A network medicine approach to investigation and population-based validation of disease manifestations and drug repurposing for COVID-19

Underestimated virus impaired cognition-more evidence and more work to do

Neurodegenerative disorders (NDs) are chronic neurological diseases that can be of idiopathic, genetic, or potentially infectious origin. Although the exact cause of neurodegeneration is unknown, it might be result of a confluence of age, genetic susceptibility factors, and environmental stresses. The blood-brain barrier shields the brain from the majority of viral infections, however neurotropic viruses are able to breach this barrier and infect central nervous system. Growing research points to a possible connection between viruses and neurodegenerative diseases, indicating that virus-induced neuroinflammation and disruption of neuronal protein quality control may play a role in the initial stages of disease progression. The diagnosis and treatment of NDs are urgent and challenging. Even though there is limited clinical evidence to support the use of antiviral medications and their dose regimens within the central nervous system (CNS), with the exception of acyclovir, they are currently utilized to treat various viral CNS infections. Understanding the neuropathogenesis of viral CNS infection may help with targeted diagnosis and treatment plans by focusing on the molecular mechanisms of the CNS. It may also be helpful in the search for new antiviral drugs, which are crucial for better managing these neurotropic viral infections. This review focuses on new findings linking viral infection to NDs and explores how viral modifications of cellular functions can impact the development of neurodegeneration and will also explore the therapeutic potential of antiviral drugs in NDs.

Artificial intelligence streamlines scientific discovery of drug-target interactions

Drug discovery is a complicated process through which new therapeutics are identified to prevent and treat specific diseases. Identification of drug-target interactions (DTIs) stands as a pivotal aspect within the realm of drug discovery and development. The traditional process of drug discovery, especially identification of DTIs, is marked by its high costs of experimental assays and low success rates. Computational methods have emerged as indispensable tools, especially those employing artificial intelligence (AI) methods, which could streamline the process, thereby reducing costs and time consumption and potentially increasing success rates. In this review, we focus on the application of AI techniques in DTI prediction. Specifically, we commence with a comprehensive overview of drug discovery and development, along with systematic prediction and validation of DTIs. We proceed to highlight the prominent databases and toolkits used in developing AI methods for DTI prediction, as well as with methodologies for evaluating their efficacy. We further extend the exploration into three primary types of state-of-the-art AI methods used in DTI prediction, including classical machine learning, deep learning and network-based methods. Finally, we summarize the key findings and outline the current challenges and future directions that AI methods face in scientific drug discovery and development.

Neuroinflammation inhibition and neuroprotective effects of purpurin, a potential anti-AD compound, screened via network proximity and gene enrichment analyses

Alzheimer's disease (AD) is a complex neurodegenerative disease without any effective preventive or therapeutic drugs. Natural products with stable structures and pharmacological characteristics are valuable sources for the development of novel drugs for many complex diseases. This study aimed to discover potential natural compounds for the treatment of AD using new technologies and methods and explore the efficacy and mechanism of candidate compounds. AD-related large-scale genetic datasets were collated to construct disease-PPIs and natural products were collected from six databases to construct compound-protein interactions (CPIs). Potential relationships between natural compounds and AD were predicted via network proximity and gene enrichment analyses. Then, five AD-related cell models and d-galactose-induced aging rat model were established to evaluate the neuroprotective effects of candidate compounds in vitro and in vivo. We identified that 267 natural compounds were predicted to have close connections with AD and 19 compounds could exert protective effect in at least one cell model. Notably, purpurin exerted protective effect in three cell models and significantly improved the cognitive learning and memory functions, reduced the oxidative stress injuries and neuroinflammation, and enhanced the synaptic plasticity and neurotrophic effect in the brain of d-galactose-treated rats. In this study, AD-related natural compounds were identified via network proximity and gene enrichment analyses. In vivo and in vitro experiments revealed the therapeutic potential of purpurin for AD treatment, laying the foundation for further in-depth research and providing valuable information for the development of novel anti-AD drugs.

A structurally informed human protein-protein interactome reveals proteome-wide perturbations caused by disease mutations

To assist the translation of genetic findings to disease pathobiology and therapeutics discovery, we present an ensemble deep learning framework, termed PIONEER (Protein-protein InteractiOn iNtErfacE pRediction), that predicts protein-binding partner-specific interfaces for all known protein interactions in humans and seven other common model organisms to generate comprehensive structurally informed protein interactomes. We demonstrate that PIONEER outperforms existing state-of-the-art methods and experimentally validate its predictions. We show that disease-associated mutations are enriched in PIONEER-predicted protein-protein interfaces and explore their impact on disease prognosis and drug responses. We identify 586 significant protein-protein interactions (PPIs) enriched with PIONEER-predicted interface somatic mutations (termed oncoPPIs) from analysis of approximately 11,000 whole exomes across 33 cancer types and show significant associations of oncoPPIs with patient survival and drug responses. PIONEER, implemented as both a web server platform and a software package, identifies functional consequences of disease-associated alleles and offers a deep learning tool for precision medicine at multiscale interactome network levels.

Network medicine analysis for dissecting the therapeutic mechanism of consensus TCM formulae in treating hepatocellular carcinoma with different TCM syndromes

Introduction

Hepatocellular carcinoma (HCC) is a major cause of cancer-related mortality worldwide. Traditional Chinese Medicine (TCM) is widely utilized as an adjunct therapy, improving patient survival and quality of life. TCM categorizes HCC into five distinct syndromes, each treated with specific herbal formulae. However, the molecular mechanisms underlying these treatments remain unclear.

Methods

We employed a network medicine approach to explore the therapeutic mechanisms of TCM in HCC. By constructing a protein-protein interaction (PPI) network, we integrated genes associated with TCM syndromes and their corresponding herbal formulae. This allowed for a quantitative analysis of the topological and functional relationships between TCM syndromes, HCC, and the specific formulae used for treatment.

Results

Our findings revealed that genes related to the five TCM syndromes were closely associated with HCC-related genes within the PPI network. The gene sets corresponding to the five TCM formulae exhibited significant proximity to HCC and its related syndromes, suggesting the efficacy of TCM syndrome differentiation and treatment. Additionally, through a random walk algorithm applied to a heterogeneous network, we prioritized active herbal ingredients, with results confirmed by literature.

Discussion

The identification of these key compounds underscores the potential of network medicine to unravel the complex pharmacological actions of TCM. This study provides a molecular basis for TCM's therapeutic strategies in HCC and highlights specific herbal ingredients as potential leads for drug development and precision medicine.

Computational Approaches to Drug Repurposing: Methods, Challenges, and Opportunities

Drug repurposing refers to the inference of therapeutic relationships between a clinical indication and existing compounds. As an emerging paradigm in drug development, drug repurposing enables more efficient treatment of rare diseases, stratified patient populations, and urgent threats to public health. However, prioritizing well-suited drug candidates from among a nearly infinite number of repurposing options continues to represent a significant challenge in drug development. Over the past decade, advances in genomic profiling, database curation, and machine learning techniques have enabled more accurate identification of drug repurposing candidates for subsequent clinical evaluation. This review outlines the major methodologic classes that these approaches comprise, which rely on (a) protein structure, (b) genomic signatures, (c) biological networks, and (d) real-world clinical data. We propose that realizing the full impact of drug repurposing methodologies requires a multidisciplinary understanding of each method's advantages and limitations with respect to clinical practice.

Circadian-driven tissue specificity is constrained under caloric restricted feeding conditions

Tissue specificity is a fundamental property of an organ that affects numerous biological processes, including aging and longevity, and is regulated by the circadian clock. However, the distinction between circadian-affected tissue specificity and other tissue specificities remains poorly understood. Here, using multi-omics data on circadian rhythms in mice, we discovered that approximately 35% of tissue-specific genes are directly affected by circadian regulation. These circadian-affected tissue-specific genes have higher expression levels and are associated with metabolism in hepatocytes. They also exhibit specific features in long-reads sequencing data. Notably, these genes are associated with aging and longevity at both the gene level and at the network module level. The expression of these genes oscillates in response to caloric restricted feeding regimens, which have been demonstrated to promote longevity. In addition, aging and longevity genes are disrupted in various circadian disorders. Our study indicates that the modulation of circadian-affected tissue specificity is essential for understanding the circadian mechanisms that regulate aging and longevity at the genomic level.

