Drug repositioning for dengue haemorrhagic fever by integrating multiple omics analyses

Dengue overview: An updated systemic review

Dengue is caused by the dengue virus (DENVs) infection and clinical manifestations include dengue fever (DF), dengue hemorrhagic fever (DHF), or dengue shock syndrome (DSS). Due to a lack of antiviral drugs and effective vaccines, several therapeutic and control strategies have been proposed. A systemic literature review was conducted according to PRISMA guidelines to select proper references to give an overview of DENV infection. Results indicate that understanding the virus characteristics and epidemiology are essential to gain the basic and clinical knowledge as well as dengue disseminated pattern and status. Different factors and mechanisms are thought to be involved in the presentation of DHF and DSS, including antibody-dependent enhancement, immune dysregulation, viral virulence, host genetic susceptibility, and preexisting dengue antibodies. This study suggests that dissecting pathogenesis and risk factors as well as developing different types of therapeutic and control strategies against DENV infection are urgently needed.

Prediction of Protein-Protein Interactions Using Vision Transformer and Language Model

The knowledge of protein-protein interaction (PPI) helps us to understand proteins' functions, the causes and growth of several diseases, and can aid in designing new drugs. The majority of existing PPI research has relied mainly on sequence-based approaches. With the availability of multi-omics datasets (sequence, 3D structure) and advancements in deep learning techniques, it is feasible to develop a deep multi-modal framework that fuses the features learned from different sources of information to predict PPI. In this work, we propose a multi-modal approach utilizing protein sequence and 3D structure. To extract features from the 3D structure of proteins, we use a pre-trained vision transformer model that has been fine-tuned on the structural representation of proteins. The protein sequence is encoded into a feature vector using a pre-trained language model. The feature vectors extracted from the two modalities are fused and then fed to the neural network classifier to predict the protein interactions. To showcase the effectiveness of the proposed methodology, we conduct experiments on two popular PPI datasets, namely, the human dataset and the S. cerevisiae dataset. Our approach outperforms the existing methodologies to predict PPI, including multi-modal approaches. We also evaluate the contributions of each modality by designing uni-modal baselines. We perform experiments with three modalities as well, having gene ontology as the third modality.

A systems biology approach to better understand human tick-borne diseases

Tick-borne diseases (TBDs) are a growing global health concern. Despite extensive studies, ill-defined tick-associated pathologies remain with unknown aetiologies. Human immunological responses after tick bite, and inter-individual variations of immune-response phenotypes, are not well characterised. Current reductive experimental methodologies limit our understanding of more complex tick-associated illness, which results from the interactions between the host, tick, and microbes. An unbiased, systems-level integration of clinical metadata and biological host data - obtained via transcriptomics, proteomics, and metabolomics - offers to drive the data-informed generation of testable hypotheses in TBDs. Advanced computational tools have rendered meaningful analysis of such large data sets feasible. This review highlights the advantages of integrative system biology approaches as essential for understanding the complex pathobiology of TBDs.

UTX deficiency in neural stem/progenitor cells results in impaired neural development, fetal ventriculomegaly, and postnatal death

Recent studies have demonstrated that epigenetic modifications are deeply involved in neurogenesis; however, the precise mechanisms remain largely unknown. To determine the role of UTX (also known as KDM6A), a demethylase of histone H3K27, in neural development, we generated Utx-deficient mice in neural stem/progenitor cells (NSPCs). Since Utx is an X chromosome-specific gene, the genotypes are sex-dependent; female mice lose both Utx alleles (Utx^Δ/Δ ), and male mice lose one Utx allele yet retain one Uty allele, the counterpart of Utx on the Y chromosome (Utx^Δ/Uty ). We found that Utx^Δ/Δ mice exhibited fetal ventriculomegaly and died soon after birth. Immunofluorescence staining and EdU labeling revealed a significant increase in NSPCs and a significant decrease in intermediate-progenitor and differentiated neural cells. Molecular analyses revealed the downregulation of pathways related to DNA replication and increased H3K27me3 levels around the transcription start sites in Utx^Δ/Δ NSPCs. These results indicate that UTX globally regulates the expression of genes required for proper neural development in NSPCs, and UTX deficiency leads to impaired cell cycle exit, reduced differentiation, and neonatal death. Interestingly, although Utx^Δ/Uty mice survived the postnatal period, most died of hydrocephalus, a clinical feature of Kabuki syndrome, a congenital anomaly involving UTX mutations. Our findings provide novel insights into the role of histone modifiers in neural development and suggest that Utx^Δ/Uty mice are a potential disease model for Kabuki syndrome.

