Host fecal DNA specific methylation signatures mark gut dysbiosis and inflammation in children affected by autism spectrum disorder.

The gut-brain axis involves several bidirectional pathway communications including microbiome, bacterial metabolites, neurotransmitters as well as immune system and is perturbed both in brain and in gastrointestinal disorders. Consistently, microbiota-gut-brain axis has been found altered in autism spectrum disorder (ASD). We reasoned that such alterations occurring in ASD may impact both on methylation signatures of human host fecal DNA (HFD) and possibly on the types of human cells shed in the stools from intestinal tract giving origin to HFD. To test this hypothesis, we have performed whole genome methylation analysis of HFD from an age-restricted cohort of young children with ASD (N = 8) and healthy controls (N = 7). In the same cohort we have previously investigated the fecal microbiota composition and here we refined such analysis and searched for eventual associations with data derived from HFD methylome analysis. Our results showed that specific epigenetic signatures in human fecal DNA, especially at genes related to inflammation, associated with the disease. By applying methylation-based deconvolution algorithm, we found that the HFD derived mainly from immune cells and the relative abundance of those differed between patients and controls. Consistently, most of differentially methylated regions fitted with genes involved in inflammatory response. Interestingly, using Horvath epigenetic clock, we found that ASD affected children showed both epigenetic and microbiota age accelerated. We believe that the present unprecedented approach may be useful for the identification of the ASD associated HFD epigenetic signatures and may be potentially extended to other brain disorders and intestinal inflammatory diseases.

Targeted inhibition of the methyltransferase SETD8 synergizes with the Wee1 inhibitor adavosertib in restraining glioblastoma growth.

Despite intense research efforts, glioblastoma remains an incurable brain tumor with a dismal median survival time of 15 months. Thus, identifying new therapeutic targets is an urgent need. Here, we show that the lysine methyltransferase SETD8 is overexpressed in 50% of high-grade gliomas. The small molecule SETD8 inhibitor UNC0379, as well as siRNA-mediated inhibition of SETD8, blocked glioblastoma cell proliferation, by inducing DNA damage and activating cell cycle checkpoints. Specifically, in p53-proficient glioblastoma cells, SETD8 inhibition and DNA damage induced p21 accumulation and G1/S arrest whereas, in p53-deficient glioblastoma cells, DNA damage induced by SETD8 inhibition resulted in G2/M arrest mediated by Chk1 activation. Checkpoint abrogation, by the Wee1 kinase inhibitor adavosertib, induced glioblastoma cell lines and primary cells, DNA-damaged by UNC0379, to progress to mitosis where they died by mitotic catastrophe. Finally, UNC0379 and adavosertib synergized in restraining glioblastoma growth in a murine xenograft model, providing a strong rationale to further explore this novel pharmacological approach for adjuvant glioblastoma treatment.

A novel workflow for the qualitative analysis of DNA methylation data.

DNA methylation is an epigenetic modification that plays a pivotal role in major biological mechanisms, such as gene regulation, genomic imprinting, and genome stability. Different combinations of methylated cytosines for a given DNA locus generate different epialleles and alterations of these latter have been associated with several pathological conditions. Existing computational methods and statistical tests relevant to DNA methylation analysis are mostly based on the comparison of average CpG sites methylation levels and they often neglect non-CG methylation. Here, we present EpiStatProfiler, an R package that allows the analysis of CpG and non-CpG based epialleles starting from bisulfite sequencing data through a collection of dedicated extraction functions and statistical tests. EpiStatProfiler is provided with a set of useful auxiliary features, such as customizable genomic ranges, strand-specific epialleles analysis, locus annotation and gene set enrichment analysis. We showcase the package functionalities on two public datasets by identifying putative relevant loci in mice harboring the Huntington's disease-causing Htt gene mutation and in Ctcf +/- mice compared to their wild-type counterparts. To our knowledge, EpiStatProfiler is the first package providing functionalities dedicated to the analysis of epialleles composition derived from any kind of bisulfite sequencing experiment.

Epigenetic remodelling of Fxyd1 promoters in developing heart and brain tissues.

FXYD1 is a key protein controlling ion channel transport. FXYD1 exerts its function by regulating Na+/K+-ATPase activity, mainly in brain and cardiac tissues. Alterations of the expression level of the FXYD1 protein cause diastolic dysfunction and arrhythmias in heart and decreased neuronal dendritic tree and spine formation in brain. Moreover, FXYD1, a target of MeCP2, plays a crucial role in the pathogenesis of the Rett syndrome, a neurodevelopmental disorder. Thus, the amount of FXYD1 must be strictly controlled in a tissue specific manner and, likely, during development. Epigenetic modifications, particularly DNA methylation, represent the major candidate mechanism that may regulate Fxyd1 expression. In the present study, we performed a comprehensive DNA methylation analysis and mRNA expression level measurement of the two Fxyd1 transcripts, Fxyd1a and Fxyd1b, in brain and heart tissues during mouse development. We found that DNA methylation at Fxyd1a increased during brain development and decreased during heart development along with coherent changes in mRNA expression levels. We also applied ultra-deep methylation analysis to detect cell to cell methylation differences and to identify possible distinct methylation profile (epialleles) distribution between heart and brain and in different developmental stages. Our data indicate that the expression of Fxyd1 transcript isoforms inversely correlates with DNA methylation in developing brain and cardiac tissues suggesting the existence of a temporal-specific epigenetic program. Moreover, we identified a clear remodeling of epiallele profiles which were distinctive for single developmental stage both in brain and heart tissues.

