From Genotype to Phenotype: Through Chromatin

Salvia officinalis L. exerts oncostatic effects in rodent and in vitro models of breast carcinoma

Introduction: Based on extensive data from oncology research, the use of phytochemicals or plant-based nutraceuticals is considered an innovative tool for cancer management. This research aimed to analyze the oncostatic properties of Salvia officinalis L. [Lamiaceae; Salviae officinalis herba] using animal and in vitro models of breast carcinoma (BC). Methods: The effects of dietary administered S. officinalis in two concentrations (0.1%/SAL 0.1/and 1%/SAL 1/) were assessed in both syngeneic 4T1 mouse and chemically induced rat models of BC. The histopathological and molecular evaluations of rodent carcinoma specimens were performed after the autopsy. Besides, numerous in vitro analyses using two human cancer cell lines were performed. Results and Conclusion: The dominant metabolites found in S. officinalis propylene glycol extract (SPGE) were representatives of phenolics, specifically rosmarinic, protocatechuic, and salicylic acids. Furthermore, the occurrence of triterpenoids ursolic and oleanolic acid was proved in SPGE. In a mouse model, a non-significant tumor volume decrease after S. officinalis treatment was associated with a significant reduction in the mitotic activity index of 4T1 tumors by 37.5% (SAL 0.1) and 31.5% (SAL 1) vs. controls (set as a blank group with not applied salvia in the diet). In addition, salvia at higher doses significantly decreased necrosis/whole tumor area ratio by 46% when compared to control tumor samples. In a rat chemoprevention study, S. officinalis at a higher dose significantly lengthened the latency of tumors by 8.5 days and significantly improved the high/low-grade carcinomas ratio vs. controls in both doses. Analyses of the mechanisms of anticancer activities of S. officinalis included well-validated prognostic, predictive, and diagnostic biomarkers that are applied in both oncology practice and preclinical investigation. Our assessment in vivo revealed numerous significant changes after a comparison of treated vs. untreated cancer cells. In this regard, we found an overexpression in caspase-3, an increased Bax/Bcl-2 ratio, and a decrease in MDA, ALDH1, and EpCam expression. In addition, salvia reduced TGF-β serum levels in rats (decrease in IL-6 and TNF-α levels were with borderline significance). Evaluation of epigenetic modifications in rat cancer specimens in vivo revealed a decline in the lysine methylations of H3K4m3 and an increase in lysine acetylation in H4K16ac levels in treated groups. Salvia decreased the relative levels of oncogenic miR21 and tumor-suppressive miR145 (miR210, miR22, miR34a, and miR155 were not significantly altered). The methylation of ATM and PTEN promoters was decreased after S. officinalis treatment (PITX2, RASSF1, and TIMP3 promoters were not altered). Analyzing plasma metabolomics profile in tumor-bearing rats, we found reduced levels of ketoacids derived from BCAAs after salvia treatment. In vitro analyses revealed significant anti-cancer effects of SPGE extract in MCF-7 and MDA-MB-231 cell lines (cytotoxicity, caspase-3/-7, Bcl-2, Annexin V/PI, cell cycle, BrdU, and mitochondrial membrane potential). Our study demonstrates the significant chemopreventive and treatment effects of salvia haulm using animal or in vitro BC models.

Emotional dysregulation, alexithymia and neuroticism: a systematic review on the genetic basis of a subset of psychological traits

Neuroticism, alexithymia and emotion dysregulation are key traits and known risk factors for several psychiatric conditions. In this systematic review, the aim is to evaluate the genetic contribution to these psychological phenotypes. A systematic review of articles found in PubMed was conducted. Search terms included 'genetic', 'GWAS', 'neuroticism', 'alexithymia' and 'emotion dysregulation'. Risk of bias was assessed utilizing the STREGA checklist. Two hundred two papers were selected from existing literature based on the inclusion and exclusion criteria. Among these, 27 were genome-wide studies and 175 were genetic association studies. Single gene association studies focused on selected groups of genes, mostly involved in neurotransmission, with conflicting results. GWAS studies on neuroticism, on the other hand, found several relevant and replicated intergenic and intronic loci affecting the expression and regulation of crucial and well-known genes (such as DRD2 and CRHR1). Mutations in genes coding for trascriptional factors were also found to be associated with neuroticism (DCC, XKR6, TCF4, RBFOX1), as well as a noncoding regulatory RNA (LINC00461). On the other hand, little GWAS data are available on alexythima and emotional dysregulation.

