Uncovering the interplay between hypertension and the inflammatory response for the patient affected by COVID-19 via mathematical modeling and computer-based analysis

We explore here the systems-based regulatory mechanisms that determine human blood pressure patterns. This in the context of the reported negative association between hypertension and COVID-19 disease. We are particularly interested in the key role that plays angiotensin converting enzyme 2 (ACE2), one of the first identified receptors that enable the entry of the SARS-CoV-2 virus into a cell. Taking into account the two main systems involved in the regulation of blood pressure, that is, the Renin-Angiotensin system and the Kallikrein-Kinin system, we follow a Bottom-Up systems biology modeling approach in order to built the discrete Boolean model of the gene regulatory network that underlies both the typical hypertensive phenotype and the hypotensive/normotensive phenotype. These phenotypes correspond to the dynamic attractors of the regulatory network modeled on the basis of publicly available experimental information. Our model recovers the observed phenotypes and shows the key role played by the inflammatory response in the emergence of hypertension. Source code go to the next url: https://github.com/cxro-cc/red_ras_kks © 2023, EasyChair. All rights reserved.

Mucormycosis Metabolic Network Modeling: A Constraint-Based Approach

Mucormycosis is an uncommon illness caused by the fungus Mucorales. India was concerned about mucormycosis and COVID-19 in 2020. To minimize morbidity and occurrence, prevent, and treat mucormycosis, analysis is required. Combining systems biology and bioinformatics-based mucormycosis research, this study simulates the Genome-scale metabolic model (GSSM) of a Rhizopus oryzae strain for the comprehension of the organism's metabolic mechanism. Several key metabolic pathways for a mucormycosis-causing fungus strain were identified in research publications and targeted for inclusion in a model of a metabolic network. Based on the Flux Balance Analysis (FBA) approach, an integrated model of these pathways at the scale of the genome's metabolism was developed and appropriate constraints were applied to the numerous reactions involved in Rhizopus oryzae's metabolism using the COBRA package in MATLAB. Hence, unique evidence of pharmacological targets and biomarkers that may function as diagnostic, early analytic, and therapeutic agents in mucormycosis was discovered. Our study investigates the role of key metabolites in the model by applying constraints and altering fluxes, which provides valuable candidates for drug development. . © 2023 IEEE.

Noncovalent antibody catenation on a target surface greatly increases the antigen-binding avidity.

Immunoglobulin G (IgG) antibodies are widely used for diagnosis and therapy. Given the unique dimeric structure of IgG, we hypothesized that, by genetically fusing a homodimeric protein (catenator) to the C-terminus of IgG, reversible catenation of antibody molecules could be induced on a surface where target antigen molecules are abundant, and that it could be an effective way to greatly enhance the antigen-binding avidity. A thermodynamic simulation showed that quite low homodimerization affinity of a catenator, e.g. dissociation constant of 100 µM, can enhance nanomolar antigen-binding avidity to a picomolar level, and that the fold enhancement sharply depends on the density of the antigen. In a proof-of-concept experiment where antigen molecules are immobilized on a biosensor tip, the C-terminal fusion of a pair of weakly homodimerizing proteins to three different antibodies enhanced the antigen-binding avidity by at least 110 or 304 folds from the intrinsic binding avidity. Compared with the mother antibody, Obinutuzumab(Y101L) which targets CD20, the same antibody with fused catenators exhibited significantly enhanced binding to SU-DHL5 cells. Together, the homodimerization-induced antibody catenation would be a new powerful approach to improve antibody applications, including the detection of scarce biomarkers and targeted anticancer therapies.

Systems biology in COVID-19

Coronavirus disease 2019 (COVID-19), the disease cause by the novel severe acute respiratory syndrome coronavirus 2 represents a global, unresolved challenge for researchers and clinicians alike. In the shadow of overwhelmed healthcare systems, the pressure to produce knowledge, standard operating procedures, efficacious treatments, and prophylactic agents has been unlike any other occasion in recent history. Systems biology, an assortment of methods that aim to model biological systems and their properties has risen to meet this multifaceted challenge. In this chapter, we review approaches and breakthroughs of systems biology research in COVID-19, along with the nascent clade of phenomics, a deep-phenotyping systems concept that has enabled the real-time integration of big data and analytical methods in clinical decision making. © 2023 Elsevier Inc. All rights reserved.

