Systems Biology Approaches for Therapeutics Development Against COVID-19

ScRNA-Seq reveals T cell immunity in COVID-19 patients and implications for immunotherapy

SARS-CoV-2, the virus causing COVID-19, poses significant health threats due to its high transmissibility and potential for severe respiratory complications. T cells, central to adaptive immunity, also interact with innate immunity, playing a pivotal role in coordinating defenses and eliminating infected cells. Single-cell RNA sequencing (scRNA-seq) has provided more subtle heterogeneity, rare subpopulations, or new subpopulations that are at the district differentiation stage or with specific function. Thus, elucidating how T cell heterogeneity impacts COVID-19 disease severity remains a critical question requiring comprehensive analysis. This review revealed the heterogeneity of the host T cells, including conventional T cells (CD8+, CD4+ T cells) and unconventional T cells, including natural killer T (NKT) cells, mucosal-associated invariant T (MAIT) and gamma-delta T (γδT) cells in COVID-19 patients with different clinical manifestations. Severe COVID-19 had marked lymphopenia, excessive activation, elevated exhaustion and reduced functional diversity of T cells. Pathogenic contributions arise from dysregulated cytotoxic T cells, Treg cells and unconventional T cells collectively driving systemic hyperinflammation and tissue injury. Current therapeutic strategies targeting T cells-such as enhancing virus-specific T cell responses, reverting T-cell exhaustion and alleviating inflammation-exhibit inconsistent efficacy, underscoring the need for combinatorial approaches. This review highlights how scRNA-seq deciphers T cell heterogeneity and dysfunction in COVID-19. By targeting T cell exhaustion, inflammation, and subset-specific deficits, these insights pave the way for therapies and vaccines.

Uncovering the Effect of SARS-CoV-2 on Liver Metabolism via Genome-Scale Metabolic Modeling for Reprogramming and Therapeutic Strategies

Genome-scale metabolic models (GEMs) are promising computational tools that contribute to elucidating host-virus interactions at the system level and developing therapeutic strategies against viral infection. In this study, the effect of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) on liver metabolism was investigated using integrated GEMs of human hepatocytes and SARS-CoV-2. They were generated for uninfected and infected hepatocytes using transcriptome data. Reporter metabolite analysis resulted in significant transcriptional changes around several metabolites involved in xenobiotics, drugs, arachidonic acid, and leukotriene metabolisms due to SARS-CoV-2 infection. Flux balance analysis and minimization of metabolic adjustment approaches unraveled possible virus-induced hepatocellular reprogramming in fatty acid, glycerophospholipid, sphingolipid cholesterol, and folate metabolisms, bile acid biosynthesis, and carnitine shuttle among others. Reaction knockout analysis provided critical reactions in glycolysis, oxidative phosphorylation, purine metabolism, and reactive oxygen species detoxification subsystems. Computational analysis also showed that administration of dopamine, glucosamine, D-xylose, cysteine, and (R)-3-hydroxybutanoate contributes to alleviating viral infection. In essence, the reconstructed host-virus GEM helps us understand metabolic programming and develop therapeutic strategies to battle SARS-CoV-2.

Identification of Disalicyloyl Curcumin as a Potential DNA Polymerase Inhibitor for Marek's Disease Herpesvirus: A Computational Study Using Virtual Screening and Molecular Dynamics Simulations

Marek's disease virus (MDV) is a highly contagious and persistent virus that causes T-lymphoma in chickens, posing a significant threat to the poultry industry despite the availability of vaccines. The emergence of new virulent strains has further intensified the challenge of designing effective antiviral drugs for MDV. In this study, our main objective was to identify novel antiviral phytochemicals through in silico analysis. We employed Alphafold to construct a three-dimensional (3D) structure of the MDV DNA polymerase, a crucial enzyme involved in viral replication. To ensure the accuracy of the structural model, we validated it using tools available at the SAVES server. Subsequently, a diverse dataset containing thousands of compounds, primarily derived from plant sources, was subjected to molecular docking with the MDV DNA polymerase model, utilizing AutoDock software V 4.2. Through comprehensive analysis of the docking results, we identified Disalicyloyl curcumin as a promising drug candidate that exhibited remarkable binding affinity, with a minimum energy of -12.66 Kcal/mol, specifically targeting the DNA polymerase enzyme. To further assess its potential, we performed molecular dynamics simulations, which confirmed the stability of Disalicyloyl curcumin within the MDV system. Experimental validation of its inhibitory activity in vitro can provide substantial support for its effectiveness. The outcomes of our study hold significant implications for the poultry industry, as the discovery of efficient antiviral phytochemicals against MDV could substantially mitigate the economic losses associated with this devastating disease.

