Evaluation of expression changes, proteins interaction network, and microRNAs targeting catalase and superoxide dismutase genes under cold stress in rapeseed (Brassica napus L.)

Rapeseed is the third-largest source of plant oil and one of the essential oil plants worldwide. Cold stress is one of the critical factors that affect plant yield. Therefore, improving cold stress tolerance is necessary for yield increase. The present study investigated BnCAT1 and BnCSD1 genes’ expression behavior in a tolerant and sensitive cultivar under cold stress (4 °C). Besides, protein-protein interaction networks of CATs and CSDs enzymes, and their association with other antioxidant enzymes were analyzed. Moreover, the microRNAs targeting BnCAT1 and BnCSD1 genes were predicted. This study indicated many direct and indirect interactions and the association between the components of the plant antioxidant system. However, not only did the CATs and CSDs enzymes have a relationship with each other, but they also interacted directly with ascorbate peroxidase and glutathione reductase enzymes. Also, 23 and 35 effective microRNAs were predicted for BnCAT1 and BnCSD1 genes, respectively. The gene expression results indicated an elevated expression of BnCAT1 and BnCSD1 in both tolerant and sensitive cultivars. However, this increase was more noticeable in the tolerant cultivar. Thus, the BnCSD1 gene had the highest expression in the early hour of cold stress, especially in the 12th h, and the BnCAT1 gene showed the highest expression in the 48th h. This result may indicate a functional relationship between these enzymes.

Download Full-text

Transcriptional landscape of cellular networks reveal interactions driving the dormancy mechanisms in cancer

Scientific Reports ◽

10.1038/s41598-021-94005-x ◽

2021 ◽

Vol 11 (1) ◽

Author(s):

Dilara Uzuner ◽

Yunus Akkoç ◽

Nesibe Peker ◽

Pınar Pir ◽

Devrim Gözüaçık ◽

...

Keyword(s):

Cancer Cells ◽

Regulatory Networks ◽

Metastatic Cancer ◽

Interaction Network ◽

Current Treatment ◽

Cellular Mechanisms ◽

Protein Protein Interaction ◽

Cancer Dormancy ◽

Protein Protein Interaction Networks ◽

First Time

AbstractPrimary cancer cells exert unique capacity to disseminate and nestle in distant organs. Once seeded in secondary sites, cancer cells may enter a dormant state, becoming resistant to current treatment approaches, and they remain silent until they reactivate and cause overt metastases. To illuminate the complex mechanisms of cancer dormancy, 10 transcriptomic datasets from the literature enabling 21 dormancy–cancer comparisons were mapped on protein–protein interaction networks and gene-regulatory networks to extract subnetworks that are enriched in significantly deregulated genes. The genes appearing in the subnetworks and significantly upregulated in dormancy with respect to proliferative state were scored and filtered across all comparisons, leading to a dormancy–interaction network for the first time in the literature, which includes 139 genes and 1974 interactions. The dormancy interaction network will contribute to the elucidation of cellular mechanisms orchestrating cancer dormancy, paving the way for improvements in the diagnosis and treatment of metastatic cancer.

Download Full-text

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

BioMed Research International ◽

10.1155/2016/4658506 ◽

2016 ◽

Vol 2016 ◽

pp. 1-12 ◽

Cited By ~ 10

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.

Download Full-text

An Algorithm for Finding Functional Modules and Protein Complexes in Protein-Protein Interaction Networks

Journal of Biomedicine and Biotechnology ◽

10.1155/2008/860270 ◽

2008 ◽

Vol 2008 ◽

pp. 1-10 ◽

Cited By ~ 9

Author(s):

Guangyu Cui ◽

Yu Chen ◽

De-Shuang Huang ◽

Kyungsook Han

Keyword(s):