A critical appraisal of the relative contribution of tissue architecture, genetics, epigenetics and cell metabolism to carcinogenesis

Here we contrast several carcinogenesis models. The somatic-mutation-theory posits mutations as main causes of malignancy. However, inconsistencies led to alternative explanations. For example, the tissue-organization-field-theory considers disrupted tissue-architecture as main cause. Both models can be reconciled using systems-biology-approaches, according to which tumors hover in states of self-organized criticality between order and chaos, are emergent results of multiple deviations and are subject to general laws of nature: inevitable variation(mutation) explainable by increased entropy(second-law-of-thermodynamics) or indeterminate decoherence upon measurement of superposed quantum systems(quantum mechanics), followed by Darwinian-selection. Genomic expression is regulated by epigenetics. Both systems cooperate. So cancer is neither just a mutational nor an epigenetic problem. Rather, epigenetics links environmental cues to endogenous genetics engendering a regulatory machinery that encompasses specific cancer-metabolic-networks. Interestingly, mutations occur at all levels of this machinery (oncogenes/tumor-suppressors, epigenetic-modifiers, structure-genes, metabolic-genes). Therefore, in most cases, DNA mutations may be the initial and crucial cancer-promoting triggers.

Exploring the Genetic Predisposition to Epigenetic Changes in Alzheimer's Disease

Alzheimer's disease (AD) is a prevalent type of dementia in elderly populations with a significant genetic component. The accumulating evidence suggests that AD involves a reconfiguration of the epigenetic landscape, including DNA methylation, post-translational modification of histone proteins, and chromatin remodeling. Along with environmental factors, individual specific genetic features play a considerable role in the formation of epigenetic architecture. In this study, we attempt to identify the non-coding regulatory SNPs (rSNPs) able to affect the epigenetic mechanisms in AD. To this end, the multi-omics approach is used. The GEO (Gene Expression Omnibus) available data (GSE153875) for AD patients and controls are integrated to reveal the rSNPs that display allele-specific features in both ChIP-seq profiles of four histone modifications and RNA-seq. Furthermore, we analyze the presence of rSNPs in the promoters of genes reported to be differentially expressed between AD and the normal brain (AD-related genes) and involved in epigenetic regulation according to the EpiFactors database. We also searched for the rSNPs in the promoters of the genes coding for transcription regulators of the identified AD-related genes. These regulators were selected based on the corresponding ChIP-seq peaks (ENCODE) in the promoter regions of these genes. Finally, we formed a panel of rSNPs localized to the promoters of genes that contribute to the epigenetic landscape in AD and, thus, to the genetic predisposition for this disease.

Repressive epigenetic mechanisms, such as the H3K27me3 histone modification, were predicted to affect muscle gene expression and its mineral content in Nelore cattle

Epigenetic repression has been linked to the regulation of different cell states. In this study, we focus on the influence of this repression, mainly by H3K27me3, over gene expression in muscle cells, which may affect mineral content, a phenotype that is relevant to muscle function and beef quality. Based on the inverse relationship between H3K27me3 and gene expression (i.e., epigenetic repression) and on contrasting sample groups, we computationally predicted regulatory genes that affect muscle mineral content. To this end, we applied the TRIAGE predictive method followed by a rank product analysis. This methodology can predict regulatory genes that might be affected by repressive epigenetic regulation related to mineral concentration. Annotation of orthologous genes, between human and bovine, enabled our investigation of gene expression in the Longissimus thoracis muscle of Bos indicus cattle. The animals under study had a contrasting mineral content in their muscle cells. We identified candidate regulatory genes influenced by repressive epigenetic mechanisms, linking histone modification to mineral content in beef samples. The discovered candidate genes take part in multiple biological pathways, i.e., impulse transmission, cell signalling, immunological, and developmental pathways. Some of these genes were previously associated with mineral content or regulatory mechanisms. Our findings indicate that epigenetic repression can partially explain the gene expression profiles observed in muscle samples with contrasting mineral content through the candidate regulators here identified.