Multi-Omics and Artificial Intelligence-Guided Drug Repositioning: Prospects, Challenges, and Lessons Learned from COVID-19

Drug repurposing is of interest for therapeutics innovation in many human diseases including coronavirus disease 2019 (COVID-19). Methodological innovations in drug repurposing are currently being empowered by convergence of omics systems science and digital transformation of life sciences. This expert review article offers a systematic summary of the application of artificial intelligence (AI), particularly machine learning (ML), to drug repurposing and classifies and introduces the common clustering, dimensionality reduction, and other methods. We highlight, as a present-day high-profile example, the involvement of AI/ML-based drug discovery in the COVID-19 pandemic and discuss the collection and sharing of diverse data types, and the possible futures awaiting drug repurposing in an era of AI/ML and digital technologies. The article provides new insights on convergence of multi-omics and AI-based drug repurposing. We conclude with reflections on the various pathways to expedite innovation in drug development through drug repurposing for prompt responses to the current COVID-19 pandemic and future ecological crises in the 21st century.

Disturbed lipid and amino acid metabolisms in COVID-19 patients

The Coronavirus disease 2019 (COVID-19) pandemic is overwhelming the healthcare systems. Identification of systemic reactions underlying COVID-19 will lead to new biomarkers and therapeutic targets for monitoring and early intervention in this viral infection. We performed targeted metabolomics covering up to 630 metabolites within several key metabolic pathways in plasma samples of 20 hospitalized COVID-19 patients and 37 matched controls. Plasma metabolic signatures specifically differentiated severe COVID-19 from control patients. The identified metabolic signatures indicated distinct alterations in both lipid and amino acid metabolisms in COVID-19 compared to control patient plasma. Systems biology-based analyses identified sphingolipid, tryptophan, tyrosine, glutamine, arginine, and arachidonic acid metabolism as mostly impacted pathways in COVID-19 patients. Notably, gamma-aminobutyric acid (GABA) was significantly reduced in COVID-19 patients and GABA plasma levels allowed for stratification of COVID-19 patients with high sensitivity and specificity. The data reveal large metabolic disturbances in COVID-19 patients and suggest use of GABA as potential biomarker and therapeutic target for the infection.

Isolation and Identification of Dengue Virus Interactome with Human Plasma Proteins by Affinity Purification-Mass Spectrometry

Viral proteins evolve to benefit the interaction with host proteins during the infection and replication processes. A comprehensive understanding of virus interactome with host proteins may thus lead to the identification of molecular targets for infection inhibition. We present a procedure for isolating and identifying the dengue virus interactome with human plasma proteins. It comprises the fractionation of human plasma by anion exchange chromatography, followed by affinity purification and mass spectrometry identification of the captured proteins. This procedure was applied to the characterization of the interactions of the four serotypes of dengue virus with human plasma proteins, mediated by the domain III of the envelope protein of the virus. The resulting interactome comprises 62 proteins, six of which were validated as new direct interactions of the virus with its human host.

Prediction of severe adverse events, modes of action and drug treatments for COVID-19's complications

Following SARS-CoV-2 infection, some COVID-19 patients experience severe host driven adverse events. To treat these complications, their underlying etiology and drug treatments must be identified. Thus, a novel AI methodology MOATAI-VIR, which predicts disease-protein-pathway relationships and repurposed FDA-approved drugs to treat COVID-19's clinical manifestations was developed. SARS-CoV-2 interacting human proteins and GWAS identified respiratory failure genes provide the input from which the mode-of-action (MOA) proteins/pathways of the resulting disease comorbidities are predicted. These comorbidities are then mapped to their clinical manifestations. To assess each manifestation's molecular basis, their prioritized shared proteins were subject to global pathway analysis. Next, the molecular features associated with hallmark COVID-19 phenotypes, e.g. unusual neurological symptoms, cytokine storms, and blood clots were explored. In practice, 24/26 of the major clinical manifestations are successfully predicted. Three major uncharacterized manifestation categories including neoplasms are also found. The prevalence of neoplasms suggests that SARS-CoV-2 might be an oncovirus due to shared molecular mechanisms between oncogenesis and viral replication. Then, repurposed FDA-approved drugs that might treat COVID-19's clinical manifestations are predicted by virtual ligand screening of the most frequent comorbid protein targets. These drugs might help treat both COVID-19's severe adverse events and lesser ones such as loss of taste/smell.

