Stratification of Patients with Coronary Artery Disease by Circulating Cytokines Profile: A Pilot Study

Coronary artery disease (CAD) is a long-term inflammatory process, with atherosclerosis as its underlying pathophysiological mechanism. Endothelial dysfunction is the first step towards atherosclerosis, where damaged endothelial cells release large amounts of pro-inflammatory cytokines and mediators, thus promoting vascular inflammation and disease progression. However, the correlation between serum cytokines and CAD severity remains to be defined. Serum samples from patients performing cardiac computed tomography for suspected CAD (n = 75) were analyzed with a multiplex bead-based immunoassay panel for simultaneous assessment of the concentration of 11 cytokines using flow cytometric technology. The analysis showed statistically significant increases in sRAGE, CCL2_MCP1, FLT1, and IL6 levels in CAD patients compared with healthy subjects and a gradual increase trend towards a more severe form of the disease for most cytokines (e.g., sCD40L, FLT1, sRAGE, CCL2-MCP1, TNFα). Lastly, we explored the performance of cytokines in predicting the diagnosis of CAD and found that an increase in IL6 levels will increase the odds of being non-obstructive CAD-positive. In contrast, an increase in CCL2-MCP1 or FLT1 levels will increase the probability of being obstructive CAD-positive. These results suggest that the combination of serum cytokines may contribute to the not-invasive stratification risk for patients with suspected CAD.

4D-Flow Cardiovascular Magnetic Resonance Sequence for Aortic Assessment: Multi-Vendor and Multi-Magnetic Field Reproducibility in Healthy Volunteers

OBJECTIVES

Four-dimensional (4D) flow cardiac magnetic resonance (CMR) represents an emerging technique for non-invasive evaluation of the aortic flow. The aim of this study was to investigate a 4D-flow CMR sequence for the assessment of thoracic aorta comparing different vendors and different magnetic fields of MR scanner in fifteen healthy volunteers.

METHODS

CMR was performed on three different MRI scanners: one at 1.5 T and two at 3 T. Flow parameters and planar wall shear stress (WSS) were extracted from six transversal planes along the full thoracic aorta by three operators. Inter-vendor comparability as well as scan-rescan, intra- and interobserver reproducibility were examined.

RESULTS

A high heterogeneity was found in the comparisons for each operator and for each scanner in the six transversal planes analysis (Friedman rank-sum test; p-value ≤ 0.05). Among all, the most reproducible measures were extracted for the sinotubular junction plane and for the flow parameters.

CONCLUSIONS

Our results suggest that standardized procedures have to be defined to make more comparable and reproducible 4D-flow parameters and mainly, clinical impactfulness. Further studies on sequences development are needed to validate 4D-flow MRI assessment across vendors and magnetic fields also compared to a missing gold standard.

Targeted genetic analysis unveils novel associations between ACE I/D and APO T158C polymorphisms with D-dimer levels in severe COVID-19 patients with pulmonary embolism

Only a percentage of COVID-19 patients develop thrombotic complications. We hypothesized that genetic profiles may explain part of the inter-individual differences. Our goal was to evaluate the genotypic distribution of targeted DNA polymorphisms in COVID-19 patients complicated (PE+) or not (PE-) by pulmonary embolism. We designed a retrospective observational study enrolling N = 94 consecutive patients suffering severe COVID-19 with pulmonary embolism (PE+, N = 47) or not (PE-, N = 47) during hospitalization. A panel of N = 13 prothrombotic DNA polymorphisms (FV R506Q and H1299R, FII G20210A, MTHFR C677T and A1298C, CBS 844ins68, PAI-1 4G/5G, GPIIIa HPA-1 a/b, ACE I/D, AGT T9543C, ATR-1 A1166C, FGB - 455G > A, FXIII103G > T) and N = 2 lipid metabolism-related DNA polymorphisms (APOE T 112C and T158C) were investigated using Reverse Dot Blot technique. Then, we investigated possible associations between genotypic subclasses and demographic, clinical, and laboratory parameters including age, obesity, smoking, pro-inflammatory cytokines, drug therapy, and biomarkers of thrombotic risk such as D-dimer (DD). We found that 58.7% of PE+ had homozygous mutant D/D genotype at ACE I/D locus vs. PE- (40.4%) and 87% of PE+ had homozygous mutant C/C genotype at APOE T158C locus vs. PE- (68.1%). In PE+ group, DD levels were significantly higher in D/D and I/D genotypes at ACE I/D locus (P = 0.00066 and P = 0.00023, respectively) and in C/C and T/C genotypes at APOE T158C locus (P = 1.6e-06 and P = 0.0012, respectively) than PE- group. For the first time, we showed significant associations between higher DD levels and ACE I/D and APOE T158C polymorphisms in PE+ vs. PE- patients suggesting potential useful biomarkers of poor clinical outcome.

