Significant Unresolved Questions and Opportunities for Bioengineering in Understanding and Treating COVID-19 Disease Progression

Abstract

COVID-19 is a disease that manifests itself in a multitude of ways across a wide range of tissues. Many factors are involved, and though impressive strides have been made in studying this novel disease in a very short time, there is still a great deal that is unknown about how the virus functions. Clinical data has been crucial for providing information on COVID-19 progression and determining risk factors. However, the mechanisms leading to the multi-tissue pathology are yet to be fully established. Although insights from SARS-CoV-1 and MERS-CoV have been valuable, it is clear that SARS-CoV-2 is different and merits its own extensive studies. In this review, we highlight unresolved questions surrounding this virus including the temporal immune dynamics, infection of non-pulmonary tissue, early life exposure, and the role of circadian rhythms. Risk factors such as sex and exposure to pollutants are also explored followed by a discussion of ways in which bioengineering approaches can be employed to help understand COVID-19. The use of sophisticated in vitro models can be employed to interrogate intercellular interactions and also to tease apart effects of the virus itself from the resulting immune response. Additionally, spatiotemporal information can be gleaned from these models to learn more about the dynamics of the virus and COVID-19 progression. Application of advanced tissue and organ system models into COVID-19 research can result in more nuanced insight into the mechanisms underlying this condition and elucidate strategies to combat its effects.

Introduction

As SARS-CoV-2 (the virus) infection continues to spread worldwide, the scientific community is working to characterize the pathophysiology of COVID-19 (the disease) in the hopes of developing effective therapeutics to ease the burden being caused by this pandemic. Whereas insights from SARS-CoV-1 and MERS-CoV have been helpful, it has become clear that SARS-CoV-2 is more contagious and displays idiosyncrasies that separate it from the other zoonotic coronaviruses.33 Impressive strides have been made across various fields, but a great deal of information on this virus continues to elude us. With a heavy reliance on endpoint clinical data, it is difficult to determine the dynamics and timing of this dangerous infection. Infection seemingly occurs in the nasal passage and continues to spread to the lower airway, the gut, the heart, and other tissues through an unidentified route.39,131 In addition to location of spread, the timing seems to be of utmost importance. The innate and adaptive immune system work in concert to respond to this virus, and when this rhythm is disrupted, it can lead to unhampered viral spread and hyperinflammation, causing cytokine storm.138,192 The release of inflammatory cytokines at the wrong time can be the difference between mild and severe disease. Even the symptoms of COVID-19 are unpredictable and appear to abruptly change over time. Given the disease’s effects on a wide range of tissues as well as its mysterious progression, there are many unresolved questions. Engineering approaches and innovative model design could aid in understanding the pathophysiology of this disease. There is an unmet need for models that allow for collection of spatiotemporal information on how the virus spreads and how the body reacts to it. In this perspective, we will highlight critical areas of need, such as determination of how and where the virus propagates through the body and the intricate immune response that follows, factors that could increase risk, and the ramifications of early life exposure, as well as emerging engineering tools that could address these needs.

The Respiratory Tract: SARS-CoV-2 Viral Infection

Unsurprisingly with a coronavirus, the respiratory tract is the primary focus of attention to understand pathophysiology. SARS-CoV-2, along with SARS-CoV-1, uses the transmembrane protein angiotensin converting enzyme (ACE) 2 as an entry point into cells.103,278 SARS-CoV-2 cell entry is mediated by the viral spike protein (S) binding to transmembrane ACE2 on the outer cell membrane via a receptor-binding domain (RBD). The transmembrane serine protease TMPRSS2 cleaves S protein enabling cell internalization by virus-plasma membrane fusion.103,206 Investigation of scRNA-seq datasets from the Human Cell Atlas Consortium showed that TMPRSS2 is more broadly expressed throughout the body compared to ACE2, suggesting that ACE2 expression is the limiting factor for viral entry.221 Indeed, ACE2 is expressed in alveolar epithelial type II cells along with nasal epithelial cells.221 The nasal epithelium was found to express more ACE2 compared to the lower airway epithelium, and in vitro experiments based on infecting primary cultures with recombinant SARS-CoV-2 showed an infection gradient that favored the upper airways.106 The same study found that infected nasal epithelial cells displayed more robust viral replication compared to large airway epithelial cells.106

In severe cases, COVID-19 appears to result in acute respiratory distress syndrome (ARDS), which is caused by widespread pulmonary inflammation.208,258 ARDS, which is commonly caused by sepsis and pneumonia, is characterized by pulmonary capillary permeability and neutrophil invasion, causing an acute inflammatory response along with edema.93 However, COVID-19 patients appear to present with an atypical form of ARDS with relatively high respiratory system compliance, indicating preserved lung gas volume, and low shunt fraction, while displaying severe hypoxemia.87

Temporal Immune Dynamics in COVID-19

As with any viral infection, a sophisticated immune response is critical in resolving disease and hindering the propagation of damage. Immune kinetics in successful responses are fine-tuned, and the temporal control of events ensures the balance of the inflammatory state is maintained. Similar to many airborne viruses, SARS-CoV-2 infects the pulmonary epithelium, initiating the first stage in the immune response. Inflammatory cytokines, such as interferons and interleukin (IL) -6, are released by infected epithelial cells to recruit innate immune intervention.137 At this stage, tissue resident macrophages in the lung parenchyma and leukocytes from the blood arrive at the site of infection. Through a variety of mechanisms, this leg of the response is designed to clear extracellular virus and viral debris, minimizing the burden in the lung. At the same time, tissue resident dendritic cells capture viral antigens and migrate to nearby lymph nodes. It is here in the lymph node that antigen presentation is performed to activate T and B lymphocytes, initiating the adaptive immune response.215 This phase of the response is much more specific and targeted, as infected cells are destroyed, and the production of antibodies aids in obstruction of virus and labeling of virus for further processing and disposal by immune cells.62 While the adaptive immune response is incredibly robust and efficient in responding to pathogens, the main cost of this specificity is the time required to develop the response. It is paramount that during this transient period of priming the adaptive immunity that the innate immune response sufficiently holds off progression of the infection. For this reason, timing and coordination of the response can be one of the most crucial factors in disease outcome. Early clinical findings indicate that SARS-CoV-2 actively avoids some of these early innate immune mechanisms and can dysregulate proper timing of the response.63,73 This disruption of the response and delayed resolution has been shown to lead to a hyperinflammatory state in COVID-19 patients and can result in cytokine storm, or hypercytokinemia, and ARDS.110,143

Although data on SARS-CoV-2 has been limited and will likely be more readily available in the coming months, its similarity to SARS-CoV-1 and MERS-CoV, 80 and 50% RNA homology respectively, provides a place to start in the investigation of these immune evasion mechanisms.150 One such mechanism characterized in SARS-CoV-1 and believed to be involved in SARS-CoV-2, is the suppression of the type 1 interferon (T1IFN) response.73,192 Typically, the presence of viral RNA is discovered by pathogen recognition receptors (PRRs), such as toll-like receptors (TLRs), on the cell membrane or inside endosomes and retinoic-acid inducible gene I (RIG-I)-like receptors in the cytoplasm (RLRs).135,148 In SARS-CoV-1, these important signaling receptors are suppressed, which prevents the nuclear translocation and activation of NF-κB and IRF3. Both of these transcription factors are involved in the expression of major pro-inflammatory cytokines such as T1IFN, IL-1, IL-6, and TNF-α.145,192 By evading immune recognition transiently after infection, the innate immunity is delayed, and the finely tuned timing of the response is disrupted. In SARS-CoV-1, this pathway of immune evasion leads to rapid viral replication and highly correlates to poor disease outcome. It is suspected that the immune response to SARS-CoV-2 similarly involves this mechanism.36,121

In addition to immune evasion techniques that allow the virus to rapidly replicate in the lung, evidence is also emerging that the virus can directly infect or obstruct immune cells. Some early studies have shown evidence that SARS-CoV-2 is capable of infecting CD169+ macrophages within the lymph node.38 The infection of these macrophages resulted in the destruction of lymph node architecture, lymphocyte death, and upregulation of inflammatory markers such as IL-6. The removal of an effective adaptive immune response would directly contribute to a poor disease resolution, characteristic of the severe cases of COVID-19. While still early, this initial finding confirms similarities to SARS-CoV-1, which also results in monocyte and macrophage infection.264 Additionally, this mechanism could contribute to the consistent findings of lymphopenia in severe COVID-19 cases.194 One COVID-19 hallmark includes depressed lymphocyte counts and a high neutrophil-lymphocyte ratio (NL ratio), pointing to an overactivation of non-specific innate immune responses and/or depressed adaptive response. Thus, NL ratio is an effective tool for determining disease prognosis, and high NL ratio is a predictor of poor outcomes.259 Unfortunately, the exact mechanism of systemic lymphopenia in SARS-CoV-2 infection remains unclear with numerous outstanding hypotheses, including a weak or delayed overall adaptive response, poor lung-specific lymphocyte recruitment, T cell exhaustion, or triggered apoptosis following SARS-CoV-2 infection.6,224

Another hallmark of SARS-CoV-2 infection is the “cytokine storm” or hypercytokinemia. Severe cases of hypercytokinemia lead to critical conditions such as ARDS or multiple organ failure.7,162,230 In SARS-CoV-1 and MERS-CoV, the delay in initial immune response at the beginning of infection gives the virus time for uncontrolled replication. This replication is then met with the influx of activated leukocytes, specifically neutrophils and inflammatory monocytes, that contribute to the high NL ratio consistently observed in severe cases.187 Finally, the combination of increased presence of virus and innate immune cells at the site of infection results in the high expression of T1IFN, IL-1, IL-6, and TNF-α.73 While counterintuitive, early-stage immune evasion could potentially be the cause of the hyperinflammatory state seen in later-stage severe COVID-19 pathology192 (Fig. 1). In over-correcting for the initial sluggish response, the non-antigen-specific innate immune response is unleashed and runs unchecked, contributing to the morbidity and mortality of the disease.275 In fact, immunocompromised rhesus macaques infected with MERS-CoV displayed high viral load, yet minimal lung pathology.248 This result illustrates the significance of the immune response in patient outcome and points to a potential target for therapeutic interventions by halting the runaway producers of these pro-inflammatory cytokines. Studies on COVID-19 patients in Wuhan show increased neutrophils, decreased total lymphocytes, and increased serum IL-6 and c-reactive protein,278 which is further exaggerated in ICU patients compared to non-ICU patients.256 In addition to increased plasma IL-6 levels, increased bronchoalveolar lavage fluid (BALF) IL-6 levels are associated with poor outcomes in ARDS.157 IL-6 inhibition has emerged as an intriguing target in the fight against COVID-19. However, IL-6 KO mice displayed greater bronchoalveolar inflammation compared to wild-type mice in a model of ARDS, indicating that an approach more nuanced than simply blocking IL-6 may be needed.238 Additionally, the timing of IL-6 is critical as IL-6 is necessary for the early immune response in fighting COVID-19 and other viral infections in the early stages of infection. Low IL-6 levels in the early stages of infection could result in uncontrolled viral replication. Rather than attempting to abolish IL-6 release from macrophages, a better strategy may be to regulate it.

Figure 1
figure1

Overview of potential immune effects of SARS-CoV-2. Immune evasion, lymphopenia, and NETosis are all potential mechanisms leading to the severe immunopathology seen in patients infected with SARS-CoV-2, which includes hypercytokinemia, endothelial damage, edema, and fibrosis.

Another component of the hyperinflammation associated with cytokine storm are the T helper 17 (Th17) cells. Not only have elevated Th17 responses previously been seen in MERS and SARS patients, but COVID-19 patients also display elevated Th17 cells in their peripheral blood.255 Increased hyperactivation and concentration of proinflammatory Th17 cells were also observed in a COVID-19 case study.258 Similarly, severe H1N1 response is associated with overaction of Th17 cells.17 Th17 activity causes increased IL-17 release, and its reduction reduces mortality, inflammation, and lung damage in a murine model of influenza-induced lung injury.50 Therapies that suppress Th17 function have already been suggested as a way to combat COVID-19-associated mortality.255

Although we have much more to learn about the specifics of SARS-CoV-2, its similarity to SARS-CoV-1 and MERS-CoV has allowed us to recognize connections in immunopathology. It is clear that the immune response is dysregulated, and the kinetics of this response play a major role in disease progression. Inflammatory signaling, innate immunity, and adaptive immunity are designed to work in unison and in a timely manner to respond to the disease. However, the potential for immune evasion mechanisms by SARS-CoV-2 could point to the delayed and uncontrolled innate immune response, the poor involvement of the adaptive immune response, and the hypercytokinemia consistent with severe COVID-19 cases.

ACE2/RAS Connecting Vascular/BP to Gut and Renal

To virologists, ACE2 is the SARS-CoV-2 receptor, however to physiologists, ACE2 is a vital enzyme in the renin angiotensin system (RAS), which plays a major role in maintaining blood pressure. The systemic RAS functions mainly though cleavage of one main peptide, angiotensinogen (agt), to peptides of different lengths and, therefore, different functions (Fig. 2). Importantly, an enzyme similar to ACE2, ACE, is responsible for the production of angII. AngII typically functions as a vasoconstrictive peptide, but also has pro-inflammatory and pro-fibrotic functions.16 ACE2 cleaves the vasoconstrictive peptide angII to the vasodilatory peptide ang(1–7) thereby reducing the concentration of angII and increasing the concentration of ang(1–7). The balance between ACE/ACE2 and angII/ang(1–7) is important for homeostasis and can cause disease when dysregulated.228,251 For example, ACE2 has been shown to be protective against experimentally induced fibrosis and is down-regulated in human lung fibrosis.139 Interestingly, ACE2 is downregulated by SARS-CoV-1,126 and a similar mechanism is believed to occur in SARS-CoV-2 infection as well. Downregulation of ACE2 leads to increased levels of angII252 and decreased levels of ang(1–7) which can lead to lung injury108,126 as well as cardiac and renal injury.174 However, soluble ACE2 decreased SARS-CoV-1 pseudovirion transduction in HEK cells,114 and exogenous ACE2 partially rescues lung injury in vivo.108 Therefore, the use of ACE2 as the viral entry receptor for SARS-CoV-2 may contribute to the pathology of COVID-19.22

Figure 2
figure2

The RAS is vital for maintaining blood pressure. The RAS functions in the systemic circulation after renin is released from the kidneys. ACE2 is expressed in the pulmonary endothelium to cleave angII to protective ang(1–7). SARS-CoV-1, and likely SARS-CoV-2, down regulate ACE2.

ACE2 is generally expressed as a membrane bound protein by alveolar epithelium, vascular endothelium, and gastrointestinal epithelium.98,113 The function of ACE2 in the vascular endothelium (blood pressure maintenance59,229) and GI epithelium (amino acid transport25) is known, while the function in the alveolar epithelium is not well characterized. However, it is at this site where SARS-CoV-2 initially enters cells to begin an infection.

Underlying conditions, specifically those related to the cardiovascular system, are large risk factors for COVID-19. However, the question remains of whether the underlying condition itself puts patients at higher risk for becoming infected or having a more severe case, or if the underlying physiology that put the patient at risk for the co-morbidity also puts the patient at risk for becoming infected with SARS-CoV-2. ACE2 has been shown to be expressed by cardiac pericytes, but not cardiomyocytes39; however direct infection of the heart has not been common. In order for other organ systems to be infected, the virus must cross the epithelial barrier of the lung and the endothelial barrier in the capillaries to enter the blood. How this translocation occurs is unclear; however the pulmonary epithelium is damaged and leaky due to the infection, so it is not unreasonable that the virus would be able to access endothelial cells from the basal surface. Expression of ACE2 must then be present on the basal surface, which has not been established as of now. From in vitro studies, the vascular endothelium can become infected163 which has been seen to cause inflammation234,272 and impaired function of the endothelium clinically. The endothelium, specifically in the lung, highly expresses ACE2 as part of the systemic RAS. After pulmonary vascular infection, resulting dysregulation of the ACE/ACE2 and angII/ang(1–7) balance is likely. Increased angII concentrations result in lung injury and vasoconstriction leading to hypertension. Accordingly, hypertension is the most common co-morbidity with COVID-19, including a higher proportion admitted to the ICU.241

The kidney is highly involved in the RAS and regulation of blood pressure. The kidney secretes renin, the first enzyme in the RAS pathway, and regulates water content in the blood to regulate blood pressure. ACE2 is strongly expressed in the brush border of proximal tubular cells and weakly expressed in parietal epithelium and podocytes.98 There is elevated risk of acute kidney infection in COVID-19 patients, and it is associated with higher disease severity and is an adverse prognostic sign.41 As the kidney is so heavily involved in the RAS, dysregulation of the system plays a major role in kidney function. Therefore, it is not surprising that acute kidney infection is correlated with worse COVID-19 outcomes.

The other common site of initial infection, as it is also exposed to the environment, is the GI tract. ACE2 is highly expressed in the upper and stratified epithelium of the esophagus, as well as in the absorptive enterocytes of the ileum and colon.270 Clinically, symptoms of nausea and diarrhea provide evidence that many people can become infected with SARS-CoV-2 within the gut. In fact, viral DNA has been found in stool104,225; however whether or not the samples are infectious has yet to be clarified. Interestingly, ACE2 expression in the gut epithelium is known to be involved in amino acid transport, not RAS regulation. ACE2 is commonly co-localized with an amino acid transporter, B0AT1. Using computer modeling, it was shown that when ACE2 is complexed with B0AT1, interaction with TMPRSS2, the cleavage enzyme necessary for viral entry, is reduced.207 B0AT1 is not commonly expressed in pulmonary epithelium, so the complexation between B0AT1 and ACE2 in the gut epithelium may be protective against infection.

There is much to be understood about the interaction and regulation between the virus and ACE2. Since SARS-CoV-1 downregulates ACE2,126 SARS-CoV-2 is expected to downregulate ACE2 as well. This downregulation is what disrupts the systemic and pulmonary RAS. However, the downregulation is thought to occur because ACE2 is internalized with the virus, meaning that only epithelial cells at the site of infection, most likely pulmonary, experience the downregulation of ACE2. Since the endogenous function of ACE2 is unknown in the pulmonary epithelium, we do not know what the downregulation does in this system. However, it is assumed that the downregulation occurs systemically as well, as that is where ACE2 functions in the RAS to reduce vasoconstriction and hypertension. How the systemic ACE2 concentration becomes reduced if most of the viral infection occurs in the epithelium is yet to be answered. There is also a question as to why ACE2 is downregulated. Why and how the virus downregulates ACE2 is important to know to understand the pathology of the infection and potentially to be able to treat the disease. In addition, ACE2 expression is known to be homeostatically regulated through many intra- and extra-cellular pathways which are not well understood.45,46,74,125,130,184,203,231 Correctly interpreting responses of dynamically regulated systems to perturbations is complicated by the presence of regulation, and usually requires a model-based approach.44 Animal models that can be infected by SARS-CoV-2 and show similar dysregulation in the RAS would be excellent tools to study the connection between these two factors.