New Ways to Protect the Host from SARS-CoV-2? Lung Microbiome Metabolites Inhibit STAT3 and Modulate the Immunological Network

COVID-19 caused by the SARS-CoV-2 infection is a systemic disease that affects multiple organs, biological pathways, and cell types. A systems biology approach would benefit the study of COVID-19 in the pandemic as well as the endemic state. Notably, patients with COVID-19 have dysbiosis of lung microbiota whose functional relevance to the host is largely unknown. We carried out a systems biology investigation of the impact of lung microbiome-derived metabolites on host immune system during COVID-19. RNAseq was performed to identify the host-specific pro- and anti-inflammatory differentially expressed genes (DEGs) in bronchial epithelium and alveolar cells during SARS-CoV-2 infection. The overlapping DEGs were harnessed to construct an immune network while their key transcriptional regulator was deciphered. We identified 68 overlapping genes from both cell types to construct the immune network, and Signal Transducer and Activator of Transcription 3 (STAT3) was found to regulate the majority of the network proteins. Furthermore, thymidine diphosphate produced from the lung microbiome had the highest affinity with STAT3 (-6.349 kcal/mol) than the known STAT3 inhibitors (n = 410), with an affinity ranging from -5.39 to 1.31 kcal/mol. In addition, the molecular dynamic studies showed distinguishable changes in the behavior of the STAT3 complex when compared with free STAT3. Overall, our results provide new observations on the importance of lung microbiome metabolites that regulate the host immune system in patients with COVID-19, and may open up new avenues for preventive medicine and therapeutics innovation.

Reductionist methodology and the ambiguity of the categories of race and ethnicity in biomedical research: an exploratory study of recent evidence

In this article, we analyse how researchers use the categories of race and ethnicity with reference to genetics and genomics. We show that there is still considerable conceptual "messiness" (despite the wide-ranging and popular debate on the subject) when it comes to the use of ethnoracial categories in genetics and genomics that among other things makes it difficult to properly compare and interpret research using ethnoracial categories, as well as draw conclusions from them. Finally, we briefly reconstruct some of the biases of reductionism to which geneticists (as well as other researchers referring to genetic methods and explanations) are particularly exposed to, and we analyse the problem in the context of the biologization of ethnoracial categories. Our work constitutes a novel, in-depth contribution to the debate about reporting race and ethnicity in biomedical and health research. First, we reconstruct the theoretical background assumptions about racial ontology which researchers implicitly presume in their studies with the aid of a sample of recent papers published in medical journals about COVID-19. Secondly, we use the typology of the biases of reductionism to the problem of biologization of ethnoracial categories with reference to genetics and genomics.

Artificial intelligence assessment of the potential of tocilizumab along with corticosteroids therapy for the management of COVID-19 evoked acute respiratory distress syndrome

Acute respiratory distress syndrome (ARDS), associated with high mortality rate, affects up to 67% of hospitalized COVID-19 patients. Early evidence indicated that the pathogenesis of COVID-19 evoked ARDS is, at least partially, mediated by hyperinflammatory cytokine storm in which interleukin 6 (IL-6) plays an essential role. The corticosteroid dexamethasone is an effective treatment for severe COVID-19 related ARDS. However, trials of other immunomodulatory therapies, including anti-IL6 agents such as tocilizumab and sarilumab, have shown limited evidence of benefit as monotherapy. But recently published large trials have reported added benefit of tocilizumab in combination with dexamethasone in severe COVID-19 related ARDS. In silico tools can be useful to shed light on the mechanisms evoked by SARS-CoV-2 infection and of the potential therapeutic approaches. Therapeutic performance mapping system (TPMS), based on systems biology and artificial intelligence, integrate available biological, pharmacological and medical knowledge to create mathematical models of the disease. This technology was used to identify the pharmacological mechanism of dexamethasone, with or without tocilizumab, in the management of COVID-19 evoked ARDS. The results showed that while dexamethasone would be addressing a wider range of pathological processes with low intensity, tocilizumab might provide a more direct and intense effect upon the cytokine storm. Based on this in silico study, we conclude that the use of tocilizumab alongside dexamethasone is predicted to induce a synergistic effect in dampening inflammation and subsequent pathological processes, supporting the beneficial effect of the combined therapy in critically ill patients. Future research will allow identifying the ideal subpopulation of patients that would benefit better from this combined treatment.

