scholarly journals Weighted Gene Co-Expression Network Analysis and Machine learning Identify Critical Genes in the Development of Osteomyelitis after Staphylococcus aureus Infections

2021 ◽  
Author(s):  
Baixing Chen ◽  
Shaoshuo Li ◽  
Zhaoqi Lu ◽  
Mingling Huang ◽  
Shi Lin ◽  
...  
Keyword(s):  
Staphylococcus Aureus ◽  
Network Analysis ◽  
Protein Function ◽  
Actin Polymerization ◽  
Rna Binding ◽  
Area Under The Curve ◽  
Recursive Feature Elimination ◽  
Protein Protein Interaction ◽  
External Data ◽  
Highly Correlated

Abstract Background: Staphylococcus aureus (S. aureus) is the most common pathogen that causes osteomyelitis (OM). However, OM's pathogenesis, which is not clear, involves many factors such as environment, genetics and immunity dysregulation. This study aims to explore the key genes involved in the pathogenesis and development of OM following S. aureus infection. Methods: After obtaining the datasets of GSE6269 and GSE16129, we performed weighted gene co-expression network analysis (WGCNA) to find clusters modules of highly correlated genes and recursive feature elimination (RFE) method to narrow the range of feature genes. For determining the effect of feature genes, we constructed a random forest (RF) model with feature genes and validated the predictive validity of the RF model using independent data from GSE11908. The protein-protein interaction (PPI) network identifies essential proteins that contributed to OM development. Results: There were 12,401 genes from 77 samples that 48 S. aureus patients developed to OM and 29 of those without OM. We divided 31 significant gene modules into different modules, and the brown module significantly related to OM. Biological Functions of the brown module mainly enriched in the inflammatory response, metabolic, cancer, viral pathways, protein binding and RNA binding. After screening, 19 genes, including CYP2E1, BBS10, ARPC5L, GAPVD1, PURA, RBMS1, BTN2A2, EXOSC8, METTL8, FYCO1, KHK, PRPF38B, CD72, C2CD5, ABHD6, CD200, FAM53C, HCP5 and ELP1, were defined as feature genes for constructing RF model. After validating the external data, the average area under the curve was 85%, and the accuracy of the RF model was 85.7%. The protein function of modules enriched in the RNA exosome complex's catalytic component and regulation of actin polymerization. Conclusions: This study aimed to identify related genes involved in the occurrence and development of OM. We constructed the RF model with 19 genes, which effectively classify the patients with OM or non-OM. Despite its limitations, the study certainly adds to our understanding of OM's pathogenesis, and therefore, has significant implications for potential therapeutic targets and the predicted value of OM.

scholarly journals Identification of Infertility-Associated Topologically Important Genes Using Weighted Co-expression Network Analysis

2021 ◽  
Vol 12 ◽  
Author(s):  
Jingni Wu ◽  
Xiaomeng Xia ◽  
Ye Hu ◽  
Xiaoling Fang ◽  
Sandra Orsulic
Keyword(s):  
Network Analysis ◽  
Topological Analysis ◽  
Interaction Network ◽  
Enrichment Analysis ◽  
Gene Expression Omnibus ◽  
Gene Set Enrichment Analysis ◽  
Messenger Rnas ◽  
Hub Genes ◽  
Protein Protein Interaction ◽  
Highly Correlated

Endometriosis has been associated with a high risk of infertility. However, the underlying molecular mechanism of infertility in endometriosis remains poorly understood. In our study, we aimed to discover topologically important genes related to infertility in endometriosis, based on the structure network mining. We used microarray data from the Gene Expression Omnibus (GEO) database to construct a weighted gene co-expression network for fertile and infertile women with endometriosis and to identify gene modules highly correlated with clinical features of infertility in endometriosis. Additionally, the protein–protein interaction network analysis was used to identify the potential 20 hub messenger RNAs (mRNAs) while the network topological analysis was used to identify nine candidate long non-coding RNAs (lncRNAs). Functional annotations of clinically significant modules and lncRNAs revealed that hub genes might be involved in infertility in endometriosis by regulating G protein-coupled receptor signaling (GPCR) activity. Gene Set Enrichment Analysis showed that the phospholipase C-activating GPCR signaling pathway is correlated with infertility in patients with endometriosis. Taken together, our analysis has identified 29 hub genes which might lead to infertility in endometriosis through the regulation of the GPCR network.

scholarly journals Investigation of hub gene associated with the infection of Staphylococcus aureus via weighted gene co-expression network analysis

