Tracking the immune response profiles elicited by the BNT162b2 vaccine in COVID-19 unexperienced and experienced individuals.

Multiple vaccines have been approved to control COVID-19 pandemic, with Pfizer/BioNTech (BNT162b2) being widely used. We conducted a longitudinal analysis of the immune response elicited after three doses of the BNT162b2 vaccine in individuals who have previously experienced SARS-CoV-2 infection and in unexperienced ones. We conducted immunological analyses and single-cell transcriptomics of circulating T and B lymphocytes, combined to CITE-seq or LIBRA-seq, and VDJ-seq. We found that antibody levels against SARS-CoV-2 Spike, NTD and RBD from wild-type, delta and omicron VoCs show comparable dynamics in both vaccination groups, with a peak after the second dose, a decline after six months and a restoration after the booster dose. The antibody neutralization activity was maintained, with lower titers against the omicron variant. Spike-specific memory B cell response was sustained over the vaccination schedule. Clonal analysis revealed that Spike-specific B cells were polyclonal, with a partial clone conservation from natural infection to vaccination. Spike-specific T cell responses were oriented towards effector and effector memory phenotypes, with similar trends in unexperienced and experienced individuals. The CD8 T cell compartment showed a higher clonal expansion and persistence than CD4 T cells. The first two vaccinations doses tended to induce new clones rather than promoting expansion of pre-existing clones. However, we identified a fraction of Spike-specific CD8 T cell clones persisting from natural infection that were boosted by vaccination and clones specifically induced by vaccination. Collectively, our observations revealed a moderate effect of the second dose in enhancing the immune responses elicited after the first vaccination. Differently, we found that a third dose was necessary to restore comparable levels of neutralizing antibodies and Spike-specific T and B cell responses in individuals who experienced a natural SARS-CoV-2 infection.

Deep Phenotyping of T-Cells Derived From the Aneurysm Wall in a Pediatric Case of Subarachnoid Hemorrhage.

Intracranial aneurysms (IAs) are very rare in children, and the characteristics of the T-cells in the IA wall are largely unknown. A comatose 7-years-old child was admitted to our center because of a subarachnoid hemorrhage due to a ruptured giant aneurysm of the right middle cerebral artery. Two days after the aneurysm clipping the patient was fully awake with left hemiparesis. T-cells from the IA wall and from peripheral blood of this patient were analyzed by multi-dimensional flow cytometry. Unbiased analysis, based on the use of FlowSOM clustering and dimensionality reduction technique UMAP, indicated that there was virtually no overlap between circulating and tissue-infiltrating T-cells. Thus, naïve T-cells and canonical memory T-cells were largely restricted to peripheral blood, while CD4-CD8-T-cells were strongly enriched in the IA wall. The unique CD4+, CD8+ and CD4-CD8-T-cell clusters from the IA wall expressed high levels of CCR5, Granzyme B and CD69, displaying thus characteristics of cytotoxic and tissue-resident effector cells. Low Ki67 expression indicated that they were nevertheless in a resting state. Among regulatory T-cell subsets, Eomes+Tr1-like cells were strongly enriched in the IA wall. Finally, analysis of cytokine producing capacities unveiled that the IA wall contained poly-functional T-cells, which expressed predominantly IFN-γ, TNF and IL-2. CD4+T-cells co-expressed also CD40L, and produced some IL-17, GM-CSF and IL-10. This report provides to our knowledge the first detailed characterization of the human T-cell compartment in the IA wall.

Identification, semantic annotation and comparison of combinations of functional elements in multiple biological conditions.

MOTIVATION: Approaches such as chromatin immunoprecipitation followed by sequencing (ChIP-seq) represent the standard for the identification of binding sites of DNA-associated proteins, including transcription factors and histone marks. Public repositories of omics data contain a huge number of experimental ChIP-seq data, but their reuse and integrative analysis across multiple conditions remain a daunting task. RESULTS: We present the Combinatorial and Semantic Analysis of Functional Elements (CombSAFE), an efficient computational method able to integrate and take advantage of the valuable and numerous, but heterogeneous, ChIP-seq data publicly available in big data repositories. Leveraging natural language processing techniques, it integrates omics data samples with semantic annotations from selected biomedical ontologies; then, using hidden Markov models, it identifies combinations of static and dynamic functional elements throughout the genome for the corresponding samples. CombSAFE allows analyzing the whole genome, by clustering patterns of regions with similar functional elements and through enrichment analyses to discover ontological terms significantly associated with them. Moreover, it allows comparing functional states of a specific genomic region to analyze their different behavior throughout the various semantic annotations. Such findings can provide novel insights by identifying unexpected combinations of functional elements in different biological conditions. AVAILABILITY AND IMPLEMENTATION: The Python implementation of the CombSAFE pipeline is freely available for non-commercial use at: https://github.com/DEIB-GECO/CombSAFE. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Integrated longitudinal immunophenotypic, transcriptional and repertoire analyses delineate immune responses in COVID-19 patients.

