An integrative systems biology view of host-pathogen interactions: The regulation of immunity and homeostasis is concomitant, flexible, and smart.

The systemic bio-organization of humans and other mammals is essentially "preprogrammed", and the basic interacting units, the cells, can be crudely mapped into discrete sets of developmental lineages and maturation states. Over several decades, however, and focusing on the immune system, we and others invoked evidence - now overwhelming - suggesting dynamic acquisition of cellular properties and functions, through tuning, re-networking, chromatin remodeling, and adaptive differentiation. The genetically encoded "algorithms" that govern the integration of signals and the computation of new states are not fully understood but are believed to be "smart", designed to enable the cells and the system to discriminate meaningful perturbations from each other and from "noise". Cellular sensory and response properties are shaped in part by recurring temporal patterns, or features, of the signaling environment. We compared this phenomenon to associative brain learning. We proposed that interactive cell learning is subject to selective pressures geared to performance, allowing the response of immune cells to injury or infection to be progressively coordinated with that of other cell types across tissues and organs. This in turn is comparable to supervised brain learning. Guided by feedback from both the tissue itself and the neural system, resident or recruited antigen-specific and innate immune cells can eradicate a pathogen while simultaneously sustaining functional homeostasis. As informative memories of immune responses are imprinted both systemically and within the targeted tissues, it is desirable to enhance tissue preparedness by incorporating attenuated-pathogen vaccines and informed choice of tissue-centered immunomodulators in vaccination schemes. Fortunately, much of the "training" that a living system requires to survive and function in the face of disturbances from outside or within is already incorporated into its design, so it does not need to deep-learn how to face a new challenge each time from scratch. Instead, the system learns from experience how to efficiently select a built-in strategy, or a combination of those, and can then use tuning to refine its organization and responses. Efforts to identify and therapeutically augment such strategies can take advantage of existing integrative modeling approaches. One recently explored strategy is boosting the flux of uninfected cells into and throughout an infected tissue to rinse and replace the infected cells.

Preclinical evaluation of a COVID-19 vaccine candidate based on a recombinant RBD fusion heterodimer of SARS-CoV-2

Current COVID-19 vaccines have been associated with a decline in infection rates, prevention of severe disease and a decrease in mortality rates. However, SARS-CoV-2 variants are continuously evolving, and development of new accessible COVID-19 vaccines is essential to mitigate the pandemic. Here, we present data on preclinical studies in mice of a receptor-binding domain (RBD)-based recombinant protein vaccine (PHH-1V) consisting of an RBD fusion heterodimer comprising the B.1.351 and B.1.1.7 SARS-CoV-2 variants formulated in SQBA adjuvant, an oil-in-water emulsion. A prime-boost immunisation with PHH-1V in BALB/c and K18-hACE2 mice induced a CD4+ and CD8+ T cell response and RBD-binding antibodies with neutralising activity against several variants, and also showed a good tolerability profile. Significantly, RBD fusion heterodimer vaccination conferred 100% efficacy, preventing mortality in SARS-CoV-2 infected K18-hACE2 mice, but also reducing Beta, Delta and Omicron infection in lower respiratory airways. These findings demonstrate the feasibility of this recombinant vaccine strategy. Graphical

Preclinical evaluation of a COVID-19 vaccine candidate based on a recombinant RBD fusion heterodimer of SARS-CoV-2.

Current COVID-19 vaccines have been associated with a decline in infection rates, prevention of severe disease, and a decrease in mortality rates. However, SARS-CoV-2 variants are continuously evolving, and development of new accessible COVID-19 vaccines is essential to mitigate the pandemic. Here, we present data on preclinical studies in mice of a receptor-binding domain (RBD)-based recombinant protein vaccine (PHH-1V) consisting of an RBD fusion heterodimer comprising the B.1.351 and B.1.1.7 SARS-CoV-2 variants formulated in SQBA adjuvant, an oil-in-water emulsion. A prime-boost immunisation with PHH-1V in BALB/c and K18-hACE2 mice induced a CD4+ and CD8+ T cell response and RBD-binding antibodies with neutralizing activity against several variants, and also showed a good tolerability profile. Significantly, RBD fusion heterodimer vaccination conferred 100% efficacy, preventing mortality in SARS-CoV-2 infected K18-hACE2 mice, but also reducing Beta, Delta and Omicron infection in lower respiratory airways. These findings demonstrate the feasibility of this recombinant vaccine strategy.