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Jia-xin Li ◽  
Xun-jie Cao ◽  
Yuan-yi Huang ◽  
Ya-ping Li ◽  
Zi-yuan Yu ◽  
...  
Keyword(s):  
Staphylococcus Aureus ◽  
Network Analysis ◽  
Roc Curve ◽  
Enrichment Analysis ◽  
Gene Set Enrichment Analysis ◽  
Hub Genes ◽  
Hub Gene ◽  
Protein Protein Interaction ◽  
Serious Infection ◽  
Diagnostic Values

Abstract Introduction Staphylococcus aureus is a gram-positive bacterium that causes serious infection. With the increasing resistance of bacteria to current antibiotics, it is necessary to learn more about the molecular mechanism and cellular pathways involved in the Staphylococcus aureus infection. Methods We downloaded the GSE33341 dataset from the GEO database and applied the weighted gene co-expression network analysis (WGCNA), from which we obtained some critical modules. Kyoto Encyclopedia of Genes and Genomes (KEGG) and Gene Ontology (GO) were applied to illustrate the biological functions of genes in these modules. We constructed the protein-protein interaction (PPI) network by Cytoscape and selected five candidate hub genes. Five potential hub genes were validated in GSE30119 by GraphPad Prism 8.0. The diagnostic values of these genes were calculated and present in the ROC curve based on the GSE13670 dataset. Their gene functions were analyzed by Gene Set Enrichment Analysis (GSEA). Results A co-expression network was built with 5000 genes divided into 11 modules. The genes in green and turquoise modules demonstrated a high correlation. According to the KEGG and GO analyses, genes in the green module were closely related to ubiquitination and autophagy. Subsequently, we picked out the top five hub genes in the green module. And UBB was determined as the hub gene in the GSE30119 dataset. The expression level of UBB, ASB, and MKRN1 could significantly differentiate between Staphylococcus aureus infection and healthy controls based on the ROC curve. The GSEA analysis indicated that lower expression levels of UBB were associated with the P53 signal pathway. Conclusions We identified some hub genes and significant signal enrichment pathways in Staphylococcus aureus infection via bioinformatics analysis, which may facilitate the development of potential clinical therapeutic strategies.

Reading the phosphorylation code: binding of the 14-3-3 protein to multivalent client phosphoproteins

2020 ◽  
Vol 477 (7) ◽  
pp. 1219-1225 ◽  
Author(s):  
Nikolai N. Sluchanko
Keyword(s):  
Protein Function ◽  
Intrinsically Disordered Protein ◽  
Structural Basis ◽  
Major Protein ◽  
Post Translational Modifications ◽  
Protein Protein Interaction ◽  
Intrinsically Disordered ◽  
Biochemical Journal ◽  
Multiple Binding ◽  
And Control

Many major protein–protein interaction networks are maintained by ‘hub’ proteins with multiple binding partners, where interactions are often facilitated by intrinsically disordered protein regions that undergo post-translational modifications, such as phosphorylation. Phosphorylation can directly affect protein function and control recognition by proteins that ‘read’ the phosphorylation code, re-wiring the interactome. The eukaryotic 14-3-3 proteins recognizing multiple phosphoproteins nicely exemplify these concepts. Although recent studies established the biochemical and structural basis for the interaction of the 14-3-3 dimers with several phosphorylated clients, understanding their assembly with partners phosphorylated at multiple sites represents a challenge. Suboptimal sequence context around the phosphorylated residue may reduce binding affinity, resulting in quantitative differences for distinct phosphorylation sites, making hierarchy and priority in their binding rather uncertain. Recently, Stevers et al. [Biochemical Journal (2017) 474: 1273–1287] undertook a remarkable attempt to untangle the mechanism of 14-3-3 dimer binding to leucine-rich repeat kinase 2 (LRRK2) that contains multiple candidate 14-3-3-binding sites and is mutated in Parkinson's disease. By using the protein-peptide binding approach, the authors systematically analyzed affinities for a set of LRRK2 phosphopeptides, alone or in combination, to a 14-3-3 protein and determined crystal structures for 14-3-3 complexes with selected phosphopeptides. This study addresses a long-standing question in the 14-3-3 biology, unearthing a range of important details that are relevant for understanding binding mechanisms of other polyvalent proteins.