To understand how a protective immune response against SARS-CoV-2 develops over time, we integrated phenotypic, transcriptional and repertoire analyses on PBMCs from mild and severe COVID-19 patients during and after infection, and compared them to healthy donors (HD). A type I IFN-response signature marked all the immune populations from severe patients during the infection. Humoral immunity was dominated by IgG production primarily against the RBD and N proteins, with neutralizing antibody titers increasing post infection and with disease severity. Memory B cells, including an atypical FCRL5+ T-BET+ memory subset, increased during the infection, especially in patients with mild disease. A significant reduction of effector memory, CD8+ T cells frequency characterized patients with severe disease. Despite such impairment, we observed robust clonal expansion of CD8+ T lymphocytes, while CD4+ T cells were less expanded and skewed toward TCM and TH2-like phenotypes. MAIT cells were also expanded, but only in patients with mild disease. Terminally differentiated CD8+ GZMB+ effector cells were clonally expanded both during the infection and post-infection, while CD8+ GZMK+ lymphocytes were more expanded post-infection and represented bona fide memory precursor effector cells. TCR repertoire analysis revealed that only highly proliferating T cell clonotypes, which included SARS-CoV-2-specific cells, were maintained post-infection and shared between the CD8+ GZMB+ and GZMK+ subsets. Overall, this study describes the development of immunity against SARS-CoV-2 and identifies an effector CD8+ T cell population with memory precursor-like features.

Genome-wide dynamics of RNA synthesis, processing, and degradation without RNA metabolic labeling.

The quantification of the kinetic rates of RNA synthesis, processing, and degradation are largely based on the integrative analysis of total and nascent transcription, the latter being quantified through RNA metabolic labeling. We developed INSPEcT-, a computational method based on the mathematical modeling of premature and mature RNA expression that is able to quantify kinetic rates from steady-state or time course total RNA-seq data without requiring any information on nascent transcripts. Our approach outperforms available solutions, closely recapitulates the kinetic rates obtained through RNA metabolic labeling, improves the ability to detect changes in transcript half-lives, reduces the cost and complexity of the experiments, and can be adopted to study experimental conditions in which nascent transcription cannot be readily profiled. Finally, we applied INSPEcT- to the characterization of post-transcriptional regulation landscapes in dozens of physiological and disease conditions. This approach was included in the INSPEcT Bioconductor package, which can now unveil RNA dynamics from steady-state or time course data, with or without the profiling of nascent RNA.

Ontology-driven integrative analysis of omics data through Onassis.

Public repositories of large-scale omics datasets represent a valuable resource for researchers. In fact, data re-analysis can either answer novel questions or provide critical data able to complement in-house experiments. However, despite the development of standards for the compilation of metadata, the identification and organization of samples still constitutes a major bottleneck hampering data reuse. We introduce Onassis, an R package within the Bioconductor environment providing key functionalities of Natural Language Processing (NLP) tools. Leveraging biomedical ontologies, Onassis greatly simplifies the association of samples from large-scale repositories to their representation in terms of ontology-based annotations. Moreover, through the use of semantic similarity measures, Onassis hierarchically organizes the datasets of interest, thus supporting the semantically aware analysis of the corresponding omics data. In conclusion, Onassis leverages NLP techniques, biomedical ontologies, and the R statistical framework, to identify, relate, and analyze datasets from public repositories. The tool was tested on various large-scale datasets, including compendia of gene expression, histone marks, and DNA methylation, illustrating how it can facilitate the integrative analysis of various omics data.

A dual role of dLsd1 in oogenesis: regulating developmental genes and repressing transposons.