Integrative In Silico Investigation Reveals the Host-Virus Interactions in Repurposed Drugs Against SARS-CoV-2

The ongoing COVID-19 outbreak have posed a significant threat to public health worldwide. Recently Toll-like receptor (TLR) has been proposed to be the drug target of SARS-CoV-2 treatment, the specificity and efficacy of such treatments remain unknown. In the present study we performed the investigation of repurposed drugs via a framework comprising of Search Tool for Interacting Chemicals (STITCH), Kyoto Encyclopedia of Genes and Genomes (KEGG), molecular docking, and virus-host-drug interactome mapping. Chloroquine (CQ) and hydroxychloroquine (HCQ) were utilized as probes to explore the interaction network that is linked to SARS-CoV-2. 47 drug targets were shown to be overlapped with SARS-CoV-2 network and were enriched in TLR signaling pathway. Molecular docking analysis and molecular dynamics simulation determined the direct binding affinity of TLR9 to CQ and HCQ. Furthermore, we established SARS-CoV-2-human-drug protein interaction map and identified the axis of TLR9-ERC1-Nsp13 and TLR9-RIPK1-Nsp12. Therefore, the elucidation of the interactions of SARS-CoV-2 with TLR9 axis will not only provide pivotal insights into SARS-CoV-2 infection and pathogenesis but also improve the treatment against COVID-19.

COVID-19 challenges: From SARS-CoV-2 infection to effective point-of-care diagnosis by electrochemical biosensing platforms

In January 2020, the World Health Organization (WHO) identified a new zoonotic virus, SARS-CoV-2, responsible for causing the COVID-19 (coronavirus disease 2019). Since then, there has been a collaborative trend between the scientific community and industry. Multidisciplinary research networks try to understand the whole SARS-CoV-2 pathophysiology and its relationship with the different grades of severity presented by COVID-19. The scientific community has gathered all the data in the quickly developed vaccines that offer a protective effect for all variants of the virus and promote new diagnostic alternatives able to have a high standard of efficiency, added to shorter response analysis time and portability. The industry enters in the context of accelerating the path taken by science until obtaining the final product. In this review, we show the principal diagnostic methods developed during the COVID-19 pandemic. However, when we observe the diagnostic tools section of an efficient infection outbreak containment report and the features required for such tools, we could observe a highlight of electrochemical biosensing platforms. Such devices present a high standard of analytical performance, are low-cost tools, easy to handle and interpret, and can be used in the most remote and low-resource regions. Therefore, probably, they are the ideal point-of-care diagnostic tools for pandemic scenarios.

Races of small molecule clinical trials for the treatment of COVID-19: An up-to-date comprehensive review

The coronavirus disease‐19 (COVID‐19) pandemic has become a global threat since its first outbreak at the end of 2019. Several review articles have been published recently, focusing on the aspects of target biology, drug repurposing, and mechanisms of action (MOAs) for potential treatment. This review gathers all small molecules currently in active clinical trials, categorizes them into six sub‐classes, and summarizes their clinical progress. The aim is to provide the researchers from both pharmaceutical industries and academic institutes with the handful information and dataset to accelerate their research programs in searching effective small molecule therapy for treatment of COVID‐19.