Association Between Circulating CD4+ T Cell Methylation Signatures of Network-Oriented SOCS3 Gene and Hemodynamics in Patients Suffering Pulmonary Arterial Hypertension

Pathogenic DNA methylation changes may be involved in pulmonary arterial hypertension (PAH) onset and its progression, but there is no data on potential associations with patient-derived hemodynamic parameters. The reduced representation bisulfite sequencing (RRBS) platform identified N = 631 differentially methylated CpG sites which annotated to N = 408 genes (DMGs) in circulating CD4⁺ T cells isolated from PAH patients vs. healthy controls (CTRLs). A promoter-restricted network analysis established the PAH subnetwork that included 5 hub DMGs (SOCS3, GNAS, ITGAL, NCOR2, NFIC) and 5 non-hub DMGs (NR4A2, GRM2, PGK1, STMN1, LIMS2). The functional analysis revealed that the SOCS3 gene was the most recurrent among the top ten significant pathways enriching the PAH subnetwork, including the growth hormone receptor and the interleukin-6 signaling. Correlation analysis showed that the promoter methylation levels of each network-oriented DMG were associated individually with hemodynamic parameters. In particular, SOCS3 hypomethylation was negatively associated with right atrial pressure (RAP) and positively associated with cardiac index (CI) (|r|≥ 0.6). A significant upregulation of the SOCS3, ITGAL, NFIC, NCOR2, and PGK1 mRNA levels (qRT-PCR) in peripheral blood mononuclear cells from PAH patients vs. CTRLs was found (P ≤ 0.05). By immunoblotting, a significant upregulation of the SOCS3 protein was confirmed in PAH patients vs. CTRLs (P < 0.01). This is the first network-oriented study which integrates circulating CD4⁺ T cell DNA methylation signatures, hemodynamic parameters, and validation experiments in PAH patients at first diagnosis or early follow-up. Our data suggests that SOCS3 gene might be involved in PAH pathogenesis and serve as potential prognostic biomarker.

Integrated Bioinformatics Analysis Reveals Novel miRNA as Biomarkers Associated with Preeclampsia

Preeclampsia is a leading cause of perinatal maternal-foetal mortality and morbidity. This study aims to identify the key microRNAs (miRNA) in preeclampsia and uncover their potential functions. We downloaded the miRNA expression profile of GSE119799 for plasma and GSE177049 for the placenta. Each dataset consisted of five patients (PE) and five controls (N). From a technical point of view, we analysed the counts per million (CPM) for both datasets, highlighting 358 miRNAs in common, 78 unique for plasma and 298 unique for placenta. At the same time, we performed an expression differential analysis (|logFC| ≥ 1|and FDR ≤ 0.05) to evaluate the biological impact of the miRNAs. This approach allowed us to highlight 321 miRNAs in common between plasma and placenta, within which four were upregulated in plasma. Furthermore, the same analysis revealed five miRNAs expressed exclusively in plasma; these were also upregulated. In conclusion, the in-depth bioinformatics analysis conducted during our study will allow us, on the one hand, to verify the targets of each of the nine identified miRNAs; on the other hand, to use them both as new non-invasive biomarkers and as therapeutic targets for the development of personalised treatments.

