scholarly journals Lung disease network reveals impact of comorbidity on SARS-CoV-2 infection and opportunities of drug repurposing

2021 ◽  
Vol 14 (1) ◽  
Author(s):  
Asim Bikas Das

Abstract Background Higher mortality of COVID-19 patients with lung disease is a formidable challenge for the health care system. Genetic association between COVID-19 and various lung disorders must be understood to comprehend the molecular basis of comorbidity and accelerate drug development. Methods Lungs tissue-specific neighborhood network of human targets of SARS-CoV-2 was constructed. This network was integrated with lung diseases to build a disease–gene and disease-disease association network. Network-based toolset was used to identify the overlapping disease modules and drug targets. The functional protein modules were identified using community detection algorithms and biological processes, and pathway enrichment analysis. Results In total, 141 lung diseases were linked to a neighborhood network of SARS-CoV-2 targets, and 59 lung diseases were found to be topologically overlapped with the COVID-19 module. Topological overlap with various lung disorders allows repurposing of drugs used for these disorders to hit the closely associated COVID-19 module. Further analysis showed that functional protein–protein interaction modules in the lungs, substantially hijacked by SARS-CoV-2, are connected to several lung disorders. FDA-approved targets in the hijacked protein modules were identified and that can be hit by exiting drugs to rescue these modules from virus possession. Conclusion Lung diseases are clustered with COVID-19 in the same network vicinity, indicating the potential threat for patients with respiratory diseases after SARS-CoV-2 infection. Pathobiological similarities between lung diseases and COVID-19 and clinical evidence suggest that shared molecular features are the probable reason for comorbidity. Network-based drug repurposing approaches can be applied to improve the clinical conditions of COVID-19 patients.

2020 ◽  
Author(s):  
ASIM BIKAS DAS

Abstract Higher mortality of COVID-19 patients with comorbidity is the formidable challenge faced by the health care system. In response to the present crisis, understanding the molecular basis of comorbidity is essential to accelerate the development of drugs. To address this, the genetic association between COVID-19 and various lung disorders was measured and notable molecular resemblance was observed. 141 lung diseases were linked to a neighborhood network of SARS-CoV-2 targets, and 59 lung diseases topologically overlapped with COVID-19 module. This demonstrates the clustering of lung diseases with COVID-19 in the same network vicinity, indicating the potential threat for lung patients upon SARS-CoV-2 infection. Pathobiological similarities between lung diseases and COVID-19, and clinical evidences suggest that shared molecular features probably the reason for comorbidity. Additionally, topological overlap with various lung disorders provides an opportunity to repurpose the drugs used for lung disease to hit the closely associated COVID-19 module. Further analysis showed that the functional protein-protein interaction modules in the lungs, substantially hijacked by SARS-CoV-2, were connected to several lung disorders. The network-based proximity measure identified the FDA approved targets in hijacked protein modules which can be hit by existing drugs to rescue these modules from viral possessions, and can lead to the improvement of clinical conditions.


2018 ◽  
Author(s):  
Juaquim Aguirre-Plans ◽  
Janet Piñero ◽  
Jörg Menche ◽  
Ferran Sanz ◽  
Laura I Furlong ◽  
...  

AbstractThe traditional drug discovery paradigm has shaped around the idea of “one target, one disease”. Recently, it has become clear that not only it is hard to achieve single target specificity but also it is often more desirable to tinker the complex cellular network by targeting multiple proteins, causing a paradigm shift towards polypharmacology (multiple targets, one disease). Given the lack of clear-cut boundaries across disease (endo)phenotypes and genetic heterogeneity across patients, a natural extension to the current polypharmacology paradigm is targeting common biological pathways involved in diseases, giving rise to “endopharmacology” (multiple targets, multiple diseases). In this study, leveraging powerful network medicine tools, we describe a recipe for first, identifying common pathways pertaining to diseases and then, prioritizing drugs that target these pathways towards endopharmacology. We present proximal pathway enrichment analysis (PxEA) that uses the topology information of the network of interactions between disease genes, pathway genes, drug targets and other proteins to rank drugs for their interactome-based proximity to pathways shared across multiple diseases, providing unprecedented drug repurposing opportunities. As a proof of principle, we focus on nine autoimmune disorders and using PxEA, we show that many drugs indicated for these conditions are not necessarily specific to the condition of interest, but rather target the common biological pathways across these diseases. Finally, we provide the high scoring drug repurposing candidates that can target common mechanisms involved in type 2 diabetes and Alzheimer’s disease, two phenotypes that have recently gained attention due to the increased comorbidity among patients.