Differential Gene Expression Prediction by Ensemble Deep Networks on Histone Modification Data

Predicting differential gene expression (DGE) from Histone modifications (HM) signal is crucial to understand how HM controls cell functional heterogeneity through influencing differential gene regulation. Most existing prediction methods use fixed-length bins to represent HM signals and transmit these bins into a single machine learning model to predict differential expression genes of single cell type or cell type pair. However, the inappropriate bin length may cause the splitting of the important HM segment and lead to information loss. Furthermore, the bias of single learning model may limit the prediction accuracy. Considering these problems, in this paper, we proposes an Ensemble deep neural networks framework for predicting Differential Gene Expression (EnDGE). EnDGE employs different feature extractors on input HM signal data with different bin lengths and fuses the feature vectors for DGE prediction. Ensemble multiple learning models with different HM signal cutting strategies helps to keep the integrity and consistency of genetic information in each signal segment, and offset the bias of individual models. Besides the popular feature extractors, we also propose a new Residual Network based model with higher prediction accuracy to increase the diversity of feature extractors. Experiments on the real datasets from the Roadmap Epigenome Project (REMC) show that for all cell type pairs, EnDGE significantly outperforms the state-of-the-art baselines for differential gene expression prediction.

Cerebral palsy and related neuromotor disorders: Overview of genetic and genomic studies

Cerebral palsy (CP) is a debilitating condition characterized by abnormal movement or posture, beginning early in development. Early family and twin studies and more recent genomic investigations clearly demonstrate that genetic factors of major effect contribute to the etiology of CP. Most copy number variants and small alterations of nucleotide sequence that cause CP arise as a result of de novo mutations, so studies that estimate heritability on basis of recurrence frequency within families substantially underestimate genetic contributions to the etiology. At least 4% of patients with typical CP have disease-causing CNVs, and at least 14% have disease-causing single nucleotide variants or indels. The rate of pathogenic genomic lesions is probably more than twice as high among patients who have atypical CP, i.e., neuromotor dysfunction with additional neurodevelopmental abnormalities or malformations, or with MRI findings and medical history that are not characteristic of a perinatal insult. Mutations of many different genetic loci can produce a CP-like phenotype. The importance of genetic variants of minor effect and of epigenetic modifications in producing a multifactorial predisposition to CP is less clear. Recognizing the specific cause of CP in an affected individual is essential to providing optimal clinical management. An etiological diagnosis provides families an "enhanced compass" that improves overall well-being, facilitates access to educational and social services, permits accurate genetic counseling, and, for a subset of patients such as those with underlying inherited metabolic disorders, may make precision therapy that targets the pathophysiology available. Trio exome sequencing with assessment of copy number or trio genome sequencing with bioinformatics analysis for single nucleotide variants, indels, and copy number variants is clinically indicated in the initial workup of CP patients, especially those with additional malformations or neurodevelopmental abnormalities.

