Search

To understand the determinants of long-term immune responses to severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) and the concurrent impact of vaccination and emerging variants, we follow a prospective cohort of 332 patients with coronavirus disease 2019 (COVID-19) over more than a year after symptom onset. We evaluate plasma-neutralizing activity using HIV-based pseudoviruses expressing the spike of different SARS-CoV-2 variants and analyze them longitudinally using mixed-effects models. Long-term neutralizing activity is stable beyond 1 year after infection in mild/asymptomatic and hospitalized participants. However, longitudinal models suggest that hospitalized individuals generate both short- and long-lived memory B cells, while the responses of non-hospitalized individuals are dominated by long-lived B cells. In both groups, vaccination boosts responses to natural infection. Long-term (>300 days from infection) responses in unvaccinated participants show a reduced efficacy against beta, but not alpha nor delta, variants. Multivariate analysis identifies the severity of primary infection as an independent determinant of higher magnitude and lower relative cross-neutralization activity of long-term neutralizing responses., Graphical abstract, Pradenas and Trinité et al. analyze the kinetics of neutralizing antibodies in response to natural SARS-CoV-2 infection and evaluate the long-term (beyond 1 year) stability and breadth of the neutralizing response before subsequent vaccination.

Cancer progression is a complex phenomenon that spans multiple scales from molecular to cellular and intercellular. Simulations can be used to perturb the underlying mechanisms of those systems and to generate hypotheses on novel therapies. We present a new version of PhysiBoSS, a multiscale modelling framework designed to cover multiple temporal and spatial scales, that improves its integration with PhysiCell, decoupling the cell agent simulations with the internal Boolean model in an easy-to-maintain computational framework. PhysiBoSS 2.0 is a redesign and reimplementation of PhysiBoSS, conceived as an add-on that expands the PhysiCell agent-based functionalities with intracellular cell signalling using MaBoSS having a decoupled, maintainable and model-agnostic design. PhysiBoSS 2.0 successfully reproduces simulations reported in the former version and expands its functionalities such as using user-defined models and cells' specifications, having mechanistic submodels of substrate internalisation with ODEs and enabling the study of drug synergies. PhysiBoSS 2.0 is open-source and publicly available on GitHub (https://github.com/PhysiBoSS/PhysiBoSS) under the BSD 3-clause license with several repositories of accompanying interoperable tools. Additionally, a nanoHUB tool has been set up to ease the use of PhysiBoSS 2.0 (https://nanohub.org/tools/pba4tnf/).

Importance Autism spectrum disorder (ASD) and attention-deficit/hyperactivity disorder (ADHD) are childhood-onset disorders that may persist into adulthood. Several studies have suggested that they may be associated with an increased risk of mortality; however, the results are inconsistent. Objective To assess the risk of mortality among persons with ASD or ADHD and their first-degree relatives. Data Sources A search of MEDLINE, Embase, Scopus, Web of Science, and PsycINFO (published from inception to April 1, 2021) was supplemented by searching reference lists of the retrieved articles. Study Selection Cohort and case-control studies that reported mortality rate ratios (RRs) in persons with ASD or ADHD and/or their first-degree relatives compared with the general population or those without ASD/ADHD were included. Data Extraction and Synthesis Screening, data extraction, and quality assessment were performed by at least 2 researchers independently. A random-effects model was used to meta-analyze individual studies and assessed heterogeneity (I2). Main Outcomes and Measures All-cause mortality in association with ASD or ADHD. Secondary outcome was cause-specific mortality. Results Twenty-seven studies were included, with a total of 642 260 individuals. All-cause mortality was found to be higher for persons with ASD (154 238 participants; 12 studies; RR, 2.37; 95% CI, 1.97-2.85; I2, 89%; moderate confidence) and persons with ADHD (396 488 participants; 8 studies; RR, 2.13; 95% CI, 1.13-4.02; I2, 98%; low confidence) than for the general population. Among persons with ASD, deaths from natural causes (4 studies; RR, 3.80; 95% CI, 2.06-7.01; I2, 96%; low confidence) and deaths from unnatural causes were increased (6 studies; RR, 2.50; 95% CI, 1.49-4.18; I2, 95%; low confidence). Among persons with ADHD, deaths from natural causes were not significantly increased (4 studies; RR, 1.62; 95% CI, 0.89-2.96; I2, 88%; low confidence), but deaths from unnatural causes were higher than expected (10 studies; RR, 2.81; 95% CI, 1.73-4.55; I2, 92%; low confidence). Conclusions and Relevance This systematic review and meta-analysis found that ASD and ADHD are associated with a significantly increased risk of mortality. Understanding the mechanisms of these associations may lead to targeted strategies to prevent avoidable deaths in high-risk groups. The substantial heterogeneity between studies should be explored further. This study was supported by the Institute of Health Carlos III and Generalitat Valenciana. Drs Catalá-López and Tabarés-Seisdedos received funding from the Centro de Investigación Biomédica en Red de Salud Mental, Institute of Health Carlos III, and Generalitat Valencia. Dr Page received support from an Australian Research Council Discovery Early Career Researcher Award. Dr Hutton received support from a new investigator award from the Canadian Institutes of Health Research and the Drug Safety and Effectiveness Network. Dr Ridao received support from the Spanish Health Services Research on Chronic Patients Network and Institute of Health Carlos III.

