Aberrant DNA Methylation Patterns of Deleted in Liver Cancer 1 Isoforms in Hepatocellular Carcinoma.

Hepatocellular carcinoma (HCC), a common primary liver cancer, is the third leading cause of death worldwide. DNA methylation changes are common in HCC and have been studied to be associated with hepatocarcinogenesis. In our study, we used the MassARRAY® EpiTYPER technology to investigate the methylation differences of deleted in liver cancer 1 (DLC1) (isoform 1 and 3) promoter between HCC tissues and corresponding adjacent noncancerous tissues and the association between methylation levels and clinicopathological features. In addition, the modified CRISPR-Cas9 system and the DNA methyltransferase inhibitor (DNMTi) were utilized to explore the functional correlation of epigenetic modifications and DLC1 gene regulation. The methylation levels of the DLC1 isoforms in HCC samples were found significantly lower than those in the adjacent noncancerous tissues (all p < 0.0001). Also, we found that the expression of DLC1 could be bidirectionally regulated by the modified CRISPR-Cas9 system and the DNMTi. Moreover, the hypomethylation of DLC1 in HCC samples was connected with the presence of satellite lesions (p = 0.0305) and incomplete tumor capsule (p = 0.0204). Receiver operator characteristic curve analysis demonstrated that the methylation levels of DLC1 could be applied to discriminate HCC patients (area under the curve = 0.728, p < 0.0001). The hypomethylation status was a key regulatory mechanism of DLC1 expression and might serve as a potential biomarker for HCC.

Bicuspid Aortic Valve-Associated Regulatory Regions Reveal GATA4 Regulation and Function During Human-Induced Pluripotent Stem Cell-Based Endothelial-Mesenchymal Transition-Brief Report.

BACKGROUND: The endothelial-mesenchymal transition (EndoMT) is a fundamental process for heart valve formation and defects in EndoMT cause aortic valve abnormalities. Our previous genome-wide association study identified multiple variants in a large chromosome 8 segment as significantly associated with bicuspid aortic valve (BAV). The objective of this study is to determine the biological effects of this large noncoding segment in human induced pluripotent stem cell (hiPSC)-based EndoMT. METHODS: A large genomic segment enriched for BAV-associated variants was deleted in hiPSCs using 2-step CRISPR/Cas9 editing. To address the effects of the variants on GATA4 expression, we generated CRISPR repression hiPSC lines (CRISPRi) as well as hiPSCs from BAV patients. The resulting hiPSCs were differentiated to mesenchymal/myofibroblast-like cells through cardiovascular-lineage endothelial cells for molecular and cellular analysis. Single-cell RNA sequencing was also performed at different stages of EndoMT induction. RESULTS: The large deletion impaired hiPSC-based EndoMT in multiple biallelic clones compared with their isogenic control. It also reduced GATA4 transcript and protein levels during EndoMT, sparing the other genes nearby the deletion segment. Single-cell trajectory analysis revealed the molecular reprogramming during EndoMT. Putative GATA-binding protein targets during EndoMT were uncovered, including genes implicated in endocardial cushion formation and EndoMT process. Differentiation of cells derived from BAV patients carrying the rs117430032 variant as well as CRISPRi repression of the rs117430032 locus resulted in lower GATA4 expression in a stage-specific manner. TWIST1 was identified as a potential regulator of GATA4 expression, showing specificity to the locus tagged by rs117430032. CONCLUSIONS: BAV-associated distal regions regulate GATA4 expression during hiPSC-based EndoMT, which in turn promotes EndoMT progression, implicating its contribution to heart valve development.

Prediction model for synergistic anti-tumor multi-compound combinations from traditional Chinese medicine based on extreme gradient boosting, targets and gene expression data.

Traditional Chinese medicine (TCM) is characterized by synergistic therapeutic effect involving multiple compounds and targets, which provide potential new therapy for the treatment of complex cancer conditions. However, the main contributors and the underlying mechanisms of synergistic TCM cancer therapies remain largely undetermined. Machine learning now provides a new approach to determine synergistic compound combinations from complex components of TCM. In this study, a prediction model based on extreme gradient boosting (XGBoost) algorithm was constructed by integrating gene expression data of different cancer cell lines, targets information of natural compounds and drug response data. Radix Paeoniae Rubra (RPR) was selected as a model herbal sample to evaluate the reliability of the constructed model. The optimal XGBoost prediction model achieved a good performance with Mean Square Error (MSE) of 0.66, Mean Absolute Error (MAE) of 0.61, and the Root Mean Squared Error (RMSE) of 0.81 on test dataset. The superior synergistic anti-tumor combinations of D15 (Paeonol[Formula: see text][Formula: see text][Formula: see text]Ethyl gallate) and D13 (Paeoniflorin[Formula: see text][Formula: see text][Formula: see text]Paeonol) were successfully predicted from RPR and experimentally validated on MCF-7 cells. Moreover, the combination of D13 could work as a main contributor to a synergistic anti-proliferative activity in the compatibility of RPR and Cortex Moutan (CM). Our XGBoost model could be a reliable tool for the efficient prediction of synergistic anti-tumor multi-compound combinations from TCM.