Early Life Exposure

Viral infection during pregnancy can result in an array of complications (reviewed in Ref. 266). The effects of SARS-CoV-2 on the fetus are unclear. It is important to consider the impact that the virus has on the mother’s health and immune profile, placental function, and whether it is able to reach the developing fetus.

Vertical transmission between mother and fetus has not been demonstrated and is not a pre-requisite for maternal viral infection to cause fetal harm.266 For example, elevated maternal IL-17a release from RORγt-dependent Th17 cells is associated with greater IL-17a in the fetal brain, resulting in autism spectrum disorder-like phenotypes in offspring in a rodent model of maternal immune activation (MIA).43 This effect can be induced simply through IL-6 injection,212 suggesting that future studies on the immune response to COVID-19 in the placenta would be important.

It has not conclusively been determined whether or not the placenta can be infected with SARS-CoV-2.185 The epithelium of the placenta expresses ACE2, TMPRSS2, as well as other genes necessary for viral replication and budding. Single cell RNA-seq reveals that stromal cells, decidual perivascular cells, villous cytotrophoblasts, and syncytiotrophoblasts (STB) in the human placenta express ACE2.136 Specifically, ACE2 and TMPRSS2 are expressed by first trimester STB and second trimester extravillous trophoblasts (EVT).8 Although EVTs did not express ACE2 in early stages, ACE2 expression increased by 24 weeks gestation.136 Histological studies performed on human placenta similarly indicated ACE2 expression in the cytotrophoblasts and STB, as well as the villous blood vessel endothelium and vascular smooth muscle cells, with increased STB ACE2 expression in tissues from spontaneous miscarriages.232 ACE2 expression was also observed in the intravascular trophoblasts and decidual cells of the maternal stroma and in the umbilical cord endothelial and smooth muscle cells.232

While the effect of maternal infection has yet to have been able to be fully studied, as the virus has not been circulating in the human population longer than the gestational length of human pregnancy, it is becoming clear that the placenta is not resistant from SARS-CoV-2 infection. Placentas from women infected with SARS-CoV-2 were more likely to have histological pathologies associated, such as maternal vascular malperfusion, atherosis and fibrinoid necrosis, and hypertrophy of arterioles;205 however the placentas were not tested for SARS-CoV-2. To date, there has been no cases of vertical transmission of SARS-CoV-2; however neonatal infants can contract the disease.4

The matter of whether or not vertical SARS-CoV-2 transmission occurs is controversial.132 A study performed on six SARS-CoV-2 positive pregnant women found no evidence of SARS-CoV-2 RNA in the newborns but did indicate elevated virus-specific antibodies in their sera.267 There are reports of two neonates born to SARS-CoV-2 positive mothers testing positive for SARS-CoV-2 infection based on nasopharyngeal swab.181 In these cases, the placentas displayed chronic intervillositis with macrophage infiltration, and SARS-CoV-2 RNA was detected in the STB.181 Others have similarly detected SARS-CoV-2 RNA in placental samples though it was unclear whether it occurred on the maternal or fetal side, and there was no evidence of fetal infection.185 Drawing conclusions from these studies is difficult as current SARS-CoV-2 tests often suffer from poor accuracy. Apart from vertical transmission, there are questions regarding the impact of maternal COVID-19 diagnosis on gestational outcomes. One case study displayed placental abruption with acute fetal distress in a SARS-CoV-2 positive mother.127 While the link to COVID-19 is inconclusive, the patient’s lack of risk factors along with to the relative rarity of this phenomenon led the authors to recommend increased surveillance in SARS-CoV-2 positive women. In a cohort study on pregnant women with severe COVID-19, 75% delivered preterm with no neonatal deaths or evidence of vertical transmission.190 It is important to note that the majority of these patients were receiving treatment in the form of hydroxychloroquine or remdesivir.190 These drugs have had limited to no controlled studies regarding their safety during pregnancy; however the limited data has not shown an increased risk to fetal health.49,78 A meta-analysis confirmed elevated risk of preterm birth, preeclampsia, and perinatal death associated with COVID-19.57

Controlled studies on pregnant women with COVID-19 present ethical limitations. It is therefore important to develop models that allow for deeper insight regarding the likelihood of vertical transmission as well as mechanisms through which maternal immune response could cause harm to the developing fetus. 3D in vitro models of the placenta that capture the transport dynamics and infection potential at this interface throughout gestation could prove useful in determining whether the virus is capable of crossing the fetal barrier. Apart from fetal infection, it is also important to determine how maternal infection could alter placental phenotype. Trophoblast behavior, placental endothelial cell barrier function, and immune cell cytokine release are all important outputs for determining how maternal infection could possibly affect placental function. These factors also have implications for pre-term birth, which is known to be associated with inflammation and infection. There would also be great value in applying models of neuroinflammation and development to determine how COVID-19-associated cytokine profiles may cause changes in the developing brain.

Exacerbants

Circadian Rhythms and Potential Chronotherapeutics

One intriguing component of this disease is its cyclic nature. Anecdotal reports of patients feeling well during the day and taking a turn for the worse at night are common. This is not surprising as pulmonary illnesses such as ARDS and chronic obstructive pulmonary disease (COPD) have long been understood to follow circadian rhythms.220 Infectious diseases, such as influenza A or bacterial infection, are known to disrupt these circadian rhythms. Greater circadian disruption is associated with poor outcomes following viral infection.156 A shift in the circadian patterns could cause a discordance across tissues and exacerbate disease state, especially given the importance of these rhythms in the immune system. The importance of understanding the influence of circadian rhythms becomes especially apparent when one considers the fact that over a third of medical workers displayed symptoms of insomnia during the COVID-19 outbreak in China.274 Furthermore, in addition to existing vulnerable shift workers, shelter-in-place orders may result in labor rearrangements that cause an increase in circadian rhythm sleep-wake disorders in the work force.112

Circadian rhythms largely rely on fluctuations in a collection of transcription factors. Disruptions to these fluctuations can strengthen or weaken immune function. Interestingly, macrophages are able maintain circadian rhythms ex vivo. In fact, ~8% of the macrophage transcriptome displays circadian oscillations, including parts of the TLR4/TNFα pathway.119 Similarly, expression of TLR9, which plays an important role in viral recognition, fluctuates throughout the day, and the time of infection impacts disease severity in a murine model of sepsis.210 Modulation of circadian rhythms has long been appreciated as a method of regulating the immune response. Melatonin, a molecule that is strongly associated with regulating sleep cycles, has been suggested as a possible adjuvant treatment for COVID-19 due to its anti-inflammatory and anti-oxidation qualities.273 Bmal1 is an important transcription factor that maintains circadian rhythms and exerts anti-inflammatory effects, and Bmal1 knock out mouse models display enhanced viral infection and viral load,154 as well as increased reactive oxygen species accumulation and IL-1b release.67 Circadian rhythms are also important in lymph node biology because they modulate promigratory factor expression, resulting in oscillations in lymphocyte activity with homing peaking at night and egress peaking during the day.61 Healthy lung function also displays circadian rhythms, with peak performance midday and relatively decreased performance early morning.13,220 Interestingly, ARDS survivors display high incidences of disturbed sleep patterns following hospital discharge, and chronotherapy (in which a drug is administered at the optimal time based on circadian rhythms) has been recommended as a method to offset this phenomenon and improve patients’ quality of life.261 ACE2 expression also displays circadian rhythms in some tissues,101 indicating that similar patterns may occur in the airway and that the virus’s ability to enter cells may vary throughout the day.

REV-ERBα and RORγ are counteracting nuclear receptors that stabilize the oscillations of circadian rhythms.69 They both act as transcription co-factors that control the expression of other circadian genes as well as genes related to metabolism, immune function, and other important processes. REV-ERBα activity is linked to the repression of pro-inflammatory signals in macrophages,69 particularly IL-6,90 which could be important in staving off the hyperinflammation associated with cytokine storm. Interestingly, REV-ERBα activity is associated with decreased IL-6 release in LPS-challenged alveolar macrophages.90 Furthermore, high tidal volume mechanical ventilation decreases REV-ERBα mRNA and protein in rats,142 suggesting a mechanism through which standard end-stage COVID-19 treatment could exacerbate inflammation. In fact, REV-ERBα agonism reduced ventilator induced lung injury-associated edema and inflammation in the same model.

RORγ activity is crucial for Th17 differentiation and activity,214 and others have already suggested the use of RORγ inhibitors for COVID-19 treatment in the hopes of suppressing excessive Th17 activity.255 PPARγ has been found to repress RORγ in models of autoimmunity in the CNS, resulting in decreased Th17 differentiation.124 PPARγ is generally considered to be anti-inflammatory and has also been suggested as a potential target for COVID-19 treatment28,68 as its anti-inflammatory actions may lessen the severity of the disease. PPARγ also displays circadian rhythms, and its activation results in increased REV-ERBα transcription and decreased RORγ transcription.40,77

Given that both immune and pulmonary function are highly cyclical, it is important to consider temporal fluctuations in any in vitro or in vivo model. Targets such as REV-ERBα, RORγ, Bmal1, and Per2 could reveal interesting infection patterns. Detecting any type of phase shift in viral replication and the immune response could be important in fighting this disease. Establishing a zeitgeber into experiments and incorporating greater temporal resolution would aid in understanding how timing of infection impacts the course of the disease and what time of day interventions are most appropriate. Understanding how circadian rhythms impact the progression of COVID-19 could aid in the development of chronotherapeutics.51 Simply knowing what time of day is optimal for administering a therapeutic could make a difference in the body’s ability to clear the virus. Maintaining a regular sleep schedule promotes healthy immune function and is a simple step that can help lower the chances of a poorly timed immune response.

Pollution, Smoking, and Vaping as Environmental Exacerbants

Particulate matter (PM) in the form of air pollution is quickly emerging as a risk factor for COVID-19 mortality. SARS patients from regions with a high or moderate air pollution index had an 84% increased risk of death compared to patients from regions with low air pollution.52 Similar trends are occurring for SARS-CoV-2 infection in Northern Italy, which is one of the most polluted areas in Europe, experiencing some of the highest lethality rates in the world.48 A study in China found that a 10 µg/m3 increase in PM was associated with ~3% increase in daily confirmed cases, and a similar American study showed that an increase of 1 µg/m3 of PM resulted in an 8% increase in COVID-19 mortality.253,279 Traffic related PM has also been shown to increase ACE2 expression in both pulmonary and nasal epithelial cells in vitro.159 Independent of COVID-19, exposure to PM is heavily correlated to cardiovascular disease.222,233 PM exposure causes barrier dysfunction and elevated IL-6 production in HUVEC cultures55 as well as the formation of intracellular reactive oxygen species (ROS) and eventual cellular senescence, even at relatively low levels.24 The effects of PM exposure on lung function have been widely studied in the context of both air pollution, cigarette smoke, and nanoparticles. For example, diesel exhaust particles (DEP) are readily taken up by the pulmonary epithelium, resulting in altered cytokine production leading to inflammation.20 Nanoparticle inhalation in mice results in lung inflammation through increased NF-kB, IFNα, IFNβ, IL-1β, and IL-6.146 PM in general is associated with an increased inflammatory response, causing increased production of TNFα and IL-6 in LPS-challenged macrophages.88 In fact, mice treated with PM displayed a dose-dependent increase in pro-inflammatory cytokines in the lung and systemically along with greater cell infiltration into the alveoli.179 PM-induced cytokine production in macrophages could also cause a systemic inflammatory response with increased circulating platelets, leukocytes, and prothrombotic proteins,102 further contributing to COVID-19-associated mortality. Additionally, one of the hallmarks of ARDS is increased pulmonary vascular permeability, which allows excess fluid and immune cell infiltration into the lung.93,128 PM is also known to decrease vascular barrier function,54,55,245 suggesting that exposure could render COVID-19 patients more susceptible to ARDS. PM exposure likewise causes oxidative stress and inflammation in the pulmonary endothelium.56,97

Meanwhile, exposure to PM in the form of cigarette smoke is associated with decreased REV-ERBα transcript in the lung tissue and dysregulated cytokine release in response to LPS challenge262 and greater inflammation and cellular senescence in REV-ERBα KO mice.219 Given that cigarette smoke is associated with greater RORγ and IL-17 expression in a murine model of COPD, it is likely that PM exposure could play a role in Th17 hyperactivation in COVID-19.64 Interestingly, cigarette smoke and air pollution, both of which are risk factors for COVID-19 mortality, also disrupt circadian rhythms,242 further suggesting a role for the dysregulation of these crucial patterns in COVID-19. E-cigarettes represent a growing public health concern, especially among youths, and they are associated with exposure to PM in the form of ultrafine particles, as well as nicotine.75,193 Even sub-chronic exposure to e-cigarettes resulted in increased macrophage and T cell influx into the lung along with increased inflammatory cytokine release and increased ACE2 expression.244

Exposure to PM comes with a litany of health risks (hypertension, obesity, diabetes), many of which are common co-morbidities for severe COVID-19. Recapitulating these phenotypes could aid in the development of effective therapeutics for the most vulnerable populations. Co-culture with PM-exposed immune cells or even conditioned media from PM-exposed immune cells could more accurately model the pathogenesis of severe cases.

Outstanding Questions in Prevalence

Sex-Differences, Infection vs. Death Prevalence

Trends indicate that males are more susceptible to COVID-19-associated mortality.257 Whereas the higher mortality from SARS-CoV-2 infection in men may be in part due to behavior reasons (higher risk taking, more likely to smoke, less likely to follow safety guidelines),18,116 there is also a genetic basis. ACE2 is on the X-chromosome, and ACE2 transcript expression is higher in Asian men than Asian women.277 Higher expression of ACE2 may result in a higher risk for contracting the disease. Publicly available data from China indicates that the proportion of males who succumbed to COVID-19 is 2.4 times that of females, independent of age.115 This is not particularly shocking as immune function and phenotype strongly vary between the sexes.176 For example, females have a higher proportion of CD4+ T cells, and males have a higher number of CD8+ T cells.134 Females also generate more activated CD4+ T cells in response to T cell receptor (TCR) signaling and exhibit stronger antibody responses, higher B cell numbers, and basal immunoglobulin levels.85,199 Generally speaking, females would appear to have more responsive immune systems. Females also have more numerous tissue resident leukocytes along with a higher density of toll-like receptors (TLRs). This is further illustrated by the fact that females are more prone to autoimmune diseases, while males are more likely to succumb to sepsis.176,202 Looking more specifically at COVID-19 pathology, it is possible that males are more prone to elevated IL-17 similar to what has been observed in males with ankylosing spondylitis, an autoimmune disorder that is associated with increased Th17 counts in males but not females.95

One possible factor in the observed differences in male and female immune systems is the transcription factor PPARγ. Interestingly, females express more PPARγ in their T cells.66,271 PPARγ acts as a negative regulator of T cell activation and suppresses cytokine production.260 Female PPARγ-deficient cells display increased cytokine levels following TCR activation, but this effect is not seen in males, indicating that PPARγ is more important in female immunity than male immunity. PPARγ deficiency in female T cells also skews T cell differentiation. Interestingly, treating male T cells with estradiol results in greater PPARγ expression.178 PPARγ appears to have a stronger effect on inflammatory diseases in an estrogen-replete environment. These differences are also observed in the lymph node as female PPARγ knock out mice show increased germinal center responses, as well as increased spontaneous antibody production, whereas male mice do not.177 Interestingly, many of the immune differences observed between males and females become less pronounced if the ovaries are removed,202 indicating hormones produced and released by the ovaries, progesterone and estrogen, are driving factors in this difference.

Racial/Ethnic Differences

ACE2 expression is regulated by a variety of factors including behavioral, environmental, and genetic conditions. Asthma, COPD, hypertension, smoking, obesity, and male sex are associated with higher expression of ACE2 in bronchial biopsies, BAL, or blood samples.195 An ACE2 allele associated with higher expression is more common in the East Asian population compared to Europeans.27 Higher expression of ACE2 may result in a higher risk of contracting COVID-19, but it also may be protective of lung injury as more ACE2 is available to compensate for downregulation. Expression of ACE2 is at a lower level in the black population, which puts this group at higher risk of arterial hypertension and end organ damage.47 However, the black population in the United States is being affected by the virus at higher rates. While there are confounding factors, such as socioeconomic status, it is thought decreased expression of ACE2 may limit initial infection rates by SARS-CoV-2. However, if infection does occur, it is more severe due to physiology that is directly related to the limited ACE2 expression, like arterial hypertension.236 However, there may also be genetic factors involved for the severity of the disease as well.

Opportunities for Bioengineers to study Respiratory Viral Lifecycle

Immune System Therapeutics

Using SARS-CoV-1 and MERS-CoV along with the stream of new clinical reports, potential therapeutic targets for COVID-19 continue to be identified. Although many antiviral therapeutics are being investigated, such as remdesivir which targets viral polymerase,172 managing the host immune response is equivalently, if not more so, important. In the same way different antivirals can target a distinct point of the viral life cycle, the timing coordination of the immune system allows for targeting distinct stages in the response.

Halting the contributions of infiltrating neutrophils and inflammatory monocytes is a clear opportunity for therapeutic intervention. One potential target could be the neutrophil extracellular traps (NETs). NETs are extracellular networks of fibers created by neutrophils, to capture and destroy pathogen. While NETs are highly protective of tissue damage during acute inflammation, they have been increasingly implicated in pathology of immune related disorders.175 From clinical data, it has been found that a higher NL ratio is seen in more severe cases and in SARS-CoV-1, increased neutrophil infiltration is associated to poor prognosis. One reason for this is the linkage between NETs and thrombosis, as many severe cases of COVID-19 show incidence of thrombosis and hypercoagulation.123 NETs can act as a thrombosis promoter, as they act as a scaffold to collect platelets and induce coagulation.133 In multiple clinical studies of COVID-19, patients with severe disease state presented with elevated levels of NET remnants, such as cell-free DNA, myeloperoxidase-DNA complexes, and citrullinated histone H3.14,280 Targeting these complexes has been a major interest for potential therapeutics for SARS-CoV-2 and requires further investigation. Beyond identification of NET-specific inhibitors, halting the migration of inflammatory cells into the airspace may represent an additional approach. Previous work has demonstrated that intravenous injection of relatively inert, biocompatible nanoparticles can serve as “distractions” to otherwise migratory leukocytes, stopping their travel to sites of chronic inflammation and halting their pathological contribution. Particles with no added stimulatory or tolerizing moieties demonstrated robust anti-inflammatory preclinical responses to treat West Nile virus, inflammatory bowel disease,89,198 sepsis,34 and acute lung injury,80 driven by particle phagocytosis and subsequent cell “distraction” of inflammatory monocytes and neutrophils, respectively. This approach has shown potential for mitigating ARDS in COVID-19,166 with a multitude of other immune-engineering materials approaches currently in development.