Network-based approach for drug repurposing against mpox

The current outbreak of mpox presents a significant threat to the global community. However, the lack of mpox-specific drugs necessitates the identification of additional candidates for clinical trials. In this study, a network medicine framework was used to investigate poxviruses-human interactions to identify potential drugs effective against the mpox virus (MPXV). The results indicated that poxviruses preferentially target hubs on the human interactome, and that these virally-targeted proteins (VTPs) tend to aggregate together within specific modules. Comorbidity analysis revealed that mpox is closely related to immune system diseases. Based on predicted drug-target interactions, 268 drugs were identified using the network proximity approach, among which 23 drugs displaying the least side-effects and significant proximity to MPXV were selected as the final candidates. Lastly, specific drugs were explored based on VTPs, differentially expressed proteins, and intermediate nodes, corresponding to different categories. These findings provide novel insights that can contribute to a deeper understanding of the pathogenesis of MPXV and development of ready-to-use treatment strategies based on drug repurposing.

Molecular docking as a tool for the discovery of novel insight about the role of acid sphingomyelinase inhibitors in SARS- CoV-2 infectivity

Recently, COVID-19, caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) and its variants, caused > 6 million deaths. Symptoms included respiratory strain and complications, leading to severe pneumonia. SARS-CoV-2 attaches to the ACE-2 receptor of the host cell membrane to enter. Targeting the SARS-CoV-2 entry may effectively inhibit infection. Acid sphingomyelinase (ASMase) is a lysosomal protein that catalyzes the conversion of sphingolipid (sphingomyelin) to ceramide. Ceramide molecules aggregate/assemble on the plasma membrane to form "platforms" that facilitate the viral intake into the cell. Impairing the ASMase activity will eventually disrupt viral entry into the cell. In this review, we identified the metabolism of sphingolipids, sphingolipids' role in cell signal transduction cascades, and viral infection mechanisms. Also, we outlined ASMase structure and underlying mechanisms inhibiting viral entry 40 with the aid of inhibitors of acid sphingomyelinase (FIASMAs). In silico molecular docking analyses of FIASMAs with inhibitors revealed that dilazep (S = - 12.58 kcal/mol), emetine (S = - 11.65 kcal/mol), pimozide (S = - 11.29 kcal/mol), carvedilol (S = - 11.28 kcal/mol), mebeverine (S = - 11.14 kcal/mol), cepharanthine (S = - 11.06 kcal/mol), hydroxyzin (S = - 10.96 kcal/mol), astemizole (S = - 10.81 kcal/mol), sertindole (S = - 10.55 kcal/mol), and bepridil (S = - 10.47 kcal/mol) have higher inhibition activity than the candidate drug amiodarone (S = - 10.43 kcal/mol), making them better options for inhibition.

Structurally-informed human interactome reveals proteome-wide perturbations by disease mutations

Human genome sequencing studies have identified numerous loci associated with complex diseases. However, translating human genetic and genomic findings to disease pathobiology and therapeutic discovery remains a major challenge at multiscale interactome network levels. Here, we present a deep-learning-based ensemble framework, termed PIONEER (Protein-protein InteractiOn iNtErfacE pRediction), that accurately predicts protein binding partner-specific interfaces for all known protein interactions in humans and seven other common model organisms, generating comprehensive structurally-informed protein interactomes. We demonstrate that PIONEER outperforms existing state-of-the-art methods. We further systematically validated PIONEER predictions experimentally through generating 2,395 mutations and testing their impact on 6,754 mutation-interaction pairs, confirming the high quality and validity of PIONEER predictions. We show that disease-associated mutations are enriched in PIONEER-predicted protein-protein interfaces after mapping mutations from ~60,000 germline exomes and ~36,000 somatic genomes. We identify 586 significant protein-protein interactions (PPIs) enriched with PIONEER-predicted interface somatic mutations (termed oncoPPIs) from pan-cancer analysis of ~11,000 tumor whole-exomes across 33 cancer types. We show that PIONEER-predicted oncoPPIs are significantly associated with patient survival and drug responses from both cancer cell lines and patient-derived xenograft mouse models. We identify a landscape of PPI-perturbing tumor alleles upon ubiquitination by E3 ligases, and we experimentally validate the tumorigenic KEAP1-NRF2 interface mutation p.Thr80Lys in non-small cell lung cancer. We show that PIONEER-predicted PPI-perturbing alleles alter protein abundance and correlates with drug responses and patient survival in colon and uterine cancers as demonstrated by proteogenomic data from the National Cancer Institute's Clinical Proteomic Tumor Analysis Consortium. PIONEER, implemented as both a web server platform and a software package, identifies functional consequences of disease-associated alleles and offers a deep learning tool for precision medicine at multiscale interactome network levels.

Transcriptome-based analysis of human peripheral blood reveals regulators of immune response in different viral infections

Introduction

There are difficulties in creating direct antiviral drugs for all viruses, including new, suddenly arising infections, such as COVID-19. Therefore, pathogenesis-directed therapy is often necessary to treat severe viral infections and comorbidities associated with them. Despite significant differences in the etiopathogenesis of viral diseases, in general, they are associated with significant dysfunction of the immune system. Study of common mechanisms of immune dysfunction caused by different viral infections can help develop novel therapeutic strategies to combat infections and associated comorbidities.

Methods

To identify common mechanisms of immune functions disruption during infection by nine different viruses (cytomegalovirus, Ebstein-Barr virus, human T-cell leukemia virus type 1, Hepatitis B and C viruses, human immunodeficiency virus, Dengue virus, SARS-CoV, and SARS-CoV-2), we analyzed the corresponding transcription profiles from peripheral blood mononuclear cells (PBMC) using the originally developed pipeline that include transcriptome data collection, processing, normalization, analysis and search for master regulators of several viral infections. The ten datasets containing transcription data from patients infected by nine viruses and healthy people were obtained from Gene Expression Omnibus. The analysis of the data was performed by Genome Enhancer pipeline.

Results

We revealed common pathways, cellular processes, and master regulators for studied viral infections. We found that all nine viral infections cause immune activation, exhaustion, cell proliferation disruption, and increased susceptibility to apoptosis. Using network analysis, we identified PBMC receptors, representing proteins at the top of signaling pathways that may be responsible for the observed transcriptional changes and maintain the current functional state of cells.

Discussion

The identified relationships between some of them and virus-induced alteration of immune functions are new and have not been found earlier, e.g., receptors for autocrine motility factor, insulin, prolactin, angiotensin II, and immunoglobulin epsilon. Modulation of the identified receptors can be investigated as one of therapeutic strategies for the treatment of severe viral infections.

Safety and efficacy of melatonin as an adjuvant therapy in COVID-19 patients: Systematic review and meta-analysis

BACKGROUND

Melatonin might be beneficial to coronavirus disease 2019 (COVID-19) patients in terms of both prevention and treatment. We investigated how melatonin affected various clinical and laboratory results in COVID-19 patients.

METHODS

PubMed, Scopus, Cochrane Library and Web of Science databases were utilized for searching eligible articles fulfilling our inclusion criteria up to December 2022. We used random effect model in case of significant heterogeneity; in other cases, a fixed model was applied. RevMan was used for meta-analysis.