Protein Interaction ◽

Protein Interaction Network ◽

Protein Complexes ◽

Interaction Network ◽

Connected Subgraph ◽

Protein Protein Interaction ◽

Group Form ◽

Protein Protein Interaction Networks ◽

Protein Protein Interaction Network ◽

Good Agreement

Biological processes are often performed by a group of proteins rather than by individual proteins, and proteins in a same biological group form a densely connected subgraph in a protein-protein interaction network. Therefore, finding a densely connected subgraph provides useful information to predict the function or protein complex of uncharacterized proteins in the highly connected subgraph. We have developed an efficient algorithm and program for finding cliques and near-cliques in a protein-protein interaction network. Analysis of the interaction network of yeast proteins using the algorithm demonstrates that 59% of the near-cliques identified by our algorithm have at least one function shared by all the proteins within a near-clique, and that 56% of the near-cliques show a good agreement with the experimentally determined protein complexes catalogued in MIPS.

Download Full-text

Co-evolution between codon usage and Protein-Protein Interaction Networks in Bacterial genomes

10.1101/2020.03.30.016956 ◽

2020 ◽

Cited By ~ 1

Author(s):

Maddalena Dilucca ◽

Giulio Cimini ◽

Sergio Forcelloni ◽

Andrea Giansanti

Keyword(s):

Codon Usage ◽

Protein Interaction ◽

Protein Interactions ◽

Codon Bias ◽

Gc Content ◽

Interaction Network ◽

Protein Interaction Networks ◽

Bacterial Genomes ◽

Protein Protein Interaction ◽

Protein Protein Interaction Networks

AbstractIn this work, we study the correlation between codon usage and the network features of the PPI in bacteria genomes. We want to extend the information by Dilucca et al. (2015) about E.Coli’s genome for a set of other 34 bacteria. We use PCA techniques in the space of codon bias indices (compAI, compAI_w, tAI, NC) and GC content to show that genes with similar patterns of codon usage feature have a significantly higher probability that their encoded proteins interact within the PPI. And vice-versa, we show that interacting in the PPI have a coherent codon usage. This work could allow for future investigations into the possible effects that codon bias signal can have on the topology of protein interaction network and, as such, to improve existing bioinformatics methods for predicting protein interactions.

Download Full-text

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

Nature Communications ◽

10.1038/s41467-021-21146-y ◽

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.

Download Full-text

Endocytic proteins with prion-like domains form viscoelastic condensates that enable membrane remodeling

Proceedings of the National Academy of Sciences ◽

10.1073/pnas.2113789118 ◽

2021 ◽

Vol 118 (50) ◽

pp. e2113789118

Author(s):

Louis-Philippe Bergeron-Sandoval ◽

Sandeep Kumar ◽

Hossein Khadivi Heris ◽

Catherine L. A. Chang ◽

Caitlin E. Cornell ◽

...

Keyword(s):

Protein Interaction ◽

Interaction Network ◽

Coat Proteins ◽

Membrane Remodeling ◽

Interfacial Tensions ◽

Protein Protein Interaction ◽

Membrane Invagination ◽

Protein Protein Interaction Networks ◽

Key Steps ◽

Protein Protein Interaction Network

Membrane invagination and vesicle formation are key steps in endocytosis and cellular trafficking. Here, we show that endocytic coat proteins with prion-like domains (PLDs) form hemispherical puncta in the budding yeast, Saccharomyces cerevisiae. These puncta have the hallmarks of biomolecular condensates and organize proteins at the membrane for actin-dependent endocytosis. They also enable membrane remodeling to drive actin-independent endocytosis. The puncta, which we refer to as endocytic condensates, form and dissolve reversibly in response to changes in temperature and solution conditions. We find that endocytic condensates are organized around dynamic protein–protein interaction networks, which involve interactions among PLDs with high glutamine contents. The endocytic coat protein Sla1 is at the hub of the protein–protein interaction network. Using active rheology, we inferred the material properties of endocytic condensates. These experiments show that endocytic condensates are akin to viscoelastic materials. We use these characterizations to estimate the interfacial tension between endocytic condensates and their surroundings. We then adapt the physics of contact mechanics, specifically modifications of Hertz theory, to develop a quantitative framework for describing how interfacial tensions among condensates, the membrane, and the cytosol can deform the plasma membrane to enable actin-independent endocytosis.

Download Full-text