Simple Summary Vulvar squamous cell carcinoma (VSCC) is the most common form of vulvar malignancy, and its incidence has increased in recent years. For better diagnosis and prognostication, and to expand available treatment options, molecular characterization of VSCC is crucial. We sought to identify aberrations in DNA methylation in VSCC, as this has been implicated in the development of several cancers. To this end, we performed genome-wide methylation sequencing on a set of VSCC and normal vulvar tissue using the Infinium MethylationEPIC BeadChip array. We detected 199 genes to be differentially methylated in VSCC compared to normal vulvar tissue. Of these, 194 genes were hyper-methylated, which leads to a loss of function of the genes. As most of these genes are involved in transcription regulator activity, our results suggest that disruption of this process plays an important role in VSCC development. Abstract DNA methylation is the most widely studied mechanism of epigenetic modification, which can influence gene expression without alterations in DNA sequences. Aberrations in DNA methylation are known to play a role in carcinogenesis, and methylation profiling has enabled the identification of biomarkers of potential clinical interest for several cancers. For vulvar squamous cell carcinoma (VSCC), however, methylation profiling remains an under-studied area. We sought to identify differentially methylated genes (DMGs) in VSCC, by performing Infinium MethylationEPIC BeadChip (Illumina) array sequencing, on a set of primary VSCC (n = 18), and normal vulvar tissue from women with no history of vulvar (pre)malignancies (n = 6). Using a false-discovery rate of 0.05, beta-difference (Δβ) of ±0.5, and CpG-island probes as cut-offs, 199 DMGs (195 hyper-methylated, 4 hypo-methylated) were identified for VSCC. Most of the hyper-methylated genes were found to be involved in transcription regulator activity, indicating that disruption of this process plays a vital role in VSCC development. The majority of VSCCs harbored amplifications of chromosomes 3, 8, and 9. We identified a set of DMGs in this exploratory, hypothesis-generating study, which we hope will facilitate epigenetic profiling of VSCCs. Prognostic relevance of these DMGs deserves further exploration in larger cohorts of VSCC and its precursor lesions.

Introduction: Graph theory has been applied to study the pathophysiology of multiple sclerosis (MS) since it provides global and focal measures of brain network properties that are affected by MS. Typically, the connection strength and, consequently, the network properties are computed by counting the number of streamlines (NOS) connecting couples of gray matter regions. However, recent studies have shown that this method is not quantitative. Methods: We evaluated diffusion-based microstructural measures extracted from three different models to assess the network properties in a group of 66 MS patients and 64 healthy subjects. Besides, we assessed their correlation with patients' disability and with a biological measure of neuroaxonal damage. Results: Graph metrics extracted from connectomes weighted by intra-axonal microstructural components were the most sensitive to MS pathology and the most related to clinical disability. In contrast, measures of network segregation extracted from the connectomes weighted by maps describing extracellular diffusivity were the most related to serum concentration of neurofilament light chain. Network properties assessed with NOS were neither sensitive to MS pathology nor correlated with clinical and pathological measures of disease impact in MS patients. Conclusion: Using tractometry-derived graph measures in MS patients, we identified a set of metrics based on microstructural components that are highly sensitive to the disease and that provide sensitive correlates of clinical and biological deterioration in MS patients. Impact statement Graph theory has been widely used to study the alterations in the structural connectivity of multiple sclerosis (MS) patients. Usually, brain graphs used for the extraction of network metrics are created by counting the number of streamlines connecting gray matter regions, however, this method is not quantitative. In this study, we used tractometry to average the values of diffusion-based microstructural maps along the reconstructed streamlines. Our results show that network metrics extracted from the connectomes weighted on microstructural maps provide sensitive information to MS pathology, which correlate with clinical and biological measures of disease impact.