EpICC: A Bayesian neural network model with uncertainty correction for a more accurate classification of cancer

Accurate classification of cancers into their types and subtypes holds the key for choosing the right treatment strategy and can greatly impact patient well-being. However, existence of large-scale variations in the molecular processes driving even a single type of cancer can make accurate classification a challenging problem. Therefore, improved and robust methods for classification are absolutely critical. Although deep learning-based methods for cancer classification have been proposed earlier, they all provide point estimates for predictions without any measure of confidence and thus, can fall short in real-world applications where key decisions are to be made based on the predictions of the classifier. Here we report a Bayesian neural network-based model for classification of cancer types as well as sub-types from transcriptomic data. This model reported a measure of confidence with each prediction through analysis of epistemic uncertainty. We incorporated an uncertainty correction step with the Bayesian network-based model to greatly enhance prediction accuracy of cancer types (> 97% accuracy) and sub-types (> 80%). Our work suggests that reporting uncertainty measure with each classification can enable more accurate and informed decision-making that can be highly valuable in clinical settings.

Using expression quantitative trait loci data and graph-embedded neural networks to uncover genotype–phenotype interactions

Motivation: A central goal of current biology is to establish a complete functional link between the genotype and phenotype, known as the so-called genotype–phenotype map. With the continuous development of high-throughput technology and the decline in sequencing costs, multi-omics analysis has become more widely employed. While this gives us new opportunities to uncover the correlation mechanisms between single-nucleotide polymorphism (SNP), genes, and phenotypes, multi-omics still faces certain challenges, specifically: 1) When the sample size is large enough, the number of omics types is often not large enough to meet the requirements of multi-omics analysis; 2) each omics’ internal correlations are often unclear, such as the correlation between genes in genomics; 3) when analyzing a large number of traits (p), the sample size (n) is often smaller than p, n << p, hindering the application of machine learning methods in the classification of disease outcomes.

Results: To solve these issues with multi-omics and build a robust classification model, we propose a graph-embedded deep neural network (G-EDNN) based on expression quantitative trait loci (eQTL) data, which achieves sparse connectivity between network layers to prevent overfitting. The correlation within each omics is also considered such that the model more closely resembles biological reality. To verify the capabilities of this method, we conducted experimental analysis using the GSE28127 and GSE95496 data sets from the Gene Expression Omnibus (GEO) database, tested various neural network architectures, and used prior data for feature selection and graph embedding. Results show that the proposed method could achieve a high classification accuracy and easy-to-interpret feature selection. This method represents an extended application of genotype–phenotype association analysis in deep learning networks.

Discovering Common miRNA Signatures Underlying Female-Specific Cancers via a Machine Learning Approach Driven by the Cancer Hallmark ERBB

Big data processing, using omics data integration and machine learning (ML) methods, drive efforts to discover diagnostic and prognostic biomarkers for clinical decision making. Previously, we used the TCGA database for gene expression profiling of breast, ovary, and endometrial cancers, and identified a top-scoring network centered on the ERBB2 gene, which plays a crucial role in carcinogenesis in the three estrogen-dependent tumors. Here, we focused on microRNA expression signature similarity, asking whether they could target the ERBB family. We applied an ML approach on integrated TCGA miRNA profiling of breast, endometrium, and ovarian cancer to identify common miRNA signatures differentiating tumor and normal conditions. Using the ML-based algorithm and the miRTarBase database, we found 205 features and 158 miRNAs targeting ERBB isoforms, respectively. By merging the results of both databases and ranking each feature according to the weighted Support Vector Machine model, we prioritized 42 features, with accuracy (0.98), AUC (0.93–95% CI 0.917–0.94), sensitivity (0.85), and specificity (0.99), indicating their diagnostic capability to discriminate between the two conditions. In vitro validations by qRT-PCR experiments, using model and parental cell lines for each tumor type showed that five miRNAs (hsa-mir-323a-3p, hsa-mir-323b-3p, hsa-mir-331-3p, hsa-mir-381-3p, and hsa-mir-1301-3p) had expressed trend concordance between breast, ovarian, and endometrium cancer cell lines compared with normal lines, confirming our in silico predictions. This shows that an integrated computational approach combined with biological knowledge, could identify expression signatures as potential diagnostic biomarkers common to multiple tumors.