Sección: A fondo [ES] “¿Las máquinas pueden pensar?”. Esta pregunta se la formuló Alan Turing1, considerado como el padre de la computación, ya en el año 1950 (Turing, 1950). Al mismo tiempo, formuló un pequeño juego al que acuñó el nombre de “juego de las imitaciones”. El juego consiste en que una persona A interactúa con una máquina B y una persona C, e intentará adivinar cuál de ellos es la máquina. A no tiene acceso visual ni sonoro a B ni a C: sólo se puede comunicar a través de una terminal con ambas. El nombre de “imitación” se refiere a que la máquina B intentará replicar el comportamiento de la persona C. Este juego ha pasado a conocerse como el Test de Turing, que además ha extendido la idea básica del juego de las imitaciones: ¿podemos distinguir entre una persona y una máquina, por ejemplo, durante una conversación por mensajes de texto? ¿Y durante una llamada telefónica? Puede que un día tengamos incluso que preguntarnos si podemos distinguir a una persona de un robot.

The regulation of gene transcription by transcription factors is a fundamental biological process, yet the relations between transcription factors (TF) and their target genes (TG) are still only sparsely covered in databases. Text-mining tools can offer broad and complementary solutions to help locate and extract mentions of these biological relationships in articles. We have generated ExTRI, a knowledge graph of TF-TG relationships, by applying a high recall text-mining pipeline to MedLine abstracts identifying over 100,000 candidate sentences with TF-TG relations. Validation procedures indicated that about half of the candidate sentences contain true TF-TG relationships. Post-processing identified 53,000 high confidence sentences containing TF-TG relationships, with a cross-validation F1-score close to 75%. The resulting collection of TF-TG relationships covers 80% of the relations annotated in existing databases. It adds 11,000 other potential interactions, including relationships for ~100 TFs currently not in public TF-TG relation databases. The high confidence abstract sentences contribute 25,000 literature references not available from other resources and offer a wealth of direct pointers to functional aspects of the TF-TG interactions. Our compiled resource encompassing ExTRI together with publicly available resources delivers literature-derived TF-TG interactions for more than 900 of the 1500–1600 proteins considered to function as specific DNA binding TFs. The obtained result can be used by curators, for network analysis and modelling, for causal reasoning or knowledge graph mining approaches, or serve to benchmark text mining strategies. We thank the participants of the COST Action GREEKC (CA15205) for fruitful discussions during workshops supported by COST (European Cooperation in Science and Technology).

The world has gone through unprecedented changes since the global pandemic hit. During the early phase of the pandemic, the absence of known drugs or pharmaceutical treatments forced governments to introduce different policies in order to help reduce contagion rates and manage the economic consequences of the pandemic. This paper analyses the causal impact on mobility and COVID19 incidence from policy makers in Cataluña, Spain. We use annonimized phone-based mobility data together with reported incidence and apply a series of causal impact models frequently used in econometrics and policy evaluation in order to measure the policies impact.. We analyse the case of Cataluña and the public policy decision of closing all bars and restaurants down for a 5 week period between the 2020-16-10 to 2020-23-11. We find that this decision led to a significant reduction in mobility. It not only led to reductions in mobility but from a behavioural economics standpoint we highlight how people responded to the policy decision. Moreover, the policy of closing bars and restaurants slowed the incidence rate of COVID19 after a time lag has been taken into account. These finding are significant since governments worldwide want to restrict movements of people in order to slow down COVID19 incidence without infringing on their rights directly.

