Epigenetic modifications are correlated to environmental factors. Exposure to ambient air pollution may contribute to the development of different diseases such as cancer, cardiovascular diseases, and neurological and metabolic disorders. Looking for the association between DNA methylation and exposure biomarkers may help in the prevention of adverse effects. Association analysis can be carried out through regression modeling. When dealing with the association between DNA methylation and pollutants, the response variable is beta-distributed, and linear regression models are not appropriate when the range is limited to (0, 1). Beta regression models are more suitable for this situation. Methylation levels can also be measured through the M-value statistic and association studies may be performed using classical linear regression models or robust linear regression models in the presence of outliers. An alternative to these models when the variable of interest does not behave linearly in all the predictors is given by a generalized linear model framework that incorporates non-linear terms and interactions. In this paper, we applied these models to a case study constituted of a cohort of healthy people living in regions exposed to different levels of pollution to investigate the association between DNA methylation and cadmium exposure.

Electrostimulation is the object of the study of a variety of clinical approaches, ranging from bioelectronic medicine where the aim is to elicit the activity of the autonomic nervous system (ANS), to electroacupuncture with the general objective to restore homeostasis, to transcutaneous electrical nerve stimulation (TENS) to control pain and degeneration, to name a few. Among the numerous obstacles preventing from a clear adoption or rejection of these approaches in mainstream clinical practice, is the difficulty in standardizing experimental systems for testing and validation. Consequently, indications on the appropriate magnitude of an effective stimulus (duration, frequency, intensity) remain unclear. To approach this issue we present preliminary results on the differential molecular activity elicited in a 3D bioprinted construct containing fibroblasts and keratinocytes in a collagen matrix, by two diverse types of electrical stimulation (direct and alternate current). Two conditions, physiology and inflammation induced by TNF? perfusion were tested with anelectrobiomedical device. The system mimics a simplified model of skin, the largest and most accessible of our organs, in inflamed or physiological states, treated by electrostimulation. The bioprinted sample is constructed to yield an appropriate number of cell enabling high-throughput screens. We report here our preliminary results on RNA-seq differential expression comparing direct and alternate current stimuli, with a focus on wound healing and inflammation as part of the greater inflammatory pathway. Our construct offer reproducibility of the experience, and direct comparison among potentially numerous conditions and types of stimulation. Our preliminary results shows that electrostimulation offers differential elicitation of biological functions. In particular, direct and alternate current provoke differential activation of proliferation and development associated functions.

In this paper, we consider the problem of estimating the graphs of conditional dependencies between variables (i.e., graphical models) from multiple datasets under Gaussian settings. We present jewel 2.0, which improves our previous method jewel 1.0 by modeling commonality and class-specific differences in the graph structures and better estimating graphs with hubs, making this new approach more appealing for biological data applications. We introduce these two improvements by modifying the regression-based problem formulation and the corresponding minimization algorithm. We also present, for the first time in the multiple graphs setting, a stability selection procedure to reduce the number of false positives in the estimated graphs. Finally, we illustrate the performance of jewel 2.0 through simulated and real data examples. The method is implemented in the new version of the R package jewel

Objective The immune response arises from a fne balance of mechanisms that provide for surveillance, tolerance, and elimination of dangers. Sulfavant A (SULF A) is a sulfolipid with a promising adjuvant activity. Here we studied the mechanismof action of SULF A and addressed the identifcation of its molecular target in human dendritic cells (hDCs).Methods Adjuvant efect and immunological response to SULF A were assessed on DCs derived from human donors. Inaddition to testing various reporter cells, target identifcation and downstream signalling was supported by a reverse pharmacology approach based on antibody blocking and gene silencing, crosstalk with TLR pathways, use of human allogeneicmixed lymphocyte reaction.Results SULF A binds to the Triggering Receptor Expressed on Myeloid cells-2 (TREM2) and initiates an unconventionalmaturation of hDCs leading to enhanced migration activity and up-regulation of MHC and co-stimulatory molecules without release of conventional cytokines. This response involves the SYK-NFAT axis and is compromised by blockade orgene silencing of TREM2. Activation by SULF A preserved the DC functions to excite the allogeneic T cell response, andincreased interleukin-10 release after lipopolysaccharide stimulation.Conclusion SULF A is the frst synthetic small molecule that binds to TREM2. The receptor engagement drives diferentiation of an unprecedented DC phenotype (homeDCs) that contributes to immune homeostasis without compromising lymphocyte activation and immunogenic response. This mechanism fully supports the adjuvant and immunoregulatory activityof SULF A. We also propose that the biological p

