Cadmium influence on lipid metabolism in Sprague-Dawley rats through linoleic acid and glycerophospholipid metabolism pathways.

Cadmium (Cd) is widely distributed in the environment and easy adsorbed by living organisms with adverse effects. Exposure to Cd-contaminated food may disrupt lipid metabolism and increase human health risk. To study the perturbation effect of Cd on lipid metabolism in vivo, 24 male Sprague-Dawley (SD) rats were randomly assigned four groups and treated by Cd chloride solution (0, 1.375 mg/kg, 5.5 mg/kg, 22 mg/kg) for 14 days. The characteristic indexes of serum lipid metabolism were analyzed. Afterwards, untargeted metabolomics analysis was applied to explore the adverse effects of Cd on rats by liquid chromatography coupled with mass spectrometry (LC-MS). The results revealed that Cd exposure obviously decreased the average serum of triglycerides (TG) and low-density lipoprotein cholesterol (LDL-C) and caused an imbalance of endogenous compounds in the 22 mg/kg Cd-exposed group. Compared with the control group, 30 metabolites with significant differences were identified in the serum. Our results indicated that Cd caused lipid metabolic disorders in rats by disrupting linoleic acid and glycerophospholipid metabolism pathways. Furthermore, there were three kinds of remarkable differential metabolites-9Z,12Z-octadecadienoic acid, PC(20:4(8Z,11Z,14Z,17Z)/0:0), and PC(15:0/18:2(9Z,12Z)), which enriched the two significant metabolism pathways and could be the potential biomarkers.

DeepHiC: A generative adversarial network for enhancing Hi-C data resolution.

Hi-C is commonly used to study three-dimensional genome organization. However, due to the high sequencing cost and technical constraints, the resolution of most Hi-C datasets is coarse, resulting in a loss of information and biological interpretability. Here we develop DeepHiC, a generative adversarial network, to predict high-resolution Hi-C contact maps from low-coverage sequencing data. We demonstrated that DeepHiC is capable of reproducing high-resolution Hi-C data from as few as 1% downsampled reads. Empowered by adversarial training, our method can restore fine-grained details similar to those in high-resolution Hi-C matrices, boosting accuracy in chromatin loops identification and TADs detection, and outperforms the state-of-the-art methods in accuracy of prediction. Finally, application of DeepHiC to Hi-C data on mouse embryonic development can facilitate chromatin loop detection. We develop a web-based tool (DeepHiC, http://sysomics.com/deephic) that allows researchers to enhance their own Hi-C data with just a few clicks.

Combination of biotransformation and metabolomics reveals tolfenpyrad-induced hepatocytotoxicity.

Tolfenpyrad (TFP) is an extensively used pesticide that inevitably leads to human exposure to both TFP and its transformation product residues. However, the biotransformation of TFP in humans has not been elucidated, and the toxicity of TFP along with its biotransformation products remains largely unknown. In this study, the biotransformation process of TFP was investigated using human liver microsomes and human hepatic cells. Endogenous metabolic changes in the cells were studied to investigate the hepatocytotoxicity of TFP at environmentally relevant concentrations. Fourteen phase I biotransformation products and four phase II TFP products were characterized, among which twelve products were identified for the first time. The oxidative product tolfenpyrad-benzoic acid (PT-CA) was particularly abundant and stable. Further hepatotoxicity assessments and metabolic studies demonstrated comparable metabolic profiles for TFP and PT-CA in HepG2 cells, with both significantly disrupting purine and glutathione metabolism. These processes are closely associated with oxidative stress, mitochondrial damage, and cell death. Our results provide novel perspectives on the biotransformation, metabolism, and hepatotoxicity of TFP, thereby highlighting the non-negligible toxicity of its crucial biotransformation product PT-CA in environmental risk assessments.

Prime factorization via localized tile assembly in a DNA origami framework.

