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Most cell type-specific genes are regulated by the interaction of enhancers with their promoters. The identification of enhancers is not trivial as enhancers are diverse in their characteristics and dynamic in their interaction partners. Currently, enhancer-associated features such as histone modifications, co-activators or bi-directional transcription are used in lieu of any definitive and universal enhancer feature. We present Esearch3D, a new approach that leverages network theory approaches to identify active enhancers. Our work is based on the fact that enhancers act as a source of regulatory information to increase the rate of transcription of their target genes and that the flow of this information is mediated by the folding of chromatin in the three-dimensional (3D) nuclear space between the enhancer and the target gene promoter. Esearch3D reverse engineers this flow of information to calculate the likelihood of enhancer activity in intergenic regions by propagating the transcription levels of genes across 3D-genome networks. Regions predicted to have high enhancer activity are shown to be enriched in annotations indicative of enhancer activity. These include: enhancer-associated histone marks, bi-directional CAGE-seq, STARR-seq, P300 and RNA polymerase II ChIP-seq, and expression quantitative trait loci (eQTL). Esearch3D successfully leverages the relationship between chromatin architecture and global transcription and represents a novel approach to predict active enhancers and understand the complex underpinnings of regulatory networks. The method is available at: https://github.com/InfOmics/Esearch3D.

Brain functionality resides on finely tuned regulation of gene expression by networks of non-coding RNAs (ncRNAs) such as the one composed by the circular RNA ciRS-7, the microRNA miR-7 and the long non-coding RNA Cyrano. However, very little is known on how this network regulates stress responses in neurodegeneration. Here we describe ischemia induced alterations in the ncRNA network bothin vitroandin vivoand in transgenic mice lacking ciRS-7 or miR-7. Our data show that cortical neurons downregulate ciRS-7 and Cyrano and upregulate miR-7 expression upon ischemic insults. Mice lacking ciRS-7 show reduced lesion size and motor impairment, whilst the absence of miR-7 alone leads to an increase in the ischemia induced neuronal death. Moreover, miR-7 levels in pyramidal excitatory neurons regulate dendrite morphology and glutamatergic signaling suggesting a potential molecular link to thein vivophenotype. Our data reveal a new endogenous mechanism by which ciRS-7 and miR-7 regulate the outcome of ischemic stroke and shed new light into the pathophysiological roles of intracellular networks of non-coding RNAs in the brain.

Simple Summary In this manuscript, we offer an integrated molecular analysis of 44 combined large cell neuroendocrine carcinomas (CoLCNECs) in order to deepen the knowledge about these rare histotypes and to clarify their relationship with lung cancers. In the present state of research, molecular studies are still scant, consisting of small and heterogeneous cohorts, and the genomic landscape is poorly characterized. This study shows that CoLCNECs constitute a standalone group of neuroendocrine neoplasm, with three different molecular profiles, two of which overlap with pure LCNEC or adenocarcinoma. CoLCNECs can be considered an independent histologic category with specific genomic and transcriptomic features, different and therefore not comparable to other lung cancers. Indeed, in addition to a histological re-evaluation of lung cancer classification, our study may help to develop a new diagnostic approach for novel and personalized treatments in CoLCNECs. Background: Combined large cell neuroendocrine carcinoma (CoLCNEC) is given by the association of LCNEC with adeno or squamous or any non-neuroendocrine carcinoma. Molecular bases of CoLCNEC pathogenesis are scant and no standardized therapies are defined. Methods: 44 CoLCNECs: 26 with adenocarcinoma (CoADC), 7 with squamous cell carcinoma (CoSQC), 3 with small cell carcinoma (CoSCLC), 4 with atypical carcinoid (CoAC) and 4 napsin-A positive LCNEC (NapA+), were assessed for alterations in 409 genes and transcriptomic profiling of 20,815 genes. Results: Genes altered included TP53 (n = 30), RB1 (n = 14) and KRAS (n = 13). Targetable alterations included six KRAS G12C mutations and ALK-EML4 fusion gene. Comparison of CoLCNEC transcriptomes with 86 lung cancers of pure histology (8 AC, 19 ADC, 19 LCNEC, 11 SCLC and 29 SQC) identified CoLCNEC as a separate entity of neuroendocrine tumours with three different molecular profiles, two of which showed a non-neuroendocrine lineage. Hypomethylation, activation of MAPK signalling and association to immunotherapy signature specifically characterized each of three CoLCNEC molecular clusters. Prognostic stratification was also provided. Conclusions: CoLCNECs are an independent histologic category. Our findings support the extension of routine evaluation of KRAS mutations, fusion genes and immune-related markers to offer new perspectives in the therapeutic management of CoLCNEC.

