Modeling of microRNA-derived disease network repurposes methotrexate for the prevention and therapy of abdominal aortic aneurysm in mice

Abdominal aortic aneurysm (AAA) is a highly lethal vascular disease characterized by permanent dilatation of the abdominal aorta. The main purpose of the current study is to search for noninvasive medical therapies for abdominal aortic aneurysm (AAA), for which there is currently no effective drug therapy. Network medicine represents a cutting-edge technology, as analysis and modeling of disease networks can provide critical clues regarding the etiology of specific diseases and which therapeutics may be effective. Here, we proposed a novel algorithm to quantify disease relations based on a large accumulated microRNA-disease association dataset and then built a disease network that covered 15 disease classes and included 304 diseases. Analysis revealed a number of patterns for these diseases; for example, diseases tended to be clustered and coherent in the network. Surprisingly, we found that AAA showed the strongest similarity with rheumatoid arthritis and systemic lupus erythematosus, both of which are autoimmune diseases, suggesting that AAA could be one type of autoimmune disease in etiology. Based on this observation, we further hypothesized that drugs for autoimmune disease could be repurposed for the prevention and therapy of AAA. Finally, animal experiments confirmed that methotrexate, a drug for autoimmune disease, was able to prevent the formation and inhibit the development of AAA.

Identification of drug–disease associations by using multiple drug and disease networks

Background: Drug repositioning is a new research area in drug development. It aims to discover novel therapeutic uses of existing drugs. It could accelerate the process of designing novel drugs for some diseases and considerably decrease the cost. The traditional method to determine novel therapeutic uses of an existing drug is quite laborious. It is alternative to design computational methods to overcome such defect. Objective: This study aims to propose a novel model for the identification of drug–disease associations. Method: Twelve drug networks and three disease networks were built, which were fed into a powerful network-embedding algorithm called Mashup to produce informative drug and disease features. These features were combined to represent each drug–disease association. Classic classification algorithm, random forest, was used to build the model. Results: Tenfold cross-validation results indicated that the MCC, AUROC, and AUPR were 0.7156, 0.9280, and 0.9191, respectively. Conclusion: The proposed model showed good performance. Some tests indicated that a small dimension of drug features and a large dimension of disease features were beneficial for constructing the model. Moreover, the model was quite robust even if some drug or disease properties were not available.

Mathematical model of the feedback between global supply chain disruption and COVID-19 dynamics

The pandemic of COVID-19 has become one of the greatest threats to human health, causing severe disruptions in the global supply chain, and compromising health care delivery worldwide. Although government authorities sought to contain the spread of SARS-CoV-2, by restricting travel and in-person activities, failure to deploy time-sensitive strategies in ramping-up of critical resource production exacerbated the outbreak. Here, we developed a mathematical model to analyze the effects of the interaction between supply chain disruption and infectious disease dynamics using coupled production and disease networks built on global data. Analysis of the supply chain model suggests that time-sensitive containment strategies could be created to balance objectives in pandemic control and economic losses, leading to a spatiotemporal separation of infection peaks that alleviates the societal impact of the disease. A lean resource allocation strategy can reduce the impact of supply chain shortages from 11.91 to 1.11% in North America. Our model highlights the importance of cross-sectoral coordination and region-wise collaboration to optimally contain a pandemic and provides a framework that could advance the containment and model-based decision making for future pandemics.

Identifying Disease Comorbidity Patterns Using Centrality Measures in Computing

Social network analysis has been increasingly employed to study patterns in diverse areas of disciplines such as crowd management, air passenger and freight transportation, business modelling and analysis, online social movements and bioinformatics. Over the years, human disease networks have been studied to analyze Human Disease, Genotype, and Phenotype networks. This study explores human Disease Network based on their symptoms by employing different social network analysis such as centrality measures of network, community detection, overlapping communities. We studied relationships of symptoms with diseases on meso-level in order to detect comorbidity pattern of communities in disease network. This help us to understand the underlying patterns of diseases based on symptoms and find out that how different disease communities are correlated by detecting overlapping communities.

Assembling Disease Networks From Causal Interaction Resources

The development of high-throughput high-content technologies and the increased ease in their application in clinical settings has raised the expectation of an important impact of these technologies on diagnosis and personalized therapy. Patient genomic and expression profiles yield lists of genes that are mutated or whose expression is modulated in specific disease conditions. The challenge remains of extracting from these lists functional information that may help to shed light on the mechanisms that are perturbed in the disease, thus setting a rational framework that may help clinical decisions. Network approaches are playing an increasing role in the organization and interpretation of patients' data. Biological networks are generated by connecting genes or gene products according to experimental evidence that demonstrates their interactions. Till recently most approaches have relied on networks based on physical interactions between proteins. Such networks miss an important piece of information as they lack details on the functional consequences of the interactions. Over the past few years, a number of resources have started collecting causal information of the type protein A activates/inactivates protein B, in a structured format. This information may be represented as signed directed graphs where physiological and pathological signaling can be conveniently inspected. In this review we will (i) present and compare these resources and discuss the different scope in comparison with pathway resources; (ii) compare resources that explicitly capture causality in terms of data content and proteome coverage (iii) review how causal-graphs can be used to extract disease-specific Boolean networks.

