In silico dynamics of COVID-19 phenotypes for optimizing clinical management

Understanding the underlying mechanisms of COVID-19 progression and the impact of various pharmaceutical interventions is crucial for the clinical management of the disease. We developed a comprehensive mathematical framework based on the known mechanisms of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection, incorporating the renin−angiotensin system and ACE2, which the virus exploits for cellular entry, key elements of the innate and adaptive immune responses, the role of inflammatory cytokines, and the coagulation cascade for thrombus formation. The model predicts the evolution of viral load, immune cells, cytokines, thrombosis, and oxygen saturation based on patient baseline condition and the presence of comorbidities. Model predictions were validated with clinical data from healthy people and COVID-19 patients, and the results were used to gain insight into identified risk factors of disease progression including older age; comorbidities such as obesity, diabetes, and hypertension; and dysregulated immune response. We then simulated treatment with various drug classes to identify optimal therapeutic protocols. We found that the outcome of any treatment depends on the sustained response rate of activated CD8⁺ T cells and sufficient control of the innate immune response. Furthermore, the best treatment—or combination of treatments—depends on the preinfection health status of the patient. Our mathematical framework provides important insight into SARS-CoV-2 pathogenesis and could be used as the basis for personalized, optimal management of COVID-19.

COVID-19 has created unprecedented challenges for the health care system, and, until an effective vaccine is developed and made widely available, treatment options are limited. A challenge to the development of optimal treatment strategies is the extreme heterogeneity of presentation. Infection with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) results in a syndrome that ranges in severity from asymptomatic to multiorgan failure and death. In addition to local complications in the lung, the virus can cause systemic inflammation and disseminated microthrombosis, which can cause stroke, myocardial infarction, or pulmonary emboli (1–4). Risk factors for poor COVID-19 outcome include advanced age, obesity, diabetes, and hypertension (5–13).Computational analyses can provide insights into the transmission, control, progression, and underlying mechanisms of infectious diseases. Indeed, epidemiological and statistical modeling has been used for COVID-19, providing powerful insights into comorbidities, transmission dynamics, and control of the disease (14–17). However, to date, these analyses have been population dynamics models of SARS-CoV-2 infection and transmission or correlative analyses of COVID-19 comorbidities and treatment response. Simple viral dynamics models have been also developed and used to predict the SARS-CoV-2 response to antiviral drugs (18, 19). These models, however, do not explicitly consider the biological or physiological mechanisms underlying disease progression or the time course of response to various therapeutic interventions, and only a few more-sophisticated models have been developed toward this direction (20, 21).Several therapies targeting various aspects of COVID-19 pathogenesis have been proposed and have either completed—or are currently being tested in—clinical trials (22). Despite strong biologic rationale, these treatments have generally produced conflicting results in the clinic. For example, trials of antiviral therapies (e.g., remdesivir) have been mixed: The original trial from China failed (23), a subsequent trial in the United States led to approval of remdesivir in the United States and other countries (24), and the recent results of the World Health Organization Solidarity trial again show no benefit (25). Other antiviral drugs alone or in combination are also showing promise (26).Other potential treatments include antiinflammatory drugs and antithrombotic agents. Because of the systemic inflammation seen in many patients, antiinflammatory drugs have been tested, including anti-IL6/IL6R therapy (e.g., tocilizumab, siltuximab) and anti-JAK1/2 drugs (e.g., barcitinib). It is not clear whether these drugs will be effective as stand-alone treatments, particularly after the recent failure of tocilizumab in a phase III trial (1, 27–29). In addition, given that a common complication of COVID-19 is the development of coagulopathies with microvascular thrombi potentially leading to the dysfunction of multiple organ systems (2, 3), antithrombotic drugs (e.g., low molecular weight heparin) are being tested. Recognizing the interactions of COVID-19 with the immune system (30), the corticosteroid dexamethasone has been tested, showing some promising results. Given the large range of patient comorbidities, disease severities, and variety of complications such as thrombosis, it is likely that patients will have heterogeneous responses to any given therapy, and such heterogeneity will continue to be a challenge for clinical trials of unselected COVID-19 patients (31).Here, we developed a systems biology-based mathematical model to address this urgent need. Our model incorporates the known mechanisms of SARS-CoV-2 pathogenesis and the potential mechanisms of action of various therapeutic interventions that have been tested in COVID-19 patients. In previous work, we have exploited angiotensin receptor blockers (ARBs) and angiotensin converting enzyme inhibitors (ACEis) for the improvement of cancer therapies and developed mathematical models of the renin−angiotensin system in the context of cancer desmoplasia (32–35). Using a similar approach, we developed a detailed model that includes lung infection by the SARS-CoV-2 virus and a pharmacokinetic/pharmacodynamic (PK/PD) model of infection and thrombosis to simulate events that take place throughout the body during COVID-19 progression (Fig. 1 and SI Appendix, Fig. S1). The model is first validated against clinical data of healthy people and COVID-19 patients and then used to simulate disease progression in patients with specific comorbidities. Subsequently, we present model predictions for various therapies currently employed for treatment of COVID-19 alone or in combination, and we identify protocols for optimal clinical management for each of the clinically observed COVID-19 phenotypes.Open in a separate windowFig. 1.Schematic of the detailed lung model. The model incorporates the virus infection of epithelial and endothelial cells, the RAS, T cells activation and immune checkpoints, the known IL6 pathways, neutrophils, and macrophages, as well as the formation of NETs, and the coagulation cascade. The lung model is coupled with a PK/PD model for the virus and thrombi dissemination through the body.     

