Malnutrition and Dietary Habits Alter the Immune System Which May Consequently Influence SARS-CoV-2 Virulence: A Review.

COVID-19, resulting from the SARS-CoV-2 virus, is a major pandemic that the world is fighting. SARS-CoV-2 primarily causes lung infection by attaching to the ACE2 receptor on the alveolar epithelial cells. However, the ACE2 receptor is also present in intestinal epithelial cells, suggesting a link between nutrition, virulence and clinical outcomes of COVID-19. Respiratory viral infections perturb the gut microbiota. The gut microbiota is shaped by our diet; therefore, a healthy gut is important for optimal metabolism, immunology and protection of the host. Malnutrition causes diverse changes in the immune system by repressing immune responses and enhancing viral vulnerability. Thus, improving gut health with a high-quality, nutrient-filled diet will improve immunity against infections and diseases. This review emphasizes the significance of dietary choices and its subsequent effects on the immune system, which may potentially impact SARS-CoV-2 vulnerability.

Conserved topology of virus glycoepitopes presents novel targets for repurposing HIV antibody 2G12.

Complex glycans decorate viral surface proteins and play a critical role in virus-host interactions. Viral surface glycans shield vulnerable protein epitopes from host immunity yet can also present distinct "glycoepitopes" that can be targeted by host antibodies such as the potent anti-HIV antibody 2G12 that binds high-mannose glycans on gp120. Two recent publications demonstrate 2G12 binding to high mannose glycans on SARS-CoV-2 and select Influenza A (Flu) H3N2 viruses. Previously, our lab observed 2G12 binding and functional inhibition of a range of Flu viruses that include H3N2 and H1N1 lineages. In this manuscript, we present these data alongside structural analyses to offer an expanded picture of 2G12-Flu interactions. Further, based on the remarkable breadth of 2G12 N-glycan recognition and the structural factors promoting glycoprotein oligomannosylation, we hypothesize that 2G12 glycoepitopes can be defined from protein structure alone according to N-glycan site topology. We develop a model describing 2G12 glycoepitopes based on N-glycan site topology, and apply the model to identify viruses within the Protein Data Bank presenting putative 2G12 glycoepitopes for 2G12 repurposing toward analytical, diagnostic, and therapeutic applications.

Influence of heterogeneous age-group contact patterns on critical vaccination rates for herd immunity to SARS-CoV-2.

Currently, several western countries have more than half of their population fully vaccinated against COVID-19. At the same time, some of them are experiencing a fourth or even a fifth wave of cases, most of them concentrated in sectors of the populations whose vaccination coverage is lower than the average. So, the initial scenario of vaccine prioritization has given way to a new one where achieving herd immunity is the primary concern. Using an age-structured vaccination model with waning immunity, we show that, under a limited supply of vaccines, a vaccination strategy based on minimizing the basic reproduction number allows for the deployment of a number of vaccine doses lower than the one required for maximizing the vaccination coverage. Such minimization is achieved by giving greater protection to those age groups that, for a given social contact pattern, have smaller fractions of susceptible individuals at the endemic equilibrium without vaccination, that is, to those groups that are more vulnerable to infection.

A novel definition and treatment of hyperinflammation in COVID-19 based on purinergic signalling.

Hyperinflammation plays an important role in severe and critical COVID-19. Using inconsistent criteria, many researchers define hyperinflammation as a form of very severe inflammation with cytokine storm. Therefore, COVID-19 patients are treated with anti-inflammatory drugs. These drugs appear to be less efficacious than expected and are sometimes accompanied by serious adverse effects. SARS-CoV-2 promotes cellular ATP release. Increased levels of extracellular ATP activate the purinergic receptors of the immune cells initiating the physiologic pro-inflammatory immune response. Persisting viral infection drives the ATP release even further leading to the activation of the P2X7 purinergic receptors (P2X7Rs) and a severe yet physiologic inflammation. Disease progression promotes prolonged vigorous activation of the P2X7R causing cell death and uncontrolled ATP release leading to cytokine storm and desensitisation of all other purinergic receptors of the immune cells. This results in immune paralysis with co-infections or secondary infections. We refer to this pathologic condition as hyperinflammation. The readily available and affordable P2X7R antagonist lidocaine can abrogate hyperinflammation and restore the normal immune function. The issue is that the half-maximal effective concentration for P2X7R inhibition of lidocaine is much higher than the maximal tolerable plasma concentration where adverse effects start to develop. To overcome this, we selectively inhibit the P2X7Rs of the immune cells of the lymphatic system inducing clonal expansion of Tregs in local lymph nodes. Subsequently, these Tregs migrate throughout the body exerting anti-inflammatory activities suppressing systemic and (distant) local hyperinflammation. We illustrate this with six critically ill COVID-19 patients treated with lidocaine.