RESULTS

We included 11 studies in our review. Clinical improvement rate was found to be statistically significantly higher in patients taking melatonin than in the control group (OR: 5.09; 95% CI: 2.60-9.96, p ​< ​0.001). Patients receiving melatonin showed a non-significant difference in mortality rate compared to the control group (OR: 0.37; 95% CI: 0.07-1.81, p ​= ​0.22). However, in the randomized controlled trials subgroup, melatonin-treated patients showed significantly lower mortality than did the controls (OR: 0.17; 95% CI: 0.08-0.38, p ​< ​0.001). CRP level was statistically significantly lower due to melatonin treatment (weighted mean difference [WMD] ​= ​-9.85; 95% CI: -18.54 to -1.16, p ​= ​0.03). Length of hospital stay was statistically significantly shorter in patients taking melatonin compared to controls (WMD ​= ​-4.05; 95% CI: -5.39 to -2.7, p ​< ​0.001).

CONCLUSION

Melatonin was found to have substantial effects on COVID-19 patients when used as adjuvant therapy, enhancing clinical improvement and decreasing time to recovery with a shorter length of hospital stay and a shorter duration of mechanical ventilation.

COVID-19 Metabolomic-Guided Amino Acid Therapy Protects from Inflammation and Disease Sequelae

The outbreak of coronavirus disease 2019 (COVID-19) has caused a worldwide pandemic since 2019. A metabolic disorder is a contributing factor to deaths from COVID-19. However, the underlying mechanism of metabolic dysfunction in COVID-19 patients and the potential interventions are not elucidated. Here targeted plasma metabolomic is performed, and the metabolite profiles among healthy controls, and asymptomatic, moderate, and severe COVID-19 patients are compared. Among the altered metabolites, arachidonic acid and linolenic acid pathway metabolites are profoundly up-regulated in COVID-19 patients. Arginine biosynthesis, alanine, aspartate, and glutamate metabolism pathways are significantly disturbed in asymptomatic patients. In the comparison of metabolite variances among the groups, higher levels of l-citrulline and l-glutamine are found in asymptomatic carriers and moderate or severe patients at the remission stage. Furthermore, l-citrulline and l-glutamine combination therapy is demonstrated to effectively protect mice from coronavirus infection and endotoxin-induced sepsis, and is observed to efficiently prevent the occurrence of pulmonary fibrosis and central nervous system damage. Collectively, the data reveal the metabolite profile of asymptomatic COVID-19 patients and propose a potential strategy for COVID-19 treatment.

Repurposed drug studies on the primary prevention of SARS-CoV-2 infection during the pandemic: systematic review and meta-analysis

OBJECTIVE

Current evidence on the effectiveness of SARS-CoV-2 prophylaxis is inconclusive. We aimed to systematically evaluate published studies on repurposed drugs for the prevention of laboratory-confirmed SARS-CoV-2 infection and/or COVID-19 among healthy adults.

DESIGN

Systematic review.

ELIGIBILITY

Quantitative experimental and observational intervention studies that evaluated the effectiveness of repurposed drugs for the primary prevention of SARS-CoV-2 infection and/or COVID-19 disease.

DATA SOURCE

PubMed and Embase (1 January 2020-28 September 2022).

RISK OF BIAS

Cochrane Risk of Bias 2.0 and Risk of Bias in Non-Randomised Studies of Interventions tools were applied to assess the quality of studies.

DATA ANALYSIS

Meta-analyses for each eligible drug were performed if ≥2 similar study designs were available.

RESULTS

In all, 65 (25 trials, 40 observational) and 29 publications were eligible for review and meta-analyses, respectively. Most studies pertained to hydroxychloroquine (32), ACE inhibitor (ACEi) or angiotensin receptor blocker (ARB) (11), statin (8), and ivermectin (8). In trials, hydroxychloroquine prophylaxis reduced laboratory-confirmed SARS-CoV-2 infection (risk ratio: 0.82 (95% CI 0.74 to 0.90), I2=48%), a result largely driven by one clinical trial (weight: 60.5%). Such beneficial effects were not observed in observational studies, nor for prognostic clinical outcomes. Ivermectin did not significantly reduce the risk of SARS-CoV-2 infection (RR: 0.35 (95% CI 0.10 to 1.26), I2=96%) and findings for clinical outcomes were inconsistent. Neither ACEi or ARB were beneficial in reducing SARS-CoV-2 infection. Most of the evidence from clinical trials was of moderate quality and of lower quality in observational studies.

CONCLUSIONS

Results from our analysis are insufficient to support an evidence-based repurposed drug policy for SARS-CoV-2 prophylaxis because of inconsistency. In the view of scarce supportive evidence on repurposing drugs for COVID-19, alternative strategies such as immunisation of vulnerable people are warranted to prevent the future waves of infection.

PROSPERO REGISTRATION NUMBER

CRD42021292797.

Biomedical knowledge graph learning for drug repurposing by extending guilt-by-association to multiple layers

Computational drug repurposing aims to identify new indications for existing drugs by utilizing high-throughput data, often in the form of biomedical knowledge graphs. However, learning on biomedical knowledge graphs can be challenging due to the dominance of genes and a small number of drug and disease entities, resulting in less effective representations. To overcome this challenge, we propose a "semantic multi-layer guilt-by-association" approach that leverages the principle of guilt-by-association - "similar genes share similar functions", at the drug-gene-disease level. Using this approach, our model DREAMwalk: Drug Repurposing through Exploring Associations using Multi-layer random walk uses our semantic information-guided random walk to generate drug and disease-populated node sequences, allowing for effective mapping of both drugs and diseases in a unified embedding space. Compared to state-of-the-art link prediction models, our approach improves drug-disease association prediction accuracy by up to 16.8%. Moreover, exploration of the embedding space reveals a well-aligned harmony between biological and semantic contexts. We demonstrate the effectiveness of our approach through repurposing case studies for breast carcinoma and Alzheimer's disease, highlighting the potential of multi-layer guilt-by-association perspective for drug repurposing on biomedical knowledge graphs.

Cynomolgus-rhesus hybrid macaques serve as a platform for imprinting studies

Genomic imprinting can lead to allele-specific expression (ASE), where one allele is preferentially expressed more than the other. Perturbations in genomic imprinting or ASE genes have been widely observed across various neurological disorders, notably autism spectrum disorder (ASD). In this study, we crossed rhesus cynomolgus monkeys to produce hybrid monkeys and established a framework to evaluate their allele-specific gene expression patterns using the parental genomes as a reference. Our proof-of-concept analysis of the hybrid monkeys identified 353 genes with allele-biased expression in the brain, enabling us to determine the chromosomal locations of ASE clusters. Importantly, we confirmed a significant enrichment of ASE genes associated with neuropsychiatric disorders, including ASD, highlighting the potential of hybrid monkey models in advancing our understanding of genomic imprinting.

Repurposing Drugs for the Treatment of COVID-19 and Its Cardiovascular Manifestations

COVID-19 is an infectious disease caused by SARS-CoV-2 leading to the ongoing global pandemic. Infected patients developed a range of respiratory symptoms, including respiratory failure, as well as other extrapulmonary complications. Multiple comorbidities, including hypertension, diabetes, cardiovascular diseases, and chronic kidney diseases, are associated with the severity and increased mortality of COVID-19. SARS-CoV-2 infection also causes a range of cardiovascular complications, including myocarditis, myocardial injury, heart failure, arrhythmias, acute coronary syndrome, and venous thromboembolism. Although a variety of methods have been developed and many clinical trials have been launched for drug repositioning for COVID-19, treatments that consider cardiovascular manifestations and cardiovascular disease comorbidities specifically are limited. In this review, we summarize recent advances in drug repositioning for COVID-19, including experimental drug repositioning, high-throughput drug screening, omics data-based, and network medicine-based computational drug repositioning, with particular attention on those drug treatments that consider cardiovascular manifestations of COVID-19. We discuss prospective opportunities and potential methods for repurposing drugs to treat cardiovascular complications of COVID-19.