Deep fusion learning facilitates anatomical therapeutic chemical recognition in drug repurposing and discovery

The advent of large-scale biomedical data and computational algorithms provides new opportunities for drug repurposing and discovery. It is of great interest to find an appropriate data representation and modeling method to facilitate these studies. The anatomical therapeutic chemical (ATC) classification system, proposed by the World Health Organization (WHO), is an essential source of information for drug repurposing and discovery. Besides, computational methods are applied to predict drug ATC classification. We conducted a systematic review of ATC computational prediction studies and revealed the differences in data sets, data representation, algorithm approaches, and evaluation metrics. We then proposed a deep fusion learning (DFL) framework to optimize the ATC prediction model, namely DeepATC. The methods based on graph convolutional network, inferring biological network and multimodel attentive fusion network were applied in DeepATC to extract the molecular topological information and low-dimensional representation from the molecular graph and heterogeneous biological networks. The results indicated that DeepATC achieved superior model performance with area under the curve (AUC) value at 0.968. Furthermore, the DFL framework was performed for the transcriptome data-based ATC prediction, as well as another independent task that is significantly relevant to drug discovery, namely drug-target interaction. The DFL-based model achieved excellent performance in the above-extended validation task, suggesting that the idea of aggregating the heterogeneous biological network and node's (molecule or protein) self-topological features will bring inspiration for broader drug repurposing and discovery research.

Multi-Tissue Transcriptomic-Informed In Silico Investigation of Drugs for the Treatment of Dengue Fever Disease

Transcriptomics, proteomics and pathogen-host interactomics data are being explored for the in silico–informed selection of drugs, prior to their functional evaluation. The effectiveness of this kind of strategy has been put to the test in the current COVID-19 pandemic, and it has been paying off, leading to a few drugs being rapidly repurposed as treatment against SARS-CoV-2 infection. Several neglected tropical diseases, for which treatment remains unavailable, would benefit from informed in silico investigations of drugs, as performed in this work for Dengue fever disease. We analyzed transcriptomic data in the key tissues of liver, spleen and blood profiles and verified that despite transcriptomic differences due to tissue specialization, the common mechanisms of action, “Adrenergic receptor antagonist”, “ATPase inhibitor”, “NF-kB pathway inhibitor” and “Serotonin receptor antagonist”, were identified as druggable (e.g., oxprenolol, digoxin, auranofin and palonosetron, respectively) to oppose the effects of severe Dengue infection in these tissues. These are good candidates for future functional evaluation and clinical trials.

Application of multiple omics and network projection analyses to drug repositioning for pathogenic mosquito-borne viruses

Pathogenic mosquito-borne viruses are a serious public health issue in tropical and subtropical regions and are increasingly becoming a problem in other climate zones. Drug repositioning is a rapid, pharmaco-economic approach that can be used to identify compounds that target these neglected tropical diseases. We have applied a computational drug repositioning method to five mosquito-borne viral infections: dengue virus (DENV), zika virus (ZIKV), West Nile virus (WNV), Japanese encephalitis virus (JEV) and Chikungunya virus (CHIV). We identified signature molecules and pathways for each virus infection based on omics analyses, and determined 77 drug candidates and 146 proteins for those diseases by using a filtering method. Based on the omics analyses, we analyzed the relationship among drugs, target proteins and the five viruses by projecting the signature molecules onto a human protein-protein interaction network. We have classified the drug candidates according to the degree of target proteins in the protein-protein interaction network for the five infectious diseases.