Brief Research Report: Anti-SARS-CoV-2 Immunity in Long Lasting Responders to Cancer Immunotherapy Through mRNA-Based COVID-19 Vaccination.

Cancer patients (CPs) have been identified as particularly vulnerable to SARS-CoV-2 infection, and therefore are a priority group for receiving COVID-19 vaccination. From the patients with advanced solid tumors, about 20% respond very efficiently to immunotherapy with anti-PD1/PD-L1 antibodies and achieve long lasting cancer responses. It is unclear whether an efficient cancer-specific immune response may also correlate with an efficient response upon COVID-19 vaccination. Here, we explored the antiviral immune response to the mRNA-based COVID-19 vaccine BNT162b2 in a group of 11 long-lasting cancer immunotherapy responders. We analysed the development of SARS-CoV-2-specific IgG serum antibodies, virus neutralizing capacities and T cell responses. Control groups included patients treated with adjuvant cancer immunotherapy (IMT, cohort B), CPs not treated with immunotherapy (no-IMT, cohort C) and healthy controls (cohort A). The median ELISA IgG titers significantly increased after the prime-boost COVID vaccine regimen in all cohorts (Cohort A: pre-vaccine = 900 (100-2700), 3 weeks (w) post-boost = 24300 (2700-72900); Cohort B: pre-vaccine = 300 (100-2700), 3 w post-boost = 8100 (300-72900); Cohort C: pre-vaccine = 500 (100-2700), 3 w post-boost = 24300 (300-72900)). However, at the 3 w post-prime time-point, only the healthy control group showed a statistically significant increase in antibody levels (Cohort A = 8100 (900-8100); Cohort B = 900 (300-8100); Cohort C = 900 (300-8100)) (P < 0.05). Strikingly, while all healthy controls generated high-level antibody responses after the complete prime-boost regimen (Cohort A = 15/15 (100%), not all CPs behaved alike [Cohort B= 12/14 (84'6%); Cohort C= 5/6 (83%)]. Their responses, including those of the long-lasting immunotherapy responders, were more variable (Cohort A: 3 w post-boost (median nAb titers = 95.32 (84.09-96.93), median Spike-specific IFN-γ response = 64 (24-150); Cohort B: 3 w post-boost (median nAb titers = 85.62 (8.22-97.19), median Spike-specific IFN-γ response (28 (1-372); Cohort C: 3 w post-boost (median nAb titers = 95.87 (11.8-97.3), median Spike-specific IFN-γ response = 67 (20-84)). Two long-lasting cancer responders did not respond properly to the prime-boost vaccination and did not generate S-specific IgGs, neutralizing antibodies or virus-specific T cells, although their cancer immune control persisted for years. Thus, although mRNA-based vaccines can induce both antibody and T cell responses in CPs, the immune response to COVID vaccination is independent of the capacity to develop an efficient anti-cancer immune response to anti PD-1/PD-L1 antibodies.

Anti-SARS-COV-2 specific immunity in HIV immunological non-responders after mRNA-based COVID-19 vaccination.

Individuals infected with the human immunodeficiency virus type 1 (HIV-1) belong to the group of people most vulnerable to SARS-CoV-2 infections and the associated disease COVID-19. Here we describe SARS-CoV-2-specific antibody and cellular immune responses in a small cohort of immunological non-responder HIV-1 patients (HIV-INRs) after receiving the COVID-19 mRNA-based BioNTech/Pfizer vaccine. Compared to the control group of vaccinated healthy individuals that all developed a virus-specific immune response, 5 of 10 vaccinated HIV-1 patients showed insufficient immune responses. The lack of response was not directly correlated with patients CD4 cell counts. Three of the five non-responders that agreed to receive a booster vaccination subsequently generated a virus-specific response. Thus, even HIV-INRs can be efficiently vaccinated against COVID-19 but may require a follow-up by virus-specific immune monitoring to guarantee clinical vaccine benefits.