Target-agnostic drug prediction integrated with medical record analysis uncovers differential associations of statins with increased survival in COVID-19 patients

Drug repurposing requires distinguishing established drug class targets from novel molecule-specific mechanisms and rapidly derisking their therapeutic potential in a time-critical manner, particularly in a pandemic scenario. In response to the challenge to rapidly identify treatment options for COVID-19, several studies reported that statins, as a drug class, reduce mortality in these patients. However, it is unknown if different statins exhibit consistent function or may have varying therapeutic benefit. A Bayesian network tool was used to predict drugs that shift the host transcriptomic response to SARS-CoV-2 infection towards a healthy state. Drugs were predicted using 14 RNA-sequencing datasets from 72 autopsy tissues and 465 COVID-19 patient samples or from cultured human cells and organoids infected with SARS-CoV-2. Top drug predictions included statins, which were then assessed using electronic medical records containing over 4,000 COVID-19 patients on statins to determine mortality risk in patients prescribed specific statins versus untreated matched controls. The same drugs were tested in Vero E6 cells infected with SARS-CoV-2 and human endothelial cells infected with a related OC43 coronavirus. Simvastatin was among the most highly predicted compounds (14/14 datasets) and five other statins, including atorvastatin, were predicted to be active in > 50% of analyses. Analysis of the clinical database revealed that reduced mortality risk was only observed in COVID-19 patients prescribed a subset of statins, including simvastatin and atorvastatin. In vitro testing of SARS-CoV-2 infected cells revealed simvastatin to be a potent direct inhibitor whereas most other statins were less effective. Simvastatin also inhibited OC43 infection and reduced cytokine production in endothelial cells. Statins may differ in their ability to sustain the lives of COVID-19 patients despite having a shared drug target and lipid-modifying mechanism of action. These findings highlight the value of target-agnostic drug prediction coupled with patient databases to identify and clinically evaluate non-obvious mechanisms and derisk and accelerate drug repurposing opportunities.

Shared molecular signatures between coronavirus infection and neurodegenerative diseases provide targets for broad-spectrum drug development

Growing evidences have suggested the association between coronavirus infection and neurodegenerative diseases. However, the molecular mechanism behind the association is complex and remains to be clarified. This study integrated human genes involved in infections of three coronaviruses including SARS-CoV-2, SARS-CoV and MERS-CoV from multi-omics data, and investigated the shared genes and molecular functions between coronavirus infection and two neurodegenerative diseases, namely Alzheimer's Disease (AD) and Parkinson's Disease (PD). Seven genes including HSP90AA1, ALDH2, CAV1, COMT, MTOR, IGF2R and HSPA1A, and several inflammation and stress response-related molecular functions such as MAPK signaling pathway, NF-kappa B signaling pathway, responses to oxidative or chemical stress were common to both coronavirus infection and neurodegenerative diseases. These genes were further found to interact with more than 20 other viruses. Finally, drugs targeting these genes were identified. The study would not only help clarify the molecular mechanism behind the association between coronavirus infection and neurodegenerative diseases, but also provide novel targets for the development of broad-spectrum drugs against both coronaviruses and neurodegenerative diseases.

Nitric oxide mediated hypoxia dynamics in COVID-19

Several COVID-19 patients frequently experience with happy hypoxia. Sometimes, the level of nitric oxide (NO) in COVID-19 patients was found to be greater than in non-COVID-19 hypoxemics and most of the cases lower. Induced or inhaled NO has a long history of usage as a therapy for hypoxemia. Excessive production of ROS and oxidative stress lower the NO level and stimulates mitochondrial malfunction is the primary cause of hypoxia-mediated mortality in COVID-19. Higher level of NO in mitochondria also the cause of dysfunction, because, excess NO can also diffuse quickly into mitochondria or through mitochondrial nitric oxide synthase (NOS). A precise dose of NO may increase oxygenation while also acting as an effective inhibitor of cytokine storm. NOS inhibitors may be used in conjunction with iNO therapy to compensate for the patient's optimal NO level. NO play a key role in COVID-19 happy hypoxia and a crucial component in the COVID-19 pathogenesis that demands a reliable and easily accessible biomarker to monitor.

Network modeling helps to tackle the complexity of drug-disease systems

From the (patho)physiological point of view, diseases can be considered as emergent properties of living systems stemming from the complexity of these systems. Complex systems display some typical features, including the presence of emergent behavior and the organization in successive hierarchic levels. Drug treatments increase this complexity scenario, and from some years the use of network models has been introduced to describe drug-disease systems and to make predictions about them with regard to several aspects related to drug discovery. Here, we review some recent examples thereof with the aim to illustrate how network science tools can be very effective in addressing both tasks. We will examine the use of bipartite networks that lead to the important concept of "disease module", as well as the introduction of more articulated models, like multi-scale and multiplex networks, able to describe disease systems at increasing levels of organization. Examples of predictive models will then be discussed, considering both those that exploit approaches purely based on graph theory and those that integrate machine learning methods. A short account of both kinds of methodological applications will be provided. Finally, the point will be made on the present situation of modeling complex drug-disease systems highlighting some open issues. This article is categorized under: Neurological Diseases > Computational Models Infectious Diseases > Computational Models Cardiovascular Diseases > Computational Models.

Network Biology and Medicine to Rescue: Applications for Retinal Disease Mechanisms and Therapy

Inherited retinal degenerations (IRDs) are clinically and genetically heterogenous blinding diseases that manifest through dysfunction of target cells, photoreceptors, and retinal pigment epithelium (RPE) in the retina. Despite knowledge of numerous underlying genetic defects, current therapeutic approaches, including gene centric applications, have had limited success, thereby asserting the need of new directions for basic and translational research. Human diseases have commonalities that can be represented in a network form, called diseasome, which captures relationships among disease genes, proteins, metabolites, and patient meta-data. Clinical and genetic information of IRDs suggest shared relationships among pathobiological factors, making these a model case for network medicine. Characterization of the diseasome would considerably improve our understanding of retinal pathologies and permit better design of targeted therapies for disrupted regions within the integrated disease network. Network medicine in synergy with the ongoing artificial intelligence revolution can boost therapeutic developments, especially gene agnostic treatment opportunities.

Drug screening and biomarker gene investigation in cancer therapy through the human transcriptional regulatory network

A complex and vast biological network regulates all biological functions in the human body in a sophisticated manner, and abnormalities in this network can lead to disease and even cancer. The construction of a high-quality human molecular interaction network is possible with the development of experimental techniques that facilitate the interpretation of the mechanisms of drug treatment for cancer. We collected 11 molecular interaction databases based on experimental sources and constructed a human protein-protein interaction (PPI) network and a human transcriptional regulatory network (HTRN). A random walk-based graph embedding method was used to calculate the diffusion profiles of drugs and cancers, and a pipeline was constructed by using five similarity comparison metrics combined with a rank aggregation algorithm, which can be implemented for drug screening and biomarker gene prediction. Taking NSCLC as an example, curcumin was identified as a potentially promising anticancer drug from 5450 natural small molecules, and combined with differentially expressed genes, survival analysis, and topological ranking, we obtained BIRC5 (survivin), which is both a biomarker for NSCLC and a key target for curcumin. Finally, the binding mode of curcumin and survivin was explored using molecular docking. This work has a guiding significance for antitumor drug screening and the identification of tumor markers.

