Browsing by Author "Skelton, Michelle"

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    Open Access
    Investigating the multifaceted host contribution to COVID-19 disease risk, progression and treatment: an integrative multi-omics network-based approach study
    (2024) Agamah, Francis Adem; Martin, Darrin P; Skelton, Michelle
    Coronavirus Disease-2019 (COVID-19) is a contagious respiratory disorder caused by Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2), a newly emerged β coronavirus belonging to the Coronaviridae family. Since its discovery in Wuhan, China, in December 2019, COVID-19 disease has transformed into a devastating global pandemic that has created disruptions across healthcare, economic, and social systems with approximately seven hundred and seventy million reported cases and close to seven million reported deaths as of January 2024. The clinical presentation of COVID-19 is very heterogeneous, ranging from mild disease states to severe disease states, associated with varying transcription, protein expression, lipid synthesis, and metabolic profiles. As a result of the heterogeneity of COVID-19 disease, the efficacy of drugs used to treat it may vary depending on the disease states when the medicines are administered. Molecular biology studies investigating COVID-19 clinical heterogeneity and the drugs that might be used to treat the disease have varied in terms of the approaches implemented. These approaches have focused on single features of SARS-CoV-2 infected cells such as gene transcription levels or protein expression levels (so-called “single omics” approaches) to approaches that attempt to simultaneously examine multiple molecular features of infected cells (so-called “multi-omics approaches”). This project adopts the latter approach, implementing a network-based method that integrates multi-omics data and drug-related data to investigate the contribution of host physiology to COVID-19 disease progression and identify promising drug repurposing candidates as potential treatment options. This involved evaluating existing computational integrative multi-omics network-based methods, determining their strengths and limitations, and adapting them to fit the aims of this project: identifying and characterizing biosignatures associated with various COVID-19 disease phases, as well as identifying drug repurposing candidates tailored for mild, moderate, and severe COVID-19 disease phases. The World Health Organization (WHO) Ordinal Scale (WOS) was used as a disease severity reference to harmonize COVID-19 patient metadata across two studies. A unified COVID-19 knowledge graph was constructed by assembling a disease-specific interactome from the literature and databases. We leveraged a multi-omics network-based approach to construct disease-state and omics-specific graphs by integrating proteomics, transcriptomics, metabolomics, and lipidomics data with the unified COVID-19 knowledge graph. We used an adapted random walk with restart algorithm, called multiXrank, to explore the disease-state and omics-specific graphs, and drug data to search for and prioritize not only biosignatures associated with COVID-19 disease phases but also drug candidates with the potential for treating mild, moderate, and severe COVID-19. The network analysis identified critical biosignatures for each COVID-19 phase. Mild cases displayed unique signatures like CCL4 and IRF1, potentially driving chemotaxis and interferon signaling. The moderate phase was characterized by biosignatures like HGF, MMP12, IL-10, and NFKB1, implicating enhanced inflammation, matrix remodeling, and immune regulation. In severe cases, biosignatures such as lysophosphatidylcholines, diglyceride, and sphingomyelin appeared, suggesting profound tissue damage, dysregulated lipid metabolism, and disrupted repair pathways. As expected, the abundance of shared chemokine and cytokine biosignatures in severe and moderate COVID-19 disease phases as compared to either mild vs moderate or mild vs severe disease phases suggests a closer molecular relatedness between these phases. This finding, along with biosignatures that discriminate between the disease states, and interactions between biosignatures that are either common between or associated with COVID-19 disease phases sheds light on the nuanced progression of the illness. We further investigated the differential influence of interleukin-6 (IL6) and interleukin 6 receptor (IL6R) on disease progression. We found that IL6 interaction with features of different omics data types increased with disease severity, thus indicating the differential association of IL6 with the different disease states. More specifically, IL6 interaction with proteins (e.g., IFNB, IFIT3), transcripts (e.g., CXCL1, CXCL2, CCL3), and metabolites (e.g., 1-(1-enyl-palmitoyl)-GPC, 1-(1-enyl-palmitoyl)-2-oleoyl-GPC (P-16:0/18:1)) may contribute to its major role in disease severity. We also observed that IL6R interactions mainly with proteins and transcripts increase more clearly with disease severity than do interactions with metabolites and lipids. We present a multilayered visualization tool (hosted at http://cytoscape.h3africa.org, last accessed on February 6, 2024) to navigate and analyze complex interactions across different biological layers, offering a valuable resource for uncovering key drivers of disease severity. Interestingly, cross-layer interactions between different omics profiles increased with disease severity. These potential association patterns could be useful for providing insights into the underlying molecular causes and consequences of the clinical heterogeneity of COVID-19, enabling early disease diagnosis, and optimal treatment prediction. The network-based integration of drug data and multi-omics data assisted in drug prediction. Most importantly, we prioritized twenty Food and Drug Administration approved agents with potential utility for mild, moderate, and severe COVID-19 disease phases. For mild COVID-19, stimulating immune cell recruitment and activation is key. Drugs like histamine, curcumin, and paclitaxel show potential in this regard due to their ability to stimulate immune cell recruitment, potentially mitigating disease progression. Similarly, non-steroidal anti-inflammatory drugs like indomethacin and diclofenac may offer symptomatic relief in mild cases. In mild to moderate COVID-19, drugs like omacetaxine, crizotinib, and vorinostat, known for their antiviral properties, can potentially hinder viral replication and offer additional treatment options. Moreover, glutathione, a potent antioxidant, could be valuable in moderate cases due to its potential to counteract inflammation and potentially prevent the dangerous "cytokine storm" seen in patients with antioxidant deficiencies. In severe COVID-19, the excessive immune response triggers a dangerous "inflammatory cascade." To combat this, drugs with strong anti-inflammatory effects, including anti-inflammatory drugs (sarilumab, tocilizumab), corticosteroids (dexamethasone, hydrocortisone), and immunosuppressives (sirolimus, cyclosporine), emerge as potential candidates for controlling this harmful process. Moreover, we further explore the interactions involving the drug repurposing candidates and key biosignatures. The findings could be useful for personalized treatment options tailored to individual patients based on their disease severity level. This project identified both biosignatures of different omics types (proteins, transcripts, metabolites, and lipids) enriched in disease-state pathways and their associated interactions that are either common between, or unique to mild, moderate, and severe COVID-19. These biosignatures include molecular features that underlie the observed clinical heterogeneity of COVID-19 and emphasize the need for disease-phase-specific treatment strategies. In addition, we explored the potential of multi-omics and drug-related data to predict therapeutics for different phases of COVID-19. This project demonstrates that the integrative analysis of drug data and multi-omics data enables the prioritization of biosignatures and potential drug candidates for COVID-19 disease phases. These findings hold promise for guiding future experimental studies towards potential clinical applications, requiring further investigation to definitively translate into therapeutic advancements.
