Enteroviruses (EVs) are highly prevalent viruses world-wide, causing a wide range of diseases in both children and adults. Insight in the global prevalence of EVs is important to define their clinical significance and total disease burden, and assists in making therapeutic decisions. While many studies have been conducted to describe epidemiology of EVs in specific (sub)populations and patient cohorts, little effort has been made to aggregate the available evidence. In the current study, we conducted a search in the PubMed and Embase (Ovid) databases to identify articles reporting EV prevalence and type distribution. We summarized the findings of 153 included studies. We found that EVs are highly prevalent viruses in all continents. Enterovirus B was the most detected species worldwide, while the other species showed continent-specific differences, with Enterovirus C more detected in Africa and Enterovirus A more detected in Asia. Echovirus 30 was by far the most detected type, especially in studies conducted in Europe. EV types in species Enterovirus B—including echovirus 30—were often detected in patient groups with neurological infections and in cerebrospinal fluid, while Enterovirus C types were often found in stool samples.

Cancer is characterized by pervasive epigenetic alterations with enhancer dysfunction orchestrating the aberrant cancer transcriptional programs and transcriptional dependencies. Here, we epigenetically characterize human colorectal cancer (CRC) using de novo chromatin state discovery on a library of different patient-derived organoids. By exploring this resource, we unveil a tumor-specific deregulated enhancerome that is cancer cell-intrinsic and independent of interpatient heterogeneity. We show that the transcriptional coactivators YAP/TAZ act as key regulators of the conserved CRC gained enhancers. The same YAP/TAZ-bound enhancers display active chromatin profiles across diverse human tumors, highlighting a pan-cancer epigenetic rewiring which at single-cell level distinguishes malignant from normal cell populations. YAP/TAZ inhibition in established tumor organoids causes extensive cell death unveiling their essential role in tumor maintenance. This work indicates a common layer of YAP/TAZ-fueled enhancer reprogramming that is key for the cancer cell state and can be exploited for the development of improved therapeutic avenues.

The brain is an extraordinarily complex system that facilitates the optimal integration of information from different regions to execute its functions. With the recent advances in technology, researchers can now collect enormous amounts of data from the brain using neuroimaging at different scales and from numerous modalities. With that comes the need for sophisticated tools for analysis. The field of network neuroscience has been trying to tackle these challenges, and graph theory has been one of its essential branches through the investigation of brain networks. Recently, topological data analysis has gained more attention as an alternative framework by providing a set of metrics that go beyond pairwise connections and offer improved robustness against noise. In this hands-on tutorial, our goal is to provide the computational tools to explore neuroimaging data using these frameworks and to facilitate their accessibility, data visualisation, and comprehension for newcomers to the field. We will start by giving a concise (and by no means complete) overview of the field to introduce the two frameworks and then explain how to compute both well-established and newer metrics on resting-state functional magnetic resonance imaging. We use an open-source language (Python) and provide an accompanying publicly available Jupyter Notebook that uses the 1000 Functional Connectomes Project dataset. Moreover, we would like to highlight one part of our notebook dedicated to the realistic visualisation of high order interactions in brain networks. This pipeline provides three-dimensional (3-D) plots of pairwise and higher-order interactions projected in a brain atlas, a new feature tailor-made for network neuroscience.

Airway organoids are polarized 3D epithelial structures that recapitulate the organization and many of the key functions of the in vivo tissue. They present an attractive model that can overcome some of the limitations of traditional 2D and Air–Liquid Interface (ALI) models, yet the limited accessibility of the organoids’ apical side has hindered their applications in studies focusing on host–pathogen interactions. Here, we describe a scalable, fast and efficient way to generate airway organoids with the apical side externally exposed. These apical-out airway organoids are generated in an Extracellular Matrix (ECM)-free environment from 2D-expanded bronchial epithelial cells and differentiated in suspension to develop uniformly-sized organoid cultures with robust ciliogenesis. Differentiated apical-out airway organoids are susceptible to infection with common respiratory viruses and show varying responses upon treatment with antivirals. In addition to the ease of apical accessibility, these apical-out airway organoids offer an alternative in vitro model to study host–pathogen interactions in higher throughput than the traditional air–liquid interface model.

The brain is an extraordinarily complex system that facilitates the efficient integration of information from different regions to execute its functions. With the recent advances in technology, researchers can now collect enormous amounts of data from the brain using neuroimaging at different scales and from numerous modalities. With that comes the need for sophisticated tools for analysis. The field of network neuroscience has been trying to tackle these challenges, and graph theory has been one of its essential branches through the investigation of brain networks. Recently, topological data analysis has gained more attention as an alternative framework by providing a set of metrics that go beyond pair-wise connections and offer improved robustness against noise. In this hands-on tutorial, our goal is to provide the computational tools to explore neuroimaging data using these frameworks and to facilitate their accessibility, data visualisation, and comprehension for newcomers to the field. We will start by giving a concise (and by no means complete) overview of the field to introduce the two frameworks, and then explain how to compute both well-established and newer metrics on resting-state functional magnetic resonance imaging. We use an open-source language (Python) and provide an accompanying publicly available Jupyter Notebook that uses data from the 1000 Functional Connectomes Project. Moreover, we would like to highlight one part of our notebook that is solely dedicated to realistic visualisation of high order interactions in brain networks. This pipeline provides three-dimensional (3-D) plots of pair-wise and higher-order interactions projected in a brain atlas, a new feature tailor-made for network neuroscience.

Disease modelling plays a fundamental role in biomedical research, even more in virology where the virus depends strictly on its host for replication. Although animal models are extensively used in virology, there is an increasing demand for animal‐free research. Therefore, during this transition, it is crucial to learn and take advantage of animal research to better implement new emerging models. In this study, we aim to systematically review the translation from animal models to humans for the well‐characterized viral disease polio, as a reference for novel in vitro models in virology. We found a high risk of bias in the included studies and a large diversity of animal models. Moreover, we showed that animal models for studying poliovirus pathogenesis are mainly discrimination models focusing on specific aspects of the disease allowing an insightful understanding of the complex poliovirus infection. Our review underlines the importance of proper standardization of new emerging models and a careful interpretation of the results from discrimination models.

Respiratory syncytial virus (RSV) lower respiratory tract infection (LRTI) causes a major burden of disease. The host response in RSV-LRTI is characterized by airway epithelial injury, inflammation and neutrophil influx, with the formation of neutrophil extracellular traps (NETs). However, the precise role of NETs in the pathophysiology of RSV-LRTI remains to be elucidated. Here, we used well-differentiated human airway epithelial cultures (HAE) of a pediatric and adult donor to study whether NETs cause airway epithelial injury and inflammation in the setting of RSV infection. The exposure of uninfected and RSV-infected HAE cultures to NETs, as produced by stimulation of neutrophils by a low dose of phorbol 12-myristate 13-acetate (PMA), did not induce or aggravate cell injury or inflammation. RSV infection of HAE cultures caused release of pro-inflammatory cytokines such as IL-6 and RANTES in both adult and pediatric cultures, but the differential gene expression for regulated cell death differed between culture donors. In this in vitro airway epithelial model, NETs in the setting of RSV infection did not cause or aggravate epithelial injury or inflammation.

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