ABCA1, TCF7, NFATC1, PRKCZ and PDGFA DNA methylation as potential epigenetic-sensitive targets in acute coronary syndrome via network analysis

Background

Acute coronary syndrome (ACS) is the most severe clinical manifestation of coronary heart disease and the leading cause of death worldwide.

Purpose

To perform an epigenome-wide analysis in circulating CD4+ and CD8+ T cells of ACS patients and healthy subjects (HS) enrolled in the DIANA clinical trial (NCT04371809) in order to identify differentially methylated genes (DMGs).

Methods

Genomic DNA was extracted from CD4+ and CD8+ T cells of all subjects and sequenced by the reduced representation bisulfite sequencing (RRBS) platform. Functional pathway analysis was performed and significant DMGs were selected for gene expression validation by qRT-PCR in ACS patients and HS. GeneMANIA was used to built a prediction gene network. Correlation analyses between molecular data and clinical variables were performed.

Results

In CD4+ T cells we identified 61 differentially methylated regions (DMRs) associated to 57 annotated genes of which 53% (n=32) were hyper- and 47% (n=29) were hypo-methylated in ACS patients vs HS. In CD8+ T cells we identified 613 DMRs associated to 569 annotated genes of which 28% (n=173) were hyper- and 72% (n=440) were hypo-methylated between two groups. In both cell type of ACS patients, 175 DMRs were associated to 157 annotated genes of which 41% (n=72) were hyper- and 59% (n=103) were hypo-methylated. From functional analysis, we selected the top 5 DMGs in the prevalent pathways with the highest differential of methylation values. Specifically, we considered 6 hub genes: NFATC1, TCF7, PDGFA, PRKCB, PRKCZ and ABCA1 and determined their respective expression levels by q-RT-PCR. We found a significant up-regulation of the selected genes in ACS patients vs HS (P&lt;0.001 for all comparisons). Correlation analysis showed both common and cell specific correlation patterns. In CD4+ T cells, PDGFA promoter methylation was negatively correlated with CK-MB concentrations (r=−0.79, P=0.018). ABCA1, TCF7, PDGFA and PRKCZ gene expression was positively associated to CK-MB concentrations (r=0.75, P=0.03; r=0.760, P=0.029; r=0.72, P=0.044; r=0.74, P=0.035, respectively).

Conlusions

This study is the first single-base resolution map of DNA methylome by RRBS in CD4+ and CD8+ T cells, providing specific methylation signatures that could help to clarify the role of aberrant methylation in ACS pathogenesis, and provide the basis for the search of novel epigenetic-sensitive biomarkers in the prevention and early diagnosis of this pathology.

Funding Acknowledgement

Type of funding sources: Public grant(s) – National budget only. Main funding source(s): Italian Ministry of Health;Italian Ministry of Research and University

Circulating CD4+T/methylation signatures of network-oriented SOCS3, ITGAL, NFIC, NCOR2, PGK1 genes associate with hemodynamics in pulmonary arterial hypertension patients

Background

CD4+ T cells are associated with pulmonary arterial hypertension (PAH) pathogenesis but mechanistic insights are limited.

Purpose

To identify differential CD4+ T methylation signatures in healthy controls vs PAH and evaluate a putative association with the cardiopulmonary hemodynamic profile of affected patients.

Methods

We used RRBS platform to profile CD4+ T DNA methylome in the CLEOPAHTRA clinical trial.