In addition to cellular targets, modulation of humoral immunity and soluble factors has been a potential solution to COVID-19 pathology. Convalescent plasma therapy has been tested for safety and efficacy. Due to the importance of neutralizing antibodies for SARS-CoV-2 and the delayed production typically seen, a study has shown that delivering convalescent plasma from recently recovered patients was successful in improving patient outcomes.19 Clinical symptoms were improved as patients displayed increased oxyhemoglobin saturation, increased lymphocyte counts, and decreased C-reactive protein.65 This therapy has been shown to have great potential but has some logistical issues to consider, such as patient compliance in donating plasma, collecting high enough quantities to effectively treat enough patients, and determining what populations are most in need of convalescent plasma therapy. Additionally, cytokine-based interventions have been suggested as a response to hypercytokinemia.110 Drugs such as Tocilizumab, an anti-IL-6R mAb, have been successful in the blocking of IL-6 as treatment for various autoimmune disorders.276 While these therapies might have potential, the most important cytokine to target and the correct dosing need to be determined, as a full blocking of the pro-inflammatory signature could have deleterious effects.

Finally, as with many highly infectious viruses, many are looking into the development of vaccines for prophylactic protection. Possibilities for vaccines are wide ranging, including RNA/DNA vaccines, recombinant protein vaccines, vectored vaccines, inactivated vaccines, or live attenuated vaccines.5 Certainly, consideration to the route of administration may prove most valuable, with intranasal and pulmonary vaccination leading to the most robust mucosal protection for respiratory infections.81,149,169 With over 100 COVID-19 vaccines currently in development and many techniques at the disposal of vaccine developers, time remains one of the most crucial factors. Typical vaccines take many years to test for safety and efficacy, but a pandemic scenario has mandated the speeding up of this process.153 However, caution is still warranted in SARS-CoV-2 vaccine development. Evidence from prior SARS-CoV-1 vaccination studies in rhesus macaques demonstrated that generation of antibodies towards some Spike protein domains led to antibody-dependent enhancement (ADE).144,246 These non-neutralizing antibodies instead promote viral uptake into host innate cells such as macrophages and monocytes through Fc-mediated receptors, leading to activation and exacerbation of their pro-inflammatory response.105,109 While such potential epitopes are being removed from most vaccine candidates, care must be taken in promoting protective and complete immune responses.105 The difficulty of promoting neutralizing antibody responses while avoiding ADE is the reason why no highly effective vaccine exists for HIV, and why most HIV vaccine candidates actually increased infection rates.72,282 So while developing a vaccine remains a major promise, the likelihood of aiding in our current pandemic status remains unclear.

Molecular Pathways and Medical Dosing

Great strides are being made in developing or re-appropriating therapeutics for COVID-19, as there are many clinical trials occurring in parallel. An important factor that needs to be considered is dosing to maximize efficacy and minimize negative side-effects. For example, there have been multiple studies on hydroxychloroquine dosing,86,186 which has proven ineffective at treating COVID-19.21 With all therapeutic clinical trials, it would be beneficial to time the administration of any drugs targeting the immune system with the natural circadian rhythms of the immune response.189 Autoimmune treatments are shown to be more effective when administered at night,53,92 and it is not unreasonable to assume that similar trends may emerge in immune-based COVID-19 treatments. Antiviral therapies should also be administered to optimize antiviral effect.94 Not much is known about optimal antiviral dosing, but circadian viral oscillations have been observed in other systems,71,204 and it is possible that there are times of day that are optimal for viral targeting. Finally, given the proven risk of cytokine storm events in COVID-19,110,143 it could be beneficial to optimize dosing and timing of vaccine administration to minimize the risk of these occurring during vaccination while also maximizing the development of a protective immune response.122

Dynamic treatment scheduling of this nature has been explored extensively in other contexts, especially in the treatment of HIV2,216,283 and cancer.10,151,284 These approaches have their roots in control theory, in which a predictive mathematical system model is updated in real-time based on measurements, and interventions are chosen to optimize the predicted behavior of the system.60 Mathematical models exist for many of the phenomena described above, including within-host viral dynamics,30,31,173 circadian rhythms,11,201 and cytokine storm dynamics,79,265 which could easily be adapted to match COVID-19 infection behavior. Experiments will need to be designed to collect data for model tuning and validation (a process known as system identification in control theory).26,152,158 From there appropriate open-loop strategies (in which a fixed intervention schedule is applied based on measured starting conditions) and/or closed-loop strategies (in which the schedule is continually adapted based on new measurements) can be developed.32,239,281

Microphysiological Systems

COVID-19 is a complex, multifactorial disease that manifests with a broad range of pathologies, but microphysiological systems and other engineering approaches can be used to study different components of this disease (Fig. 3). For example, one feature in many COVID-19 cases is fibrosis. Some believe that the presence of fibrosis on a CT scan is good news as it indicates that healing has begun, while others claim that it is a precursor to the peak stage of the disease and could escalate into pulmonary interstitial fibrosis disease.263 It is important to understand how SARS-CoV-2 infection impacts ECM generation and how any pre-existing lung injury could impact disease progression. Studying the vascular compartment is also of utmost importance as endothelial dysfunction has proven to be a crucial factor in the pathophysiology of COVID-19.1,227 There are numerous 3D in vitro models including lung-on-a-chip models that have been useful for studying the role of ECM and vasculature161,164,180,209,243 (reviewed in Ref. 167). The incorporation of an immune component into these pulmonary models would have obvious implications for COVID-19 research, and the field is moving towards the development of such models (reviewed in Ref. 191). Being able to study how SARS-CoV-2 infection impacts lymph node activity would elucidate many of the immune phenomena that have been observed. Understanding the cytokine profile and the activity of trafficking immune cells would provide insight into the T cell exhaustion and other irregularities that have been observed in COVID-19 patients. The application of kidney models would also be relevant as acute kidney injury has been observed to occur with COVID-19.41,217 Kidney-on-a-chip devices have been used for drug transport and nephrotoxicity studies,111,140 and advances are reviewed in Refs. 120 and 171. Applying these models to COVID-19 studies could help clinicians understand how SARS-CoV-2 causes these renal pathologies and what the role of ACE2 may be. Finally, assaying COVID-19 pathology with neural models would be useful for examining long term effects of SARS-CoV-2, after the initial infection has subsided. Coronavirus infections have been shown to have deleterious effects on neurological structure and function and have the potential to lead to long term damage. Coronaviruses have been linked to diseases such as infectious toxic encephalopathy, viral encephalitis, and acute cerebrovascular disease through direct pathogen presence in the CNS, hypoxia associated with respiratory issues, or injury due to neuroinflammation.12,254 Models that can investigate the neurotropic mechanisms of the virus, hypoxia injury, and immune injury would be a critical tool for understanding the long term effects of SARS-CoV-2 and help guide therapeutic strategies to overcome them.99

Figure 3
figure3

Models that could be helpful in understanding the SARS-CoV-2 infection. Examples of existing model systems may be used or novel models that could be developed to study the pathology of the infection and disease.

For all of these models, it is important to look at samples from both sexes when studying the pathophysiology of COVID-19. Donor sex alone has been found to influence phenotype in pulmonary microvascular endothelial cells.268,269 Since hormones can heavily influence immune and lung function, it is important to account for the presence of hormones in media.29,223 Phenol red-free media is recommended for such studies because phenol red can interact with estrogen receptors. It is preferable to use hormone-free media and supplement hormones as desired to more closely mimic physiological conditions. Looking at cells from donors of different ethnicities is also important. Ethnic differences in pharmacokinetics have long been appreciated,37 and there have been calls to standardize reporting of observed ethnic differences in lung function.23 Given that COVID-19 is disproportionately affecting black populations in the United States, there is value in accounting for that fact in cell-based studies. Unfortunately, access to diverse cell sources can be limited, and therefore efforts should be made to obtain these resources and distribute them to research labs.

Ex Vivo Culture Systems

One potential area of exploration for bioengineers that is being underutilized, is the use of ex vivo models. While these platforms are highly complex and challenging to develop, there is a corresponding increase in control and ability to separate spatiotemporal responses, with less sacrifice of physiologic conditions. These models are common for studying organ development91,165,168,200 and bioreactors for organ and lung conditioning for transplant,96,188 but less common with applications to studying disease pathophysiology. This is especially critical in studying SARS-CoV-2 pathology and the progression of COVID-19. While evidence is pointing towards the shift in the immune response from protective to hyperinflammatory, there is a need to discretize this complicated spatiotemporal process, which is very difficult to achieve with patient data alone. Researchers are unable to obtain direct, real-time cell population kinetic data from human patients through the course of the infection, which will be necessary to elucidate the fundamental components of the pathology. Animal models bring our level of control closer to being able to separate and longitudinally analyze responses to viral respiratory infections, but still fail to give the necessary resolution. Therefore, models of complete lung tissue with tailorable introduction of soluble factors and immune components, could be a highly beneficial solution to this. Pathogen (pathogen associated molecular patterns, pseudovirus, or actual SARS-CoV-2) can be delivered to the airway epithelium, and immune constituents (damage associated molecular patterns, cytokines, and immune cells) can then be supplied at designated time points. Due to the nature of these systems, outputs can be measured in real-time via compartment sampling or live imaging to assess the kinetics of the response. Some models currently being used, such as ex vivo lung culture models and precision cut lung slices, can be adapted to study both the effects of viral infection and tissue damage, as well as the interaction between lung tissue and infiltrating immune responses.15,100 Further, development of microfluidic ex vivo lymph node models that allow observation of immune activity within the lymph node have been developed using mouse tissue.35,196,197 Advancing these approaches for comparable studies in human tissue would provide invaluable insight into viral life cycle and immune response.

Preclinical Airway Models

If the COVID-19 pandemic has illuminated one major opportunity for bioengineers, it is the opportunity to advance understanding of transport within the pulmonary system. Examples of areas in which studying transport could be beneficial include assessing the number of viral aerosols that penetrate a facemask or lead to infection, identifying the precise location of initial viral entry, determining how infection migrates downward through the respiratory tract, understanding the confounding COVID-19 hypoxia,87 and the development of inhaled therapeutics and/or vaccines. However, these studies are made difficult by the fact that the lung presents significant multi-scale, non-equilibrium transport challenges. Advancing fundamental knowledge in these areas will inform not only COVID-19 disease progression and assessment of new inhaled therapeutics, but advance understanding for a host of other respiratory diseases. Currently, there remains a lack of integrated whole lung preclinical tools or models capable of predicting how an inhaled virus will move dynamically in the lung. An optimal in vitro assessment would exactly recreate all of the anatomical features of the human lung; however, the incredible structural complexity, size, and constant motion of the airway necessitates approximations.155,170 While animal and ex vivo models can provide some insight into aspects of transport phenomena within the human respiratory tract, they do not recreate the asymmetric bifurcations of the human airway-tree needed to accurately model particulate deposition, mucocilliary transport, and localized cellular response.170 Preclinical pulmonary models that consider these multi-scale transport challenges are currently de-coupled, with experimental and computational approaches independently advancing understanding of aerosol transport through static upper and lower airspaces, particulate transport penetrating through mucosal interfaces, or cellular response in lung tissue mimics or alveolar, air-liquid-interface (ALI) microfluidic models.3,76,84,107,155,183,213,218,226,235,237 An outstanding opportunity in the field is to integrate these approaches under physiological breathing conditions. Integrated whole-lung models that incorporate airway structure, breathing maneuvers, and a mucocilliary escalator are still on the horizon, but will provide valuable insight to pathogen and host cell dynamics within the lung.

Mucus

The role of mucus in COVID-19 pathology needs to be addressed. One autopsy report indicated the presence of high viscosity gelatinous mucus in the bronchi, which could explain the dry cough observed in COVID-19 patients.263 Increased mucus secretion along with an imbalance in surface liquid volume can cause mucus to become more elastic, making it difficult to clear.70,129 Indeed, aberrant MUC5B secretion was observed in the alveolar region in COVID-19 autopsy samples.106 Being able to model these alterations in mucus would be valuable both for understanding how SARS-CoV-2 causes the dysregulation of mucus production in the epithelium and for understanding transport dynamics with regard to virus secretion and possible uptake of inhaled therapeutics. Mucin based hydrogels have proven to be an attractive option for modeling the mechanical properties of mucus.117 The presence and qualities of mucus are known to impact diffusion of inhaled therapeutic agents. For example, inflammation can reduce the residence time of Fab’ fragments, but PEGylation of these fragments results in prolonged residence time in both inflamed and healthy lungs.182 Thus, it is important to consider the mechanical properties and mucin-balance of COVID-19-associated mucus when performing drug delivery studies aimed at the airway.147 Understanding alterations in the airway surface liquid as well as any changes in mucin expression is important with regard to lung function and mucociliary transport (reviewed in Ref. 9). Another concern is aerosolization of mucus as this could impact healthcare workers by exposing them to the virus during intubation, endoscopic surgery, and tracheotomies.240 Modeling the likelihood and extent of mucus aerosolization could underlie strategies to minimize transmission and has clear public health benefits.

Ventilation/ARDS Models

Another group of models with excellent utility in studying the complete life cycle of COVID-19 are ventilator models or models capable of examining the development of ARDS. Much of the research will be focused on the early stages of disease progression; however these later stage models can be valuable in addressing severe disease outcomes and identifying therapeutic targets. Whereas ventilator-induced lung injury (VILI) has been well studied, there is still no other option for COVID-19 patients with severe respiratory issues.211 Clinical reports show approximately 5–15% of patients infected with SARS-CoV-2 require a ventilator and intensive care observation.160 There are a variety of in vivo and ex vivo models that have been used to study VILI, and their adaptation to COVID-19 studies can help provide information as to how these severe cases and pathologies are made worse by ventilation.42,249 In addition to VILI, SARS-CoV-2 has been shown to lead to edema, fibrosis, and ARDS. Although the definition for ARDS is broad and can be applied to many diseases, interestingly, clinical data has shown that COVID-19 presents in a very specific form of ARDS.87 This highlights the need for models that mimic the specific pathophysiology seen in COVID-19 as a way to identify therapeutic targets. One main challenge is the lack of ARDS models currently in use, as a murine ARDS model does not exist. This points toward a potential for model development in larger animal models or synthetic approaches that more appropriately represent human disease. There are a number of swine models capable of reproducing ARDS immunopathology being seen in COVID-19 patients, and further development of these models will help researchers in understanding SARS-CoV-2 infection. These approaches will have major implications on discovering and testing therapeutics for this virus but can broadly be applied to a number of ARDS inducing conditions such as pulmonary infections, sepsis, contact with toxic materials, and physical injury.7 Additionally, more advanced models can be used to allow direct comparisons between clinical outcomes observed in patients and the ARDS model to improve the transition of therapeutics and treatment strategies into clinical trials.

Conclusions

COVID-19 simultaneously affects multiple tissues in ways that we are only beginning to understand. Clinical samples have been enormously helpful in providing information that aids in characterizing the disease, but performing controlled experiments is still difficult. Due to animal model limitations, there is an opportunity to apply human cell-based models to SARS-CoV-2 research to investigate the effects of the virus on a tissue by tissue basis with high spatiotemporal resolution. An added benefit is the ability to perform controlled experiments that are impossible in a clinical setting. The ability to study the SARS-CoV-2-induced cytokine profiles in the context of different stressors would make it possible to look at specific mechanisms underlying the pathophysiology of COVID-19 to answer unresolved questions regarding viral entry, the resulting immune cascade, and the factors that determine whether the virus is cleared or if hyperinflammation occurs. Engineered models can also be used to investigate the ways in which known risk factors exacerbate disease progression at the cellular and molecular level and help dictate avenues for additional therapies for at-risk populations. A benefit of complex models over traditional in vitro models is the ability to look at intercellular interactions and to consider multiple aspects at one time. Hypercytokinemia, ARDS, and hypercoagulopathy are some of the most obvious features in COVID-19-related mortality and morbidity, and a vascularized, perfusable lung model that incorporates immune function could aid in investigating these phenomena individually or in combination with one another. Models of other tissues such as the placenta, gut, kidney, or neurons could also aid in understanding how the virus potentially infects or otherwise impacts the body apart from the lung (Fig. 4). These strategies could dissect direct effects that are caused by the virus itself from indirect effects caused by systemic inflammation. They could also determine appropriate therapies to target individual tissues to minimize dysfunction, if necessary. Questions regarding aerosol generation and deposition and mechanical properties of lung tissue and mucus would also benefit from an engineering approach. By characterizing and then controlling these phenomena to learn how the virus moves and is deposited throughout the body, we can determine the impact on disease progression and possibly how to minimize these observed effects.

Figure 4
figure4

SARS-CoV-2 impacts a variety of tissues. The implementation of models to study factors such as infection dynamics, inflammation, and other factors would greatly benefit the field.

There is value in investigating multiple factors and the ways in which they interact. For example, obesity and PM exposure are both independent risk factors for poor COVID-19 outcomes,58,118,253 but PM exposure can also increase risk of obesity.247,250 Furthermore, circadian disruption is associated with obesity,82,83 and PM exposure can perturb circadian rhythms.141,242 Developing strategies to investigate these factors separately and in tandem could help determine if there is a synergistic effect or if both risk factors increase vulnerability through similar mechanisms. Furthermore, PM exposure and circadian rhythm disruption could impact cytokine release, and additional studies are needed to confirm the mechanism through which this occurs and the implications for COVID-19 treatment. Models that allow for high temporal resolution and separate control of interdependent physiological and environmental factors and phenotypes would be invaluable for COVID-19 pathophysiological studies.

The impact of biomedical engineering on COVID-19 research has the potential to be far-reaching, especially if partnerships and collaborations with clinicians and clinical centers are established. In addition to the repurposing of existing tools and models for COVID-19 investigations, our community should endeavor to develop approaches and model systems that integrate the investigation of the complex physiological, temporal, and environmental, racial, and sex-based variables outlined herein to not only be applied to this disease but that can be applied for studies on pathogen response and lung injury more broadly. The use of engineered systems will undoubtedly accelerate the pace of COVID-19 research, bringing us closer to viable treatment strategies.

References

  1. 1.

    Ackermann, M., S. E. Verleden, M. Kuehnel, A. Haverich, T. Welte, F. Laenger, A. Vanstapel, C. Werlein, H. Stark, A. Tzankov, W. W. Li, V. W. Li, S. J. Mentzer, and D. Jonigk. Pulmonary vascular endothelialitis, thrombosis, and angiogenesis in Covid-19. N. Engl. J. Med. 383:120–128, 2020.

    Google Scholar 

  2. 2.

    Adams, B. M., H. T. Banks, M. Davidian, H.-D. Kwon, H. T. Tran, S. N. Wynne, and E. S. Rosenberg. HIV dynamics: modeling, data analysis, and optimal treatment protocols. J. Comput. Appl. Math. 184:10–49, 2005.

    MathSciNet  MATH  Google Scholar 

  3. 3.

    Ali, M., M. K. Mazumder, and T. B. Martonen. Measurements of electrodynamic effects on the deposition of MDI and DPI aerosols in a replica cast of human oral-pharyngeal-laryngeal airways. J. Aerosol. Med. Pulm. Drug. Deliv. 22:35–44, 2009.

    Google Scholar 

  4. 4.

    Alzamora, M. C., T. Paredes, D. Caceres, C. M. Webb, L. M. Valdez, and M. La Rosa. Severe COVID-19 during pregnancy and possible vertical transmission. Am. J. Perinatol. 2020. https://doi.org/10.1055/s-0040-1710050.