Prevalence and predictors of delayed gastric emptying among Indian patients with long-standing type 2 diabetes mellitus

Background and Aims

Upper gastrointestinal symptoms are more prevalent among type 2 diabetes mellitus (T2DM) patients. The prevalence of delayed gastric emptying (GE) and factors predictive of it have not been studied in Indian T2DM patients and the present study aimed to study the same.

Methods

This hospital-based cross-sectional study involved adult (age between 18 and 65 years) outpatients with T2DM of ≥5-year duration. Measurements of GE of a labelled standardized solid rice idli meal by gastric emptying scintigraphy (GES), symptoms of delayed GE (by standardized questionnaire) and autonomic function by cardiovascular autonomic function tests (AFTs) were carried out. Thirty healthy subjects served as controls for GES and AFTs.

Results

One hundred and forty T2DM patients (age range: 32–65 years) were studied. Delayed GE was documented in ≈29 % (40/140) and rapid GE in 2 % (3/140) of T2DM patients. Univariate analysis showed significant positive association between delayed GE and duration of DM, body mass index (BMI), HbA1c, retinopathy, peripheral neuropathy, autonomic dysfunction and coronary artery disease (p < 0.05 for all). However, there was no significant correlation of age, sex, symptoms suggestive of gastroparesis and nephropathy with delayed GE. Hypoglycemic episodes were significantly more frequent in those with delayed GE (p < 0.05). Multiple logistic regression analysis revealed only BMI and HbA1c to be significant independent predictors of delayed GE.

Conclusion

Presence and severity of symptoms of gastroparesis did not predict delayed GE. Delayed GE, irrespective of symptoms, was associated with microvascular and macrovascular diabetic complications and increased risk of hypoglycemic episodes. HbA_1c and BMI were independent predictors of delayed GE.

     

The prevalence of abnormal ECG in trained sportsmen

Background

Competitive sports training causes structural and conductive system changes manifesting by various electrocardiographic alterations. We undertook this study to assess the prevalence of abnormal ECG in trained Indian athletes and correlate it with the nature of sports training, that is endurance or strength training.

Methods

We evaluated a standard resting, lying 12 lead Electrocardiogram (ECG) in 66 actively training Indian athletes. Standard diagnostic criteria were used to define various morphological ECG abnormalities.

Results

33/66 (50%) of the athletes were undertaking endurance training while the other 33 (50%) were involved in a strength-training regimen. Overall 54/66 (81%) sportsmen had significant ECG changes. 68% of these changes were considered as normal training related features, while the remaining 32% were considered abnormal. There were seven common training related ECG changes–Sinus Bradycardia (21%), Sinus Arrhythmia (16%), 1st degree Atrioventricular Heart Block (6%), Type 1 2nd-degree Atrioventicular Heart Block (3%), Incomplete Right bundle branch block (RBBB) (24%), Early Repolarization (42%), Left Ventricular Hypertrophy (LVH) (14%); while three abnormal ECG changes--T-wave inversion (13%), RBBB(4%), Right ventricular hypertrophy (RVH) with strain (29%) were noted. Early repolarization (commonest change), sinus bradycardia, and incomplete RBBB were the commoner features noticed, with a significantly higher presence in the endurance trained athletes.

Conclusion

A high proportion of athletes undergoing competitive level sports training are likely to have abnormal ECG recordings. Majority of these are benign, and related to the physiological adaptation to the extreme levels of exertion. These changes are commoner during endurance training (running) than strength training (weightlifting).     

