scholarly journals Inference of Gene Regulatory Network Uncovers the Linkage Between Circadian Clock and Crassulacean Acid Metabolism in Kalanchoë fedtschenkoi

2019 ◽  
Author(s):  
Robert C. Moseley ◽  
Francis Motta ◽  
Gerald A. Tuskan ◽  
Steve Haase ◽  
Xiaohan Yang
Keyword(s):  
Gene Expression ◽  
Circadian Clock ◽  
Gene Regulatory Network ◽  
Regulatory Network ◽  
Crassulacean Acid Metabolism ◽  
Acid Metabolism ◽  
Clock Genes ◽  
Specific Gene ◽  
Cam Plants ◽  
Gene Regulatory

AbstractThe circadian clock drives time-specific gene expression, allowing for associated biological processes to be active during certain times of the 24 h day. Crassulacean acid metabolism (CAM) photosynthetic plants represent an interesting case of circadian regulation of gene expression as CO2 fixation and stomatal movement in CAM plants display strong circadian dynamics. The molecular mechanisms behind how the circadian clock enabled these physiological differences is not well understood. Therefore, we set out to investigate whether core circadian elements in CAM plants were re-phased during evolution, or whether networks of phase-specific genes were simply connected to different core elements. We utilized a new metric for identifying candidate core genes of a periodic gene network and then applied the Local Edge Machine (LEM) algorithm to infer regulatory relationships between the candidate core clock genes and orthologs of known core clock genes in K. fedtschenkoi. We also used LEM to identify stomata-related gene targets for K. fedtschenkoi core clock genes and constructed a subsequent gene regulatory network. Our results provide new insights into the mechanism of circadian control of CAM-related genes in K. fedtschenkoi, facilitating the engineering of CAM machinery into non-CAM plants for sustainable crop production in water-limited environments.

scholarly journals Inference of Gene Regulatory Network Uncovers the Linkage between Circadian Clock and Crassulacean Acid Metabolism in Kalanchoë fedtschenkoi

Cells ◽  
2021 ◽  
Vol 10 (9) ◽  
pp. 2217
Author(s):  
Robert C. Moseley ◽  
Francis Motta ◽  
Gerald A. Tuskan ◽  
Steven B. Haase ◽  
Xiaohan Yang
Keyword(s):  
Gene Expression ◽  
Circadian Clock ◽  
Gene Regulatory Network ◽  
Regulatory Network ◽  
Crassulacean Acid Metabolism ◽  
Acid Metabolism ◽  
Clock Genes ◽  
Stomatal Movement ◽  
Cam Plants ◽  
Gene Regulatory

The circadian clock drives time-specific gene expression, enabling biological processes to be temporally controlled. Plants that conduct crassulacean acid metabolism (CAM) photosynthesis represent an interesting case of circadian regulation of gene expression as stomatal movement is temporally inverted relative to stomatal movement in C3 plants. The mechanisms behind how the circadian clock enabled physiological differences at the molecular level is not well understood. Recently, the rescheduling of gene expression was reported as a mechanism to explain how CAM evolved from C3. Therefore, we investigated whether core circadian clock genes in CAM plants were re-phased during evolution, or whether networks of phase-specific genes were simply re-wired to different core clock genes. We identified candidate core clock genes based on gene expression features and then applied the Local Edge Machine (LEM) algorithm to infer regulatory relationships between this new set of core candidates and known core clock genes in Kalanchoë fedtschenkoi. We further inferred stomata-related gene targets for known and candidate core clock genes and constructed a gene regulatory network for core clock and stomata-related genes. Our results provide new insight into the mechanism of circadian control of CAM-related genes in K. fedtschenkoi, facilitating the engineering of CAM machinery into non-CAM plants for sustainable crop production in water-limited environments.

scholarly journals Predicting genotype-specific gene regulatory networks

2021 ◽  
Author(s):  
Deborah Weighill ◽  
Marouen Ben Guebila ◽  
Kimberly Glass ◽  
John Quackenbush ◽  
John Platig
Keyword(s):  
Gene Expression ◽  
Gene Regulatory Network ◽  
Gene Regulatory Networks ◽  
Genetic Variants ◽  
Regulatory Network ◽  
Regulatory Networks ◽  
Specific Gene ◽  
Disease Etiology ◽  
Gene Regulatory ◽  
The Impact