    Article  Google Scholar 

  5. 5.

    Amanat, F., and F. Krammer. SARS-CoV-2 vaccines: status report. Immunity 52:583–589, 2020.

    Google Scholar 

  6. 6.

    Anft, M., K. Paniskaki, A. Blazquez-Navarro, A. A. N. Doevelaar, F. Seibert, B. Hoelzer, S. Skrzypczyk, E. Kohut, J. Kurek, J. Zapka, P. Wehler, S. Kaliszczyk, S. Bajda, C. Thieme, T. Roch, M. J. Konik, T. Brenner, C. Tempfer, C. Watzl, S. Dolff, U. Dittmer, T. Westhoff, O. Witzke, U. Stervbo, and N. Babel. COVID-19 progression is potentially driven by T cell immunopathogenesis. Allergy Immunol. 2020. https://doi.org/10.1101/2020.04.28.20083089.

    Article  Google Scholar 

  7. 7.

    ARDS Definition Task Force, V. M. Ranieri, G. D. Rubenfeld, B. T. Thompson, N. D. Ferguson, E. Caldwell, E. Fan, L. Camporota, and A. S. Slutsky. Acute respiratory distress syndrome: the Berlin Definition. JAMA 307:2526–2533, 2012.

    Google Scholar 

  8. 8.

    Ashray, N., A. Bhide, P. Chakarborty, S. Colaco, A. Mishra, K. Chhabria, M. K. Jolly, and D. Modi. Single-cell RNA-seq identifies cell subsets in human placenta that highly expresses factors to drive pathogenesis of SARS-CoV-2. Cell Dev. Biol. 2020. https://doi.org/10.20944/preprints202005.0195.v1.

    Article  Google Scholar 

  9. 9.

    Atanasova, K. R., and L. R. Reznikov. Strategies for measuring airway mucus and mucins. Respir. Res. 20:261, 2019.

    Google Scholar 

  10. 10.

    Bagheri, N., M. Shiina, D. A. Lauffenburger, and W. M. Korn. A dynamical systems model for combinatorial cancer therapy enhances oncolytic adenovirus efficacy by MEK-inhibition. PLoS Comput. Biol. 7:e1001085, 2011.

    Google Scholar 

  11. 11.

    Bagheri, N., J. Stelling, and F. J. D. Iii. Circadian phase resetting via single and multiple control targets. PLoS Comput. Biol. 4:e1000104, 2008.

    MathSciNet  Google Scholar 

  12. 12.

    Baig, A. M., A. Khaleeq, U. Ali, and H. Syeda. Evidence of the COVID-19 virus targeting the CNS: tissue distribution, host–virus interaction, and proposed neurotropic mechanisms. ACS Chem. Neurosci. 11:995–998, 2020.

    Google Scholar 

  13. 13.

    Barnes, P. J. Circadian variation in airway function. Am. J. Med. 79:5–9, 1985.

    Google Scholar 

  14. 14.

    Barnes, B. J., J. M. Adrover, A. Baxter-Stoltzfus, A. Borczuk, J. Cools-Lartigue, J. M. Crawford, J. Daßler-Plenker, P. Guerci, C. Huynh, J. S. Knight, M. Loda, M. R. Looney, F. McAllister, R. Rayes, S. Renaud, S. Rousseau, S. Salvatore, R. E. Schwartz, J. D. Spicer, C. C. Yost, A. Weber, Y. Zuo, and M. Egeblad. Targeting potential drivers of COVID-19: neutrophil extracellular traps. J. Exp. Med 2020. https://doi.org/10.1084/jem.20200652.

    Article  Google Scholar 

  15. 15.

    Beck-Broichsitter, M., J. Gauss, C. B. Packhaeuser, K. Lahnstein, T. Schmehl, W. Seeger, T. Kissel, and T. Gessler. Pulmonary drug delivery with aerosolizable nanoparticles in an ex vivo lung model. Int. J. Pharm. 367:169–178, 2009.

    Google Scholar 

  16. 16.

    Benigni, A., P. Cassis, and G. Remuzzi. Angiotensin II revisited: new roles in inflammation, immunology and aging. EMBO Mol. Med. 2:247–257, 2010.

    Google Scholar 

  17. 17.

    Bermejo-Martin, J. F., et al. Th1 and Th17 hypercytokinemia as early host response signature in severe pandemic influenza. Crit. Care 13:R201, 2009.

    Google Scholar 

  18. 18.

    Bertakis, K. D., R. Azari, L. J. Helms, E. J. Callahan, and J. A. Robbins. Gender differences in the utilization of health care services. J. Fam. Pract. 49:147–152, 2000.

    Google Scholar 

  19. 19.

    Bloch, E. M., S. Shoham, A. Casadevall, B. S. Sachais, B. Shaz, J. L. Winters, C. van Buskirk, B. J. Grossman, M. Joyner, J. P. Henderson, A. Pekosz, B. Lau, A. Wesolowski, L. Katz, H. Shan, P. G. Auwaerter, D. Thomas, D. J. Sullivan, N. Paneth, E. Gehrie, S. Spitalnik, E. A. Hod, L. Pollack, W. T. Nicholson, L. Pirofski, J. A. Bailey, and A. A. R. Tobian. Deployment of convalescent plasma for the prevention and treatment of COVID-19. J. Clin. Invest. 130:2757–2765, 2020.

    Google Scholar 

  20. 20.

    Boland, S., A. Baeza-Squiban, T. Fournier, O. Houcine, M.-C. Gendron, M. Chévrier, G. Jouvenot, A. Coste, M. Aubier, and F. Marano. Diesel exhaust particles are taken up by human airway epithelial cells in vitro and alter cytokine production. Am. J. Physiol. Lung Cell. Mol. Physiol. 276:L604–L613, 1999.

    Google Scholar 

  21. 21.

    Boulware, D. R., M. F. Pullen, A. S. Bangdiwala, K. A. Pastick, S. M. Lofgren, E. C. Okafor, C. P. Skipper, A. A. Nascene, M. R. Nicol, M. Abassi, N. W. Engen, M. P. Cheng, D. LaBar, S. A. Lother, L. J. MacKenzie, G. Drobot, N. Marten, R. Zarychanski, L. E. Kelly, I. S. Schwartz, E. G. McDonald, R. Rajasingham, T. C. Lee, and K. H. Hullsiek. A randomized trial of hydroxychloroquine as postexposure prophylaxis for Covid-19. N. Engl. J. Med. 2020. https://doi.org/10.1056/NEJMoa2016638.

    Article  Google Scholar 

  22. 22.

    Bourgonje, A. R., A. E. Abdulle, W. Timens, J.-L. Hillebrands, G. J. Navis, S. J. Gordijn, M. C. Bolling, G. Dijkstra, A. A. Voors, A. D. M. E. Osterhaus, P. H. J. van der Voort, D. J. Mulder, and H. van Goor. Angiotensin-converting enzyme-2 (ACE2), SARS-CoV-2 and pathophysiology of coronavirus disease, COVID-19. J. Pathol. 2019. https://doi.org/10.1002/path.5471.

    Article  Google Scholar 

  23. 23.

    Braun, L., M. Wolfgang, and K. Dickersin. Defining race/ethnicity and explaining difference in research studies on lung function. Eur. Respir. J. 41:1362–1370, 2013.

    Google Scholar 

  24. 24.

    Büchner, N., N. Ale-Agha, S. Jakob, U. Sydlik, K. Kunze, K. Unfried, J. Altschmied, and J. Haendeler. Unhealthy diet and ultrafine carbon black particles induce senescence and disease associated phenotypic changes. Exp. Gerontol. 48:8–16, 2013.

    Google Scholar 

  25. 25.

    Camargo, S. M. R., D. Singer, V. Makrides, K. Huggel, K. M. Pos, C. A. Wagner, K. Kuba, U. Danilczyk, F. Skovby, R. Kleta, J. M. Penninger, and F. Verrey. Tissue-specific amino acid transporter partners ACE2 and collectrin differentially interact with hartnup mutations. Gastroenterology 136:872–882.e3, 2009.

    Google Scholar 

  26. 26.

    Cannon, L., C. A. Vargas-Garcia, A. Jagarapu, M. J. Piovoso, and R. Zurakowski. HIV 2-LTR experiment design optimization. PLoS ONE 13:e0206700, 2018.

    Google Scholar 

  27. 27.

    Cao, Y., L. Li, Z. Feng, S. Wan, P. Huang, X. Sun, F. Wen, X. Huang, G. Ning, and W. Wang. Comparative genetic analysis of the novel coronavirus (2019-nCoV/SARS-CoV-2) receptor ACE2 in different populations. Cell Discov. 6:1–4, 2020.

    Google Scholar 

  28. 28.

    Carboni, E., A. R. Carta, and E. Carboni. Can pioglitazone be potentially useful therapeutically in treating patients with covid-19? Med. Hypoth. 2020. https://doi.org/10.1016/j.mehy.2020.109776.

    Article  Google Scholar 

  29. 29.

    Card, J. W., and D. C. Zeldin. Hormonal influences on lung function and response to environmental agents. Proc. Am. Thorac. Soc. 6:588–595, 2009.

    Google Scholar 

  30. 30.

    Cardozo, E. F., D. Ji, G. Lau, R. F. Schinazi, G.-F. Chen, R. M. Ribeiro, and A. S. Perelson. Disentangling the lifespans of hepatitis C virus-infected cells and intracellular vRNA replication-complexes during direct-acting anti-viral therapy. J. Viral Hepat. 27:261–269, 2020.

    Google Scholar 

  31. 31.

    Cardozo, E. F., R. Luo, M. J. Piovoso, and R. Zurakowski. Spatial modeling of HIV cryptic viremia and 2-LTR formation during raltegravir intensification. J. Theor. Biol. 345:61–69, 2014.

    MathSciNet  MATH  Google Scholar 

  32. 32.

    Cardozo, E. F., and R. Zurakowski. Robust closed-loop minimal sampling method for HIV therapy switching strategies. IEEE Trans. Biomed. Eng. 59:2227–2234, 2012.

    Google Scholar 

  33. 33.

    Cascella, M., M. Rajnik, A. Cuomo, S. C. Dulebohn, and R. Di Napoli. Features, evaluation and treatment coronavirus (COVID-19). In: StatPearls, edited by M. Cascella, M. Rajnik, A. Cuomo, S. C. Dulebohn, and R. Di Napoli. Treasure Island, FL: StatPearls Publishing, 2020.

    Google Scholar 

  34. 34.

    Casey, L. M., S. Kakade, J. T. Decker, J. A. Rose, K. Deans, L. D. Shea, and R. M. Pearson. Cargo-less nanoparticles program innate immune cell responses to toll-like receptor activation. Biomaterials 218:119333, 2019.

    Google Scholar 

  35. 35.

    Catterton, M. A., A. F. Dunn, and R. R. Pompano. User-defined local stimulation of live tissue through a movable microfluidic port. Lab. Chip 18:2003–2012, 2018.

    Google Scholar 

  36. 36.

    Channappanavar, R., and S. Perlman. Pathogenic human coronavirus infections: causes and consequences of cytokine storm and immunopathology. Semin. Immunopathol. 39:529–539, 2017.

    Google Scholar 

  37. 37.

    Chen, M.-L. Ethnic or racial differences revisited. Clin. Pharmacokinet. 45:957–964, 2006.

    Google Scholar 

  38. 38.

    Chen, Y., Z. Feng, B. Diao, R. Wang, G. Wang, C. Wang, Y. Tan, L. Liu, C. Wang, Y. Liu, Y. Liu, Z. Yuan, L. Ren, and Y. Wu. The novel severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) directly decimates human spleens and lymph nodes. Infect. Dis. (except HIV/AIDS) 2020. https://doi.org/10.1101/2020.03.27.20045427.

    Article  Google Scholar 

  39. 39.

    Chen, L., X. Li, M. Chen, Y. Feng, and C. Xiong. The ACE2 expression in human heart indicates new potential mechanism of heart injury among patients infected with SARS-CoV-2. Cardiovasc. Res. 116:1097–1100, 2020.

    Google Scholar 

  40. 40.

    Chen, L., and G. Yang. PPARs integrate the mammalian clock and energy metabolism. PPAR Res. 2014. https://doi.org/10.1155/2014/653017.

    Article  Google Scholar 

  41. 41.

    Cheng, Y., R. Luo, K. Wang, M. Zhang, Z. Wang, L. Dong, J. Li, Y. Yao, S. Ge, and G. Xu. Kidney disease is associated with in-hospital death of patients with COVID-19. Kidney Int. 97:829–838, 2020.

    Google Scholar 

  42. 42.

    Choi, W.-I., D. A. Quinn, K. M. Park, R. K. Moufarrej, B. Jafari, O. Syrkina, J. V. Bonventre, and C. A. Hales. Systemic microvascular leak in an in vivo rat model of ventilator-induced lung injury. Am. J. Respir. Crit. Care Med. 167:1627–1632, 2003.

    Google Scholar 

  43. 43.

    Choi, G. B., Y. S. Yim, H. Wong, S. Kim, H. Kim, S. V. Kim, C. A. Hoeffer, D. R. Littman, and J. R. Huh. The maternal interleukin-17a pathway in mice promotes autism-like phenotypes in offspring. Science 351:933–939, 2016.

    Google Scholar 

  44. 44.

    Chung, S.-W., C. R. Cooper, M. C. Farach-Carson, and B. A. Ogunnaike. A control engineering approach to understanding the TGF-β paradox in cancer. J. R. Soc. Interface 9:1389–1397, 2012.

    Google Scholar 

  45. 45.

    Clarke, N. E., N. D. Belyaev, D. W. Lambert, and A. J. Turner. Epigenetic regulation of angiotensin-converting enzyme 2 (ACE2) by SIRT1 under conditions of cell energy stress. Clin. Sci. 126:507–516, 2014.

    Google Scholar 

  46. 46.

    Clarke, N. E., and A. J. Turner. Angiotensin-converting enzyme 2: the first decade. Int. J. Hypertens. 2011. https://doi.org/10.1155/2012/307315.

    Article  Google Scholar 

  47. 47.

    Cohall, D., N. Ojeh, C. M. Ferrario, O. P. Adams, and M. Nunez-Smith. Is hypertension in African-descent populations contributed to by an imbalance in the activities of the ACE2/Ang-(1–7)/Mas and the ACE/Ang II/AT1 axes? J. Renin. Angiotensin Aldosterone Syst. 2020. https://doi.org/10.1177/1470320320908186.

    Article  Google Scholar 

  48. 48.

    Conticini, E., B. Frediani, and D. Caro. Can atmospheric pollution be considered a co-factor in extremely high level of SARS-CoV-2 lethality in Northern Italy? Environ. Pollut. 2020. https://doi.org/10.1016/j.envpol.2020.114465.

    Article  Google Scholar 

  49. 49.

    Costedoat-Chalumeau, N., Z. Amoura, P. Duhaut, D. L. T. Huong, D. Sebbough, B. Wechsler, D. Vauthier, I. Denjoy, J.-M. Lupoglazoff, and J.-C. Piette. Safety of hydroxychloroquine in pregnant patients with connective tissue diseases: a study of one hundred thirty-three cases compared with a control group. Arthritis Rheum. 48:3207–3211, 2003.

    Google Scholar 

  50. 50.

    Crowe, C. R., K. Chen, D. A. Pociask, J. F. Alcorn, C. Krivich, R. I. Enelow, T. M. Ross, J. L. Witztum, and J. K. Kolls. Critical role of IL-17RA in immunopathology of influenza infection. J. Immunol. 183:5301–5310, 2009.

    Google Scholar 

  51. 51.

    Cruz, M., M. Miyazawa, and D. Gozal. Putative contributions of circadian clock and sleep in the context of SARS-CoV-2 infection. Eur. Respir. J. 2020. https://doi.org/10.1183/13993003.01023-2020.

    Article  Google Scholar 

  52. 52.

    Cui, Y., Z.-F. Zhang, J. Froines, J. Zhao, H. Wang, S.-Z. Yu, and R. Detels. Air pollution and case fatality of SARS in the People’s Republic of China: an ecologic study. Environ. Health 2:15, 2003.

    Google Scholar 

  53. 53.

    Cutolo, M. Glucocorticoids and chronotherapy in rheumatoid arthritis. RMD Open 2:e000203, 2016.

    Google Scholar 

  54. 54.

    Dai, J., W. Chen, Y. Lin, S. Wang, X. Guo, and Q.-Q. Zhang. Exposure to concentrated ambient fine particulate matter induces vascular endothelial dysfunction via miR-21. Int. J. Biol. Sci. 13:868–877, 2017.

    Google Scholar 

  55. 55.

    Dai, J., C. Sun, Z. Yao, W. Chen, L. Yu, and M. Long. Exposure to concentrated ambient fine particulate matter disrupts vascular endothelial cell barrier function via the IL-6/HIF-1α signaling pathway. FEBS Open Bio 6:720–728, 2016.

    Google Scholar 

  56. 56.

    Davel, A. P., M. Lemos, L. M. Pastro, S. C. Pedro, P. A. de André, C. Hebeda, S. H. Farsky, P. H. Saldiva, and L. V. Rossoni. Endothelial dysfunction in the pulmonary artery induced by concentrated fine particulate matter exposure is associated with local but not systemic inflammation. Toxicology 295:39–46, 2012.

    Google Scholar 

  57. 57.

    Di Mascio, D., A. Khalil, G. Saccone, G. Rizzo, D. Buca, M. Liberati, J. Vecchiet, L. Nappi, G. Scambia, V. Berghella, and F. D’Antonio. Outcome of coronavirus spectrum infections (SARS, MERS, COVID-19) during pregnancy: a systematic review and meta-analysis. Am. J. Obstet. Gynecol. MFM 2:100107, 2020.

    Google Scholar 

  58. 58.

    Dietz, W., and C. Santos-Burgoa. Obesity and its implications for COVID-19 mortality. Obesity 28:1005, 2020.

    Google Scholar 

  59. 59.

    Donoghue, M., F. Hsieh, E. Baronas, K. Godbout, M. Gosselin, N. Stagliano, M. Donovan, B. Woolf, K. Robison, R. Jeyaseelan, R. E. Breitbart, and S. Acton. A novel angiotensin-converting enzyme-related carboxypeptidase (ACE2) converts angiotensin I to angiotensin 1-9. Circulat. Res. 2000. https://doi.org/10.1161/01.res.87.5.e1.

    Article  Google Scholar 

  60. 60.

    Doyle, F., L. Jovanovic, D. Seborg, R. S. Parker, B. W. Bequette, A. M. Jeffrey, X. Xia, I. K. Craig, and T. McAvoy. A tutorial on biomedical process control. J. Process Control 17:571–572, 2007.

    Google Scholar 

  61. 61.

