scholarly journals Prenatal testosterone triggers long-term behavioral changes in male zebra finches: unravelling the neurogenomic mechanisms

BMC Genomics ◽  
2021 ◽  
Vol 22 (1) ◽  
Author(s):  
Alexandra B. Bentz ◽  
Chad E. Niederhuth ◽  
Laura L. Carruth ◽  
Kristen J. Navara
Keyword(s):  
Protein Interaction ◽  
Protein Interaction Networks ◽  
Interaction Networks ◽  
Differentially Expressed ◽  
Zebra Finches ◽  
Hub Genes ◽  
Prenatal Testosterone ◽  
Protein Protein Interaction ◽  
Protein Protein Interaction Networks

Abstract Background Maternal hormones, like testosterone, can strongly influence developing offspring, even generating long-term organizational effects on adult behavior; yet, the mechanisms facilitating these effects are still unclear. Here, we experimentally elevated prenatal testosterone in the eggs of zebra finches (Taeniopygia guttata) and measured male aggression in adulthood along with patterns of neural gene expression (RNA-seq) and DNA methylation (MethylC-Seq) in two socially relevant brain regions (hypothalamus and nucleus taenia of the amygdala). We used enrichment analyses and protein-protein interaction networks to find candidate processes and hub genes potentially affected by the treatment. We additionally identified differentially expressed genes that contained differentially methylated regions. Results We found that males from testosterone-injected eggs displayed more aggressive behaviors compared to males from control eggs. Hundreds of genes were differentially expressed, particularly in the hypothalamus, including potential aggression-related hub genes (e.g., brain derived neurotrophic factor). There were also enriched processes with well-established links to aggressive phenotypes (e.g., somatostatin and glutamate signaling). Furthermore, several highly connected genes identified in protein-protein interaction networks also showed differential methylation, including adenylate cyclase 2 and proprotein convertase 2. Conclusions These results highlight genes and processes that may play an important role in mediating the effects of prenatal testosterone on long-term phenotypic outcomes, thereby providing insights into the molecular mechanisms that facilitate hormone-mediated maternal effects.

scholarly journals Functional module detection through integration of single-cell RNA sequencing data with protein–protein interaction networks

2019 ◽  
Author(s):  
Florian Klimm ◽  
Enrique M. Toledo ◽  
Thomas Monfeuga ◽  
Fang Zhang ◽  
Charlotte M. Deane ◽  
...  
Keyword(s):  
Single Cell ◽  
Rna Sequencing ◽  
Protein Interaction ◽  
Protein Interaction Networks ◽  
Interaction Networks ◽  
Differentially Expressed ◽  
Sequencing Data ◽  
Protein Protein Interaction ◽  
Single Cell Rna Sequencing ◽  
Protein Protein Interaction Networks

AbstractRecent advances in single-cell RNA sequencing (scRNA-seq) have allowed researchers to explore transcriptional function at a cellular level. In this study, we present scPPIN, a method for integrating single-cell RNA sequencing data with protein–protein interaction networks (PPINs) that detects active modules in cells of different transcriptional states. We achieve this by clustering RNA-sequencing data, identifying differentially expressed genes, constructing node-weighted PPINs, and finding the maximum-weight connected subgraphs with an exact Steiner-tree approach. As a case study, we investigate RNA-sequencing data from human liver spheroids but the techniques described here are applicable to other organisms and tissues. scPPIN allows us to expand the output of differential expressed genes analysis with information from protein interactions. We find that different transcriptional states have different subnetworks of the PPIN significantly enriched which represent biological pathways. In these pathways, scPPIN also identifies proteins that are not differentially expressed but have a crucial biological function (e.g., as receptors) and therefore reveals biology beyond a standard differentially expressed gene analysis.

scholarly journals Functional module detection through integration of single-cell RNA sequencing data with protein–protein interaction networks

BMC Genomics ◽  
2020 ◽  
Vol 21 (1) ◽  
Author(s):  
Florian Klimm ◽  
Enrique M. Toledo ◽  
Thomas Monfeuga ◽  
Fang Zhang ◽  
Charlotte M. Deane ◽  
...  
Keyword(s):  
Single Cell ◽  
Rna Sequencing ◽  
Protein Interaction ◽  
Protein Interaction Networks ◽  
Interaction Networks ◽  
Differentially Expressed ◽  
Sequencing Data ◽  
Protein Protein Interaction ◽  
Single Cell Rna Sequencing ◽  
Protein Protein Interaction Networks

