scholarly journals Constrained Network Modularity

2012 ◽  
Vol 2012 ◽  
pp. 1-7
Author(s):  
Enrico Capobianco
Keyword(s):  
Gene Expression ◽  
Protein Interactions ◽  
Time Course ◽  
Activity Level ◽  
Modular Organization ◽  
Just In Time ◽  
Disease States ◽  
Yeast Dataset ◽  
Entropy Measures ◽  
Differential Networks

Static representations of protein interactions networks or PIN reflect measurements referred to a variety of conditions, including time. To partially bypass such limitation, gene expression information is usually integrated in the network to measure its “activity level.” In general, the entire PIN modular organization (complexes, pathways) can reveal changes of configuration whose functional significance depends on biological annotation. However, since network dynamics are based on the presence of different conditions leading to comparisons between normal and disease states, or between networks observed sequentially in time, our working hypothesis refers to the analysis of differential networks based on varying modularity and uncertainty. Two popular methods were applied and evaluated, k-core and Q-modularity, over a reference yeast dataset comprising a PIN of literature-curated data obtained from the fusion of heterogeneous measurements sources. While the functional aspect of interest is cell cycle and the corresponding interactions were isolated, the PIN dynamics were externally induced by time-course measured gene expression values, which we consider one of the “modularity drivers.” Notably, due to the nature of such expression values referred to the “just-in-time method,” we could specialize our approach according to three constrained modular configurations then comparatively assessed through local entropy measures.

Identifying differential networks based on multi-platform gene expression data

2017 ◽  
Vol 13 (1) ◽  
pp. 183-192 ◽  
Author(s):  
Le Ou-Yang ◽  
Hong Yan ◽  
Xiao-Fei Zhang
Keyword(s):  
Gene Expression ◽  
Gene Regulatory Network ◽  
Gene Expression Data ◽  
Regulatory Network ◽  
Disease Development ◽  
Expression Data ◽  
Biological Mechanisms ◽  
Disease States ◽  
Gene Regulatory ◽  
Differential Networks

Exploring how the structure of a gene regulatory network differs between two different disease states is fundamental for understanding the biological mechanisms behind disease development and progression.

Role of small nuclear RNAs in eukaryotic gene expression

2013 ◽  
Vol 54 ◽  
pp. 79-90 ◽  
Author(s):  
Saba Valadkhan ◽  
Lalith S. Gunawardane
Keyword(s):  
Gene Expression ◽  
Protein Interactions ◽  
Rna Stability ◽  
Splice Sites ◽  
Branch Site ◽  
Small Nuclear Rnas ◽  
Eukaryotic Gene Expression ◽  
Eukaryotic Gene ◽  
Rna Biogenesis

Eukaryotic cells contain small, highly abundant, nuclear-localized non-coding RNAs [snRNAs (small nuclear RNAs)] which play important roles in splicing of introns from primary genomic transcripts. Through a combination of RNA–RNA and RNA–protein interactions, two of the snRNPs, U1 and U2, recognize the splice sites and the branch site of introns. A complex remodelling of RNA–RNA and protein-based interactions follows, resulting in the assembly of catalytically competent spliceosomes, in which the snRNAs and their bound proteins play central roles. This process involves formation of extensive base-pairing interactions between U2 and U6, U6 and the 5′ splice site, and U5 and the exonic sequences immediately adjacent to the 5′ and 3′ splice sites. Thus RNA–RNA interactions involving U2, U5 and U6 help position the reacting groups of the first and second steps of splicing. In addition, U6 is also thought to participate in formation of the spliceosomal active site. Furthermore, emerging evidence suggests additional roles for snRNAs in regulation of various aspects of RNA biogenesis, from transcription to polyadenylation and RNA stability. These snRNP-mediated regulatory roles probably serve to ensure the co-ordination of the different processes involved in biogenesis of RNAs and point to the central importance of snRNAs in eukaryotic gene expression.