Gene Targeting in Disease Networks

Profiling of whole transcriptomes has become a cornerstone of molecular biology and an invaluable tool for the characterization of clinical phenotypes and the identification of disease subtypes. Analyses of these data are becoming ever more sophisticated as we move beyond simple comparisons to consider networks of higher-order interactions and associations. Gene regulatory networks (GRNs) model the regulatory relationships of transcription factors and genes and have allowed the identification of differentially regulated processes in disease systems. In this perspective, we discuss gene targeting scores, which measure changes in inferred regulatory network interactions, and their use in identifying disease-relevant processes. In addition, we present an example analysis for pancreatic ductal adenocarcinoma (PDAC), demonstrating the power of gene targeting scores to identify differential processes between complex phenotypes, processes that would have been missed by only performing differential expression analysis. This example demonstrates that gene targeting scores are an invaluable addition to gene expression analysis in the characterization of diseases and other complex phenotypes.

Feedbacks between global supply chain disruption and the spread of SARS-CoV-2

The pandemic of COVID-19 has become one of the greatest threats to human health, causing severe disruptions in the global supply chain, and compromising health care delivery worldwide. Although government authorities sought to contain the spread of SARS-CoV-2, the virus that causes COVID-19, by restricting travel and in-person activities, failure to deploy time-sensitive strategies in ramping-up of critical resource production exacerbated the outbreak. Here, we analyze the interactive effects of supply chain disruption and infectious disease dynamics using coupled production and disease networks built on global data. We find that time-sensitive containment strategies could be created to balance objectives in pandemic control and economic losses, leading to a spatiotemporal separation of infection peaks that alleviate the societal impact of the disease. A lean resource allocation strategy is discovered that effectively counteracts the positive feedback between transmission and production such that stockpiles of health care resources may be manufactured and distributed to limit future shortage and disease. The study highlights the importance of cross-sectoral coordination and region-wise collaboration to optimally contain a pandemic while accounting for production.

Exploring Young Adulthood Psychopathology Networks Related to Depression Using Partial Correlation Network and Bayesian Network

To characterize young adulthood depression is complicated because it is entangled with a broad spectrum of symptoms as well as traumatic experiences during development. However, previous symptom network studies have focused on undirected transdiagnostic association among depression and anxiety symptoms. Our study investigated both undirected and directed connections among variables potentially associated with depression, such as anxiety, addiction, subjective distress caused by traumatic events, perceived emotional adversities, and support systems. Both the regularized partial correlation network analysis and Bayesian network analysis were applied to 579 subjects screened for depression. Anxiety-related symptoms played a role as a hub node in the partial correlation network and Bayesian network. The vulnerability analysis of the partial correlation network showed that verbal abuse, social anxiety, concentration problems, and suicidal ideation had the strongest influence on changes in the network’s topology. In the Bayesian network analysis, loss of interest, depressed mood, and parental verbal abuse were located as parent nodes in the directed acyclic graph. In the aspect of disease networks, more attention should be paid to certain variables encompassing various domains as well as depressive symptoms in young adults’ mental health management.

Dynamic Methylome Modification associated with mutational signatures in ageing and etiology of disease

Epigenetic markers and reversible change in the loci of genes regulating critical cell processes, have recently emerged as important biomarkers in the study of disease pathology. The epigenetic changes that accompany ageing in the context of population health risk needs to be explored. Additionally, the interplay between dynamic methylation changes that accompany ageing in relation to mutations that accrue in an individual’s genome over time needs further investigation. Our current study captures the role for variants acting in concurrence with dynamic methylation in an individual analysed over time, in essence reflecting the genome-epigenome interplay, affecting biochemical pathways controlling physiological processes. In our current study, we completed the whole genome methylation and variant analysis in one Zoroastrian-Parsi non-smoking individual, collected at an interval of 12 years apart (at 53 and 65 years respectively) (ZPMetG-Hv2a-1A (old, t0), ZPMetG-Hv2a-1B (recent, t0+12)) using Grid-ion Nanopore sequencer at 13X genome coverage overall. We further identified the Single Nucleotide Variants (SNVs) and indels in known CpG islands by employing GATK and MuTect2 variant caller pipeline with GRCh37 (patch 13) human genome as the reference.We found 5258 disease relevant genes differentially methylated across this individual over 12 years. Employing the GATK pipeline, we found 24,948 genes corresponding to 4,58,148 variants specific to ZPMetG-Hv2a-1B, indicative of variants that accrued over time. 242/24948 gene variants occurred within the CpG regions that were differentially methylated with 67/247 exactly occurring on the CpG site. Our analysis yielded a critical cluster of 10 genes which are significantly methylated and have variants at the CpG site or the ±4bp CpG region window. KEGG enrichment network analysis, Reactome and STRING analysis of mutational signatures of gene specific variants indicated an impact in biological process regulating immune system, disease networks implicated in cancer and neurodegenerative diseases and transcriptional control of processes regulating cellular senescence and longevity.Our current study provides an understanding of the ageing methylome over time through the interplay between differentially methylated genes and variants in the etiology of disease.