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 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 Individualized interactomes for network-based precision medicine in hypertrophic cardiomyopathy with implications for other clinical pathophenotypes

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Bradley A. Maron ◽  
Rui-Sheng Wang ◽  
Sergei Shevtsov ◽  
Stavros G. Drakos ◽  
Elena Arons ◽  
...  
Keyword(s):  
Hypertrophic Cardiomyopathy ◽  
Precision Medicine ◽  
Protein Interaction ◽  
Interaction Network ◽  
Protein Interaction Networks ◽  
Janus Kinase ◽  
Interaction Networks ◽  
Patient Specific ◽  
Protein Protein Interaction ◽  
Protein Protein Interaction Networks

AbstractProgress in precision medicine is limited by insufficient knowledge of transcriptomic or proteomic features in involved tissues that define pathobiological differences between patients. Here, myectomy tissue from patients with obstructive hypertrophic cardiomyopathy and heart failure is analyzed using RNA-Seq, and the results are used to develop individualized protein-protein interaction networks. From this approach, hypertrophic cardiomyopathy is distinguished from dilated cardiomyopathy based on the protein-protein interaction network pattern. Within the hypertrophic cardiomyopathy cohort, the patient-specific networks are variable in complexity, and enriched for 30 endophenotypes. The cardiac Janus kinase 2-Signal Transducer and Activator of Transcription 3-collagen 4A2 (JAK2-STAT3-COL4A2) expression profile informed by the networks was able to discriminate two hypertrophic cardiomyopathy patients with extreme fibrosis phenotypes. Patient-specific network features also associate with other important hypertrophic cardiomyopathy clinical phenotypes. These proof-of-concept findings introduce personalized protein-protein interaction networks (reticulotypes) for characterizing patient-specific pathobiology, thereby offering a direct strategy for advancing precision medicine.

scholarly journals Comparative analysis of Pure Hubs and Pure Bottlenecks in Human Protein-protein Interaction Networks

2021 ◽  
Author(s):  
Nithya Chandramohan ◽  
Manjari Kiran ◽  
Hampapathalu Adimurthy Nagarajaram
Keyword(s):  
Protein Interaction ◽  
Splice Variants ◽  
Protein Interaction Networks ◽  
Interaction Networks ◽  
Human Protein ◽  
Cancer Genes ◽  
Protein Protein Interaction ◽  
High Betweenness ◽  
Protein Protein Interaction Networks ◽  
High Degree

Bottlenecks and hubs form a set of topologically important nodes in a network. In this communication, we have made a detailed investigation on hubs and bottlenecks in human protein-protein interaction networks. We find that, three distinct groups exist which we refer to as: a) pure hubs (PHs, nodes having high degree but low betweenness values), b) mix proteins (MXs, nodes having both high degree and high betweenness values) and c) pure bottlenecks (PBs, nodes having high betweenness values but low degree values). Our investigations have revealed that pure hubs, as compared with MXs and PBs, (i) are more disordered, (ii) have higher potential to bind to multiple partners, (iii) are enriched with essential proteins as well as enriched with a higher number of splice variants. The MX proteins, as compared with PHs and PBs, (i) show slower evolutionary patterns, (ii) are involved in multiple pathways, (iii) enriched with the products of genes associated with various diseases and (iv) are more often targeted by bacteria, viruses, protozoa, and fungi pathogens. PBs, as compared with the PHs and MXs, (i) are associated with cancer genes and (ii) are the targets or the nearest neighbors of the targets of most of the approved drugs. Furthermore, our study revealed that these three categories of proteins are involved in distinct functional roles; PHs are involved in housekeeping processes such as transcription and replication; MXs proteins are involved in core signaling pathways whereas PBs are involved in signal transduction processes. Our work, therefore, has identified the distinct characteristics features associated with pure hubs, mix proteins and pure bottlenecks and thus helps in prioritizing proteins based on their degree and betweenness centrality values.

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