A comprehensive SARS-CoV-2-human protein-protein interactome reveals COVID-19 pathobiology and potential host therapeutic targets

Studying viral-host protein-protein interactions can facilitate the discovery of therapies for viral infection. We use high-throughput yeast two-hybrid experiments and mass spectrometry to generate a comprehensive SARS-CoV-2-human protein-protein interactome network consisting of 739 high-confidence binary and co-complex interactions, validating 218 known SARS-CoV-2 host factors and revealing 361 novel ones. Our results show the highest overlap of interaction partners between published datasets and of genes differentially expressed in samples from COVID-19 patients. We identify an interaction between the viral protein ORF3a and the human transcription factor ZNF579, illustrating a direct viral impact on host transcription. We perform network-based screens of >2,900 FDA-approved or investigational drugs and identify 23 with significant network proximity to SARS-CoV-2 host factors. One of these drugs, carvedilol, shows clinical benefits for COVID-19 patients in an electronic health records analysis and antiviral properties in a human lung cell line infected with SARS-CoV-2. Our study demonstrates the value of network systems biology to understand human-virus interactions and provides hits for further research on COVID-19 therapeutics.

Knowledge Mapping of Drug Repositioning's Theme and Development

Background

In recent years, the emergence of new diseases and resistance to known diseases have led to increasing demand for new drugs. By means of bibliometric analysis, this paper studied the relevant articles on drug repositioning in recent years and analyzed the current research foci and trends.

Methodology

The Web of Science database was searched to collect all relevant literature on drug repositioning from 2001 to 2022. These data were imported into CiteSpace and bibliometric online analysis platforms for bibliometric analysis. The processed data and visualized images predict the development trends in the research field.

Results

The quality and quantity of articles published after 2011 have improved significantly, with 45 of them cited more than 100 times. Articles posted by journals from different countries have high citation values. Authors from other institutions have also collaborated to analyze drug rediscovery. Keywords found in the literature include molecular docking (N=223), virtual screening (N=170), drug discovery (N=126), machine learning (N=125), and drug-target interaction (N=68); these words represent the core content of drug repositioning.

Conclusion

The key focus of drug research and development is related to the discovery of new indications for drugs. Researchers are starting to retarget drugs after analyzing online databases and clinical trials. More and more drugs are being targeted at other diseases to treat more patients, based on saving money and time. It is worth noting that researchers need more financial and technical support to complete drug development.

Deep generative molecular design reshapes drug discovery

Recent advances and accomplishments of artificial intelligence (AI) and deep generative models have established their usefulness in medicinal applications, especially in drug discovery and development. To correctly apply AI, the developer and user face questions such as which protocols to consider, which factors to scrutinize, and how the deep generative models can integrate the relevant disciplines. This review summarizes classical and newly developed AI approaches, providing an updated and accessible guide to the broad computational drug discovery and development community. We introduce deep generative models from different standpoints and describe the theoretical frameworks for representing chemical and biological structures and their applications. We discuss the data and technical challenges and highlight future directions of multimodal deep generative models for accelerating drug discovery.

Systematic comparison of differential expression networks in MTB mono-, HIV mono- and MTB/HIV co-infections for drug repurposing

The synergy between human immunodeficiency virus (HIV) and Mycobacterium tuberculosis (MTB) could accelerate the deterioration of immunological functions. Previous studies have explored the pathogenic mechanisms of HIV mono-infection (HMI), MTB mono-infection (MMI) and MTB/HIV co-infection (MHCI), but their similarities and specificities remain to be profoundly investigated. We thus designed a computational framework named IDEN to identify gene pairs related to these states, which were then compared from different perspectives. MMI-related genes showed the highest enrichment level on a greater number of chromosomes. Genes shared by more states tended to be more evolutionarily conserved, posttranslationally modified and topologically important. At the expression level, HMI-specific gene pairs yielded higher correlations, while the overlapping pairs involved in MHCI had significantly lower correlations. The correlation changes of common gene pairs showed that MHCI shared more similarities with MMI. Moreover, MMI- and MHCI-related genes were enriched in more identical pathways and biological processes, further illustrating that MTB may play a dominant role in co-infection. Hub genes specific to each state could promote pathogen infections, while those shared by two states could enhance immune responses. Finally, we improved the network proximity measure for drug repurposing by considering the importance of gene pairs, and approximately ten drug candidates were identified for each disease state.

Comprehensive Analysis of Ubiquitome Changes in Nicotiana benthamiana after Rice Stripe Virus Infection

Rice stripe virus (RSV) is one of the most devastating viruses affecting rice production. During virus infection, ubiquitination plays an important role in the dynamic regulation of host defenses. We combined the ubiquitomics approach with the label-free quantitation proteomics approach to investigate potential ubiquitination status changes of Nicotiana benthamiana infected with RSV. Bioinformatics analyses were performed to elucidate potential associations between proteins with differentially ubiquitinated sites (DUSs) and various cellular components/pathways during virus infection. In total, 399 DUSs in 313 proteins were identified and quantified, among them 244 ubiquitinated lysine (Kub) sites in 186 proteins were up-regulated and 155 Kub sites in 127 proteins were down-regulated at 10 days after RSV infection. Gene Ontology and Kyoto Encyclopedia of Genes and Genomes pathway enrichment analyses indicated that proteins with up-regulated Kub sites were significantly enriched in the ribosome. Silencing of 3-isopropylmalate dehydratase large subunit through virus-induced gene silencing delayed RSV infection, while silencing of mRNA-decapping enzyme-like protein promoted RSV symptom in the late stage of infection. Moreover, ubiquitination was observed in all seven RSV-encoded proteins. Our study supplied the comprehensive analysis of the ubiquitination changes in N. benthamiana after RSV infection, which is helpful for understanding RSV pathogenesis and RSV-host interactions.

Recent computational drug repositioning strategies against SARS-CoV-2

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We performed a comprehensive review of computational drug repositioning methods applied to COVID-19 based on differing data types including sequence data, expression data, structure data and interaction data.

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We found that graph theory and neural network were the most used strategies for drug repositioning in the case of COVID-19.

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Integrating different levels of data may improve the success rate for drug repositioning.

Since COVID-19 emerged in 2019, significant levels of suffering and disruption have been caused on a global scale. Although vaccines have become widely used, the virus has shown its potential for evading immunities or acquiring other novel characteristics. Whether current drug treatments are still effective for people infected with Omicron remains unclear. Due to the long development cycles and high expense requirements of de novo drug development, many researchers have turned to consider drug repositioning in the search to find effective treatments for COVID-19. Here, we review such drug repositioning and combination efforts towards providing better handling. For potential drugs under consideration, aspects of both structure and function require attention, with specific categories of sequence, expression, structure, and interaction, the key parameters for investigation. For different data types, we show the corresponding differing drug repositioning methods that have been exploited. As incorporating drug combinations can increase therapeutic efficacy and reduce toxicity, we also review computational strategies to reveal drug combination potential. Taken together, we found that graph theory and neural network were the most used strategy with high potential towards drug repositioning for COVID-19. Integrating different levels of data may further improve the success rate of drug repositioning.

Rational drug repositioning for coronavirus-associated diseases using directional mapping and side-effect inference

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), the pathogen of coronavirus disease 2019 (COVID-19), has infected hundreds of millions of people and caused millions of deaths. Looking for valid druggable targets with minimal side effects for the treatment of COVID-19 remains critical. After discovering host genes from multiscale omics data, we developed an end-to-end network method to investigate drug-host gene(s)-coronavirus (CoV) paths and the mechanism of action between the drug and the host factor in a directional network. We also inspected the potential side effect of the candidate drug on several common comorbidities. We established a catalog of host genes associated with three CoVs. Rule-based prioritization yielded 29 Food and Drug Administration (FDA)-approved drugs via accounting for the effects of drugs on CoVs, comorbidities, and drug-target confidence information. Seven drugs are currently undergoing clinical trials as COVID-19 treatment. This catalog of druggable host genes associated with CoVs and the prioritized repurposed drugs will provide a new sight in therapeutics discovery for severe COVID-19 patients.

