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.

CBX Family Members in Two Major Subtypes of Renal Cell Carcinoma: A Comparative Bioinformatic Analysis.

The biological function and clinical values of Chromobox (CBX) family proteins in renal cell carcinoma (RCC) are still poorly investigated. This study aimed to compare the expression profiles and clinical relevance of CBXs between the two most frequent subtypes of RCC, clear cell renal cell carcinomas (ccRCC) and papillary renal cell carcinomas (pRCC), and to investigate whether CBXs would play a more or less similar role in the pathogenesis and progression of these RCC subtypes. Considering these two RCC populations in the TCGA database, we built a bioinformatics framework by integrating a computational pipeline with several online tools. CBXs showed a similar trend in ccRCC and pRCC tissues but with some features specific for each subtype. Specifically, the relative expressions of CBX3 and CBX2 were, respectively, the highest and lowest among all CBXs in both RCC subtypes. These data also found confirmation in cellular validation. Except for CBX4 and CBX8, all others were deregulated in the ccRCC subtype. CBX1, CBX6, and CBX7 were also significantly associated with the tumor stage. Further, low expression levels of CBX1, CBX5, CBX6, CBX7, and high expression of CBX8 were associated with poor prognosis. Otherwise, in the pRCC subtype, CBX2, CBX3, CBX7, and CBX8 were deregulated, and CBX2, CBX6, and CBX7 were associated with the tumor stage. In addition, in pRCC patients, low expression levels of CBX2, CBX4, and CBX7 were associated with an unfavorable prognosis. Similarly, CBX3, CBX6, and CBX7 presented the highest alteration rate in both subtypes and were found to be functionally related to histone binding, nuclear chromosomes, and heterochromatin. Furthermore, CBX gene expression levels correlated with immune cell infiltration, suggesting that CBXs might reflect the immune status of RCC subtypes. Our results highlight similarities and differences of CBXs within the two major RCC subtypes, providing new insights for future eligible biomarkers or possible molecular therapeutic targets for these diseases.

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.

Evolution of intra-tumoral heterogeneity across different pathological stages in papillary thyroid carcinoma.

BACKGROUND: Intra-tumor heterogeneity (ITH) results from the continuous accumulation of mutations during disease progression, thus impacting patients' clinical outcome. How the ITH evolves across papillary thyroid carcinoma (PTC) different tumor stages is lacking. METHODS: We used the whole-exome sequencing data from The Cancer Genome Atlas Thyroid Cancer (TCGA-THCA) cohort to track the ITH and assessed its relationship with clinical features through different stages of the PTC progression. We further assayed the expression levels of the specific genes in papillary thyroid cancer cell lines compared to an immortalized normal thyroid epithelial cell line by qRT-PCR. RESULTS: We revealed the timing of mutational processes and the dynamics of the temporal acquisition of somatic events during the lifetime of the PTC. ITH significantly influences the PTC patient's survival rate and, as genetic heterogeneity increases, the prognosis gets worse in advanced tumor stages. ITH also affects the mutational architecture of each clinical stage which is subject to periodic fluctuations. Different mutational processes may cooperate to shape a stage-specific mutational spectrum during the progression from early to advanced tumor stages. Moreover, different evolutionary paths characterize PTC progression across pathological stages due to both mutations recurrently occurring in all stages in hotspot positions and distinct codon changes dominating in different stages. A different expression level of specific genes also exists in different thyroid cancer cell lines. CONCLUSIONS: Our findings suggest ITH as a potential unfavorable prognostic factor in PTC and highlight the dynamic changes in different clinical stages of PTC, providing some clues for the precision medicine and suggesting different diagnostic decisions depending on the clinical stages of patients. Finally, complete clear guidelines to define risk stratification of PTC patients are lacking; thus, this work could contribute to defining patients who need more aggressive treatments and, in turn, could reduce the social burden of this cancer.

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.

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.

MuSA: a graphical user interface for multi-OMICs data integration in radiogenomic studies.

Analysis of large-scale omics data along with biomedical images has gaining a huge interest in predicting phenotypic conditions towards personalized medicine. Multiple layers of investigations such as genomics, transcriptomics and proteomics, have led to high dimensionality and heterogeneity of data. Multi-omics data integration can provide meaningful contribution to early diagnosis and an accurate estimate of prognosis and treatment in cancer. Some multi-layer data structures have been developed to integrate multi-omics biological information, but none of these has been developed and evaluated to include radiomic data. We proposed to use MultiAssayExperiment (MAE) as an integrated data structure to combine multi-omics data facilitating the exploration of heterogeneous data. We improved the usability of the MAE, developing a Multi-omics Statistical Approaches (MuSA) tool that uses a Shiny graphical user interface, able to simplify the management and the analysis of radiogenomic datasets. The capabilities of MuSA were shown using public breast cancer datasets from TCGA-TCIA databases. MuSA architecture is modular and can be divided in Pre-processing and Downstream analysis. The pre-processing section allows data filtering and normalization. The downstream analysis section contains modules for data science such as correlation, clustering (i.e., heatmap) and feature selection methods. The results are dynamically shown in MuSA. MuSA tool provides an easy-to-use way to create, manage and analyze radiogenomic data. The application is specifically designed to guide no-programmer researchers through different computational steps. Integration analysis is implemented in a modular structure, making MuSA an easily expansible open-source software.