Purpose Calcium ions are involved in the regulation of several cellular processes and may also influence viral replication. Hypocalcemia has been frequently reported during infectious diseases and in critically ill patients, including also COVID-19 patients, significantly related with the pro-inflammatory state and mortality. The aim of this study is to investigate the prevalence of hypocalcemia at admission in patients hospitalized for COVID-19 (Coronavirus disease 2019) and to evaluate association of hypocalcemia with in-hospital COVID-19 outcomes. Methods Retrospective analysis on 118 consecutive patients, hospitalized for COVID-19 between March and May 2020. Clinical characteristics, inflammation markers, biochemical routine and mineral metabolism parameters at admission were collected. Hypocalcemia was defined as total serum calcium Results Overall prevalence of hypocalcemia was 76.6%, with just 6.7% of patients reporting levels of 25-(OH)-vitamin D > 30 ng/ml. Total serum calcium was inversely related with selected inflammatory biomarkers (p p = 0.01) compared with other groups. Conclusion Total serum calcium detected on admission is inversely related with proinflammatory biomarkers of severe COVID-19 and is useful to better define risk stratification for adverse in-hospital outcome.

OBJECTIVE Identifying metabolic factors associated with critical disease can help to improve management of patients hospitalized for coronavirus disease 2019 (COVID-19). High triglycerides and low HDL levels characterize the atherogenic dyslipidemia closely related to insulin resistance and diabetes. We examined associations of atherogenic dyslipidemia detected on admission with outcome of COVID-19 during hospitalization. RESEARCH DESIGN AND METHODS We retrospectively analyzed clinical reports of 118 consecutive patients hospitalized for COVID-19 in Rome, Italy, between March and May 2020. Clinical characteristics, inflammation markers, and glucose and lipid metabolism parameters at admission were collected. Critical disease was defined as in-hospital death or need for endotracheal intubation. Associations were tested using logistic regression analysis. RESULTS Patients with critical COVID-19 (n = 43) were significantly older than those with noncritical disease (n = 75) and presented higher levels of fasting glucose, triglycerides, C-reactive protein, interleukin-6, procalcitonin, and d-dimer (P < 0.01 for all), whereas HDL levels were lower (P = 0.003). Atherogenic dyslipidemia was more frequent in patients with critical COVID-19 (46 vs. 24%, P = 0.011), as well as diabetes (37 vs. 19%, P = 0.026), and significantly associated with death or intubation (odds ratio 2.53 [95% CI 1.16–6.32], P = 0.018). Triglycerides were significantly associated with selected inflammatory biomarkers (P < 0.05 for all) and poorer outcome of COVID-19 during hospitalization in both the overall population and the subgroup with atherogenic dyslipidemia. CONCLUSIONS Atherogenic dyslipidemia detected on admission can be associated with critical in-hospital course of COVID-19. Further investigations are needed to elucidate the hypothetical role of insulin resistance and related lipid abnormalities in severe acute respiratory syndrome coronavirus 2 pathogenesis. Assessment of lipid profile should be encouraged in patients hospitalized for COVID-19.

BackgroundMicroglia are the endogenous immune cells of the brain and act as sensors of pathology to maintain brain homeostasis and eliminate potential threats. In Alzheimer’s disease (AD), toxic amyloid beta (Aβ) accumulates in the brain and forms stiff plaques. In late-onset AD accounting for 95% of all cases, this is thought to be due to reduced clearance of Aβ. Human genome-wide association studies and animal models suggest that reduced clearance results from aberrant function of microglia. While the impact of neurochemical pathways on microglia have been broadly studied, mechanical receptors regulating microglial functions remain largely unexplored.MethodsHere we showed that a mechanotransduction ion channel, PIEZO1, is expressed and functional in human and mouse microglia. We used a small molecule agonist, Yoda1, to study how activation of PIEZO1 affects AD-related functions in human induced pluripotent stem cell (iPSC) -derived microglia-like cells (iMGL) under controlled laboratory experiments. Cell survival, metabolism, phagocytosis and lysosomal activity were assessed using real-time functional assays. To evaluate the effect of activation of PIEZO1 in vivo, 5-month-old 5xFAD male mice were infused daily with Yoda1 for two weeks through intracranial cannulas. Microglial Iba1 expression and Aβ pathology were quantified with immunohistochemistry and confocal microscopy. Published human and mouse AD datasets were used for in-depth analysis of PIEZO1 gene expression and related pathways in microglial subpopulations.ResultsWe show that PIEZO1 orchestrates Aβ clearance by enhancing microglial survival, phagocytosis, and lysosomal activity. Aβ inhibited PIEZO1-mediated calcium transients, whereas activation of PIEZO1 with a selective agonist, Yoda1, improved microglial phagocytosis resulting in Aβ clearance both in human and mouse models of AD. Moreover, PIEZO1 expression was associated with a unique microglial transcriptional phenotype in AD as indicated by assessment of cellular metabolism, and human and mouse single cell datasets.ConclusionThese results indicate that the compromised function of microglia in AD could be improved by controlled activation of PIEZO1 channels resulting in alleviated Aβ burden. Pharmacological regulation of these mechanoreceptors in microglia could represent a novel therapeutic paradigm for AD.GRAPHICAL ABSTRACT