    Druzd, D., O. Matveeva, L. Ince, U. Harrison, W. He, C. Schmal, H. Herzel, A. H. Tsang, N. Kawakami, A. Leliavski, O. Uhl, L. Yao, L. E. Sander, C.-S. Chen, K. Kraus, A. de Juan, S. M. Hergenhan, M. Ehlers, B. Koletzko, R. Haas, W. Solbach, H. Oster, and C. Scheiermann. Lymphocyte circadian clocks control lymph node trafficking and adaptive immune responses. Immunity 46:120–132, 2017.

    Google Scholar 

  62. 62.

    Du, L., Y. He, Y. Zhou, S. Liu, B.-J. Zheng, and S. Jiang. The spike protein of SARS-CoV—a target for vaccine and therapeutic development. Nat. Rev. Microbiol. 7:226–236, 2009.

    Google Scholar 

  63. 63.

    Du, S. Q., and W. Yuan. Mathematical modeling of interaction between innate and adaptive immune responses in COVID-19 and implications for viral pathogenesis. J. Med. Virol. 2020. https://doi.org/10.1002/jmv.25866.

    Article  Google Scholar 

  64. 64.

    Duan, M.-C., H.-J. Tang, X.-N. Zhong, and Y. Huang. Persistence of Th17/Tc17 cell expression upon smoking cessation in mice with cigarette smoke-induced emphysema. Clin. Dev. Immunol. 2013. https://doi.org/10.1155/2013/350727.

    Article  Google Scholar 

  65. 65.

    Duan, K., et al. Effectiveness of convalescent plasma therapy in severe COVID-19 patients. Proc. Natl. Acad. Sci. 117:9490–9496, 2020.

    Google Scholar 

  66. 66.

    Dunn, S. E., S. S. Ousman, R. A. Sobel, L. Zuniga, S. E. Baranzini, S. Youssef, A. Crowell, J. Loh, J. Oksenberg, and L. Steinman. Peroxisome proliferator-activated receptor (PPAR)alpha expression in T cells mediates gender differences in development of T cell-mediated autoimmunity. J. Exp. Med. 204:321–330, 2007.

    Google Scholar 

  67. 67.

    Early, J. O., D. Menon, C. A. Wyse, M. P. Cervantes-Silva, Z. Zaslona, R. G. Carroll, E. M. Palsson-McDermott, S. Angiari, D. G. Ryan, S. E. Corcoran, G. Timmons, S. S. Geiger, D. J. Fitzpatrick, D. O’Connell, R. J. Xavier, K. Hokamp, L. A. J. O’Neill, and A. M. Curtis. Circadian clock protein BMAL1 regulates IL-1β in macrophages via NRF2. Proc. Natl. Acad. Sci. 115:E8460–E8468, 2018.

    Google Scholar 

  68. 68.

    Erol, A. Pioglitazone treatment for the COVID-19-associated cytokine storm. OSF Preprints 2020. https://doi.org/10.31219/osf.io/avfpb.

    Article  Google Scholar 

  69. 69.

    Everett, L. J., and M. A. Lazar. Nuclear receptor Rev-erbα: up, down, and all around. Trends Endocrinol. Metab. 25:586–592, 2014.

    Google Scholar 

  70. 70.

    Fahy, J. V., and B. F. Dickey. Airway mucus function and dysfunction. N. Engl. J. Med. 363:2233–2247, 2010.

    Google Scholar 

  71. 71.

    Fan, A., and K. Wang. A viral infection model with immune circadian rhythms. Appl. Math. Comput. 215:3369–3374, 2010.

    MathSciNet  MATH  Google Scholar 

  72. 72.

    Fauci, A. S., and H. D. Marston. Toward an HIV vaccine: a scientific journey. Science 349:386–387, 2015.

    Google Scholar 

  73. 73.

    Felsenstein, S., J. A. Herbert, P. S. McNamara, and C. M. Hedrich. COVID-19: immunology and treatment options. Clin. Immunol. 215:108448, 2020.

    Google Scholar 

  74. 74.

    Fernandes, T., N. Y. Hashimoto, F. C. Magalhães, F. B. Fernandes, D. E. Casarini, A. K. Carmona, J. E. Krieger, M. I. Phillips, and E. M. Oliveira. Aerobic exercise training-induced left ventricular hypertrophy involves regulatory MicroRNAs, decreased angiotensin-converting enzyme-angiotensin ii, and synergistic regulation of angiotensin-converting enzyme 2-angiotensin (1–7). Hypertension 58:182–189, 2011.

    Google Scholar 

  75. 75.

    Fernández, E., M. Ballbè, X. Sureda, M. Fu, E. Saltó, and J. M. Martínez-Sánchez. Particulate matter from electronic cigarettes and conventional cigarettes: a systematic review and observational study. Curr. Environ. Health Reep. 2:423–429, 2015.

    Google Scholar 

  76. 76.

    Fishman, J. M., K. Wiles, M. W. Lowdell, P. De Coppi, M. J. Elliott, A. Atala, and M. A. Birchall. Airway tissue engineering: an update. Expert Opin. Biol. Ther. 14:1477–1491, 2014.

    Google Scholar 

  77. 77.

    Fontaine, C., G. Dubois, Y. Duguay, T. Helledie, N. Vu-Dac, P. Gervois, F. Soncin, S. Mandrup, J.-C. Fruchart, J. Fruchart-Najib, and B. Staels. The orphan nuclear receptor Rev-Erbalpha is a peroxisome proliferator-activated receptor (PPAR) gamma target gene and promotes PPARgamma-induced adipocyte differentiation. J. Biol. Chem. 278:37672–37680, 2003.

    Google Scholar 

  78. 78.

    Food and Drug Administration. Fact Sheet for Health Care Providers Emergency Use Authorization (EUA) of Remdesivir (GS-5734TM). London: Food Press, 2020.

    Google Scholar 

  79. 79.

    Foteinou, P. T., S. E. Calvano, S. F. Lowry, and I. P. Androulakis. In silico simulation of corticosteroids effect on an NFkB-dependent physicochemical model of systemic inflammation. PLoS ONE 2009. https://doi.org/10.1371/journal.pone.0004706.

    Article  Google Scholar 

  80. 80.

    Fromen, C. A., W. J. Kelley, M. B. Fish, R. Adili, J. Noble, M. J. Hoenerhoff, M. Holinstat, and O. Eniola-Adefeso. Neutrophil–particle interactions in blood circulation drive particle clearance and alter neutrophil responses in acute inflammation. ACS Nano 11:10797–10807, 2017.

    Google Scholar 

  81. 81.

    Fromen, C. A., G. R. Robbins, T. W. Shen, M. P. Kai, J. P. Y. Ting, and J. M. DeSimone. Controlled analysis of nanoparticle charge on mucosal and systemic antibody responses following pulmonary immunization. Proc. Natl. Acad. Sci. 112:488–493, 2015.

    Google Scholar 

  82. 82.

    Froy, O. Metabolism and Circadian rhythms—implications for obesity. Endocr. Rev. 31:1–24, 2010.

    Google Scholar 

  83. 83.

    Froy, O. Circadian rhythms and obesity in mammals. ISRN Obes. 2012. https://doi.org/10.5402/2012/437198.

    Article  Google Scholar 

  84. 84.

    Fulcher, M. L., S. E. Gabriel, J. C. Olsen, J. R. Tatreau, M. Gentzsch, E. Livanos, M. T. Saavedra, P. Salmon, and S. H. Randell. Novel human bronchial epithelial cell lines for cystic fibrosis research. Am. J. Physiol. Lung Cell Mol. Physiol. 296:L82–L91, 2009.

    Google Scholar 

  85. 85.

    Furman, D. Sexual dimorphism in immunity: improving our understanding of vaccine immune responses in men. Expert Rev. Vaccines 14:461–471, 2015.

    Google Scholar 

  86. 86.

    Garcia-Cremades, M., B. P. Solans, E. Hughes, J. P. Ernest, E. Wallender, F. Aweeka, A. F. Luetkemeyer, and R. M. Savic. Optimizing hydroxychloroquine dosing for patients with COVID-19: an integrative modeling approach for effective drug repurposing. Clin. Pharmacol. Ther. 2020. https://doi.org/10.1002/cpt.1856.

    Article  Google Scholar 

  87. 87.

    Gattinoni, L., S. Coppola, M. Cressoni, M. Busana, S. Rossi, and D. Chiumello. COVID-19 does not lead to a “typical” acute respiratory distress syndrome. Am. J. Respir. Crit. Care Med. 201:1299–1300, 2020.

    Google Scholar 

  88. 88.

    Gawda, A., G. Majka, B. Nowak, M. Śróttek, M. Walczewska, and J. Marcinkiewicz. Air particulate matter SRM 1648a primes macrophages to hyperinflammatory response after LPS stimulation. Inflamm. Res. 67:765–776, 2018.

    Google Scholar 

  89. 89.

    Getts, D. R., R. L. Terry, M. T. Getts, C. Deffrasnes, M. Müller, C. van Vreden, T. M. Ashhurst, B. Chami, D. McCarthy, H. Wu, J. Ma, A. Martin, L. D. Shae, P. Witting, G. S. Kansas, J. Kühn, W. Hafezi, I. L. Campbell, D. Reilly, J. Say, L. Brown, M. Y. White, S. J. Cordwell, S. J. Chadban, E. B. Thorp, S. Bao, S. D. Miller, and N. J. C. King. Therapeutic inflammatory monocyte modulation using immune-modifying microparticles. Sci. Transl. Med. 6:219ra7, 2014.

    Google Scholar 

  90. 90.

    Gibbs, J. E., J. Blaikley, S. Beesley, L. Matthews, K. D. Simpson, S. H. Boyce, S. N. Farrow, K. J. Else, D. Singh, D. W. Ray, and A. S. I. Loudon. The nuclear receptor REV-ERBα mediates circadian regulation of innate immunity through selective regulation of inflammatory cytokines. Proc. Natl. Acad. Sci. 109:582–587, 2012.

    Google Scholar 

  91. 91.

    Gilbert, R. M., J. T. Morgan, E. S. Marcin, and J. P. Gleghorn. Fluid mechanics as a driver of tissue-scale mechanical signaling in organogenesis. Curr. Pathobiol. Rep. 4:199–208, 2016.

    Google Scholar 

  92. 92.

    Glass-Marmor, L., T. Paperna, Y. Ben-Yosef, and A. Miller. Chronotherapy using corticosteroids for multiple sclerosis relapses. J. Neurol. Neurosurg. Psychiatry 78:886–888, 2007.

    Google Scholar 

  93. 93.

    Gonzales, J., R. Lucas, and A. Verin. The acute respiratory distress syndrome: mechanisms and perspective therapeutic approaches. Austin. J. Vasc. Med. 2:1009, 2015.

    Google Scholar 

  94. 94.

    Goyal, A., E. F. Cardozo-Ojeda, and J. T. Schiffer. Potency and timing of antiviral therapy as determinants of duration of SARS CoV-2 shedding and intensity of inflammatory response. medRxiv 2020. https://doi.org/10.1101/2020.04.10.20061325.

    Article  Google Scholar 

  95. 95.

    Gracey, E., Y. Yao, B. Green, Z. Qaiyum, Y. Baglaenko, A. Lin, A. Anton, R. Ayearst, P. Yip, and R. D. Inman. Sexual dimorphism in the Th17 signature of ankylosing spondylitis. Arthritis Rheumatol. 68:679–689, 2016.

    Google Scholar 

  96. 96.

    Guenthart, B. A., J. D. O’Neill, J. Kim, D. Queen, S. Chicotka, K. Fung, M. Simpson, R. Donocoff, M. Salna, C. C. Marboe, K. Cunningham, S. P. Halligan, H. M. Wobma, A. E. Hozain, A. Romanov, G. Vunjak-Novakovic, and M. Bacchetta. Regeneration of severely damaged lungs using an interventional cross-circulation platform. Nat. Commun. 10:1985, 2019.

    Google Scholar 

  97. 97.

    Haberzettl, P., D. J. Conklin, W. T. Abplanalp, A. Bhatnagar, and T. E. O’Toole. Inhalation of fine particulate matter impairs endothelial progenitor cell function via pulmonary oxidative stress. Arterioscl. Thromb. Vasc. Biol. 38:131–142, 2018.

    Google Scholar 

  98. 98.

    Hamming, I., W. Timens, M. L. C. Bulthuis, A. T. Lely, G. J. Navis, and H. van Goor. Tissue distribution of ACE2 protein, the functional receptor for SARS coronavirus: a first step in understanding SARS pathogenesis. J. Pathol. 203:631–637, 2004.

    Google Scholar 

  99. 99.

    Haring, A. P., H. Sontheimer, and B. N. Johnson. Microphysiological human brain and neural systems-on-a-chip: potential alternatives to small animal models and emerging platforms for drug discovery and personalized medicine. Stem Cell Rev. Rep. 13:381–406, 2017.

    Google Scholar 

  100. 100.

    Henjakovic, M., K. Sewald, S. Switalla, D. Kaiser, M. Müller, T. Z. Veres, C. Martin, S. Uhlig, N. Krug, and A. Braun. Ex vivo testing of immune responses in precision-cut lung slices. Toxicol. Appl. Pharmacol. 231:68–76, 2008.

    Google Scholar 

  101. 101.

    Herichová, I., D. Šoltésová, K. Szántóová, B. Mravec, D. Neupauerová, A. Veselá, and M. Zeman. Effect of angiotensin II on rhythmic per2 expression in the suprachiasmatic nucleus and heart and daily rhythm of activity in Wistar rats. Regul. Pept. 186:49–56, 2013.

    Google Scholar 

  102. 102.

    Hiraiwa, K., and S. F. van Eeden. Contribution of lung macrophages to the inflammatory responses induced by exposure to air pollutants. Mediators Inflamm. 2013. https://doi.org/10.1155/2013/619523.

    Article  Google Scholar 

  103. 103.

    Hoffmann, M., H. Kleine-Weber, S. Schroeder, N. Krüger, T. Herrler, S. Erichsen, T. S. Schiergens, G. Herrler, N.-H. Wu, A. Nitsche, M. A. Müller, C. Drosten, and S. Pöhlmann. SARS-CoV-2 cell entry depends on ACE2 and TMPRSS2 and is blocked by a clinically proven protease inhibitor. Cell 181:271–280.e8, 2020.

    Google Scholar 

  104. 104.

    Holshue, M. L., C. DeBolt, S. Lindquist, K. H. Lofy, J. Wiesman, H. Bruce, C. Spitters, K. Ericson, S. Wilkerson, A. Tural, G. Diaz, A. Cohn, L. Fox, A. Patel, S. I. Gerber, L. Kim, S. Tong, X. Lu, S. Lindstrom, M. A. Pallansch, W. C. Weldon, H. M. Biggs, T. M. Uyeki, and S. K. Pillai. First case of 2019 novel coronavirus in the United States. N. Engl. J. Med. 382:929–936, 2020.

    Google Scholar 

  105. 105.

    Hotez, P. J., D. B. Corry, and M. E. Bottazzi. COVID-19 vaccine design: the Janus face of immune enhancement. Nat. Rev. Immunol. 20:347–348, 2020.

    Google Scholar 

  106. 106.

    Hou, Y. J., et al. SARS-CoV-2 reverse genetics reveals a variable infection gradient in the respiratory tract. Cell 2020. https://doi.org/10.1016/j.cell.2020.05.042.

    Article  Google Scholar 

  107. 107.

    Huh, D., B. D. Matthews, A. Mammoto, M. Montoya-Zavala, H. Y. Hsin, and D. E. Ingber. Reconstituting organ-level lung functions on a chip. Science 328:1662–1668, 2010.

    Google Scholar 

  108. 108.

    Imai, Y., K. Kuba, S. Rao, Y. Huan, F. Guo, B. Guan, P. Yang, R. Sarao, T. Wada, H. Leong-Poi, M. A. Crackower, A. Fukamizu, C. C. Hui, L. Hein, S. Uhlig, A. S. Slutsky, C. Jiang, and J. M. Penninger. Angiotensin-converting enzyme 2 protects from severe acute lung failure. Nature 436:112–116, 2005.

    Google Scholar 

  109. 109.

    Iwasaki, A., and Y. Yang. The potential danger of suboptimal antibody responses in COVID-19. Nat. Rev. Immunol. 20:339–341, 2020.

    Google Scholar 

  110. 110.

    Jamilloux, Y., T. Henry, A. Belot, S. Viel, M. Fauter, T. El Jammal, T. Walzer, B. François, and P. Sève. Should we stimulate or suppress immune responses in COVID-19? Cytokine and anti-cytokine interventions. Autoimmunity Rev. 2020. https://doi.org/10.1016/j.autrev.2020.102567.

    Article  Google Scholar 

  111. 111.

    Jang, K.-J., A. P. Mehr, G. A. Hamilton, L. A. McPartlin, S. Chung, K.-Y. Suh, and D. E. Ingber. Human kidney proximal tubule-on-a-chip for drug transport and nephrotoxicity assessment. Int. Bio (Cam) 5:1119–1129, 2013.

    Google Scholar 

  112. 112.

    Jeff, B. W. Circadian rhythm sleep-wake disorders and the COVID-19 pandemic. J. Clin. Sleep Med. 2020. https://doi.org/10.5664/jcsm.8540.

    Article  Google Scholar 

  113. 113.

    Jia, H. P., D. C. Look, L. Shi, M. Hickey, L. Pewe, J. Netland, M. Farzan, C. Wohlford-Lenane, S. Perlman, and P. B. McCray. ACE2 receptor expression and severe acute respiratory syndrome coronavirus infection depend on differentiation of human airway epithelia. J. Virol. 79:14614–14621, 2005.

    Google Scholar 

  114. 114.

    Jia, H. P., D. C. Look, P. Tan, L. Shi, M. Hickey, L. Gakhar, M. C. Chappell, C. Wohlford-Lenane, and P. B. McCray. Ectodomain shedding of angiotensin converting enzyme 2 in human airway epithelia. Am. J. Physiol. Lung Cell. Mol. Physiol. 297:84, 2009.

    Google Scholar 

  115. 115.

    Jin, J.-M., P. Bai, W. He, F. Wu, X.-F. Liu, D.-M. Han, S. Liu, and J.-K. Yang. Gender differences in patients with COVID-19: focus on severity and mortality. Front. Public Health 2020. https://doi.org/10.3389/fpubh.2020.00152.

    Article  Google Scholar 

  116. 116.

    Johnson, H. D., D. Sholcosky, K. Gabello, R. Ragni, and N. Ogonosky. Sex differences in public restroom handwashing behavior associated with visual behavior prompts. Percept. Mot. Skills 97:805–810, 2003.

    Google Scholar 

  117. 117.

    Joyner, K., D. Song, R. F. Hawkins, R. D. Silcott, and G. A. Duncan. A rational approach to form disulfide linked mucin hydrogels. Soft Matter 15:9632–9639, 2019.

    Google Scholar 

  118. 118.

    Kassir, R. Risk of COVID-19 for patients with obesity. Obes Rev 2020. https://doi.org/10.1111/obr.13034.

    Article  Google Scholar 

  119. 119.

    Keller, M., J. Mazuch, U. Abraham, G. D. Eom, E. D. Herzog, H.-D. Volk, A. Kramer, and B. Maier. A circadian clock in macrophages controls inflammatory immune responses. Proc. Natl. Acad. Sci. 106:21407–21412, 2009.

