scholarly journals Diagnostic biomarkers for invasive aspergillosis utilizing weighted gene co-expression network analysis

2020 ◽  
Author(s):  
Weiwei Deng ◽  
Yubo Ma ◽  
Panpan Liang ◽  
Chen Huang ◽  
Yi Zhang ◽  
...  
Keyword(s):  
Network Analysis ◽  
Invasive Aspergillosis ◽  
Bacterial Infections ◽  
High Sensitivity ◽  
Roc Curves ◽  
Microarray Dataset ◽  
High Expression ◽  
Hub Genes ◽  
Diagnostic Biomarkers ◽  
Hematological Patients

Abstract Background: Invasive aspergillosis (IA) has a significant mortality in immunocompromised patients. In recent years, with more aggressive immunosuppressed therapies, the incidence of IA was increasing. However, diagnostic biomarkers with high sensitivity and specificity remain rare. To get new diagnostic biomarkers, microarray dataset GSE78000 was analyzed. Methods: Weighted gene co-expression network analysis (WGCNA) was used to identify hub genes. Roc curves were employed for investigating diagnostic biomarkers for IA.Results: Hub genes were TLR4, TP53I3/PIG3, TMTC1, ITGAM, CYSTM1, FAR1, GAS7 and MKNK1. However, after we compared gene expression of hematological patients suffering from IA with non-IA patients, only TLR4, TP53I3/PIG3 and TMTC1 were significantly high expression in IA patients. At the optimal cut‐off value, TLR4 can diagnose patients with IA with 78.3% sensitivity and 72.7% specificity. TP53I3/PIG3 can diagnose patients with IA with 91.3% sensitivity and 54.5% specificity. TMTC1 can diagnose patients with IA with 78.3% sensitivity and 81.8% specificity. In addition, the data of hematological patients suffering from Staphylococcus aureus (S. aureus) and Escherichia coli (E.coli) infections were also analyzed. The results showed that TLR4 and TP53I3/PIG3 were also significantly high expression in S. aureus and E.coli infections, while only TP53I3/PIG3 was obviously higher expression in patients with bacterial infections compared with IA. As for TMTC1, we cannot annotate the gene from the microarray data. Conclusions: our results suggested that TLR4, TP53I3/PIG3 and TMTC1 might be used for the diagnosis of IA, and TP53I3/PIG3 can also be used to discriminate hematological aspergillosis and bacterial infections.

In Silico Protein Interaction Network Analysis of Virulence Proteins Associated with Invasive Aspergillosis for Drug Discovery

2019 ◽  
Vol 19 (2) ◽  
pp. 146-155 ◽  
Author(s):  
Renu Chaudhary ◽  
Meenakshi Balhara ◽  
Deepak Kumar Jangir ◽  
Mehak Dangi ◽  
Mrridula Dangi ◽  
...  
Keyword(s):  
Network Analysis ◽  
Invasive Aspergillosis ◽  
Protein Interaction ◽  
Drug Target ◽  
Interaction Network ◽  
Enrichment Analysis ◽  
Homology Model ◽  
Ppi Network ◽  
Virulence Proteins ◽  
Hub Proteins

<P>Background: Protein-Protein interaction (PPI) network analysis of virulence proteins of Aspergillus fumigatus is a prevailing strategy to understand the mechanism behind the virulence of A. fumigatus. The identification of major hub proteins and targeting the hub protein as a new antifungal drug target will help in treating the invasive aspergillosis. </P><P> Materials & Method: In the present study, the PPI network of 96 virulence (drug target) proteins of A. fumigatus were investigated which resulted in 103 nodes and 430 edges. Topological enrichment analysis of the PPI network was also carried out by using STRING database and Network analyzer a cytoscape plugin app. The key enriched KEGG pathway and protein domains were analyzed by STRING.Conclusion:Manual curation of PPI data identified three proteins (PyrABCN-43, AroM-34, and Glt1- 34) of A. fumigatus possessing the highest interacting partners. Top 10% hub proteins were also identified from the network using cytohubba on the basis of seven algorithms, i.e. betweenness, radiality, closeness, degree, bottleneck, MCC and EPC. Homology model and the active pocket of top three hub proteins were also predicted.</P>

scholarly journals Weighted gene co-expression network analysis identified underlying hub genes and mechanisms in the occurrence and development of viral myocarditis