Differentially expressed genes prediction by multiple self-attention on epigenetics data

Predicting differentially expressed genes (DEGs) from epigenetics signal data is the key to understand how epigenetics controls cell functional heterogeneity by gene regulation. This knowledge can help developing 'epigenetics drugs' for complex diseases like cancers. Most of existing machine learning-based methods suffer defects in prediction accuracy, interpretability or training speed. To address these problems, in this paper, we propose a Multiple Self-Attention model for predicting DEGs on Epigenetic data (Epi-MSA). Epi-MSA first uses convolutional neural networks for neighborhood bins information embedding, and then employs multiple self-attention encoders on different input epigenetics factors data to learn which locations of genes are important for predicting DEGs. Next it trains a soft attention module to pick out which epigenetics factors are significant. The attention mechanism makes the model interpretable, and the pure matrix operation of self-attention enables the model to be parallel calculated and speeds up the training. Experiments on datasets from the Roadmap Epigenome Project and BluePrint Data Analysis Portal (BDAP) show that the performance of Epi-MSA is better than existing competitive methods, and Epi-MSA also has a smaller standard deviation, which shows that Epi-MSA is effective and stable. In addition, Epi-MSA has a good interpretability, this is confirmed by referring its attention weight matrix with existing biological knowledge.

Sirtuins are crucial regulators of T cell metabolism and functions

It is well known that metabolism underlies T cell differentiation and functions. The pathways regulating T cell metabolism and function are interconnected, and changes in T cell metabolic activity directly impact the effector functions and fate of T cells. Thus, understanding how metabolic pathways influence immune responses and ultimately affect disease progression is paramount. Epigenetic and posttranslational modification mechanisms have been found to control immune responses and metabolic reprogramming. Sirtuins are NAD⁺-dependent histone deacetylases that play key roles during cellular responses to a variety of stresses and have recently been reported to have potential roles in immune responses. Therefore, sirtuins are of significant interest as therapeutic targets to treat immune-related diseases and enhance antitumor immunity. This review aims to illustrate the potential roles of sirtuins in different subtypes of T cells during the adaptive immune response.

Sirtuins, enzymes that regulate how cells respond to stress, regulate T cell metabolism and functions, and therefore blocking or boosting sirtuins influences immune responses. As part of the immune system, some types of T cells attack specific targets; others keep the immune response in check. Imene Hamaidi and Sungjune Kim at H. Lee Moffitt Cancer Center, Tampa, USA, have reviewed how sirtuins affect different subsets of T cells to either promote or suppress immune responses. Boosting sirtuins that increase the function of inflammation-suppressing T cells can improve outcomes for transplant recipients or help treat autoimmune diseases. Conversely, stimulating immune-activating sirtuins can help re-energize exhausted antitumor T cells. Understanding the complex web of sirtuin–T cell interactions may help in developing therapeutic strategies for improving transplant outcomes, and for treating autoimmune diseases and cancer.

Associations of age, sex, sexual abuse, and genotype with monoamine oxidase a gene methylation

Epigenome-wide studies report higher methylation among women than men with decreasing levels with age. Little is known about associations of sex and age with methylation of monoamine oxidase A (MAOA). Methylation of the first exonic and partial first intronic region of MAOA has been shown to strengthen associations of interactions of MAOA-uVNTR genotypes and adversity with aggression and substance misuse. Our study examined associations of sex and age with MAOA first exon and intron methylation levels in 252 women and 157 men aged 14–73 years. Participants included adolescents recruited at a substance misuse clinic, their siblings and parents, and healthy women. Women showed ~ 50% higher levels of exonic, and ~ 15% higher intronic, methylation than men. Methylation levels were similar between younger (M = 22.7 years) and older (M = 46.1 years) participants, and stable across age. Age modified few associations of methylation levels with sex. MAOA genotypes modified few associations of methylation with sex and age. Higher methylation levels among women were not explained by genotype, nor interaction of genotype and sexual abuse. Findings were similar after adjusting for lifetime diagnoses of substance dependence (women = 24.3%; men = 34.2%). Methylation levels were higher among women who experienced sexual abuse than women who did not. Results extend on prior studies by showing that women display higher levels of methylation than men within first intronic/exonic regions of MAOA, which did not decrease with age in either sex. Findings were not conditioned by genotype nor interactions of genotype and trauma, and indicate X-chromosome inactivation.