Quantifying Neutralizing Antibodies in Patients with COVID-19 by a Two-Variable Generalized Additive Model.

Considering the urgent demand for faster methods to quantify neutralizing antibody titers in patients with coronavirus (CoV) disease 2019 (COVID-19), developing an analytical model or method to replace the conventional virus neutralization test (NT) is essential. Moreover, a "COVID-19 immunity passport" is currently being proposed as a certification for people who travel internationally. Therefore, an enzyme-linked immunosorbent assay (ELISA) was designed to detect severe acute respiratory syndrome CoV 2 (SARS-CoV-2)-neutralizing antibodies in serum, which is based on the binding affinity of SARS-CoV-2 viral spike protein 1 (S1) and the viral spike protein receptor-binding domain (RBD) to antibodies. The RBD is considered the major binding region of neutralizing antibodies. Furthermore, S1 covers the RBD and several other regions, which are also important for neutralizing antibody binding. In this study, we assessed 144 clinical specimens, including those from patients with PCR-confirmed SARS-CoV-2 infections and healthy donors, using both the NT and ELISA. The ELISA results analyzed by spline regression and the two-variable generalized additive model precisely reflected the NT value, and the correlation between predicted and actual NT values was as high as 0.917. Therefore, our method serves as a surrogate to quantify neutralizing antibody titer. The analytic method and platform used in this study present a new perspective for serological testing of SARS-CoV-2 infection and have clinical potential to assess vaccine efficacy. IMPORTANCE Herein, we present a new approach for serological testing for SARS-CoV-2 antibodies using innovative laboratory methods that demonstrate a combination of biology and mathematics. The traditional virus neutralization test is the gold standard method; however, it is time-consuming and poses a risk to medical personnel. Thus, there is a demand for methods that rapidly quantify neutralizing antibody titers in patients with COVID-19 or examine vaccine efficacy at a biosafety level 2 containment facility. Therefore, we used a two-variable generalized additive model to analyze the results of the enzyme-linked immunosorbent assay and found the method to serve as a surrogate to quantify neutralizing antibody titers. This methodology has potential for clinical use in assessing vaccine efficacy.

Secondary Immunodeficiency Related to Kidney Disease (SIDKD)-Definition, Unmet Need, and Mechanisms.

Kidney disease is a known risk factor for poor outcomes of COVID-19 and many other serious infections. Conversely, infection is the second most common cause of death in patients with kidney disease. However, little is known about the underlying secondary immunodeficiency related to kidney disease (SIDKD). In contrast to cardiovascular disease related to kidney disease, which has triggered countless epidemiologic, clinical, and experimental research activities or interventional trials, investments in tracing, understanding, and therapeutically targeting SIDKD have been sparse. As a call for more awareness of SIDKD as an imminent unmet medical need that requires rigorous research activities at all levels, we review the epidemiology of SIDKD and the numerous aspects of the abnormal immunophenotype of patients with kidney disease. We propose a definition of SIDKD and discuss the pathogenic mechanisms of SIDKD known thus far, including more recent insights into the unexpected immunoregulatory roles of elevated levels of FGF23 and hyperuricemia and shifts in the secretome of the intestinal microbiota in kidney disease. As an ultimate goal, we should aim to develop therapeutics that can reduce mortality due to infections in patients with kidney disease by normalizing host defense to pathogens and immune responses to vaccines.

