TNF-alpha+ CD4+ Tcells dominate the SARS-CoV-2 specific T cell response in COVID-19 outpatients and are associated with durable antibodies.
Cell reports. Medicine
Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2)-specific CD4+ Tcells are likely important in immunity against coronavirus 2019 (COVID-19), but our understanding of CD4+ longitudinal dynamics following infection and of specific features that correlate with the maintenance of neutralizing antibodies remains limited. Here, we characterize SARS-CoV-2-specific CD4+ Tcells in a longitudinal cohort of 109 COVID-19 outpatients enrolled during acute infection. The quality of the SARS-CoV-2-specific CD4+ response shifts from cells producing interferon gamma (IFNgamma) to tumor necrosis factor alpha (TNF-alpha) from 5days to 4months post-enrollment, with IFNgamma-IL-21-TNF-alpha+ CD4+ Tcells the predominant population detected at later time points. Greater percentages of IFNgamma-IL-21-TNF-alpha+ CD4+ Tcells on day 28 correlate with SARS-CoV-2-neutralizing antibodies measured 7months post-infection (⍴= 0.4, p= 0.01). mRNA vaccination following SARS-CoV-2 infection boosts both IFNgamma- and TNF-alpha-producing, spike-protein-specific CD4+ Tcells. These data suggest that SARS-CoV-2-specific, TNF-alpha-producing CD4+ Tcells may play an important role in antibody maintenance following COVID-19.
View details for DOI 10.1016/j.xcrm.2022.100640
View details for PubMedID 35588734
CELLULAR CORRELATES FOR PROTECTION AGAINST MALARIA ACQUIRED ACROSS MULTIPLE PREGNANCIES
AMER SOC TROP MED & HYGIENE. 2021: 219
View details for Web of Science ID 000778105602375
Generalizations of the 'Linear Chain Trick': incorporating more flexible dwell time distributions into mean field ODE models.
Journal of mathematical biology
In this paper we generalize the Linear Chain Trick (LCT; aka the Gamma Chain Trick) to help provide modelers more flexibility to incorporate appropriate dwell time assumptions into mean field ODEs, and help clarify connections between individual-level stochastic model assumptions and the structure of corresponding mean field ODEs. The LCT is a technique used to construct mean field ODE models from continuous-time stochastic state transition models where the time an individual spends in a given state (i.e., the dwell time) is Erlang distributed (i.e., gamma distributed with integer shape parameter). Despite the LCT's widespread use, we lack general theory to facilitate the easy application of this technique, especially for complex models. Modelers must therefore choose between constructing ODE models using heuristics with oversimplified dwell time assumptions, using time consuming derivations from first principles, or to instead use non-ODE models (like integro-differential or delay differential equations) which can be cumbersome to derive and analyze. Here, we provide analytical results that enable modelers to more efficiently construct ODE models using the LCT or related extensions. Specifically, we provide (1) novel LCT extensions for various scenarios found in applications, including conditional dwell time distributions; (2) formulations of these LCT extensions that bypass the need to derive ODEs from integral equations; and (3) a novel Generalized Linear Chain Trick (GLCT) framework that extends the LCT to a much broader set of possible dwell time distribution assumptions, including the flexible phase-type distributions which can approximate distributions on [Formula: see text] and can be fit to data.
View details for DOI 10.1007/s00285-019-01412-w
View details for PubMedID 31410551