AbstractThe majority of disease-associated genetic variants are thought to have regulatory effects, including the disruption of transcription factor (TF) binding and the alteration of downstream gene expression. Identifying how a person’s genotype affects their individual gene regulatory network has the potential to provide important insights into disease etiology and to enable improved genotype-specific disease risk assessments and treatments. However, the impact of genetic variants is generally not considered when constructing gene regulatory networks. To address this unmet need, we developed EGRET (Estimating the Genetic Regulatory Effect on TFs), which infers a genotype-specific gene regulatory network (GRN) for each individual in a study population by using message passing to integrate genotype-informed TF motif predictions - derived from individual genotype data, the predicted effects of variants on TF binding and gene expression, and TF motif predictions - with TF protein-protein interactions and gene expression. Comparing EGRET networks for two blood-derived cell lines identified genotype-associated cell-line specific regulatory differences which were subsequently validated using allele-specific expression, chromatin accessibility QTLs, and differential TF binding from ChIP-seq. In addition, EGRET GRNs for three cell types across 119 individuals captured regulatory differences associated with disease in a cell-type-specific manner. Our analyses demonstrate that EGRET networks can capture the impact of genetic variants on complex phenotypes, supporting a novel fine-scale stratification of individuals based on their genetic background. EGRET is available through the Network Zoo R package (netZooR v0.9; netzoo.github.io).

scholarly journals NeTFactor, a framework for identifying transcriptional regulators of gene expression-based biomarkers

2019 ◽  
Vol 9 (1) ◽  
Author(s):  
Mehmet Eren Ahsen ◽  
Yoojin Chun ◽  
Alexander Grishin ◽  
Galina Grishina ◽  
Gustavo Stolovitzky ◽  
...  
Keyword(s):  
Gene Expression ◽  
Gene Regulatory Network ◽  
Regulatory Network ◽  
Regulatory Mechanisms ◽  
Epithelial Cell Line ◽  
Specific Gene ◽  
Peroxisome Proliferator ◽  
Peroxisome Proliferator Activated Receptor ◽  
Network Analyses ◽  
Gene Regulatory

Abstract Biological and regulatory mechanisms underlying many multi-gene expression-based disease biomarkers are often not readily evident. We describe an innovative framework, NeTFactor, that combines network analyses with gene expression data to identify transcription factors (TFs) that significantly and maximally regulate such a biomarker. NeTFactor uses a computationally-inferred context-specific gene regulatory network and applies topological, statistical, and optimization methods to identify regulator TFs. Application of NeTFactor to a multi-gene expression-based asthma biomarker identified ETS translocation variant 4 (ETV4) and peroxisome proliferator-activated receptor gamma (PPARG) as the biomarker’s most significant TF regulators. siRNA-based knock down of these TFs in an airway epithelial cell line model demonstrated significant reduction of cytokine expression relevant to asthma, validating NeTFactor’s top-scoring findings. While PPARG has been associated with airway inflammation, ETV4 has not yet been implicated in asthma, thus indicating the possibility of novel, disease-relevant discovery by NeTFactor. We also show that NeTFactor’s results are robust when the gene regulatory network and biomarker are derived from independent data. Additionally, our application of NeTFactor to a different disease biomarker identified TF regulators of interest. These results illustrate that the application of NeTFactor to multi-gene expression-based biomarkers could yield valuable insights into regulatory mechanisms and biological processes underlying disease.

scholarly journals Study on the Relationship between the miRNA-centered ceRNA Regulatory Network and Fatigue

2021 ◽  
Author(s):  
Xingzhe Yang ◽  
Feng Li ◽  
Jie Ma ◽  
Yan Liu ◽  
Xuejiao Wang ◽  
...  
Keyword(s):  
Gene Expression ◽  
Gene Regulatory Network ◽  
Regulatory Network ◽  
Hot Spot ◽  
Genetic Research ◽  
Noncoding Rnas ◽  
Messenger Rnas ◽  
Effective Prevention ◽  
Gene Regulatory ◽  
The Relationship

