outcomes (AOs). To validate the reliability of the network, comprehensive literature verification was conducted. We demonstrated that 164 of the 354 AOs identified have been previously characterized in the literature. The results for 136 of these AOs were consistent with the predictions of the AATA catalogue, representing an accuracy rate of 82.9%. Besides, distinct patterns in molecular KEs and AOs among categories of substances, such as biocides and metals, were demonstrated. Some representative substances, including atrazine and copper, pose significant risks to fish at various levels of biological organization. Moreover, experimental verification of the AATA predictions was conducted, including exposures of zebrafish to perfluorooctanesulfonate, cresyl diphenyl phosphate, and lanthanum. Results demonstrated consistency with predictions of the AATA catalogue, with an accuracy rate of 92.3%. Collectively, the present findings support the AATA catalogue as an efficient and promising platform for identifying environmental toxicants to fish and thereby provide novel insights into the understanding of potential risks of environmental contaminants.

Mid-infrared spectroscopy of hemispherical water droplets.

As an important component of atmospheric aerosols, water is profoundly related with aerosol hygroscopicity and provides a medium for atmospheric heterogeneous reactions. The quantitative analysis of water content in aerosol droplets is instrumental to understanding atmospheric chemistry, as well as to addressing the related environmental issues, such as air pollution and climate change. Fourier transform infrared (FTIR) spectroscopy has been widely adopted to quantify the amount of water content in atmospheric aerosols, which is based on the absorbance of OH functional group in proportion to water content. However, in the OH stretching vibration band around 3400 cm-1, spectral distortions may occur, making a quantitative analysis impossible. In addressing this issue, here we put forth a model to simulate the FTIR absorption of hemispherical water droplets, along with a quantitative description of the spectral distortion. Our model prediction was benchmarked with the microscopic-FTIR experiments conducted on sodium sulfate droplets, and good agreements between theoretical and experimental results were found. We observed that the absorbance spanning across the mid-wavenumber infrared region increases with water absorption coefficients; while such an increasing trend was not seen in the 3400 cm-1 band. We speculate that the spectral saturated absorption is related to the absorption coefficient of water and the ratio of the projected area of droplets to the aperture area. In addition, the effects of droplet size and number density on the absorption spectra were investigated. The waveband range of the saturated absorption broadens with an increase in droplet radius. On the other hand, as the number density of water droplets increases, the absorption at 3400 cm-1 is enhanced, and the characteristic peak of condensed water becomes increasingly sharper, asymptotic to the typical infrared spectra of water collected by the pressing method.

Expression and Gene Regulation Network of Adenosine Receptor A2B in Lung Adenocarcinoma: A Potential Diagnostic and Prognostic Biomarker.

Adenosine receptor A2B (ADORA2B) encodes a protein belonging to the G protein-coupled receptor superfamily. Abnormal expression of ADORA2B may play a pathophysiological role in some human cancers. We investigated whether ADORA2B is a potential diagnostic and prognostic biomarker for lung adenocarcinoma (LUAD). The expression, various mutations, copy number variations, mRNA expression levels, and related network signaling pathways of ADORA2B were analyzed using bioinformatics-related websites, including Oncomine, UALCAN, cBioPortal, GeneMANIA, LinkedOmics, KM Plotter, and TIMER. We found that ADORA2B was overexpressed and amplified in LUAD, and a high ADORA2B expression predicted a poor prognosis for LUAD patients. Pathway analyses of ADORA2B in LUAD revealed ADORA2B-correlated signaling pathways, and the expression level of ADORA2B was associated with immune cell infiltration. Furthermore, ADORA2B mRNA and protein levels were significantly higher in human LUAD cell lines (A549 cells and NCl-H1299 cells) than in normal human bronchial epithelial (HBE) cells, and the transcript levels of genes positively or negatively correlated with ADORA2B were consistent and statistically significant. siRNA transfection experiments and functional experiments further confirmed these results. In vitro results were also consistent with those of bioinformatics analysis. Our findings provide a foundation for studying the role of ADORA2B in tumorigenesis and support the development of new drug targets for LUAD.

Network pharmacology-based study to explore the mechanism of the Yiqi Gubiao pill in lung cancer treatment.

Lung cancer (LC) has been one of the most prevalent and fatal malignancies in the past 5 years. Yiqi Gubiao pills have a good clinical effect against LC. However, their complex composition limits proper understanding of their pharmacological mechanism. Therefore, the present study aimed to systemically explore the underlying mechanisms of Yiqi Gubiao pills in treatment of LC. The network pharmacology approach was employed to identify the active ingredients and LC targets associated with Yiqi Gubiao pills. Prediction of potential active ingredients and action targets was then conducted through protein-protein interaction (PPI), Gene Ontology and Kyoto Encyclopedia of Genes and Genomes pathway analyses. In vitro experiments were then performed to further verify the mechanism of action of Yiqi Gubiao pills, revealing that the anti-LC effects were mediated by regulating the expression of IL6, TP53, albumin (ALB), MAPK3 and AKT1. In total, 102 active ingredients and 229 targets of Yiqi Gubiao pills were identified. The PPI network further revealed that AKT1, TP53, ALB, IL6 and MAPK3 were the top five hub genes associated with LC treatment. Targets of the Yiqi Gubiao pills were mainly enriched in the PI3K-Akt and Advanced glycation end products (AGE)-receptors for AGEs (RAGE) signaling pathways. Overall, network pharmacology deciphered the active ingredients and potential targets of the Yiqi Gubiao pills. Yiqi Gubiao pills partially inhibited the progression of LC by regulating the expression of hub genes (AKT1, TP53, ALB, IL6 and MAPK3) through the PI3K-Akt and AGE-RAGE signaling pathways. The findings of the present study may provide a theoretical basis for the clinical application of Yiqi Gubiao pills in LC treatment.

A comparison of food crispness based on the cloud model.

The cloud model is a typical model which transforms the qualitative concept into the quantitative description. The cloud model has been used less extensively in texture studies before. The purpose of this study was to apply the cloud model in food crispness comparison. The acoustic signals of carrots, white radishes, potatoes, Fuji apples, and crystal pears were recorded during compression. And three time-domain signal characteristics were extracted, including sound intensity, maximum short-time frame energy, and waveform index. The three signal characteristics and the cloud model were used to compare the crispness of the samples mentioned above. The crispness based on the Ex value of the cloud model, in a descending order, was carrot > potato > white radish > Fuji apple > crystal pear. To verify the results of the acoustic signals, mechanical measurement and sensory evaluation were conducted. The results of the two verification experiments confirmed the feasibility of the cloud model. The microstructures of the five samples were also analyzed. The microstructure parameters were negatively related with crispness (p < .01). PRACTICAL APPLICATIONS: The cloud model method can be used for crispness comparison of different kinds of foods. The method is more accurate than the traditional methods such as mechanical measurement and sensory evaluation. The cloud model method can also be applied to other texture studies extensively.