Identification of epigenetic modifications mediating the antagonistic effect of selenium against cadmium-induced breast carcinogenesis

The RNA helicase DHX35 functions as a co-sensor for RIG-I-mediated innate immunity

RNA helicases are involved in the innate immune response against pathogens, including bacteria and viruses; however, their mechanism in the human airway epithelial cells is still not fully understood. Here, we demonstrated that DEAH (Asp-Glu-Ala-His) box polypeptide 35 (DHX35), a member of the DExD/H (Asp-Glu-x-Asp/His)-box helicase family, boosts antiviral innate immunity in human airway epithelial cells. DHX35 knockdown attenuated the production of interferon-β (IFN-β), IL6, and CXCL10, whereas DHX35 overexpression increased their production. Upon stimulation, DHX35 was constitutively expressed, but it translocated from the nucleus into the cytosol, where it recognized cytosolic poly(I:C) and poly(dA:dT) via its HELICc domain. Mitochondrial antiviral signaling protein (MAVS) acted as an adaptor for DHX35 and interacted with the HELICc domain of DHX35 using amino acids 360-510. Interestingly, DHX35 interacted with retinoic acid-inducible gene 1 (RIG-I), enhanced the binding affinity of RIG-I with poly(I:C) and poly(dA:dT), and formed a signalsome with MAVS to activate interferon regulatory factor 3 (IRF3), NF-κB-p65, and MAPK signaling pathways. These results indicate that DHX35 not only acted as a cytosolic nucleic acid sensor but also synergized with RIG-I to enhance antiviral immunity in human airway epithelial cells. Our results demonstrate a novel molecular mechanism for DHX35 in RIG-I-mediated innate immunity and provide a novel candidate for drug and vaccine design to control viral infections in the human airway.

Construction and testing of a risk prediction classifier for cardia carcinoma

OBJECTIVES

This research aimed to construct a prediction model for stages II and III cardia carcinoma (CC), and provide an effective preoperative evaluation tool for clinicians.

METHODS

CC mRNA expression matrix was obtained from Gene Expression Omnibus and The Cancer Genome Atlas databases. Non-negative matrix factorization was used to cluster data to obtain subgroup information, and weighted gene co-expression network analysis was used to uncover key modules linked to different subgroups. Gene-set enrichment analysis analyzed biological pathways of different subgroups. The related pathways of multiple modules were scrutinized with Kyoto Encyclopedia of Genes and Genomes. Key modules were manually annotated to screen CC-related genes. Subsequently, quantitative real-time polymerase chain reaction assessed CC-related gene expression in fresh tissues and paraffin samples, and Pearson correlation analysis was performed. A classification model was constructed and the predictive ability was evaluated by the receiver operating characteristic curve.

RESULTS

CC patients had four subgroups that were associated with brown, turquoise, red, and black modules, respectively. The CC-related modules were mainly associated with abnormal cell metabolism and inflammatory immune pathways. Then, 76 CC-elated genes were identified. Pearson correlation analysis presented that THBS4, COL14A1, DPYSL3, FGF7, and SVIL levels were relatively stable in fresh and paraffin tissues. The area under the curve of 5-gene combined prediction for staging was 0.8571, indicating good prediction ability.

CONCLUSIONS

The staging classifier for CC based on THBS4, COL14A1, DPYSL3, FGF7, and SVIL has a good predictive effect, which may provide effective guidance for whether CC patients need emergency surgery.

Association of Dietary Intake of Zinc and Selenium with Breast Cancer Risk: A Case-Control Study in Chinese Women

As major nonenzymatic antioxidant components in the body, dietary Zinc (Zn) and Selenium (Se) may have an impact on breast cancer development. This study aimed to investigate the relationship between dietary Zn, Se intake and breast cancer risk in Chinese women. The case-control study included 1591 cases and 1622 age-frequency matched controls. Dietary intake was collected using a validated food frequency questionnaire. Dietary Zn and Se were divided into four categories: Zn/Se from plants, Zn/Se from meat, Zn/Se from red meat, and Zn/Se from white meat. Unconditional logistic regression models and restricted cubic spline analyses were performed to identify potential associations. Zn from white meat intake was linearly and inversely associated with breast cancer risk, and Se from red meat intake was linearly and positively associated with breast cancer risk, with adjusted odds ratio and 95% confidence interval of 0.76 (0.61-0.95) and 1.36 (1.04-1.77), respectively. Non-linear relationships were found between total dietary Zn, Zn from meat, Zn from red meat intake and breast cancer risk (p_{non-linearity} < 0.05). In conclusion, dietary Zn and Se intake were associated with breast cancer risk in Chinese women, and the optimal intake of Zn may be beneficial for breast cancer prevention.

Identification of genetic biomarkers associated with pharmacokinetics and pharmacodynamics of apixaban in Chinese healthy volunteers

BACKGROUND

Apixaban is a superior direct oral anticoagulant exihibiting interindividual variability in concentration and response in the real world. The present study aimed to identify genetic biomarkers associated with pharmacokinetics (PK) and pharmacodynamics (PD) of apixaban in healthy Chinese subjects.

METHODS

This multicenter study included 181 healthy Chinese adults taking a single dose of 2.5 mg or 5 mg apixaban and assessed their PK and PD parameters. Genome-wide single nucleotide polymorphism (SNP) genotyping was performed using the Affymetrix Axiom CBC_PMRA Array. Candidate gene association analysis and genome-wide association study were conducted to identify genes with a predictive value for PK and PD parameters of apixaban.