Strategies to minimize heterogeneity and optimize clinical trials in Acute Respiratory Distress Syndrome (ARDS): Insights from mathematical modelling.

BACKGROUND: Mathematical modelling may aid in understanding the complex interactions between injury and immune response in critical illness. METHODS: We utilize a system biology model of COVID-19 to analyze the effect of altering baseline patient characteristics on the outcome of immunomodulatory therapies. We create example parameter sets meant to mimic diverse patient types. For each patient type, we define the optimal treatment, identify biologic programs responsible for clinical responses, and predict biomarkers of those programs. FINDINGS: Model states representing older and hyperinflamed patients respond better to immunomodulation than those representing obese and diabetic patients. The disparate clinical responses are driven by distinct biologic programs. Optimal treatment initiation time is determined by neutrophil recruitment, systemic cytokine expression, systemic microthrombosis and the renin-angiotensin system (RAS) in older patients, and by RAS, systemic microthrombosis and trans IL6 signalling for hyperinflamed patients. For older and hyperinflamed patients, IL6 modulating therapy is predicted to be optimal when initiated very early (<4th day of infection) and broad immunosuppression therapy (corticosteroids) is predicted to be optimally initiated later in the disease (7th - 9th day of infection). We show that markers of biologic programs identified by the model correspond to clinically identified markers of disease severity. INTERPRETATION: We demonstrate that modelling of COVID-19 pathobiology can suggest biomarkers that predict optimal response to a given immunomodulatory treatment. Mathematical modelling thus constitutes a novel adjunct to predictive enrichment and may aid in the reduction of heterogeneity in critical care trials. FUNDING: C.V. received a Marie Sklodowska Curie Actions Individual Fellowship (MSCA-IF-GF-2020-101028945). R.K.J.'s research is supported by R01-CA208205, and U01-CA 224348, R35-CA197743 and grants from the National Foundation for Cancer Research, Jane's Trust Foundation, Advanced Medical Research Foundation and Harvard Ludwig Cancer Center. No funder had a role in production or approval of this manuscript.

Viral surface geometry shapes influenza and coronavirus spike evolution through antibody pressure.

The evolution of circulating viruses is shaped by their need to evade antibody response, which mainly targets the viral spike. Because of the high density of spikes on the viral surface, not all antigenic sites are targeted equally by antibodies. We offer here a geometry-based approach to predict and rank the probability of surface residues of SARS spike (S protein) and influenza H1N1 spike (hemagglutinin) to acquire antibody-escaping mutations utilizing in-silico models of viral structure. We used coarse-grained MD simulations to estimate the on-rate (targeting) of an antibody model to surface residues of the spike protein. Analyzing publicly available sequences, we found that spike surface sequence diversity of the pre-pandemic seasonal influenza H1N1 and the sarbecovirus subgenus highly correlates with our model prediction of antibody targeting. In particular, we identified an antibody-targeting gradient, which matches a mutability gradient along the main axis of the spike. This identifies the role of viral surface geometry in shaping the evolution of circulating viruses. For the 2009 H1N1 and SARS-CoV-2 pandemics, a mutability gradient along the main axis of the spike was not observed. Our model further allowed us to identify key residues of the SARS-CoV-2 spike at which antibody escape mutations have now occurred. Therefore, it can inform of the likely functional role of observed mutations and predict at which residues antibody-escaping mutation might arise.

Upregulation of type 1 conventional dendritic cells implicates antigen cross-presentation in multisystem inflammatory syndrome.