Abstract Background Recent advances in single-cell RNA sequencing have allowed researchers to explore transcriptional function at a cellular level. In particular, single-cell RNA sequencing reveals that there exist clusters of cells with similar gene expression profiles, representing different transcriptional states. Results In this study, we present scPPIN, a method for integrating single-cell RNA sequencing data with protein–protein interaction networks that detects active modules in cells of different transcriptional states. We achieve this by clustering RNA-sequencing data, identifying differentially expressed genes, constructing node-weighted protein–protein interaction networks, and finding the maximum-weight connected subgraphs with an exact Steiner-tree approach. As case studies, we investigate two RNA-sequencing data sets from human liver spheroids and human adipose tissue, respectively. With scPPIN we expand the output of differential expressed genes analysis with information from protein interactions. We find that different transcriptional states have different subnetworks of the protein–protein interaction networks significantly enriched which represent biological pathways. In these pathways, scPPIN identifies proteins that are not differentially expressed but have a crucial biological function (e.g., as receptors) and therefore reveals biology beyond a standard differential expressed gene analysis. Conclusions The introduced scPPIN method can be used to systematically analyse differentially expressed genes in single-cell RNA sequencing data by integrating it with protein interaction data. The detected modules that characterise each cluster help to identify and hypothesise a biological function associated to those cells. Our analysis suggests the participation of unexpected proteins in these pathways that are undetectable from the single-cell RNA sequencing data alone. The techniques described here are applicable to other organisms and tissues.

scholarly journals Positive Selection and Centrality in the Yeast and Fly Protein-Protein Interaction Networks

2016 ◽  
Vol 2016 ◽  
pp. 1-12 ◽  
Author(s):  
Sandip Chakraborty ◽  
David Alvarez-Ponce
Keyword(s):  
Positive Selection ◽  
Protein Interaction ◽  
Interaction Network ◽  
Protein Interaction Networks ◽  
Interaction Networks ◽  
Protein Protein Interaction ◽  
Protein Protein Interaction Networks ◽  
Interspecific Comparisons ◽  
High Degree

Proteins within a molecular network are expected to be subject to different selective pressures depending on their relative hierarchical positions. However, it is not obvious what genes within a network should be more likely to evolve under positive selection. On one hand, only mutations at genes with a relatively high degree of control over adaptive phenotypes (such as those encoding highly connected proteins) are expected to be “seen” by natural selection. On the other hand, a high degree of pleiotropy at these genes is expected to hinder adaptation. Previous analyses of the human protein-protein interaction network have shown that genes under long-term, recurrent positive selection (as inferred from interspecific comparisons) tend to act at the periphery of the network. It is unknown, however, whether these trends apply to other organisms. Here, we show that long-term positive selection has preferentially targeted the periphery of the yeast interactome. Conversely, in flies, genes under positive selection encode significantly more connected and central proteins. These observations are not due to covariation of genes’ adaptability and centrality with confounding factors. Therefore, the distribution of proteins encoded by genes under recurrent positive selection across protein-protein interaction networks varies from one species to another.

scholarly journals Identification of key genes as predictive biomarkers for osteosarcoma metastasis using translational bioinformatics

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Fu-peng Ding ◽  
Jia-yi Tian ◽  
Jing Wu ◽  
Dong-feng Han ◽  
Ding Zhao
Keyword(s):  
Clinical Significance ◽  
Protein Interaction ◽  
Protein Interaction Networks ◽  
Interaction Networks ◽  
Support Vector ◽  
Hub Genes ◽  
Protein Protein Interaction ◽  
Protein Protein Interaction Networks ◽  
Key Genes

