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Teterina, A.

Publications and source records attributed to Teterina, A..

2 recordsLinked to original sources

Expanding the stdpopsim species catalog, and lessons learned forrealistic genome simulations

Simulation is a key tool in population genetics for both methods development and empirical research, but producing simulations that recapitulate the main features of genomic data sets remains a major obstacle. Today, more realistic simulations are possible thanks to large increases in the quantity and quality of available genetic data, and to the sophistication of inference and simulation software. However, implementing these simulations still requires substantial time and specialized knowledge. These challenges are especially pronounced for simulating genomes for species that are not well-studied, since it is not always clear what information is required to produce simulations with a level of realism sufficient to confidently answer a given question. The community-developed framework stdpopsim seeks to lower this barrier by facilitating the simulation of complex population genetic models using up-to-date information. The initial version of stdpopsim focused on establishing this framework using six well-characterized model species (Adrion et al., 2020). Here, we report on major improvements made in the new release of stdpopsim (version 0.2), which includes a significant expansion of the species catalog and substantial additions to simulation capabilities. Features added to improve the realism of the simulated genomes include non-crossover recombination and provision of species-specific genomic annotations. Through community-driven efforts, we expanded the number of species in the catalog more than three-fold and broadened coverage across the tree of life. During the process of expanding the catalog, we have identified common sticking points and developed best practices for setting up genome-scale simulations. We describe the input data required for generating a realistic simulation, suggest good practices for obtaining the relevant information from the literature, and discuss common pitfalls and major considerations. These improvements to stdpopsim aim to further promote the use of realistic whole-genome population genetic simulations, especially in non-model organisms, making them available, transparent, and accessible to everyone.

bioinformatics↗

Tocilizumab treatment leads to early resolution of myeloid dysfunction and lymphopenia in patients hospitalized with COVID-19

High interleukin (IL)-6 levels are associated with more severe clinical manifestations in patients hospitalized with COVID-19, but the complex role of IL-6 in antiviral and inflammatory processes has made it difficult to decipher its involvement in the disease. IL-6 receptor blockade by tocilizumab (anti-IL6R; Actemra) is used globally for the treatment of severe COVID-19, yet a molecular understanding of the therapeutic benefit remains unclear. We characterized the immune profile and identified cellular and molecular pathways directly modified by tocilizumab in peripheral blood samples collected from patients enrolled in the COVACTA study, a phase 3, randomized, double-blind, placebo-controlled trial that assessed the efficacy and safety of tocilizumab in hospitalized patients with severe COVID-19 pneumonia. We identified factors predicting disease severity and clinical outcomes, including markers of inflammation, lymphopenia, myeloid dysregulation, and organ injury. Proteomic analysis confirmed a pharmacodynamic effect for tocilizumab in addition to identifying novel pharmacodynamic biomarkers. Transcriptomic analysis revealed that tocilizumab treatment leads to faster resolution of lymphopenia and myeloid dysregulation associated with severe COVID-19, indicating greater anti-inflammatory activity relative to standard of care and potentially leading to faster recovery in patients hospitalized with COVID-19. One sentence summaryInterleukin-6 receptor blockade with tocilizumab accelerated resolution of myeloid dysfunction and lymphopenia in patients hospitalized with COVID-19

immunology↗