2021 ◽  
Author(s):  
Tilman Hinnerichs ◽  
Robert Hoehndorf

AbstractMotivationIn silico drug–target interaction (DTI) prediction is important for drug discovery and drug repurposing. Approaches to predict DTIs can proceed indirectly, top-down, using phenotypic effects of drugs to identify potential drug targets, or they can be direct, bottom-up and use molecular information to directly predict binding potentials. Both approaches can be combined with information about interaction networks.ResultsWe developed DTI-Voodoo as a computational method that combines molecular features and ontology-encoded phenotypic effects of drugs with protein–protein interaction networks, and uses a graph convolutional neural network to predict DTIs. We demonstrate that drug effect features can exploit information in the interaction network whereas molecular features do not. DTI-Voodoo is designed to predict candidate drugs for a given protein; we use this formulation to show that common DTI datasets contain intrinsic biases with major affects on performance evaluation and comparison of DTI prediction methods. Using a modified evaluation scheme, we demonstrate that DTI-Voodoo improves significantly over state of the art DTI prediction methods.AvailabilityDTI-Voodoo source code and data necessary to reproduce results are freely available at https://github.com/THinnerichs/DTI-VOODOO.Supplementary informationSupplementary data are available at https://github.com/ THinnerichs/DTI-VOODOO.


2020 ◽  
Author(s):  
Asim Bikas Das

AbstractHigher mortality of COVID19 patients with comorbidity is the formidable challenge faced by the health care system. In response to the present crisis, understanding the molecular basis of comorbidity is essential to accelerate the development of potential drugs. To address this, we have measured the genetic association between COVID19 and various lung disorders and observed a remarkable resemblance. 141 lung disorders directly or indirectly linked to COVID19 result in a high-density disease-disease association network that shows a small-world property. The clustering of many lung diseases with COVID19 demonstrates a greater complexity and severity of SARS-CoV-2 infection. Furthermore, our results show that the functional protein-protein interaction modules involved RNA and protein metabolism, substantially hijacked by SARS-CoV-2, are connected to several lung disorders. Therefore we recommend targeting the components of these modules to inhibit the viral growth and improve the clinical conditions in comorbidity.


2020 ◽  
Author(s):  
Fang Li ◽  
Muhammad "Tuan" Amith ◽  
Grace Xiong ◽  
Jingcheng Du ◽  
Yang Xiang ◽  
...  

BACKGROUND Alzheimer’s Disease (AD) is a devastating neurodegenerative disease, of which the pathophysiology is insufficiently understood, and the curative drugs are long-awaited to be developed. Computational drug repurposing introduces a promising complementary strategy of drug discovery, which benefits from an accelerated development process and decreased failure rate. However, generating new hypotheses in AD drug repurposing requires multi-dimensional and multi-disciplinary data integration and connection, posing a great challenge in the era of big data. By integrating data with computable semantics, ontologies could infer unknown relationships through automated reasoning and fulfill an essential role in supporting computational drug repurposing. OBJECTIVE The study aimed to systematically design a robust Drug Repurposing-Oriented Alzheimer’s Disease Ontology (DROADO), which could model fundamental elements and their relationships involved in AD drug repurposing and integrate their up-to-date research advance comprehensively. METHODS We devised a core knowledge model of computational AD drug repurposing, based on both pre-genomic and post-genomic research paradigms. The model centered on the possible AD pathophysiology and abstracted the essential elements and their relationships. We adopted a hybrid strategy to populate the ontology (classes and properties), including importing from well-curated databases, extracting from high-quality papers and reusing the existing ontologies. We also leveraged n-ary relations and nanopublication graphs to enrich the object relations, making the knowledge stored in the ontology more powerful in supporting computational processing. The initially built ontology was evaluated by a semiotic-driven and web-based tool Ontokeeper. RESULTS The current version of DROADO was composed of 1,021 classes, 23 object properties and 3,207 axioms, depicting a fundamental network related to computational neuroscience concepts and relationships. Assessment using semiotic evaluation metrics by OntoKeeper indicated sufficient preliminary quality (semantics, usefulness and community-consensus) of the ontology. CONCLUSIONS As an in-depth knowledge base, DROADO would be promising in enabling computational algorithms to realize supervised mining from multi-source data, and ultimately, facilitating the discovery of novel AD drug targets and the realization of AD drug repurposing.


