scholarly journals Systematic assessment of gene co-regulation within chromatin domains determines differentially active domains across human cancers

2021 ◽  
Vol 22 (1) ◽  
Author(s):  
Marie Zufferey ◽  
Yuanlong Liu ◽  
Daniele Tavernari ◽  
Marco Mina ◽  
Giovanni Ciriello
Keyword(s):  
Complementary Information ◽  
Chromatin Domains ◽  
Algorithmic Approach ◽  
Systematic Assessment ◽  
Regulatory Interactions ◽  
Human Cancers ◽  
Domain Boundaries ◽  
Gene Regulatory ◽  
Systematic Identification ◽  
The Relationship

Abstract Background Spatial interactions and insulation of chromatin regions are associated with transcriptional regulation. Domains of frequent chromatin contacts are proposed as functional units, favoring and delimiting gene regulatory interactions. However, contrasting evidence supports the association between chromatin domains and transcription. Result Here, we assess gene co-regulation in chromatin domains across multiple human cancers, which exhibit great transcriptional heterogeneity. Across all datasets, gene co-regulation is observed only within a small yet significant number of chromatin domains. We design an algorithmic approach to identify differentially active domains (DADo) between two conditions and show that these provide complementary information to differentially expressed genes. Domains comprising co-regulated genes are enriched in the less active B sub-compartments and for genes with similar function. Notably, differential activation of chromatin domains is not associated with major changes of domain boundaries, but rather with changes of sub-compartments and intra-domain contacts. Conclusion Overall, gene co-regulation is observed only in a minority of chromatin domains, whose systematic identification will help unravel the relationship between chromatin structure and transcription.

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 The Relationship between AGAMOUS and Cytokinin Signaling in the Establishment of Carpeloid Features

Plants ◽  
2021 ◽  
Vol 10 (5) ◽  
pp. 827
Author(s):  
Andrea Gómez-Felipe ◽  
Daniel Kierzkowski ◽  
Stefan de Folter
Keyword(s):  
Regulatory Network ◽  
Dependent Manner ◽  
Mads Box ◽  
Type B ◽  
Cytokinin Signaling ◽  
Reproductive Structures ◽  
Carpel Development ◽  
Gene Regulatory ◽  
The Relationship ◽  
Gynoecium Development

Gynoecium development is dependent on gene regulation and hormonal pathway interactions. The phytohormones auxin and cytokinin are involved in many developmental programs, where cytokinin is normally important for cell division and meristem activity, while auxin induces cell differentiation and organ initiation in the shoot. The MADS-box transcription factor AGAMOUS (AG) is important for the development of the reproductive structures of the flower. Here, we focus on the relationship between AG and cytokinin in Arabidopsis thaliana, and use the weak ag-12 and the strong ag-1 allele. We found that cytokinin induces carpeloid features in an AG-dependent manner and the expression of the transcription factors CRC, SHP2, and SPT that are involved in carpel development. AG is important for gynoecium development, and contributes to regulating, or else directly regulates CRC, SHP2, and SPT. All four genes respond to either reduced or induced cytokinin signaling and have the potential to be regulated by cytokinin via the type-B ARR proteins. We generated a model of a gene regulatory network, where cytokinin signaling is mainly upstream and in parallel with AG activity.

scholarly journals Correspondence Learning for Deep Multi-Modal Recognition and Fraud Detection

Electronics ◽  
2021 ◽  
Vol 10 (7) ◽  
pp. 800
Author(s):  
Jongchan Park ◽  
Min-Hyun Kim ◽  
Dong-Geol Choi
Keyword(s):  
Deep Learning ◽  
Simple Technique ◽  
Detection Method ◽  
Fraud Detection ◽  
Complementary Information ◽  
Multiple Modalities ◽  
Vending Machines ◽  
Emotion Intensity ◽  
The Relationship ◽  
Extract Information

Deep learning-based methods have achieved good performance in various recognition benchmarks mostly by utilizing single modalities. As different modalities contain complementary information to each other, multi-modal based methods are proposed to implicitly utilize them. In this paper, we propose a simple technique, called correspondence learning (CL), which explicitly learns the relationship among multiple modalities. The multiple modalities in the data samples are randomly mixed among different samples. If the modalities are from the same sample (not mixed), then they have positive correspondence, and vice versa. CL is an auxiliary task for the model to predict the correspondence among modalities. The model is expected to extract information from each modality to check correspondence and achieve better representations in multi-modal recognition tasks. In this work, we first validate the proposed method in various multi-modal benchmarks including CMU Multimodal Opinion-Level Sentiment Intensity (CMU-MOSI) and CMU Multimodal Opinion Sentiment and Emotion Intensity (CMU-MOSEI) sentiment analysis datasets. In addition, we propose a fraud detection method using the learned correspondence among modalities. To validate this additional usage, we collect a multi-modal dataset for fraud detection using real-world samples for reverse vending machines.