Summary: ADViSELipidomics is a novel Shiny app for preprocessing, analyzing and visualizing lipidomics data. Ithandles the outputs from LipidSearch and LIQUID for lipid identification and quantification and the data fromthe Metabolomics Workbench. ADViSELipidomics extracts information by parsing lipid species (using LIPID MAPSclassification) and, together with information available on the samples, performs several exploratory and statisticalanalyses. When the experiment includes internal lipid standards, ADViSELipidomics can normalize the data matrix,providing normalized concentration values per lipids and samples. Moreover, it identifies differentially abundantlipids in simple and complex experimental designs, dealing with batch effect correction. Finally, ADViSELipidomicshas a user-friendly graphical user interface and supports an extensive series of interactive graphics.

The molecular programs involved in regulatory T (Treg) cell activation and homeostasis remain incompletely understood. Here, we show that T cell receptor (TCR) signaling in Treg cells induces the nuclear translocation of serine/threonine kinase 4 (Stk4), leading to the formation of an Stk4-NF-?B p65-Foxp3 complex that regulates Foxp3- and p65-dependent transcriptional programs. This complex was stabilized by Stk4-dependent phosphorylation of Foxp3 on serine-418. Stk4 deficiency in Treg cells, either alone or in combination with its homolog Stk3, precipitated a fatal autoimmune lymphoproliferative disease in mice characterized by decreased Treg cell p65 expression and nuclear translocation, impaired NF-?B p65-Foxp3 complex formation, and defective Treg cell activation. In an adoptive immunotherapy model, overexpression of p65 or the phosphomimetic Foxp3S418E in Stk3/4-deficient Treg cells ameliorated their immune regulatory defects. Our studies identify Stk4 as an essential TCR-responsive regulator of p65-Foxp3-dependent transcription that promotes Treg cell-mediated immune tolerance.

In this study we investigated the transcriptome and epigenome dynamics of the tomato fruit during post-harvest in a landracebelonging to a group of tomatoes (Solanum lycopersicum L.) collectively known as "Piennolo del Vesuvio", all characterized by a long shelflife. Expression of protein-coding genes and microRNAs as well as DNA methylation patterns and histone modifications were analysedin distinct post-harvest phases. Multi-omics data integration contributed to the elucidation of the molecular mechanisms underlyingprocesses leading to long shelf-life. We unveiled global changes in transcriptome and epigenome. DNA methylation increased and therepressive histone mark H3K27me3 was lost as the fruit progressed from red ripe to 150 days post-harvest. Thousands of genes weredifferentially expressed, about half of which were potentially epi-regulated as they were engaged in at least one epi-mark change inaddition to being microRNA targets in ~5% of cases. Down-regulation of the ripening regulator MADS-RIN and of genes involved inethylene response and cell wall degradation was consistent with the delayed fruit softening. Large-scale epigenome reprogrammingthat occurred in the fruit during post-harvest likely contributed to delayed fruit senescence.

TBX1 is a key regulator of pharyngeal apparatus (PhAp) development. Vitamin B12 (vB12) treatment partially rescues aortic arch patterning defects of Tbx1+/- embryos. Here, we show that it also improves cardiac outflow tract septation and branchiomeric muscle anomalies of Tbx1 hypomorphic mutants. At the molecular level, in vivo vB12 treatment enabled us to identify genes that were dysregulated by Tbx1 haploinsufficiency and rescued by treatment. We found that SNAI2, also known as SLUG, encoded by the rescued gene Snai2, identified a population of mesodermal cells that was partially overlapping with, but distinct from, ISL1+ and TBX1+ populations. In addition, SNAI2+ cells were mislocalized and had a greater tendency to aggregate in Tbx1+/- and Tbx1-/- embryos, and vB12 treatment restored cellular distribution. Adjacent neural crest-derived mesenchymal cells, which do not express TBX1, were also affected, showing enhanced segregation from cardiopharyngeal mesodermal cells. We propose that TBX1 regulates cell distribution in the core mesoderm and the arrangement of multiple lineages within the PhAp.

