The COVID-19 explorer—An integrated, whole patient knowledge model of COVID-19 disease

Abstract

Since early 2020 the COVID-19 pandemic has paralyzed the world, resulting in more than half a billion infections and over 6 million deaths within a 28-month period. Knowledge about the disease remains largely disjointed, especially when considering the molecular mechanisms driving the diversity of clinical manifestations and symptoms. Despite the recent availability of vaccines, there remains an urgent need to develop effective treatments for cases of severe disease, especially in the face of novel virus variants. The complexity of the situation is exacerbated by the emergence of COVID-19 as a complex and multifaceted systemic disease affecting independent tissues and organs throughout the body. The development of effective treatment strategies is therefore predicated on an integrated understanding of the underlying disease mechanisms and their potentially causative link to the diversity of observed clinical phenotypes. To address this need, we utilized a computational technology (the Dataome platform) to build an integrated clinico-molecular view on the most important COVID-19 clinical phenotypes. Our results provide the first integrated, whole-patient model of COVID-19 symptomatology that connects the molecular lifecycle of SARS-CoV-2 with microvesicle-mediated intercellular communication and the contact activation and kallikrein-kinin systems. The model not only explains the clinical pleiotropy of COVID-19, but also provides an evidence-driven framework for drug development/repurposing and the identification of critical risk factors. The associated knowledge is provided in the form of the open source COVID-19 Explorer (https://covid19.molecularhealth.com), enabling the global community to explore and analyze the key molecular features of systemic COVID-19 and associated implications for research priorities and therapeutic strategies. Our work suggests that knowledge modeling solutions may offer important utility in expediting the global response to future health emergencies.

1 Introduction

If there is any positive to be gleaned from the devastating COVID-19 pandemic, it could be the scale and impact of global response from the biomedical research community to the study of the SARS-CoV-2 virus. From efforts to characterize molecular disease mechanisms in the search for tractable therapeutic avenues, to drug repurposing and pandemic forecasting, we have witnessed unprecedented levels of multidisciplinary collaboration. Nevertheless, the resultant peer-reviewed insights have come at a rapid velocity and volume that makes it challenging to efficiently capture, integrate and analyze emergent data from both the clinical and molecular domains. The integration of such insights with existing knowledge is pivotal to efficient knowledge transfer and assimilation by the research community, aiding our understanding of disease mechanisms and expediting the generation and testing of associated therapeutic hypotheses. Recognizing the global urgency, we initiated a COVID-19 focused knowledge modeling effort in March 2020 that sought to rapidly address this challenge. Our goal was to develop a whole patient knowledge model of COVID-19 symptomatology and associated molecular knowledge that links the key molecular players in disease pathophysiology to: 1) common symptoms, 2) severe manifestations and 3) outcome and severity-associated risk factors.

To achieve this, we designed a stepwise, expert-driven knowledge modeling strategy that iteratively combined the capacities of both extensive data integration and human insight (see Figure 1). This supervised strategy helped us to 1) manage the rapid pace of new insights, 2) enable the flexible elaboration of more specific disease symptom models and associated hypotheses, and 3) permit the real-time inclusion of important new findings at the whole-patient level. Here, we report on our findings and the functionality of the COVID-19 Explorer web resource. Our results are provided at the level of a whole-patient knowledge model, including the possible causative pathogenic mechanisms underlying COVID-19 phenotypes (Brock et al., 2022). To accommodate usability for a variety of use-case scenarios, results are summarized in different formats: the ‘COVID-19 Explorer’ provides a detailed, comprehensive and fully interactive view of the relationships and accompanying evidence, while ‘The COVID-19 Cockpit’ (see Supplementary Table S1) is intended to support clinical researchers in diagnostic and hypothesis generation for new therapeutic strategies (see Figure 1).

FIGURE 1

By developing a patient-level molecular atlas of COVID-19 pathogenesis as an interactive open-source knowledge model, we provide the biomedical and clinical research communities with an effective tool to decipher COVID-19 hypotheses and to enable more informed development and testing of new diagnostic and therapeutic strategies.