HDAC class I inhibitor domatinostat sensitizes pancreatic cancer to chemotherapy by targeting cancer stem cell compartment via FOXM1 modulation.

BACKGROUND: Pancreatic ductal adenocarcinoma (PDAC) represents an unmet clinical need due to the very poor prognosis and the lack of effective therapy. Here we investigated the potential of domatinostat (4SC-202), a new class I histone deacetylase (HDAC) inhibitor, currently in clinical development, to sensitize PDAC to first line standard gemcitabine (G)/taxol (T) doublet chemotherapy treatment. METHODS: Synergistic anti-tumor effect of the combined treatment was assessed in PANC1, ASPC1 and PANC28 PDAC cell lines in vitro as well as on tumor spheroids and microtissues, by evaluating combination index (CI), apoptosis, clonogenic capability. The data were confirmed in vivo xenograft models of PANC28 and PANC1 cells in athymic mice. Cancer stem cells (CSC) targeting was studied by mRNA and protein expression of CSC markers, by limiting dilution assay, and by flow cytometric and immunofluorescent evaluation of CSC mitochondrial and cellular oxidative stress. Mechanistic role of forkhead box M1 (FOXM1) and downstream targets was evaluated in FOXM1-overexpressing PDAC cells. RESULTS: We showed that domatinostat sensitized in vitro and in vivo models of PDAC to chemotherapeutics commonly used in PDAC patients management and particularly to GT doublet, by targeting CSC compartment through the induction of mitochondrial and cellular oxidative stress. Mechanistically, we showed that domatinostat hampers the expression and function of FOXM1, a transcription factor playing a crucial role in stemness, oxidative stress modulation and DNA repair. Domatinostat reduced FOXM1 protein levels by downregulating mRNA expression and inducing proteasome-mediated protein degradation thus preventing nuclear translocation correlated with a reduction of FOXM1 target genes. Furthermore, by overexpressing FOXM1 in PDAC cells we significantly reduced domatinostat-inducing oxidative mitochondrial and cellular stress and abolished GT sensitization, both in adherent and spheroid cells, confirming FOXM1 crucial role in the mechanisms described. Finally, we found a correlation of FOXM1 expression with poor progression free survival in PDAC chemotherapy-treated patients. CONCLUSIONS: Overall, we suggest a novel therapeutic strategy based on domatinostat to improve efficacy and to overcome resistance of commonly used chemotherapeutics in PDAC that warrant further clinical evaluation.

Evaluation of MGMT Gene Methylation in Neuroendocrine Neoplasms.

Unresectable neuroendocrine neoplasms (NENs) often poorly respond to standard therapeutic approaches. Alkylating agents, in particular temozolomide, commonly used to treat high-grade brain tumors including glioblastomas, have recently been tested in advanced or metastatic NENs, where they showed promising response rates. In glioblastomas, prediction of response to temozolomide is based on the assessment of the methylation status of the MGMT gene, as its product, O 6-methylguanine-DNA methyltransferase, may counteract the damaging effects of the alkylating agent. However, in NENs, such a biomarker has not been validated yet. Thus, we have investigated MGMT methylation in 42 NENs of different grades and from various sites of origin by two different approaches: in contrast to methylation-specific PCR (MSP), which is commonly used in glioblastoma management, amplicon bisulfite sequencing (ABS) is based on high-resolution, next-generation sequencing and interrogates several additional CpG sites compared to those covered by MSP. Overall, we found MGMT methylation in 74% (31/42) of the NENs investigated. A higher methylation degree was observed in well-differentiated tumors and in tumors originating in the gastrointestinal tract. Comparing MSP and ABS results, we demonstrate that the region analyzed by the MSP test is sufficiently informative of the MGMT methylation status in NENs, suggesting that this predictive parameter could routinely be interrogated also in NENs.

MC profiling: a novel approach to analyze DNA methylation heterogeneity in genome-wide bisulfite sequencing data.