ESR1 Regulates the Obesity- and Metabolism-Differential Gene MMAA to Inhibit the Occurrence and Development of Hepatocellular Carcinoma.

Obesity is often regarded as a factor that promotes tumorigenesis, but the role of obesity in promoting hepatocellular carcinoma (HCC) is still controversial. We compared the trend change of 14 obesity-related genes in the formation and development of HCC in normal, adjacent, and HCC tissues. Mendelian randomization (MR) analysis was used to verify the relationship between obesity and HCC occurrence. Metabolism of cobalamin-associated A (MMAA) was discovered as an obesity- and metabolism-differential gene, and its function in HCC was tested in vitro and in vivo. Finally, we explored how obese female patients with an originally high expression of female estrogen receptor (ESR1) directly upregulated MMAA to interfere with the progression of HCC. Fourteen obesity-related genes were downregulated in adjacent and tumoral tissues compared with normal liver tissues, which indicated that obesity may be inversely related to the occurrence of HCC and was consistent with the results of MR analysis. We also discovered that MMAA is a metabolic gene closely related to the occurrence and development of HCC by mining the TCGA database, and it functioned an anti-tumor-promoting role in HCC by damaging the mitochondrial function and preserving the redox balance. We further verified that obese females with a high expression of ESR1 can regulate MMAA to protect HCC from progression. This study elucidates that obesity might be a protective factor for female HCC patients, as they originally highly expressed ESR1, which could upregulate MMAA to suppress tumor growth and participate in metabolic reprogramming.

Global Trends in Research of Lipid Metabolism in T lymphocytes From 1985 to 2022: A Bibliometric Analysis.

Lipids are involved in both energy metabolism and signaling transduction. Abnormal lipid metabolism in T cells is associated with the differentiation, longevity and activity of T cells, which has received increasing concern since its firstly reported in 1985. To evaluate the trends of lipid metabolism in T cells and map knowledge structure, we employed bibliometric analysis. A total of 286 related publications obtained from the Web of Science Core Collection published between 1985 and 2022 were analyzed using indicators of publication and citation metrics, countries, institutes, authors, cited references and key words. The present research status, the global trends and the future development directions in lipid metabolism and T cells were visualized and discussed. In summary, this study provides a comprehensive display on the field of lipid metabolism in T cells, which will help researchers explore lipid metabolism in T cells more effectively and intuitively.

Platelet-Vesicles-Encapsulated RSL-3 Enable Anti-Angiogenesis and Induce Ferroptosis to Inhibit Pancreatic Cancer Progress.

Pancreatic ductal adenocarcinoma (PDAC) is one of the most malignant cancers. It is characterized by stromal richness, lack of blood supply and special metabolic reprogramming in the tumor microenvironment, which is difficult to treat and easy to metastase. Great efforts have been made to develop new drugs which can pass through the stroma and are more effective than traditional chemotherapeutics, such as ferroptosis inducers-Erastin and RSL-3. As current anti-angiogenic therapy drugs alone are suboptimal for PDAC, novel vascular disruption agents in combination with ferroptosis inducers might provide a possible solution. Here, we designed human platelet vesicles (PVs) to camouflage RSL-3 to enhance drug uptake rate by tumor cells and circulation time in vivo, deteriorating the tumor vessels and resulting in tumor embolism to cut the nutrient supply as well as causing cell death due to excessive lipid peroxidation. The RSL-3@PVs can also cause the classic ferroptosis-related change of mitochondrial morphology, with changes in cellular redox levels. Besides that, RSL-3@PVs has been proved to have great biological safety profile in vitro and in vivo. This study demonstrates the promising potential of integrating PVs and RSL-3 as a combination therapy for improving the outcome of PDAC.

Deep learning-based multi-drug synergy prediction model for individually tailored anti-cancer therapies.