1.1 Materials and methods

We utilized Molecular Health’s (MH’s) Dataome technology platform in collaboration with disease modeling experts to capture, structure and logically connect diverse clinical and molecular features of COVID-19 pathobiology (see Figure 1). Readouts from these rapid in silico analyses were curated and organized into molecular models containing salient information for each symptom (see Figure 2 and Figure 4). The final complete comprehensive model, linking key molecular mechanisms to COVID-19 symptomatology and the related source data, is made publicly available via the web-based COVID-19 Explorer (accessible, at http://covid19.molecularhealth.com).

FIGURE 2

1.1.1 The Dataome technology

The utility of the integrated Dataome technology has been previously validated across multiple clinically important contexts, including biomarker discovery, drug safety prediction and drug repositioning (Armaiz-Pena et al., 2013; Bohnert et al., 2017; Pradeep et al., 2015; Soldatos et al., 2018; Soldatos and Jackson, 2019; Schotland et al., 2021; Schell et al., 2016). While Dataome provides a flexible approach to the automated capture and quality assurance of globally available sources of clinical and molecular data and knowledge, a supervised approach was required for the molecular modeling of COVID-19 to account for the rapid emergence of new insights. This permitted the stepwise, expert-guided elaboration of more specific COVID-19 symptomology models and associated hypotheses, flexible enough to enable the near real-time inclusion of important new findings, given the highly dynamic nature of the pandemic.

1.2 COVID-19 knowledge modeling strategy

1.2.1 “Base model” generation

To initiate the model building process, we queried the Dataome Nucleus with the previously reported SARS-CoV-2 protein interaction map of 332 high confidence interactors (Gordon et al., 2020) as a “seed” for knowledge expansion. To optimize the specificity of our analysis, we focused on the molecular determinants of host cells that define them as viral targets and the immediate impact of the viral infection on the host cell response. This was achieved through inclusion of a minimum set of elements defining host cell, host-specific response (e.g., innate immune response) and associated phenotypes (see Figure 1). Domain experts independently inspected resultant data, providing a systematic expansion of the network to include related pathways, protein interactors, and regulatory elements. This so-called “base model” centered on a converging molecular mechanism including the host proteins responsible for virus entry, TMPRSS2 and ACE2, together with significantly differentially down-regulated components of the interferon stimulated genes (ISG) induced by the virus infection (ACE2 and SERPING1) (see Figure 1).

1.2.2 Iterative expansion of the base model

FIGURE 3

1.2.3 Pre-model curation and integrated visualization

At each stage of the disease/symptom modeling process, the extracted “pre-models” were inspected and visually modeled using the free open-source software PathVisio, [version 3.2.2 (Kutmon et al., 2015)]. During this process, relevant “pre-model” references were attached to the respective objects in PathVisio and manually complemented with bibliography as necessary. Entities and relationships associated with each mechanism/symptom were presented by a JavaScript-animated SVG image. Graphical renditions were produced via the PathVisio program, with data outputs in GPML and SVG format.

1.3 The web-based knowledge explorer

To enable the effective exploration of the key findings by the community regarding the mechanisms that possibly underlie COVID-19 symptoms and the associated evidence summaries, we developed a dedicated web-based interface providing a comprehensive and fully interactive COVID-19 disease model. Results are also provided in the format of a printable Table (see Supplementary Table S1) An expanded version of this Table is found in the sister article to this one focusing in detail on the molecular hypotheses and their validation status1.

1.3.1 Whole patient pre-model integration

Linkage of the curated pre-models through related molecular protagonists resulted in a whole patient COVID-19 disease model that connects central molecular disease mechanisms (namely, aberrant contact activation system (CAS) and ACE2/DAK/B1R signaling) (see Figures 4, 5) to eight core pathogenic processes: 1) inflammatory signaling, 2) coagulation, 3) barrier permeability, 4) senescence, 5) autoimmunity, 6) fibrogenic signaling, 7) nociception and 8) exocytosis. The model is completed by functionally intersecting these mechanisms with respective symptoms, associated pathogenic pathways and affected organ-systems (see Figure 4 and Supplementary Table S1). The dynamic nature of the global developments around the pandemic provided a constant flow of updated observations that were used as real-time validation of the resulting model’s core1. Finally, domain expert curation ensured continuous synchronization with new SARS-CoV-2 data and Dataome content.