Results

Differentially methylated CpG sites (N=631) annotated to N=408 genes (DMGs). Most of them (65%) were hypermethylated and localized in distal intergenic (36%) and promoter regions (31%). Promoter-related network analysis established the PAH subnetwork highlighting 5 hub DMGs (SOCS3, GNAS, ITGAL, NCOR2, NFIC) and 5 non-hub DMGs (NR4A2, GRM2, PGK1, STMN1, LIMS2) as potential candidate genes (Figure 1). The Infinium Human MethylationEPIC BeadChip on CD4+ T cells from an independent study population confirmed the global RRBS-methylation trends. Both in idiopathic and Associated-PAH, each of these 10 network-oriented DMGs was strongly correlated with at least one hemodynamic parameter such as right atrial pressure (RAP), cardiac index (CI), mean pulmonary arterial pressure (mPAP), pulmonary vascular resistance (PVR), and pulmonary capillary wedge pressure (PCWP) (Figure 2). In addition, mRNA levels of the ITGAL, NFIC, NCOR2, PGK1 genes and the IL-6-STAT3-SOCS3 signaling axis were significantly upregulated in PBMCs from patients with PAH vs controls suggesting putative drug targets. Furthermore, both SOCS3 methylation and mRNA levels were positively correlated with cardiac index (CI) in idiopathic PAH whereas both PGK1 methylation and mRNA levels were positively correlated with RAP and inversely with CI in Associated PAH suggesting putative non-invasive biomarkers.

Conclusions

This hypothesis-generating study shows for the first time that circulating CD4+ T methylation signatures, inclusive of SOCS3, ITGAL, NFIC, NCOR2, and PGK1 genes may yield insight into pro-inflammatory mechanisms that exacerbate vascular remodeling in PAH and suggest non-invasive biomarkers to optimize patient phenotyping and, possibly, prognostication in PAH.

Funding Acknowledgement

Type of funding sources: Public grant(s) – National budget only. Main funding source(s): PRIN2017F8ZB89 from Italian Ministry of University and Research (MIUR) (PI Prof Napoli) and Ricerca Corrente (RC) 2019 from Italian Ministry of Health (PI Prof. Napoli). Figure 1Figure 2

Pancreatic Progenitor Commitment Is Marked by an Increase in Ink4a/Arf Expression

The identification of the molecular mechanisms controlling early cell fate decisions in mammals is of paramount importance as the ability to determine specific lineage differentiation represents a significant opportunity for new therapies. Pancreatic Progenitor Cells (PPCs) constitute a regenerative reserve essential for the maintenance and regeneration of the pancreas. Besides, PPCs represent an excellent model for understanding pathological pancreatic cellular remodeling. Given the lack of valid markers of early endoderm, the identification of new ones is of fundamental importance. Both products of the Ink4a/Arf locus, in addition to being critical cell-cycle regulators, appear to be involved in several disease pathologies. Moreover, the locus' expression is epigenetically regulated in ES reprogramming processes, thus constituting the ideal candidates to modulate PPCs homeostasis. In this study, starting from mouse embryonic stem cells (mESCs), we analyzed the early stages of pancreatic commitment. By inducing mESCs commitment to the pancreatic lineage, we observed that both products of the Cdkn2a locus, Ink4a and Arf, mark a naïve pancreatic cellular state that resembled PPC-like specification. Treatment with epi-drugs suggests a role for chromatin remodeling in the CDKN2a (Cycline Dependent Kinase Inhibitor 2A) locus regulation in line with previous observations in other cellular systems. Our data considerably improve the comprehension of pancreatic cellular ontogeny, which could be critical for implementing pluripotent stem cells programming and reprogramming toward pancreatic lineage commitment.

ABCA1, TCF7, NFATC1, PRKCZ, and PDGFA DNA methylation as potential epigenetic-sensitive targets in acute coronary syndrome via network analysis

