Disentangling the dynamical underpinnings of differences in SARS-CoV-2 pathology using within-host ecological models

Comparison of plasma mitochondrial DNA copy number in asymptomatic and symptomatic COVID-19 patients

Introduction

Since the beginning of the COVID-19 pandemic, a wide clinical spectrum, from asymptomatic infection to mild or severe disease and death, have been reported in COVID-19 patients. Studies have suggested several possible factors, which may affect the clinical outcome of COVID-19. A pro-inflammatory state and impaired antiviral response have been suggested as major contributing factors in severe COVID-19. Considering that mitochondria have an important role in regulating the immune responses to pathogens, pro-inflammatory signaling, and cell death, it has received much attention in SARS-CoV-2 infection. Recent studies have demonstrated that high levels of cell-free mitochondrial DNA (cf-mtDNA) are associated with an increased risk of COVID-19 intensive care unit (ICU) admission and mortality. However, there have been few studies on cf-mtDNA in SARS-CoV-2 infection, mainly focusing on critically ill COVID-19 cases. In the present study, we investigated cf-mtDNA copy number in COVID-19 patients and compared between asymptomatic and symptomatic cases, and assessed the clinical values. We also determined the cf-nuclear DNA (cf-nDNA) copy number and mitochondrial transcription factor A (TFAM) mRNA level in the studied groups.

Materials and methods

Plasma and buffy coat samples were collected from 37 COVID-19 patients and 33 controls. Briefly, after total DNA extraction, plasma cf-mtDNA, and cf-nDNA copy numbers were measured by absolute qPCR using a standard curve method. Furthermore, after total RNA extraction from buffy coat and cDNA synthesis, TFAM mRNA levels were evaluated by qPCR.

Results

The results showed that cf-mtDNA levels in asymptomatic COVID-19 patients were statistically significantly higher than in symptomatic cases (p value = 0.01). However, cf-nDNA levels were higher in symptomatic patients than in asymptomatic cases (p value = 0.00). There was no significant difference between TFAM levels in the buffy coat of these two groups (p value > 0.05). Also, cf-mtDNA levels showed good diagnostic potential in COVID-19 subgroups.

Conclusion

cf-mtDNA is probably important in the outcome of SARS-CoV-2 infection due to its role in inflammation and immune response. It can also be a promising candidate biomarker for the diagnosis of COVID-19 subgroups. Further investigation will help understanding the COVID-19 pathophysiology and effective diagnostic and therapeutic strategies.

Niche theory for within-host parasite dynamics: Analogies to food web modules via feedback loops

Why do parasites exhibit a wide dynamical range within their hosts? For instance, why does infecting dose either lead to infection or immune clearance? Why do some parasites exhibit boom-bust, oscillatory dynamics? What maintains parasite diversity, that is coinfection v single infection due to exclusion or priority effects? For insights on parasite dose, dynamics and diversity governing within-host infection, we turn to niche models. An omnivory food web model (IGP) blueprints one parasite competing with immune cells for host energy (PIE). Similarly, a competition model (keystone predation, KP) mirrors a new coinfection model (2PIE). We then drew analogies between models using feedback loops. The following three points arise: first, like in IGP, parasites oscillate when longer loops through parasites, immune cells and resource regulate parasite growth. Shorter, self-limitation loops (involving resources and enemies) stabilise those oscillations. Second, IGP can produce priority effects that resemble immune clearance. But, despite comparable loop structure, PIE cannot due to constraints imposed by production of immune cells. Third, despite somewhat different loop structure, KP and 2PIE share apparent and resource competition mechanisms that produce coexistence (coinfection) or priority effects of prey or parasites. Together, this mechanistic niche framework for within-host dynamics offers new perspective to improve individual health.