FIGURE 4

FIGURE 5

1.3.2 Webserver modules

A Flask micro web framework serves as the web application displaying the interconnected “sub-models”. The application presents an SVG-based graphic for each sub-model, animated by the d3. js JavaScript library. To project the most actual and comprehensive information, the collection of citations supporting the relations of each “sub-model”, may range from peer-reviewed articles to very recent conference content (e.g., abstracts) and even ad-hoc communications. This information is provided as auxiliary information via respective animated components.

1.4 Results

Constructed during the early stages of the COVID-19 pandemic in 2020, our COVID-19 knowledge model revealed that the multitude and complexity of observed, and seemingly disparate clinical phenotypes may be linked to the pleiotropic activity of eight core molecular mechanisms involved in the host response (see Supplementary Table S1 and 1). In addition, the model revealed functionally connected mechanisms across multiple organ systems allowing for the identification of novel hypotheses for both viral dependent and independent disease mechanisms and associated pharmacologic targets that may warrant further investigation for drug repurposing and/or development efforts1.

1.4.1 The COVID-19 explorer

The COVID-19 Explorer represents a comprehensive COVID-19 disease model linking curated molecular protagonists at the symptom specific level. The COVID-19 Explorer is openly available via an interactive web-based interface at: https://covid19.molecularhealth.com.

1.4.1.1 Organization and functionality

These different views are aimed at enabling multiple use case scenarios, relevant to both the research community and clinical users. The full scope of “COVID-19 Explorer” features and utilities is described in detail within the accompanying User documentation: https://covid19.molecularhealth.com/MHCoronaExplorer_QuickGuide.pdf.

1.4.1.2 Using the explorer: Phenotype associations and predictive potential

Our model was developed during the initial stages of the pandemic. At this time, new and seemingly unrelated clinical phenotypes were concurrently described. Using the Explorer, these could be immediately linked to core mechanisms (e.g., barrier permeability or exocytosis). For instance, first reports of silent hypoxemia emerged in April 2020. The symptoms of silent hypoxemia were mapped against the symptomatology of heritable diseases, identifying Congenital Central Hereditary Hypoventilation Syndrome (CCHS) as a phenotypically related disease (see Figure 3) (Brock et al., 2022). CCHS is caused by dysfunction of the exocytosis machinery in oxygen sensing cells, providing a direct link to our model (Supplementary Material S1, Figure 3) (see also Figure 4). A similar link between clinical phenotypes and our model was established upon the first reports on endotheliitis (Varga et al., 2020), vasculitis and the role of micro-thrombotic events in severe disease (see Supplementary Table S1). Here too, direct links between the molecular etiology of the observed symptoms and our model could be drawn and strikingly, by mid-April, first cases of new onset KWD-like disease were reported in children with COVID-19, thereby also suggesting the predictive potential of the model.

1.4.1.3 Key aspects of the COVID-19 knowledge model

The COVID-19 Explorer and associated knowledge model highlight the key molecular players involved in host responsible for SARS-CoV-2 entry (ACE2, TMPRSS2), and the host factors of the ISG response that are specifically dysregulated by the SARS-CoV host interaction (ACE2, SERPING1) (see Figure 5). In turn, these are seen to converge on unifying pleiotropic signaling pathways comprising Renin-Angiotensin System (RAS) and Kallikrein Kinin System (KKS) as part of the Contact Activation System (CAS). Concurrent downregulation of ACE2 and SERPING1 may then reciprocally amplify the deregulation of KKS thereby generating a `“perfect storm”, which may lead to extreme over-activation of downstream signaling, especially in acute COVID-19. Finally, the model indicates that viral perturbation of eight key mechanisms, alone or in combination, may contribute to the pathogenesis of primary COVID-19 phenotypes.

