scholarly journals Corynebacterium glutamicum Regulation beyond Transcription: Organizing Principles and Reconstruction of an Extended Regulatory Network Incorporating Regulations Mediated by Small RNA and Protein–Protein Interactions

2021 ◽  
Vol 9 (7) ◽  
pp. 1395
Author(s):  
Juan M. Escorcia-Rodríguez ◽  
Andreas Tauch ◽  
Julio A. Freyre-González
Keyword(s):  
Corynebacterium Glutamicum ◽  
Protein Interactions ◽  
Regulatory Network ◽  
Regulatory Networks ◽  
Network Models ◽  
Global Scale ◽  
System Level ◽  
Model Organisms ◽  
Protein Protein Interactions ◽  
Regulatory Structure

Corynebacterium glutamicum is a Gram-positive bacterium found in soil where the condition changes demand plasticity of the regulatory machinery. The study of such machinery at the global scale has been challenged by the lack of data integration. Here, we report three regulatory network models for C. glutamicum: strong (3040 interactions) constructed solely with regulations previously supported by directed experiments; all evidence (4665 interactions) containing the strong network, regulations previously supported by nondirected experiments, and protein–protein interactions with a direct effect on gene transcription; sRNA (5222 interactions) containing the all evidence network and sRNA-mediated regulations. Compared to the previous version (2018), the strong and all evidence networks increased by 75 and 1225 interactions, respectively. We analyzed the system-level components of the three networks to identify how they differ and compared their structures against those for the networks of more than 40 species. The inclusion of the sRNA-mediated regulations changed the proportions of the system-level components and increased the number of modules but decreased their size. The C. glutamicum regulatory structure contrasted with other bacterial regulatory networks. Finally, we used the strong networks of three model organisms to provide insights and future directions of the C.glutamicum regulatory network characterization.

scholarly journals Corynebacterium glutamicum regulation beyond transcription: Organizing principles and reconstruction of an extended regulatory network incorporating regulations mediated by small RNA and protein-protein interactions

2021 ◽  
Author(s):  
Juan Miguel Escorcia-Rodríguez ◽  
Andreas Tauch ◽  
Julio Augusto Freyre-González
Keyword(s):  
Corynebacterium Glutamicum ◽  
Protein Interactions ◽  
Regulatory Network ◽  
Regulatory Networks ◽  
Network Models ◽  
Global Scale ◽  
System Level ◽  
Model Organisms ◽  
Protein Protein Interactions ◽  
Regulatory Structure

Corynebacterium glutamicum is a Gram-positive bacterium found in soil where the condition changes demand plasticity of the regulatory machinery. The study of such machinery at the global scale has been challenged by the lack of data integration. Here, we report three regulatory network models for C. glutamicum: strong (3040 interactions) constructed solely with regulations previously supported by directed experiments; all evidence (4665 interactions) containing the strong network, regulations previously supported by non-directed experiments, and protein-protein interactions with a direct effect on gene transcription; and sRNA (5222 interactions) containing the all evidence network and sRNA-mediated regulations. Compared to the previous version (2018), the strong and all evidence networks increased by 75 and 1225 interactions, respectively. We analyzed the system-level components of the three networks to identify how they differ and compared their structures against those for the networks of more than 40 species. The inclusion of the sRNAs regulations changed the proportions of the system-level components and increased the number of modules but decreased their size. The C. glutamicum regulatory structure contrasted with other bacterial regulatory networks. Finally, we used the strong networks of three model organisms to provide insights and future directions of the C. glutamicum regulatory network characterization.

scholarly journals Corynebacterium Glutamicum Regulation beyond Transcription: Organizing Principles and Reconstruction of an Extended Regulatory Network Incorporating Regulations Mediated by Small Rna and Protein-Protein Interactions

2020 ◽  
Author(s):  
Juan M. Escorcia-Rodríguez ◽  
Andreas Tauch ◽  
Julio A. Freyre-González
Keyword(s):  
Corynebacterium Glutamicum ◽  
Protein Interactions ◽  
Regulatory Network ◽  
Transcriptional Regulatory Network ◽  
Network Models ◽  
Global Scale ◽  
System Level ◽  
Model Organisms ◽  
Protein Protein Interactions ◽  
Regulatory Structure