DNA methylation is an epigenetic mark implicated in crucial biological processes. Most of the knowledge about DNA methylation is based on bulk experiments, in which DNA methylation of genomic regions is reported as average methylation. However, average methylation does not inform on how methylated cytosines are distributed in each single DNA molecule. Here, we propose Methylation Class (MC) profiling as a genome-wide approach to the study of DNA methylation heterogeneity from bulk bisulfite sequencing experiments. The proposed approach is built on the concept of MCs, groups of DNA molecules sharing the same number of methylated cytosines. The relative abundances of MCs from sequencing reads incorporates the information on the average methylation, and directly informs on the methylation level of each molecule. By applying our approach to publicly available bisulfite-sequencing datasets, we individuated cell-to-cell differences as the prevalent contributor to methylation heterogeneity. Moreover, we individuated signatures of loci undergoing imprinting and X-inactivation, and highlighted differences between the two processes. When applying MC profiling to compare different conditions, we identified methylation changes occurring in regions with almost constant average methylation. Altogether, our results indicate that MC profiling can provide useful insights on the epigenetic status and its evolution at multiple genomic regions.

DNA Methylation Profiles of Tph1A and BDNF in Gut and Brain of L. Rhamnosus-Treated Zebrafish.

The bidirectional microbiota-gut-brain axis has raised increasing interest over the past years in the context of health and disease, but there is a lack of information on molecular mechanisms underlying this connection. We hypothesized that change in microbiota composition may affect brain epigenetics leading to long-lasting effects on specific brain gene regulation. To test this hypothesis, we used Zebrafish (Danio Rerio) as a model system. As previously shown, treatment with high doses of probiotics can modulate behavior in Zebrafish, causing significant changes in the expression of some brain-relevant genes, such as BDNF and Tph1A. Using an ultra-deep targeted analysis, we investigated the methylation state of the BDNF and Tph1A promoter region in the brain and gut of probiotic-treated and untreated Zebrafishes. Thanks to the high resolution power of our analysis, we evaluated cell-to-cell methylation differences. At this resolution level, we found slight DNA methylation changes in probiotic-treated samples, likely related to a subgroup of brain and gut cells, and that specific DNA methylation signatures significantly correlated with specific behavioral scores.

Artificial Intelligence for Epigenetics: Towards Personalized Medicine.

Epigenetics is a field of biological sciences focused on the study of reversible, heritable changes in gene function, not due to modifications of the genomic sequence. These changes are the result of a complex cross-talk between several molecular mechanisms that is in turn orchestrated by genetic and environmental factors. The epigenetic profile captures the unique regulatory landscape and the exposure to environmental stimuli of an individual. It thus constitutes a valuable reservoir of information for personalized medicine, which is aimed at customizing health-care interventions based on the unique characteristics of each individual. Nowadays, the complex milieu of epigenomic marks can be studied at the genome-wide level thanks to massive, high-throughput technologies. This new experimental approach is opening up new and interesting knowledge perspectives. However, the analysis of these complex omic data requires to face important analytic issues. Artificial Intelligence, and in particular Machine Learning, are emerging as powerful resources to decipher epigenomic data. In this review, we will first describe the most used ML approaches in epigenomics. We then will recapitulate some of the recent applications of ML to epigenomic analysis. Finally, we will provide some examples of how the ML approach to epigenetic data can be useful for personalized medicine.

Modeling DNA Methylation Profiles through a Dynamic Equilibrium between Methylation and Demethylation.

DNA methylation is a heritable epigenetic mark that plays a key role in regulating gene expression. Mathematical modeling has been extensively applied to unravel the regulatory mechanisms of this process. In this study, we aimed to investigate DNA methylation by performing a high-depth analysis of particular loci, and by subsequent modeling of the experimental results. In particular, we performed an in-deep DNA methylation profiling of two genomic loci surrounding the transcription start site of the D-Aspartate Oxidase and the D-Serine Oxidase genes in different samples (n = 51). We found evidence of cell-to-cell differences in DNA methylation status. However, these cell differences were maintained between different individuals, which indeed showed very similar DNA methylation profiles. Therefore, we hypothesized that the observed pattern of DNA methylation was the result of a dynamic balance between DNA methylation and demethylation, and that this balance was identical between individuals. We hence developed a simple mathematical model to test this hypothesis. Our model reliably captured the characteristics of the experimental data, suggesting that DNA methylation and demethylation work together in determining the methylation state of a locus. Furthermore, our model suggested that the methylation status of neighboring cytosines plays an important role in this balance.

Ultra-Deep DNA Methylation Analysis of X-Linked Genes: GLA and AR as Model Genes.