Background Neoantigens’ (neoAg) identification, which determines T-cell responses against tumors, has fostered the development of personalized vaccines with promising results. While the ranking of the most immunogenic neoAg can be addressed using predictive techniques, their formulation as vaccines needs to be improved. To maximize their therapeutic potential, optimal neoAg-based vaccines should be manufactured in a superb delivery platform that enhances robust new immune responses, able to bypass thymic tolerance and the humoral immunosuppressive microenvironment. These novel T cell responses generated at the periphery will not be exhausted, opposite to TILs. We aim to develop a highly immunogenic vaccine platform, based on engineered HIV-derived Virus-Like Particles (VLP) expressing approximately 2500 copies of each selected neoAg. We tested different neoAgs loaded VLPs (neoVLP) in a melanoma mouse model to evaluate their capability to generate new immunogenic specificities. Material and Methods Specific non-synonymous mutations from B16F10 cells were identified, selected and used to generate a list of prioritized peptides. NeoAgs were classified as: Tier1, acquiring a mutation that creates an anchor residue to the MHC-I, not present in the WT peptide; Tier2, acquiring a mutation in a position that largely impacts contact with the TCR respect to WT; and Tier3, acquiring a mutation in the TCR contact region but inducing a less drastic change than in Tier2. Frame shift (FS) mutations, expected to be highly immunogenic, were also included.Thirteen to fifteen selected neoAgs from each group were loaded on highly immunogenic neoVLPs. Their immunogenicity was evaluated in C57bl/6 mice by immunization with a neoVLP-coding plasmid DNA (prime) and purified neoVLPs as soluble particles (boost). Splenocytes were used to evaluate neoAg-specific T cell responses. Results We have successfully generated and purified neoVLPs, exposing neoAgs from all groups by transient transfection of Expi293 cells. Protein integrity and VLP morphology were confirmed by western blot and cryo-EM. When used for immunization assays, neoVLPs, containing neoAgs from Tier2, Tier3 and FS groups, were capable of generating humoral responses against viral proteins and T cell responses against neoAgs present in the neoVLP. B16F10 inoculated animals, but not vaccinated, did not develop detectable T cell responses against neoAgs present in any tested neoVLP, suggesting that the vaccination with neoVLPs promoted new specificities against selected neoAgs that might contribute to tumor control and eradication. Conclusion Our data show that the neoVLPs promote the generation of new antitumor-specific immune responses against selected neoepitopes, suggesting that neoVLPs vaccination could be an alternative to current therapeutic vaccine approaches and a promising candidate for future personalized immunotherapy. Disclosure Information C. Aguilar-Gurrieri: None. A. Barajas: None. A. Pons-Grifols: None. I. Varela: None. P. Amengual-Rigo: None. R. Farriol: None. M. Vazquez: None. R. Lepore: None. C. Anjos-Souza: None. J. Blanco-Heredia: None. L. de Mattos-Arruda: None. V. Guallar: None. A. Valencia: None. B. Clotet: F. Consultant/Advisory Board; Significant; Albajuna Therapeutics. J. Carrillo: Other; Significant; Albajuna Therapeutics. J. Blanco: F. Consultant/Advisory Board; Significant; Albajuna Therapeutics.

Chromatin structure and organization has a key role in gene expression regulation. Here, we integrated ChIP-seq, RNA-seq, Hi-C, epigenetic, and cancer-related mutations data to get insight into the role of Death Inducer Obliterator gene (Dido1) in RNA pol II (RNAPII) transcription and chromatin structure regulation. Analysis of ChIP-seq data of DIDO3, the largest protein isoform of Dido1, revealed binding-sites overlap about 70% with RNAPII and H3K36me3 in the mouse genome, but also significant overlap 10-30% with Polycomb, CTCF, H3K4me3, and H3K27ac. Based on this analysis we propose that DIDO3’s PHD domain interacts with H3K36me3 posttranslational modification. Integrating multi-omics data we describe how DIDO3 potentially recruit several transcription factors, including RNAPII, and also regulates genes transcribing those same transcription factors. DIDO3 regulation of the genes traduced into proteins to which it binds puts DIDO3 in the center of intricate feedback loops. We showed, by using data from a DIDO3 mutant, that DIDO3 C-terminus is responsible for most of these transcriptional regulation, and is also implicated in other very important pathways by regulating genes encoding for Polycomb-accessory proteins, subunits of the SWI/SNF chromatin remodelling, or Set1/COMPASS chromatin modifier complexes. These multi-protein complexes control gene activation or silencing and also play a role in tumour development. DIDO3 C-terminus region and splice-site for alternative DIDO2/DIDO3 protein isoforms tended to accumulate recurrent truncating mutations identified in the TCGA Pan-Cancer dataset. We hypothesize that deregulation of DIDO3, as it happens with large epigenetic complexes and long-range interactions, leads to cell differentiation deficiency and cancer development. Overall, we propose here a molecular mechanism by which DIDO3, favour RNAPII pausing and long-range chromatin interactions.