Identification of Common Dysregulated Genes in COVID-19 and Hypersensitivity Pneumonitis: A Systems Biology and Machine Learning Approach.

A comprehensive knowledge on systems biology of severe acute respiratory syndrome coronavirus 2 is crucial for differential diagnosis of COVID-19. Interestingly, the radiological and pathological features of COVID-19 mimic that of hypersensitivity pneumonitis (HP), another pulmonary fibrotic phenotype. This motivated us to explore the overlapping pathophysiology of COVID-19 and HP, if any, and using a systems biology approach. Two datasets were obtained from the Gene Expression Omnibus database (GSE147507 and GSE150910) and common differentially expressed genes (DEGs) for both diseases identified. Fourteen common DEGs, significantly altered in both diseases, were found to be implicated in complement activation and growth factor activity. A total of five microRNAs (hsa-miR-1-3p, hsa-miR-20a-5p, hsa-miR-107, hsa-miR-16-5p, and hsa-miR-34b-5p) and five transcription factors (KLF6, ZBTB7A, ELF1, NFIL3, and ZBT33) exhibited highest interaction with these common genes. Next, C3, CFB, MMP-9, and IL1A were identified as common hub genes for both COVID-19 and HP. Finally, these top-ranked genes (hub genes) were evaluated using random forest classifier to discriminate between the disease and control group (coronavirus disease 2019 [COVID-19] vs. controls, and HP vs. controls). This supervised machine learning approach demonstrated 100% and 87.6% accuracy in differentiating COVID-19 from controls, and HP from controls, respectively. These findings provide new molecular leads that inform COVID-19 and HP diagnostics and therapeutics research and innovation.

Network-based methods with heterogeneous data to identify severe COVID immune-related genes

Bioinformatics and systems biology play a vital role in the computational prediction of disease-associated genes using multi-omics data. The network-based approach is one of the most potent tools in disease-associated gene prediction. The two commonly used methods are neighborhood-based and network diffusion techniques. However, there is still a lack of studies comparing the performance of these methods, especially in terms of functional pathway discovery. Thus, this study demonstrated the performance comparison of these two techniques in both numerical accuracies based on the area under the receiver operating characteristic curve (AUROC) and biological meaning efficiency based on functional pathway enrichment. In this study, we analyzed data of severe COVID-19 immune-related genes using heterogeneous data. The prediction results of the COVID-19 immune-related genes in the human protein-protein interaction (PPI) network showed that the network diffusion had better performance in both AUROC and pathway enrichment even though it provided a longer computational time than the neighborhood method. © 2022 IEEE.

Perspective: Multiomics and Machine Learning Help Unleash the Alternative Food Potential of Microalgae.

Food security has become a pressing issue in the modern world. The ever-increasing world population, ongoing COVID-19 pandemic, and political conflicts together with climate change issues make the problem very challenging. Therefore, fundamental changes to the current food system and new sources of alternative food are required. Recently, the exploration of alternative food sources has been supported by numerous governmental and research organizations, as well as by small and large commercial ventures. Microalgae are gaining momentum as an effective source of alternative laboratory-based nutritional proteins as they are easy to grow under variable environmental conditions, with the added advantage of absorbing carbon dioxide. Despite their attractiveness, the utilization of microalgae faces several practical limitations. Here, we discuss both the potential and challenges of microalgae in food sustainability and their possible long-term contribution to the circular economy of converting food waste into feed via modern methods. We also argue that systems biology and artificial intelligence can play a role in overcoming some of the challenges and limitations; through data-guided metabolic flux optimization, and by systematically increasing the growth of the microalgae strains without negative outcomes, such as toxicity. This requires microalgae databases rich in omics data and further developments on its mining and analytics methods.

Computational framework to understand the clinical stages of COVID-19 and visualization of time course for various treatment strategies.