BACKGROUND: Multisystem inflammatory syndrome in children (MIS-C) is an acute, febrile, severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2)-associated syndrome, often with cardiohemodynamic dysfunction. Insight into mechanism of disease is still incomplete. OBJECTIVE: Our objective was to analyze immunologic features of MIS-C patients compared to febrile controls (FC). METHODS: MIS-C patients were defined by narrow criteria, including having evidence of cardiohemodynamic involvement and no macrophage activation syndrome. Samples were collected from 8 completely treatment-naive patients with MIS-C (SARS-CoV-2 serology positive), 3 patients with unclassified MIS-C-like disease (serology negative), 14 FC, and 5 MIS-C recovery (RCV). Three healthy controls (HCs) were used for comparisons of normal range. Using spectral flow cytometry, we assessed 36 parameters in antigen-presenting cells (APCs) and 29 in T cells. We used biaxial analysis and uniform manifold approximation and projection (UMAP). RESULTS: Significant elevations in cytokines including CXCL9, M-CSF, and IL-27 were found in MIS-C compared to FC. Classic monocytes and type 2 dendritic cells (DCs) were downregulated (decreased CD86, HLA-DR) versus HCs; however, type 1 DCs (CD11c+CD141+CLEC9A+) were highly activated in MIS-C patients versus FC, expressing higher levels of CD86, CD275, and atypical conventional DC markers such as CD64, CD115, and CX3CR1. CD169 and CD38 were upregulated in multiple monocyte subtypes. CD56dim/CD57-/KLRGhi/CD161+/CD38- natural killer (NK) cells were a unique subset in MIS-C versus FC without macrophage activation syndrome. CONCLUSION: Orchestrated by complex cytokine signaling, type 1 DC activation and NK dysregulation are key features in the pathophysiology of MIS-C. NK cell findings may suggest a relationship with macrophage activation syndrome, while type 1 DC upregulation implies a role for antigen cross-presentation.

Data-driven multi-scale mathematical modeling of SARS-CoV-2 infection reveals heterogeneity among COVID-19 patients.

Patients with coronavirus disease 2019 (COVID-19) often exhibit diverse disease progressions associated with various infectious ability, symptoms, and clinical treatments. To systematically and thoroughly understand the heterogeneous progression of COVID-19, we developed a multi-scale computational model to quantitatively understand the heterogeneous progression of COVID-19 patients infected with severe acute respiratory syndrome (SARS)-like coronavirus (SARS-CoV-2). The model consists of intracellular viral dynamics, multicellular infection process, and immune responses, and was formulated using a combination of differential equations and stochastic modeling. By integrating multi-source clinical data with model analysis, we quantified individual heterogeneity using two indexes, i.e., the ratio of infected cells and incubation period. Specifically, our simulations revealed that increasing the host antiviral state or virus induced type I interferon (IFN) production rate can prolong the incubation period and postpone the transition from asymptomatic to symptomatic outcomes. We further identified the threshold dynamics of T cell exhaustion in the transition between mild-moderate and severe symptoms, and that patients with severe symptoms exhibited a lack of naïve T cells at a late stage. In addition, we quantified the efficacy of treating COVID-19 patients and investigated the effects of various therapeutic strategies. Simulations results suggested that single antiviral therapy is sufficient for moderate patients, while combination therapies and prevention of T cell exhaustion are needed for severe patients. These results highlight the critical roles of IFN and T cell responses in regulating the stage transition during COVID-19 progression. Our study reveals a quantitative relationship underpinning the heterogeneity of transition stage during COVID-19 progression and can provide a potential guidance for personalized therapy in COVID-19 patients.

Roles of the tissue-type plasminogen activator in immune response.

The COVID-19 pandemic has once again brought to the forefront the existence of a tight link between the coagulation/fibrinolytic system and the immunologic processes. Tissue-type plasminogen activator (tPA) is a serine protease with a key role in fibrinolysis by converting plasminogen into plasmin that can finally degrade fibrin clots. tPA is released in the blood by endothelial cells and hepatocytes but is also produced by various types of immune cells including T cells and monocytes. Beyond its role on hemostasis, tPA is also a potent modulator of inflammation and is involved in the regulation of several inflammatory diseases. Here, after a brief description of tPA structure, we review its new functions in adaptive immunity focusing on T cells and antigen presenting cells. We intend to synthesize the recent knowledge on proteolysis- and receptor-mediated effects of tPA on immune response in physiological and pathological context.

SARS-CoV-2 immunity and functional recovery of COVID-19 patients 1-year after infection.

