The Transient IFN Response and the Delay of Adaptive Immunity Feature the Severity of COVID-19

COVID-19 patients show heterogeneous and dynamic immune features which determine the clinical outcome. Here, we built a single-cell RNA sequencing (scRNA-seq) dataset for dissecting these complicated immune responses through a longitudinal survey of COVID-19 patients with various categories of outcomes. The data reveals a highly fluctuating peripheral immune landscape in severe COVID-19, whereas the one in asymptomatic/mild COVID-19 is relatively steady. Then, the perturbed immune landscape in peripheral blood returned to normal state in those recovered from severe COVID-19. Importantly, the imbalance of the excessively strong innate immune response and delayed adaptive immunity in the early stage of viral infection accelerates the progression of the disease, indicated by a transient strong IFN response and weak T/B-cell specific response. The proportion of abnormal monocytes appeared early and rose further throughout the severe disease. Our data indicate that a dynamic immune landscape is associated with the progression and recovery of severe COVID-19, and have provided multiple immune biomarkers for early warning of severe COVID-19.

ACE2 is the critical in vivo receptor for SARS-CoV-2 in a novel COVID-19 mouse model with TNF- and IFNγ-driven immunopathology

In silico modelling revealed how only three Spike mutations of maVie16 enhanced interaction with murine ACE2. MaVie16 induced profound pathology in BALB/c and C57BL/6 mice and the resulting mouse COVID-19 (mCOVID-19) replicated critical aspects of human disease, including early lymphopenia, pulmonary immune cell infiltration, pneumonia and specific adaptive immunity. Inhibition of the proinflammatory cytokines IFNg and TNF substantially reduced immunopathology. Importantly, genetic ACE2-deficiency completely prevented mCOVID-19 development. Finally, inhalation therapy with recombinant ACE2 fully protected mice from mCOVID-19, revealing a novel and efficient treatment. Thus, we here present maVie16 as a new tool to model COVID-19 for the discovery of new therapies and show that disease severity is determined by cytokine-driven immunopathology and critically dependent on ACE2 in vivo.

Phylogeny, Structure, Functions, and Role of AIRE in the Formation of T-Cell Subsets

It is well known that the most important feature of adaptive immunity is the specificity that provides highly precise recognition of the self, altered-self, and non-self. Due to the high specificity of antigen recognition, the adaptive immune system participates in the maintenance of genetic homeostasis, supports multicellularity, and protects an organism from different pathogens at a qualitatively different level than innate immunity. This seemingly simple property is based on millions of years of evolution that led to the formation of diversification mechanisms of antigen-recognizing receptors and later to the emergence of a system of presentation of the self and non-self antigens. The latter could have a crucial significance because the presentation of nearly complete diversity of auto-antigens in the thymus allows for the “calibration” of the forming repertoires of T-cells for the recognition of self, altered-self, and non-self antigens that are presented on the periphery. The central role in this process belongs to promiscuous gene expression by the thymic epithelial cells that express nearly the whole spectrum of proteins encoded in the genome, meanwhile maintaining their cellular identity. This complex mechanism requires strict control that is executed by several transcription factors. One of the most important of them is AIRE. This noncanonical transcription factor not only regulates the processes of differentiation and expression of peripheral tissue-specific antigens in the thymic medullar epithelial cells but also controls intercellular interactions in the thymus. Besides, it participates in an increase in the diversity and transfer of presented antigens and thus influences the formation of repertoires of maturing thymocytes. Due to these complex effects, AIRE is also called a transcriptional regulator. In this review, we briefly described the history of AIRE discovery, its structure, functions, and role in the formation of antigen-recognizing receptor repertoires, along with other transcription factors. We focused on the phylogenetic prerequisites for the development of modern adaptive immunity and emphasized the importance of the antigen presentation system.

Dynamical analysis, optimal control and spatial pattern in an influenza model with adaptive immunity in two stratified population

<abstract><p>Consistently, influenza has become a major cause of illness and mortality worldwide and it has posed a serious threat to global public health particularly among the immuno-compromised people all around the world. The development of medication to control influenza has become a major challenge now. This work proposes and analyzes a structured model based on two geographical areas, in order to study the spread of influenza. The overall underlying population is separated into two sub populations: urban and rural. This geographical distinction is required as the immunity levels are significantly higher in rural areas as compared to urban areas. Hence, this paper is a novel attempt to proposes a linear and non-linear mathematical model with adaptive immunity and compare the host immune response to disease. For both the models, disease-free equilibrium points are obtained which are locally as well as globally stable if the reproduction number is less than 1 (<italic>R</italic>₀₁ &lt; 1 &amp; <italic>R</italic>₀₂ &lt; 1) and the endemic point is stable if the reproduction number is greater then 1 (<italic>R</italic>₀₁ &gt; 1 &amp; <italic>R</italic>₀₂ &gt; 1). Next, we have incorporated two treatments in the model that constitute the effectiveness of antidots and vaccination in restraining viral creation and slow down the production of new infections and analyzed an optimal control problem. Further, we have also proposed a spatial model involving diffusion and obtained the local stability for both the models. By the use of local stability, we have derived the Turing instability condition. Finally, all the theoretical results are verified with numerical simulation using MATLAB.</p></abstract>