Recessive X-linked disorders may occasionally evolve in clinical manifestations of variable severity also in female carriers. For some of such diseases, the frequency of the symptoms' appearance during women's life may be particularly relevant. This phenomenon has been largely attributed to the potential skewness of the X-inactivation process leading to variable phenotypes. Nonetheless, in many cases, no correlation with X-inactivation unbalance was demonstrated. However, methods for analyzing skewness have been mainly limited to Human Androgen Receptor methylation analysis (HUMARA). Recently, the X-inactivation process has been largely revisited, highlighting the heterogeneity existing among loci in the epigenetic state within inactive and, possibly, active X-chromosomes. We reasoned that gene-specific and ultra-deep DNA methylation analyses could greatly help to unravel details of the X-inactivation process and the roles of specific X genes inactivation in disease manifestations. We recently provided evidence that studying DNA methylation at specific autosomic loci at a single-molecule resolution (epiallele distribution analysis) allows one to analyze cell-to-cell methylation differences in a given cell population. We here apply the epiallele analysis at two X-linked loci to investigate whether females show allele-specific epiallelic patterns. Due to the high potential of this approach, the method allows us to obtain clearly distinct allele-specific epiallele profiles.

Prevalence of GLA gene mutations and polymorphisms in patients with multiple sclerosis: A cross-sectional study.

PURPOSE: Fabry Disease (FD) has been frequently proposed as possible underestimated differential diagnosis of Multiple Sclerosis (MS), but no study has been performed to test prevalence of GLA gene mutations in a population fulfilling diagnostic criteria of MS. Aim of this study is to determine the prevalence of GLA gene mutations in a large and representative population diagnosed with MS, simultaneously providing a critical revision of current literature reports of coexistence or misdiagnosis between these two conditions. METHODS: In this mono-centric cross-sectional study, 927 patients fulfilling McDonald diagnostic criteria and encompassing all MS phenotypes were enrolled. Patients underwent evaluation of α-GalA activity and genotyping. Both genetic variants annotated as pathogenic and GVUS were considered. Estimated alleles frequencies were then compared to the ones reported in the gnomAD database. RESULTS: GLA gene variants were found in seven individuals. Five patients carried variants previously described having controversial impact on FD phenotype, and the analysis of exome database revealed that they are not rare among healthy individuals. One patient showed a new variant never described before, and another one carried a late-onset FD cardiac variant. CONCLUSIONS: The overall prevalence of GLA gene variants in MS patients is comparable to the one estimated in healthy population. This result is further supported by critical revision of current literature evidences of misdiagnosis between MS and FD, arguing in favour of independence between these disorders.

Nucleotide distance influences co-methylation between nearby CpG sites.

The tendency of individual CpG sites to be methylated is distinctive, non-random and well-regulated throughout the genome. We investigated the structural and spatial factors influencing CpGs methylation by performing an ultra-deep targeted methylation analysis on human, mouse and zebrafish genes. We found that methylation is not a random process and that closer neighboring CpG sites are more likely to share the same methylation status. Moreover, if the distance between CpGs increases, the degree of co-methylation decreases. We set up a simulation model to analyze the contribution of both the intrinsic susceptibility and the distance effect on the probability of a CpG to be methylated. Our finding suggests that the establishment of a specific methylation pattern follows a universal rule that must take into account of the synergistic and dynamic interplay of these two main factors: the intrinsic methylation susceptibility of specific CpG and the nucleotide distance between two CpG sites.

Age and dPCR can predict relapse in CML patients who discontinued imatinib: the ISAV study.

Imatinib is effective for the treatment of chronic myeloid leukemia (CML). However even undetectable BCR-ABL1 by Q-RT-PCR does not equate to eradication of the disease. Digital-PCR (dPCR), able to detect 1 BCR-ABL1 positive cell out of 10(7) , has been recently developed. The ISAV study is a multicentre trial aimed at validating dPCR to predict relapses after imatinib discontinuation in CML patients with undetectable Q-RT-PCR. CML patients under imatinib therapy since more than 2 years and with undetectable PCR for at least 18 months were eligible. Patients were monitored by standard Q-RT-PCR for 36 months. Patients losing molecular remission (two consecutive positive Q-RT-PCR with at least 1 BCR-ABL1/ABL1 value above 0.1%) resumed imatinib. The study enrolled 112 patients, with a median follow-up of 21.6 months. Fifty-two of the 108 evaluable patients (48.1%), relapsed; 73.1% relapsed in the first 9 months but 14 late relapses were observed between 10 and 22 months. Among the 56 not-relapsed patients, 40 (37.0% of total) regained Q-RT-PCR positivity but never lost MMR. dPCR results showed a significant negative predictive value ratio of 1.115 [95% CI: 1.013-1.227]. An inverse relationship between patients age and risk of relapse was evident: 95% of patients <45 years relapsed versus 42% in the class ≥45 to <65 years and 33% of patients ≥65 years [P(χ(2) ) < 0.0001]. Relapse rates ranged between 100% (<45 years, dPCR+) and 36% (>45 years, dPCR-). Imatinib can be safely discontinued in the setting of continued PCR negativity; age and dPCR results can predict relapse.