Comprehensive analysis of epigenetic signatures of human transcription control

Advances in sequencing technologies have enabled exploration of epigenetic and transcriptional profiles at a genome-wide level. The epigenetic and transcriptional landscapes are now available in hundreds of mammalian cell and tissue contexts. Many studies have performed multi-omics analyses using these datasets to enhance our understanding of relationships between epigenetic modifications and transcription regulation. Nevertheless, most studies so far have focused on the promoters/enhancers and transcription start sites, and other features of transcription control including exons, introns and transcription termination remain underexplored. We investigated the interplay between epigenetic modifications and diverse transcription features using the data generated by the Roadmap Epigenomics project. A comprehensive analysis of histone modifications, DNA methylation, and RNA-seq data of thirty-three human cell lines and tissue types allowed us to confirm the generality of previously described relationships, as well as to generate new hypotheses about the interplay between epigenetic modifications and transcription features. Importantly, our analysis included previously under-explored features of transcription control, namely, transcription termination sites, exon-intron boundaries, and the exon inclusion ratio. We have made the analyses freely available to the scientific community at joshiapps.cbu.uib.no/perepigenomics_app/ for easy exploration, validation and hypothesis generation.

Linking genotype to phenotype in multi-omics data of small sample

BACKGROUND

Genome-wide association studies (GWAS) that link genotype to phenotype represent an effective means to associate an individual genetic background with a disease or trait. However, single-omics data only provide limited information on biological mechanisms, and it is necessary to improve the accuracy for predicting the biological association between genotype and phenotype by integrating multi-omics data. Typically, gene expression data are integrated to analyze the effect of single nucleotide polymorphisms (SNPs) on phenotype. Such multi-omics data integration mainly follows two approaches: multi-staged analysis and meta-dimensional analysis, which respectively ignore intra-omics and inter-omics associations. Moreover, both approaches require omics data from a single sample set, and the large feature set of SNPs necessitates a large sample size for model establishment, but it is difficult to obtain multi-omics data from a single, large sample set.

RESULTS

To address this problem, we propose a method of genotype-phenotype association based on multi-omics data from small samples. The workflow of this method includes clustering genes using a protein-protein interaction network and gene expression data, screening gene clusters with group lasso, obtaining SNP clusters corresponding to the selected gene clusters through expression quantitative trait locus data, integrating SNP clusters and corresponding gene clusters and phenotypes into three-layer network blocks, analyzing and predicting based on each block, and obtaining the final prediction by taking the average.

CONCLUSIONS

We compare this method to others using two datasets and find that our method shows better results in both cases. Our method can effectively solve the prediction problem in multi-omics data of small sample, and provide valuable resources for further studies on the fusion of more omics data.

Interplay of RNA-Binding Proteins and microRNAs in Neurodegenerative Diseases

The number of patients with neurodegenerative diseases (NDs) is increasing, along with the growing number of older adults. This escalation threatens to create a medical and social crisis. NDs include a large spectrum of heterogeneous and multifactorial pathologies, such as amyotrophic lateral sclerosis, frontotemporal dementia, Alzheimer’s disease, Parkinson’s disease, Huntington’s disease and multiple system atrophy, and the formation of inclusion bodies resulting from protein misfolding and aggregation is a hallmark of these disorders. The proteinaceous components of the pathological inclusions include several RNA-binding proteins (RBPs), which play important roles in splicing, stability, transcription and translation. In addition, RBPs were shown to play a critical role in regulating miRNA biogenesis and metabolism. The dysfunction of both RBPs and miRNAs is often observed in several NDs. Thus, the data about the interplay among RBPs and miRNAs and their cooperation in brain functions would be important to know for better understanding NDs and the development of effective therapeutics. In this review, we focused on the connection between miRNAs, RBPs and neurodegenerative diseases.

