Drug response in association with pharmacogenomics and pharmacomicrobiomics: towards a better personalized medicine.

Researchers have long been presented with the challenge imposed by the role of genetic heterogeneity in drug response. For many years, Pharmacogenomics and pharmacomicrobiomics has been investigating the influence of an individual's genetic background to drug response and disposition. More recently, the human gut microbiome has proven to play a crucial role in the way patients respond to different therapeutic drugs and it has been shown that by understanding the composition of the human microbiome, we can improve the drug efficacy and effectively identify drug targets. However, our knowledge on the effect of host genetics on specific gut microbes related to variation in drug metabolizing enzymes, the drug remains limited and therefore limits the application of joint host-microbiome genome-wide association studies. In this paper, we provide a historical overview of the complex interactions between the host, human microbiome and drugs. While discussing applications, challenges and opportunities of these studies, we draw attention to the critical need for inclusion of diverse populations and the development of an innovative and combined pharmacogenomics and pharmacomicrobiomics approach, that may provide an important basis in personalized medicine.

Webinar report: stakeholder perspectives on informed consent for the use of genomic data by commercial entities.

In July 2020, the H3Africa Ethics and Community Engagement (E&CE) Working Group organised a webinar with ethics committee members and biomedical researchers from various African institutions throughout the Continent to discuss the issue of whether and how biological samples for scientific research may be accessed by commercial entities when broad consents obtained for the samples are silent. 128 people including Research Ethics Committee members (10), H3Africa researchers (46) including members of the E&CE working group, biomedical researchers not associated with H3Africa (27), representatives from the National Institutes of Health (16) and 10 other participants attended the webinar and shared their views. Several major themes emerged during the webinar, with the topics of broad versus explicit informed consent, defining commercial use, legacy samples and benefit sharing prevailing in the discussion. This report describes the consensus concerns and recommendations raised during the meeting and will be informative for future research on ethical considerations for genomic research in the African research context.

Computational/in silico methods in drug target and lead prediction.

Drug-like compounds are most of the time denied approval and use owing to the unexpected clinical side effects and cross-reactivity observed during clinical trials. These unexpected outcomes resulting in significant increase in attrition rate centralizes on the selected drug targets. These targets may be disease candidate proteins or genes, biological pathways, disease-associated microRNAs, disease-related biomarkers, abnormal molecular phenotypes, crucial nodes of biological network or molecular functions. This is generally linked to several factors, including incomplete knowledge on the drug targets and unpredicted pharmacokinetic expressions upon target interaction or off-target effects. A method used to identify targets, especially for polygenic diseases, is essential and constitutes a major bottleneck in drug development with the fundamental stage being the identification and validation of drug targets of interest for further downstream processes. Thus, various computational methods have been developed to complement experimental approaches in drug discovery. Here, we present an overview of various computational methods and tools applied in predicting or validating drug targets and drug-like molecules. We provide an overview on their advantages and compare these methods to identify effective methods which likely lead to optimal results. We also explore major sources of drug failure considering the challenges and opportunities involved. This review might guide researchers on selecting the most efficient approach or technique during the computational drug discovery process.

Network-driven analysis of human-Plasmodium falciparum interactome: processes for malaria drug discovery and extracting in silico targets.

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.

In Silico Screening of Isocitrate Lyase for Novel Anti-Buruli Ulcer Natural Products Originating from Africa.

Buruli ulcer (BU) is caused by Mycobacterium ulcerans and is predominant in both tropical and subtropical regions. The neglected debilitating disease is characterized by chronic necrotizing skin lesions attributed to a mycolactone, which is a macrolide toxin secreted by M. ulcerans. The preferred treatment is surgical excision of the lesions followed by a prolonged combination antibiotic therapy using existing drugs such as rifampicin and streptomycin or clarithromycin. These antibiotics appear not to be adequately potent and efficacious against persistent and late stage ulcers. In addition, emerging drug resistance to treatment poses great challenges. There is a need to identify novel natural product-derived lead compounds, which are potent and efficacious for the treatment of Buruli ulcer. Natural products present a rich diversity of chemical compounds with proven activity against various infectious diseases, and therefore, are considered in this study. This study sought to computationally predict natural product-derived lead compounds with the potential to be developed further into potent drugs with better therapeutic efficacy than the existing anti-buruli ulcer compounds. The three-dimensional (3D) structure of Isocitrate lyase (ICL) of Mycobacterium ulcerans was generated using homology modeling and was further scrutinized with molecular dynamics simulations. A library consisting of 885 compounds retrieved from the AfroDb database was virtually screened against the validated ICL model using AutoDock Vina. AfroDb is a compendium of "drug-like" and structurally diverse 3D structures of natural products originating from different geographical regions in Africa. The molecular docking with the ICL model was validated by computing a Receiver Operating Characteristic (ROC) curve with a reasonably good Area Under the Curve (AUC) value of 0.89375. Twenty hit compounds, which docked firmly within the active site pocket of the ICL receptor, were assessed via in silico bioactivity and pharmacological profiling. The three compounds, which emerged as potential novel leads, comprise ZINC38143792 (Euscaphic acid), ZINC95485880, and ZINC95486305 with reasonable binding energies (high affinity) of −8.6, −8.6, and −8.8 kcal/mol, respectively. Euscaphic acid has been reported to show minimal inhibition against a drug-sensitive strain of M. tuberculosis. The other two leads were both predicted to possess dermatological activity while one was antibacterial. The leads have shown promising results pertaining to efficacy, toxicity, pharmacokinetic, and safety. These leads can be experimentally characterized to assess their anti-mycobacterial activity and their scaffolds may serve as rich skeletons for developing anti-buruli ulcer drugs.