A full analytical overview of the generated model is summarized in Supplementary Table S1 and made available via our COVID-19 Explorer. In addition, extended information about specific mechanisms, pathways, clinical phenotypes and insights into the clinico-molecular hypotheses derived from our model are reported in the associated back-to-back publication (Brock et al., 2022).

1.4.1.4 Host factors mediating SARS-CoV-2 infection

The model identified a converging molecular landscape, delineating host-factor responses to SARS-CoV-2 via host proteins responsible for virus entry, as well as significantly differentially down-regulated components of the set of ISG induced by virus infection. More specifically, the model indicates that the cell-entry mechanism (see Figure 5A) and disease-specific ISG signature provides three key active components of SARS-CoV infected cells (TMPRSS2, ACE2 and SEPRING1) that may functionally converge in the same pleiotropic signaling systems, namely the CAS and KKS pleiotropic signaling (see Figure 5B).

1.4.1.5 Host-response driven disease mechanisms

Indeed, analysis of post-mortem COVID-19 lung suggests two distinct stages of disease-progression (see Figure 5D). Early disease has high viral-load and high expression of cytokines and ISGs and sparse immune infiltrates, while in late disease, low viral loads, low local expression of cytokines and ISGs, and strong infiltration of macrophages and lymphocytes prevails. Patients who die early are unable to control SARS-CoV-2, while patients who die later suffer from diffuse tissue-damage and immunopathology (Nienhold et al., 2020) suggesting that late disease stage pathogenesis is apparently decoupled from acute viral-load.

1.4.1.6 Multiple pathologies of COVID-19 phenotypes may converge mechanistically

The model demonstrates that excess activation of inflammatory signaling may turn productive inflammatory response and recruitment of immune cells into a detrimental cytokine storm and immunopathology. Importantly, such mechanisms can be triggered by an imbalance in ACE2-DAK-B1R signaling and associated regulatory components (e.g., miR200c or SIRT1) (see Figure 5 and Supplementary Table S1).

Altogether, the model suggests that dysregulation/disturbed homeostasis of eight mechanisms, alone or in combination, may contribute to the pathogenesis of major COVID-19 phenotypes. The multiple and seemingly unrelated clinical manifestations of COVID-19, including common disease symptoms (e.g., dry cough, myalgia, anosmia, transient diabetes and silent hypoxia) and severe manifestations (e.g., ARDS, lung fibrosis, acute coronary syndromes and thromboembolic events) may largely be linked to the pleiotropic activity of these core molecular players and mechanisms involved in the host response (see Figures 3, 4). Interestingly, the model reveals that some rarer phenotypes can be matched to other diseases sharing the same symptoms. Silent hypoxemia, for instance, causes the same symptoms as CCHS. The molecular pathologies of both converge on the same molecular mechanism (see Figures 3, 4).

1.4.1.7 Validations, diagnostic and therapeutic implications

The rapid flow of new knowledge and information during the course of the pandemic enabled us to directly examine model-derived hypotheses in real-time, with several emerging as clinically validated or incorporated in specific multicenter studies. For example, COVID-19 organ damage often cannot be entirely explained by the virus’ organ tropism and local viral load. In COVID-19 associated kidney disease, for instance, viral load is low and unevenly distributed (Puelles et al., 2020; Su et al., 2020), and cannot explain the extensive kidney damage (Wang et al., 2021). These findings are in line with the proposed COVID-19 model. Currently, there are also no diagnostic tools that are associated with systemic pathophysiology of COVID-19. A systemic, virus independent disease mechanism requires systemic distribution of a signal that bears the potential to induce a broad spectrum of pathophysiological dysregulation in a variety of organs/tissues. A derailed pleiotropic signaling system such as the KKS/B1R signaling axis constitutes a likely candidate.