Mass spectrometry is a widely applied technology with a strong impact in the proteomics field. MALDI-TOF is a combined technology in mass spectrometry with many applications in characterizing biological samples from different sources, such as the identification of cancer biomarkers, the detection of food frauds, the identification of doping substances in athletes' fluids, and so on. The massive quantity of data, in the form of mass spectra, are often biased and altered by different sources of noise. Therefore, extracting the most relevant features that characterize the samples is often challenging and requires combining several computational methods. Here, we present GeenaR, a novel web tool that provides a complete workflow for pre-processing, analyzing, visualizing, and comparing MALDI-TOF mass spectra. GeenaR is user-friendly, provides many different functionalities for the analysis of the mass spectra, and supports reproducible research since it produces a human-readable report that contains function parameters, results, and the code used for processing the mass spectra. First, we illustrate the features available in GeenaR. Then, we describe its internal structure. Finally, we prove its capabilities in analyzing oncological datasets by presenting two case studies related to ovarian cancer and colorectal cancer. GeenaR is available at http://proteomics.hsanmartino.it/geenar/.

In this paper, we consider the problem of estimating multiple Gaussian Graphical Models from high-dimensional datasets. We assume that these datasets are sampled from different distributions with the same conditional independence structure, but not the same precision matrix. We propose jewel, a joint data estimation method that uses a node-wise penalized regression approach. In particular, jewel uses a group Lasso penalty to simultaneously guarantee the resulting adjacency matrix's symmetry and the graphs' joint learning. We solve the minimization problem using the group descend algorithm and propose two procedures for estimating the regularization parameter. Furthermore, we establish the estimator's consistency property. Finally, we illustrate our estimator's performance through simulated and real data examples on gene regulatory networks.

ZBTB2 is a protein belonging to the BTB/POZ zinc-finger family whose members typically contain a BTB/POZ domain at the N-terminus and several zinc-finger domains at the C-terminus. Studies have been carried out to disclose the role of ZBTB2 in cell proliferation, in human cancers and in regulating DNA methylation. Moreover, ZBTB2 has been also described as an ARF, p53 and p21 gene repressor as well as an activator of genes modulating pluripotency. In this scenario, ZBTB2 seems to play many functions likely associated with other proteins. Here we report a picture of the ZBTB2 protein partners in U87MG cell line, identified by high-resolution mass spectrometry (MS) that highlights the interplay between ZBTB2 and chromatin remodeling multiprotein complexes.In particular, our analysis reveals the presence, as ZBTB2 candidate interactors, of SMARCA5 and BAZ1B components of the chromatin remodeling complex WICH and PBRM1, a subunit of the SWI/SNF complex. Intriguingly, we identified all the subunits of the NuRD complex among the ZBTB2 interactors. By co-immunoprecipitation experiments and ChIP-seq analysis we definitely identify ZBTB2 as a new partner of the NuRD complex. Highlightso ZBTB2 co-associate with the NuRD complex.o The multiple ZBTB2 functions can be explained because of its interplay with NuRD.o ZBTB2 can be a bridge between NuRD and DNA-sequence specific transcription factors.o ZBTB2 interactome by mass-spectrometry reveals the presence of many chromatin remodeling complex subunits.o ZBTB2 interacts with ZNF639, a sequence-specific DNA-binding zinc-finger protein.

Identifying relevant genomic features that can act as prognostic markers for buildingpredictive survival models is one of the central themes in medical research, affecting the future ofpersonalized medicine and omics technologies. However, the high dimension of genome-wide omicdata, the strong correlation among the features, and the low sample size significantly increase thecomplexity of cancer survival analysis, demanding the development of specific statistical methodsand software. Here, we present a novel R package, COSMONET (COx Survival Methods based OnNETworks), that provides a complete workflow from the pre-processing of omics data to the selectionof gene signatures and prediction of survival outcomes. In particular, COSMONET implements (i) threedifferent screening approaches to reduce the initial dimension of the data from a high-dimensionalspace p to a moderate scale d, (ii) a network-penalized Cox regression algorithm to identify the genesignature, (iii) several approaches to determine an optimal cut-off on the prognostic index (PI) toseparate high- and low-risk patients, and (iv) a prediction step for patients' risk class based on theevaluation of PIs. Moreover, COSMONET provides functions for data pre-processing, visualization,survival prediction, and gene enrichment analysis. We illustrate COSMONET through a step-by-step Rvignette using two cancer datasets.