Coronavirus disease 2019 is known to be regulated by multiple factors such as delayed immune response, impaired T cell activation, and elevated levels of proinflammatory cytokines. Clinical management of the disease remains challenging due to interplay of various factors as drug candidates may elicit different responses depending on the staging of the disease. In this context, we propose a computational framework which provides insights into the interaction between viral infection and immune response in lung epithelial cells, with an aim of predicting optimal treatment strategies based on infection severity. First, we formulate the model for visualizing the nonlinear dynamics during the disease progression considering the role of T cells, macrophages and proinflammatory cytokines. Here, we show that the model is capable of emulating the dynamic and static data trends of viral load, T cell, macrophage levels, interleukin (IL)-6 and TNF-α levels. Second, we demonstrate the ability of the framework to capture the dynamics corresponding to mild, moderate, severe, and critical condition. Our result shows that, at late phase (>15 days), severity of disease is directly proportional to pro-inflammatory cytokine IL6 and tumor necrosis factor (TNF)-α levels and inversely proportional to the number of T cells. Finally, the simulation framework was used to assess the effect of drug administration time as well as efficacy of single or multiple drugs on patients. The major contribution of the proposed framework is to utilize the infection progression model for clinical management and administration of drugs inhibiting virus replication and cytokine levels as well as immunosuppressant drugs at various stages of the disease.

Application of the PHENotype SIMulator for rapid identification of potential candidates in effective COVID-19 drug repurposing.

The current, rapidly diversifying pandemic has accelerated the need for efficient and effective identification of potential drug candidates for COVID-19. Knowledge on host-immune response to SARS-CoV-2 infection, however, remains limited with few drugs approved to date. Viable strategies and tools are rapidly arising to address this, especially with repurposing of existing drugs offering significant promise. Here we introduce a systems biology tool, the PHENotype SIMulator, which -by leveraging available transcriptomic and proteomic databases-allows modeling of SARS-CoV-2 infection in host cells in silico to i) determine with high sensitivity and specificity (both>96%) the viral effects on cellular host-immune response, resulting in specific cellular SARS-CoV-2 signatures and ii) utilize these cell-specific signatures to identify promising repurposable therapeutics. Powered by this tool, coupled with domain expertise, we identify several potential COVID-19 drugs including methylprednisolone and metformin, and further discern key cellular SARS-CoV-2-affected pathways as potential druggable targets in COVID-19 pathogenesis.

Agent-Based Simulation for Infectious Disease Modelling over a Period of Multiple Days, with Application to an Airport Scenario.

With the COVID-19 pandemic, the role of infectious disease spreading in public places has been brought into focus more than ever. Places that are of particular interest regarding the spread of infectious diseases are international airport terminals, not only for the protection of staff and ground crew members but also to help minimize the risk of the spread of infectious entities such as COVID-19 around the globe. Computational modelling and simulation can help in understanding and predicting the spreading of infectious diseases in any such scenario. In this paper, we propose a model, which combines a simulation of high geometric detail regarding virus spreading with an account of the temporal progress of infection dynamics. We, thus, introduce an agent-based social force model for tracking the spread of infectious diseases by modelling aerosol traces and concentration of virus load in the air. We complement this agent-based model to have consistency over a period of several days. We then apply this model to investigate simulations in a realistic airport setting with multiple virus variants of varying contagiousness. According to our experiments, a virus variant has to be at least twelve times more contagious than the respective control to result in a level of infection of more than 30%. Combinations of agent-based models with temporal components can be valuable tools in an attempt to assess the risk of infection attributable to a particular virus and its variants.

Bioinformatics and system biology approach to identify the influences of COVID-19 on primary sjogren's syndrome patients

Background: Primary Sjogren's syndrome (pSS) is a chronic, systemic, inflammatory autoimmune disease in which existing studies have found the presence of pSS-specific antibodies anti-SSA/ Ro in acute infection with COVID-19.1 The emergence of this phenomenon makes us aware that in the context of the long-term epidemic of COVID-19, it is necessary to further study the molecular mechanisms of the high susceptibility of pSS patients to COVID-19. Method(s): The gene expression profiles of 8 COVID-19 datasets and 5 pSS datasets were downloaded from the Gene Expression Omnibus (GEO) database. The differentially expressed genes (DEGs) between COVID-19 and PSS were identified using the limma software package and Weighted Gene Co-expression Network Analysis (WGCNA). A Venn diagram was used to discover common upregulated DEGs. To explore the possible pathogenesis of both diseases, common signaling pathways were explored by enriching DEGs using Gene Ontology (GO) and the Kyoto Gene and Genome Encyclopedia (KEGG) pathway. Protein-protein interactions (PPIs) were established to identify hub genes and key modules. The analysis of key gene expression characteristics by The Connectivity Map was used to predict potentially effective drugs. Finally, the CIBERSORT method was used to comprehensively evaluate the immune infiltrates of patients with COVID-19 and PSS to study the mechanisms that may have a common immune response or immune cell infiltration. Result(s): A total of 82 upregulated DEGs were identified in both COVID-19 and PSS (Figure 1 A-E). Functional enrichment analysis illustrated the important role of enhanced signaling pathways in response to virus defense and interferon-alpha in both diseases (Figure 1F).Three key modules including 25 hub genes were identified (Figure 1G). The correlation analysis of immune cell infiltration showed the expression of B cells memory resting decreased and NK cells resting increased significantly in the two diseases (Figure 1H, I). Finally, estradiol in drug prediction outcomes has been shown to reduce susceptibility to COVID-19 and its severity through its involvement in regulating immune cells, while the most common manifestation of dry eye in pSS patients is strongly associated with low estrogen. Conclusion(s): High defense response to virus and response to interferon-alpha in pSS patients might be a crucial susceptible factor for COVID-19 and predictive drugs such as estradiol, suggested by susceptibility genes common to COVID-19 and pSS, may help in the clinical treatment of both diseases.