Although epigenetic modifications have been intensely investigated over the last decade due to their role in crop adaptation to rapid climate change, it is unclear which epigenetic changes are heritable and therefore transmitted to their progeny. The identification of epigenetic marks that are transmitted to the next generations is of primary importance for their use in breeding and for the development of new cultivars with a broad-spectrum of tolerance/resistance to abiotic and biotic stresses. In this review, we discuss general aspects of plant responses to environmental stresses and provide an overview of recent findings on the role of transgenerational epigenetic modifications in crops. In addition, we take the opportunity to describe the aims of EPI-CATCH, an international COST action consortium composed by researchers from 28 countries. The aim of this COST action launched in 2020 is: (1) to define standardized pipelines and methods used in the study of epigenetic mechanisms in plants, (2) update, share, and exchange findings in epigenetic responses to environmental stresses in plants, (3) develop new concepts and frontiers in plant epigenetics and epigenomics, (4) enhance dissemination, communication, and transfer of knowledge in plant epigenetics and epigenomics.

Summary Stress at the work place was declared by the World Health Organization as a worldwide epidemic. The stress caused by work appears when the balance between one’s own possibilities and the environment demands is disturbed, which leads to a poor mental state. The fact is that the majority of dentists find they are under constant stress, which is caused by the nature of dental work. Literature describes dentistry as an extremely stressful profession. The main stressors include the tendency toward technical perfection, the causation of pain in patients, the lack of patient cooperation during dental treatment, the failure to maintain the given appointments. Adequate education and preparation, even during dental studies, non-neglect of symptoms and difficulties at the work place, adequate and timely response to the identification of the cause and its elimination or balancing, have a key role in solving this great problem in modern dentistry.

The heritability of methylation patterns in tumor cells, as shown in recent studies, suggests that tumor heterogeneity and progression can be interpreted and predicted in the context of methylation changes. To elucidate methylation-based evolution trajectory in tumors, we introduce a novel computational method for methylation phylogeny reconstruction leveraging single cell bisulfite treated whole genome sequencing data (scBS-seq), incorporating additional copy number information inferred independently from matched single cell RNA sequencing (scRNA-seq) data, when available. We validate our method with the scBS-seq data of multi-regionally sampled colorectal cancer cells, and demonstrate that the cell lineages constructed by our method strongly correlate with original sampling regions. Our method consists of three components: (i) noise-minimizing site selection, (ii) likelihood-based sequencing error correction, and (iii) pairwise expected distance calculation for cells, all designed to mitigate the effect of noise and uncertainty due to data sparsity commonly observed in scBS-seq data. In (i), we present an integer linear program-based biclustering formulation to select a set of CpG-sites and cells so that the number of CpG-sites with non-zero coverage in the selected cells is maximized. This procedure filters out cells with read information in too few sites and CpG-sites with read information in too few cells. In (ii), we address the sequencing errors commonly encountered in currently available platforms with a maximum log likelihood approach to correct likely sequencing errors in scBS-seq reads, incorporating CpG-site copy number information in case it can be orthogonally obtained. Given the copy number and read information for a site in a cell, together with the overall sequencing error probability, we compute the log likelihood for all possible underlying allele statuses. If the mixed read statuses at the CpG-site for the cell are more likely due to sequencing error on homozygous alleles as opposed to the presence of alleles mixed methylation statuses, we correct the reads of the minority methylation status to the majority one. In (iii), we introduce a formulation to estimate distances between any pair of cells. As scBS-seq data is typically characterized by shallow read coverage, there is rarely read count evidence for two (or more, depending on CNV status) alleles at a CpG-site. Since allele-specific methylation has been shown to have increased frequency in cancer tissues, given the reads at a CpG-site, it is especially important to consider the possibility of unobserved alleles and their methylation status when determining the CpG-site9s possible methylation zygosities. Our method incorporates copy number information when available, and for each CpG-site in a cell, we compute a probability distribution across all possible methylation zygosities. Then, given specific distance values between pairs of distinct zygosities and the likelihood of each possible zygosity for each shared CpG-site in both cells, we compute the expected total distance between any pair of cells as the mean of expected distances across all shared CpG-sites. We leverage such pairwise distances in methylation phylogeny construction. Citation Format: Xuan C. Li, Yuelin Liu, Farid Rashidi, Salem Malikic, Stephen M. Mount, Eytan Ruppin, Kenneth Aldape, Cenk Sahinalp. Epigenomic tumor evolution modeling with single-cell methylation data profiling [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2021; 2021 Apr 10-15 and May 17-21. Philadelphia (PA): AACR; Cancer Res 2021;81(13_Suppl):Abstract nr LB020.