Multi-platform omics analysis reveals molecular signature for COVID-19 pathogenesis, prognosis and drug target discovery

Disease progression prediction and therapeutic drug target discovery for Coronavirus disease 2019 (COVID-19) are particularly important, as there is still no effective strategy for severe COVID-19 patient treatment. Herein, we performed multi-platform omics analysis of serial plasma and urine samples collected from patients during the course of COVID-19. Integrative analyses of these omics data revealed several potential therapeutic targets, such as ANXA1 and CLEC3B. Molecular changes in plasma indicated dysregulation of macrophage and suppression of T cell functions in severe patients compared to those in non-severe patients. Further, we chose 25 important molecular signatures as potential biomarkers for the prediction of disease severity. The prediction power was validated using corresponding urine samples and plasma samples from new COVID-19 patient cohort, with AUC reached to 0.904 and 0.988, respectively. In conclusion, our omics data proposed not only potential therapeutic targets, but also biomarkers for understanding the pathogenesis of severe COVID-19.

DrugRepV: a compendium of repurposed drugs and chemicals targeting epidemic and pandemic viruses

Viruses are responsible for causing various epidemics and pandemics with a high mortality rate e.g. ongoing SARS-CoronaVirus-2 crisis. The discovery of novel antivirals remains a challenge but drug repurposing is emerging as a potential solution to develop antivirals in a cost-effective manner. In this regard, we collated the information of repurposed drugs tested for antiviral activity from literature and presented it in the form of a user-friendly web server named ‘DrugRepV’. The database contains 8485 entries (3448 unique) with biological, chemical, clinical and structural information of 23 viruses responsible to cause epidemics/pandemics. The database harbors browse and search options to explore the repurposed drug entries. The data can be explored by some important fields like drugs, viruses, drug targets, clinical trials, assays, etc. For summarizing the data, we provide overall statistics of the repurposed candidates. To make the database more informative, it is hyperlinked to various external repositories like DrugBank, PubChem, NCBI-Taxonomy, Clinicaltrials.gov, World Health Organization and many more. ‘DrugRepV’ database (https://bioinfo.imtech.res.in/manojk/drugrepv/) would be highly useful to the research community working to develop antivirals.

Dengue Virus Non-Structural Protein 5 as a Versatile, Multi-Functional Effector in Host-Pathogen Interactions

Dengue is emerging as one of the most prevalent mosquito-borne viral diseases of humans. The 11kb RNA genome of the dengue virus encodes three structural proteins (envelope, pre-membrane, capsid) and seven non-structural proteins (NS1, NS2A, NS2B, NS3, NS4A, NS4B, and NS5), all of which are translated as a single polyprotein that is subsequently cleaved by viral and host cellular proteases at specific sites. Non-structural protein 5 (NS5) is the largest of the non-structural proteins, functioning as both an RNA-dependent RNA polymerase (RdRp) that replicates the viral RNA and an RNA methyltransferase enzyme (MTase) that protects the viral genome by RNA capping, facilitating polyprotein translation. Within the human host, NS5 interacts with several proteins such as those in the JAK-STAT pathway, thereby interfering with anti-viral interferon signalling. This mini-review presents annotated, consolidated lists of known and potential NS5 interactors in the human host as determined by experimental and computational approaches respectively. The most significant protein interactors and the biological pathways they participate in are also highlighted and their implications discussed, along with the specific serotype of dengue virus as appropriate. This information can potentially stimulate and inform further research efforts towards providing an integrative understanding of the mechanisms by which NS5 manipulates the human-virus interface in general and the innate and adaptive immune responses in particular.

Drug Repositioning: New Approaches and Future Prospects for Life-Debilitating Diseases and the COVID-19 Pandemic Outbreak

Traditionally, drug discovery utilises a de novo design approach, which requires high cost and many years of drug development before it reaches the market. Novel drug development does not always account for orphan diseases, which have low demand and hence low-profit margins for drug developers. Recently, drug repositioning has gained recognition as an alternative approach that explores new avenues for pre-existing commercially approved or rejected drugs to treat diseases aside from the intended ones. Drug repositioning results in lower overall developmental expenses and risk assessments, as the efficacy and safety of the original drug have already been well accessed and approved by regulatory authorities. The greatest advantage of drug repositioning is that it breathes new life into the novel, rare, orphan, and resistant diseases, such as Cushing's syndrome, HIV infection, and pandemic outbreaks such as COVID-19. Repositioning existing drugs such as Hydroxychloroquine, Remdesivir, Ivermectin and Baricitinib shows good potential for COVID-19 treatment. This can crucially aid in resolving outbreaks in urgent times of need. This review discusses the past success in drug repositioning, the current technological advancement in the field, drug repositioning for personalised medicine and the ongoing research on newly emerging drugs under consideration for the COVID-19 treatment.