Modern cybersecurity built on public-key cryptosystems like Rivest-Shamir-Adleman is compromised upon finding solutions to the prime factorization. Nevertheless, solving the prime factorization problem, given a large N, remains computationally challenging. Here, we design DNA origami frameworks (DOFs) to direct localized assembly of double-crossover (DX) tiles for solving prime factorization with a model consisting of the computing, decision-making, and reporting motifs. The model implementation is based on the sequential assembly of different DX tiles in the DOF cavity that carries overhangs encoding the prime and composite integers. The primes are multiplied and then verified with the composite, and the result is visualized under atomic force microscopy via the presence (success) or absence (failure) of biotin-streptavidin labels on the reporting DX tile. The factorization of semiprimes 6 and 15 is realized with this DOF-based demonstration. Given the potential of massively parallel processing ability of DNA, this strategy opens an avenue to solve complex mathematical puzzles like prime factoring with molecular computing.

Study on toxicological effect and the mechanism of cadmium in rice and inorganic cadmium on ICR mice.

Cadmium (Cd) exposure may induce chronic intoxication, but the harm of cadmium in rice to human at chronic low-level Cd exposure remains unclear. This study employed a mouse model to investigate the toxicity and mechanism of cadmium in rice and CdCl2. After 8-week exposure to Cd (CdCl2 and Cd-contaminated rice), the biochemical indicators and oxidation indicators in the serum and liver of mice were determined, and used mRNA sequencing to investigate the mechanism of different forms of Cd. Results showed that the cadmium concentration of the liver in the CdCl2 + Rice-N group (CdCl2 mixed with feed and normal rice, 0.4mg/kg.bw) was higher than that in the Rice-H group (0.4mg/kg.bw). However, the cadmium concentration of the kidneys in the Rice-H group was higher than that in the CdCl2 + Rice-N group. Our study demonstrated that Cd-treated (Cd in rice and CdCl2) ICR mice generated obviously tissues injury, such as the increased biochemical studies, the activity of antioxidant enzymes debasement. Simultaneously, our data also indicated that there existed difference of the hepatic toxicity between Cd in rice and CdCl2. By means of transcriptomics, we discovered that CdCl2 and Cd in rice may affect different gene expression at the molecular level. We hope to provide some theoretical basis for the revision of food security standards.

Transcriptome Analysis of Caco-2 Cells upon the Exposure of Mycotoxin Deoxynivalenol and Its Acetylated Derivatives.

Deoxynivalenol (DON), 3-acetyldeoxynivalenol (3-ADON) and 15-acetyldeoxynivalenol (15-ADON) are type B trichothecenes; one of the major pollutants in food and feed products. Although the toxicity of DON has been well documented, information on the toxicity of its acetylated derivative remains incomplete. To acquire more detailed insight into 3-ADON and 15-ADON, Caco-2 cells under 0.5 µM DON, 3-ADON and 15-ADON treatment for 24 h were subjected to RNA-seq analysis. In the present study, 2656, 3132 and 2425 differentially expressed genes (DEGs) were selected, respectively, and were enriched utilizing the Kyoto Encyclopedia of Genes and Genomes (KEGG) and the Gene Ontology (GO) database. The upregulation of ataxia-telangiectasia mutated kinase (ATM), WEE1 homolog 2 (WEE2) and downregulation of proliferating cell nuclear antigen (PCNA), minichromosome maintenance (MCMs), cyclin dependent kinase (CDKs), and E2Fs indicate that the three toxins induced DNA damage, inhibition of DNA replication and cell cycle arrest in Caco-2 cells. Additionally, the upregulation of sestrin (SENEs) and NEIL1 implied that the reason for DNA damage may be attributable to oxidative stress. Our study provides insight into the toxic mechanism of 3-ADON and 15-ADON.

LINCS gene expression signature analysis revealed bosutinib as a radiosensitizer of breast cancer cells by targeting eIF4G1.