While synergistic drug combinations are more effective at fighting tumors with complex pathophysiology, preference compensating mechanisms, and drug resistance, the identification of novel synergistic drug combinations, especially complex higher-order combinations, remains challenging due to the size of combination space. Even though certain computational methods have been used to identify synergistic drug combinations in lieu of traditional in vitro and in vivo screening tests, the majority of previously published work has focused on predicting synergistic drug pairs for specific types of cancer and paid little attention to the sophisticated high-order combinations. The main objective of this study is to develop a deep learning-based approach that integrated multi-omics data to predict novel synergistic multi-drug combinations (DeepMDS) in a given cell line. To develop this approach, we firstly created a dataset comprising of gene expression profiles of cancer cell lines, target information of anti-cancer drugs, and drug response against a large variety of cancer cell lines. Based on the principle of a fully connected feed forward Deep Neural Network, the proposed model was constructed using this dataset, which achieved a high performance with a Mean Square Error (MSE) of 2.50 and a Root Mean Squared Error (RMSE) of 1.58 in the regression task, and gave the best classification accuracy of 0.94, an area under the Receiver Operating Characteristic curve (AUC) of 0.97, a sensitivity of 0.95, and a specificity of 0.93. Furthermore, we utilized three breast cancer cell subtypes (MCF-7, MDA-MD-468 and MDA-MB-231) and one lung cancer cell line A549 to validate the predicted results of our model, showing that the predicted top-ranked multi-drug combinations had superior anti-cancer effects to other combinations, particularly those that were widely used in clinical treatment. Our model has the potential to increase the practicality of expanding the drug combinational space and to leverage its capacity to prioritize the most effective multi-drug combinational therapy for precision oncology applications.

The LC-MS/MS-Based Measurement of Isopimaric Acid in Rat Plasma and Application of Pharmacokinetics.

Isopimaric acid (IPA) exhibits a diverse array of pharmacological activities, having been shown to function as an antihypertensive, antitumor, antibacterial, and hypocholesterolemic agent. However, few studies of the pharmacokinetics of IPA have been performed to date, and such analyses are essential to explore the in vivo mechanisms governing the biological activity of this compound. As such, we herein designed a selective LC-MS approach capable of quantifying serum IPA levels in model rats using an Agilent HC-C18 column (250 mm × 4.6 mm, 5 µm) via isocratic elution with a mobile phase composed of methanol 0.5% formic acid (91 : 9, v/v) at a 1 mL/min flow rate. Ion monitoring at m/z 301.2 [M-H]- was used to quantify IPA levels in plasma samples from these rats, while internal standard (IS) levels were assessed at m/z 455.3 [M-H]-. After validation, this approach was employed to conduct a pharmacokinetic analysis of rats administered IPA via the oral (p.o. 50, 100, or 200 mg/kg) and intravenous (i.v. 5 mg/kg) routes. Analyses of noncompartmental pharmacokinetic parameters revealed that IPA underwent secondary absorption following oral administration to these animals, with the two tested oral doses (50 and 100 mg/kg) being associated with respective absolute bioavailability values of 11.9% and 17.5%. In summary, this study may provide a foundation for future efforts to explore the mechanistic basis for the pharmacological activity of IPA, offering insights to guide its subsequent clinical utilization.

Noncoding RNAs link metabolic reprogramming to immune microenvironment in cancers.

Altered metabolic patterns in tumor cells not only meet their own growth requirements but also shape an immunosuppressive microenvironment through multiple mechanisms. Noncoding RNAs constitute approximately 60% of the transcriptional output of human cells and have been shown to regulate numerous cellular processes under developmental and pathological conditions. Given their extensive action mechanisms based on motif recognition patterns, noncoding RNAs may serve as hinges bridging metabolic activity and immune responses. Indeed, recent studies have shown that microRNAs, long noncoding RNAs and circRNAs are widely involved in tumor metabolic rewiring, immune cell infiltration and function. Hence, we summarized existing knowledge of the role of noncoding RNAs in the remodeling of tumor metabolism and the immune microenvironment, and notably, we established the TIMELnc manual, which is a free and public manual for researchers to identify pivotal lncRNAs that are simultaneously correlated with tumor metabolism and immune cell infiltration based on a bioinformatic approach.

Genetic Studies of Natural Glycosphingolipid Ligands for NKT Cells.

