Sanchez, P. G. | bioRxiv Science

bioRxiv2021

CD4+ T cell lymphopenia and dysfunction in severe COVID-19 disease is autocrine TNF-α/TNFRI-dependent

Lymphopenia is common in severe COVID-19 disease, yet the mechanisms are poorly understood. In 148 patients with severe COVID-19, we found lymphopenia was associated with worse survival. CD4+ lymphopenia predominated, with lower CD4+/CD8+ ratios in severe COVID-19 compared to recovered, mild disease (p<0.0001). In severe disease, immunodominant CD4+ T cell responses to Spike-1(S1) produced increased in vitro TNF-, but impaired proliferation and increased susceptibility to activation-induced cell death (AICD). CD4+TNF-+ T cell responses inversely correlated with absolute CD4+ counts from severe COVID-19 patients (n=76; R=-0.744, P<0.0001). TNF- blockade including infliximab or anti-TNFRI antibodies strikingly rescued S1-specific CD4+ proliferation and abrogated S1-AICD in severe COVID-19 patients (P<0.001). Single-cell RNAseq demonstrated downregulation of Type-1 cytokines and NF{kappa}B signaling in S1-stimulated CD4+ cells with infliximab treatment. Lung CD4+ T cells in severe COVID-19 were reduced and produced higher TNF- versus PBMC. Together, our findings show COVID-19-associated CD4+ lymphopenia and dysfunction is autocrine TNF-/TNFRI-dependent and therapies targeting TNF- may be beneficial in severe COVID-19. One Sentence SummaryAutocrine TNF-/TNFRI regulates CD4+ T cell lymphopenia and dysfunction in severe COVID-19 disease.

immunology↗

bioRxiv2020

Top-down engineering of complex communities by directed evolution

Directed evolution has been used for decades to engineer biological systems from the top-down. Generally, it has been applied at or below the organismal level, by iteratively sampling the mutational landscape in a guided search for genetic variants of higher function. Above the organismal level, a small number of studies have attempted to artificially select microbial communities and ecosystems, with uneven and generally modest success. Our theoretical understanding of artificial ecosystem selection is still limited, particularly for large assemblages of asexual organisms, and we know little about designing efficient methods to direct their evolution. To address this issue, we have developed a flexible modeling framework that allows us to systematically probe any arbitrary selection strategy on any arbitrary set of communities and selected functions, in a wide range of ecological conditions. By artificially selecting hundreds of in-silico microbial metacommunities under identical conditions, we examine the fundamental limits of the two main breeding methods used so far, and prescribe modifications that significantly increase their power. We identify a range of directed evolution strategies that, particularly when applied in combination, are better suited for the top-down engineering of large, diverse, and stable microbial consortia. Our results emphasize that directed evolution allows an ecological structure-function landscape to be navigated in search for dynamically stable and ecologically and functionally resilient high-functioning communities.

evolutionary biology↗