CriSNPr, a single interface for the curated and de novo design of gRNAs for CRISPR diagnostics using diverse Cas systems.

CRISPR-based diagnostics (CRISPRDx) have improved clinical decision-making, especially during the COVID-19 pandemic, by detecting nucleic acids and identifying variants. This has been accelerated by the discovery of new and engineered CRISPR effectors, which have expanded the portfolio of diagnostic applications to include a broad range of pathogenic and non-pathogenic conditions. However, each diagnostic CRISPR pipeline necessitates customized detection schemes based on the fundamental principles of the Cas protein used, its guide RNA (gRNA) design parameters, and the assay readout. This is especially relevant for variant detection, a low-cost alternative to sequencing-based approaches for which no in silico pipeline for the ready-to-use design of CRISPRDx currently exists. In this manuscript, we fill this lacuna using a unified web server, CriSNPr (CRISPR-based SNP recognition), which provides the user with the opportunity to de novo design gRNAs based on six CRISPRDx proteins of choice (Fn/enFnCas9, LwCas13a, LbCas12a, AaCas12b, and Cas14a) and query for ready-to-use oligonucleotide sequences for validation on relevant samples. Furthermore, we provide a database of curated pre-designed gRNAs as well as target/off-target for all human and SARS-CoV-2 variants reported thus far. CriSNPr has been validated on multiple Cas proteins, demonstrating its broad and immediate applicability across multiple detection platforms. CriSNPr can be found at http://crisnpr.igib.res.in/.

Characterization of exogenous αSN response genes and their relation to Parkinson's disease using network analyses.

Despite extensive research, the molecular mechanisms underlying the toxicity of αSN in Parkinson's disease (PD) pathology are still poorly understood. To address this, we used a microarray dataset to identify genes that are induced and differentially expressed after exposure to toxic αSN aggregates, which we call exogenous αSN response (EASR) genes. Using systems biology approaches, we then determined, at multiple levels of analysis, how these EASR genes could be related to PD pathology. A key result was the identification of functional connections between EASR genes and previously identified PD-related genes by employing the proteins' interactions networks and 9 brain region-specific co-expression networks. In each brain region, co-expression modules of EASR genes were enriched for gene sets whose expression are altered by SARS-CoV-2 infection, leading to the hypothesis that EASR co-expression genes may explain the observed links between COVID-19 and PD. An examination of the expression pattern of EASR genes in different non-neurological healthy brain regions revealed that regions with lower mean expression of the upregulated EASR genes, such as substantia nigra, are more vulnerable to αSN aggregates and lose their neurological functions during PD progression. Gene Set Enrichment Analysis of healthy and PD samples from substantia nigra revealed that a specific co-expression network, "TNF-α signaling via NF-κB", is an upregulated pathway associated with the PD phenotype. Inhibitors of the "TNF-α signaling via NF-κB" pathway may, therefore, decrease the activity level of this pathway and thereby provide therapeutic benefits for PD patients. We virtually screened FDA-approved drugs against these upregulated genes (NR4A1, DUSP1, and FOS) using docking-based drug discovery and identified several promising drugs. Altogether, our study provides a better understanding of αSN toxicity mechanisms in PD and identifies potential therapeutic targets and small molecules for treatment of PD.

Exploring Innovation with Scientific Thinking.