Acute coronary syndrome (ACS) is the most severe clinical manifestation of coronary heart disease.We performed an epigenome-wide analysis of circulating CD4⁺ and CD8⁺ T cells isolated from ACS patients and healthy subjects (HS), enrolled in the DIANA clinical trial, by reduced-representation bisulphite sequencing (RRBS). In CD4⁺ T cells, we identified 61 differentially methylated regions (DMRs) associated with 57 annotated genes (53% hyper- and 47% hypo-methylated) by comparing ACS patients vs HS. In CD8⁺ T cells, we identified 613 DMRs associated with 569 annotated genes (28% hyper- and 72% hypo-methylated) in ACS patients as compared to HS. In CD4⁺ vs CD8⁺ T cells of ACS patients we identified 175 statistically significant DMRs associated with 157 annotated genes (41% hyper- and 59% hypo-methylated). From pathway analyses, we selected six differentially methylated hub genes (NFATC1, TCF7, PDGFA, PRKCB, PRKCZ, ABCA1) and assessed their expression levels by q-RT-PCR. We found an up-regulation of selected genes in ACS patients vs HS (P < 0.001). ABCA1, TCF7, PDGFA, and PRKCZ gene expression was positively associated with CK-MB serum concentrations (r = 0.75, P = 0.03; r = 0.760, P = 0.029; r = 0.72, P = 0.044; r = 0.74, P = 0.035, respectively).This pilot study is the first single-base resolution map of DNA methylome by RRBS in CD4⁺ and CD8⁺ T cells and provides specific methylation signatures to clarify the role of aberrant methylation in ACS pathogenesis, thus supporting future research for novel epigenetic-sensitive biomarkers in the prevention and early diagnosis of this pathology.

An Integrative Computational Approach Based on Expression Similarity Signatures to Identify Protein-Protein Interaction Networks in Female-Specific Cancers

Breast, ovarian, and endometrial cancers have a major impact on mortality in women. These tumors share hormone-dependent mechanisms involved in female-specific cancers which support tumor growth in a different manner. Integrated computational approaches may allow us to better detect genomic similarities between these different female-specific cancers, helping us to deliver more sophisticated diagnosis and precise treatments. Recently, several initiatives of The Cancer Genome Atlas (TCGA) have encouraged integrated analyses of multiple cancers rather than individual tumors. These studies revealed common genetic alterations (driver genes) even in clinically distinct entities such as breast, ovarian, and endometrial cancers. In this study, we aimed to identify expression similarity signatures by extracting common genes among TCGA breast (BRCA), ovarian (OV), and uterine corpus endometrial carcinoma (UCEC) cohorts and infer co-regulatory protein-protein interaction networks that might have a relationship with the estrogen signaling pathway. Thus, we carried out an unsupervised principal component analysis (PCA)-based computational approach, using RNA sequencing data of 2,015 female cancer and 148 normal samples, in order to simultaneously capture the data heterogeneity of intertumors. Firstly, we identified tumor-associated genes from gene expression profiles. Secondly, we investigated the signaling pathways and co-regulatory protein-protein interaction networks underlying these three cancers by leveraging the Ingenuity Pathway Analysis software. In detail, we discovered 1,643 expression similarity signatures (638 downregulated and 1,005 upregulated genes, with respect to normal phenotype), denoted as tumor-associated genes. Through functional genomic analyses, we assessed that these genes were involved in the regulation of cell-cycle-dependent mechanisms, including metaphase kinetochore formation and estrogen-dependent S-phase entry. Furthermore, we generated putative co-regulatory protein-protein interaction networks, based on upstream regulators such as the ERBB2/HER2 gene. Moreover, we provided an ad-hoc bioinformatics workflow with a manageable list of intertumor expression similarity signatures for the three female-specific cancers. The expression similarity signatures identified in this study might uncover potential estrogen-dependent molecular mechanisms promoting carcinogenesis.

DNA methylation profiling of CD04+/CD08+ T cells reveals pathogenic mechanisms in increasing hyperglycemia: PIRAMIDE pilot study

Background

DNA methylation can play a pathogenic role in the early stages of hyperglycemia linking homeostasis imbalance and vascular damage.

Material and methods

We investigated DNA methylome by RRBS in CD04⁺ and CD08⁺ T cells from healthy subjects (HS) to pre-diabetics (Pre-Diab) and type 2 diabetic (T2D) patients to identify early biomarkers of glucose impairment and vascular damage. Our cross-sectional study enrolled 14 individuals from HS state to increasing hyperglycemia (pilot study, PIRAMIDE trial, NCT03792607).