The long-term immunity and functional recovery after SARS-CoV-2 infection have implications in preventive measures and patient quality of life. Here we analyzed a prospective cohort of 121 recovered COVID-19 patients from Xiangyang, China at 1-year after diagnosis. Among them, chemiluminescence immunoassay-based screening showed 99% (95% CI, 98-100%) seroprevalence 10-12 months after infection, comparing to 0.8% (95% CI, 0.7-0.9%) in the general population. Total anti-receptor-binding domain (RBD) antibodies remained stable since discharge, while anti-RBD IgG and neutralization levels decreased over time. A predictive model estimates 17% (95% CI, 11-24%) and 87% (95% CI, 80-92%) participants were still 50% protected against detectable and severe re-infection of WT SARS-CoV-2, respectively, while neutralization levels against B.1.1.7 and B.1.351 variants were significantly reduced. All non-severe patients showed normal chest CT and 21% reported COVID-19-related symptoms. In contrast, 53% severe patients had abnormal chest CT, decreased pulmonary function or cardiac involvement and 79% were still symptomatic. Our findings suggest long-lasting immune protection after SARS-CoV-2 infection, while also highlight the risk of immune evasive variants and long-term consequences for COVID-19 survivors.

From Infection to Immunity: Understanding the Response to SARS-CoV2 Through In-Silico Modeling.

Background: Immune system conditions of the patient is a key factor in COVID-19 infection survival. A growing number of studies have focused on immunological determinants to develop better biomarkers for therapies. Aim: Studies of the insurgence of immunity is at the core of both SARS-CoV-2 vaccine development and therapies. This paper attempts to describe the insurgence (and the span) of immunity in COVID-19 at the population level by developing an in-silico model. We simulate the immune response to SARS-CoV-2 and analyze the impact of infecting viral load, affinity to the ACE2 receptor, and age in an artificially infected population on the course of the disease. Methods: We use a stochastic agent-based immune simulation platform to construct a virtual cohort of infected individuals with age-dependent varying degrees of immune competence. We use a parameter set to reproduce known inter-patient variability and general epidemiological statistics. Results: By assuming the viremia at day 30 of the infection to be the proxy for lethality, we reproduce in-silico several clinical observations and identify critical factors in the statistical evolution of the infection. In particular, we evidence the importance of the humoral response over the cytotoxic response and find that the antibody titers measured after day 25 from the infection are a prognostic factor for determining the clinical outcome of the infection. Our modeling framework uses COVID-19 infection to demonstrate the actionable effectiveness of modeling the immune response at individual and population levels. The model developed can explain and interpret observed patterns of infection and makes verifiable temporal predictions. Within the limitations imposed by the simulated environment, this work proposes quantitatively that the great variability observed in the patient outcomes in real life can be the mere result of subtle variability in the infecting viral load and immune competence in the population. In this work, we exemplify how computational modeling of immune response provides an important view to discuss hypothesis and design new experiments, in particular paving the way to further investigations about the duration of vaccine-elicited immunity especially in the view of the blundering effect of immunosenescence.

Beginnings of coinhibition.

Costimulatory and coinhibitory mechanisms appear to be involved throughout immune responses to control their specificity and level. Many mechanisms operate; therefore, various theoretical models should be considered complementary rather than competing. One such coinhibitory model, pictured in 1971, involves the crosslinking of antigen receptors with inhibitory Fc receptors by antigen/antibody complexes. This model was prompted by observations that the Fc portion of antibody was required for potent suppression of immune responses by antibody. The signal via the antigen receptor wakes up T or B cells, providing specificity, while costimulators and coinhibitors stimulate or inhibit these awoken cells. The recent observations that administration of monoclonal anti-SARS-CoV-2 spike antibodies in early COVID-19 patients inhibits the induction of clinically damaging autoimmune antibodies suggest they may provide negative Fc signals that are blocked in COVID-19 patients. Furthermore, the reduced ability of SARS-CoV-2 antigen to localize to germinal centres in COVID-19 patients also suggests a block in binding of the Fc of antibody bound to antigen on FcγRIIb of follicular dendritic cells. The distinction between self and foreign is made not only at the beginning of immune responses but also throughout, and involves multiple mechanisms and models. There are past beginnings (history of models) and current and future beginnings for solving serious clinical problems (such as COVID-19) and different types of models used for understanding the complexities of fundamental immunology.