Neuroserpin in Bipolar Disorder

2020 ◽  
Vol 20 (7) ◽  
pp. 518-523
Author(s):  
Rugül Köse Çinar
Keyword(s):  
Gene Expression ◽  
Bipolar Disorder ◽  
Polymerase Chain Reaction Method ◽  
Type I ◽  
Healthy Controls ◽  
Neuroprotective Effects ◽  
Expression Levels ◽  
Disease States ◽  
Cord Injury ◽  
System Functioning

Objective: Neuroserpin is a serine protease inhibitor predominantly expressed in the nervous system functioning mainly in neuronal migration and axonal growth. Neuroprotective effects of neuroserpin were shown in animal models of stroke, brain, and spinal cord injury. Postmortem studies confirmed the involvement of neuroserpin in Alzheimer’s disease. Since altered adult neurogenesis was postulated as an aetiological mechanism for bipolar disorder, the possible effect of neuroserpin gene expression in the disorder was evaluated. Methods: Neuroserpin mRNA expression levels were examined in the peripheral blood of bipolar disorder type I manic and euthymic patients and healthy controls using the polymerase chain reaction method. The sample comprised of 60 physically healthy, middle-aged men as participants who had no substance use disorder. Results: The gene expression levels of neuroserpin were found lower in the bipolar disorder patients than the healthy controls (p=0.000). The neuroserpin levels did not differ between mania and euthymia (both 96% down-regulated compared to the controls). Conclusion: Since we detected differences between the patients and the controls, not the disease states, the dysregulation in the neuroserpin gene could be interpreted as a result of the disease itself.

scholarly journals MNBDR: A Module Network Based Method for Drug Repositioning

Genes ◽  
2020 ◽  
Vol 12 (1) ◽  
pp. 25
Author(s):  
He-Gang Chen ◽  
Xiong-Hui Zhou
Keyword(s):  
Gene Expression ◽  
Protein Interactions ◽  
Drug Response ◽  
Drug Repositioning ◽  
Expression Profiles ◽  
Drug Repurposing ◽  
Recent Decade ◽  
Data Set ◽  
Module Network ◽  
The Cross

Drug repurposing/repositioning, which aims to find novel indications for existing drugs, contributes to reducing the time and cost for drug development. For the recent decade, gene expression profiles of drug stimulating samples have been successfully used in drug repurposing. However, most of the existing methods neglect the gene modules and the interactions among the modules, although the cross-talks among pathways are common in drug response. It is essential to develop a method that utilizes the cross-talks information to predict the reliable candidate associations. In this study, we developed MNBDR (Module Network Based Drug Repositioning), a novel method that based on module network to screen drugs. It integrated protein–protein interactions and gene expression profile of human, to predict drug candidates for diseases. Specifically, the MNBDR mined dense modules through protein–protein interaction (PPI) network and constructed a module network to reveal cross-talks among modules. Then, together with the module network, based on existing gene expression data set of drug stimulation samples and disease samples, we used random walk algorithms to capture essential modules in disease development and proposed a new indicator to screen potential drugs for a given disease. Results showed MNBDR could provide better performance than popular methods. Moreover, functional analysis of the essential modules in the network indicated our method could reveal biological mechanism in drug response.

scholarly journals Compendium of Methods to Uncover RNA-Protein Interactions In Vivo

2021 ◽  
Vol 4 (1) ◽  
pp. 22
Author(s):  
Mrinmoyee Majumder ◽  
Viswanathan Palanisamy
Keyword(s):  
Gene Expression ◽  
Protein Interactions ◽  
Rna Binding ◽  
Rna Binding Proteins ◽  
Expression Patterns ◽  
Control Of Gene Expression ◽  
Regulatory Pathways ◽  
Advantages And Disadvantages ◽  
Protein Nucleic Acid