Network-based integrative multi-omics approach reveals biosignatures specific to COVID-19 disease phases.

Background: COVID-19 disease is characterized by a spectrum of disease phases (mild, moderate, and severe). Each disease phase is marked by changes in omics profiles with corresponding changes in the expression of features (biosignatures). However, integrative analysis of multiple omics data from different experiments across studies to investigate biosignatures at various disease phases is limited. Exploring an integrative multi-omics profile analysis through a network approach could be used to determine biosignatures associated with specific disease phases and enable the examination of the relationships between the biosignatures. Aim: To identify and characterize biosignatures underlying various COVID-19 disease phases in an integrative multi-omics data analysis. Method: We leveraged a multi-omics network-based approach to integrate transcriptomics, metabolomics, proteomics, and lipidomics data. The World Health Organization Ordinal Scale WHO Ordinal Scale was used as a disease severity reference to harmonize COVID-19 patient metadata across two studies with independent data. A unified COVID-19 knowledge graph was constructed by assembling a disease-specific interactome from the literature and databases. Disease-state specific omics-graphs were constructed by integrating multi-omics data with the unified COVID-19 knowledge graph. We expanded on the network layers of multiXrank, a random walk with restart on multilayer network algorithm, to explore disease state omics-specific graphs and perform enrichment analysis. Results: Network analysis revealed the biosignatures involved in inducing chemokines and inflammatory responses as hubs in the severe and moderate disease phases. We observed distinct biosignatures between severe and moderate disease phases as compared to mild-moderate and mild-severe disease phases. Mild COVID-19 cases were characterized by a unique biosignature comprising C-C Motif Chemokine Ligand 4 (CCL4), and Interferon Regulatory Factor 1 (IRF1). Hepatocyte Growth Factor (HGF), Matrix Metallopeptidase 12 (MMP12), Interleukin 10 (IL10), Nuclear Factor Kappa B Subunit 1 (NFKB1), and suberoylcarnitine form hubs in the omics network that characterizes the moderate disease state. The severe cases were marked by biosignatures such as Signal Transducer and Activator of Transcription 1 (STAT1), Superoxide Dismutase 2 (SOD2), HGF, taurine, lysophosphatidylcholine, diacylglycerol, triglycerides, and sphingomyelin that characterize the disease state. Conclusion: This study identified both biosignatures of different omics types enriched in disease-related pathways and their associated interactions (such as protein-protein, protein-transcript, protein-metabolite, transcript-metabolite, and lipid-lipid interactions) that are unique to mild, moderate, and severe COVID-19 disease states. These biosignatures include molecular features that underlie the observed clinical heterogeneity of COVID-19 and emphasize the need for disease-phase-specific treatment strategies. The approach implemented here can be used to find associations between transcripts, proteins, lipids, and metabolites in other diseases.

Computational approaches for network-based integrative multi-omics analysis.

Advances in omics technologies allow for holistic studies into biological systems. These studies rely on integrative data analysis techniques to obtain a comprehensive view of the dynamics of cellular processes, and molecular mechanisms. Network-based integrative approaches have revolutionized multi-omics analysis by providing the framework to represent interactions between multiple different omics-layers in a graph, which may faithfully reflect the molecular wiring in a cell. Here we review network-based multi-omics/multi-modal integrative analytical approaches. We classify these approaches according to the type of omics data supported, the methods and/or algorithms implemented, their node and/or edge weighting components, and their ability to identify key nodes and subnetworks. We show how these approaches can be used to identify biomarkers, disease subtypes, crosstalk, causality, and molecular drivers of physiological and pathological mechanisms. We provide insight into the most appropriate methods and tools for research questions as showcased around the aetiology and treatment of COVID-19 that can be informed by multi-omics data integration. We conclude with an overview of challenges associated with multi-omics network-based analysis, such as reproducibility, heterogeneity, (biological) interpretability of the results, and we highlight some future directions for network-based integration.

Insilico Functional Analysis of Genome-Wide Dataset From 17,000 Individuals Identifies Candidate Malaria Resistance Genes Enriched in Malaria Pathogenic Pathways.

Recent genome-wide association studies (GWASs) of severe malaria have identified several association variants. However, much about the underlying biological functions are yet to be discovered. Here, we systematically predicted plausible candidate genes and pathways from functional analysis of severe malaria resistance GWAS summary statistics (N = 17,000) meta-analysed across 11 populations in malaria endemic regions. We applied positional mapping, expression quantitative trait locus (eQTL), chromatin interaction mapping, and gene-based association analyses to identify candidate severe malaria resistance genes. We further applied rare variant analysis to raw GWAS datasets (N = 11,000) of three malaria endemic populations including Kenya, Malawi, and Gambia and performed various population genetic structures of the identified genes in the three populations and global populations. We performed network and pathway analyses to investigate their shared biological functions. Our functional mapping analysis identified 57 genes located in the known malaria genomic loci, while our gene-based GWAS analysis identified additional 125 genes across the genome. The identified genes were significantly enriched in malaria pathogenic pathways including multiple overlapping pathways in erythrocyte-related functions, blood coagulations, ion channels, adhesion molecules, membrane signalling elements, and neuronal systems. Our population genetic analysis revealed that the minor allele frequencies (MAF) of the single nucleotide polymorphisms (SNPs) residing in the identified genes are generally higher in the three malaria endemic populations compared to global populations. Overall, our results suggest that severe malaria resistance trait is attributed to multiple genes, highlighting the possibility of harnessing new malaria therapeutics that can simultaneously target multiple malaria protective host molecular pathways.