AbstractIn recent years, the incidence of fatigue has been increasing, and the effective prevention and treatment of fatigue has become an urgent problem. As a result, the genetic research of fatigue has become a hot spot. Transcriptome-level regulation is the key link in the gene regulatory network. The transcriptome includes messenger RNAs (mRNAs) and noncoding RNAs (ncRNAs). MRNAs are common research targets in gene expression profiling. Noncoding RNAs, including miRNAs, lncRNAs, circRNAs and so on, have been developed rapidly. Studies have shown that miRNAs are closely related to the occurrence and development of fatigue. MiRNAs can regulate the immune inflammatory reaction in the central nervous system (CNS), regulate the transmission of nerve impulses and gene expression, regulate brain development and brain function, and participate in the occurrence and development of fatigue by regulating mitochondrial function and energy metabolism. LncRNAs can regulate dopaminergic neurons to participate in the occurrence and development of fatigue. This has certain value in the diagnosis of chronic fatigue syndrome (CFS). CircRNAs can participate in the occurrence and development of fatigue by regulating the NF-κB pathway, TNF-α and IL-1β. The ceRNA hypothesis posits that in addition to the function of miRNAs in unidirectional regulation, mRNAs, lncRNAs and circRNAs can regulate gene expression by competitive binding with miRNAs, forming a ceRNA regulatory network with miRNAs. Therefore, we suggest that the miRNA-centered ceRNA regulatory network is closely related to fatigue. At present, there are few studies on fatigue-related ncRNA genes, and most of these limited studies are on miRNAs in ncRNAs. However, there are a few studies on the relationship between lncRNAs, cirRNAs and fatigue. Less research is available on the pathogenesis of fatigue based on the ceRNA regulatory network. Therefore, exploring the complex mechanism of fatigue based on the ceRNA regulatory network is of great significance. In this review, we summarize the relationship between miRNAs, lncRNAs and circRNAs in ncRNAs and fatigue, and focus on exploring the regulatory role of the miRNA-centered ceRNA regulatory network in the occurrence and development of fatigue, in order to gain a comprehensive, in-depth and new understanding of the essence of the fatigue gene regulatory network.

scholarly journals Deciphering the genetic links between NAFLD and co-occurring conditions using a liver gene regulatory network

2021 ◽  
Author(s):  
Sreemol Gokuladhas ◽  
William Schierding ◽  
Roan Eltigani Zaied ◽  
Tayaza Fadason ◽  
Murim Choi ◽  
...  
Keyword(s):  
Gene Regulatory Network ◽  
Regulatory Network ◽  
Complex Traits ◽  
Target Genes ◽  
Specific Gene ◽  
Fat Percentage ◽  
Alcoholic Fatty Liver ◽  
Regulatory Interactions ◽  
Hepatic Diseases ◽  
Gene Regulatory

Background & Aims: Non-alcoholic fatty liver disease (NAFLD) is a multi-system metabolic disease that co-occurs with various hepatic and extra-hepatic diseases. The phenotypic manifestation of NAFLD is primarily observed in the liver. Therefore, identifying liver-specific gene regulatory interactions between variants associated with NAFLD and multimorbid conditions may help to improve our understanding of underlying shared aetiology. Methods: Here, we constructed a liver-specific gene regulatory network (LGRN) consisting of genome-wide spatially constrained expression quantitative trait loci (eQTLs) and their target genes. The LGRN was used to identify regulatory interactions involving NAFLD-associated genetic modifiers and their inter-relationships to other complex traits. Results and Conclusions: We demonstrate that MBOAT7 and IL32, which are associated with NAFLD progression, are regulated by spatially constrained eQTLs that are enriched for an association with liver enzyme levels. MBOAT7 transcript levels are also linked to eQTLs associated with cirrhosis, and other traits that commonly co-occur with NAFLD. In addition, genes that encode interacting partners of NAFLD-candidate genes within the liver-specific protein-protein interaction network were affected by eQTLs enriched for phenotypes relevant to NAFLD (e.g. IgG glycosylation patterns, OSA). Furthermore, we identified distinct gene regulatory networks formed by the NAFLD-associated eQTLs in normal versus diseased liver, consistent with the context-specificity of the eQTLs effects. Interestingly, genes targeted by NAFLD-associated eQTLs within the LGRN were also affected by eQTLs associated with NAFLD-related traits (e.g. obesity and body fat percentage). Overall, the genetic links identified between these traits expand our understanding of shared regulatory mechanisms underlying NAFLD multimorbidities.

scholarly journals Odd-paired controls frequency doubling in Drosophila segmentation by altering the pair-rule gene regulatory network

eLife ◽  
2016 ◽  
Vol 5 ◽  
Author(s):  
Erik Clark ◽  
Michael Akam
Keyword(s):  
Gene Expression ◽  
Gene Regulatory Network ◽  
Regulatory Network ◽  
Frequency Doubling ◽  
Drosophila Embryo ◽  
Anteroposterior Patterning ◽  
Regulatory Interactions ◽  
Pair Rule Gene ◽  
Gene Regulatory ◽  
Pair Rule