Control of gene expression is critical in shaping the pro-and eukaryotic organisms’ genotype and phenotype. The gene expression regulatory pathways solely rely on protein–protein and protein–nucleic acid interactions, which determine the fate of the nucleic acids. RNA–protein interactions play a significant role in co- and post-transcriptional regulation to control gene expression. RNA-binding proteins (RBPs) are a diverse group of macromolecules that bind to RNA and play an essential role in RNA biology by regulating pre-mRNA processing, maturation, nuclear transport, stability, and translation. Hence, the studies aimed at investigating RNA–protein interactions are essential to advance our knowledge in gene expression patterns associated with health and disease. Here we discuss the long-established and current technologies that are widely used to study RNA–protein interactions in vivo. We also present the advantages and disadvantages of each method discussed in the review.

scholarly journals Androgen signaling uses a writer and a reader of ADP-ribosylation to regulate protein complex assembly

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Chun-Song Yang ◽  
Kasey Jividen ◽  
Teddy Kamata ◽  
Natalia Dworak ◽  
Luke Oostdyk ◽  
...  
Keyword(s):  
Gene Expression ◽  
Protein Interactions ◽  
Prostate Cancer Cells ◽  
Protein Protein Interactions ◽  
Post Translational Modification ◽  
Complex Assembly ◽  
Adp Ribosylation ◽  
Signaling Axis ◽  
Regulate Protein ◽  
Protein Complex Assembly

AbstractAndrogen signaling through the androgen receptor (AR) directs gene expression in both normal and prostate cancer cells. Androgen regulates multiple aspects of the AR life cycle, including its localization and post-translational modification, but understanding how modifications are read and integrated with AR activity has been difficult. Here, we show that ADP-ribosylation regulates AR through a nuclear pathway mediated by Parp7. We show that Parp7 mono-ADP-ribosylates agonist-bound AR, and that ADP-ribosyl-cysteines within the N-terminal domain mediate recruitment of the E3 ligase Dtx3L/Parp9. Molecular recognition of ADP-ribosyl-cysteine is provided by tandem macrodomains in Parp9, and Dtx3L/Parp9 modulates expression of a subset of AR-regulated genes. Parp7, ADP-ribosylation of AR, and AR-Dtx3L/Parp9 complex assembly are inhibited by Olaparib, a compound used clinically to inhibit poly-ADP-ribosyltransferases Parp1/2. Our study reveals the components of an androgen signaling axis that uses a writer and reader of ADP-ribosylation to regulate protein-protein interactions and AR activity.

scholarly journals Impact of Environmental Stressors on Gene Expression in the Embryo of the Italian Wall Lizard

2021 ◽  
Vol 11 (11) ◽  
pp. 4723
Author(s):  
Rosaria Scudiero ◽  
Chiara Maria Motta ◽  
Palma Simoniello
Keyword(s):  
Gene Expression ◽  
Protein Interactions ◽  
Membrane Trafficking ◽  
Translational Regulation ◽  
Cellular Protection ◽  
Terrestrial Vertebrate ◽  
Expression Of Genes ◽  
Stress Changes ◽  
Probable Consequence ◽  
The Impact

The cleidoic eggs of oviparous reptiles are protected from the external environment by membranes and a parchment shell permeable to water and dissolved molecules. As a consequence, not only physical but also chemical insults can reach the developing embryos, interfering with gene expression. This review provides information on the impact of the exposure to cadmium contamination or thermal stress on gene expression during the development of Italian wall lizards of the genus Podarcis. The results obtained by transcriptomic analysis, although not exhaustive, allowed to identify some stress-reactive genes and, consequently, the molecular pathways in which these genes are involved. Cadmium-responsive genes encode proteins involved in cellular protection, metabolism and proliferation, membrane trafficking, protein interactions, neuronal transmission and plasticity, immune response, and transcription regulatory factors. Cold stress changes the expression of genes involved in transcriptional/translational regulation and chromatin remodeling and inhibits the transcription of a histone methyltransferase with the probable consequence of modifying the epigenetic control of DNA. These findings provide transcriptome-level evidence of how terrestrial vertebrate embryos cope with stress, giving a key to use in population survival and environmental change studies. A better understanding of the genes contributing to stress tolerance in vertebrates would facilitate methodologies and applications aimed at improving resistance to unfavourable environments.