Identification of Essential Proteins in Yeast Using Mean Weighted Average and Recursive Feature Elimination

2019 ◽  
Vol 12 (1) ◽  
pp. 5-10 ◽  
Author(s):  
Sivagnanam Rajamanickam Mani Sekhar ◽  
Siddesh Gaddadevara Matt ◽  
Sunilkumar S. Manvi ◽  
Srinivasa Krishnarajanagar Gopalalyengar
Keyword(s):  
Drug Design ◽  
Weighted Average ◽  
Living Organism ◽  
Experimental Result ◽  
Recursive Feature Elimination ◽  
Protein Interaction Data ◽  
Essential Proteins ◽  
Protein Protein Interaction ◽  
Result Show ◽  
Better Than

Background: Essential proteins are significant for drug design, cell development, and for living organism survival. A different method has been developed to predict essential proteins by using topological feature, and biological features. Objective: Still it is a challenging task to predict essential proteins effectively and timely, as the availability of protein protein interaction data depends on network correctness. Methods: In the proposed solution, two approaches Mean Weighted Average and Recursive Feature Elimination is been used to predict essential proteins and compared to select the best one. In Mean Weighted Average consecutive slot data to be taken into aggregated count, to get the nearest value which considered as prescription for the best proteins for the slot, where as in Recursive Feature Elimination method whole data is spilt into different slots and essential protein for each slot is determined. Results: The result shows that the accuracy using Recursive Feature Elimination is at-least nine percentages superior when compared to Mean Weighted Average and Betweenness centrality. Conclusion: Essential proteins are made of genes which are essential for living being survival and drug design. Different approaches have been proposed to anticipate essential proteins using either experimental or computation methods. The experimental result show that the proposed work performs better than other approaches.

scholarly journals Diabetic Retinopathy and Laser Therapy in Rats: A Protein-Protein Interaction Network Analysis

2017 ◽  
Vol 8 (Suppl 1) ◽  
pp. S20-S21 ◽  
Author(s):  
Akram Safaei ◽  
Mostafa Rezaei Tavirani ◽  
Mona Zamanian Azodi ◽  
Alireza Lashay ◽  
Seyed Farzad Mohammadi ◽  
...  
Keyword(s):  
Diabetic Retinopathy ◽  
Network Analysis ◽  
Laser Therapy ◽  
Protein Interaction ◽  
Protein Interaction Network ◽  
Interaction Network ◽  
Protein Protein Interaction ◽  
Protein Protein Interaction Network

scholarly journals PPM1G promotes the progression of hepatocellular carcinoma via phosphorylation regulation of alternative splicing protein SRSF3

2021 ◽  
Vol 12 (8) ◽  
Author(s):  
Dawei Chen ◽  
Zhenguo Zhao ◽  
Lu Chen ◽  
Qinghua Li ◽  
Jixue Zou ◽  
...  
Keyword(s):  
Hepatocellular Carcinoma ◽  
Alternative Splicing ◽  
Protein Function ◽  
Vital Role ◽  
Normal Tissues ◽  
Mechanistic Analysis ◽  
Highly Correlated ◽  
Splicing Patterns

AbstractEmerging evidence has demonstrated that alternative splicing has a vital role in regulating protein function, but how alternative splicing factors can be regulated remains unclear. We showed that the PPM1G, a protein phosphatase, regulated the phosphorylation of SRSF3 in hepatocellular carcinoma (HCC) and contributed to the proliferation, invasion, and metastasis of HCC. PPM1G was highly expressed in HCC tissues compared to adjacent normal tissues, and higher levels of PPM1G were observed in adverse staged HCCs. The higher levels of PPM1G were highly correlated with poor prognosis, which was further validated in the TCGA cohort. The knockdown of PPM1G inhibited the cell growth and invasion of HCC cell lines. Further studies showed that the knockdown of PPM1G inhibited tumor growth in vivo. The mechanistic analysis showed that the PPM1G interacted with proteins related to alternative splicing, including SRSF3. Overexpression of PPM1G promoted the dephosphorylation of SRSF3 and changed the alternative splicing patterns of genes related to the cell cycle, the transcriptional regulation in HCC cells. In addition, we also demonstrated that the promoter of PPM1G was activated by multiple transcription factors and co-activators, including MYC/MAX and EP300, MED1, and ELF1. Our study highlighted the essential role of PPM1G in HCC and shed new light on unveiling the regulation of alternative splicing in malignant transformation.

Exploring fake news identification using word and sentence embeddings

2021 ◽  
pp. 1-8
Author(s):  
V.T Priyanga ◽  
J.P Sanjanasri ◽  
Vijay Krishna Menon ◽  
E.A Gopalakrishnan ◽  
K.P Soman
Keyword(s):  
Machine Learning ◽  
Social Media ◽  
Network Analysis ◽  
Supervised Machine Learning ◽  
Breeding Ground ◽  
Fake News ◽  
Data Set ◽  
Highly Correlated ◽  
Use Of Social Media ◽  
The Liar