BACKGROUNDPathway-based patient classification is a supervised learning task which implies a model learning pathways as features to predict the classes of patients. The counterpart of enrichment tools for the pathway analysis are fundamental methods for clinicians and biomedical scientists. They allow to find signature cellular functions which help to define and annotate a disease phenotype. They provide results which lead human experts to manually classify patients. It is a paradox that pathwaybased classifiers which natively resolve this objective are not strongly developed. They could simulate the human way of thinking, decipher hidden multivariate relationships between the deregulated pathways and the disease phenotype, and provide more information than a probability value. Instead, there are currently only two classifiers of such kind, they require a nontrivial hyperparameter tuning, are difficult to interpret and lack in providing new insights. There is the need of new classifiers which can provide novel perspectives about pathways, be easy to apply with different biological omics and produce new data enabling a further analysis of the patients.RESULTSWe propose Simpati, an innovative and interpretable patient classifier based on pathway-specific patient similarity networks. The first classifier to adopt ad-hoc novel algorithms for such graph type. It standardizes the biological high-throughput dataset of patient’s profiles with a propagation algorithm that considers the interconnected nature of the cell’s molecules for inferring a new activity score. This allows Simpati to classify with dense, sparse, and non-homogenous omic data. Simpati organizes patient’s molecules in pathways represented by patient similarity networks for being interpretable, handling missing data and preserving the patient privacy. A network represents patients as nodes and a novel similarity measure determines how much every pair act co-ordinately in a pathway. Simpati detects signature biological processes based on how much the topological properties of the related networks separate the patient classes. In this step, it includes a new cohesive subgroup detection algorithm to handle patients not showing the same pathway activity as the other class members. An unknown patient is then classified by a unique recommender system which considers how much is similar to known patients and distant from being an outlier. Simpati outperforms previously published classifiers on five cancer datasets described with two biological omics, classifies well with sparse data, identifies more relevant pathways associated to the patient’s disease than the competitors and has the lowest computational requirements.CONCLUSIONSimpati can serve as generic-purpose pathway-based classifier of patient classes. It provides signature pathways to unveil the altered biological mechanisms of a disease phenotype and to classify patients according to the learnt pathway-specific similarities. The signature condition and patient prediction can be deciphered considering the patient similarity networks which must reveal the members of a patient class more cohesive and similar than the non-members. Simpati divides the pathways in up and downinvolved. Upinvolved when the signaling cascades generated by the altered molecules of the disease patients impact stronger the pathway than the ones of the control class. We provide an R implementation, a graphical user interface and a visualization function for the patient similarity networks. The software is available at: https://github.com/LucaGiudice/Simpati

Aim: Mechanosensitive Piezo1 ion channels emerged recently as important contributors to various vital functions including modulation of the blood supply to skeletal muscles. The specific Piezo1 channel agonist Yoda1 was shown to regulate the tone of blood vessels similarly to physical exercise. However, the direct role of Piezo1 channels in muscle function has been little studied so far. We therefore investigated the action of Yoda1 on the functional state of skeletal muscle precursors (satellite cells and myotubes) as well as on adult muscle fibers. Methods: Immunostaining, electrophysiological intracellular recordings and Ca2+ imaging experiments were performed to localize and assess the effect of the chemical activation of Piezo1 channels with Yoda1, on myogenic precursors, adult myofibers and at the adult neuromuscular junction. Results: Piezo1 channels were detected by immunostaining in satellite cells and myotubes as well as in adult myofibers. In the skeletal muscle precursors, Yoda1 treatment stimulated the differentiation and cell fusion rather than the proliferation of satellite cells. Moreover, in myotubes, Yoda1 induced significant [Ca2+ ]i transients, without detectable [Ca2+ ]i response in adult myofibers. Furthermore, although expression of Piezo1 channels was detected around the muscle endplate region, Yoda1 application did not alter either the nerve-evoked or spontaneous synaptic activity or muscle contractions in adult myofibers. Conclusion: Our data indicate that the chemical activation of Piezo1 channels specifically enhances the differentiation of skeletal muscle precursors, suggesting a possible new strategy to promote muscle regeneration.