Modeling recapitulates the heterogeneous outcomes of SARS-CoV-2 infection and quantifies the differences in the innate immune and CD8 T-cell responses between patients experiencing mild and severe symptoms

SARS-CoV-2 infection results in highly heterogeneous outcomes, from cure without symptoms to acute respiratory distress and death. Empirical evidence points to the prominent roles of innate immune and CD8 T-cell responses in determining the outcomes. However, how these immune arms act in concert to elicit the outcomes remains unclear. Here, we developed a mathematical model of within-host SARS-CoV-2 infection that incorporates the essential features of the innate immune and CD8 T-cell responses. Remarkably, by varying the strengths and timings of the two immune arms, the model recapitulated the entire spectrum of outcomes realized. Furthermore, model predictions offered plausible explanations of several confounding clinical observations, including the occurrence of multiple peaks in viral load, viral recrudescence after symptom loss, and prolonged viral positivity. We applied the model to analyze published datasets of longitudinal viral load measurements from patients exhibiting diverse outcomes. The model provided excellent fits to the data. The best-fit parameter estimates indicated a nearly 80-fold stronger innate immune response and an over 200-fold more sensitive CD8 T-cell response in patients with mild compared to severe infection. These estimates provide quantitative insights into the likely origins of the dramatic inter-patient variability in the outcomes of SARS-CoV-2 infection. The insights have implications for interventions aimed at preventing severe disease and for understanding the differences between viral variants.

Why do some coronaviruses become pandemic threats when others do not?

Despite multiple spillover events and short chains of transmission on at least 4 continents, Middle East Respiratory Syndrome Coronavirus (MERS-CoV) has never triggered a pandemic. By contrast, its relative, Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) has, despite apparently little, if any, previous circulation in humans. Resolving the unsolved mystery of the failure of MERS-CoV to trigger a pandemic could help inform how we understand the pandemic potential of pathogens, and probing it underscores a need for a more holistic understanding of the ways in which viral genetic changes scale up to population-level transmission.

Therapeutic potential of induced iron depletion using iron chelators in Covid-19

Ferritin, which includes twenty-four light and heavy chains in varying proportions in different tissues, is primarily responsible for maintaining the body's iron metabolism. Its normal value is between 10 and 200 ngmL^-1 in men and between 30 and 300 ngmL^-1 in women. Iron is delivered to the tissue via them, and they act as immunomodulators, signaling molecules, and inflammatory markers. When ferritin level exceeds 1000 µgL^-1, the patient is categorized as having hyperferritinemia. Iron chelators such as deferiprone, deferirox, and deferoxamine are currently FDA approved to treat iron overload. The inflammation cascade and poor prognosis of COVID-19 may be attributed to high ferritin levels. Critically ill patients can benefit from deferasirox, an iron chelator administered orally at 20-40 mgkg^-1 once daily, as well as intravenous deferoxamine at 1000 mg initially followed by 500 mg every 4 to 12 h. It can be combined with monoclonal antibodies, antioxidants, corticosteroids, and lactoferrin to make iron chelation therapy effective for COVID-19 victims. In this article, we analyze the antiviral and antifibrotic activity of iron chelators, thereby promoting iron depletion therapy as a potentially innovative treatment strategy for COVID-19.

Spatially distributed infection increases viral load in a computational model of SARS-CoV-2 lung infection

A key question in SARS-CoV-2 infection is why viral loads and patient outcomes vary dramatically across individuals. Because spatial-temporal dynamics of viral spread and immune response are challenging to study in vivo, we developed Spatial Immune Model of Coronavirus (SIMCoV), a scalable computational model that simulates hundreds of millions of lung cells, including respiratory epithelial cells and T cells. SIMCoV replicates viral growth dynamics observed in patients and shows how spatially dispersed infections can lead to increased viral loads. The model also shows how the timing and strength of the T cell response can affect viral persistence, oscillations, and control. By incorporating spatial interactions, SIMCoV provides a parsimonious explanation for the dramatically different viral load trajectories among patients by varying only the number of initial sites of infection and the magnitude and timing of the T cell immune response. When the branching airway structure of the lung is explicitly represented, we find that virus spreads faster than in a 2D layer of epithelial cells, but much more slowly than in an undifferentiated 3D grid or in a well-mixed differential equation model. These results illustrate how realistic, spatially explicit computational models can improve understanding of within-host dynamics of SARS-CoV-2 infection.