Glycosphingolipids (GSL) are natural ligands of NKT cells. Several laboratories have reported the in vitro activity of isoglobotriosylceramide (iGb3) in stimulating NKT cells. However, the knockout mice of iGb3 synthase showed no deficiency in development and function of NKT cells. There is a lack of knowledge on the genetics of redundant natural glycosphingolipid ligands. We have identified additional glycosphingolipid with stimulatory activity to NKT cells, including fucosyl lactosylceramide (H antigen). Here we describe the procedures to generate mice with deficiencies in Fut1, Fut2, and Sec1 genes to deplete H antigen through BAC engineering for the generation of ES cell-targeting construct, as well as the mice with deficiency of both blood group H-GSL ligand and isoglobotriosylceramide.

Selection of appropriate post-harvest processing methods based on the metabolomics analysis of Salvia miltiorrhiza Bunge.

Post-harvest processing is a leading cause of metabolic changes and quality loss in food products. An untargeted metabolomics approach based on UHPLC-QTOF-MS was conducted to explain metabolic changes during post-harvest processing of Salvia miltiorrhiza. A rapid identification method was established for comprehensive characterization of 56 phenolic acids and 45 tanshinones. Enzymatic browning was found to be the primary factor impacting the metabolic profile. A decreasing in free phenolic acids along with increasing in bound polyphenols was observed correlated with the deepening of browning degree. The various substructures of bound polyphenols were explored to interpret the composition of browning-associated products. It has also been found that the steaming process and control of the moisture content during slicing can effectively reduce the influence of enzymatic browning. This metabolomics study will contribute to select the optimal post-harvest processing methods for S. miltiorrhiza and provide information for post-harvest processing of similar products.

A nanofluidic device for ultrasensitive and label-free detection of tetracycline in association with γ-cyclodextrin and GO.

Herein, an ultrasensitive and selective nanofluidic device for tetracycline (TC) was developed in association with γ-cyclodextrin and graphene oxide (GO). The assay was designed based on the change of the nanochannel surface charge due to the selective recognition ability of GO between aptamers and TC-aptamer complexes. And γ-cyclodextrin was utilized to eliminate the excess TC since the amine group molecules were inclined to be adsorbed onto the nanochannel surface and affected the adsorption efficiency of the nanochannel. In the presence of TC, TC specifically binded to the aptamer to form TC-aptamer and was separated from GO. The TC-aptamer complexes could be quantitated with conical nanochannels coated with polyethyleneimine (PEI)/Zr4+. The redundant TC was removed by γ-cyclodextrin. The detection limit of the nanofluidic device was as low as 2 ng L-1 (S/N = 3) and the linear range was 10 ng L-1 to 10 µg L-1. Moreover, the nanofluidic device provided high specificity and good recovery rates of 94.8-109.3% in natural river, tapwater and wastewater samples. The results revealed that our study provided a new rapid detection method for trace contaminant analysis.

Cancer and COVID-19 Susceptibility and Severity: A Two-Sample Mendelian Randomization and Bioinformatic Analysis.

The clinical management of patients with COVID-19 and cancer is a Gordian knot that has been discussed widely but has not reached a consensus. We introduced two-sample Mendelian randomization to investigate the causal association between a genetic predisposition to cancers and COVID-19 susceptibility and severity. Moreover, we also explored the mutation landscape, expression pattern, and prognostic implications of genes involved with COVID-19 in distinct cancers. Among all of the cancer types we analyzed, only the genetic predisposition to lung adenocarcinoma was causally associated with increased COVID-19 severity (OR = 2.93, ß = 1.074, se = 0.411, p = 0.009) with no obvious heterogeneity (Q = 17.29, p = 0.24) or symmetry of the funnel plot. In addition, the results of the pleiotropy test demonstrated that instrument SNPs were less likely to affect COVID-19 severity via approaches other than lung adenocarcinoma cancer susceptibility (p = 0.96). Leave-one-out analysis showed no outliers in instrument SNPs, whose elimination rendered alterations in statistical significance, which further supported the reliability of the MR results. Broad mutation and differential expression of these genes were also found in cancers, which may provide valuable information for developing new treatment modalities for patients with both cancer and COVID-19. For example, ERAP2, a risk factor for COVID-19-associated death, is upregulated in lung squamous cancer and negatively associated with patient prognosis. Hence, ERAP2-targeted treatment may simultaneously reduce COVID-19 disease severity and restrain cancer progression. Our results highlighted the importance of strengthening medical surveillance for COVID-19 deterioration in patients with lung adenocarcinoma by showing their causal genetic association. For these patients, a delay in anticancer treatment, such as chemotherapy and surgery, should be considered.