The mode of scientific thinking is undergoing rapid and profound changes. In the 21st century, macro and micro civilizations go parallel. A systematic and scientific methodology is required for the study of complex things. The thinking mode in modern medicine is gradually shifting from analytical, reductive thinking to holistic and systematic thinking. As such Western medicine and traditional Chinese medicine are gradually approaching the epistemology of health and disease state. The importance of scientific thinking in innovation has been expounded in this study. The development trends in medicine in the current era are analyzed, the importance of systems theory in the study of human bodies is discussed, and a new medical model named Novel Systems Medicine is proposed.

Systems Biology of COVID-19 and Human Diseases: Beyond a Bird's Eye View, and Toward One Health.

As we gaze into the future beyond the current coronavirus disease 2019 (COVID-19) pandemic, there is a need to rethink our priorities in planetary health, research funding, and, importantly, the concepts and unchecked assumptions by which we attempt to understand health and prevent illness. Next-generation quantitative omics technologies promise a more profound and panoptic understanding of the dynamic pathophysiological processes and their aberrations in diverse diseased conditions. Systems biology research is highly relevant for COVID-19, a systemic disease affecting multiple organs and biological pathways. In addition, expanding the concept of health beyond humans so as to capture the importance of ecosystem health and recognizing the interdependence of human, animal, and plant health are enormously relevant and timely in the current historical moment of the pandemic. Notably, severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), the virus causing COVID-19, can affect our body clock, and the circadian aspects of this viral infection and host immunity need to be considered for its effective clinical management. Finally, we need to rethink and expand beyond the false binaries such as humans versus nature, and deploy multiomics systems biology research if we intend to design effective, innovative, and socioecological planetary health interventions to prevent future pandemics and ecological crises. We argue here that juxtaposing ecology and human health sciences scholarship is one of the key emerging tenets of 21st-century integrative biology.

Pulmonary Arterial Hypertension: Emerging Principles of Precision Medicine across Basic Science to Clinical Practice.

Pulmonary arterial hypertension (PAH) is an enigmatic and deadly vascular disease with no known cure. Recent years have seen rapid advances in our understanding of the molecular underpinnings of PAH, with an expanding knowledge of the molecular, cellular, and systems-level drivers of disease that are being translated into novel therapeutic modalities. Simultaneous advances in clinical technology have led to a growing list of tools with potential application to diagnosis and phenotyping. Guided by fundamental biology, these developments hold the potential to usher in a new era of personalized medicine in PAH with broad implications for patient management and great promise for improved outcomes.

Drug Repurposing Using Gene Co-Expression and Module Preservation Analysis in Acute Respiratory Distress Syndrome (ARDS), Systemic Inflammatory Response Syndrome (SIRS), Sepsis, and COVID-19.

SARS-CoV-2 infections are highly correlated with the overexpression of pro-inflammatory cytokines in what is known as a cytokine storm, leading to high fatality rates. Such infections are accompanied by SIRS, ARDS, and sepsis, suggesting a potential link between the three phenotypes. Currently, little is known about the transcriptional similarity between these conditions. Herein, weighted gene co-expression network analysis (WGCNA) clustering was applied to RNA-seq datasets (GSE147902, GSE66890, GSE74224, GSE177477) to identify modules of highly co-expressed and correlated genes, cross referenced with dataset GSE160163, across the samples. To assess the transcriptome similarities between the conditions, module preservation analysis was performed and functional enrichment was analyzed in DAVID webserver. The hub genes of significantly preserved modules were identified, classified into upregulated or downregulated, and used to screen candidate drugs using Connectivity Map (CMap) to identify repurposed drugs. Results show that several immune pathways (chemokine signaling, NOD-like signaling, and Th1 and Th2 cell differentiation) are conserved across the four diseases. Hub genes screened using intramodular connectivity show significant relevance with the pathogenesis of cytokine storms. Transcriptomic-driven drug repurposing identified seven candidate drugs (SB-202190, eicosatetraenoic-acid, loratadine, TPCA-1, pinocembrin, mepacrine, and CAY-10470) that targeted several immune-related processes. These identified drugs warrant further study into their efficacy for treating cytokine storms, and in vitro and in vivo experiments are recommended to confirm the findings of this study.

Bioinformatics and systems biology approaches to identify molecular targeting mechanism influenced by COVID-19 on heart failure.