Predicting the Kinetic Coordination of Immune Response Dynamics in SARS-CoV-2 Infection: Implications for Disease Pathogenesis

A calibrated mathematical model of antiviral immune response to SARS-CoV-2 infection is developed. The model considers the innate and antigen-specific responses to SARS-CoV-2 infection. Recently published data sets from human challenge studies with SARS-CoV-2 were used for parameter evaluation. The calibration of the mathematical model of SARS-CoV-2 infection is based on combining the parameter guesses from our earlier study of influenza A virus infection, some recent quantitative models of SARS-CoV-2 infection and clinical data-based parameter estimation of a subset of the model parameters. Hence, the calibrated mathematical model represents a theoretical exploration type of study, i.e., 'in silico patient' with mild-to-moderate severity phenotype, rather than a completely validated quantitative model of COVID-19 with respect to all its state-space variables. Understanding the regulation of multiple intertwined reaction components of the immune system is necessary for linking the kinetics of immune responses with the clinical phenotypes of COVID-19. Consideration of multiple immune reaction components in a single calibrated mathematical model allowed us to address some fundamental issues related to the pathogenesis of COVID-19, i.e., the sensitivity of the peak viral load to the parameters characterizing the antiviral specific response components, the kinetic coordination of the individual innate and adaptive immune responses, and the factors favoring a prolonged viral persistence. The model provides a tool for predicting the infectivity of patients, i.e., the amount of virus which is transmitted via droplets from the person infected with SARS-CoV-2, depending on the time of infection. The thresholds for variations of the innate and adaptive response parameters which lead to a prolonged persistence of SARS-CoV-2 due to the loss of a kinetic response synchrony/coordination between them were identified.

Brief Research Report: Anti-SARS-CoV-2 Immunity in Long Lasting Responders to Cancer Immunotherapy Through mRNA-Based COVID-19 Vaccination

Cancer patients (CPs) have been identified as particularly vulnerable to SARS-CoV-2 infection, and therefore are a priority group for receiving COVID-19 vaccination. From the patients with advanced solid tumors, about 20% respond very efficiently to immunotherapy with anti-PD1/PD-L1 antibodies and achieve long lasting cancer responses. It is unclear whether an efficient cancer-specific immune response may also correlate with an efficient response upon COVID-19 vaccination. Here, we explored the antiviral immune response to the mRNA-based COVID-19 vaccine BNT162b2 in a group of 11 long-lasting cancer immunotherapy responders. We analysed the development of SARS-CoV-2-specific IgG serum antibodies, virus neutralizing capacities and T cell responses. Control groups included patients treated with adjuvant cancer immunotherapy (IMT, cohort B), CPs not treated with immunotherapy (no-IMT, cohort C) and healthy controls (cohort A). The median ELISA IgG titers significantly increased after the prime-boost COVID vaccine regimen in all cohorts (Cohort A: pre-vaccine = 900 (100-2700), 3 weeks (w) post-boost = 24300 (2700-72900);Cohort B: pre-vaccine = 300 (100-2700), 3 w post-boost = 8100 (300-72900);Cohort C: pre-vaccine = 500 (100-2700), 3 w post-boost = 24300 (300-72900)). However, at the 3 w post-prime time-point, only the healthy control group showed a statistically significant increase in antibody levels (Cohort A = 8100 (900-8100);Cohort B = 900 (300-8100);Cohort C = 900 (300-8100)) (P < 0.05). Strikingly, while all healthy controls generated high-level antibody responses after the complete prime-boost regimen (Cohort A = 15/15 (100%), not all CPs behaved alike [Cohort B= 12/14 (84'6%);Cohort C= 5/6 (83%)]. Their responses, including those of the long-lasting immunotherapy responders, were more variable (Cohort A: 3 w post-boost (median nAb titers = 95.32 (84.09-96.93), median Spike-specific IFN-γ response = 64 (24-150);Cohort B: 3 w post-boost (median nAb titers = 85.62 (8.22-97.19), median Spike-specific IFN-γ response (28 (1-372);Cohort C: 3 w post-boost (median nAb titers = 95.87 (11.8-97.3), median Spike-specific IFN-γ response = 67 (20-84)). Two long-lasting cancer responders did not respond properly to the prime-boost vaccination and did not generate S-specific IgGs, neutralizing antibodies or virus-specific T cells, although their cancer immune control persisted for years. Thus, although mRNA-based vaccines can induce both antibody and T cell responses in CPs, the immune response to COVID vaccination is independent of the capacity to develop an efficient anti-cancer immune response to anti PD-1/PD-L1 antibodies.

Sensitivity of SARS-CoV-2 Life Cycle to IFN Effects and ACE2 Binding Unveiled with a Stochastic Model.