Motivation Inflammation is part of the complex function that addresses harmful stimuli, and the first phase of wound healing (WH), which guarantees living systems' homeostasis. Deviances from physiology make inflammation turn acute (sepsis, 11M death/y) or chronic (non-communicable diseases, 41M death/y). Therefore, tackling inflammation is a key priority. We recently proposed (Maturo et al., 2020) to revise the conventional inflammatory pathway (innate immune response) to include WH (expanded inflammatory pathway). Methods We manually identified the Reactome pathways that include all reactions and species relevant to WH. Cytoscape was then used to perform the union of the SBML converted pathways, with the largest connected component being retained (732 nodes, 13.944 edges). The same was done for the innate immune response (R-HSA-168249.8) with 487 nodes, 11.744 edges. We then focused on: NF-kB (fundamental hub in all inflammatory reactions), TNF-? (renown target of inflammatory diseases) and RAC1 (key player in mechanotransduction events of WH). Results Preliminary topological results highlight the stability of closeness centrality, i.e. all molecules preserve their efficiency in spreading information. Conversely, betweenness centrality is stable for NF-kB (0.068), confirming NF-kB relevance, while halving its (very low) value in the expanded pathway for TNF-? (from 2.85E-06 to 1.29E-06). This indicates that the ability to bridge different parts of the graphs is less effective if we consider inflammation as an expanded concept, possibly contributing to explain the many side effects of anti-TNF-? therapies. Interestingly, RAC1 presents stable betweenness (from 0.094 to 0.093), comparable to NF-kB, supporting the hypothesis that WH-leveraging therapies could act on a relevant and stable target, so far neglected (Nardini et al., 2016).

The poles of the heart and branchiomeric muscles of the face and neck are formed from the cardiopharyngeal mesoderm within the pharyngeal apparatus. They are disrupted in patients with 22q11.2 deletion syndrome, due to haploinsufficiency of TBX1, encoding a T-box transcription factor. Here, using single cell RNA-sequencing, we now identify a multilineage primed population within the cardiopharyngeal mesoderm, marked by Tbx1, which has bipotent properties to form cardiac and branchiomeric muscle cells. The multilineage primed cells are localized within the nascent mesoderm of the caudal lateral pharyngeal apparatus and provide a continuous source of cardiopharyngeal mesoderm progenitors. Tbx1 regulates the maturation of multilineage primed progenitor cells to cardiopharyngeal mesoderm derivatives while restricting ectopic non-mesodermal gene expression. We further show that TBX1 confers this balance of gene expression by direct and indirect regulation of enriched genes in multilineage primed progenitors and downstream pathways, partly through altering chromatin accessibility, the perturbation of which can lead to congenital defects in individuals with 22q11.2 deletion syndrome.

ZFP57 is required to maintain the germline-marked differential methylation at imprinting control regions (ICRs) in mouse embryonic stem cells (ESCs). Although DNA methylation has a key role in genomic imprinting, several imprinted genes are controlled by different mechanisms, and a comprehensive study of the relationship between DMR methylation and imprinted gene expression is lacking. To address the latter issue, we differentiated wild-type and Zfp57-/- hybrid mouse ESCs into neural precursor cells (NPCs) and evaluated allelic expression of imprinted genes. In mutant NPCs, we observed a reduction of allelic bias of all the 32 genes that were imprinted in wild-type cells, demonstrating that ZFP57-dependent methylation is required for maintaining or acquiring imprinted gene expression during differentiation. Analysis of expression levels showed that imprinted genes expressed from the non-methylated chromosome were generally up-regulated, and those expressed from the methylated chromosome were down-regulated in mutant cells. However, expression levels of several imprinted genes acquiring biallelic expression were not affected, suggesting the existence of compensatory mechanisms that control their RNA level. Since neural differentiation was partially impaired in Zfp57-mutant cells, this study also indicates that imprinted genes and/or non-imprinted ZFP57-target genes are required for proper neurogenesis in cultured ESCs.

During last years "irreproducibility" became a general problem in omics data analysis due to the use of sophisticated and poorly described computational procedures. For avoiding misleading results, it is necessary to inspect and reproduce the entire data analysis as a unified product. Reproducible Research (RR) provides general guidelines for public access to the analytic data and related analysis code combined with natural language documentation, allowing third-parties to reproduce the findings. We developed easyreporting, a novel R/Bioconductor package, to facilitate the implementation of an RR layer inside reports/tools. We describe the main functionalities and illustrate the organization of an analysis report using a typical case study concerning the analysis of RNA-seq data. Then, we show how to use easyreporting in other projects to trace R functions automatically. This latter feature helps developers to implement procedures that automatically keep track of the analysis steps. Easyreporting can be useful in supporting the reproducibility of any data analysis project and shows great advantages for the implementation of R packages and GUIs. It turns out to be very helpful in bioinformatics, where the complexity of the analyses makes it extremely difficult to trace all the steps and parameters used in the study.