The histone demethylase LSD1 is a key chromatin regulator that is often deregulated in cancer. Its ortholog, dLsd1 plays a crucial role in Drosophila oogenesis; however, our knowledge of dLsd1 function is insufficient to explain its role in the ovary. Here, we have performed genome-wide analysis of dLsd1 binding in the ovary, and we document that dLsd1 is preferentially associated to the transcription start site of developmental genes. We uncovered an unanticipated interplay between dLsd1 and the GATA transcription factor Serpent and we report an unexpected role for Serpent in oogenesis. Besides, our transcriptomic data show that reducing dLsd1 levels results in ectopic transposable elements (TE) expression correlated with changes in H3K4me2 and H3K9me2 at TE loci. In addition, our results suggest that dLsd1 is required for Piwi dependent TE silencing. Hence, we propose that dLsd1 plays crucial roles in establishing specific gene expression programs and in repressing transposons during oogenesis.

m6A-Dependent RNA Dynamics in T Cell Differentiation.

N6-methyladenosine (m6A) is the most abundant RNA modification. It has been involved in the regulation of RNA metabolism, including degradation and translation, in both physiological and disease conditions. A recent study showed that m6A-mediated degradation of key transcripts also plays a role in the control of T cells homeostasis and IL-7 induced differentiation. We re-analyzed the omics data from that study and, through the integrative analysis of total and nascent RNA-seq data, we were able to comprehensively quantify T cells RNA dynamics and how these are affected by m6A depletion. In addition to the expected impact on RNA degradation, we revealed a broader effect of m6A on RNA dynamics, which included the alteration of RNA synthesis and processing. Altogether, the combined action of m6A on all major steps of the RNA life-cycle closely re-capitulated the observed changes in the abundance of premature and mature RNA species. Ultimately, our re-analysis extended the findings of the initial study, focused on RNA stability, and proposed a yet unappreciated role for m6A in RNA synthesis and processing dynamics.

Ontology-based annotations and semantic relations in large-scale (epi)genomics data.

Public repositories of large-scale biological data currently contain hundreds of thousands of experiments, including high-throughput sequencing and microarray data. The potential of using these resources to assemble data sets combining samples previously not associated is vastly unexplored. This requires the ability to associate samples with clear annotations and to relate experiments matched with different annotation terms. In this study, we illustrate the semantic annotation of Gene Expression Omnibus samples metadata using concepts from biomedical ontologies, focusing on the association of thousands of chromatin immunoprecipitation followed by high-throughput sequencing (ChIP-seq) samples with a given target, tissue and disease state. Next, we demonstrate the feasibility of quantitatively measuring the semantic similarity between different samples, with the aim of combining experiments associated with the same or similar semantic annotations, thus allowing the generation of large data sets without the need of additional experiments. We compared tools based on Unified Medical Language System with tools that use topic-specific ontologies, showing that the second approach outperforms the first both in the annotation process and in the computation of semantic similarity measures. Finally, we demonstrated the potential of this approach by identifying semantically homogeneous groups of ChIP-seq samples targeting the Myc transcription factor, and expanding this data set with semantically coherent epigenetic samples. The semantic information of these data sets proved to be coherent with the ChIP-seq signal and with the current knowledge about this transcription factor.

Integrated Systems for NGS Data Management and Analysis: Open Issues and Available Solutions.

Next-generation sequencing (NGS) technologies have deeply changed our understanding of cellular processes by delivering an astonishing amount of data at affordable prices; nowadays, many biology laboratories have already accumulated a large number of sequenced samples. However, managing and analyzing these data poses new challenges, which may easily be underestimated by research groups devoid of IT and quantitative skills. In this perspective, we identify five issues that should be carefully addressed by research groups approaching NGS technologies. In particular, the five key issues to be considered concern: (1) adopting a laboratory management system (LIMS) and safeguard the resulting raw data structure in downstream analyses; (2) monitoring the flow of the data and standardizing input and output directories and file names, even when multiple analysis protocols are used on the same data; (3) ensuring complete traceability of the analysis performed; (4) enabling non-experienced users to run analyses through a graphical user interface (GUI) acting as a front-end for the pipelines; (5) relying on standard metadata to annotate the datasets, and when possible using controlled vocabularies, ideally derived from biomedical ontologies. Finally, we discuss the currently available tools in the light of these issues, and we introduce HTS-flow, a new workflow management system conceived to address the concerns we raised. HTS-flow is able to retrieve information from a LIMS database, manages data analyses through a simple GUI, outputs data in standard locations and allows the complete traceability of datasets, accompanying metadata and analysis scripts.