In this context, circulating MVs enriched in B1R and mir200c or circulating mir200c itself could serve as biomarker candidates. Indeed, serum, plasma or PBMC levels of miR200c has been identified as a diagnostic biomarker candidate in different COVID-19 related disease contexts, namely Kawasaki Disease (KWD) (Zhang et al., 2017), pneumonia (Liu et al., 2017), interstitial lung disease (Jiang et al., 2017), COPD (Cao et al., 2014) and fibrosis of multiple tissues (Yang et al., 2012; Ramachandran et al., 2013; Chen et al., 2017). It has also been shown that upregulated circulating miR-200c in plasma may increase the risk of obese individuals to severe COVID-19 (Papannarao et al., 2021).

In addition, the generated COVID-19 disease mechanism model contains target structures with implications for host-directed therapies. According to the model, pharmaceutically tractable target structures include the KKS at multiple levels such as Kallikrein inhibitors, SerpinG1 enzyme replacement or B1R inhibitors for example, may represent a preferred therapeutic target, having been under evaluation for the treatment of hyperalgesia and osteoarthritis during the past decade. However, to date, no relevant clinical results have been published and most of the reported trials are inactive or have been stopped or suspended (see (Brock et al., 2022)). Interestingly, the induction of B1R also appears to be sensitive to treatment with dexamethasone. Modulation of ACE2 activity represents another potential candidate for host directed therapy either by direct activation or indirectly by induction (e.g., through SIRT-1 activators such as Melatonin, Resveratrol and Metformin or activators of PPARγ (Dambha-Miller et al., 2020)). Several of these are currently investigated and readouts of such trials utilizing drugs that potentially induce ACE2 expression (Dambha-Miller et al., 2020) are summarized in the associated back-to-back paper (Brock et al., 2022).

3 Discussion

A key value of the COVID-19 Explorer is the way it permits the capture and contextualization of disease specific clinical and molecular information. With a plurality of emergent symptoms reported weekly during March-April 2020, it was critical to connect such clinical phenotypes and risk factors with both existing (e.g., from SARS-CoV-1) and emergent molecular findings. Our work highlights the speed at which bespoke clinico-molecular models can be built, emphasizing the important role that computer augmented disease modeling by domain experts can play in response to global health emergencies. Importantly, the COVID-19 model was built and organized into an updateable data framework driven by data capture, integration and curation activities. The model details molecular factors and systems that may drive COVID-19 and links the pleiotropic symptomology to possible underlying molecular pathology mechanisms. In comparison to other works, the ability to connect such disparate information layers facilitates a unique view in approaching COVID-19 at a whole patient, system-based level (Brock et al., 2022).

The COVID-19 Explorer is one of several valuable COVID-19 knowledge resources to emerge during the course of the pandemic. Prime examples of complementary open-source initiatives include COVID-19 UniprotKB (UniProt Consortium, 2021), Open Targets’ COVID-19 Target Prioritization Tool (Carvalho-Silva et al., 2018), and Reactome’s SARS-CoV-2 (COVID-19) infection pathway (Acencio, 2020). While these resources add significant value to our armamentarium of COVID-19 focused knowledgebases, the COVID-19 Explorer is the first to contextualize such data at the level of whole patient symptomatology. https://blog.opentargets.org/covid-19-target-prioritisation-tool-released/https://reactome.org/about/news/161-version-74-released.

Direct comparison of resources complicated by the diversity of starting motivations and utilities. For example, a recent large scale structural analysis has provided unique insights into complex and potentially important mechanisms regarding COVID-19, including viral protein self-assembly, molecular mimicry of human proteins, reversal of post-translational modifications, blockage of host translation, and the disabling of host defenses. In another instance, hospitalized COVID-19 patients were found to be positively correlated with (auto-) immune responses, not only providing confirmatory observations to our findings, but also highlighting the importance of laboratory-based validation of model-based hypotheses. By providing a whole patient perspective on the molecular etiology of COVID-19 symptomatology, our COVID-19 Explorer resource is complementary to the value.