    Google Scholar 

  120. 120.

    Kim, S., and S. Takayama. Organ-on-a-chip and the kidney. Kidney Res. Clin. Pract. 34:165–169, 2015.

    Google Scholar 

  121. 121.

    Kindler, E., V. Thiel, and F. Weber. Interaction of SARS and MERS coronaviruses with the antiviral interferon response. Adv. Virus Res. 96:219–243, 2016.

    Google Scholar 

  122. 122.

    Kitamura, H., C. Sedlik, A. Jacquet, B. Zaragoza, M. Dusseaux, V. Premel, X. Sastre-Garau, and O. Lantz. Long peptide vaccination can lead to lethality through CD4+ T cell-mediated cytokine storm. J. Immunol. 185:892–901, 2010.

    Google Scholar 

  123. 123.

    Klok, F. A., M. J. H. A. Kruip, N. J. M. van der Meer, M. S. Arbous, D. Gommers, K. M. Kant, F. H. J. Kaptein, J. van Paassen, M. A. M. Stals, M. V. Huisman, and H. Endeman. Confirmation of the high cumulative incidence of thrombotic complications in critically ill ICU patients with COVID-19: an updated analysis. Thromb. Res. 2020. https://doi.org/10.1016/j.thromres.2020.04.041.

    Article  Google Scholar 

  124. 124.

    Klotz, L., S. Burgdorf, I. Dani, K. Saijo, J. Flossdorf, S. Hucke, J. Alferink, N. Novak, M. Beyer, G. Mayer, B. Langhans, T. Klockgether, A. Waisman, G. Eberl, J. Schultze, M. Famulok, W. Kolanus, C. Glass, C. Kurts, and P. A. Knolle. The nuclear receptor PPARγ selectively inhibits Th17 differentiation in a T cell–intrinsic fashion and suppresses CNS autoimmunity. J. Exp. Med. 206:2079–2089, 2009.

    Google Scholar 

  125. 125.

    Kuan, T.-C., T.-H. Yang, C.-H. Wen, M.-Y. Chen, I.-L. Lee, and C.-S. Lin. Identifying the regulatory element for human angiotensin-converting enzyme 2 (ACE2) expression in human cardiofibroblasts. Peptides 32:1832–1839, 2011.

    Google Scholar 

  126. 126.

    Kuba, K., Y. Imai, S. Rao, H. Gao, F. Guo, B. Guan, Y. Huan, P. Yang, Y. Zhang, W. Deng, L. Bao, B. Zhang, G. Liu, Z. Wang, M. Chappell, Y. Liu, D. Zheng, A. Leibbrandt, T. Wada, A. S. Slutsky, D. Liu, C. Qin, C. Jiang, and J. M. Penninger. A crucial role of angiotensin converting enzyme 2 (ACE2) in SARS coronavirus-induced lung injury. Nat. Med. 11:875–879, 2005.

    Google Scholar 

  127. 127.

    Kuhrt, K., J. McMicking, S. Nanda, C. Nelson-Piercy, and A. Shennan. Placental abruption in a twin pregnancy at 32 weeks’ gestation complicated by COVID-19, without vertical transmission to the babies. Am. J. Obstet. Gynecol. MFM 2020. https://doi.org/10.1016/j.ajogmf.2020.100135.

    Article  Google Scholar 

  128. 128.

    Kushimoto, S., Y. Taira, Y. Kitazawa, K. Okuchi, T. Sakamoto, H. Ishikura, T. Endo, S. Yamanouchi, T. Tagami, J. Yamaguchi, K. Yoshikawa, M. Sugita, Y. Kase, T. Kanemura, H. Takahashi, Y. Kuroki, H. Izumino, H. Rinka, R. Seo, M. Takatori, T. Kaneko, T. Nakamura, T. Irahara, N. Saito, A. Watanabe, and PiCCO Pulmonary Edema Study Group. The clinical usefulness of extravascular lung water and pulmonary vascular permeability index to diagnose and characterize pulmonary edema: a prospective multicenter study on the quantitative differential diagnostic definition for acute lung injury/acute respiratory distress syndrome. Crit Care 16:R232, 2012.

    Google Scholar 

  129. 129.

    Lai, S. K., Y.-Y. Wang, D. Wirtz, and J. Hanes. Micro- and macrorheology of mucus. Adv. Drug Deliv. Rev. 61:86–100, 2009.

    Google Scholar 

  130. 130.

    Lambert, D. W., L. A. Lambert, N. E. Clarke, N. M. Hooper, K. E. Porter, and A. J. Turner. Angiotensin-converting enzyme 2 is subject to post-transcriptional regulation by miR-421. Clin Sci (Lond) 127:243–249, 2014.

    Google Scholar 

  131. 131.

    Lamers, M. M., J. Beumer, J. van der Vaart, K. Knoops, J. Puschhof, T. I. Breugem, R. B. G. Ravelli, J. P. van Schayck, A. Z. Mykytyn, H. Q. Duimel, E. van Donselaar, S. Riesebosch, H. J. H. Kuijpers, D. Schippers, W. J. van de Wetering, M. de Graaf, M. Koopmans, E. Cuppen, P. J. Peters, B. L. Haagmans, and H. Clevers. SARS-CoV-2 productively infects human gut enterocytes. Science 2020. https://doi.org/10.1126/science.abc1669.

    Article  Google Scholar 

  132. 132.

    Lamouroux, A., T. Attie-Bitach, J. Martinovic, M. Leruez-Ville, and Y. Ville. Evidence for and against vertical transmission for SARS-CoV-2 (COVID-19). Am. J. Obstet. Gynecol. 2020. https://doi.org/10.1016/j.ajog.2020.04.039.

    Article  Google Scholar 

  133. 133.

    Laridan, E., K. Martinod, and S. F. De Meyer. Neutrophil extracellular traps in arterial and venous thrombosis. Semin. Thromb. Hemost. 45:86–93, 2019.

    Google Scholar 

  134. 134.

    Lee, B. W., H. K. Yap, F. T. Chew, T. C. Quah, K. Prabhakaran, G. S. Chan, S. C. Wong, and C. C. Seah. Age- and sex-related changes in lymphocyte subpopulations of healthy Asian subjects: from birth to adulthood. Cytometry 26:8–15, 1996.

    Google Scholar 

  135. 135.

    Lester, S. N., and K. Li. Toll-like receptors in antiviral innate immunity. J. Mol. Biol. 426:1246–1264, 2014.

    Google Scholar 

  136. 136.

    Li, M., L. Chen, J. Zhang, C. Xiong, and X. Li. The SARS-CoV-2 receptor ACE2 expression of maternal-fetal interface and fetal organs by single-cell transcriptome study. PLoS ONE 15:e0230295, 2020.

    Google Scholar 

  137. 137.

    Li, G., Y. Fan, Y. Lai, T. Han, Z. Li, P. Zhou, P. Pan, W. Wang, D. Hu, X. Liu, Q. Zhang, and J. Wu. Coronavirus infections and immune responses. J. Med. Virol. 92:424–432, 2020.

    Google Scholar 

  138. 138.

    Li, X., M. Geng, Y. Peng, L. Meng, and S. Lu. Molecular immune pathogenesis and diagnosis of COVID-19. J. Pharm. Anal. 2020. https://doi.org/10.1016/j.jpha.2020.03.001.

    Article  Google Scholar 

  139. 139.

    Li, X., M. Molina-Molina, A. Abdul-Hafez, V. Uhal, A. Xaubet, and B. D. Uhal. Angiotensin converting enzyme-2 is protective but downregulated in human and experimental lung fibrosis. Am. J. Physiol. Lung Cell. Mol. Physiol. 2008. https://doi.org/10.1152/ajplung.00009.2008.

    Article  Google Scholar 

  140. 140.

    Li, Z., W. Su, Y. Zhu, T. Tao, D. Li, X. Peng, and J. Qin. Drug absorption related nephrotoxicity assessment on an intestine-kidney chip. Biomicrofluidics 11:034114, 2017.

    Google Scholar 

  141. 141.

    Li, R., Y. Wang, R. Chen, W. Gu, L. Zhang, J. Gu, Z. Wang, Y. Liu, Q. Sun, K. Zhang, and C. Liu. Ambient fine particulate matter disrupts hepatic circadian oscillation and lipid metabolism in a mouse model. Environ. Pollut. 262:114179, 2020.

    Google Scholar 

  142. 142.

    Li, H., C. Wang, J. Hu, and J. Tan. A study on circadian rhythm disorder of rat lung tissue caused by mechanical ventilation induced lung injury. Int. Immunopharmacol. 18:249–254, 2014.

    Google Scholar 

  143. 143.

    Liu, Y., W. Sun, J. Li, L. Chen, Y. Wang, L. Zhang, and L. Yu. Clinical features and progression of acute respiratory distress syndrome in coronavirus disease 2019. medRxiv 2020. https://doi.org/10.1101/2020.02.17.20024166.

    Article  Google Scholar 

  144. 144.

    Liu, L., Q. Wei, Q. Lin, J. Fang, H. Wang, H. Kwok, H. Tang, K. Nishiura, J. Peng, Z. Tan, T. Wu, K.-W. Cheung, K.-H. Chan, X. Alvarez, C. Qin, A. Lackner, S. Perlman, K.-Y. Yuen, and Z. Chen. Anti–spike IgG causes severe acute lung injury by skewing macrophage responses during acute SARS-CoV infection. JCI Insight 2019. https://doi.org/10.1172/jci.insight.123158.

    Article  Google Scholar 

  145. 145.

    Liu, T., L. Zhang, D. Joo, and S.-C. Sun. NF-κB signaling in inflammation. Signal Transduct. Target Ther. 2:1–9, 2017.

    Google Scholar 

  146. 146.

    Liu, D., J.-L. Zhou, F. Hong, and Y.-Q. Zhang. Lung inflammation caused by long-term exposure to titanium dioxide in mice involving in NF-κB signaling pathway. J. Biomed. Mater. Res., Part A 105:720–727, 2017.

    Google Scholar 

  147. 147.

    Lock, J. Y., T. Carlson, and R. L. Carrier. Mucus models to evaluate the diffusion of drugs and particles. Adv. Drug Deliv. Rev. 124:34–49, 2018.

    Google Scholar 

  148. 148.

    Loo, Y.-M., and M. Gale. Immune signaling by RIG-I-like receptors. Immunity 34:680–692, 2011.

    Google Scholar 

  149. 149.

    Lu, D., and A. J. Hickey. Pulmonary vaccine delivery. Expert Rev. Vaccines 6:213–226, 2007.

    Google Scholar 

  150. 150.

    Lu, R., et al. Genomic characterisation and epidemiology of 2019 novel coronavirus: implications for virus origins and receptor binding. Lancet 395:565–574, 2020.

    Google Scholar 

  151. 151.

    Luo, R., L. Cannon, J. Hernandez, M. J. Piovoso, and R. Zurakowski. Controlling the evolution of resistance. J. Process Control 21:367–378, 2011.

    Google Scholar 

  152. 152.

    Luo, R., M. J. Piovoso, J. Martinez-Picado, and R. Zurakowski. HIV model parameter estimates from interruption trial data including drug efficacy and reservoir dynamics. PLoS ONE 7:e40198, 2012.

    Google Scholar 

  153. 153.

    Lurie, N., M. Saville, R. Hatchett, and J. Halton. Developing Covid-19 Vaccines At Pandemic Speed. N. Engl. J. Med. 382:1969–1973, 2020.

    Google Scholar 

  154. 154.

    Majumdar, T., J. Dhar, S. Patel, R. Kondratov, and S. Barik. Circadian transcription factor BMAL1 regulates innate immunity against select RNA viruses. Innate Immun 23:147–154, 2017.

    Google Scholar 

  155. 155.

    Marshall, L. J., W. Oguejiofor, R. S. Willetts, H. R. Griffiths, and A. Devitt. Developing accurate models of the human airways. J. Pharm. Pharmacol. 67:464–472, 2015.

    Google Scholar 

  156. 156.

    Mazzoccoli, G., M. Vinciguerra, A. Carbone, and A. Relógio. The circadian clock, the immune system, and viral infections: the intricate relationship between biological time and host-virus interaction. Pathogens 9:83, 2020.

    Google Scholar 

  157. 157.

    McGonagle, D., K. Sharif, A. O’Regan, and C. Bridgewood. The role of Cytokines including interleukin-6 in COVID-19 induced pneumonia and macrophage activation syndrome-like disease. Autoimmunity Rev. 2020. https://doi.org/10.1016/j.autrev.2020.102537.

    Article  Google Scholar 

  158. 158.

    Miao, H., X. Xia, A. S. Perelson, and H. Wu. On identifiability of nonlinear ODE models and applications in viral dynamics. SIAM Rev. 53:3–39, 2011.

    MathSciNet  MATH  Google Scholar 

  159. 159.

    Miyashita, L., G. Foley, S. Semple, and J. Grigg. Traffic-derived particulate matter and angiotensin-converting enzyme 2 expression in human airway epithelial cells. bioRxiv 2020. https://doi.org/10.1101/2020.05.15.097501.

    Article  Google Scholar 

  160. 160.

    Möhlenkamp, S., and H. Thiele. Ventilation of COVID-19 patients in intensive care units. Herz 2020. https://doi.org/10.1007/s00059-020-04923-1.

    Article  Google Scholar 

  161. 161.

    Mondrinos, M. J., Y.-S. Yi, N.-K. Wu, X. Ding, and D. Huh. Native extracellular matrix-derived semipermeable, optically transparent, and inexpensive membrane inserts for microfluidic cell culture. Lab. Chip 17:3146–3158, 2017.

    Google Scholar 

  162. 162.

    Mong, M. A., J. A. Awkal, and P. E. Marik. Accelerated hyaluronan concentration as the primary driver of morbidity and mortality in high-risk COVID-19 patients: with therapeutic introduction of an oral hyaluronan inhibitor in the prevention of “Induced Hyaluronan Storm” syndrome. medRxiv 2020. https://doi.org/10.1101/2020.04.19.20071647.

    Article  Google Scholar 

  163. 163.

    Monteil, V., H. Kwon, P. Prado, A. Hagelkrüys, R. A. Wimmer, M. Stahl, A. Leopoldi, E. Garreta, C. Hurtado del Pozo, F. Prosper, J. P. Romero, G. Wirnsberger, H. Zhang, A. S. Slutsky, R. Conder, N. Montserrat, A. Mirazimi, and J. M. Penninger. Inhibition of SARS-CoV-2 infections in engineered human tissues using clinical-grade soluble human ACE2. Cell 2020. https://doi.org/10.1016/j.cell.2020.04.004.

    Article  Google Scholar 

  164. 164.

    Morgan, J. T., J. Shirazi, E. M. Comber, C. Eschenburg, and J. P. Gleghorn. Fabrication of centimeter-scale and geometrically arbitrary vascular networks using in vitro self-assembly. Biomaterials 189:37–47, 2019.

    Google Scholar 

  165. 165.

    Morgan, J. T., W. G. Stewart, R. A. McKee, and J. P. Gleghorn. The mechanosensitive ion channel TRPV4 is a regulator of lung development and pulmonary vasculature stabilization. Cell. Mol. Bioeng. 11:309–320, 2018.

    Google Scholar 

  166. 166.

    Myerson, J. W., P. N. Patel, N. Habibi, L. R. Walsh, Y.-W. Lee, D. C. Luther, L. T. Ferguson, M. H. Zaleski, M. E. Zamora, O. A. Marcos-Contreras, P. M. Glassman, I. Johnston, E. D. Hood, T. Shuvaeva, J. V. Gregory, R. Y. Kiseleva, J. Nong, K. M. Rubey, C. F. Greineder, S. Mitragotri, G. S. Worthen, V. M. Rotello, J. Lahann, V. R. Muzykantov, and J. S. Brenner. Supramolecular organization predicts protein nanoparticle delivery to neutrophils for acute lung inflammation diagnosis and treatment. bioRxiv 2020. https://doi.org/10.1101/2020.04.15.037564.

    Article  Google Scholar 

  167. 167.

    Nam, H., Y. Choi, and J. Jang. Vascularized lower respiratory-physiology-on-a-chip. Appl. Sci. 10:900, 2020.

    Google Scholar 

  168. 168.

    Nelson, C. M., J. P. Gleghorn, M.-F. Pang, J. M. Jaslove, K. Goodwin, V. D. Varner, E. Miller, D. C. Radisky, and H. A. Stone. Microfluidic chest cavities reveal that transmural pressure controls the rate of lung development. Development 144:4328–4335, 2017.

    Google Scholar 

  169. 169.

    Neutra, M. R., and P. A. Kozlowski. Mucosal vaccines: the promise and the challenge. Nat. Rev. Immunol. 6:148–158, 2006.

    Google Scholar 

  170. 170.

    Newman, S. P., and H.-K. Chan. In vitro/in vivo comparisons in pulmonary drug delivery. J. Aerosol. Med. Pulm. Drug Deliv. 21:77–84, 2008.

    Google Scholar 

  171. 171.

    Nieskens, T. T. G., and M. J. Wilmer. Kidney-on-a-chip technology for renal proximal tubule tissue reconstruction. Eur. J. Pharmacol. 790:46–56, 2016.

    Google Scholar 

  172. 172.

    Oberfeld, B., A. Achanta, K. Carpenter, P. Chen, N. M. Gilette, P. Langat, J. T. Said, A. E. Schiff, A. S. Zhou, A. K. Barczak, and S. Pillai. SnapShot: COVID-19. Cell 181:954–954.e1, 2020.

    Google Scholar 

  173. 173.

    Osuna, C. E., S.-Y. Lim, C. Deleage, B. D. Griffin, D. Stein, L. T. Schroeder, R. W. Omange, K. Best, M. Luo, P. T. Hraber, H. Andersen-Elyard, E. F. C. Ojeda, S. Huang, D. L. Vanlandingham, S. Higgs, A. S. Perelson, J. D. Estes, D. Safronetz, M. G. Lewis, and J. B. Whitney. Zika viral dynamics and shedding in rhesus and cynomolgus macaques. Nat. Med. 22:1448–1455, 2016.

    Google Scholar 

  174. 174.

    Oudit, G. Y., Z. Kassiri, C. Jiang, P. P. Liu, S. M. Poutanen, J. M. Penninger, and J. Butany. SARS-coronavirus modulation of myocardial ACE2 expression and inflammation in patients with SARS. Eur. J. Clin. Investig. 39:618–625, 2009.

    Google Scholar 

  175. 175.

    Papayannopoulos, V. Neutrophil extracellular traps in immunity and disease. Nat. Rev. Immunol. 18:134–147, 2018.

    Google Scholar 

  176. 176.

    Park, H.-J., and J.-M. Choi. Sex-specific regulation of immune responses by PPARs. Exp. Mol. Med. 49:e364, 2017.

    Google Scholar 

  177. 177.

    Park, H.-J., H.-S. Park, J.-U. Lee, A. L. M. Bothwell, and J.-M. Choi. Gender-specific differences in PPARγ regulation of follicular helper T cell responses with estrogen. Sci. Rep. 6:28495, 2016.