Macrophages retain hematopoietic stem cells in the spleen via VCAM-1

Splenic myelopoiesis provides a steady flow of leukocytes to inflamed tissues, and leukocytosis correlates with cardiovascular mortality. Yet regulation of hematopoietic stem cell (HSC) activity in the spleen is incompletely understood. Here, we show that red pulp vascular cell adhesion molecule 1 (VCAM-1)⁺ macrophages are essential to extramedullary myelopoiesis because these macrophages use the adhesion molecule VCAM-1 to retain HSCs in the spleen. Nanoparticle-enabled in vivo RNAi silencing of the receptor for macrophage colony stimulation factor (M-CSFR) blocked splenic macrophage maturation, reduced splenic VCAM-1 expression and compromised splenic HSC retention. Both, depleting macrophages in CD169 iDTR mice or silencing VCAM-1 in macrophages released HSCs from the spleen. When we silenced either VCAM-1 or M-CSFR in mice with myocardial infarction or in ApoE^−/− mice with atherosclerosis, nanoparticle-enabled in vivo RNAi mitigated blood leukocytosis, limited inflammation in the ischemic heart, and reduced myeloid cell numbers in atherosclerotic plaques.Leukocytosis correlates closely with cardiovascular mortality. In the steady state, blood leukocytes derive exclusively from bone marrow hematopoietic stem cells (HSCs). Supporting cells (Sugiyama et al., 2006; Ding et al., 2012; Ding and Morrison, 2013), including macrophages (Winkler et al., 2010; Chow et al., 2011), maintain the bone marrow HSC niche and regulate hematopoietic stem and progenitor cell (HSPC) activity by supplying various cytokines and retention factors. Systemic inflammation can stimulate extramedullary hematopoiesis in adult mice and humans. Splenic myelopoiesis supplies inflammatory monocytes to atherosclerotic plaques (Robbins et al., 2012) and the ischemic myocardium (Leuschner et al., 2012). In ischemic heart disease, HSPCs emigrate from the bone marrow, seed the spleen, and amplify leukocyte production (Dutta et al., 2012). Splenic HSPCs localize in the red pulp near the sinusoids in parafollicular areas (Kiel et al., 2005). Likewise, after adoptive transfer of GFP⁺ HSPCs, GFP⁺ colonies populate the splenic red pulp of atherosclerotic ApoE^−/− mice (Robbins et al., 2012). During myocardial infarction (MI), proinflammatory monocytes derived from the spleen accelerate atherosclerotic progression (Dutta et al., 2012). Collectively, these data suggest that splenic myelopoiesis has promise as a therapeutic target; however, the components of the splenic hematopoietic niche are incompletely understood, especially compared with the well-studied bone marrow niche. Understanding HSC retention factors and their regulation in the spleen was the purpose of this study.Because the spleen harbors very few HSCs in the steady state, we investigated the splenic hematopoietic niche after injecting the Toll-like receptor ligand LPS to activate extramedullary hematopoiesis. In the bone marrow, macrophages are an integral part of the HSC niche (Winkler et al., 2010; Chow et al., 2011) and differentiation depends on the receptor for macrophage colony-stimulating factor (M-CSFR, CD115; Auffray et al., 2009). We thus hypothesized that splenic hematopoietic niche assembly also requires M-CSFR signaling. In line with knockout studies (Takahashi et al., 1994; Dai et al., 2002), in vivo knockdown of M-CSFR with nanoparticle-encapsulated siRNA reduced splenic macrophage numbers substantially. Interestingly, decreased macrophage numbers were associated with a reduction of splenic HSCs. Depleting macrophages with diphtheria toxin (DT) in CD169 iDTR mice reproduced the findings obtained with M-CSF–directed siRNA treatment, thereby indicating that macrophages have a key role in splenic HSC maintenance. To investigate how splenic macrophages retain HSCs, we measured changes in splenic expression of major bone marrow retention factors after M-CSFR silencing. Silencing M-CSFR selectively reduced splenic VCAM-1, and the adhesion molecule was primarily expressed by macrophages. Inhibiting macrophage expression of VCAM-1 with siRNA targeting this adhesion molecule reduced splenic HSPC numbers. Finally, we found that M-CSFR and macrophage-directed VCAM-1 silencing in mice with atherosclerosis mitigated blood leukocytosis and dampened inflammation in atherosclerotic plaques and the infarcted myocardium. These data reveal the importance of VCAM-1 expression by splenic macrophages for extramedullary hematopoiesis and illustrate the therapeutic potential of RNAi as an antiinflammatory that mutes emergency overproduction and provision of myeloid cells.