2020 ◽  
Vol 8 (21) ◽  
pp. 1348-1348
Author(s):  
Zetao Ma ◽  
Zhida Shen ◽  
Yingchao Gong ◽  
Jiaqi Zhou ◽  
Xiaoou Chen ◽  
...  
Keyword(s):  
Network Analysis ◽  
Viral Myocarditis ◽  
Hub Genes

scholarly journals Identification of key modules and hub genes in glioblastoma multiforme based on co‐expression network analysis

FEBS Open Bio ◽  
2021 ◽  
Author(s):  
Chun Li ◽  
Bangming Pu ◽  
Long Gu ◽  
Mingwei Zhang ◽  
Hongping Shen ◽  
...  
Keyword(s):  
Glioblastoma Multiforme ◽  
Network Analysis ◽  
Hub Genes

scholarly journals The Child Behavior Checklist as a Screening Instrument for PTSD in Refugee Children

Children ◽  
2021 ◽  
Vol 8 (6) ◽  
pp. 521
Author(s):  
Ina Nehring ◽  
Heribert Sattel ◽  
Maesa Al-Hallak ◽  
Martin Sack ◽  
Peter Henningsen ◽  
...  
Keyword(s):  
Child Behavior ◽  
Child Behavior Checklist ◽  
High Sensitivity ◽  
Roc Curves ◽  
Structured Interview ◽  
Screening Instrument ◽  
Screening Tools ◽  
Refugee Children ◽  
Post Traumatic Stress ◽  
Sensitivity Specificity

Thousands of refugees who have entered Europe experienced threatening conditions, potentially leading to post traumatic stress disorder (PTSD), which has to be detected and treated early to avoid chronic manifestation, especially in children. We aimed to evaluate and test suitable screening tools to detect PTSD in children. Syrian refugee children aged 4–14 years were examined using the PTSD-semi-structured interview, the Kinder-DIPS, and the Child Behavior Checklist (CBCL). The latter was evaluated as a potential screening tool for PTSD using (i) the CBCL-PTSD subscale and (ii) an alternative subscale consisting of a psychometrically guided selection of items with an appropriate correlation to PTSD and a sufficient prevalence (presence in more than 20% of the cases with PTSD). For both tools we calculated sensitivity, specificity, and a receiver operating characteristic (ROC) curve. Depending on the sum score of the items, the 20-item CBCL-PTSD subscale as used in previous studies yielded a maximal sensitivity of 85% and specificity of 76%. The psychometrically guided item selection resulted in a sensitivity of 85% and a specificity of 83%. The areas under the ROC curves were the same for both tools (0.9). Both subscales may be suitable as screening instrument for PTSD in refugee children, as they reveal a high sensitivity and specificity.

scholarly journals Trends in Nursing Research on Infections: Semantic Network Analysis and Topic Modeling

2021 ◽  
Vol 18 (13) ◽  
pp. 6915
Author(s):  
Jongsoon Won ◽  
Kyunghee Kim ◽  
Kyeong-Yae Sohng ◽  
Sung-Ok Chang ◽  
Seung-Kyo Chaung ◽  
...  
Keyword(s):  
Urinary Tract ◽  
Network Analysis ◽  
Infection Control ◽  
Urinary Tract Infections ◽  
Bacterial Infections ◽  
Semantic Network ◽  
People Living With Hiv ◽  
Transmitted Infection ◽  
Sexually Transmitted ◽  
Tract Infections

Background: Many countries around the world are currently threatened by the COVID-19 pandemic, and nurses are facing increasing responsibilities and work demands related to infection control. To establish a developmental strategy for infection control, it is important to analyze, understand, or visualize the accumulated data gathered from research in the field of nursing. Methods: A total of 4854 articles published between 1978 and 2017 were retrieved from the Web of Science. Abstracts from these articles were extracted, and network analysis was conducted using the semantic network module. Results: ‘wound’, ‘injury’, ‘breast’, “dressing”, ‘temperature’, ‘drainage’, ‘diabetes’, ‘abscess’, and ‘cleaning’ were identified as the keywords with high values of degree centrality, betweenness centrality, and closeness centrality; hence, they were determined to be influential in the network. The major topics were ‘PLWH’ (people living with HIV), ‘pregnancy’, and ‘STI’ (sexually transmitted infection). Conclusions: Diverse infection research has been conducted on the topics of blood-borne infections, sexually transmitted infections, respiratory infections, urinary tract infections, and bacterial infections. STIs (including HIV), pregnancy, and bacterial infections have been the focus of particularly intense research by nursing researchers. More research on viral infections, urinary tract infections, immune topic, and hospital-acquired infections will be needed.

