scholarly journals Diversity and molecular network patterns of symptom phenotypes

2021 ◽  
Vol 7 (1) ◽  
Author(s):  
Zixin Shu ◽  
Jingjing Wang ◽  
Hailong Sun ◽  
Ning Xu ◽  
Chenxia Lu ◽  
...  
Keyword(s):  
Clinical Diagnosis ◽  
Protein Interactions ◽  
Complex Disease ◽  
Phenotypic Diversity ◽  
Molecular Network ◽  
Network Medicine ◽  
Network Diversity ◽  
Underlying Mechanisms ◽  
Network Patterns ◽  
Precision Health

AbstractSymptom phenotypes have continuously been an important clinical entity for clinical diagnosis and management. However, non-specificity of symptom phenotypes for clinical diagnosis is one of the major challenges that need be addressed to advance symptom science and precision health. Network medicine has delivered a successful approach for understanding the underlying mechanisms of complex disease phenotypes, which will also be a useful tool for symptom science. Here, we extracted symptom co-occurrences from clinical textbooks to construct phenotype network of symptoms with clinical co-occurrence and incorporated high-quality symptom-gene associations and protein–protein interactions to explore the molecular network patterns of symptom phenotypes. Furthermore, we adopted established network diversity measure in network medicine to quantify both the phenotypic diversity (i.e., non-specificity) and molecular diversity of symptom phenotypes. The results showed that the clinical diversity of symptom phenotypes could partially be explained by their underlying molecular network diversity (PCC = 0.49, P-value = 2.14E-08). For example, non-specific symptoms, such as chill, vomiting, and amnesia, have both high phenotypic and molecular network diversities. Moreover, we further validated and confirmed the approach of symptom clusters to reduce the non-specificity of symptom phenotypes. Network diversity proposes a useful approach to evaluate the non-specificity of symptom phenotypes and would help elucidate the underlying molecular network mechanisms of symptom phenotypes and thus promotes the advance of symptom science for precision health.

scholarly journals Biomolecular Network-Based Synergistic Drug Combination Discovery

2016 ◽  
Vol 2016 ◽  
pp. 1-11 ◽  
Author(s):  
Xiangyi Li ◽  
Guangrong Qin ◽  
Qingmin Yang ◽  
Lanming Chen ◽  
Lu Xie
Keyword(s):  
Drug Combination ◽  
Complex Disease ◽  
Recent Decade ◽  
Drug Combinations ◽  
Network Medicine ◽  
Disease Therapy ◽  
Biomolecular Network ◽  
Disease Related Genes ◽  
Disease Specific ◽  
Synergistic Drug Combinations

Drug combination is a powerful and promising approach for complex disease therapy such as cancer and cardiovascular disease. However, the number of synergistic drug combinations approved by the Food and Drug Administration is very small. To bridge the gap between urgent need and low yield, researchers have constructed various models to identify synergistic drug combinations. Among these models, biomolecular network-based model is outstanding because of its ability to reflect and illustrate the relationships among drugs, disease-related genes, therapeutic targets, and disease-specific signaling pathways as a system. In this review, we analyzed and classified models for synergistic drug combination prediction in recent decade according to their respective algorithms. Besides, we collected useful resources including databases and analysis tools for synergistic drug combination prediction. It should provide a quick resource for computational biologists who work with network medicine or synergistic drug combination designing.

scholarly journals Application of microRNA Database Mining in Biomarker Discovery and Identification of Therapeutic Targets for Complex Disease

2020 ◽  
Vol 4 (1) ◽  
pp. 5
Author(s):  
Jennifer L. Major ◽  
Rushita A. Bagchi ◽  
Julie Pires da Silva
Keyword(s):  
Complex Disease ◽  
Biomarker Discovery ◽  
Ischemia Reperfusion Injury ◽  
Ischemia Reperfusion ◽  
Expression Patterns ◽  
Therapeutic Targets ◽  
Human Diseases ◽  
Mirna Expression Data ◽  
Artery Disease ◽  
Underlying Mechanisms

Over the past two decades, it has become increasingly evident that microRNAs (miRNA) play a major role in human diseases such as cancer and cardiovascular diseases. Moreover, their easy detection in circulation has made them a tantalizing target for biomarkers of disease. This surge in interest has led to the accumulation of a vast amount of miRNA expression data, prediction tools, and repositories. We used the Human microRNA Disease Database (HMDD) to discover miRNAs which shared expression patterns in the related diseases of ischemia/reperfusion injury, coronary artery disease, stroke, and obesity as a model to identify miRNA candidates for biomarker and/or therapeutic intervention in complex human diseases. Our analysis identified a single miRNA, hsa-miR-21, which was casually linked to all four pathologies, and numerous others which have been detected in the circulation in more than one of the diseases. Target analysis revealed that hsa-miR-21 can regulate a number of genes related to inflammation and cell growth/death which are major underlying mechanisms of these related diseases. Our study demonstrates a model for researchers to use HMDD in combination with gene analysis tools to identify miRNAs which could serve as biomarkers and/or therapeutic targets of complex human diseases.