Graphical models are well-known mathematical objects for describing conditional dependency relationships between random variables of a complex system. Gaussian graphical models refer to the case of multivariate Gaussian variable for which the graphical model is encoded through the support of corresponding inverse covariance (precision) matrix. We consider a problem of estimating multiple Gaussian graphical models from high- dimensional data sets under the assumption that they share the same conditional independence structure. However, the individual correlation matrices can differ. Such a problem can be motivated by applications where data comes from different sources and can be collected in distinct classes or groups. We propose a joint data estimation that uses a node-wise penalized regression approach. Grouped Lasso penalty simultaneously guarantees the resulting adjacency matrix's symmetry and the joint learning of the graphs. We solve the minimization problem using the group descent algorithm and establish the proposed solution's consistency and sparsity properties. Finally, we show how the regularization parameter can be estimated using cross-validation and BIC. We provide a novel R package jewel with the implementation of the proposed method and illustrate our estimator's performance through simulated and real data examples. We compare the proposed approach with other available alternatives.

Population genetics focuses on the analysis of genetic differences within and between-group of individuals and the inference of the populations' structure. These analyses are usually carried out using Bayesian clustering or maximum likelihood estimation algorithms that assign individuals to a given population depending on specific genetic patterns. Although several tools were developed to perform population genetics analysis, their standard graphical outputs may not be sufficiently informative for users lacking interactivity and complete information. StructuRly aims to resolve this problem by offering a complete environment for population analysis. In particular, StructuRly combines the statistical power of the R language with the friendly interfaces implemented using the shiny libraries to provide a novel tool for performing population clustering, evaluating several genetic indexes, and comparing results. Moreover, graphical representations are interactive and can be easily personalized. StructuRly is available either as R package on GitHub, with detailed information for its installation and use and as shinyapps.io servers for those users who are not familiar with R and the RStudio IDE. The application has been tested on Linux, macOS and Windows operative systems and can be launched as a shiny app in every web browser.

The transcription factor CCCTC-binding factor (CTCF) modulates pleiotropic functions mostly related to gene expression regulation. The role of CTCF in large scale genome organization is also well established. A unifying model to explain relationships among many CTCF-mediated activities involves direct or indirect interactions with numerous protein cofactors recruited to specific binding sites. The co-association of CTCF with other architectural proteins such as cohesin, chromodomain helicases, and BRG1, further supports the interplay between master regulators of mammalian genome folding. Here, we report a comprehensive LC-MS/MS mapping of the components of the switch/sucrose nonfermentable (SWI/SNF) chromatin remodeling complex co-associated with CTCF including subunits belonging to the core, signature, and ATPase modules. We further show that the localization patterns of representative SWI/SNF members significantly overlap with CTCF sites on transcriptionally active chromatin regions. Moreover, we provide evidence of a direct binding of the BRK-BRG1 domain to the zinc finger motifs 4-8 of CTCF, thus, suggesting that these domains mediate the interaction of CTCF with the SWI/SNF complex. These findings provide an updated view of the cooperative nature between CTCF and the SWI/SNF ATP-dependent chromatin remodeling complexes, an important step for understanding how these architectural proteins collaborate to shape the genome.

The T-box transcription factor TBX1 has critical roles in the cardiopharyngeal lineage and the gene is haploinsufficient in DiGeorge syndrome, a typical developmental anomaly of the pharyngeal apparatus. Despite almost two decades of research, if and how TBX1 function triggers chromatin remodeling is not known. Here, we explored genome-wide gene expression and chromatin remodeling in two independent cellular models of Tbx1 loss of function, mouse embryonic carcinoma cells P19Cl6, and mouse embryonic stem cells (mESCs). The results of our study revealed that the loss or knockdown of TBX1 caused extensive transcriptional changes, some of which were cell type-specific, some were in common between the two models. However, unexpectedly we observed only limited chromatin changes in both systems. In P19Cl6 cells, differentially accessible regions (DARs) were not enriched in T-BOX binding motifs; in contrast, in mESCs, 34% (n = 47) of all DARs included a T-BOX binding motif and almost all of them gained accessibility in Tbx1 -/- cells. In conclusion, despite a clear transcriptional response of our cell models to loss of TBX1 in early cell differentiation, chromatin changes were relatively modest.