scholarly journals The potential role of chemotaxis and the complement system in the formation and progression of thoracic aortic aneurysms inferred from the weighted gene coexpression network analysis

2021 ◽  
Vol 19 (1) ◽  
Author(s):  
Chuxiang Lei ◽  
Dan Yang ◽  
Wenlin Chen ◽  
Haoxuan Kan ◽  
Fang Xu ◽  
...  
Keyword(s):  
Network Analysis ◽  
Clinical Significance ◽  
Immune Cells ◽  
Complement System ◽  
Aortic Wall ◽  
Gene Coexpression Network ◽  
Hub Genes ◽  
Gene Coexpression ◽  
The Complement System ◽  
Coexpression Network Analysis

Abstract Background Thoracic aortic aneurysm (TAA) can be life-threatening due to the progressive weakening and dilatation of the aortic wall. Once the aortic wall has ruptured, no effective pharmaceutical therapies are available. However, studies on TAA at the gene expression level are limited. Our study aimed to identify the driver genes and critical pathways of TAA through gene coexpression networks. Methods We analyzed the genetic data of TAA patients from a public database by weighted gene coexpression network analysis (WGCNA). Modules with clinical significance were identified, and the differentially expressed genes (DEGs) were intersected with the genes in these modules. Gene Ontology and pathway enrichment analyses were performed. Finally, hub genes that might be driving factors of TAA were identified. Furthermore, we evaluated the diagnostic accuracy of these genes and analyzed the composition of immune cells using the CIBERSORT algorithm. Results We identified 256 DEGs and two modules with clinical significance. The immune response, including leukocyte adhesion, mononuclear cell proliferation and T cell activation, was identified by functional enrichment analysis. CX3CR1, C3, and C3AR1 were the top 3 hub genes in the module correlated with TAA, and the areas under the curve (AUCs) by receiver operating characteristic (ROC) analysis of all the hub genes exceeded 0.7. Finally, we found that the proportions of infiltrating immune cells in TAA and normal tissues were different, especially in terms of macrophages and natural killer (NK) cells. Conclusion Chemotaxis and the complement system were identified as crucial pathways in TAA, and macrophages with interactive immune cells may regulate this pathological process.

scholarly journals Weighted gene co-expression network analysis of the salt-responsive transcriptomes reveals novel hub genes in green halophytic microalgae Dunaliella salina

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Bahman Panahi ◽  
Mohammad Amin Hejazi
Keyword(s):  
Network Analysis ◽  
Salinity Stress ◽  
Empirical Bayes ◽  
Dunaliella Salina ◽  
Sulfur Metabolism ◽  
Extensive Study ◽  
Mitogen Activated Protein Kinase ◽  
Hub Genes ◽  
Chlorophyll Metabolism ◽  
Scale Free

AbstractDespite responses to salinity stress in Dunaliella salina, a unicellular halotolerant green alga, being subject to extensive study, but the underlying molecular mechanism remains unknown. Here, Empirical Bayes method was applied to identify the common differentially expressed genes (DEGs) between hypersaline and normal conditions. Then, using weighted gene co-expression network analysis (WGCNA), which takes advantage of a graph theoretical approach, highly correlated genes were clustered as a module. Subsequently, connectivity patterns of the identified modules in two conditions were surveyed to define preserved and non-preserved modules by combining the Zsummary and medianRank measures. Finally, common and specific hub genes in non-preserved modules were determined using Eigengene-based module connectivity or module membership (kME) measures and validation was performed by using leave-one-out cross-validation (LOOCV). In this study, the power of beta = 12 (scale-free R2 = 0.8) was selected as the soft-thresholding to ensure a scale-free network, which led to the identification of 15 co-expression modules. Results also indicate that green, blue, brown, and yellow modules are non-preserved in salinity stress conditions. Examples of enriched Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways in non-preserved modules are Sulfur metabolism, Oxidative phosphorylation, Porphyrin and chlorophyll metabolism, Vitamin B6 metabolism. Moreover, the systems biology approach was applied here, proposed some salinity specific hub genes, such as radical-induced cell death1 protein (RCD1), mitogen-activated protein kinase kinase kinase 13 (MAP3K13), long-chain acyl-CoA synthetase (ACSL), acetyl-CoA carboxylase, biotin carboxylase subunit (AccC), and fructose-bisphosphate aldolase (ALDO), for the development of metabolites accumulating strains in D. salina.

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