Characteristics and anatomic location of PD-1+TCF1+ stem-like CD8 T cells in chronic viral infection and cancer.

CD8 T cells play an essential role in antitumor immunity and chronic viral infections. Recent findings have delineated the differentiation pathway of CD8 T cells in accordance with the progenitor-progeny relationship of TCF1+ stem-like and Tim-3+TCF1- more differentiated T cells. Here, we investigated the characteristics of stem-like and differentiated CD8 T cells isolated from several murine tumor models and human lung cancer samples in terms of phenotypic and transcriptional features as well as their location compared to virus-specific CD8 T cells in the chronically lymphocytic choriomeningitis virus (LCMV)-infected mice. We found that CD8 tumor-infiltrating lymphocytes (TILs) in both murine and human tumors exhibited overall similar phenotypic and transcriptional characteristics compared to corresponding subsets in the spleen of chronically infected mice. Moreover, stem-like CD8 TILs exclusively responded and produced effector-like progeny CD8 T cells in vivo after antigenic restimulation, confirming their lineage relationship and the proliferative potential of stem-like CD8 TILs. Most importantly, similar to the preferential localization of PD-1+ stem-like CD8 T cells in T cell zones of the spleen during chronic LCMV infection, we found that the PD-1+ stem-like CD8 TILs in lung cancer samples are preferentially located not in the tumor parenchyma but in tertiary lymphoid structures (TLSs). The stem-like CD8 T cells are present in TLSs located within and at the periphery of the tumor, as well as in TLSs closely adjacent to the tumor parenchyma. These findings suggest that TLSs provide a protective niche to support the quiescence and maintenance of stem-like CD8 T cells in the tumor.

OUTBREAK: a user-friendly georeferencing online tool for disease surveillance.

The current COVID-19 pandemic has already claimed more than 3.7 million victims and it will cause more deaths in the coming months. Tools that track the number and locations of cases are critical for surveillance and help in making policy decisions for controlling the outbreak. However, the current surveillance web-based dashboards run on proprietary platforms, which are often expensive and require specific computational knowledge. We developed a user-friendly web tool, named OUTBREAK, that facilitates epidemic surveillance by showing in an animated graph the timeline and geolocations of cases of an outbreak. It permits even non-specialist users to input data most conveniently and track outbreaks in real-time. We applied our tool to visualize the SARS 2003, MERS, and COVID19 epidemics, and provided them as examples on the website. Through the zoom feature, it is also possible to visualize cases at city and even neighborhood levels. We made the tool freely available at https://outbreak.sysbio.tools/ . OUTBREAK has the potential to guide and help health authorities to intervene and minimize the effects of outbreaks.

OUTBREAK: a user-friendly georeferencing online tool for disease surveillance

The current COVID-19 pandemic has already claimed more than 3.7 million victims and it will cause more deaths in the coming months. Tools that track the number and locations of cases are critical for surveillance and help in making policy decisions for controlling the outbreak. However, the current surveillance web-based dashboards run on proprietary platforms, which are often expensive and require specific computational knowledge. We developed a user-friendly web tool, named OUTBREAK, that facilitates epidemic surveillance by showing in an animated graph the timeline and geolocations of cases of an outbreak. It permits even non-specialist users to input data most conveniently and track outbreaks in real-time. We applied our tool to visualize the SARS 2003, MERS, and COVID19 epidemics, and provided them as examples on the website. Through the zoom feature, it is also possible to visualize cases at city and even neighborhood levels. We made the tool freely available at https://outbreak.sysbio.tools/. OUTBREAK has the potential to guide and help health authorities to intervene and minimize the effects of outbreaks.

Long noncoding RNAs are involved in multiple immunological pathways in response to vaccination.