Results

Globally, differentially methylated regions (DMRs) were mostly annotated to promoter regions. Hypermethylated DMRs were greater than hypomethylated in CD04⁺ T cells whereas CD08⁺ T showed an opposite trend. Moreover, DMRs overlapping between Pre-Diab and T2D patients were mostly hypermethylated in both T cells. Interestingly, SPARC was the most hypomethylated gene in Pre-Diab and its methylation level gradually decreased in T2D patients. Besides, SPARC showed a significant positive correlation with DBP (+0.76), HDL (+0.54), Creatinine (+0.83), LVDd (+0.98), LVSD (+0.98), LAD (+0.98), LVPWd (+0.84), AODd (+0.81), HR (+0.72), Triglycerides (+0.83), LAD (+0.69) and AODd (+0.52) whereas a negative correlation with Cholesterol (−0.52) and LDL (−0.71) in T2D.

Conclusion

SPARC hypomethylation in CD08⁺ T cells may be a useful biomarker of vascular complications in Pre-Diab with a possible role for primary prevention warranting further multicenter clinical trials to validate our findings.

•

We conducted the first methylome analysis by RRBS platform in circulating CD04⁺ and CD08⁺ T cells in increasing hyperglycemia.

•

This approach has revealed possible biomarkers for cardiovascular and kidney complications in prediabetes.

•

SPARC hypomethylation may underly a pro-fibrotic endophenotype to be validated in larger multicenter trials.

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.

Association between DNA methylation profile and malignancy in follicular-patterned thyroid neoplasms

Molecular differentiation between benign (follicular thyroid adenoma, FTA) and malignant (follicular thyroid carcinoma, FTC) thyroid neoplasms is challenging. Here, we explored the genome-wide DNA methylation profile of FTA (n.10) and FTC (n.11) compared to normal thyroid (NT) (n.7) tissues. FTC featured 3,564 differentially-methylated CpGs (DMCpG), most (84%) of them hypermethylated, with respect to normal controls. At the principal component analysis (PCA), the methylation profile of FTA occupied an intermediate position between FTC and normal tissue. A large fraction (n. 2,385) of FTC-associated DMCpG were related (intragenic or within 1500 bp from the transcription start site) to annotated genes (n. 1,786). FTC-hypermethylated genes were enriched for targets of the Polycomb transcriptional repressor complex and the specific histone H3 marks (H3K4me2/me3-H3K27me3) found in chromatin domains known as "bivalent". Transcriptome profiling by RNAseq showed that 7.9% of the DMCpGs-associated genes were differentially expressed in FTC compared to NT, suggesting that altered DNA methylation may contribute to their altered expression. Overall, this study suggests that perturbed DNA methylation, in particular hypermethylation, is a component of the molecular mechanisms leading to the formation of FTC and that DNA methylation profiling may help differentiating FTCs from their benign counterpart.

DNA methylation landscape of the genes regulating D-serine and D-aspartate metabolism in post-mortem brain from controls and subjects with schizophrenia

The spatio-temporal regulation of genes involved in the synthesis and degradation of D-serine and D-aspartate such as serine racemase (SR), D-amino acid oxidase (DAO), G72 and D-aspartate oxidase (DDO), play pivotal roles in determining the correct levels of these D-amino acids in the human brain. Here we provide a comprehensive analysis of mRNA expression and DNA methylation status of these genes in post-mortem samples from hippocampus, dorsolateral prefrontal cortex, and cerebellum from patients with schizophrenia and non-psychiatric controls. DNA methylation analysis was performed at an ultradeep level, measuring individual epialleles frequency by single molecule approach. Differential CpG methylation and expression was detected across different brain regions, although no significant correlations were found with diagnosis. G72 showed the highest CpG and non-CpG methylation degree, which may explain the repression of G72 transcription in the brain regions considered here. Conversely, in line with the sustained SR mRNA expression in the analyzed areas, very low methylation levels were detected at this gene’s regulatory regions. Furthermore, for DAO and DDO, our single-molecule methylation approach demonstrated that analysis of epiallele distribution was able to detect differences in DNA methylation representing area-specific methylation signatures, which are likely not detectable with targeted or genome-wide classic methylation analyses.