Precision Medicine in Patients with Differential Diabetic Phenotypes: Novel Opportunities from Network Medicine

2021 ◽  
Vol 18 ◽  
Author(s):  
Claudio Napoli ◽  
Giuditta Benincasa ◽  
Samer Ellahham
Keyword(s):  
Precision Medicine ◽  
Protein Interactions ◽  
Randomized Clinical Trials ◽  
Growth Factor Receptor ◽  
Cardiovascular Complications ◽  
Personalized Therapy ◽  
Network Medicine ◽  
Prediabetic State ◽  
Relevant Role ◽  
Approved Drugs

Introduction: Diabetes mellitus (DM) comprises differential clinical phenotypes ranging from rare monogenic to common polygenic forms, such as type 1 (T1DM), type 2 (T2DM), and gestational diabetes, which are associated with cardiovascular complications. Also, the high-risk prediabetic state is rising worldwide, suggesting the urgent need for early personalized strategies to prevent and treat a hyperglycemic state. Objective: We aim to discuss the advantages and challenges of Network Medicine approaches in clarifying disease-specific molecular pathways, which may open novel ways for repurposing approved drugs to reach diabetes precision medicine and personalized therapy. Conclusion: The interactome [or protein-protein interactions (PPIs)] is a useful tool to identify subtle molecular differences between precise diabetic phenotypes and predict putative novel drugs. Despite being previously unappreciated as T2DM determinants, the growth factor receptor-bound protein 14 (GRB14), calmodulin 2 (CALM2), and protein kinase C-alpha (PRKCA) might have a relevant role in disease pathogenesis. Besides, in silico platforms have suggested that diflunisal, nabumetone, niflumic acid, and valdecoxib may be suitable for the treatment of T1DM; phenoxybenzamine and idazoxan for the treatment of T2DM by improving insulin secretion; and hydroxychloroquine reduce the risk of coronary heart disease (CHD) by counteracting inflammation. Network medicine has the potential to improve precision medicine in diabetes care and enhance personalized therapy. However, only randomized clinical trials will confirm the clinical utility of network-oriented biomarkers and drugs in the management of DM.

Progressive Alterations in Biomechanical Response of a Mouse Model of Aneurysm

2012 ◽  
Author(s):  
Darren Haskett ◽  
Urs Utzinger ◽  
Mohamad Azhar ◽  
Jonathan Vande Geest
Keyword(s):  
Mouse Model ◽  
Complex Disease ◽  
Mechanical Characteristics ◽  
Infrarenal Aorta ◽  
Unanswered Question ◽  
Significant Morbidity ◽  
Biomechanical Behavior ◽  
Abdominal Aortic ◽  
Biomechanical Response ◽  
Underlying Mechanisms

Abdominal aortic aneurysm (AAA) is a complex disease that leads to a localized dilation of the infrarenal aorta, the rupture of which is associated with significant morbidity and mortality, however the underlying mechanisms by which such changes remains an important unanswered question in the literature. Animal models of AAA can be used to study how changes in the microstructural and biomechanical behavior of aortic tissues develop as disease progresses in these animals. We chose here to investigate changes in mechanical characteristics with time in the established Apolipoprotein E deficient (ApoE−/−) angiotensin II (AngII) infused mouse model of AAA.

scholarly journals Maternal nutrition and the developmental origins of osteoporosis in offspring: Potential mechanisms and clinical implications

2018 ◽  
Vol 243 (10) ◽  
pp. 836-842 ◽  
Author(s):  
Jia Zheng ◽  
Qianyun Feng ◽  
Sheng Zheng ◽  
Xinhua Xiao
Keyword(s):  
Early Life ◽  
Theoretical Basis ◽  
Complex Disease ◽  
Maternal Nutrition ◽  
Developmental Origins ◽  
Early Prevention ◽  
Environment Exposure ◽  
Prevention Of Osteoporosis ◽  
Health And Disease ◽  
Underlying Mechanisms