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.

lncRNAs-mRNAs Co-Expression Network Underlying Childhood B-Cell Acute Lymphoblastic Leukaemia: A Pilot Study.

Long non-coding RNAs (lncRNAs) are emerging as key gene regulators in the pathogenesis and development of various cancers including B lymphoblastic leukaemia (B-ALL). In this pilot study, we used RNA-Seq transcriptomic data for identifying novel lncRNA-mRNA cooperative pairs involved in childhood B-ALL pathogenesis. We conceived a bioinformatic pipeline based on unsupervised PCA feature extraction approach and stringent statistical criteria to extract potential childhood B-ALL lncRNA signatures. We then constructed a co-expression network of the aberrantly expressed lncRNAs (30) and protein-coding genes (754). We cross-validated our in-silico findings on an independent dataset and assessed the expression levels of the most differentially expressed lncRNAs and their co-expressed mRNAs through ex vivo experiments. Using the guilt-by-association approach, we predicted lncRNA functions based on their perfectly co-expressed mRNAs (Spearman's correlation) that resulted closely disease-associated. We shed light on 24 key lncRNAs and their co-expressed mRNAs which may play an important role in B-ALL pathogenesis. Our results may be of clinical utility for diagnostic and/or prognostic purposes in paediatric B-ALL management.

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.

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.

Selective demethylation of two CpG sites causes postnatal activation of the Dao gene and consequent removal of D-serine within the mouse cerebellum.

BACKGROUND: Programmed epigenetic modifications occurring at early postnatal brain developmental stages may have a long-lasting impact on brain function and complex behavior throughout life. Notably, it is now emerging that several genes that undergo perinatal changes in DNA methylation are associated with neuropsychiatric disorders. In this context, we envisaged that epigenetic modifications during the perinatal period may potentially drive essential changes in the genes regulating brain levels of critical neuromodulators such as D-serine and D-aspartate. Dysfunction of this fine regulation may contribute to the genesis of schizophrenia or other mental disorders, in which altered levels of D-amino acids are found. We recently demonstrated that Ddo, the D-aspartate degradation gene, is actively demethylated to ultimately reduce D-aspartate levels. However, the role of epigenetics as a mechanism driving the regulation of appropriate D-ser levels during brain development has been poorly investigated to date. METHODS: We performed comprehensive ultradeep DNA methylation and hydroxymethylation profiling along with mRNA expression and HPLC-based D-amino acids level analyses of genes controlling the mammalian brain levels of D-serine and D-aspartate. DNA methylation changes occurring in specific cerebellar cell types were also investigated. We conducted high coverage targeted bisulfite sequencing by next-generation sequencing and single-molecule bioinformatic analysis. RESULTS: We report consistent spatiotemporal modifications occurring at the Dao gene during neonatal development in a specific brain region (the cerebellum) and within specific cell types (astrocytes) for the first time. Dynamic demethylation at two specific CpG sites located just downstream of the transcription start site was sufficient to strongly activate the Dao gene, ultimately promoting the complete physiological degradation of cerebellar D-serine a few days after mouse birth. High amount of 5'-hydroxymethylcytosine, exclusively detected at relevant CpG sites, strongly evoked the occurrence of an active demethylation process. CONCLUSION: The present investigation demonstrates that robust and selective demethylation of two CpG sites is associated with postnatal activation of the Dao gene and consequent removal of D-serine within the mouse cerebellum. A single-molecule methylation approach applied at the Dao locus promises to identify different cell-type compositions and functions in different brain areas and developmental stages.

Whole transcriptome targeted gene quantification provides new insights on pulmonary sarcomatoid carcinomas.

Pulmonary sarcomatoid carcinomas (PSC) are a rare group of lung cancer with a median overall survival of 9-12 months. PSC are divided into five histotypes, challenging to diagnose and treat. The identification of PSC biomarkers is warranted, but PSC molecular profile remains to be defined. Herein, a targeted whole transcriptome analysis was performed on 14 PSC samples, evaluated also for the presence of the main oncogene mutations and rearrangements. PSC expression data were compared with transcriptome data of lung adenocarcinomas (LUAD) and squamous cell carcinomas (LUSC) from The Cancer Genome Atlas. Deregulated genes were used for pathway enrichment analysis; the most representative genes were tested by immunohistochemistry (IHC) in an independent cohort (30 PSC, 31 LUAD, 31 LUSC). All PSC cases were investigated for PD-L1 expression. Thirty-eight genes deregulated in PSC were identified, among these IGJ and SLMAP were confirmed by IHC. Moreover, Forkhead box signaling and Fanconi anemia pathways were specifically enriched in PSC. Finally, some PSC harboured alterations in genes targetable by tyrosine kinase inhibitors, as EGFR and MET. We provide a deep molecular characterization of PSC; the identification of specific molecular profiles, besides increasing our knowledge on PSC biology, might suggest new strategies to improve patients management.

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.

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.