Corynebacterium glutamicum is a Gram-positive bacterium found in soil where the condition changes demand plasticity of the regulatory interactions, which study at the global scale has been challenged by the lack of data integration. Here, we update the manually-curated C. glutamicum transcriptional regulatory network, now including protein-protein interactions having a direct effect on gene transcription. The network model with regulations supported by any experimental evidence increased by 557 interactions regarding the previous (2018) version. 73 interactions supported by directed experiments were also included in a second model. We included 545 sRNA-mediated regulations in a third model with a total of 5164 interactions. We deposited the three network models in Abasy Atlas v2.4. We study the C. glutamicum regulatory structure by comparing it against the networks for more than 40 species, finding it to contrast in several global structural properties. We analyze the system-level components of the networks, finding that the inclusion of the sRNAs regulations changes their proportions, transferring part of the basal machinery to the modular class and increasing the number of modules while decreasing their size. Finally, we use strong networks of three model organisms to provide insights in future directions of the C. glutamicum network characterization.

scholarly journals The tapeworm interactome: inferring confidence scored protein-protein interactions from the proteome of Hymenolepis microstoma

2019 ◽  
Author(s):  
Katherine James ◽  
Peter D. Olson
Keyword(s):  
Transcription Factors ◽  
Protein Interaction ◽  
Protein Interactions ◽  
System Level ◽  
Model Organisms ◽  
Phylogenetic Distance ◽  
Interaction Data ◽  
Protein Protein Interactions ◽  
Gene Models ◽  
Hymenolepis Microstoma

AbstractReference genome and transcriptome assemblies of helminths have reached a level of completion whereby secondary analyses that rely on accurate gene estimation or syntenic relationships can be now conducted with a high level of confidence. Recent public release of the v.3 assembly of the mouse bile-duct tapeworm, Hymenolepis microstoma, provides chromosome-level characterisation of the genome and a stabilised set of protein coding gene models underpinned by both bioinformatic and empirical data. However, interactome data have not been produced. Conserved protein-protein interactions in other organisms, termed interologs, can be used to transfer interactions between species, allowing systems-level analysis in non-model organisms. Here, we describe a probabilistic, integrated network of interologs for the H. microstoma proteome, based on conserved protein interactions found in eukaryote model species. Almost a third of the 10,139 gene models in the v.3 assembly could be assigned interaction data and assessment of the resulting network indicates that topologically-important proteins are related to essential cellular pathways, and that the network clusters into biologically meaningful components. Moreover, network parameters are similar to those of single-species interaction networks that we constructed in the same way for S. cerevisiae, C. elegans and H. sapiens, demonstrating that information-rich, system-level analyses can be conducted even on species separated by a large phylogenetic distance from the major model organisms from which most protein interaction evidence is based. Using the interolog network, we then focused on sub-networks of interactions assigned to discrete suites of genes of interest, including signalling components and transcription factors, germline ‘multipotency’ genes, and differentially-expressed genes between larval and adult worms. These analyses not only showed an expected bias toward highly-conserved proteins, such as components of intracellular signal transduction, but in some cases predicted interactions with transcription factors that aid in identifying their target genes. With the completion of key helminth genomes, such systems level analyses can provide an important predictive framework to guide basic and applied research on helminths and will become increasingly informative as protein-protein interaction data accumulate.

Algorithmic and Stochastic Representations of Gene Regulatory Networks and Protein-Protein Interactions

2019 ◽  
Vol 19 (6) ◽  
pp. 413-425 ◽  
Author(s):  
Athanasios Alexiou ◽  
Stylianos Chatzichronis ◽  
Asma Perveen ◽  
Abdul Hafeez ◽  
Ghulam Md. Ashraf
Keyword(s):  
Protein Interactions ◽  
Biological Networks ◽  
Regulatory Networks ◽  
Review Paper ◽  
Boolean Networks ◽  
Diagnostic Tools ◽  
Complex Nature ◽  
Cellular Interactions ◽  
Protein Protein Interactions ◽  
Deterministic Models

Background:Latest studies reveal the importance of Protein-Protein interactions on physiologic functions and biological structures. Several stochastic and algorithmic methods have been published until now, for the modeling of the complex nature of the biological systems.Objective:Biological Networks computational modeling is still a challenging task. The formulation of the complex cellular interactions is a research field of great interest. In this review paper, several computational methods for the modeling of GRN and PPI are presented analytically.Methods:Several well-known GRN and PPI models are presented and discussed in this review study such as: Graphs representation, Boolean Networks, Generalized Logical Networks, Bayesian Networks, Relevance Networks, Graphical Gaussian models, Weight Matrices, Reverse Engineering Approach, Evolutionary Algorithms, Forward Modeling Approach, Deterministic models, Static models, Hybrid models, Stochastic models, Petri Nets, BioAmbients calculus and Differential Equations.Results:GRN and PPI methods have been already applied in various clinical processes with potential positive results, establishing promising diagnostic tools.Conclusion:In literature many stochastic algorithms are focused in the simulation, analysis and visualization of the various biological networks and their dynamics interactions, which are referred and described in depth in this review paper.