Osteoporosis, the most frequent metabolic disorder of bone, is a complex disease with a multifactorial origin that is influenced by genes and environments. However, the pathogenesis of osteoporosis has not been fully elucidated. The theory of “Developmental Origins of Health and Disease” indicates that early life environment exposure determines the risks of cardiometabolic diseases in adulthood. However, investigations into the effects of maternal nutrition and nutrition exposure during early life on the development of osteoporosis are limited. Recently, emerging evidence has strongly suggested that maternal nutrition has long-term influences on bone metabolism in offspring, and epigenetic modifications maybe the underlying mechanisms of this process. This review aimed to address maternal nutrition and its implications for the developmental origins of osteoporosis in offspring. It is novel in providing a theoretical basis for the early prevention of osteoporosis. Impact statement Our review aimed to address maternal nutrition and its implications for the developmental origins of osteoporosis in offspring, that can novelly provide a theoretical basis for the early prevention of osteoporosis.

scholarly journals Issues in performance evaluation for host–pathogen protein interaction prediction

2016 ◽  
Vol 14 (03) ◽  
pp. 1650011 ◽  
Author(s):  
Wajid Arshad Abbasi ◽  
Fayyaz Ul Amir Afsar Minhas
Keyword(s):  
Machine Learning ◽  
Protein Interactions ◽  
Cross Validation ◽  
Protein Protein Interactions ◽  
Evaluation Scheme ◽  
Host Pathogen ◽  
Pathogen Protein ◽  
Protein Interaction Prediction ◽  
Underlying Mechanisms ◽  
Fold Cross Validation

The study of interactions between host and pathogen proteins is important for understanding the underlying mechanisms of infectious diseases and for developing novel therapeutic solutions. Wet-lab techniques for detecting protein–protein interactions (PPIs) can benefit from computational predictions. Machine learning is one of the computational approaches that can assist biologists by predicting promising PPIs. A number of machine learning based methods for predicting host–pathogen interactions (HPI) have been proposed in the literature. The techniques used for assessing the accuracy of such predictors are of critical importance in this domain. In this paper, we question the effectiveness of K-fold cross-validation for estimating the generalization ability of HPI prediction for proteins with no known interactions. K-fold cross-validation does not model this scenario, and we demonstrate a sizable difference between its performance and the performance of an alternative evaluation scheme called leave one pathogen protein out (LOPO) cross-validation. LOPO is more effective in modeling the real world use of HPI predictors, specifically for cases in which no information about the interacting partners of a pathogen protein is available during training. We also point out that currently used metrics such as areas under the precision-recall or receiver operating characteristic curves are not intuitive to biologists and propose simpler and more directly interpretable metrics for this purpose.

scholarly journals Trifunctional lipid probes for comprehensive studies of single lipid species in living cells

2017 ◽  
Vol 114 (7) ◽  
pp. 1566-1571 ◽  
Author(s):  
Doris Höglinger ◽  
André Nadler ◽  
Per Haberkant ◽  
Joanna Kirkpatrick ◽  
Martina Schifferer ◽  
...  
Keyword(s):  
Protein Interactions ◽  
Protein Translocation ◽  
Light And Electron Microscopy ◽  
Cellular Processes ◽  
Diagnostic Potential ◽  
Lipid Species ◽  
Signaling Events ◽  
Niemann Pick ◽  
Underlying Mechanisms ◽  
Lipid Protein Interactions

Lipid-mediated signaling events regulate many cellular processes. Investigations of the complex underlying mechanisms are difficult because several different methods need to be used under varying conditions. Here we introduce multifunctional lipid derivatives to study lipid metabolism, lipid−protein interactions, and intracellular lipid localization with a single tool per target lipid. The probes are equipped with two photoreactive groups to allow photoliberation (uncaging) and photo–cross-linking in a sequential manner, as well as a click-handle for subsequent functionalization. We demonstrate the versatility of the design for the signaling lipids sphingosine and diacylglycerol; uncaging of the probe for these two species triggered calcium signaling and intracellular protein translocation events, respectively. We performed proteomic screens to map the lipid-interacting proteome for both lipids. Finally, we visualized a sphingosine transport deficiency in patient-derived Niemann−Pick disease type C fibroblasts by fluorescence as well as correlative light and electron microscopy, pointing toward the diagnostic potential of such tools. We envision that this type of probe will become important for analyzing and ultimately understanding lipid signaling events in a comprehensive manner.

scholarly journals Probing the Unknowns in Cytokinin-Mediated Immune Defense in Arabidopsis with Systems Biology Approaches

