scholarly journals Temporal correlations robustly reveal regulatory coherence upon environmental perturbation

2021 ◽  
Author(s):  
Haoran Cai ◽  
David L. Des Marais
Keyword(s):  
Regulatory Networks ◽  
Expression Profiles ◽  
Gene Expression Profiles ◽  
Population Level ◽  
Cross Sectional ◽  
Regulatory Interactions ◽  
Environmental Perturbation ◽  
Temporal Correlations ◽  
Environmental Perturbations ◽  
Cross Sectional Studies

AbstractTranscriptional Regulatory Networks (TRNs) orchestrate the timing, magnitude, and rate of organismal response to many environmental perturbations. Regulatory interactions in TRNs are context-dependent, affording the opportunity to detect responsive links upon environmental perturbation. To measure the transition between regulatory status directly, one powerful approach is chromatin immuno-precipitation binding, ChIP-Seq. However, genome-wide ChIP-Seq is costly and currently inaccessible for many transcription factors (TFs) across multiple conditions in most organisms. Seeking to exploit the abundance of RNASequencing data now available, many past studies have relied upon population-level statistics from cross-sectional studies, generating pairwise gene co-expression, to capture transient regulatory activity. Here, we employ a minimal stochastic model of transcriptional regulation and demonstrate that population correlations from cross-sectional studies may fail to capture transient regulatory activities. To characterize network rewiring in response to environmental perturbations, we use the dynamic correlation between time-series gene expression profiles in Oryza sativa in a network prior. Overall, stronger regulatory interactions are observed following environmental perturbation, which we term regulatory coherence. Previously known regulators with changing regulatory activities are reliably identified with our method. Exploiting dynamic correlations has the potential to prioritize stress-responsive regulators, affording greater detection power as compared to traditional differential expression approaches.

Revisiting regulatory decoherence: Accounting for temporal bias in a co-expression analysis reveals novel candidates controlling environmental response

2022 ◽  
Author(s):  
Haoran Cai ◽  
David Des Marais
Keyword(s):  
Regulatory Networks ◽  
Temporal Structure ◽  
Temporal Correlation ◽  
Population Level ◽  
Rapid Onset ◽  
Environmental Response ◽  
Regulatory Activity ◽  
Cross Sectional ◽  
Rice Plants ◽  
Environmental Perturbations

Abstract Transcriptional Regulatory Networks (TRNs) orchestrate the timing, magnitude, and rate of organismal response to many environmental perturbations. Regulatory interactions in TRNs are dynamic but exploiting temporal variation to understand gene regulation requires a careful appreciation of both molecular biology and confounders in statistical analysis. Seeking to exploit the abundance of RNASequencing data now available, many past studies have relied upon population-level statistics from cross-sectional studies, estimating gene co-expression interactions to capture transient changes of regulatory activity. We show that population-level co-expression exhibits biases when capturing transient changes of regulatory activity in rice plants responding to elevated temperature. An apparent cause of this bias is regulatory saturation, the observation that detectable co-variance between a regulator and its target may be low as their transcript abundances are induced. This phenomenon appears to be particularly acute for rapid onset environmental stressors. However, exploiting temporal correlations appears to be a reliable means to detect transient regulatory activity following rapid onset environmental perturbations such as temperature stress. Such temporal correlation may lose information along a more gradual-onset stressor (e.g., dehydration). We here show that rice plants exposed to a dehydration stress exhibit temporal structure of coexpression in their response that can not be unveiled by temporal correlation alone. Collectively, our results point to the need to account for the nuances of molecular interactions and the possibly confounding effects that these can introduce into conventional approaches to study transcriptome datasets.

scholarly journals Associations between dietary patterns and gene expression profiles of healthy men and women: a cross-sectional study

2013 ◽  
Vol 12 (1) ◽  
Author(s):  
Annie Bouchard-Mercier ◽  
Ann-Marie Paradis ◽  
Iwona Rudkowska ◽  
Simone Lemieux ◽  
Patrick Couture ◽  
...  
Keyword(s):  
Gene Expression ◽  
Dietary Patterns ◽  
Expression Profiles ◽  
Gene Expression Profiles ◽  
Cross Sectional Study ◽  
Sectional Study ◽  
Cross Sectional ◽  
Men And Women ◽  
Healthy Men

scholarly journals Comparative transcriptomics identifies differences in the regulation of the floral transition between Arabidopsis and Brassica rapa cultivars

2020 ◽  
Author(s):  
Alexander Calderwood ◽  
Jo Hepworth ◽  
Shannon Woodhouse ◽  
Lorelei Bilham ◽  
D. Marc Jones ◽  
...  
Keyword(s):  
Gene Expression ◽  
Brassica Rapa ◽  
Regulatory Networks ◽  
Expression Profiles ◽  
Expression Patterns ◽  
Gene Expression Profiles ◽  
Detailed Comparison ◽  
Developmental Time ◽  
Floral Transition ◽  
Gene Regulatory