Background: Diffuse large B-cell lymphoma (DLBCL), marginal zone lymphoma (MZL) and follicular lymphoma (FL) are the most common B-cell lymphomas (BCL) in dogs. Recent investigations have demonstrated overlaps of these histotypes with the human counterparts, including clinical presentation, biologic behavior, tumor genetics, and treatment response. The molecular mechanisms that underlie canine BCL are still unknown and new studies to improve diagnosis, therapy, and the utilization of canine species as spontaneous animal tumor models are undeniably needed. Recent work using human DLBCL transcriptomes has suggested that long non-coding RNAs (lncRNAs) play a key role in lymphoma pathogenesis and pinpointed a restricted number of lncRNAs as potential targets for further studies. Results: To expand the knowledge of non-coding molecules involved in canine BCL, we used transcriptomes obtained from a cohort of 62 dogs with newly-diagnosed multicentric DLBCL, MZL and FL that had undergone complete staging work-up and were treated with chemotherapy or chemo-immunotherapy. We developed a customized R pipeline performing a transcriptome assembly by multiple algorithms to uncover novel lncRNAs, and delineate genome-wide expression of unannotated and annotated lncRNAs. Our pipeline also included a new package for high performance system biology analysis, which detects high-scoring network biological neighborhoods to identify functional modules. Moreover, our customized pipeline quantified the expression of novel and annotated lncRNAs, allowing us to subtype DLBCLs into two main groups. The DLBCL subtypes showed statistically different survivals, indicating the potential use of lncRNAs as prognostic biomarkers in future studies. Conclusions: In this manuscript, we describe the methodology used to identify lncRNAs that differentiate B-cell lymphoma subtypes and we interpreted the biological and clinical values of the results. We inferred the potential functions of lncRNAs to obtain a comprehensive and integrative insight that highlights their impact in this neoplasm.

Marginal zone lymphoma (MZL) and follicular lymphoma (FL) are classified as indolent B-cell lymphomas in dogs. Aside from the clinical and histopathological similarities with the human counterpart, the molecular pathogenesis remains unclear. We integrated transcriptome, genome-wide DNA methylation and copy number aberration analysis to provide insights on the pathogenesis of canine MZL (n = 5) and FL (n = 7), also comparing them with diffuse large B-cell lymphoma (DLBCL). Transcriptome profiling highlighted the presence of similar biological processes affecting both histotypes, including BCR and TLR signalling pathways. However, FLs showed an enrichment of E2F targets, whereas MZLs were characterized by MYC-driven transcriptional activation signatures. FLs showed a distinctive loss on chr1 containing CEACAM23 and 24, conversely MZLs presented multiple recurrent gains on chr13, where MYC is located. The distribution of methylation peaks was similar between the two histotypes. Integrating data from the three omics, FLs resulted clearly separated from MZLs and DLBCL dataset. MZLs showed the enrichment of FoxM1 network and TLR associated TICAM1-dependent IRFs activation pathway. However, no specific signatures differentiated MZLs from DLBCLs. In conclusion, our study presents the first comprehensive analysis of molecular and epigenetic pathogenesis of canine FL and MZL.

Long non-coding RNAs (lncRNAs) have recently acquired a boost of interest for their implication in several biological conditions. However, many of these elements are not yet characterized. LErNet is a method to in silico define and predict the roles of IncRNAs. The core of the approach is a network expansion algorithm which enriches the genomic context of IncRNAs. The context is built by integrating the genes encoding proteins that are found next to the non-coding elements both at genomic and system level. The pipeline is particularly useful in situations where the functions of discovered IncRNAs are not yet known. The results show both the outperformance of LErNet compared to enrichment approaches in literature and its robustness in case of partially missing context information. LErNet is provided as an R package. It is available at https://github.com/InfOmics/LErNet.