Fabrication of a Biomimetic Nanochannel Logic Platform and Its Applications in the Intelligent Detection of miRNA Related to Liver Cancer.

Nanochannel-based analytical techniques have great potential applications for nucleic acid sequencing and high sensitivity detection of biological molecules. However, the sensitivity of conventional solid-state nanochannel sensors is hampered by a lack of effective signal amplification strategies, which has limited its utility in the field of analytical chemistry. Here we selected a solid-state nanochannnel modified with polyethylenimine and Zr4+ in combination with graphene oxide as the sensing platform. The high-performance sensor is based upon the change of the surface charge of the nanochannel, which is resulted from DNA cascade signal amplification in solution. The target miRNA (miR-122) can be indirectly quantitated with a detection limit of 97.2 aM with an excellent selectivity. Depending on the nucleic acid's hybridization and configuration transform, the designed nanochannel sensing systems can realize the intelligent detection of multiple liver cancer-related miRNA (miR-122 and miR Let-7a) integrating with cascaded INHIBIT-OR logic gate to provide theoretical guidance and technical support for clinical diagnosis and therapeutic evaluation of liver cancer.

Layered MoS2 nanosheets modified by biomimetic phospholipids: Enhanced stability and its synergistic treatment of cancer with chemo-photothermal therapy.

Cancer is a huge challenge humanity facing today, and single chemical treatments inevitably have shortcomings such as poor selectivity and large side effects. This paper constructed an egg yolk phospholipids modified molybdenum disulfide (MoS2) nanocarrier system for the treatment of tumors via the combination of chemotherapy and photothermal therapy. The lipid-modified layered MoS2 (MoS2-Lipid) nanocomposite was synthesized by simple physical adsorption. The lipid modification strongly enhanced the stability of MoS2 nanosheets and the nanocarrier has a large drug loading amount with pH dependent DOX release profile, an excellent photothermal property, and an ideal cellular uptake property. Therefore, we combined chemotherapy and photothermal therapy to treat tumors synergistically. Through in vitro cell experiments, pure nanocomposite had no obvious cytotoxicity to cells, and the synergistic treatment of tumors by chemotherapy and photothermal therapy was more effective than any single treatment. More importantly, in vivo experiments indicated that lipid modification enhanced the accumulation of the nanocarrier in mice tumors, thus a better photothermal performance could be seen compared with original MoS2 nanosheets. In summary, the MoS2-lipid nanocomposite is a promising nanocarrier for the treatment of tumors by chemo and photothermal therapy.

Dynamics of nonlinear waves in two-dimensional cubic-quintic nonlinear Schrödinger equation with spatially modulated nonlinearities and potentials.

We derive analytical solutions to the cubic-quintic nonlinear Schrödinger equation with potentials and nonlinearities depending on both propagation distance and transverse space. Among other, circle solitons and multi-peaked vortex solitons are found. These solitary waves propagate self-similarly and are characterized by three parameters, the modal numbers m and n, and the modulation depth of intensity. We find that the stable fundamental solitons with m = 0 and the low-order solitons with m = 1, n ≤ 2 can be supported with the energy eigenvalues E = 0 and E ≠ 0. However, higher-order solitons display unstable propagation over prolonged distances. The stability of solutions is examined by numerical simulations.

Supramolecular solvent-based extraction coupled with vortex-mixing for determination of palladium and silver in water samples by flame atomic absorption spectrometry.

A simple and practical extraction method of supramolecular solvents (SUPRAS) was developed for separation and enrichment of trace amounts of palladium (Pd) and silver (Ag) in water samples prior to flame atomic absorption spectrometry (FAAS) analysis. The SUPRAS selected was made up of an aqueous solution containing tetrahydrofuran and nonanoic acid. Pd and Ag reacted with diethyldithiocarbamate to form hydrophobic chelates, which were extracted into the vesicles of SUPRAS. Different parameters such as the concentration of chelating agent, sample pH, supramolecular solvent and the effect of foreign ions were studied. Under the optimal conditions, the linear ranges of Pd and Ag were from 10 to 1,000 µg/L. The relative recoveries of Pd and Ag in tap and river water samples at the spiking level of 10 ug/mL ranged from 90.8 to 116%. The relative standard deviations were 3.6-4.0% (n = 9), the limits of detection were 2.8 and 1.9 µg/L and the enrichment factors were 36 and 18 for Pd and Ag, respectively. The quantification limits were 3.2 and 2.4 µg/L. The method was successfully applied to the determination of Pd and Ag in water samples.