AbstractThe timing of the floral transition affects reproduction and yield, however its regulation in crops remains poorly understood. Here, we use RNA-Seq to determine and compare gene expression dynamics through the floral transition in the model species Arabidopsis thaliana and the closely related crop Brassica rapa. A direct comparison of gene expression over time between species shows little similarity, which could lead to the inference that different gene regulatory networks are at play. However, these differences can be largely resolved by synchronisation, through curve registration, of gene expression profiles. We find that different registration functions are required for different genes, indicating that there is no common ‘developmental time’ to which Arabidopsis and B. rapa can be mapped through gene expression. Instead, the expression patterns of different genes progress at different rates. We find that co-regulated genes show similar changes in synchronisation between species, suggesting that similar gene regulatory sub-network structures may be active with different wiring between them. A detailed comparison of the regulation of the floral transition between Arabidopsis and B. rapa, and between two B. rapa accessions reveals different modes of regulation of the key floral integrator SOC1, and that the floral transition in the B. rapa accessions is triggered by different pathways, even when grown under the same environmental conditions. Our study adds to the mechanistic understanding of the regulatory network of flowering time in rapid cycling B. rapa under long days and highlights the importance of registration methods for the comparison of developmental gene expression data.

scholarly journals Analysis of ceRNA networks and identification of potential drug targets for drug-resistant leukemia cell K562/ADR

PeerJ ◽  
2021 ◽  
Vol 9 ◽  
pp. e11429
Author(s):  
Zhaoping Liu ◽  
Yanyan Wang ◽  
Zhenru Xu ◽  
Shunling Yuan ◽  
Yanglin Ou ◽  
...  
Keyword(s):  
Drug Resistance ◽  
Drug Targets ◽  
Regulatory Networks ◽  
Expression Profiles ◽  
Leukemia Cell ◽  
Gene Expression Profiles ◽  
Leukemia Cells ◽  
Mrna Levels ◽  
Drug Resistant ◽  
Cerna Network

Background Drug resistance is the main obstacle in the treatment of leukemia. As a member of the competitive endogenous RNA (ceRNA) mechanism, underlying roles of lncRNA are rarely reported in drug-resistant leukemia cells. Methods The gene expression profiles of lncRNAs and mRNAs in doxorubicin-resistant K562/ADR and sensitive K562 cells were established by RNA sequencing (RNA-seq). Expression of differentially expressed lncRNAs (DElncRNAs) and DEmRNAs was validated by qRT-PCR. The potential biological functions of DElncRNAs targets were identified by GO and KEGG pathway enrichment analyses, and the lncRNA-miRNA-mRNA ceRNA network was further constructed. K562/ADR cells were transfected with CCDC26 and LINC01515 siRNAs to detect the mRNA levels of GLRX5 and DICER1, respectively. The cell survival rate after transfection was detected by CCK-8 assay. Results The ceRNA network was composed of 409 lncRNA-miRNA pairs and 306 miRNA-mRNA pairs based on 67 DElncRNAs, 58 DEmiRNAs and 192 DEmRNAs. Knockdown of CCDC26 and LINC01515 increased the sensitivity of K562/ADR cells to doxorubicin and significantly reduced the half-maximal inhibitory concentration (IC50) of doxorubicin. Furthermore, knockdown of GLRX5 and DICER1 increased the sensitivity of K562/ADR cells to doxorubicin and significantly reduced the IC50 of doxorubicin. Conclusions The ceRNA regulatory networks may play important roles in drug resistance of leukemia cells. CCDC26/miR-140-5p/GLRX5 and LINC01515/miR-425-5p/DICER1 may be potential targets for drug resistance in K562/ADR cells. This study provides a promising strategy to overcome drug resistance and deepens the understanding of the ceRNA regulatory mechanism related to drug resistance in CML cells.

scholarly journals Viral fitness predicts the magnitude and direction of perturbations in the infected host transcriptome

2017 ◽  
Author(s):  
Héctor Cervera ◽  
Silvia Ambrós ◽  
Guillermo P. Bernet ◽  
Guillermo Rodrigo ◽  
Santiago F. Elena
Keyword(s):  
Rna Silencing ◽  
Regulatory Networks ◽  
Expression Profiles ◽  
Gene Expression Profiles ◽  
Viral Fitness ◽  
Evolutionary Potential ◽  
Expression Of Genes ◽  
Darwinian Fitness ◽  
Functional Classes ◽  
Host Genes

Determining the fitness of viral genotypes has become a standard practice in virology as it is essential to evaluate their evolutionary potential. Darwinian fitness, defined as the advantage of a given genotype with respect to a reference one, is a mesoscopic property that captures into a single figure differences in performance at every stage of viral infection. But to which extent viral fitness results from particular molecular interactions with host factors and regulatory networks during infection? Can we identify host genes, and then functional classes, whose expression depends on viral fitness? Here, we compared the transcriptomes of tobacco plants infected with seven genotypes of tobacco etch potyvirus (TEV) that differ in fitness. We found that the larger the fitness differences among genotypes, the more dissimilar the transcriptomic profiles are. Consistently, two different mutations, one in the viral RNA polymerase and another in the viral suppressor of RNA silencing, that led to close fitness values, also resulted in significantly similar gene expression profiles. Moreover, we identified host genes whose expression showed a significant correlation, positive or negative, with TEV fitness. Over-expression of genes with positive correlation activates hormone-and RNA silencing-mediated pathways of plant defense. By contrast, under-expression of genes negatively correlated reduces metabolism, growth, and development. Overall, these results reveal the high information content of viral fitness, and suggest its potential use to predict differences in genomic profiles of infected hosts.