Antioxidant, Anti-inflammatory, and Immunomodulatory Roles of Nonvitamin Antioxidants in Anti-SARS-CoV-2 Therapy

Viral pathologies encompass activation of pro-oxidative pathways and inflammatory burst. Alleviating overproduction of reactive oxygen species and cytokine storm in COVID-19 is essential to counteract the immunogenic damage in endothelium and alveolar membranes. Antioxidants alleviate oxidative stress, cytokine storm, hyperinflammation, and diminish the risk of organ failure. Direct antiviral roles imply: impact on viral spike protein, interference with the ACE2 receptor, inhibition of dipeptidyl peptidase 4, transmembrane protease serine 2 or furin, and impact on of helicase, papain-like protease, 3-chyomotrypsin like protease, and RNA-dependent RNA polymerase. Prooxidative environment favors conformational changes in the receptor binding domain, promoting the affinity of the spike protein for the host receptor. Viral pathologies imply a vicious cycle, oxidative stress promoting inflammatory responses, and vice versa. The same was noticed with respect to the relationship antioxidant impairment-viral replication. Timing, dosage, pro-oxidative activities, mutual influences, and interference with other antioxidants should be carefully regarded. Deficiency is linked to illness severity.

Transcriptomics-based network medicine approach identifies metformin as a repurposable drug for atrial fibrillation

Effective drugs for atrial fibrillation (AF) are lacking, resulting in significant morbidity and mortality. This study demonstrates that network proximity analysis of differentially expressed genes from atrial tissue to drug targets can help prioritize repurposed drugs for AF. Using enrichment analysis of drug-gene signatures and functional testing in human inducible pluripotent stem cell (iPSC)-derived atrial-like cardiomyocytes, we identify metformin as a top repurposed drug candidate for AF. Using the active compactor, a new design analysis of large-scale longitudinal electronic health record (EHR) data, we determine that metformin use is significantly associated with a reduced risk of AF (odds ratio = 0.48, 95%, confidence interval [CI] 0.36-0.64, p < 0.001) compared with standard treatments for diabetes. This study utilizes network medicine methodologies to identify repurposed drugs for AF treatment and identifies metformin as a candidate drug.

Gene networks under circadian control exhibit diurnal organization in primate organs

Mammalian organs are individually controlled by autonomous circadian clocks. At the molecular level, this process is defined by the cyclical co-expression of both core transcription factors and their downstream targets across time. While interactions between these molecular clocks are necessary for proper homeostasis, these features remain undefined. Here, we utilize integrative analysis of a baboon diurnal transcriptome atlas to characterize the properties of gene networks under circadian control. We found that 53.4% (8120) of baboon genes are oscillating body-wide. Additionally, two basic network modes were observed at the systems level: daytime and nighttime mode. Daytime networks were enriched for genes involved in metabolism, while nighttime networks were enriched for genes associated with growth and cellular signaling. A substantial number of diseases only form significant disease modules at either daytime or nighttime. In addition, a majority of SARS-CoV-2-related genes and modules are rhythmically expressed, which have significant network proximities with circadian regulators. Our data suggest that synchronization amongst circadian gene networks is necessary for proper homeostatic functions and circadian regulators have close interactions with SARS-CoV-2 infection.

Integrative analysis of the high-resolution baboon diurnal transcriptome, provides insights into the effect of circadian rhythm on the whole-body primate gene network.

Target-agnostic drug prediction integrated with medical record analysis uncovers differential associations of statins with increased survival in COVID-19 patients

Importance

Drug repurposing requires distinguishing established drug class targets from novel molecule-specific mechanisms and rapidly derisking their therapeutic potential in a time-critical manner, particularly in a pandemic scenario. In response to the challenge to rapidly identify treatment options for COVID-19, several studies reported that statins, as a drug class, reduce mortality in these patients. However, it is unknown if different statins exhibit consistent function or may have varying therapeutic benefit.

Objectives

To test if different statins differ in their ability to exert protective effects based on molecular computational predictions and electronic medical record analysis.

Main Outcomes and Measures

A Bayesian network tool was used to predict drugs that shift the host transcriptomic response to SARS-CoV-2 infection towards a healthy state. Drugs were predicted using 14 RNA-sequencing datasets from 72 autopsy tissues and 465 COVID-19 patient samples or from cultured human cells and organoids infected with SARS-CoV-2, with a total of 2,436 drugs investigated. Top drug predictions included statins, which were then assessed using electronic medical records containing over 4,000 COVID-19 patients on statins to determine mortality risk in patients prescribed specific statins versus untreated matched controls. The same drugs were tested in Vero E6 cells infected with SARS-CoV-2 and human endothelial cells infected with a related OC43 coronavirus.

Results

Simvastatin was among the most highly predicted compounds (14/14 datasets) and five other statins, including atorvastatin, were predicted to be active in > 50% of analyses. Analysis of the clinical database revealed that reduced mortality risk was only observed in COVID-19 patients prescribed a subset of statins, including simvastatin and atorvastatin. In vitro testing of SARS-CoV-2 infected cells revealed simvastatin to be a potent direct inhibitor whereas most other statins were less effective. Simvastatin also inhibited OC43 infection and reduced cytokine production in endothelial cells.

Conclusions and Relevance

Different statins may differ in their ability to sustain the lives of COVID-19 patients despite having a shared drug target and lipid-modifying mechanism of action. These findings highlight the value of target-agnostic drug prediction coupled with patient databases to identify and clinically evaluate non-obvious mechanisms and derisk and accelerate drug repurposing opportunities.

A Review of the Potential Effects of Melatonin in Compromised Mitochondrial Redox Activities in Elderly Patients With COVID-19

Melatonin, an endogenous indoleamine, is an antioxidant and anti-inflammatory molecule widely distributed in the body. It efficiently regulates pro-inflammatory and anti-inflammatory cytokines under various pathophysiological conditions. The melatonin rhythm, which is strongly associated with oxidative lesions and mitochondrial dysfunction, is also observed during the biological process of aging. Melatonin levels decline considerably with age and are related to numerous age-related illnesses. The signs of aging, including immune aging, increased basal inflammation, mitochondrial dysfunction, significant telomeric abrasion, and disrupted autophagy, contribute to the increased severity of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection. These characteristics can worsen the pathophysiological response of the elderly to SARS-CoV-2 and pose an additional risk of accelerating biological aging even after recovery. This review explains that the death rate of coronavirus disease (COVID-19) increases with chronic diseases and age, and the decline in melatonin levels, which is closely related to the mitochondrial dysfunction in the patient, affects the virus-related death rate. Further, melatonin can enhance mitochondrial function and limit virus-related diseases. Hence, melatonin supplementation in older people may be beneficial for the treatment of COVID-19.

Network approaches for modeling the effect of drugs and diseases

The network approach is quickly becoming a fundamental building block of computational methods aiming at elucidating the mechanism of action (MoA) and therapeutic effect of drugs. By modeling the effect of drugs and diseases on different biological networks, it is possible to better explain the interplay between disease perturbations and drug targets as well as how drug compounds induce favorable biological responses and/or adverse effects. Omics technologies have been extensively used to generate the data needed to study the mechanisms of action of drugs and diseases. These data are often exploited to define condition-specific networks and to study whether drugs can reverse disease perturbations. In this review, we describe network data mining algorithms that are commonly used to study drug’s MoA and to improve our understanding of the basis of chronic diseases. These methods can support fundamental stages of the drug development process, including the identification of putative drug targets, the in silico screening of drug compounds and drug combinations for the treatment of diseases. We also discuss recent studies using biological and omics-driven networks to search for possible repurposed FDA-approved drug treatments for SARS-CoV-2 infections (COVID-19).

Computational drug repurposing based on electronic health records: a scoping review

Computational drug repurposing methods adapt Artificial intelligence (AI) algorithms for the discovery of new applications of approved or investigational drugs. Among the heterogeneous datasets, electronic health records (EHRs) datasets provide rich longitudinal and pathophysiological data that facilitate the generation and validation of drug repurposing. Here, we present an appraisal of recently published research on computational drug repurposing utilizing the EHR. Thirty-three research articles, retrieved from Embase, Medline, Scopus, and Web of Science between January 2000 and January 2022, were included in the final review. Four themes, (1) publication venue, (2) data types and sources, (3) method for data processing and prediction, and (4) targeted disease, validation, and released tools were presented. The review summarized the contribution of EHR used in drug repurposing as well as revealed that the utilization is hindered by the validation, accessibility, and understanding of EHRs. These findings can support researchers in the utilization of medical data resources and the development of computational methods for drug repurposing.