RESULTS

Several ABCG2 variants were associated with C_max and AUC_0-t of apixaban (p < 6.12 × 10^-5) and also presented significant differences of anti-Xa_3h activity and dPT_3h according to different ABCG2 genotypes (p < 0.05). Besides, ABLIM2 variants were found to be associated with PK characteristics and F13A1 and C3 variants were associated with PD characteristics of apixaban (p < 9.46 × 10^-8).

CONCLUSION

ABCG2 variants were found to be ideal genetic biomarkers for both PK and PD characteristics of apixaban. ABLIM2, F13A1 and C3 were identified as potential candidate genes associated with inter-individual variability of apixaban. This study was registered on ClinicalTrials.gov NCT03259399.

A review on the epigenetics modifications to nanomaterials in humans and animals: novel epigenetic regulator

In the nanotechnology era, nanotechnology applications have been intensifying their prospects to embrace all the vigorous sectors persuading human health and animal. The safety and concerns regarding the widespread use of engineered nanomaterials (NMA) and their potential effect on human health still require further clarification. Literature elucidated that NMA exhibited significant adverse effects on various molecular and cellular alterations. Epigenetics is a complex process resulting in the interactions between an organism’s environment and genome. The epigenetic modifications, including histone modification and DNA methylation, chromatin structure and DNA accessibility alteration, regulate gene expression patterns. Disturbances of epigenetic markers induced by NMA might promote the sensitivity of humans and animals to several diseases. Also, this paper focus on the epigenetic regulators of some dietary nutrients that have been confirmed to stimulate the epigenome and, more exactly, DNA histone modifications and non-histone proteins modulation by acetylation, and phosphorylation inhibition, which counteracts oxidative stress generations. The present review epitomizes the recent evidence of the potential effects of NMA on histone modifications, in addition to in vivo and in vitro cytosine DNA methylation and its toxicity. Furthermore, the part of epigenetic fluctuations as possible translational biomarkers for uncovering untoward properties of NMA is deliberated.

Integrated multi-omics approach revealed cellular senescence landscape

Cellular senescence is a complex multifactorial biological phenomenon that plays essential roles in aging, and aging-related diseases. During this process, the senescent cells undergo gene expression altering and chromatin structure remodeling. However, studies on the epigenetic landscape of senescence using integrated multi-omics approaches are limited. In this research, we performed ATAC-seq, RNA-seq and ChIP-seq on different senescent types to reveal the landscape of senescence and identify the prime regulatory elements. We also obtained 34 key genes and deduced that NAT1, PBX1 and RRM2, which interacted with each other, could be the potential markers of aging and aging-related diseases. In summary, our work provides the landscape to study accessibility dynamics and transcriptional regulations in cellular senescence. The application of this technique in different types of senescence allows us to identify the regulatory elements responsible for the substantial regulation of transcription, providing the insights into molecular mechanisms of senescence.

Gene-environment interactions in Alzheimer disease: the emerging role of epigenetics

With the exception of a few monogenic forms, Alzheimer disease (AD) has a complex aetiology that is likely to involve multiple susceptibility genes and environmental factors. The role of environmental factors is difficult to determine and, until a few years ago, the molecular mechanisms underlying gene-environment (G × E) interactions in AD were largely unknown. Here, we review evidence that has emerged over the past two decades to explain how environmental factors, such as diet, lifestyle, alcohol, smoking and pollutants, might interact with the human genome. In particular, we discuss how various environmental AD risk factors can induce epigenetic modifications of key AD-related genes and pathways and consider how epigenetic mechanisms could contribute to the effects of oxidative stress on AD onset. Studies on early-life exposures are helping to uncover critical time windows of sensitivity to epigenetic influences from environmental factors, thereby laying the foundations for future primary preventative approaches. We conclude that epigenetic modifications need to be considered when assessing G × E interactions in AD.

Application of Feature Selection and Deep Learning for Cancer Prediction Using DNA Methylation Markers

DNA methylation is a process that can affect gene accessibility and therefore gene expression. In this study, a machine learning pipeline is proposed for the prediction of breast cancer and the identification of significant genes that contribute to the prediction. The current study utilized breast cancer methylation data from The Cancer Genome Atlas (TCGA), specifically the TCGA-BRCA dataset. Feature engineering techniques have been utilized to reduce data volume and make deep learning scalable. A comparative analysis of the proposed approach on Illumina 27K and 450K methylation data reveals that deep learning methodologies for cancer prediction can be coupled with feature selection models to enhance prediction accuracy. Prediction using 450K methylation markers can be accomplished in less than 13 s with an accuracy of 98.75%. Of the list of 685 genes in the feature selected 27K dataset, 578 were mapped to Ensemble Gene IDs. This reduced set was significantly (FDR < 0.05) enriched in five biological processes and one molecular function. Of the list of 1572 genes in the feature selected 450K data set, 1290 were mapped to Ensemble Gene IDs. This reduced set was significantly (FDR < 0.05) enriched in 95 biological processes and 17 molecular functions. Seven oncogene/tumor suppressor genes were common between the 27K and 450K feature selected gene sets. These genes were RTN4IP1, MYO18B, ANP32A, BRF1, SETBP1, NTRK1, and IGF2R. Our bioinformatics deep learning workflow, incorporating imputation and data balancing methods, is able to identify important methylation markers related to functionally important genes in breast cancer with high accuracy compared to deep learning or statistical models alone.