    Google Scholar 

  178. 178.

    Park, H.-J., H.-S. Park, J.-U. Lee, A. L. M. Bothwell, and J.-M. Choi. Sex-based selectivity of PPARγ regulation in Th1, Th2, and Th17 differentiation. Int. J. Mol. Sci. 2016. https://doi.org/10.3390/ijms17081347.

    Article  Google Scholar 

  179. 179.

    Park, E.-J., J. Roh, Y. Kim, K. Park, D.-S. Kim, and S.-D. Yu. PM 2.5 collected in a residential area induced Th1-type inflammatory responses with oxidative stress in mice. Environ. Res. 111:348–355, 2011.

    Google Scholar 

  180. 180.

    Park, Y. K., T.-Y. Tu, S. H. Lim, I. J. M. Clement, S. Y. Yang, and R. D. Kamm. In vitro microvessel growth and remodeling within a three-dimensional microfluidic environment. Cell. Mol. Bioeng. 7:15–25, 2014.

    Google Scholar 

  181. 181.

    Patanè, L., D. Morotti, M. R. Giunta, C. Sigismondi, M. G. Piccoli, L. Frigerio, G. Mangili, M. Arosio, and G. Cornolti. Vertical transmission of COVID-19: SARS-CoV-2 RNA on the fetal side of the placenta in pregnancies with COVID-19 positive mothers and neonates at birth. Am. J. Obstet. Gynecol. MFM 2020. https://doi.org/10.1016/j.ajogmf.2020.100145.

    Article  Google Scholar 

  182. 182.

    Patil, H. P., D. Freches, L. Karmani, G. A. Duncan, B. Ucakar, J. S. Suk, J. Hanes, B. Gallez, and R. Vanbever. Fate of PEGylated antibody fragments following delivery to the lungs: influence of delivery site, PEG size and lung inflammation. J. Control. Release 272:62–71, 2018.

    Google Scholar 

  183. 183.

    Paur, H.-R., F. R. Cassee, J. Teeguarden, H. Fissan, S. Diabate, M. Aufderheide, W. G. Kreyling, O. Hänninen, G. Kasper, M. Riediker, B. Rothen-Rutishauser, and O. Schmid. In-vitro cell exposure studies for the assessment of nanoparticle toxicity in the lung—a dialog between aerosol science and biology. J. Aerosol Sci. 42:668–692, 2011.

    Google Scholar 

  184. 184.

    Pedersen, K. B., K. H. Chhabra, V. K. Nguyen, H. Xia, and E. Lazartigues. The transcription factor HNF1α induces expression of angiotensin-converting enzyme 2 (ACE2) in pancreatic islets from evolutionarily conserved promoter motifs. Biochim. Biophys. Acta 1225–1235:2013, 1829.

    Google Scholar 

  185. 185.

    Penfield, C. A., S. G. Brubaker, M. A. Limaye, J. Lighter, A. J. Ratner, K. M. Thomas, J. Meyer, and A. S. Roman. Detection of SARS-COV-2 in placental and fetal membrane samples. Am. J. Obstet. Gynecol. MFM 2020. https://doi.org/10.1016/j.ajogmf.2020.100133.

    Article  Google Scholar 

  186. 186.

    Perinel, S., M. Launay, É. Botelho-Nevers, É. Diconne, A. Louf-Durier, R. Lachand, M. Murgier, D. Page, R. Vermesch, G. Thierry, and X. Delavenne. towards optimization of hydroxychloroquine dosing in intensive care unit COVID-19 patients. Clin. Infect. Dis. 2020. https://doi.org/10.1093/cid/ciaa394.

    Article  Google Scholar 

  187. 187.

    Perlman, S., and A. A. Dandekar. Immunopathogenesis of coronavirus infections: implications for SARS. Nat. Rev. Immunol. 5:917–927, 2005.

    Google Scholar 

  188. 188.

    Petersen, T. H., E. A. Calle, L. Zhao, E. J. Lee, L. Gui, M. B. Raredon, K. Gavrilov, T. Yi, Z. W. Zhuang, C. Breuer, E. Herzog, and L. E. Niklason. Tissue-engineered lungs for in vivo implantation. Science 329:538–541, 2010.

    Google Scholar 

  189. 189.

    Petrovsky, N. Temporal regulation of the human immune system. Expert Rev. Clin. Immunol. 1:379–383, 2005.

    Google Scholar 

  190. 190.

    Pierce-Williams, R. A. M., J. Burd, L. Felder, R. Khoury, P. S. Bernstein, K. Avila, C. A. Penfield, A. S. Roman, C. A. DeBolt, J. L. Stone, A. Bianco, A. R. Kern-Goldberger, A. Hirshberg, S. K. Srinivas, J. S. Jayakumaran, J. S. Brandt, H. Anastasio, M. Birsner, D. S. O’Brien, H. M. Sedev, C. D. Dolin, W. T. Schnettler, A. Suhag, S. Ahluwalia, R. S. Navathe, A. Khalifeh, K. Anderson, and V. Berghella. Clinical course of severe and critical COVID-19 in hospitalized pregnancies: a US cohort study. Am. J. Obstet. Gynecol. MFM 2020. https://doi.org/10.1016/j.ajogmf.2020.100134.

    Article  Google Scholar 

  191. 191.

    Polini, A., L. L. del Mercato, A. Barra, Y. S. Zhang, F. Calabi, and G. Gigli. Towards the development of human immune-system-on-a-chip platforms. Drug Discov. Today 24:517–525, 2019.

    Google Scholar 

  192. 192.

    Prompetchara, E., C. Ketloy, and T. Palaga. Immune responses in COVID-19 and potential vaccines: lessons learned from SARS and MERS epidemic. Asian Pac. J. Allergy Immunol. 38:1–9, 2020.

    Google Scholar 

  193. 193.

    Protano, C., P. Avino, M. Manigrasso, V. Vivaldi, F. Perna, F. Valeriani, and M. Vitali. Environmental electronic vape exposure from four different generations of electronic cigarettes: airborne particulate matter levels. Int. J. Environ. Res. Public Health 2018. https://doi.org/10.3390/ijerph15102172.

    Article  Google Scholar 

  194. 194.

    Qin, C., L. Zhou, Z. Hu, S. Zhang, S. Yang, Y. Tao, C. Xie, K. Ma, K. Shang, W. Wang, and D.-S. Tian. Dysregulation of immune response in patients with coronavirus (COVID-19) in Wuhan, China. Clin. Infect. Dis. 2019. https://doi.org/10.1093/cid/ciaa248.

    Article  Google Scholar 

  195. 195.

    Radzikowska, U., M. Ding, G. Tan, D. Zhakparov, Y. Peng, P. Wawrzyniak, M. Wang, S. Li, H. Morita, C. Altunbulakli, M. Reiger, A. Neumann, N. Lunjani, C. Traidl-Hoffmann, K. Nadeau, L. O’Mahony, C. Akdis, and M. Sokolowska. Distribution of ACE2, CD147, cyclophilins, CD26 and other SARS-CoV-2 associated molecules in human tissues and immune cells in health and disease. bioRxiv 2020. https://doi.org/10.1101/2020.05.14.090332.

    Article  Google Scholar 

  196. 196.

    Ross, A. E., M. C. Belanger, J. F. Woodroof, and R. R. Pompano. Spatially resolved microfluidic stimulation of lymphoid tissue ex vivo. Analyst 142:649–659, 2017.

    Google Scholar 

  197. 197.

    Ross, A. E., and R. R. Pompano. Diffusion of cytokines in live lymph node tissue using microfluidic integrated optical imaging. Anal. Chim. Acta 1000:205–213, 2018.

    Google Scholar 

  198. 198.

    Saito, E., R. Kuo, R. M. Pearson, N. Gohel, B. Cheung, N. J. C. King, S. D. Miller, and L. D. Shea. Designing drug-free biodegradable nanoparticles to modulate inflammatory monocytes and neutrophils for ameliorating inflammation. J. Control. Release 300:185–196, 2019.

    Google Scholar 

  199. 199.

    Sankaran-Walters, S., M. Macal, I. Grishina, L. Nagy, L. Goulart, K. Coolidge, J. Li, A. Fenton, T. Williams, M. K. Miller, J. Flamm, T. Prindiville, M. George, and S. Dandekar. Sex differences matter in the gut: effect on mucosal immune activation and inflammation. Biol. Sex Differ. 4:10, 2013.

    Google Scholar 

  200. 200.

    Schappell, L. E., D. J. Minahan, and J. Gleghorn. A microfluidic system to measure neonatal lung compliance over late stage development as a functional measure of lung tissue mechanics. J. Biomech. Eng. 2020. https://doi.org/10.1115/1.4047133.

    Article  Google Scholar 

  201. 201.

    Scheff, J. D., S. E. Calvano, S. F. Lowry, and I. P. Androulakis. Modeling the influence of circadian rhythms on the acute inflammatory response. J. Theor. Biol. 264:1068–1076, 2010.

    MathSciNet  MATH  Google Scholar 

  202. 202.

    Scotland, R. S., M. J. Stables, S. Madalli, P. Watson, and D. W. Gilroy. Sex-differences in resident immune cell phenotype underlies more efficient acute inflammatory responses in female mice. Blood 118:5918–5927, 2011.

    Google Scholar 

  203. 203.

    Senkel, S., B. Lucas, L. Klein-Hitpass, and G. U. Ryffel. Identification of target genes of the transcription factor HNF1β and HNF1α in a human embryonic kidney cell line. Biochim. Biophys. Acta (BBA) Gene Struct. Expr. 1731:179–190, 2005.

    Google Scholar 

  204. 204.

    Shadan, F. F. A circadian model for viral persistence. Med. Hypoth. 68:546–553, 2007.

    Google Scholar 

  205. 205.

    Shanes, E. D., L. B. Mithal, S. Otero, H. A. Azad, E. S. Miller, and J. A. Goldstein. Placental pathology in COVID-19. medRxiv 2020. https://doi.org/10.1101/2020.05.08.20093229.

    Article  Google Scholar 

  206. 206.

    Shang, J., Y. Wan, C. Luo, G. Ye, Q. Geng, A. Auerbach, and F. Li. Cell entry mechanisms of SARS-CoV-2. Proc. Natl. Acad. Sci. 117:11727–11734, 2020.

    Google Scholar 

  207. 207.

    Sharma, S., I. Singh, S. Haider, M. Z. Malik, K. Ponnusamy, and E. Rai. ACE2 homo-dimerization, human genomic variants and interaction of host proteins explain high population specific differences in outcomes of COVID19. bioRxiv 2020. https://doi.org/10.1101/2020.04.24.050534.

    Article  Google Scholar 

  208. 208.

    Shi, Y., Y. Wang, C. Shao, J. Huang, J. Gan, X. Huang, E. Bucci, M. Piacentini, G. Ippolito, and G. Melino. COVID-19 infection: the perspectives on immune responses. Cell Death Differ. 2020. https://doi.org/10.1038/s41418-020-0530-3.

    Article  Google Scholar 

  209. 209.

    Shirazi, J., J. T. Morgan, E. M. Comber, and J. P. Gleghorn. Generation and morphological quantification of large scale, three-dimensional, self-assembled vascular networks. MethodsX 6:1907–1918, 2019.

    Google Scholar 

  210. 210.

    Silver, A. C., A. Arjona, W. E. Walker, and E. Fikrig. The Circadian Clock Controls toll-like receptor 9-mediated innate and adaptive immunity. Immunity 36:251–261, 2012.

    Google Scholar 

  211. 211.

    Slutsky, A. S., and V. M. Ranieri. Ventilator-Induced Lung Injury. N. Engl. J. Med. 369:2126–2136, 2013.

    Google Scholar 

  212. 212.

    Smith, S. E. P., J. Li, K. Garbett, K. Mirnics, and P. H. Patterson. Maternal immune activation alters fetal brain development through interleukin-6. J. Neurosci. 27:10695–10702, 2007.

    Google Scholar 

  213. 213.

    Smithmyer, M. E., S. E. Cassel, and A. M. Kloxin. Bridging 2D and 3D culture: probing impact of extracellular environment on fibroblast activation in layered hydrogels. AIChE J. 65:e16837, 2019.

    Google Scholar 

  214. 214.

    Solt, L. A., N. Kumar, P. Nuhant, Y. Wang, J. L. Lauer, J. Liu, M. A. Istrate, T. M. Kamenecka, W. R. Roush, D. Vidović, S. C. Schürer, J. Xu, G. Wagoner, P. D. Drew, P. R. Griffin, and T. P. Burris. Suppression of T H 17 differentiation and autoimmunity by a synthetic ROR ligand. Nature 472:491–494, 2011.

    Google Scholar 

  215. 215.

    Steinman, R. M., and H. Hemmi. Dendritic cells: translating innate to adaptive immunity. In: From Innate Immunity to Immunological Memory, edited by B. Pulendran, and R. Ahmed. Berlin, Heidelberg: Springer, 2006, pp. 17–58. https://doi.org/10.1007/3-540-32636-7_2.

    Google Scholar 

  216. 216.

    Stengel, R. F. Mutation and control of the human immunodeficiency virus. Math. Biosci. 213:93–102, 2008.

    MathSciNet  MATH  Google Scholar 

  217. 217.

    Su, H., M. Yang, C. Wan, L.-X. Yi, F. Tang, H.-Y. Zhu, F. Yi, H.-C. Yang, A. B. Fogo, X. Nie, and C. Zhang. Renal histopathological analysis of 26 postmortem findings of patients with COVID-19 in China. Kidney Int. 2020. https://doi.org/10.1016/j.kint.2020.04.003.

    Article  Google Scholar 

  218. 218.

    Sul, B., Z. Oppito, S. Jayasekera, B. Vanger, A. Zeller, M. Morris, K. Ruppert, T. Altes, V. Rakesh, S. Day, R. Robinson, J. Reifman, and A. Wallqvist. Assessing airflow sensitivity to healthy and diseased lung conditions in a computational fluid dynamics model validated in vitro. J. Biomech. Eng. 2018. https://doi.org/10.1115/1.4038896.

    Article  Google Scholar 

  219. 219.

    Sundar, I. K., K. Rashid, M. T. Sellix, and I. Rahman. The nuclear receptor and clock gene REV-ERBα regulates cigarette smoke-induced lung inflammation. Biochem. Biophys. Res. Commun. 493:1390–1395, 2017.

    Google Scholar 

  220. 220.

    Sundar, I. K., H. Yao, M. T. Sellix, and I. Rahman. Circadian molecular clock in lung pathophysiology. Am. J. Physiol. Lung Cell. Mol. Physiol. 309:L1056–L1075, 2015.

    Google Scholar 

  221. 221.

    Sungnak, W., N. Huang, C. Bécavin, M. Berg, R. Queen, M. Litvinukova, C. Talavera-López, H. Maatz, D. Reichart, F. Sampaziotis, K. B. Worlock, M. Yoshida, and J. L. Barnes. SARS-CoV-2 entry factors are highly expressed in nasal epithelial cells together with innate immune genes. Nat. Med. 26:681–687, 2020.

    Google Scholar 

  222. 222.

    Suwa, T., J. C. Hogg, K. B. Quinlan, A. Ohgami, R. Vincent, and S. F. van Eeden. Particulate air pollution induces progression of atherosclerosis. J. Am. Coll. Cardiol. 39:935–942, 2002.

    Google Scholar 

  223. 223.

    Tam, A., D. Morrish, S. Wadsworth, D. Dorscheid, S. P. Man, and D. D. Sin. The role of female hormones on lung function in chronic lung diseases. BMC Womens Health 11:24, 2011.

    Google Scholar 

  224. 224.

    Tan, L., Q. Wang, D. Zhang, J. Ding, Q. Huang, Y.-Q. Tang, Q. Wang, and H. Miao. Lymphopenia predicts disease severity of COVID-19: a descriptive and predictive study. Signal Transduct. Targeted Ther. 5:1–3, 2020.

    Google Scholar 

  225. 225.

    Tang, A., Z.-D. Tong, H.-L. Wang, Y.-X. Dai, K.-F. Li, J.-N. Liu, W.-J. Wu, C. Yuan, M.-L. Yu, P. Li, and J.-B. Yan. Detection of novel coronavirus by RT-PCR in stool specimen from asymptomatic child. China. Emerg. Infect. Dis. 26:1337–1339, 2020.

    Google Scholar 

  226. 226.

    Tenenbaum-Katan, J., A. Artzy-Schnirman, R. Fishler, N. Korin, and J. Sznitman. Biomimetics of the pulmonary environment in vitro: a microfluidics perspective. Biomicrofluidics 12:042209, 2018.

    Google Scholar 

  227. 227.

    Teuwen, L.-A., V. Geldhof, A. Pasut, and P. Carmeliet. COVID-19: the vasculature unleashed. Nat. Rev. Immunol. 2020. https://doi.org/10.1038/s41577-020-0343-0.

    Article  Google Scholar 

  228. 228.

    Tikellis, C., and M. C. Thomas. Angiotensin-converting enzyme 2 (ACE2) is a key modulator of the renin angiotensin system in health and disease. Int. J. Peptides 2012. https://doi.org/10.1155/2012/256294.

    Article  Google Scholar 

  229. 229.

    Tipnis, S. R., N. M. Hooper, R. Hyde, E. Karran, G. Christie, and A. J. Turner. A human homolog of angiotensin-converting enzyme: cloning and functional expression as a captopril-insensitive carboxypeptidase. J. Biol. Chem. 275:33238–33243, 2000.

    Google Scholar 

  230. 230.

    Umbrello, M., P. Formenti, L. Bolgiaghi, and D. Chiumello. Current concepts of ARDS: a narrative review. Int. J. Mol. Sci. 2016. https://doi.org/10.3390/ijms18010064.

    Article  Google Scholar 

  231. 231.

    Unger, T., U. M. Steckelings, and V. J. Dzau. The Protective Arm of the Renin Angiotensin System (RAS). New York: Elsevier, 2015. https://doi.org/10.1016/C2013-0-23135-4.

    Book  Google Scholar 

  232. 232.

    Valdés, G., L. A. A. Neves, L. Anton, J. Corthorn, C. Chacón, A. M. Germain, D. C. Merrill, C. M. Ferrario, R. Sarao, J. Penninger, and K. B. Brosnihan. Distribution of angiotensin-(1-7) and ACE2 in human placentas of normal and pathological pregnancies. Placenta 27:200–207, 2006.

    Google Scholar 

  233. 233.

    van Eeden, S. F., and J. C. Hogg. Systemic inflammatory response induced by particulate matter air pollution: the importance of bone-marrow stimulation. J. Toxicol. Environ. Health Part A 65:1597–1613, 2002.

    Google Scholar 

  234. 234.

    Varga, Z., A. J. Flammer, P. Steiger, M. Haberecker, R. Andermatt, A. S. Zinkernagel, M. R. Mehra, R. A. Schuepbach, F. Ruschitzka, and H. Moch. Endothelial cell infection and endotheliitis in COVID-19. Lancet 395:1417–1418, 2020.