The evolution of powerful yet perilous immune systems

The mammalian immune system packs serious punch against infection but can also cause harm: for example, coronavirus disease 2019 (COVID-19) made headline news of the simultaneous power and peril of human immune responses. In principle, natural selection leads to exquisite adaptation and therefore cytokine responsiveness that optimally balances the benefits of defense against its costs (e.g., immunopathology suffered and resources expended). Here, we illustrate how evolutionary biology can predict such optima and also help to explain when/why individuals exhibit apparently maladaptive immunopathological responses. Ultimately, we argue that the evolutionary legacies of multicellularity and life-history strategy, in addition to our coevolution with symbionts and our demographic history, together explain human susceptibility to overzealous, pathology-inducing cytokine responses. Evolutionary insight thereby complements molecular/cellular mechanistic insights into immunopathology.

Challenges in modeling the emergence of novel pathogens

The emergence of infectious agents with pandemic potential present scientific challenges from detection to data interpretation to understanding determinants of risk and forecasts. Mathematical models could play an essential role in how we prepare for future emergent pathogens. Here, we describe core directions for expansion of the existing tools and knowledge base, including: using mathematical models to identify critical directions and paths for strengthening data collection to detect and respond to outbreaks of novel pathogens; expanding basic theory to identify infectious agents and contexts that present the greatest risks, over both the short and longer term; by strengthening estimation tools that make the most use of the likely range and uncertainties in existing data; and by ensuring modelling applications are carefully communicated and developed within diverse and equitable collaborations for increased public health benefit.

Scenario analysis of COVID-19 transmission dynamics in Malaysia with the possibility of reinfection and limited medical resources scenarios

COVID-19 is a major health threat across the globe, which causes severe acute respiratory syndrome (SARS), and it is highly contagious with significant mortality. In this study, we conduct a scenario analysis for COVID-19 in Malaysia using a simple universality class of the SIR system and extensions thereof (i.e., the inclusion of temporary immunity through the reinfection problems and limited medical resources scenarios leads to the SIRS-type model). This system has been employed in order to provide further insights on the long-term outcomes of COVID-19 pandemic. As a case study, the COVID-19 transmission dynamics are investigated using daily confirmed cases in Malaysia, where some of the epidemiological parameters of this system are estimated based on the fitting of the model to real COVID-19 data released by the Ministry of Health Malaysia (MOH). We observe that this model is able to mimic the trend of infection trajectories of COVID-19 pandemic in Malaysia and it is possible for transmission dynamics to be influenced by the reinfection force and limited medical resources problems. A rebound effect in transmission could occur after several years and this situation depends on the intensity of reinfection force. Our analysis also depicts the existence of a critical value in reinfection threshold beyond which the infection dynamics persist and the COVID-19 outbreaks are rather hard to eradicate. Therefore, understanding the interplay between distinct epidemiological factors using mathematical modelling approaches could help to support authorities in making informed decisions so as to control the spread of this pandemic effectively.

SARS-CoV-2: Cross-scale Insights from Ecology and Evolution

Ecological and evolutionary processes govern the fitness, propagation, and interactions of organisms through space and time, and viruses are no exception. While coronavirus disease 2019 (COVID-19) research has primarily emphasized virological, clinical, and epidemiological perspectives, crucial aspects of the pandemic are fundamentally ecological or evolutionary. Here, we highlight five conceptual domains of ecology and evolution – invasion, consumer-resource interactions, spatial ecology, diversity, and adaptation – that illuminate (sometimes unexpectedly) the emergence and spread of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). We describe the applications of these concepts across levels of biological organization and spatial scales, including within individual hosts, host populations, and multispecies communities. Together, these perspectives illustrate the integrative power of ecological and evolutionary ideas and highlight the benefits of interdisciplinary thinking for understanding emerging viruses.