scholarly journals Convergent evolution of venom gland transcriptomes across Metazoa

2021 ◽  
Author(s):  
Giulia Zancolli ◽  
Maarten Reijnders ◽  
Robert Waterhouse ◽  
Marc Robinson-Rechavi
Keyword(s):  
Gene Expression ◽  
Regulatory Networks ◽  
Molecular Mechanisms ◽  
Expression Profiles ◽  
Gene Expression Profiles ◽  
Venom Gland ◽  
Bioactive Molecules ◽  
Specific Gene ◽  
Animal Kingdom ◽  
Venom Glands

Animals have repeatedly evolved specialized organs and anatomical structures to produce and deliver a cocktail of potent bioactive molecules to subdue prey or predators: venom. This makes it one of the most widespread convergent functions in the animal kingdom. Whether animals have adopted the same genetic toolkit to evolved venom systems is a fascinating question that still eludes us. Here, we performed the first comparative analysis of venom gland transcriptomes from 20 venomous species spanning the main Metazoan lineages, to test whether different animals have independently adopted similar molecular mechanisms to perform the same function. We found a strong convergence in gene expression profiles, with venom glands being more similar to each other than to any other tissue from the same species, and their differences closely mirroring the species phylogeny. Although venom glands secrete some of the fastest evolving molecules (toxins), their gene expression does not evolve faster than evolutionarily older tissues. We found 15 venom gland specific gene modules enriched in endoplasmic reticulum stress and unfolded protein response pathways, indicating that animals have independently adopted stress response mechanisms to cope with mass production of toxins. This, in turns, activates regulatory networks for epithelial development, cell turnover and maintenance which seem composed of both convergent and lineage-specific factors, possibly reflecting the different developmental origins of venom glands. This study represents the first step towards an understanding of the molecular mechanisms underlying the repeated evolution of one of the most successful adaptive traits in the animal kingdom.

scholarly journals Technique of Gene Expression Profiles Extraction Based on the Complex Use of Clustering and Classification Methods

Diagnostics ◽  
2020 ◽  
Vol 10 (8) ◽  
pp. 584
Author(s):  
Sergii Babichev ◽  
Jiří Škvor
Keyword(s):  
Gene Expression ◽  
Regulatory Networks ◽  
Fuzzy Inference ◽  
Expression Profiles ◽  
Gene Expression Profiles ◽  
Quality Criterion ◽  
Gene Expressions ◽  
Inference System ◽  
Stepwise Procedure ◽  
Binary Classifiers

In this paper, we present the results of the research concerning extraction of informative gene expression profiles from high-dimensional array of gene expressions considering the state of patients’ health using clustering method, ML-based binary classifiers and fuzzy inference system. Applying of the proposed stepwise procedure can allow us to extract the most informative genes taking into account both the subtypes of disease or state of the patient’s health for further reconstruction of gene regulatory networks based on the allocated genes and following simulation of the reconstructed models. We used the publicly available gene expressions data as the experimental ones which were obtained using DNA microarray experiments and contained two types of patients’ gene expression profiles—the patients with lung cancer tumor and healthy patients. The stepwise procedure of the data processing assumes the following steps—in the beginning, we reduce the number of genes by removing non-informative genes in terms of statistical criteria and Shannon entropy; then, we perform the stepwise hierarchical clustering of gene expression profiles at hierarchical levels from 1 to 10 using the SOTA (Self-Organizing Tree Algorithm) clustering algorithm with correlation distance metric. The quality of the obtained clustering was evaluated using the complex clustering quality criterion which is considered both the gene expression profiles distribution relative to center of the clusters where these gene expression profiles are allocated and the centers of the clusters distribution. The result of this stage execution was a selection of the optimal cluster at each of the hierarchical levels which corresponded to the minimum value of the quality criterion. At the next step, we have implemented a classification procedure of the examined objects using four well known binary classifiers—logistic regression, support-vector machine, decision trees and random forest classifier. The effectiveness of the appropriate technique was evaluated based on the use of ROC (Receiver Operating Characteristic) analysis using criteria, included as the components, the errors of both the first and the second kinds. The final decision concerning the extraction of the most informative subset of gene expression profiles was taken based on the use of the fuzzy inference system, the inputs of which are the results of the appropriate single classifiers operation and the output is the final solution concerning state of the patient’s health. To our mind, the implementation of the proposed stepwise procedure of the informative gene expression profiles extraction create the conditions for the increasing effectiveness of the further procedure of gene regulatory networks reconstruction and the following simulation of the reconstructed models considering the subtypes of the disease and/or state of the patient’s health.

Sign in / Sign up

Export Citation Format

Share Document