    Google Scholar 

  235. 235.

    Verbanck, S., G. Ghorbaniasl, M. F. Biddiscombe, D. Dragojlovic, N. Ricks, C. Lacor, B. Ilsen, J. de Mey, D. Schuermans, S. R. Underwood, P. J. Barnes, W. Vincken, and O. S. Usmani. Inhaled aerosol distribution in human airways: a scintigraphy-guided study in a 3D printed model. J. Aerosol. Med. Pulm. Drug Deliv. 29:525–533, 2016.

    Google Scholar 

  236. 236.

    Vinciguerra, M., and E. Greco. Sars-CoV-2 and black population: ACE2 as shield or blade? Infect. Genet. Evol. 2020. https://doi.org/10.1016/j.meegid.2020.104361.

    Article  Google Scholar 

  237. 237.

    Vlahakis, N. E., M. A. Schroeder, A. H. Limper, and R. D. Hubmayr. Stretch induces cytokine release by alveolar epithelial cells in vitro. Am. J. Physiol. Lung Cell. Mol. Physiol. 277:L167–L173, 1999.

    Google Scholar 

  238. 238.

    Voiriot, G., K. Razazi, V. Amsellem, J. Tran Van Nhieu, S. Abid, S. Adnot, A. Mekontso Dessap, and B. Maitre. Interleukin-6 displays lung anti-inflammatory properties and exerts protective hemodynamic effects in a double-hit murine acute lung injury. Respir. Res. 18:64, 2017.

    Google Scholar 

  239. 239.

    Vozeh, S., and J. L. Steimer. Feedback control methods for drug dosage optimisation: CONCEPTS, classification and clinical application. Clin. Pharmacokinet. 10:457–476, 1985.

    Google Scholar 

  240. 240.

    Vukkadala, N., Z. J. Qian, F. C. Holsinger, Z. M. Patel, and E. Rosenthal. COVID-19 and the otolaryngologist: preliminary evidence-based review. Laryngoscope 2020. https://doi.org/10.1002/lary.28672.

    Article  Google Scholar 

  241. 241.

    Wang, D., B. Hu, C. Hu, F. Zhu, X. Liu, J. Zhang, B. Wang, H. Xiang, Z. Cheng, Y. Xiong, Y. Zhao, Y. Li, X. Wang, and Z. Peng. Clinical Characteristics of 138 hospitalized patients with 2019 novel coronavirus–infected pneumonia in Wuhan, China. JAMA 323:1061–1069, 2020.

    Google Scholar 

  242. 242.

    Wang, Y., R. Li, R. Chen, W. Gu, L. Zhang, J. Gu, Z. Wang, Y. Liu, Q. Sun, K. Zhang, and C. Liu. Ambient fine particulate matter exposure perturbed circadian rhythm and oscillations of lipid metabolism in adipose tissues. Chemosphere 251:126392, 2020.

    Google Scholar 

  243. 243.

    Wang, X., D. T. T. Phan, S. C. George, C. C. W. Hughes, and A. P. Lee. Engineering anastomosis between living capillary networks and endothelial cell-lined microfluidic channels. Lab. Chip 16:282–290, 2016.

    Google Scholar 

  244. 244.

    Wang, Q., I. K. Sundar, D. Li, J. H. Lucas, T. Muthumalage, S. R. McDonough, and I. Rahman. E-cigarette-induced pulmonary inflammation and dysregulated repair are mediated by nAChR α7 receptor: role of nAChR α7 in ACE2 Covid-19 receptor regulation. Respir. Res. 2020. https://doi.org/10.21203/rs.2.23829/v2.

    Article  Google Scholar 

  245. 245.

    Wang, T., L. Wang, L. Moreno-Vinasco, G. D. Lang, J. H. Siegler, B. Mathew, P. V. Usatyuk, J. M. Samet, A. S. Geyh, P. N. Breysse, V. Natarajan, and J. G. N. Garcia. Particulate matter air pollution disrupts endothelial cell barrier via calpain-mediated tight junction protein degradation. Particle Fibre Toxicol. 9:35, 2012.

    Google Scholar 

  246. 246.

    Wang, Q., L. Zhang, K. Kuwahara, L. Li, Z. Liu, T. Li, H. Zhu, J. Liu, Y. Xu, J. Xie, H. Morioka, N. Sakaguchi, C. Qin, and G. Liu. Immunodominant SARS coronavirus epitopes in humans elicited both enhancing and neutralizing effects on infection in non-human primates. ACS Infect. Dis. 2:361–376, 2016.

    Google Scholar 

  247. 247.

    Wei, Y., J. Zhang, Z. Li, A. Gow, K. F. Chung, M. Hu, Z. Sun, L. Zeng, T. Zhu, G. Jia, X. Li, M. Duarte, and X. Tang. Chronic exposure to air pollution particles increases the risk of obesity and metabolic syndrome: findings from a natural experiment in Beijing. FASEB J. 30:2115–2122, 2016.

    Google Scholar 

  248. 248.

    Williamson, B. N., F. Feldmann, B. Schwarz, K. Meade-White, D. P. Porter, J. Schulz, N. van Doremalen, I. Leighton, C. K. Yinda, L. Pérez-Pérez, A. Okumura, J. Lovaglio, P. W. Hanley, G. Saturday, C. M. Bosio, S. Anzick, K. Barbian, T. Cihlar, C. Martens, D. P. Scott, V. J. Munster, and E. de Wit. Clinical benefit of remdesivir in rhesus macaques infected with SARS-CoV-2. Microbiology 2020. https://doi.org/10.1101/2020.04.15.043166.

    Article  Google Scholar 

  249. 249.

    Wilson, M. R., S. Choudhury, M. E. Goddard, K. P. O’Dea, A. G. Nicholson, and M. Takata. High tidal volume upregulates intrapulmonary cytokines in an in vivo mouse model of ventilator-induced lung injury. J. Appl. Physiol. 95:1385–1393, 2003.

    Google Scholar 

  250. 250.

    Woodward, N. C., A. L. Crow, Y. Zhang, S. Epstein, J. Hartiala, R. Johnson, H. Kocalis, A. Saffari, I. Sankaranarayanan, O. Akbari, G. Ramanathan, J. A. Araujo, C. E. Finch, S. G. Bouret, C. Sioutas, T. E. Morgan, and H. Allayee. Exposure to nanoscale particulate matter from gestation to adulthood impairs metabolic homeostasis in mice. Sci. Rep. 9:1–11, 2019.

    Google Scholar 

  251. 251.

    Wösten-Van Asperen, R. M., R. Lutter, P. A. Specht, G. N. Moll, J. B. Van Woensel, C. M. Van Der Loos, H. Van Goor, J. Kamilic, S. Florquin, and A. P. Bos. Acute respiratory distress syndrome leads to reduced ratio of ACE/ACE2 activities and is prevented by angiotensin-(17) or an angiotensin II receptor antagonist. J. Pathol. 225:618–627, 2011.

    Google Scholar 

  252. 252.

    Wu, Y. Compensation of ACE2 Function for Possible Clinical Management of 2019-nCoV-induced acute lung injury. Virol. Sin. 12250:2019–2021, 2020.

    Google Scholar 

  253. 253.

    Wu, X., R. C. Nethery, B. M. Sabath, D. Braun, and F. Dominici. Exposure to air pollution and COVID-19 mortality in the United States: a nationwide cross-sectional study. MedRxiv 2020. https://doi.org/10.1101/2020.04.05.20054502.

    Article  Google Scholar 

  254. 254.

    Wu, Y., X. Xu, Z. Chen, J. Duan, K. Hashimoto, L. Yang, C. Liu, and C. Yang. Nervous system involvement after infection with COVID-19 and other coronaviruses. Brain Behav. Immun. 2020. https://doi.org/10.1016/j.bbi.2020.03.031.

    Article  Google Scholar 

  255. 255.

    Wu, D., and X. O. Yang. TH17 responses in cytokine storm of COVID-19: An emerging target of JAK2 inhibitor Fedratinib. J. Microbiol. Immunol. Infect. 2020. https://doi.org/10.1016/j.jmii.2020.03.005.

    Article  Google Scholar 

  256. 256.

    Wu, F., S. Zhao, B. Yu, Y.-M. Chen, W. Wang, Z.-G. Song, Y. Hu, Z.-W. Tao, J.-H. Tian, Y.-Y. Pei, M.-L. Yuan, Y.-L. Zhang, F.-H. Dai, Y. Liu, Q.-M. Wang, J.-J. Zheng, L. Xu, E. C. Holmes, and Y.-Z. Zhang. A new coronavirus associated with human respiratory disease in China. Nature 579:265–269, 2020.

    Google Scholar 

  257. 257.

    Xie, J., Z. Tong, X. Guan, B. Du, and H. Qiu. Clinical characteristics of patients who died of coronavirus disease 2019 in China. JAMA Netw. Open 3:e205619–e205619, 2020.

    Google Scholar 

  258. 258.

    Xu, Z., L. Shi, Y. Wang, J. Zhang, L. Huang, C. Zhang, S. Liu, P. Zhao, H. Liu, L. Zhu, Y. Tai, C. Bai, T. Gao, J. Song, P. Xia, J. Dong, J. Zhao, and F.-S. Wang. Pathological findings of COVID-19 associated with acute respiratory distress syndrome. Lancet Respir. Med. 8:420–422, 2020.

    Google Scholar 

  259. 259.

    Yang, A.-P., J. Liu, W. Tao, and H. Li. The diagnostic and predictive role of NLR, d-NLR and PLR in COVID-19 patients. Int. Immunopharmacol. 2020. https://doi.org/10.1016/j.intimp.2020.106504.

    Article  Google Scholar 

  260. 260.

    Yang, X. Y., L. H. Wang, T. Chen, D. R. Hodge, J. H. Resau, L. DaSilva, and W. L. Farrar. Activation of human T lymphocytes is inhibited by peroxisome proliferator-activated receptor gamma (PPARgamma) agonists. PPARgamma co-association with transcription factor NFAT. J. Biol. Chem. 275:4541–4544, 2000.

    Google Scholar 

  261. 261.

    Yang, P.-L., T. M. Ward, R. L. Burr, V. K. Kapur, S. M. McCurry, M. V. Vitiello, C. L. Hough, and E. C. Parsons. Sleep and Circadian Rhythms In Survivors Of Acute Respiratory Failure. Front Neurol 2020. https://doi.org/10.3389/fneur.2020.0009.

    Article  Google Scholar 

  262. 262.

    Yao, H., I. K. Sundar, Y. Huang, J. Gerloff, M. T. Sellix, P. J. Sime, and I. Rahman. Disruption of sirtuin 1–mediated control of circadian molecular clock and inflammation in chronic obstructive pulmonary disease. Am. J. Respir. Cell Mol. Biol. 53:782–792, 2015.

    Google Scholar 

  263. 263.

    Ye, Z., Y. Zhang, Y. Wang, Z. Huang, and B. Song. Chest Ct manifestations of new coronavirus disease 2019 (COVID-19): a pictorial review. Eur. Radiol. 2019. https://doi.org/10.1007/s00330-020-06801-0.

    Article  Google Scholar 

  264. 264.

    Yilla, M., B. H. Harcourt, C. J. Hickman, M. McGrew, A. Tamin, C. S. Goldsmith, W. J. Bellini, and L. J. Anderson. SARS-coronavirus replication in human peripheral monocytes/macrophages. Virus Res. 107:93–101, 2005.

    Google Scholar 

  265. 265.

    Yiu, H. H., A. L. Graham, and R. F. Stengel. Dynamics of a cytokine storm. PLoS ONE 7:e45027, 2012.

    Google Scholar 

  266. 266.

    Yockey, L. J., C. Lucas, and A. Iwasaki. Contributions of maternal and fetal antiviral immunity in congenital disease. Science 368:608–612, 2020.

    Google Scholar 

  267. 267.

    Zeng, H., C. Xu, J. Fan, Y. Tang, Q. Deng, W. Zhang, and X. Long. Antibodies in infants born to mothers with COVID-19 pneumonia. JAMA 323:1848–1849, 2020.

    Google Scholar 

  268. 268.

    Zhang, Y., C. Coarfa, X. Dong, W. Jiang, B. Hayward-Piatkovskyi, J. P. Gleghorn, and K. Lingappan. MicroRNA-30a as a candidate underlying sex-specific differences in neonatal hyperoxic lung injury: implications for BPD. Am. J. Physiol. Lung Cell Mol. Physiol. 316:L144–L156, 2019.

    Google Scholar 

  269. 269.

    Zhang, Y., X. Dong, J. Shirazi, J. P. Gleghorn, and K. Lingappan. Pulmonary endothelial cells exhibit sexual dimorphism in their response to hyperoxia. Am. J. Physiol. Heart Circul. Physiol. 315:H1287–H1292, 2018.

    Google Scholar 

  270. 270.

    Zhang, H., Z. Kang, H. Gong, D. Xu, J. Wang, Z. Li, Z. Li, X. Cui, J. Xiao, J. Zhan, T. Meng, W. Zhou, J. Liu, and H. Xu. Digestive system is a potential route of COVID-19: an analysis of single-cell coexpression pattern of key proteins in viral entry process. Gut 69:1010–1018, 2020.

    Google Scholar 

  271. 271.

    Zhang, M. A., D. Rego, M. Moshkova, H. Kebir, A. Chruscinski, H. Nguyen, R. Akkermann, F. Z. Stanczyk, A. Prat, L. Steinman, and S. E. Dunn. Peroxisome proliferator-activated receptor (PPAR)α and -γ regulate IFNγ and IL-17A production by human T cells in a sex-specific way. Proc. Natl. Acad. Sci. 109:9505–9510, 2012.

    Google Scholar 

  272. 272.

    Zhang, T., L. X. Sun, and R. E. Feng. Comparison of clinical and pathological features between severe acute respiratory syndrome and coronavirus disease 2019. Zhonghua Jie He He Hu Xi Za Zhi 43:E040, 2020.

    Google Scholar 

  273. 273.

    Zhang, R., X. Wang, L. Ni, X. Di, B. Ma, S. Niu, C. Liu, and R. J. Reiter. COVID-19: melatonin as a potential adjuvant treatment. Life Sci. 250:117583, 2020.

    Google Scholar 

  274. 274.

    Zhang, C., L. Yang, S. Liu, S. Ma, Y. Wang, Z. Cai, H. Du, R. Li, L. Kang, M. Su, J. Zhang, Z. Liu, and B. Zhang. Survey of insomnia and related social psychological factors among medical staff involved in the novel coronavirus disease outbreak. Front. Psychiatry 11:2020, 2019. https://doi.org/10.3389/fpsyt.2020.00306.

    Article  Google Scholar 

  275. 275.

    Zhang, W., Y. Zhao, F. Zhang, Q. Wang, T. Li, Z. Liu, J. Wang, Y. Qin, X. Zhang, X. Yan, X. Zeng, and S. Zhang. The use of anti-inflammatory drugs in the treatment of people with severe coronavirus disease 2019 (COVID-19): the perspectives of clinical immunologists from China. Clin. Immunol. 214:108393, 2020.

    Google Scholar 

  276. 276.

    Zhao, M. Cytokine storm and immunomodulatory therapy in COVID-19: role of chloroquine and anti-IL-6 monoclonal antibodies. Int. J. Antimicrob. Agents 2020. https://doi.org/10.1016/j.ijantimicag.2020.105982.

    Article  Google Scholar 

  277. 277.

    Zhao, Y., Z. Zhao, Y. Wang, Y. Zhou, Y. Ma, and W. Zuo. Single-cell RNA expression profiling of ACE2, the putative receptor of Wuhan 2019-nCov. bioRxiv 2020. https://doi.org/10.1101/2020.01.26.919985.

    Article  Google Scholar 

  278. 278.

    Zhou, P., X.-L. Yang, X.-G. Wang, B. Hu, L. Zhang, W. Zhang, H.-R. Si, Y. Zhu, B. Li, C.-L. Huang, H.-D. Chen, J. Chen, Y. Luo, H. Guo, R.-D. Jiang, M.-Q. Liu, Y. Chen, X.-R. Shen, X. Wang, X.-S. Zheng, K. Zhao, Q.-J. Chen, F. Deng, L.-L. Liu, B. Yan, F.-X. Zhan, Y.-Y. Wang, G.-F. Xiao, and Z.-L. Shi. A pneumonia outbreak associated with a new coronavirus of probable bat origin. Nature 579:270–273, 2020.

    Google Scholar 

  279. 279.

    Zhu, Y., J. Xie, F. Huang, and L. Cao. Association between short-term exposure to air pollution and COVID-19 infection: evidence from China. Sci. Total Environ. 727:138704, 2020.

    Google Scholar 

  280. 280.

    Zuo, Y., M. Zuo, S. Yalavarthi, K. Gockman, J. A. Madison, H. Shi, J. S. Knight, and Y. Kanthi. Neutrophil extracellular traps and thrombosis in COVID-19. medRxiv 2020. https://doi.org/10.1101/2020.04.30.20086736.

    Article  Google Scholar 

  281. 281.

    Zurakowski, R. Nonlinear observer output-feedback MPC treatment scheduling for HIV. BioMed. Eng. OnLine 10:40, 2011.

    Google Scholar 

  282. 282.

    Zurakowski, R. Using the tools we have: low-efficacy vaccines and HIV. EBioMedicine 2:1867–1868, 2015.

    Google Scholar 

  283. 283.

    Zurakowski, R., and A. R. Teel. A model predictive control based scheduling method for HIV therapy. J. Theor. Biol. 238:368–382, 2006.

    MathSciNet  MATH  Google Scholar 

  284. 284.

    Zurakowski, R., and D. Wodarz. Model-driven approaches for in vitro combination therapy using ONYX-015 replicating oncolytic adenovirus. J. Theor. Biol. 245:1–8, 2007.

    MathSciNet  Google Scholar 

Download references

Acknowledgments

This work was supported in part by grants from the National Institutes of Health [R01HL133163 (JPG), R01HL144775 (JPG), ACCEL CTR U54GM104941 (JPG, CF), R03AI136710 (RZ)], the National Science Foundation [CBET1943686 (JPG)], and the March of Dimes Basil O’Connor Award [5-FY16-33 (JPG)]. Figures were created using Biorender.com.

Author information

Affiliations

Authors

Corresponding author

Correspondence to Jason P. Gleghorn.

Ethics declarations

Conflict of interest

The authors declare that they have no conflict of interest.

Human and Animal Studies

No human studies were carried out by the authors for this article. No animal studies were carried out by the authors for this article.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Associate Editor Owen McCarty oversaw the review of this article.

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Shirazi, J., Donzanti, M.J., Nelson, K.M. et al. Significant Unresolved Questions and Opportunities for Bioengineering in Understanding and Treating COVID-19 Disease Progression. Cel. Mol. Bioeng. 13, 259–284 (2020). https://doi.org/10.1007/s12195-020-00637-w

Download citation

Keywords

  • COVID-19
  • ACE2
  • Circadian rhythms
  • Organotypic models
  • Placenta
  • Cytokine storm
  • ARDS
  • Host response
  • Immune
  • Sex differences