scholarly journals Bayesian approach for predicting responses to therapy from high-dimensional time-course gene expression profiles

2021 ◽  
Vol 22 (1) ◽  
Author(s):  
Arika Fukushima ◽  
Masahiro Sugimoto ◽  
Satoru Hiwa ◽  
Tomoyuki Hiroyasu
Keyword(s):  
Gene Expression ◽  
Time Course ◽  
Expression Profiles ◽  
Gene Expression Profiles ◽  
Response To Therapy ◽  
Hcv Infection ◽  
Entire Treatment Period ◽  
Time Points ◽  
Time Course Data ◽  
Conventional Methods

Abstract Background Historical and updated information provided by time-course data collected during an entire treatment period proves to be more useful than information provided by single-point data. Accurate predictions made using time-course data on multiple biomarkers that indicate a patient’s response to therapy contribute positively to the decision-making process associated with designing effective treatment programs for various diseases. Therefore, the development of prediction methods incorporating time-course data on multiple markers is necessary. Results We proposed new methods that may be used for prediction and gene selection via time-course gene expression profiles. Our prediction method consolidated multiple probabilities calculated using gene expression profiles collected over a series of time points to predict therapy response. Using two data sets collected from patients with hepatitis C virus (HCV) infection and multiple sclerosis (MS), we performed numerical experiments that predicted response to therapy and evaluated their accuracies. Our methods were more accurate than conventional methods and successfully selected genes, the functions of which were associated with the pathology of HCV infection and MS. Conclusions The proposed method accurately predicted response to therapy using data at multiple time points. It showed higher accuracies at early time points compared to those of conventional methods. Furthermore, this method successfully selected genes that were directly associated with diseases.

scholarly journals SPLICE-q: a Python tool for genome-wide quantification of splicing efficiency

2021 ◽  
Vol 22 (1) ◽  
Author(s):  
Verônica R. de Melo Costa ◽  
Julianus Pfeuffer ◽  
Annita Louloupi ◽  
Ulf A. V. Ørom ◽  
Rosario M. Piro
Keyword(s):  
Gene Expression ◽  
Cancer Progression ◽  
Time Course ◽  
Progressive Increase ◽  
Rna Seq ◽  
Transcript Processing ◽  
Aberrant Transcript ◽  
Genome Wide ◽  
Splicing Efficiency ◽  
Nascent Rna

Abstract Background Introns are generally removed from primary transcripts to form mature RNA molecules in a post-transcriptional process called splicing. An efficient splicing of primary transcripts is an essential step in gene expression and its misregulation is related to numerous human diseases. Thus, to better understand the dynamics of this process and the perturbations that might be caused by aberrant transcript processing it is important to quantify splicing efficiency. Results Here, we introduce SPLICE-q, a fast and user-friendly Python tool for genome-wide SPLICing Efficiency quantification. It supports studies focusing on the implications of splicing efficiency in transcript processing dynamics. SPLICE-q uses aligned reads from strand-specific RNA-seq to quantify splicing efficiency for each intron individually and allows the user to select different levels of restrictiveness concerning the introns’ overlap with other genomic elements such as exons of other genes. We applied SPLICE-q to globally assess the dynamics of intron excision in yeast and human nascent RNA-seq. We also show its application using total RNA-seq from a patient-matched prostate cancer sample. Conclusions Our analyses illustrate that SPLICE-q is suitable to detect a progressive increase of splicing efficiency throughout a time course of nascent RNA-seq and it might be useful when it comes to understanding cancer progression beyond mere gene expression levels. SPLICE-q is available at: https://github.com/vrmelo/SPLICE-q

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