BackgroundUnderstanding the determinants of long-term immune responses to SARS-CoV-2 and the concurrent impact of vaccination and emerging variants of concern will guide optimal strategies to achieve global protection against the COVID-19 pandemic.MethodsA prospective cohort of 332 COVID-19 patients was followed beyond one year. Plasma neutralizing activity was evaluated using HIV-based reporter pseudoviruses expressing different SARS-CoV-2 spikes and was longitudinally analyzed using mixed-effects models.FindingsLong-term neutralizing activity was stable beyond one year after infection in mild/asymptomatic and hospitalized participants. However, longitudinal models suggest that hospitalized individuals generate both short- and long-lived memory B cells, while outpatient responses were dominated by long-lived B cells. In both groups, vaccination boosted responses to natural infection, although viral variants, mainly B.1.351, reduced the efficacy of neutralization. Importantly, despite showing higher neutralization titers, hospitalized patients showed lower cross-neutralization of B.1.351 variant compared to outpatients. Multivariate analysis identified severity of primary infection as the factor that independently determines both the magnitude and the inferior cross-neutralization activity of long-term neutralizing responses.ConclusionsNeutralizing response induced by SARS-CoV-2 is heterogeneous in magnitude but stable beyond one year after infection. Vaccination boosts these long-lasting natural neutralizing responses, counteracting the significant resistance to neutralization of new viral variants. Severity of primary infection determines higher magnitude but poorer quality of long-term neutralizing responses.

SARS-CoV-2 variants display enhanced transmissibility and/or immune evasion and can be generated in humans or animals, like minks, thus generating new reservoirs. The continuous surveillance of animal susceptibility to new variants is necessary to predict pandemic evolution. In this study we demonstrate that, compared to the B.1 SARS-CoV-2 variant, K18-hACE2 transgenic mice challenged with the B.1.351 variant displayed a faster progression of infection. Furthermore, we also report that B.1.351 can establish infection in wildtype mice, while B.1 cannot. B.1.351-challenged wildtype mice showed a milder infection than transgenic mice, confirmed by detectable viral loads in oropharyngeal swabs and tissues, lung pathology, immunohistochemistry and serology. In silico models supported these findings by demonstrating that the Spike mutations in B.1.351 resulted in increased affinity for both human and murine ACE2 receptors. Overall, this study highlights the plasticity of SARS-CoV-2 animal susceptibility landscape, which may contribute to viral persistence and expansion.

The Protein Data Bank in Europe-Knowledge Base (PDBe-KB, https://pdbe-kb.org) is a community-driven, collaborative resource for literature-derived, manually curated and computationally predicted structural and functional annotations of macromolecular structure data, contained in the Protein Data Bank (PDB). The goal of PDBe-KB is two-fold: (i) to increase the visibility and reduce the fragmentation of annotations contributed by specialist data resources, and to make these data more findable, accessible, interoperable and reusable (FAIR) and (ii) to place macromolecular structure data in their biological context, thus facilitating their use by the broader scientific community in fundamental and applied research. Here, we describe the guidelines of this collaborative effort, the current status of contributed data, and the PDBe-KB infrastructure, which includes the data exchange format, the deposition system for added value annotations, the distributable database containing the assembled data, and programmatic access endpoints. We also describe a series of novel web-pages—the PDBe-KB aggregated views of structure data—which combine information on macromolecular structures from many PDB entries. We have recently released the first set of pages in this series, which provide an overview of available structural and functional information for a protein of interest, referenced by a UniProtKB accession.