The Drosophila embryo transiently exhibits a double-segment periodicity, defined by the expression of seven 'pair-rule' genes, each in a pattern of seven stripes. At gastrulation, interactions between the pair-rule genes lead to frequency doubling and the patterning of 14 parasegment boundaries. In contrast to earlier stages of Drosophila anteroposterior patterning, this transition is not well understood. By carefully analysing the spatiotemporal dynamics of pair-rule gene expression, we demonstrate that frequency-doubling is precipitated by multiple coordinated changes to the network of regulatory interactions between the pair-rule genes. We identify the broadly expressed but temporally patterned transcription factor, Odd-paired (Opa/Zic), as the cause of these changes, and show that the patterning of the even-numbered parasegment boundaries relies on Opa-dependent regulatory interactions. Our findings indicate that the pair-rule gene regulatory network has a temporally modulated topology, permitting the pair-rule genes to play stage-specific patterning roles.

PAGeneRN

2020 ◽  
pp. 1052-1075 ◽  
Author(s):  
Dina Elsayad ◽  
A. Ali ◽  
Howida A. Shedeed ◽  
Mohamed F. Tolba
Keyword(s):  
Gene Expression ◽  
Data Analysis ◽  
Network Analysis ◽  
Gene Regulatory Network ◽  
Gene Expression Data ◽  
Regulatory Network ◽  
Regulatory Networks ◽  
Expression Data ◽  
Gene Expression Data Analysis ◽  
Gene Regulatory

The gene expression analysis is an important research area of Bioinformatics. The gene expression data analysis aims to understand the genes interacting phenomena, gene functionality and the genes mutations effect. The Gene regulatory network analysis is one of the gene expression data analysis tasks. Gene regulatory network aims to study the genes interactions topological organization. The regulatory network is critical for understanding the pathological phenotypes and the normal cell physiology. There are many researches that focus on gene regulatory network analysis but unfortunately some algorithms are affected by data size. Where, the algorithm runtime is proportional to the data size, therefore, some parallel algorithms are presented to enhance the algorithms runtime and efficiency. This work presents a background, mathematical models and comparisons about gene regulatory networks analysis different techniques. In addition, this work proposes Parallel Architecture for Gene Regulatory Network (PAGeneRN).

SIN-KNO: A method of gene regulatory network inference using single-cell transcription and gene knockout data

2019 ◽  
Vol 17 (06) ◽  
pp. 1950035
Author(s):  
Huiqing Wang ◽  
Yuanyuan Lian ◽  
Chun Li ◽  
Yue Ma ◽  
Zhiliang Yan ◽  
...  
Keyword(s):  
Gene Expression ◽  
Steady State ◽  
Single Cell ◽  
Gene Regulatory Network ◽  
Regulatory Network ◽  
Gene Knockout ◽  
Cell Heterogeneity ◽  
State Information ◽  
Gene Regulatory ◽  
Cell Data

As a tool of interpreting and analyzing genetic data, gene regulatory network (GRN) could reveal regulatory relationships between genes, proteins, and small molecules, as well as understand physiological activities and functions within biological cells, interact in pathways, and how to make changes in the organism. Traditional GRN research focuses on the analysis of the regulatory relationships through the average of cellular gene expressions. These methods are difficult to identify the cell heterogeneity of gene expression. Existing methods for inferring GRN using single-cell transcriptional data lack expression information when genes reach steady state, and the high dimensionality of single-cell data leads to high temporal and spatial complexity of the algorithm. In order to solve the problem in traditional GRN inference methods, including the lack of cellular heterogeneity information, single-cell data complexity and lack of steady-state information, we propose a method for GRN inference using single-cell transcription and gene knockout data, called SINgle-cell transcription data-KNOckout data (SIN-KNO), which focuses on combining dynamic and steady-state information of regulatory relationship contained in gene expression. Capturing cell heterogeneity information could help understand the gene expression difference in different cells. So, we could observe gene expression changes more accurately. Gene knockout data could observe the gene expression levels at steady-state of all other genes when one gene is knockout. Classifying the genes before analyzing the single-cell data could determine a large number of non-existent regulation, greatly reducing the number of regulation required for inference. In order to show the efficiency, the proposed method has been compared with several typical methods in this area including GENIE3, JUMP3, and SINCERITIES. The results of the evaluation indicate that the proposed method can analyze the diversified information contained in the two types of data, establish a more accurate gene regulation network, and improve the computational efficiency. The method provides a new thinking for dealing with large datasets and high computational complexity of single-cell data in the GRN inference.

Sign in / Sign up

Export Citation Format

Share Document