In Silico Recognition of Protein-Protein Interaction

2011 ◽  
pp. 248-268
Author(s):  
Byung-Hoon Park ◽  
Phuongan Dam ◽  
Chongle Pan ◽  
Ying Xu ◽  
Al Geist ◽  
...  
Keyword(s):  
Protein Interactions ◽  
Regulatory Networks ◽  
Machine Learning Techniques ◽  
Translation Regulation ◽  
Future Research ◽  
Protein Protein Interactions ◽  
Protein Protein Interaction ◽  
Protein Levels ◽  
Protein Functions ◽  
Model Protein

Protein-protein interactions are fundamental to cellular processes. They are responsible for phenomena like DNA replication, gene transcription, protein translation, regulation of metabolic pathways, immunologic recognition, signal transduction, etc. The identification of interacting proteins is therefore an important prerequisite step in understanding their physiological functions. Due to the invaluable importance to various biophysical activities, reliable computational methods to infer protein-protein interactions from either structural or genome sequences are in heavy demand lately. Successful predictions, for instance, will facilitate a drug design process and the reconstruction of metabolic or regulatory networks. In this chapter, we review: (a) high-throughput experimental methods for identification of protein-protein interactions, (b) existing databases of protein-protein interactions, (c) computational approaches to predicting protein-protein interactions at both residue and protein levels, (d) various statistical and machine learning techniques to model protein-protein interactions, and (e) applications of protein-protein interactions in predicting protein functions. We also discuss intrinsic drawbacks of the existing approaches and future research directions.

scholarly journals An Improved Phenotype-Driven Tool for Rare Mendelian Variant Prioritization: Benchmarking Exomiser on Real Patient Whole-Exome Data

Genes ◽  
2020 ◽  
Vol 11 (4) ◽  
pp. 460
Author(s):  
Valentina Cipriani ◽  
Nikolas Pontikos ◽  
Gavin Arno ◽  
Panagiotis I. Sergouniotis ◽  
Eva Lenassi ◽  
...  
Keyword(s):  
Molecular Diagnosis ◽  
Protein Interactions ◽  
Model Organisms ◽  
Protein Protein Interactions ◽  
Sequencing Data ◽  
Variant Prioritization ◽  
Real Patient ◽  
Human Phenotype ◽  
Support Tool ◽  
Variant Frequency

Next-generation sequencing has revolutionized rare disease diagnostics, but many patients remain without a molecular diagnosis, particularly because many candidate variants usually survive despite strict filtering. Exomiser was launched in 2014 as a Java tool that performs an integrative analysis of patients’ sequencing data and their phenotypes encoded with Human Phenotype Ontology (HPO) terms. It prioritizes variants by leveraging information on variant frequency, predicted pathogenicity, and gene-phenotype associations derived from human diseases, model organisms, and protein–protein interactions. Early published releases of Exomiser were able to prioritize disease-causative variants as top candidates in up to 97% of simulated whole-exomes. The size of the tested real patient datasets published so far are very limited. Here, we present the latest Exomiser version 12.0.1 with many new features. We assessed the performance using a set of 134 whole-exomes from patients with a range of rare retinal diseases and known molecular diagnosis. Using default settings, Exomiser ranked the correct diagnosed variants as the top candidate in 74% of the dataset and top 5 in 94%; not using the patients’ HPO profiles (i.e., variant-only analysis) decreased the performance to 3% and 27%, respectively. In conclusion, Exomiser is an effective support tool for rare Mendelian phenotype-driven variant prioritization.

scholarly journals A massively parallel barcoded sequencing pipeline enables generation of the first ORFeome and interactome map for rice

2020 ◽  
Vol 117 (21) ◽  
pp. 11836-11842 ◽  
Author(s):  
Shayne D. Wierbowski ◽  
Tommy V. Vo ◽  
Pascal Falter-Braun ◽  
Timothy O. Jobe ◽  
Lars H. Kruse ◽  
...  
Keyword(s):  
Protein Interactions ◽  
Massively Parallel ◽  
Model Organisms ◽  
Protein Protein Interactions ◽  
Numerous Model ◽  
High Quality Protein ◽  
Protein Interactome ◽  
Wide Range ◽  
Dna Elements ◽  
General Tool