Artificial Intelligence in Epigenetic Studies: Shedding Light on Rare Diseases

More than 7,000 rare diseases (RDs) exist worldwide, affecting approximately 350 million people, out of which only 5% have treatment. The development of novel genome sequencing techniques has accelerated the discovery and diagnosis in RDs. However, most patients remain undiagnosed. Epigenetics has emerged as a promise for diagnosis and therapies in common disorders (e.g., cancer) with several epimarkers and epidrugs already approved and used in clinical practice. Hence, it may also become an opportunity to uncover new disease mechanisms and therapeutic targets in RDs. In this “big data” age, the amount of information generated, collected, and managed in (bio)medicine is increasing, leading to the need for its rapid and efficient collection, analysis, and characterization. Artificial intelligence (AI), particularly deep learning, is already being successfully applied to analyze genomic information in basic research, diagnosis, and drug discovery and is gaining momentum in the epigenetic field. The application of deep learning to epigenomic studies in RDs could significantly boost discovery and therapy development. This review aims to collect and summarize the application of AI tools in the epigenomic field of RDs. The lower number of studies found, specific for RDs, indicate that this is a field open to expansion, following the results obtained for other more common disorders.

Degradation of BRD4 - a promising treatment approach not only for hematologic but also for solid cancer

Bromodomain (BRD) and extra-terminal (BET) proteins are epigenetic readers that regulate gene expression and promote cancer evolution. Pharmacological inactivation of BRD4 has recently been introduced as a promising anti-neoplastic approach that targets MYC oncogene expression. However, resistance against BRD4-targeting drugs has been described. We compared the efficacy of the small-molecule-type BET BRD inhibitor JQ1 with the recently developed BET protein degraders dBET1 and dBET6 in colon, breast, melanoma, ovarian, lung and prostate cancer cell lines. As determined by qPCR, all BRD4 targeting drugs dose-dependently decreased MYC expression, with dBET6 introducing the strongest downregulation of MYC. This correlated with the anti-proliferative activity of these drugs, which was at least one order of magnitude higher for dBET6 (IC₅₀ 0.001-0.5 µM) than for dBET1 or JQ1 (IC₅₀ 0.5-5 µM). Interestingly, when combined with commonly used cytotoxic therapeutics, dBET6 was found to promote anti-neoplastic effects and to counteract chemoresistance in most cancer cell lines. Moreover, JQ1 and both BET degraders strongly downregulated baseline and interferon-gamma induced expression of the immune checkpoint molecule PD-L1 in all cancer cell lines. Together, our data suggest that dBET6 outperforms first-generation BRD4 targeting drugs like dBET1 and JQ1, and decreases chemoresistance and immune resistance of cancer.

The neuroendocrine modulation of global DNA methylation in neuropsychiatric disorders

There is an increasing body of knowledge on the influence of differential DNA methylation of specific genomic regions in psychiatric disorders. However, fewer studies have addressed global DNA methylation (GMe) levels. GMe is an estimative of biological functioning that is regulated by pervasive mechanisms able to capture the big picture of metabolic and environmental influences upon gene expression. In the present perspective article, we highlighted evidence for the relationships between cortisol and sex hormones and GMe in psychiatric disorders. We argue that the far-reaching effects of cortisol and sexual hormones on GMe may lie on the pathways linking stress and mental health. Further research on these endocrine-epigenetic links may help to explain the role of environmental stress as well as sex differences in the prevalence of psychiatric disorders.