DNA Methylation variability among individuals is related to CpGs cluster density and evolutionary signatures

BACKGROUND

In recent years, epigenetics has gained a central role in the understanding of the process of natural selection. It is now clear how environmental impacts on the methylome could promote methylation variability with direct effects on disease etiology as well as phenotypic and genotypic variations in evolutionary processes. To identify possible factors influencing inter-individual methylation variability, we studied methylation values standard deviation of 166 healthy individuals searching for possible associations with genomic features and evolutionary signatures.

RESULTS

We analyzed methylation variability values in relation to CpG cluster density and we found a strong association between them (p-value < 2.2 × 10- 16). Furthermore, we found that genes related to CpGs with high methylation variability values were enriched for immunological pathways; instead, those associated with low ones were enriched for pathways related to basic cellular functions. Finally, we found an association between methylation variability values and signals of both ancient (p-value < 2.2 × 10- 16) and recent selective pressure (p-value < 1 × 10- 4).

CONCLUSION

Our results indicate the presence of an intricate interplay between genetics, epigenetic code and evolutionary constraints in humans.

Candidate genes and pathways downstream of PAX8 involved in ovarian high-grade serous carcinoma

Understanding the biology and molecular pathogenesis of ovarian epithelial cancer (EOC) is key to developing improved diagnostic and prognostic indicators and effective therapies. Although research has traditionally focused on the hypothesis that high-grade serous carcinoma (HGSC) arises from the ovarian surface epithelium (OSE), recent studies suggest that additional sites of origin exist and a substantial proportion of cases may arise from precursor lesions located in the Fallopian tubal epithelium (FTE). In FTE cells, the transcription factor PAX8 is a marker of the secretory cell lineage and its expression is retained in 96% of EOC. We have recently reported that PAX8 is involved in the tumorigenic phenotype of ovarian cancer cells. In this study, to uncover genes and pathways downstream of PAX8 involved in ovarian carcinoma we have determined the molecular profiles of ovarian cancer cells and in parallel of Fallopian tube epithelial cells by means of a silencing approach followed by an RNA-seq analysis. Interestingly, we highlighted the involvement of pathways like WNT signaling, epithelial-mesenchymal transition, p53 and apoptosis. We believe that our analysis has led to the identification of candidate genes and pathways regulated by PAX8 that could be additional targets for the therapy of ovarian carcinoma.

ampliMethProfiler: a pipeline for the analysis of CpG methylation profiles of targeted deep bisulfite sequenced amplicons

Background

CpG sites in an individual molecule may exist in a binary state (methylated or unmethylated) and each individual DNA molecule, containing a certain number of CpGs, is a combination of these states defining an epihaplotype. Classic quantification based approaches to study DNA methylation are intrinsically unable to fully represent the complexity of the underlying methylation substrate. Epihaplotype based approaches, on the other hand, allow methylation profiles of cell populations to be studied at the single molecule level.

For such investigations, next-generation sequencing techniques can be used, both for quantitative and for epihaplotype analysis. Currently available tools for methylation analysis lack output formats that explicitly report CpG methylation profiles at the single molecule level and that have suited statistical tools for their interpretation.

Results

Here we present ampliMethProfiler, a python-based pipeline for the extraction and statistical epihaplotype analysis of amplicons from targeted deep bisulfite sequencing of multiple DNA regions.

Conclusions

ampliMethProfiler tool provides an easy and user friendly way to extract and analyze the epihaplotype composition of reads from targeted bisulfite sequencing experiments. ampliMethProfiler is written in python language and requires a local installation of BLAST and (optionally) QIIME tools. It can be run on Linux and OS X platforms. The software is open source and freely available at http://amplimethprofiler.sourceforge.net.