Understanding the mechanisms of vaccine-elicited protection contributes to the development of new vaccines. The emerging field of systems vaccinology provides detailed information on host responses to vaccination and has been successfully applied to study the molecular mechanisms of several vaccines. Long noncoding RNAs (lncRNAs) are crucially involved in multiple biological processes, but their role in vaccine-induced immunity has not been explored. We performed an analysis of over 2,000 blood transcriptome samples from 17 vaccine cohorts to identify lncRNAs potentially involved with antibody responses to influenza and yellow fever vaccines. We have created an online database where all results from this analysis can be accessed easily. We found that lncRNAs participate in distinct immunological pathways related to vaccine-elicited responses. Among them, we showed that the expression of lncRNA FAM30A was high in B cells and correlates with the expression of immunoglobulin genes located in its genomic vicinity. We also identified altered expression of these lncRNAs in RNA-sequencing (RNA-seq) data from a cohort of children following immunization with intranasal live attenuated influenza vaccine, suggesting a common role across several diverse vaccines. Taken together, these findings provide evidence that lncRNAs have a significant impact on immune responses induced by vaccination.

webCEMiTool: Co-expression Modular Analysis Made Easy.

Co-expression analysis has been widely used to elucidate the functional architecture of genes under different biological processes. Such analysis, however, requires substantial knowledge about programming languages and/or bioinformatics skills. We present webCEMiTool, a unique online tool that performs comprehensive modular analyses in a fully automated manner. The webCEMiTool not only identifies co-expression gene modules but also performs several functional analyses on them. In addition, webCEMiTool integrates transcriptomic data with interactome information (i.e., protein-protein interactions) and identifies potential hubs on each network. The tool generates user-friendly html reports that allow users to search for specific genes in each module, as well as check if a module contains genes overrepresented in specific pathways or altered in a specific sample phenotype. We used webCEMiTool to perform a modular analysis of single-cell RNA-seq data of human cells infected with either Zika virus or dengue virus.

Canonical PI3Kγ signaling in myeloid cells restricts Trypanosoma cruzi infection and dampens chagasic myocarditis.

Chagas disease is caused by infection with the protozoan Trypanosoma cruzi (T. cruzi) and is an important cause of severe inflammatory heart disease. However, the mechanisms driving Chagas disease cardiomyopathy have not been completely elucidated. Here, we show that the canonical PI3Kγ pathway is upregulated in both human chagasic hearts and hearts of acutely infected mice. PI3Kγ-deficient mice and mutant mice carrying catalytically inactive PI3Kγ are more susceptible to T. cruzi infection. The canonical PI3Kγ signaling in myeloid cells is essential to restrict T. cruzi heart parasitism and ultimately to avoid myocarditis, heart damage, and death of mice. Furthermore, high PIK3CG expression correlates with low parasitism in human Chagas' hearts. In conclusion, these results indicate an essential role of the canonical PI3Kγ signaling pathway in the control of T. cruzi infection, providing further insight into the molecular mechanisms involved in the pathophysiology of chagasic heart disease.

CEMiTool: a Bioconductor package for performing comprehensive modular co-expression analyses.

BACKGROUND: The analysis of modular gene co-expression networks is a well-established method commonly used for discovering the systems-level functionality of genes. In addition, these studies provide a basis for the discovery of clinically relevant molecular pathways underlying different diseases and conditions. RESULTS: In this paper, we present a fast and easy-to-use Bioconductor package named CEMiTool that unifies the discovery and the analysis of co-expression modules. Using the same real datasets, we demonstrate that CEMiTool outperforms existing tools, and provides unique results in a user-friendly html report with high quality graphs. Among its features, our tool evaluates whether modules contain genes that are over-represented by specific pathways or that are altered in a specific sample group, as well as it integrates transcriptomic data with interactome information, identifying the potential hubs on each network. We successfully applied CEMiTool to over 1000 transcriptome datasets, and to a new RNA-seq dataset of patients infected with Leishmania, revealing novel insights of the disease's physiopathology. CONCLUSION: The CEMiTool R package provides users with an easy-to-use method to automatically implement gene co-expression network analyses, obtain key information about the discovered gene modules using additional downstream analyses and retrieve publication-ready results via a high-quality interactive report.