Rhus coriaria L. (Sumac) Demonstrates Oncostatic Activity in the Therapeutic and Preventive Model of Breast Carcinoma

Comprehensive scientific data provide evidence that isolated phytochemicals or whole plant foods may beneficially modify carcinogenesis. The aim of this study was to evaluate the oncostatic activities of Rhus coriaria L. (sumac) using animal models (rat and mouse), and cell lines of breast carcinoma. R. coriaria (as a powder) was administered through the diet at two concentrations (low dose: 0.1% (w/w) and high dose: 1 % (w/w)) for the duration of the experiment in a syngeneic 4T1 mouse and chemically-induced rat mammary carcinoma models. After autopsy, histopathological and molecular analyses of tumor samples in rodents were performed. Moreover, in vitro analyses using MCF-7 and MDA-MB-231 cells were conducted. The dominant metabolites present in tested R. coriaria methanolic extract were glycosides of gallic acid (possible gallotannins). In the mouse model, R. coriaria at a higher dose (1%) significantly decreased tumor volume by 27% when compared to controls. In addition, treated tumors showed significant dose-dependent decrease in mitotic activity index by 36.5% and 51% in comparison with the control group. In the chemoprevention study using rats, R. coriaria at a higher dose significantly reduced the tumor incidence by 20% and in lower dose non-significantly reduced tumor frequency by 29% when compared to controls. Evaluations of the mechanism of oncostatic action using valid clinical markers demonstrated several positive alterations in rat tumor cells after the treatment with R. coriaria. In this regard, histopathological analysis of treated tumor specimens showed robust dose-dependent decrease in the ratio of high-/low-grade carcinomas by 66% and 73% compared to controls. In treated rat carcinomas, we found significant caspase-3, Bax, and Bax/Bcl-2 expression increases; on the other side, a significant down-regulation of Bcl-2, Ki67, CD24, ALDH1, and EpCam expressions and MDA levels. When compared to control specimens, evaluation of epigenetic alterations in rat tumor cells in vivo showed significant dose-dependent decrease in lysine methylation status of H3K4m3 and H3K9m3 and dose-dependent increase in lysine acetylation in H4K16ac levels (H4K20m3 was not changed) in treated groups. However, only in lower dose of sumac were significant decreases in the expression of oncogenic miR210 and increase of tumor-suppressive miR145 (miR21, miR22, and miR155 were not changed) observed. Finally, only in lower sumac dose, significant decreases in methylation status of three out of five gene promoters-ATM, PTEN, and TIMP3 (PITX2 and RASSF1 promoters were not changed). In vitro evaluations using methanolic extract of R. coriaria showed significant anticancer efficacy in MCF-7 and MDA-MB-231 cells (using Resazurin, cell cycle, annexin V/PI, caspase-3/7, Bcl-2, PARP, and mitochondrial membrane potential analyses). In conclusion, sumac demonstrated significant oncostatic activities in rodent models of breast carcinoma that were validated by mechanistic studies in vivo and in vitro.

Fluctuations of Histone Chemical Modifications in Breast, Prostate, and Colorectal Cancer: An Implication of Phytochemicals as Defenders of Chromatin Equilibrium

Natural substances of plant origin exert health beneficiary efficacy due to the content of various phytochemicals. Significant anticancer abilities of natural compounds are mediated via various processes such as regulation of a cell's epigenome. The potential antineoplastic activity of plant natural substances mediated by their action on posttranslational histone modifications (PHMs) is currently a highly evaluated area of cancer research. PHMs play an important role in maintaining chromatin structure and regulating gene expression. Aberrations in PHMs are directly linked to the process of carcinogenesis in cancer such as breast (BC), prostate (PC), and colorectal (CRC) cancer, common malignant diseases in terms of incidence and mortality among both men and women. This review summarizes the effects of plant phytochemicals (isolated or mixtures) on cancer-associated PHMs (mainly modulation of acetylation and methylation) resulting in alterations of chromatin structure that are related to the regulation of transcription activity of specific oncogenes, which are crucial in the development of BC, PC, and CRC. Significant effectiveness of natural compounds in the modulation of aberrant PHMs were confirmed by a number of in vitro or in vivo studies in preclinical cancer research. However, evidence concerning PHMs-modulating abilities of plant-based natural substances in clinical trials is insufficient.