Electronic supplementary material

The online version of this article (doi:10.1186/s12859-016-1380-3) contains supplementary material, which is available to authorized users.

Tracking the evolution of epialleles during neural differentiation and brain development: D-Aspartate oxidase as a model gene

We performed ultra-deep methylation analysis at single molecule level of the promoter region of developmentally regulated D-Aspartate oxidase (Ddo), as a model gene, during brain development and embryonic stem cell neural differentiation. Single molecule methylation analysis enabled us to establish the effective epiallele composition within mixed or pure brain cell populations. In this framework, an epiallele is defined as a specific combination of methylated CpG within Ddo locus and can represent the epigenetic haplotype revealing a cell-to-cell methylation heterogeneity. Using this approach, we found a high degree of polymorphism of methylated alleles (epipolymorphism) evolving in a remarkably conserved fashion during brain development. The different sets of epialleles mark stage, brain areas, and cell type and unravel the possible role of specific CpGs in favoring or inhibiting local methylation. Undifferentiated embryonic stem cells showed non-organized distribution of epialleles that apparently originated by stochastic methylation events on individual CpGs. Upon neural differentiation, despite detecting no changes in average methylation, we observed that the epiallele distribution was profoundly different, gradually shifting toward organized patterns specific to the glial or neuronal cell types. Our findings provide a deep view of gene methylation heterogeneity in brain cell populations promising to furnish innovative ways to unravel mechanisms underlying methylation patterns generation and alteration in brain diseases.

Modeling DNA methylation by analyzing the individual configurations of single molecules

DNA methylation is often analyzed by reporting the average methylation degree of each cytosine. In this study, we used a single molecule methylation analysis in order to look at the methylation conformation of individual molecules. Using D-aspartate oxidase as a model gene, we performed an in-depth methylation analysis through the developmental stages of 3 different mouse tissues (brain, lung, and gut), where this gene undergoes opposite methylation destiny. This approach allowed us to track both methylation and demethylation processes at high resolution. The complexity of these dynamics was markedly simplified by introducing the concept of methylation classes (MCs), defined as the number of methylated cytosines per molecule, irrespective of their position. The MC concept smooths the stochasticity of the system, allowing a more deterministic description. In this framework, we also propose a mathematical model based on the Markov chain. This model aims to identify the transition probability of a molecule from one MC to another during methylation and demethylation processes. The results of our model suggest that: 1) both processes are ruled by a dominant class of phenomena, namely, the gain or loss of one methyl group at a time; and 2) the probability of a single CpG site becoming methylated or demethylated depends on the methylation status of the whole molecule at that time.

Evidence for evolutionary and nonevolutionary forces shaping the distribution of human genetic variants near transcription start sites

The regions surrounding transcription start sites (TSSs) of genes play a critical role in the regulation of gene expression. At the same time, current evidence indicates that these regions are particularly stressed by transcription-related mutagenic phenomena. In this work we performed a genome-wide analysis of the distribution of single nucleotide polymorphisms (SNPs) inside the 10 kb region flanking human TSSs by dividing SNPs into four classes according to their frequency (rare, two intermediate classes, and common). We found that, in this 10 kb region, the distribution of variants depends on their frequency and on their localization relative to the TSS. We found that the distribution of variants is generally different for TSSs located inside or outside of CpG islands. We found a significant relationship between the distribution of rare variants and nucleosome occupancy scores. Furthermore, our analysis suggests that evolutionary (purifying selection) and nonevolutionary (biased gene conversion) forces both play a role in determining the relative SNP frequency around TSSs. Finally, we analyzed the potential pathogenicity of each class of variant using the Combined Annotation Dependent Depletion score. In conclusion, this study provides a novel and detailed view of the distribution of genomic variants around TSSs, providing insight into the forces that instigate and maintain variability in such critical regions.