Melatonin drugs inhibit SARS-CoV-2 entry into the brain and virus-induced damage of cerebral small vessels

COVID-19 is a complex disease with short- and long-term respiratory, inflammatory and neurological symptoms that are triggered by the infection with SARS-CoV-2. Invasion of the brain by SARS-CoV-2 has been observed in humans and is postulated to be involved in post-COVID state. Brain infection is particularly pronounced in the K18-hACE2 mouse model of COVID-19. Prevention of brain infection in the acute phase of the disease might thus be of therapeutic relevance to prevent long-lasting symptoms of COVID-19. We previously showed that melatonin or two prescribed structural analogs, agomelatine and ramelteon delay the onset of severe clinical symptoms and improve survival of SARS-CoV-2-infected K18-hACE2 mice. Here, we show that treatment of K18-hACE2 mice with melatonin and two melatonin-derived marketed drugs, agomelatine and ramelteon, prevents SARS-CoV-2 entry in the brain, thereby reducing virus-induced damage of small cerebral vessels, immune cell infiltration and brain inflammation. Molecular modeling analyses complemented by experimental studies in cells showed that SARS-CoV-2 entry in endothelial cells is prevented by melatonin binding to an allosteric-binding site on human angiotensin-converting enzyme 2 (ACE2), thus interfering with ACE2 function as an entry receptor for SARS-CoV-2. Our findings open new perspectives for the repurposing of melatonergic drugs and its clinically used analogs in the prevention of brain infection by SARS-CoV-2 and COVID-19-related long-term neurological symptoms.

A comprehensive SARS-CoV-2-human protein-protein interactome network identifies pathobiology and host-targeting therapies for COVID-19

Physical interactions between viral and host proteins are responsible for almost all aspects of the viral life cycle and the host's immune response. Studying viral-host protein-protein interactions is thus crucial for identifying strategies for treatment and prevention of viral infection. Here, we use high-throughput yeast two-hybrid and affinity purification followed by mass spectrometry to generate a comprehensive SARS-CoV-2-human protein-protein interactome network consisting of both binary and co-complex interactions. We report a total of 739 high-confidence interactions, showing the highest overlap of interaction partners among published datasets as well as the highest overlap with genes differentially expressed in samples (such as upper airway and bronchial epithelial cells) from patients with SARS-CoV-2 infection. Showcasing the utility of our network, we describe a novel interaction between the viral accessory protein ORF3a and the host zinc finger transcription factor ZNF579 to illustrate a SARS-CoV-2 factor mediating a direct impact on host transcription. Leveraging our interactome, we performed network-based drug screens for over 2,900 FDA-approved/investigational drugs and obtained a curated list of 23 drugs that had significant network proximities to SARS-CoV-2 host factors, one of which, carvedilol, showed promising antiviral properties. We performed electronic health record-based validation using two independent large-scale, longitudinal COVID-19 patient databases and found that carvedilol usage was associated with a significantly lowered probability (17%-20%, P < 0.001) of obtaining a SARS-CoV-2 positive test after adjusting various confounding factors. Carvedilol additionally showed anti-viral activity against SARS-CoV-2 in a human lung epithelial cell line [half maximal effective concentration (EC 50 ) value of 4.1 µM], suggesting a mechanism for its beneficial effect in COVID-19. Our study demonstrates the value of large-scale network systems biology approaches for extracting biological insight from complex biological processes.

Melatonin and multiple sclerosis: antioxidant, anti-inflammatory and immunomodulator mechanism of action

BACKGROUND

Melatonin is an indole hormone secreted primarily by the pineal gland that showing anti-oxidant, anti-inflammatory and anti-apoptotic capacity. It can play an important role in the pathophysiological mechanisms of various diseases. In this regard, different studies have shown that there is a relationship between Melatonin and Multiple Sclerosis (MS). MS is a chronic immune-mediated disease of the Central Nervous System.

AIM

The objective of this review was to evaluate the mechanisms of action of melatonin on oxidative stress, inflammation and intestinal dysbiosis caused by MS, as well as its interaction with different hormones and factors that can influence the pathophysiology of the disease.

RESULTS

Melatonin causes a significant increase in the levels of catalase, superoxide dismutase, glutathione peroxidase, glutathione and can counteract and inhibit the effects of the NLRP3 inflammasome, which would also be beneficial during SARS-CoV-2 infection. In addition, melatonin increases antimicrobial peptides, especially Reg3β, which could be useful in controlling the microbiota.

CONCLUSION

Melatonin could exert a beneficial effect in people suffering from MS, running as a promising candidate for the treatment of this disease. However, more research in human is needed to help understand the possible interaction between melatonin and certain sex hormones, such as estrogens, to know the potential therapeutic efficacy in both men and women.

A comprehensive review of Artificial Intelligence and Network based approaches to drug repurposing in Covid-19

Conventional drug discovery and development is tedious and time-taking process; because of which it has failed to keep the required pace to mitigate threats and cater demands of viral and re-occurring diseases, such as Covid-19. The main reasons of this delay in traditional drug development are: high attrition rates, extensive time requirements, and huge financial investment with significant risk. The effective solution to de novo drug discovery is drug repurposing. Previous studies have shown that the network-based approaches and analysis are versatile platform for repurposing as the network biology is used to model the interactions between variety of biological concepts. Herein, we provide a comprehensive background of machine learning and deep learning in drug repurposing while specifically focusing on the applications of network-based approach to drug repurposing in Covid-19, data sources, and tools used. Furthermore, use of network proximity, network diffusion, and AI on network-based drug repurposing for Covid-19 is well-explained. Finally, limitations of network-based approaches in general and specific to network are stated along with future recommendations for better network-based models.

The circadian rhythm of viruses and its implications on susceptibility to infection

INTRODUCTION

Circadian genes have an impact on multiple hormonal, metabolic, and immunological pathways and have recently been implicated in some infectious diseases.

AREAS COVERED

We review aspects related to the current knowledge about circadian rhythm and viral infections, their consequences, and the potential therapeutic options.

EXPERT OPINION

Expert opinion: In order to address a problem, it is necessary to know the topic in depth. Although in recent years there has been a growing interest in the role of circadian rhythms, many relevant questions remain to be resolved. Thus, the mechanisms linking the circadian machinery against viral infections are poorly understood. In a clear approach to personalized precision medicine, in order to treat a disease in the most appropriate phase of the circadian rhythm, and in order to achieve the optimal efficacy, it is highly recommended to carry out studies that improve the knowledge about the circadian rhythm.

Classification and severity progression measure of COVID-19 patients using pairs of multi-omic factors

Early detection and effective treatment of severe COVID-19 patients remain two major challenges during the current pandemic. Analysis of molecular changes in blood samples of severe patients is one of the promising approaches to this problem. From thousands of proteomic, metabolomic, lipidomic, and transcriptomic biomarkers selected in other research, we identify several pairs of biomarkers that after additional nonlinear spline transformation are highly effective in classifying and predicting severe COVID-19 cases. The performance of these pairs is evaluated in-sample, in a cross-validation exercise, and in an out-of-sample analysis on two independent datasets. We further improve our classifier by identifying complementary pairs using hierarchical clustering. In a result, we achieve 96-98% AUC on the validation data. Our findings can help medical experts to identify small groups of biomarkers that after nonlinear transformation can be used to construct a cost-effective test for patient screening and prediction of severity progression.