Radioresistance is the predominant cause for radiotherapy failure and disease progression, resulting in increased breast cancer­associated mortality. Using gene expression signature analysis of the Library of Integrated Network­Based Cellular Signatures (LINCS) and Gene Expression Omnibus (GEO), the aim of the present study was to systematically identify potential candidate radiosensitizers from known drugs. The similarity of integrated gene expression signatures between irradiated eukaryotic translation initiation factor 4 Î³ 1 (eIF4G1)­silenced breast cancer cells and known drugs was measured using enrichment scores (ES). Drugs with positive ES were selected as potential radiosensitizers. The radiosensitizing effects of the candidate drugs were analyzed in breast cancer cell lines (MCF­7, MX­1 and MDA­MB­231) using CCK­8 and colony formation assays following exposure to ionizing radiation. Cell apoptosis was measured using flow cytometry. The expression levels of eIF4G1 and DNA damage response (DDR) proteins were analyzed by western blotting. Bosutinib was identified as a promising radiosensitizer, as its administration markedly reduced the dosage required both for the drug and for ionizing radiation, which may be associated with fewer treatment­associated adverse reactions. Moreover, combined treatment of ionizing radiation and bosutinib significantly increased cell killing in all three cell lines, compared with ionizing radiation or bosutinib alone. Among the three cell lines, MX­1 cells were identified as the most sensitive to both ionizing radiation and bosutinib. Bosutinib markedly downregulated the expression of eIF4G1 in a dose­dependent manner and also reduced the expression of DDR proteins (including ATM, XRCC4, ATRIP, and GADD45A). Moreover, eIF4G1 was identified as a key target of bosutinib that may regulate DNA damage induced by ionizing radiation. Thus, bosutinib may serve as a potential candidate radiosensitizer for breast cancer therapy.

GCG inhibits SARS-CoV-2 replication by disrupting the liquid phase condensation of its nucleocapsid protein.

Lack of detailed knowledge of SARS-CoV-2 infection has been hampering the development of treatments for coronavirus disease 2019 (COVID-19). Here, we report that RNA triggers the liquid-liquid phase separation (LLPS) of the SARS-CoV-2 nucleocapsid protein, N. By analyzing all 29 proteins of SARS-CoV-2, we find that only N is predicted as an LLPS protein. We further confirm the LLPS of N during SARS-CoV-2 infection. Among the 100,849 genome variants of SARS-CoV-2 in the GISAID database, we identify that ~37% (36,941) of the genomes contain a specific trio-nucleotide polymorphism (GGG-to-AAC) in the coding sequence of N, which leads to the amino acid substitutions, R203K/G204R. Interestingly, NR203K/G204R exhibits a higher propensity to undergo LLPS and a greater effect on IFN inhibition. By screening the chemicals known to interfere with N-RNA binding in other viruses, we find that (-)-gallocatechin gallate (GCG), a polyphenol from green tea, disrupts the LLPS of N and inhibits SARS-CoV-2 replication. Thus, our study reveals that targeting N-RNA condensation with GCG could be a potential treatment for COVID-19.

Systematic Identification and Assessment of Therapeutic Targets for Breast Cancer Based on Genome-Wide RNA Interference Transcriptomes.

With accumulating public omics data, great efforts have been made to characterize the genetic heterogeneity of breast cancer. However, identifying novel targets and selecting the best from the sizeable lists of candidate targets is still a key challenge for targeted therapy, largely owing to the lack of economical, efficient and systematic discovery and assessment to prioritize potential therapeutic targets. Here, we describe an approach that combines the computational evaluation and objective, multifaceted assessment to systematically identify and prioritize targets for biological validation and therapeutic exploration. We first establish the reference gene expression profiles from breast cancer cell line MCF7 upon genome-wide RNA interference (RNAi) of a total of 3689 genes, and the breast cancer query signatures using RNA-seq data generated from tissue samples of clinical breast cancer patients in the Cancer Genome Atlas (TCGA). Based on gene set enrichment analysis, we identified a set of 510 genes that when knocked down could significantly reverse the transcriptome of breast cancer state. We then perform multifaceted assessment to analyze the gene set to prioritize potential targets for gene therapy. We also propose drug repurposing opportunities and identify potentially druggable proteins that have been poorly explored with regard to the discovery of small-molecule modulators. Finally, we obtained a small list of candidate therapeutic targets for four major breast cancer subtypes, i.e., luminal A, luminal B, HER2+ and triple negative breast cancer. This RNAi transcriptome-based approach can be a helpful paradigm for relevant researches to identify and prioritize candidate targets for experimental validation.