2021 ◽  
Vol 10 (6) ◽  
pp. 1214
Author(s):  
Ji Tu ◽  
Jose Vargas Castillo ◽  
Abhirup Das ◽  
Ashish D. Diwan

Degenerative cervical myelopathy (DCM), earlier referred to as cervical spondylotic myelopathy (CSM), is the most common and serious neurological disorder in the elderly population caused by chronic progressive compression or irritation of the spinal cord in the neck. The clinical features of DCM include localised neck pain and functional impairment of motor function in the arms, fingers and hands. If left untreated, this can lead to significant and permanent nerve damage including paralysis and death. Despite recent advancements in understanding the DCM pathology, prognosis remains poor and little is known about the molecular mechanisms underlying its pathogenesis. Moreover, there is scant evidence for the best treatment suitable for DCM patients. Decompressive surgery remains the most effective long-term treatment for this pathology, although the decision of when to perform such a procedure remains challenging. Given the fact that the aged population in the world is continuously increasing, DCM is posing a formidable challenge that needs urgent attention. Here, in this comprehensive review, we discuss the current knowledge of DCM pathology, including epidemiology, diagnosis, natural history, pathophysiology, risk factors, molecular features and treatment options. In addition to describing different scoring and classification systems used by clinicians in diagnosing DCM, we also highlight how advanced imaging techniques are being used to study the disease process. Last but not the least, we discuss several molecular underpinnings of DCM aetiology, including the cells involved and the pathways and molecules that are hallmarks of this disease.


2021 ◽  
Vol 10 (11) ◽  
pp. 2285
Author(s):  
John N. Shumar ◽  
Abhimanyu Chandel ◽  
Christopher S. King

Progressive fibrosing interstitial lung disease (PF-ILD) describes a phenotypic subset of interstitial lung diseases characterized by progressive, intractable lung fibrosis. PF-ILD is separate from, but has radiographic, histopathologic, and clinical similarities to idiopathic pulmonary fibrosis. Two antifibrotic medications, nintedanib and pirfenidone, have been approved for use in patients with idiopathic pulmonary fibrosis. Recently completed randomized controlled trials have demonstrated the clinical efficacy of antifibrotic therapy in patients with PF-ILD. The validation of efficacy of antifibrotic therapy in PF-ILD has changed the treatment landscape for all of the fibrotic lung diseases, providing a new treatment pathway and opening the door for combined antifibrotic and immunosuppressant drug therapy to address both the fibrotic and inflammatory components of ILD characterized by mixed pathophysiologic pathways.