Systematic mappings of protein interactome networks have provided invaluable functional information for numerous model organisms. Here we developPCR-mediatedLinkage of barcodedAdaptersTo nucleic acidElements forsequencing (PLATE-seq) that serves as a general tool to rapidly sequence thousands of DNA elements. We validate its utility by generating the ORFeome forOryza sativacovering 2,300 genes and constructing a high-quality protein–protein interactome map consisting of 322 interactions between 289 proteins, expanding the known interactions in rice by roughly 50%. Our work paves the way for high-throughput profiling of protein–protein interactions in a wide range of organisms.

Regulative interactions of the osmosensing C-terminal domain in the trimeric glycine betaine transporter BetP from Corynebacterium glutamicum

2009 ◽  
Vol 390 (8) ◽  
Author(s):  
Reinhard Krämer ◽  
Christine Ziegler
Keyword(s):  
Amino Acid ◽  
Corynebacterium Glutamicum ◽  
Protein Interactions ◽  
Regulation Mechanism ◽  
Protein Protein Interactions ◽  
Molecular Switching ◽  
X Ray ◽  
X Ray Crystallography ◽  
Terminal Domain ◽  
Domain Reorientation

Abstract Activation of the osmoregulated trimeric betaine transporter BetP from Corynebacterium glutamicum was shown to depend mainly on the correct folding and integrity of its 55 amino acid long, partly α-helical C-terminal domain. Reorientation of the three C-terminal domains in the BetP trimer indicates different lipid-protein and protein-protein interactions of the C-terminal domain during osmoregulation. A regulation mechanism is suggested where this domain switches the transporter from the inactive to the active state. Interpretation of recently obtained electron and X-ray crystallography data of BetP led to a structure-function based model of C-terminal molecular switching involved in osmoregulation.

scholarly journals Conserved Central Intraviral Protein Interactome of the Herpesviridae Family

mSystems ◽  
2019 ◽  
Vol 4 (5) ◽  
Author(s):  
Anna Hernández Durán ◽  
Kay Grünewald ◽  
Maya Topf
Keyword(s):  
Protein Interactions ◽  
Computational Models ◽  
Functional Organization ◽  
Driving Forces ◽  
Protein Complexes ◽  
Structural Features ◽  
System Level ◽  
Protein Protein Interactions ◽  
Protein Interactome ◽  
Essential Components

ABSTRACT Protein interactions are major driving forces behind the functional phenotypes of biological processes. As such, evolutionary footprints are reflected in system-level collections of protein-protein interactions (PPIs), i.e., protein interactomes. We conducted a comparative analysis of intraviral protein interactomes for representative species of each of the three subfamilies of herpesviruses (herpes simplex virus 1, human cytomegalovirus, and Epstein-Barr virus), which are highly prevalent etiologic agents of important human diseases. The intraviral interactomes were reconstructed by combining experimentally supported and computationally predicted protein-protein interactions. Using cross-species network comparison, we then identified family-wise conserved interactions and protein complexes, which we defined as a herpesviral “central” intraviral protein interactome. A large number of widely accepted conserved herpesviral protein complexes are present in this central intraviral interactome, encouragingly supporting the biological coherence of our results. Importantly, these protein complexes represent most, if not all, of the essential steps required during a productive life cycle. Hence the central intraviral protein interactome could plausibly represent a minimal infectious interactome of the herpesvirus family across a variety of hosts. Our data, which have been integrated into our herpesvirus interactomics database, HVint2.0, could assist in creating comprehensive system-level computational models of this viral lineage. IMPORTANCE Herpesviruses are an important socioeconomic burden for both humans and livestock. Throughout their long evolutionary history, individual herpesvirus species have developed remarkable host specificity, while collectively the Herpesviridae family has evolved to infect a large variety of eukaryotic hosts. The development of approaches to fight herpesvirus infections has been hampered by the complexity of herpesviruses’ genomes, proteomes, and structural features. The data and insights generated by our study add to the understanding of the functional organization of herpesvirus-encoded proteins, specifically of family-wise conserved features defining essential components required for a productive infectious cycle across different hosts, which can contribute toward the conceptualization of antiherpetic infection strategies with an effect on a broader range of target species. All of the generated data have been made freely available through our HVint2.0 database, a dedicated resource of curated herpesvirus interactomics purposely created to promote and assist future studies in the field.

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