The hierarchical organization of natural protein interaction networks confers self-organization properties on pseudocells.

BACKGROUND: Cell organization is governed and maintained via specific interactions among its constituent macromolecules. Comparison of the experimentally determined protein interaction networks in different model organisms has revealed little conservation of the specific edges linking ortholog proteins. Nevertheless, some topological characteristics of the graphs representing the networks--namely non-random degree distribution and high clustering coefficient--are shared by networks of distantly related organisms. Here we investigate the role of the topological features of the protein interaction network in promoting cell organization. METHODS: We have used a stochastic model, dubbed ProtNet representing a computer stylized cell to answer questions about the dynamic consequences of the topological properties of the static graphs representing protein interaction networks. RESULTS: By using a novel metrics of cell organization, we show that natural networks, differently from random networks, can promote cell self-organization. Furthermore the ensemble of protein complexes that forms in pseudocells, which self-organize according to the interaction rules of natural networks, are more robust to perturbations. CONCLUSIONS: The analysis of the dynamic properties of networks with a variety of topological characteristics lead us to conclude that self organization is a consequence of the high clustering coefficient, whereas the scale free degree distribution has little influence on this property.

The MIntAct project--IntAct as a common curation platform for 11 molecular interaction databases.

IntAct (freely available at http://www.ebi.ac.uk/intact) is an open-source, open data molecular interaction database populated by data either curated from the literature or from direct data depositions. IntAct has developed a sophisticated web-based curation tool, capable of supporting both IMEx- and MIMIx-level curation. This tool is now utilized by multiple additional curation teams, all of whom annotate data directly into the IntAct database. Members of the IntAct team supply appropriate levels of training, perform quality control on entries and take responsibility for long-term data maintenance. Recently, the MINT and IntAct databases decided to merge their separate efforts to make optimal use of limited developer resources and maximize the curation output. All data manually curated by the MINT curators have been moved into the IntAct database at EMBL-EBI and are merged with the existing IntAct dataset. Both IntAct and MINT are active contributors to the IMEx consortium (http://www.imexconsortium.org).

A new reference implementation of the PSICQUIC web service.

The Proteomics Standard Initiative Common QUery InterfaCe (PSICQUIC) specification was created by the Human Proteome Organization Proteomics Standards Initiative (HUPO-PSI) to enable computational access to molecular-interaction data resources by means of a standard Web Service and query language. Currently providing >150 million binary interaction evidences from 28 servers globally, the PSICQUIC interface allows the concurrent search of multiple molecular-interaction information resources using a single query. Here, we present an extension of the PSICQUIC specification (version 1.3), which has been released to be compliant with the enhanced standards in molecular interactions. The new release also includes a new reference implementation of the PSICQUIC server available to the data providers. It offers augmented web service capabilities and improves the user experience. PSICQUIC has been running for almost 5 years, with a user base growing from only 4 data providers to 28 (April 2013) allowing access to 151 310 109 binary interactions. The power of this web service is shown in PSICQUIC View web application, an example of how to simultaneously query, browse and download results from the different PSICQUIC servers. This application is free and open to all users with no login requirement (http://www.ebi.ac.uk/Tools/webservices/psicquic/view/main.xhtml).

MINT, the molecular interaction database: 2012 update.

The Molecular INTeraction Database (MINT, http://mint.bio.uniroma2.it/mint/) is a public repository for protein-protein interactions (PPI) reported in peer-reviewed journals. The database grows steadily over the years and at September 2011 contains approximately 235,000 binary interactions captured from over 4750 publications. The web interface allows the users to search, visualize and download interactions data. MINT is one of the members of the International Molecular Exchange consortium (IMEx) and adopts the Molecular Interaction Ontology of the Proteomics Standard Initiative (PSI-MI) standards for curation and data exchange. MINT data are freely accessible and downloadable at http://mint.bio.uniroma2.it/mint/download.do. We report here the growth of the database, the major changes in curation policy and a new algorithm to assign a confidence to each interaction.