A medicinal chemistry perspective of drug repositioning: Recent advances and challenges in drug discovery

Drug repurposing is a strategy consisting of finding new indications for already known marketed drugs used in various clinical settings or highly characterized compounds despite they can be failed drugs. Recently, it emerges as an alternative approach for the rapid identification and development of new pharmaceuticals for various rare and complex diseases for which lack the effective drug treatments. The success rate of drugs repurposing approach accounts for approximately 30% of new FDA approved drugs and vaccines in recent years. This review focuses on the status of drugs repurposing approach for various diseases including skin diseases, infective, inflammatory, cancer, and neurodegenerative diseases. Efforts have been made to provide structural features and mode of actions of drugs.

Dengue Virus-Polymersome Hybrid Nanovesicles for Advanced Drug Screening Using Real-Time Single Nanoparticle-Virus Tracking

Dengue virus (DENV) is a major infectious viral pathogen that affects millions of individuals worldwide every year, causing a potentially fatal syndrome, while no commercial antiviral drugs are yet available. To develop an antiviral against dengue fever, it is necessary to understand the relationship between DENV and host cells, which could provide a basis for viral dynamics and identification of inhibitory drug targets. In this study, we designed DiD-loaded and BODIPY-ceramide-encapsulated DENV-polymersome hybrid nanovesicles (DENVSomes) prepared by an extrusion method, which trigger red fluorescence in the endosome and green in the Golgi. DENVSome monitors the dynamics of host cell-virus interaction and tracking in living cells with novel state-of-the-art imaging technologies that show images at high resolution. Also, DENVSome can be exploited to screen whether candidate antiviral drugs interact with DENVs. Consequently, we successfully demonstrated that DENVSome is an efficient tool for tracking and unraveling the mechanisms of replication and drug screening for antiviral drugs of DENV.

Computational approaches for identifying potential inhibitors on targeting protein interactions in drug discovery

In the era of big data, the interplay of artificial and human intelligence is the demanding job to address the concerns involving exchange of decisions between both sides. Drug discovery is one of the key sources of the big data, which involves synergy among various computational methods to achieve a clinical success. Rightful acquisition, mining and analysis of the data related to ligand and targets are crucial to accomplish reliable outcomes in the entire process. Novel designing and screening tactics are necessary to substantiate a potent and efficient lead compounds. Such methods are emphasized and portrayed in the current review targeting protein-ligand and protein-protein interactions involved in various diseases with potential applications.

Pathway Interactions Based on Drug-Induced Datasets

In this study, we identified enrichment pathway connections from MCF7 breast cancer epithelial cells that were treated with 87 drugs. We extracted drug-treated samples, where the sample size was greater than or equal to 5. The drugs included 17-allylamino-geldanamycin, LY294002, trichostatin A, valproic acid, sirolimus, and wortmannin, which had sample sizes of 11, 8, 7, 7, 7, and 5, respectively. We found meaningful pathways using gene set enrichment analysis and identified intradrug and interdrug pathway interactions, which implied the influence of drug combination. Among the top 20 enrichment pathways that were wortmannin induced, there were a total of 37 intradrug pathway interactions via common genes. Thirty-seven pathway interactions were induced by valproic acid, 11 induced by trichostatin A, 20 induced by LY294002, and 59 induced by sirolimus, all via common genes. The number of interdrug-induced pathway interactions ranged from one pair of pathways to 23. The pair of ERBB_SIGNALING and INSULIN_SIGNALING pathways showed the highest score from a pair of 2 individual drugs. The highest number of pathway interactions was observed between the drugs 17-allylamino-geldanamycin and LY294002.