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    Open Access
    Network-driven analysis of human–Plasmodium falciparum interactome: processes for malaria drug discovery and extracting in silico targets
    (2021-10-26) Agamah, Francis E.; Damena, Delesa; Skelton, Michelle; Ghansah, Anita; Mazandu, Gaston K.; Chimusa, Emile R.
    Background The emergence and spread of malaria drug resistance have resulted in the need to understand disease mechanisms and importantly identify essential targets and potential drug candidates. Malaria infection involves the complex interaction between the host and pathogen, thus, functional interactions between human and Plasmodium falciparum is essential to obtain a holistic view of the genetic architecture of malaria. Several functional interaction studies have extended the understanding of malaria disease and integrating such datasets would provide further insights towards understanding drug resistance and/or genetic resistance/susceptibility, disease pathogenesis, and drug discovery. Methods This study curated and analysed data including pathogen and host selective genes, host and pathogen protein sequence data, protein–protein interaction datasets, and drug data from literature and databases to perform human-host and P. falciparum network-based analysis. An integrative computational framework is presented that was developed and found to be reasonably accurate based on various evaluations, applications, and experimental evidence of outputs produced, from data-driven analysis. Results This approach revealed 8 hub protein targets essential for parasite and human host-directed malaria drug therapy. In a semantic similarity approach, 26 potential repurposable drugs involved in regulating host immune response to inflammatory-driven disorders and/or inhibiting residual malaria infection that can be appropriated for malaria treatment. Further analysis of host–pathogen network shortest paths enabled the prediction of immune-related biological processes and pathways subverted by P. falciparum to increase its within-host survival. Conclusions Host–pathogen network analysis reveals potential drug targets and biological processes and pathways subverted by P. falciparum to enhance its within malaria host survival. The results presented have implications for drug discovery and will inform experimental studies.
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    The pharmacologenetics of lopinavir in a cohort of black African HIV/AIDS patients
    (2015) Mpeta, Bafokeng; Dandara, Collet; Skelton, Michelle; Kampira, Elizabeth
    The Sub-Saharan African region remains the most severely affected by the HIV/AIDS epidemic. At the end of 2011, The Joint United Nations Programme on HIV/AIDS (UNAIDS) estimated that about 5% of adults were living with the HIV in this region, accounting for 69% of the global HIV prevalence. Efforts to curb the epidemic are focused on managing HIV through prevention strategies, such as advocating the use of condoms or pre-exposure or post-exposure prophylactic treatment, and prolonging life through the use of antiretroviral (ARV) therapy. Drugs used in ARV therapy target different major steps of the HIV reproductive cycle. These are nucleoside and non-nucleoside reverse transcriptase inhibitors (NRTIs/NNRTIs); fusion/entry inhibitors; integrase inhibitors; and protease inhibitors (PIs). In South Africa PIs, specifically lopinavir (LPV) boosted with another PI, ritonavir (RTV) are used in second-line ARV regimens along with a backbone of 2 NRTIs. The use of ARVs is not without issues - patients often experience side-effects to the drugs such as nausea, diarrhoea, and lipodystrophy with LPV use, which may influence their adherence to treatment and eventually lead to treatment failure. Inter-individual variability exists in patients' response to treatment despite the standard dose of 400 mg/100 mg (LPV/RTV) that is given and this may be due to differences in transport or metabolism of the drug in the liver. High plasma drug levels (associated with side-effects or toxicity) may be a result of poor metabolism or conversely, low plasma drug levels (associated with failure to suppress the virus) may be a result of extensive metabolism of the drug. Proteins involved in the disposition of LPV include the drug metabolising enzymes, CYP3A4 and CYP3A5; the hepatic uptake transporter, OATP1B1; and the efflux transporter, MRP2. Variation in the genes encoding these proteins may influence their functioning and hence LPV disposition. The aim of the study was to identify significant single nucleotide polymorphisms (SNPs) in each gene; to genotype a cohort of HIV-infected patients from Malawi and South Africa to identify the frequency of those variants; and to correlate genotypes with LPV plasma levels and other clinical parameters.