Modifiable Host Factors for the Prevention and Treatment of COVID-19: Diet and Lifestyle/Diet and Lifestyle Factors in the Prevention of COVID-19

Many studies have shown that the immune system requires adequate nutrition to work at an optimal level. Not only do optimized nutritional strategies support the immune system, but they also reduce chronic inflammation. Nutritional supplements that are recommended for patients with critical illnesses are thought to also be effective for the coronavirus disease 2019 (COVID-19) patients in the intensive care unit. Some studies have recommended fresh fruits and vegetables, soy, nuts, and antioxidants, such as omega-3 fatty acids, to improve immune system activity. Although nutritional status is considered to be an important prognostic factor for patients with COVID-19, there is to date no sufficient evidence that optimal nutritional therapies can be beneficial for these patients. Some have argued that the COVID-19 pandemic is a good opportunity to test the effectiveness of nutritional intervention for infectious diseases. Many researchers have suggested that testing the proposed nutritional approaches for infectious diseases in the context of a pandemic would be highly informative. The authors of other review papers concluded that it is important to have a diet based on fresh foods, such as fruits, vegetables, whole grains, low-fat dairy products, and healthy fats (i.e., olive oil and fish oil), and to limit the intake of sugary drinks as well as high-calorie and high-salt foods. In this review, we discuss the clinical significance of functional food ingredients as complementary therapies potentially beneficial for the prevention or treatment of COVID-19. We believe that our review will be helpful to plan and deploy future studies to conclude these potentials against COVID-19, but also to new infectious diseases that may arise in the future.

Basic mechanisms of SARS-CoV-2 infection. What endocrine systems could be implicated?

Although SARS-CoV-2 viral attacks starts by the interaction of spike protein (S Protein) to ACE2 receptor located at the cell surface of respiratory tract and digestive system cells, different endocrine targets, endocrine organs and metabolic conditions are of fundamental relevance for understanding disease progression and special outcomes, in particular those of fatal consequences for the patient. During pandemic, moreover, a specific phenotype of COVID-19 metabolic patient has been described, characterized by being at particular risk of worse outcomes. In the present paper we describe the mechanism of viral interaction with endocrine organs, emphasizing the specific endocrine molecules of particular relevance explaining COVID-19 disease evolution and outcomes.

Melatonin as adjunctive therapy in patients admitted to the Covid-19

Objective

Coronavirus has disrupted the natural order of the world since September 2019 with no specific medication. The beneficial effects of melatonin on sepsis and viral influenza were demonstrated previously, but its effects on covid-19, especially COVID -19 ICU, is unclear. Therefore, our aim was to determine the effects of melatonin in COVID-19 ICU patients.

Methods

This is a retrospective cohort study in which the records of patients admitted to COVID -19 ICU of (XXX) during March to June 2020 were reviewed. According to inclusion criteria, patients who received 15 mg of melatonin daily were called MRG and the rest were called NMRG.

Results

Thirty-one patients were included and analyzed, of which twelve patients were in MRG. Demographic and clinical characteristics, and laboratory data were similar between two groups at ICU admission. Melatonin had no significant effect on ICU duration, CRP and ESR, also the trend of changes was in favor of melatonin. Nevertheless, melatonin significantly reduced the NLR (OR = −9.81, p = 0.003), and also declined mortality marginally (p = 0.09). Melatonin was well tolerated with no major adverse effects, moreover the thrombocytopenia occurrence was significantly lower in MRG (p = 0.005). In MRG, survival increased and mortality risk decreased, although the difference between groups wasn't significant (p = 0.37), which might be related to the small sample-size.

Conclusion

Our study showed that melatonin is unlikely to reduce mortality among COVID19 patients and with no significant effect on disease-specific biochemical parameters.

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Coronavirus has disrupted the natural order of the world since September 2019 with no specific medication.

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The beneficial effects of melatonin on sepsis and viral influenza were demonstrated previously.

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Our survey showed melatonin had a beneficial effect on survival and mortality risk.

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As well as platelets and lymphocytes without life-threatening complications.

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Melatonin was an essential adjuvant therapy in patients admitted to covid-19 ICU.

Discovery of potential small molecular SARS-CoV-2 entry blockers targeting the spike protein

An epidemic of pneumonia caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is spreading worldwide. SARS-CoV-2 relies on its spike protein to invade host cells by interacting with the human receptor protein Angiotensin-Converting Enzymes 2 (ACE2). Therefore, designing an antibody or small-molecular entry blockers is of great significance for virus prevention and treatment. This study identified five potential small molecular anti-virus blockers via targeting SARS-CoV-2 spike protein by combining in silico technologies with in vitro experimental methods. The five molecules were natural products that binding to the RBD domain of SARS-CoV-2 was qualitatively and quantitively validated by both native Mass Spectrometry (MS) and Surface Plasmon Resonance (SPR). Anti-viral activity assays showed that the optimal molecule, H69C2, had a strong binding affinity (dissociation constant K_D) of 0.0947 µM and anti-virus IC₅₀ of 85.75 µM.

Mechanisms and clinical evidence to support melatonin's use in severe COVID-19 patients to lower mortality

The fear of SARS-CoV-2 infection is due to its high mortality related to seasonal flu. To date, few medicines have been developed to significantly reduce the mortality of the severe COVID-19 patients, especially those requiring tracheal intubation. The severity and mortality of SARS-CoV-2 infection not only depend on the viral virulence, but are primarily determined by the cytokine storm and the destructive inflammation driven by the host immune reaction. Thus, to target the host immune response might be a better strategy to combat this pandemic. Melatonin is a molecule with multiple activities on a virus infection. These include that it downregulates the overreaction of innate immune response to suppress inflammation, promotes the adaptive immune reaction to enhance antibody formation, inhibits the entrance of the virus into the cell as well as limits its replication. These render it a potentially excellent candidate for treatment of the severe COVID-19 cases. Several clinical trials have confirmed that melatonin when added to the conventional therapy significantly reduces the mortality of the severe COVID-19 patients. The cost of melatonin is a small fraction of those medications approved by FDA for emergency use to treat COVID-19. Because of its self-administered, low cost and high safety margin, melatonin could be made available to every country in the world at an affordable cost. We recommend melatonin be used to treat severe COVID-19 patients with the intent of reducing mortality. If successful, it would make the SARS-CoV-2 pandemic less fearful and help to return life back to normalcy.

Inhalation of nebulized omega-3 fatty acids mitigate LPS-induced acute lung inflammation in rats: Implications for treatment of COPD and COVID-19

Many current treatment options for lung inflammation and thrombosis come with unwanted side effects. The natural omega-3 fatty acids (O3FA) are generally anti-inflammatory and antithrombotic. O3FA are always administered orally and occasionally by intravenous (IV) infusion. The main goal of this study is to determine if O3FA administered by inhalation of a nebulized formulation mitigates LPS-induced acute lung inflammation in male Wistar rats. Inflammation was triggered by intraperitoneal injection of LPS once a day for 14 days. One hour post-injection, rats received nebulized treatments consisting of egg lecithin emulsified O3, Budesonide and Montelukast, and blends of O3 and Melatonin or Montelukast or Cannabidiol; O3 was in the form of free fatty acids for all groups except one group with ethyl esters. Lung histology and cytokines were determined in n = 3 rats per group at day 8 and day 15. All groups had alveolar histiocytosis severity scores half or less than that of the disease control (Cd) treated with LPS and saline only inhalation. IL-6, TNF-α, TGF-β, and IL-10 were attenuated in all O3FA groups. IL-1β was attenuated in most but not all O3 groups. O3 administered as ethyl ester was overall most effective in mitigating LPS effects. No evidence of lipid pneumonia or other chronic distress was observed. These preclinical data suggest that O3FA formulations should be further investigated as treatments in lung inflammation and thrombosis related lung disorders, including asthma, chronic obstructive pulmonary disease, lung cancer and acute respiratory distress such as COVID-19.