Mathematical modelling of infection processes in cells is of fundamental interest. It helps to understand the SARS-CoV-2 dynamics in detail and can be useful to define the vulnerability steps targeted by antiviral treatments. We previously developed a deterministic mathematical model of the SARS-CoV-2 life cycle in a single cell. Despite answering many questions, it certainly cannot accurately account for the stochastic nature of an infection process caused by natural fluctuation in reaction kinetics and the small abundance of participating components in a single cell. In the present work, this deterministic model is transformed into a stochastic one based on a Markov Chain Monte Carlo (MCMC) method. This model is employed to compute statistical characteristics of the SARS-CoV-2 life cycle including the probability for a non-degenerate infection process. Varying parameters of the model enables us to unveil the inhibitory effects of IFN and the effects of the ACE2 binding affinity. The simulation results show that the type I IFN response has a very strong effect on inhibition of the total viral progeny whereas the effect of a 10-fold variation of the binding rate to ACE2 turns out to be negligible for the probability of infection and viral production.

The HIV-1 Nucleocapsid Regulates Its Own Condensation by Phase-Separated Activity-Enhancing Sequestration of the Viral Protease during Maturation.

A growing number of studies indicate that mRNAs and long ncRNAs can affect protein populations by assembling dynamic ribonucleoprotein (RNP) granules. These phase-separated molecular 'sponges', stabilized by quinary (transient and weak) interactions, control proteins involved in numerous biological functions. Retroviruses such as HIV-1 form by self-assembly when their genomic RNA (gRNA) traps Gag and GagPol polyprotein precursors. Infectivity requires extracellular budding of the particle followed by maturation, an ordered processing of ∼2400 Gag and ∼120 GagPol by the viral protease (PR). This leads to a condensed gRNA-NCp7 nucleocapsid and a CAp24-self-assembled capsid surrounding the RNP. The choreography by which all of these components dynamically interact during virus maturation is one of the missing milestones to fully depict the HIV life cycle. Here, we describe how HIV-1 has evolved a dynamic RNP granule with successive weak-strong-moderate quinary NC-gRNA networks during the sequential processing of the GagNC domain. We also reveal two palindromic RNA-binding triads on NC, KxxFxxQ and QxxFxxK, that provide quinary NC-gRNA interactions. Consequently, the nucleocapsid complex appears properly aggregated for capsid reassembly and reverse transcription, mandatory processes for viral infectivity. We show that PR is sequestered within this RNP and drives its maturation/condensation within minutes, this process being most effective at the end of budding. We anticipate such findings will stimulate further investigations of quinary interactions and emergent mechanisms in crowded environments throughout the wide and growing array of RNP granules.

Intracellular Life Cycle Kinetics of SARS-CoV-2 Predicted Using Mathematical Modelling.

SARS-CoV-2 infection represents a global threat to human health. Various approaches were employed to reveal the pathogenetic mechanisms of COVID-19. Mathematical and computational modelling is a powerful tool to describe and analyze the infection dynamics in relation to a plethora of processes contributing to the observed disease phenotypes. In our study here, we formulate and calibrate a deterministic model of the SARS-CoV-2 life cycle. It provides a kinetic description of the major replication stages of SARS-CoV-2. Sensitivity analysis of the net viral progeny with respect to model parameters enables the identification of the life cycle stages that have the strongest impact on viral replication. These three most influential parameters are (i) degradation rate of positive sense vRNAs in cytoplasm (negative effect), (ii) threshold number of non-structural proteins enhancing vRNA transcription (negative effect), and (iii) translation rate of non-structural proteins (positive effect). The results of our analysis could be used for guiding the search for antiviral drug targets to combat SARS-CoV-2 infection.

Back to Normal: An Old Physics Route to Reduce SARS-CoV-2 Transmission in Indoor Spaces.

We advocate the widespread use of UV-C light as a short-term, easily deployable, and affordable way to limit virus spread in the current SARS-CoV-2 pandemic. Radical social distancing with the associated shutdown of schools, restaurants, sport clubs, workplaces, and traveling has been shown to be effective in reducing virus spread, but its economic and social costs are unsustainable in the medium term. Simple measures like frequent handwashing, facial masks, and other physical barriers are being commonly adopted to prevent virus transmission. However, their efficacy may be limited, particularly in shared indoor spaces, where, in addition to airborne transmission, elements with small surface areas such as elevator buttons, door handles, and handrails are frequently used and can also mediate transmission. We argue that additional measures are necessary to reduce virus transmission when people resume attending schools and jobs that require proximity or some degree of physical contact. Among the available alternatives, UV-C light satisfies the requirements of rapid, widespread, and economically viable deployment. Its implementation is only limited by current production capacities, an increase of which requires swift intervention by industry and authorities.