Characterization by Quantitative Serum Proteomics of Immune-Related Prognostic Biomarkers for COVID-19 Symptomatology

The COVID-19 pandemic caused by SARS-CoV-2 challenges the understanding of factors affecting disease progression and severity. The identification of prognostic biomarkers and physiological processes associated with disease symptoms is relevant for the development of new diagnostic and therapeutic interventions to contribute to the control of this pandemic. To address this challenge, in this study, we used a quantitative proteomics together with multiple data analysis algorithms to characterize serum protein profiles in five cohorts from healthy to SARS-CoV-2-infected recovered (hospital discharge), nonsevere (hospitalized), and severe [at the intensive care unit (ICU)] cases with increasing systemic inflammation in comparison with healthy individuals sampled prior to the COVID-19 pandemic. The results showed significantly dysregulated proteins and associated biological processes and disorders associated to COVID-19. These results corroborated previous findings in COVID-19 studies and highlighted how the representation of dysregulated serum proteins and associated BPs increases with COVID-19 disease symptomatology from asymptomatic to severe cases. The analysis was then focused on novel disease processes and biomarkers that were correlated with disease symptomatology. To contribute to translational medicine, results corroborated the predictive value of selected immune-related biomarkers for disease recovery [Selenoprotein P (SELENOP) and Serum paraoxonase/arylesterase 1 (PON1)], severity [Carboxypeptidase B2 (CBP2)], and symptomatology [Pregnancy zone protein (PZP)] using protein-specific ELISA tests. Our results contributed to the characterization of SARS-CoV-2–host molecular interactions with potential contributions to the monitoring and control of this pandemic by using immune-related biomarkers associated with disease symptomatology.

The main post-translational modifications and related regulatory pathways in the malaria parasite Plasmodium falciparum: An update

There are important challenges when investigating individual post-translational modifications (PTMs) or protein interaction network and delineating if PTMs or their changes and cross-talks are involved during infection, disease initiation or as a result of disease progression. Proteomics and in silico approaches now offer the possibility to complement each other to further understand the regulatory involvement of these modifications in parasites and infection biology. Accordingly, the current review highlights key expressed or altered proteins and PTMs are invisible switches that turn on and off the function of most of the proteins. PTMs include phosphorylation, glycosylation, ubiquitylation, palmitoylation, myristoylation, prenylation, acetylation, methylation, and epigenetic PTMs in P. falciparum which have been recently identified. But also other low-abundant or overlooked PTMs that might be important for the parasite's survival, infectivity, antigenicity, immunomodulation and pathogenesis. We here emphasize the PTMs as regulatory pathways playing major roles in the biology, pathogenicity, metabolic pathways, survival, host-parasite interactions and the life cycle of P. falciparum. Further validations and functional characterizations of such proteins might confirm the discovery of therapeutic targets and might most likely provide valuable data for the treatment of P. falciparum, the main cause of severe malaria in human.

In silico investigation of the viroporin E as a vaccine target against SARS-CoV-2

Viroporins, integral viral membrane ion channel proteins, interact with host-cell proteins deregulating physiological processes and activating inflammasomes. Severity of COVID-19 might be associated with hyperinflammation, thus we aimed at the complete immunoinformatic analysis of the SARS-CoV-2 viroporin E, P0DTC4. We also identified the human proteins interacting with P0DTC4 and the enriched molecular functions of the corresponding genes. The complete sequence of P0DTC4 in FASTA format was processed in 10 databases relative to secondary and tertiary protein structure analyses and prediction of optimal vaccine epitopes. Three more databases were accessed for the retrieval and the molecular functional characterization of the P0DTC4 human interactors. The immunoinformatics analysis resulted in the identification of 4 discontinuous B-cell epitopes along with 1 linear B-cell epitope and 11 T-cell epitopes which were found to be antigenic, immunogenic, nonallergen, nontoxin, and unable to induce autoimmunity thus fulfilling prerequisites for vaccine design. The functional enrichment analysis showed that the predicted host interactors of P0DTC4 target the cellular acetylation network. Two of the identified host-cell proteins – BRD2 and BRD4 – have been shown to be promising targets for antiviral therapy. Thus, our findings have implications for COVID-19 therapy and indicate that viroporin E could serve as a promising vaccine target against SARS-CoV-2. Validation experiments are required to complement these in silico results.