Coronavirus disease 2019 (COVID-19) caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has emerged as a contemporary hazard to people. It has been known that COVID-19 can both induce heart failure (HF) and raise the risk of patient mortality. However, the mechanism underlying the association between COVID-19 and HF remains unclear. The common molecular pathways between COVID-19 and HF were identified using bioinformatic and systems biology techniques. Transcriptome analysis was performed to identify differentially expressed genes (DEGs). To identify gene ontology terms and Kyoto Encyclopedia of Genes and Genomes pathways, common DEGs were used for enrichment analysis. The results showed that COVID-19 and HF have several common immune mechanisms, including differentiation of T helper (Th) 1, Th 2, Th 17 cells; activation of lymphocytes; and binding of major histocompatibility complex class I and II protein complexes. Furthermore, a protein-protein interaction network was constructed to identify hub genes, and immune cell infiltration analysis was performed. Six hub genes (FCGR3A, CD69, IFNG, CCR7, CCL5, and CCL4) were closely associated with COVID-19 and HF. These targets were associated with immune cells (central memory CD8 T cells, T follicular helper cells, regulatory T cells, myeloid-derived suppressor cells, plasmacytoid dendritic cells, macrophages, eosinophils, and neutrophils). Additionally, transcription factors, microRNAs, drugs, and chemicals that are closely associated with COVID-19 and HF were identified through the interaction network.

Computational pharmacology: New avenues for COVID-19 therapeutics search and better preparedness for future pandemic crises.

The COVID-19 pandemic created an unprecedented global healthcare emergency prompting the exploration of new therapeutic avenues, including drug repurposing. A large number of ongoing studies revealed pervasive issues in clinical research, such as the lack of accessible and organised data. Moreover, current shortcomings in clinical studies highlighted the need for a multi-faceted approach to tackle this health crisis. Thus, we set out to explore and develop new strategies for drug repositioning by employing computational pharmacology, data mining, systems biology, and computational chemistry to advance shared efforts in identifying key targets, affected networks, and potential pharmaceutical intervention options. Our study revealed that formulating pharmacological strategies should rely on both therapeutic targets and their networks. We showed how data mining can reveal regulatory patterns, capture novel targets, alert about side-effects, and help identify new therapeutic avenues. We also highlighted the importance of the miRNA regulatory layer and how this information could be used to monitor disease progression or devise treatment strategies. Importantly, our work bridged the interactome with the chemical compound space to better understand the complex landscape of COVID-19 drugs. Machine and deep learning allowed us to showcase limitations in current chemical libraries for COVID-19 suggesting that both in silico and experimental analyses should be combined to retrieve therapeutically valuable compounds. Based on the gathered data, we strongly advocate for taking this opportunity to establish robust practices for treating today's and future infectious diseases by preparing solid analytical frameworks.

Unpacking COVID-19 Systems Biology in Lung and Whole Blood with Transcriptomics and miRNA Regulators.

COVID-19 is a systemic disease affecting tissues and organs, including and beyond the lung. Apart from the current pandemic context, we also have vastly inadequate knowledge of consequences of repeated exposures to SARS-CoV-2 (severe acute respiratory syndrome coronavirus 2), the virus causing COVID-19, in multiple organ systems and the whole organism scales when the disease evolves from a pandemic to an endemic state. This calls for a systems biology and systems medicine approach and unpacking the effects of COVID-19 in lung as well as other tissues. We report here original findings from transcriptomics analyses and differentially expressed genes (DEGs) in lung samples from 60 patients and 27 healthy controls, and in whole blood samples from 255 patients and 103 healthy individuals. A total of 11 datasets with RNA-seq transcriptomic data were obtained from the Gene Expression Omnibus and the European Nucleotide Archive. The identified DEGs were used to construct protein interaction and functional networks and to identify related pathways and miRNAs. We found 35 DEGs common between lung and the whole blood, and importantly, 2 novel genes, namely CYP1B1 and TNFAIP6, which have not been previously implicated with COVID-19. We also identified four novel miRNA potential regulators, hsa-mir-192-5p, hsa-mir-221-3p, hsa-mir-4756-3p, and hsa-mir-10a-5p, implicated in lung or other diseases induced by coronaviruses. In summary, these findings offer new molecular leads and insights to unpack COVID-19 systems biology in a whole organism context and might inform future antiviral drug, diagnostics, and vaccine discovery efforts.