The widespread use of social media like Facebook, Twitter, Whatsapp, etc. has changed the way News is created and published; accessing news has become easy and inexpensive. However, the scale of usage and inability to moderate the content has made social media, a breeding ground for the circulation of fake news. Fake news is deliberately created either to increase the readership or disrupt the order in the society for political and commercial benefits. It is of paramount importance to identify and filter out fake news especially in democratic societies. Most existing methods for detecting fake news involve traditional supervised machine learning which has been quite ineffective. In this paper, we are analyzing word embedding features that can tell apart fake news from true news. We use the LIAR and ISOT data set. We churn out highly correlated news data from the entire data set by using cosine similarity and other such metrices, in order to distinguish their domains based on central topics. We then employ auto-encoders to detect and differentiate between true and fake news while also exploring their separability through network analysis.

scholarly journals Dynamic cortical actin remodeling by ERM proteins controls BCR microcluster organization and integrity

2011 ◽  
Vol 208 (5) ◽  
pp. 1055-1068 ◽  
Author(s):  
Bebhinn Treanor ◽  
David Depoil ◽  
Andreas Bruckbauer ◽  
Facundo D. Batista
Keyword(s):  
Actin Cytoskeleton ◽  
B Cell ◽  
Protein Function ◽  
Actin Polymerization ◽  
Lymphocyte Activation ◽  
Dominant Negative ◽  
Temporary Increase ◽  
Cortical Actin ◽  
Erm Proteins ◽  
Diffusion Dynamics

Signaling microclusters are a common feature of lymphocyte activation. However, the mechanisms controlling the size and organization of these discrete structures are poorly understood. The Ezrin-Radixin-Moesin (ERM) proteins, which link plasma membrane proteins with the actin cytoskeleton and regulate the steady-state diffusion dynamics of the B cell receptor (BCR), are transiently dephosphorylated upon antigen receptor stimulation. In this study, we show that the ERM proteins ezrin and moesin influence the organization and integrity of BCR microclusters. BCR-driven inactivation of ERM proteins is accompanied by a temporary increase in BCR diffusion, followed by BCR immobilization. Disruption of ERM protein function using dominant-negative or constitutively active ezrin constructs or knockdown of ezrin and moesin expression quantitatively and qualitatively alters BCR microcluster formation, antigen aggregation, and downstream BCR signal transduction. Chemical inhibition of actin polymerization also altered the structure and integrity of BCR microclusters. Together, these findings highlight a crucial role for the cortical actin cytoskeleton during B cell spreading and microcluster formation and function.

scholarly journals Radiogenomic modeling predicts survival-associated prognostic groups in glioblastoma

2021 ◽  
Vol 3 (1) ◽  
Author(s):  
Nicholas Nuechterlein ◽  
Beibin Li ◽  
Abdullah Feroze ◽  
Eric C Holland ◽  
Linda Shapiro ◽  
...  
Keyword(s):  
Machine Learning ◽  
Feature Selection ◽  
Molecular Subtypes ◽  
Feature Selection Method ◽  
Area Under The Curve ◽  
Selection Method ◽  
Recursive Feature Elimination ◽  
Signal Abnormality ◽  
Mri Features ◽  
Mri Scans

Abstract Background Combined whole-exome sequencing (WES) and somatic copy number alteration (SCNA) information can separate isocitrate dehydrogenase (IDH)1/2-wildtype glioblastoma into two prognostic molecular subtypes, which cannot be distinguished by epigenetic or clinical features. The potential for radiographic features to discriminate between these molecular subtypes has yet to be established. Methods Radiologic features (n = 35 340) were extracted from 46 multisequence, pre-operative magnetic resonance imaging (MRI) scans of IDH1/2-wildtype glioblastoma patients from The Cancer Imaging Archive (TCIA), all of whom have corresponding WES/SCNA data. We developed a novel feature selection method that leverages the structure of extracted MRI features to mitigate the dimensionality challenge posed by the disparity between a large number of features and the limited patients in our cohort. Six traditional machine learning classifiers were trained to distinguish molecular subtypes using our feature selection method, which was compared to least absolute shrinkage and selection operator (LASSO) feature selection, recursive feature elimination, and variance thresholding. Results We were able to classify glioblastomas into two prognostic subgroups with a cross-validated area under the curve score of 0.80 (±0.03) using ridge logistic regression on the 15-dimensional principle component analysis (PCA) embedding of the features selected by our novel feature selection method. An interrogation of the selected features suggested that features describing contours in the T2 signal abnormality region on the T2-weighted fluid-attenuated inversion recovery (FLAIR) MRI sequence may best distinguish these two groups from one another. Conclusions We successfully trained a machine learning model that allows for relevant targeted feature extraction from standard MRI to accurately predict molecularly-defined risk-stratifying IDH1/2-wildtype glioblastoma patient groups.

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