2014 ◽  
Vol 8 ◽  
pp. BBI.S13462 ◽  
Author(s):  
Muhammad Naseem ◽  
Meik Kunz ◽  
Thomas Dandekar
Keyword(s):  
Systems Biology ◽  
Immune Responses ◽  
Protein Interactions ◽  
Immune Defense ◽  
Immune Functions ◽  
Protein Protein Interactions ◽  
Cytokinin Signaling ◽  
Host Immune Responses ◽  
Arabidopsis Protein ◽  
Underlying Mechanisms

Plant hormones involving salicylic acid (SA), jasmonic acid (JA), ethylene (Et), and auxin, gibberellins, and abscisic acid (ABA) are known to regulate host immune responses. However, plant hormone cytokinin has the potential to modulate defense signaling including SA and JA. It promotes plant pathogen and herbivore resistance; underlying mechanisms are still unknown. Using systems biology approaches, we unravel hub points of immune interaction mediated by cytokinin signaling in Arabidopsis. High-confidence Arabidopsis protein—protein interactions (PPI) are coupled to changes in cytokinin-mediated gene expression. Nodes of the cellular interactome that are enriched in immune functions also reconstitute sub-networks. Topological analyses and their specific immunological relevance lead to the identification of functional hubs in cellular interactome. We discuss our identified immune hubs in light of an emerging model of cytokinin-mediated immune defense against pathogen infection in plants.

scholarly journals Understanding the Pathogenesis of Kawasaki Disease by Network and Pathway Analysis

2013 ◽  
Vol 2013 ◽  
pp. 1-17 ◽  
Author(s):  
Yu-wen Lv ◽  
Jing Wang ◽  
Ling Sun ◽  
Jian-min Zhang ◽  
Lei Cao ◽  
...  
Keyword(s):  
Immune Response ◽  
Kawasaki Disease ◽  
Protein Interactions ◽  
Complex Disease ◽  
Close Contact ◽  
Association Studies ◽  
Ion Homeostasis ◽  
Ivig Treatment ◽  
Genome Wide Association Studies ◽  
Immune Pathway

Kawasaki disease (KD) is a complex disease, leading to the damage of multisystems. The pathogen that triggers this sophisticated disease is still unknown since it was first reported in 1967. To increase our knowledge on the effects of genes in KD, we extracted statistically significant genes so far associated with this mysterious illness from candidate gene studies and genome-wide association studies. These genes contributed to susceptibility to KD, coronary artery lesions, resistance to initial IVIG treatment, incomplete KD, and so on. Gene ontology category and pathways were analyzed for relationships among these statistically significant genes. These genes were represented in a variety of functional categories, including immune response, inflammatory response, and cellular calcium ion homeostasis. They were mainly enriched in the pathway of immune response. We further highlighted the compelling immune pathway of NF-AT signal and leukocyte interactions combined with another transcription factor NF-κB in the pathogenesis of KD. STRING analysis, a network analysis focusing on protein interactions, validated close contact between these genes and implied the importance of this pathway. This data will contribute to understanding pathogenesis of KD.

scholarly journals Expression attenuation as a mechanism of robustness against gene duplication

2021 ◽  
Vol 118 (6) ◽  
pp. e2014345118
Author(s):  
Diana Ascencio ◽  
Guillaume Diss ◽  
Isabelle Gagnon-Arsenault ◽  
Alexandre K. Dubé ◽  
Alexander DeLuna ◽  
...  
Keyword(s):  
Gene Duplication ◽  
Protein Interactions ◽  
Protein Complexes ◽  
Phenotypic Diversity ◽  
26S Proteasome ◽  
Gene Duplications ◽  
Protein Protein Interactions ◽  
Individual Gene ◽  
The Impact

Gene duplication is ubiquitous and a major driver of phenotypic diversity across the tree of life, but its immediate consequences are not fully understood. Deleterious effects would decrease the probability of retention of duplicates and prevent their contribution to long-term evolution. One possible detrimental effect of duplication is the perturbation of the stoichiometry of protein complexes. Here, we measured the fitness effects of the duplication of 899 essential genes in the budding yeast using high-resolution competition assays. At least 10% of genes caused a fitness disadvantage when duplicated. Intriguingly, the duplication of most protein complex subunits had small to nondetectable effects on fitness, with few exceptions. We selected four complexes with subunits that had an impact on fitness when duplicated and measured the impact of individual gene duplications on their protein–protein interactions. We found that very few duplications affect both fitness and interactions. Furthermore, large complexes such as the 26S proteasome are protected from gene duplication by attenuation of protein abundance. Regulatory mechanisms that maintain the stoichiometric balance of protein complexes may protect from the immediate effects of gene duplication. Our results show that a better understanding of protein regulation and assembly in complexes is required for the refinement of current models of gene duplication.

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