Modelling direct and herd protection effects of vaccination against the SARS-CoV-2 Delta variant in Australia.

OBJECTIVES: To analyse the outcomes of COVID-19 vaccination by vaccine type, age group eligibility, vaccination strategy, and population coverage. DESIGN: Epidemiologic modelling to assess the final size of a COVID-19 epidemic in Australia, with vaccination program (Pfizer, AstraZeneca, mixed), vaccination strategy (vulnerable first, transmitters first, untargeted), age group eligibility threshold (5 or 15 years), population coverage, and pre-vaccination effective reproduction number ( Reffv¯ ) for the SARS-CoV-2 Delta variant as factors. MAIN OUTCOME MEASURES: Numbers of SARS-CoV-2 infections; cumulative hospitalisations, deaths, and years of life lost. RESULTS: Assuming Reffv¯ = 5, the current mixed vaccination program (vaccinating people aged 60 or more with the AstraZeneca vaccine and people under 60 with the Pfizer vaccine) will not achieve herd protection unless population vaccination coverage reaches 85% by lowering the vaccination eligibility age to 5 years. At Reffv¯ = 3, the mixed program could achieve herd protection at 60-70% population coverage and without vaccinating 5-15-year-old children. At Reffv¯ = 7, herd protection is unlikely to be achieved with currently available vaccines, but they would still reduce the number of COVID-19-related deaths by 85%. CONCLUSION: Vaccinating vulnerable people first is the optimal policy when population vaccination coverage is low, but vaccinating more socially active people becomes more important as the Reffv¯ declines and vaccination coverage increases. Assuming the most plausible Reffv¯ of 5, vaccinating more than 85% of the population, including children, would be needed to achieve herd protection. Even without herd protection, vaccines are highly effective in reducing the number of deaths.

Quantification of Cytokine Storms During Virus Infections.

Highly pathogenic virus infections usually trigger cytokine storms, which may have adverse effects on vital organs and result in high mortalities. The two cytokines interleukin (IL)-4 and interferon (IFN)-γ play key roles in the generation and regulation of cytokine storms. However, it is still unclear whether the cytokine with the largest induction amplitude is the same under different virus infections. It is unknown which is the most critical and whether there are any mathematical formulas that can fit the changing rules of cytokines. Three coronaviruses (SARS-CoV, MERS-CoV, and SARS-CoV-2), three influenza viruses (2009H1N1, H5N1 and H7N9), Ebola virus, human immunodeficiency virus, dengue virus, Zika virus, West Nile virus, hepatitis B virus, hepatitis C virus, and enterovirus 71 were included in this analysis. We retrieved the cytokine fold change (FC), viral load, and clearance rate data from these highly pathogenic virus infections in humans and analyzed the correlations among them. Our analysis showed that interferon-inducible protein (IP)-10, IL-6, IL-8 and IL-17 are the most common cytokines with the largest induction amplitudes. Equations were obtained: the maximum induced cytokine (max) FC = IFN-γ FC × (IFN-γ FC/IL-4 FC) (if IFN-γ FC/IL-4 FC > 1); max FC = IL-4 FC (if IFN-γ FC/IL-4 FC < 1). For IFN-γ-inducible infections, 1.30 × log2 (IFN-γ FC) = log10 (viral load) - 2.48 - 2.83 × (clearance rate). The clinical relevance of cytokines and their antagonists is also discussed.

SARS-CoV-2 and interferon blockade.

The response to viral infection generally includes an activation of the adaptive immune response to produce cytotoxic T cells and neutralizing antibodies. We propose that SARS-CoV-2 activates the innate immune system through the renin-angiotensin and kallikrein-bradykinin pathways, blocks interferon production and reduces an effective adaptive immune response. This model has therapeutic implications.