Abstract Background Osteosarcoma (OS) metastasis is the most common cause of cancer-related mortality, however, no sufficient clinical biomarkers have been identified. In this study, we identified five genes to help predict metastasis at diagnosis. Methods We performed weighted gene co-expression network analysis (WGCNA) to identify the most relevant gene modules associated with OS metastasis. An important machine learning algorithm, the support vector machine (SVM), was employed to predict key genes for classifying the OS metastasis phenotype. Finally, we investigated the clinical significance of key genes and their enriched pathways. Results Eighteen modules were identified in WGCNA, among which the pink, red, brown, blue, and turquoise modules demonstrated good preservation. In the five modules, the brown and red modules were highly correlated with OS metastasis. Genes in the two modules closely interacted in protein–protein interaction networks and were therefore chosen for further analysis. Genes in the two modules were primarily enriched in the biological processes associated with tumorigenesis and development. Furthermore, 65 differentially expressed genes were identified as common hub genes in both WGCNA and protein–protein interaction networks. SVM classifiers with the maximum area under the curve were based on 30 and 15 genes in the brown and red modules, respectively. The clinical significance of the 45 hub genes was analyzed. Of the 45 genes, 17 were found to be significantly correlated with survival time. Finally, 5/17 genes, including ADAP2 (P = 0.0094), LCP2 (P = 0.013), ARHGAP25 (P = 0.0049), CD53 (P = 0.016), and TLR7 (P = 0.04) were significantly correlated with the metastatic phenotype. In vitro verification, western blotting, wound healing analyses, transwell invasion assays, proliferation assays, and colony formation assays indicated that ARHGAP25 promoted OS cell migration, invasion, proliferation, and epithelial–mesenchymal transition. Conclusion We identified five genes, namely ADAP2, LCP2, ARHGAP25, CD53, and TLR7, as candidate biomarkers for the prediction of OS metastasis; ARHGAP25 inhibits MG63 OS cell growth, migration, and invasion in vitro, indicating that ARHGAP25 can serve as a promising specific and prognostic biomarker for OS metastasis.

scholarly journals Prognostic and Predictive Values of MAD2L1 in Cholangiocarcinoma

2020 ◽  
Author(s):  
Yuan Gao ◽  
Xiwu Ouyang ◽  
Tianping Luo ◽  
Chunfu Zhu ◽  
Xihu Qin
Keyword(s):  
Protein Interaction ◽  
Microarray Data ◽  
Mitotic Arrest ◽  
Protein Interaction Networks ◽  
Interaction Networks ◽  
Hub Genes ◽  
Protein Protein Interaction ◽  
Kaplan Meier ◽  
Networks Analysis ◽  
Protein Protein Interaction Networks

Abstract Background: This study aimed to investigate the expression of Mitotic arrest deficient 2-like protein 1 (MAD2L1) in cholangiocarcinoma (CCA) and its biological function. Methods: Our study performed bioinformatics to analyze the microarray data from the Gene Expression Omnibus (GEO) database and obtained the differentially expressed genes (DEGs). Enrichment assay and protein-protein interaction networks analysis were conducted to extract hub genes. Mitotic arrest deficient 2-like protein 1 (MAD2L1) was investigated in tumor and adjacent nonneoplastic biliary ducts in 42 samples by immunohistochemistry. Subsequently, MAD2L1 was manipulated, and its function was examined in CCA cell lines and in vivo model. Results: 297 DEGs were extracted from the microarray data. And seven hub genes were defined through the enrichment assay and protein-protein interaction networks analysis. MAD2L1 was picked up as a novel biomarker, according to hierarchical cluster analysis and Kaplan-Meier survival analysis. MAD2L1 was specifically expressed in cancer tissues but not in the surrounding normal tissue, and 31 (73.81%) of 42 CCAs were MAD2L1 positive by immunohistochemistry. MAD2L1 expression was significantly correlated with tumor size, pathological grade, and clinical stage. Kaplan-Meier analysis demonstrated an inverse correlation between MAD2L1 expression and overall survival. Real-time polymerase chain reaction (RT-PCR) and Immunoblotting results further confirmed the results of immunohistochemistry and bioinformatic analysis from the database. In vitro and in vivo models, decreasing MAD2L1 could significantly suppress tumor growth.Conclusion: High MAD2L1 expression predicts the advanced stage of CCA. Targeting MAD2L1 may be a potential tumor suppressor and may provide the biological basis for a new therapeutic strategy.Trial registration: [2020]KY157-01. Registered 1st April 2020 - Retrospectively registered, http://114.255.48.20/login#.

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