2020 ◽  
Vol 79 (Suppl 1) ◽  
pp. 1097.1-1097
Author(s):  
F. Zhu ◽  
X. Zhang

Background:Connective tissue disease-associated interstitial lung disease (CTD-ILD) is a class of refractory diseases.Non-specific treatment with hormone and immunosuppressive agents is mostly used at present, but the effect is limited and the long-term survival rate is not improved [1],while anti-fibrosis treatments (such as Pirfenidone and Nintedanib) have only recently been approved, the long-term efficacy is still unknown.Tofacitinib(TOFA), a JAK inhibitor, has recently been used to treat patients with severe dermatomyositis related interstitial pulmonary disease, with significantly improved survival rate [2-4].A basic study showed that TOFA improved interstitial pulmonary disease in mice by promoting the proliferation of myelogenic inhibitory cells [5].However, whether TOFA can affect the migration and invasion of human lung fibroblasts and further research to reveal the mechanism of its inhibition of pulmonary fibrosis has not been reported.Objectives:To investigate the anti - fibrosis effect of TOFA in CTD-ILD.Methods:Cell migration and invasion AssaysHLFs were incubated with TOFA for 72h, followed by TGF- β1 for 24h.DMEM serum-free medium was used to determine the cell density to 5. 0 × 107/L, 600 uL medium containing 10% fetal bovine serum was added to the lower compartment of Transwell chamber, and 200 uL cell suspension was added to the upper compartment.Incubate in incubator for 12 h.After fixation, staining and sealing, the cells were observed and counted under a microscope. At least 5 random field transmembrane cells were counted in each hole, and the mean value was taken.For the invasion assays, Transwell chamber coated with matrigel was used, and the cell incubation time was 16 h.Results:1. Effect of TOFA on HLFs migration function (Figure 1)Figure 1.Effect of TOFA on HLFs migration function(×200).Mean ± SEM. n = 5.The number of cells passing through the biofilm in the three groups was counted.It can be seen that TGF-β1 group significantly increased compared with control group (*P < 0.0001), and TOFA group significantly decreased compared with TGF- β1 group (#P < 0.0001), suggesting that TOFA can significantly inhibit TGF-β1- induced HLFs migration.2. Effect of TOFA on HLFs invasion function (Figure 2)Figure 2.Effect of TOFA on HLFs invasion function(×200).Mean ± SEM. n = 5.The number of cells passing through the matrigel in the three groups was counted.It can be seen that TGF-β1 group was significantly higher than the control group (*P < 0.0001), and TOFA group was significantly lower than TGF-β1 group(#P < 0.001), suggesting that TOFA can significantly inhibit the invasion function of HLFs induced by TGF-β1.Conclusion:TOFA can effectively inhibit the function of HLFs migration and invasion. Although further studies are needed to elucidate the mechanism by which TOFA inhibit the function of HLFs migration and invasion, our study suggests that TOFA has a potential therapeutic effect for CTD-ILD.References:[1]Aparicio, I.J. and J.S. Lee, Connective Tissue Disease-Associated Interstitial Lung Diseases: Unresolved Issues. Semin Respir Crit Care Med, 2016. 37(3): p. 468-76.[2]Kato, M., et al., Successful Treatment for Refractory Interstitial Lung Disease and Pneumomediastinum With Multidisciplinary Therapy Including Tofacitinib in a Patient With Anti-MDA5 Antibody-Positive Dermatomyositis. J Clin Rheumatol, 2019.[3]Kurasawa, K., et al., Tofacitinib for refractory interstitial lung diseases in anti-melanoma differentiation-associated 5 gene antibody-positive dermatomyositis. Rheumatology (Oxford), 2018. 57(12): p. 2114-2119.[4]Chen, Z., X. Wang, and S. Ye, Tofacitinib in Amyopathic Dermatomyositis-Associated Interstitial Lung Disease. N Engl J Med, 2019. 381(3): p. 291-293.[5]Sendo, S., et al., Tofacitinib facilitates the expansion of myeloid-derived suppressor cells and ameliorates interstitial lung disease in SKG mice. Arthritis Res Ther, 2019. 21(1): p. 184Disclosure of Interests:None declared


2021 ◽  
Vol 41 (1) ◽  
Author(s):  
Kyuto Sonehara ◽  
Yukinori Okada

AbstractGenome-wide association studies have identified numerous disease-susceptibility genes. As knowledge of gene–disease associations accumulates, it is becoming increasingly important to translate this knowledge into clinical practice. This challenge involves finding effective drug targets and estimating their potential side effects, which often results in failure of promising clinical trials. Here, we review recent advances and future perspectives in genetics-led drug discovery, with a focus on drug repurposing, Mendelian randomization, and the use of multifaceted omics data.


2021 ◽  
Vol 8 (02) ◽  
pp. 53-57
Author(s):  
Kalika Gupta ◽  
Mitin Parmar ◽  
Pranav Bhavsar ◽  
Milan Chaudhary

BACKGROUND Occupational lung diseases are diseases affecting the respiratory system, including occupational asthma, black lung disease and many more. Workers exposed to marble dust stand an increased risk of suffering from asthma symptoms, chronic bronchitis, nasal inflammation and impairment of lung functions. The recognition of occupational causes can be made difficult by years of latency between exposure in the workplace and the occurrence of disease. Through this study, authors have established the importance of early identification of symptoms of occupational lung diseases and the importance of preventive measures that can be applied to reduce incidence of such diseases. METHODS This was a cross sectional community-based study conducted on 340 marble mining or cutting workers of Rajnagar [Morwar], Rajsamand district of Rajasthan, for a duration of three months. Workers were clinically examined and asked about environmental conditions and use of preventive measures through a questionnaire designed by the investigators and with the help of pamphlets and videos, educational interventions were provided. RESULTS Almost 90 % of the workers didn’t use protective measure like mask or shield. Among the 10 % workers who were using safety measures, 60 % were using face mask and 20 % were using apron at the work place. After the educational intervention given by investigators, around 63 % had started using various safety measures. CONCLUSIONS Early interventions after development of symptoms are important as they can decrease chances of further worsening of the condition. Health education, periodic health check-ups and use of protective measures are the essence in preventing occupational lung diseases. KEYWORDS Occupational Lung Disease, Cough, Marble Workers, Silicosis


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