Despite the broad utility of the whole patient model, several limitations exist. First, our integrated COVID-19 model was generated by a small team of biomedical experts, exploiting the content, technologies and processes of our proprietary Dataome solution. To do so, we partially relied on results gained from the extensive research that has been published on the original SARS coronavirus. For instance, the pathogen specific impact on the differential expression of ISG’s has been taken from work on SARS-CoV. We then used structured interaction and pathway data, as well as TDM and subsequent manual curation to identify upstream and downstream processes. All facts are supported by peer-reviewed literature and are transparently reported, though we cannot be certain about the accuracy or reproducibility of these results. We also screened for phenotypes that are associated with molecular perturbations (genetic or pharmacologic) of members within these regulatory networks. However, we limited this work to proposed key factors and the immediate regulatory elements.

Second, we also systematically collected COVID-19 associated phenotypes and risk profiles affecting different organ systems. Since a majority of these phenotypes also occur in other disease contexts, such as symptomatically related hereditary diseases, we identified the molecular mechanisms involved in their respective molecular etiologies. We then screened for convergence/divergence between the disturbed host mechanisms and those underlying the COVID-19 phenotypes. The advantage of such an approach is that key factors and interrelated pleiotropic regulatory concepts are quickly identified. However, our results likely need to be complemented by further systematic extension of the work. It seems logical, that the manifold symptoms and manifestations of COVID-19 result from the dysregulation of a few key elements converging in a pleiotropic mechanism which is connected to completely different phenotypes, depending on the individual tissue and organ context. While in many cases there is also multiple independent evidence linking the disturbed host system to the mechanisms underlying specific symptoms, the causal association with SARS-CoV-2 has yet to be proven. In that respect, we regard our approach as an effort to aggregate and interlink facts and connect existing knowledge that results in a defendable and testable hypothetical model that can inform future targeted research.

In terms of future directions, the current model is based on the molecular phenotype of the cells targeted and infected by SARS-CoV(-2) and provides a basis for explaining the diverse clinical phenotypes, observed risk factors and tractable strategies for therapeutic interventions and prevention. However, it does not provide a detailed mechanistic model for how direct pathogen host interactions induce and modulate the observed pathogen specific host response. As our initial approach was to reduce complexity by focusing on molecular phenotypes, we now have a consistent and testable base model that permits the systematic integration of additional data such as the full pathogen-host interactome. The model will also be further expanded by the +1-interaction level. The resulting network will then be further enriched by associated disease phenotypes from various sources, disease mechanisms and mode of actions of drugs targeting any of the components in the model. The resulting graph database will lend itself to the application of advance AI analytics to identify hypernodes and, eventually, mechanisms defining causality. This will provide novel angles to detect new strategies for intervention or for the comprehensive evaluation of existing interventional programs.

While our COVID-19 model was initially based on primarily data-mining hypotheses, data from more recent developments have helped update the initial model and also validate multiple predictions. For example, for some clinical phenotypes (e.g., KWD-like syndromes) predicted by our model it appeared that the clinical reality was ultimately superseding our model in real-time given the ongoing, massive global pandemic thereby providing timely validation (Brock et al., 2022). Moreover, our model revealed functionally connected mechanisms across various organ-systems, identified hypotheses for both, viral-dependent and -independent disease mechanisms, and associated pharmacologic targets that may warrant further evaluation (Brock et al., 2022). In this context our model, combined with other laboratory and/or real-world evidence, can be used both as a hypothesis generation and validation point regarding observed experimental or clinical findings.

In summary, our COVID-19 knowledge model links key molecular players in COVID-19 disease pathophysiology to common symptoms, severe manifestations and outcome/severity-associated risk factors at the whole patient level. We have validated that our COVID-19 Explorer provides a valuable and unique resource to support clinical and translational research audiences in hypothesis generation for new diagnostic and therapeutic strategies. We also anticipate that as the trajectory of scientific discovery continues to correlate the rate of technological advancement, biomedical research will grow increasingly dependent on similar human-focused and systems-based clinico-molecular information systems, capable of summarizing diverse findings in the form of intuitive whole patient disease models. Our work suggests that computer-augmented modelling of such knowledge by domain experts currently represents the most reliable approach in this regard. Moreover, it also provides a structured format through which future publication of expert reviews may be approached.

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