A systematic assessment of the relationship between synthetic surfactants and mucosal adjuvanticity

2021 ◽  
Author(s):  
Naoto Yoshino ◽  
Hanae Kawamura ◽  
Ikumi Sugiyama ◽  
Yutaka Sasaki ◽  
Takashi Odagiri ◽  
...  
Keyword(s):  
Systematic Assessment ◽  
The Relationship ◽  
Synthetic Surfactants

scholarly journals Prediction of genome-wide effects of single nucleotide variants on transcription factor binding

2020 ◽  
Vol 10 (1) ◽  
Author(s):  
Sebastian Carrasco Pro ◽  
Katia Bulekova ◽  
Brian Gregor ◽  
Adam Labadorf ◽  
Juan Ignacio Fuxman Bass
Keyword(s):  
Binding Sites ◽  
Cancer Type ◽  
Single Nucleotide Variants ◽  
Single Nucleotide ◽  
Regulatory Regions ◽  
Genome Wide ◽  
Transcriptional Regulatory ◽  
Gene Regulatory ◽  
The Impact ◽  
The Relationship

Abstract Single nucleotide variants (SNVs) located in transcriptional regulatory regions can result in gene expression changes that lead to adaptive or detrimental phenotypic outcomes. Here, we predict gain or loss of binding sites for 741 transcription factors (TFs) across the human genome. We calculated ‘gainability’ and ‘disruptability’ scores for each TF that represent the likelihood of binding sites being created or disrupted, respectively. We found that functional cis-eQTL SNVs are more likely to alter TF binding sites than rare SNVs in the human population. In addition, we show that cancer somatic mutations have different effects on TF binding sites from different TF families on a cancer-type basis. Finally, we discuss the relationship between these results and cancer mutational signatures. Altogether, we provide a blueprint to study the impact of SNVs derived from genetic variation or disease association on TF binding to gene regulatory regions.

The Evolution of Gene Regulatory Interactions

BioScience ◽  
2010 ◽  
Vol 60 (1) ◽  
pp. 15-23 ◽  
Author(s):  
David A. Garfield ◽  
Gregory A. Wray
Keyword(s):  
Regulatory Interactions ◽  
Gene Regulatory

scholarly journals Modeling a gene regulatory network of EMT hybrid states for mouse embryonic skin cells

2019 ◽  
Author(s):  
Dan Ramirez ◽  
Vivek Kohar ◽  
Ataur Katebi ◽  
Mingyang Lu
Keyword(s):  
Gene Regulatory Network ◽  
Regulatory Network ◽  
Epithelial Mesenchymal Transition ◽  
Expression Patterns ◽  
Mesenchymal Transition ◽  
Hybrid State ◽  
Regulatory Interactions ◽  
Skin Cells ◽  
Embryonic Skin ◽  
Gene Regulatory

AbstractEpithelial-mesenchymal transition (EMT) plays a crucial role in embryonic development and tumorigenesis. Although EMT has been extensively studied with both computational and experimental methods, the gene regulatory mechanisms governing the transition are not yet well understood. Recent investigations have begun to better characterize the complex phenotypic plasticity underlying EMT using a computational systems biology approach. Here, we analyzed recently published single-cell RNA sequencing data from E9.5 to E11.5 mouse embryonic skin cells and identified the gene expression patterns of both epithelial and mesenchymal phenotypes, as well as a clear hybrid state. By integrating the scRNA-seq data and gene regulatory interactions from the literature, we constructed a gene regulatory network model governing the decision-making of EMT in the context of the developing mouse embryo. We simulated the network using a recently developed mathematical modeling method, named RACIPE, and observed three distinct phenotypic states whose gene expression patterns can be associated with the epithelial, hybrid, and mesenchymal states in the scRNA-seq data. Additionally, the model is in agreement with published results on the composition of EMT phenotypes and regulatory networks. We identified Wnt signaling as a major pathway in inducing the EMT and its role in driving cellular state transitions during embryonic development. Our findings demonstrate a new method of identifying and incorporating tissue-specific regulatory interactions into gene regulatory network modeling.Author SummaryEpithelial-mesenchymal transition (EMT) is a cellular process wherein cells become disconnected from their surroundings and acquire the ability to migrate through the body. EMT has been observed in biological contexts including development, wound healing, and cancer, yet the regulatory mechanisms underlying it are not well understood. Of particular interest is a purported hybrid state, in which cells can retain some adhesion to their surroundings but also show mesenchymal traits. Here, we examine the prevalence and composition of the hybrid state in the context of the embryonic mouse, integrating gene regulatory interactions from published experimental results as well as from the specific single cell RNA sequencing dataset of interest. Using mathematical modeling, we simulated a regulatory network based on these sources and aligned the simulated phenotypes with those in the data. We identified a hybrid EMT phenotype and revealed the inducing effect of Wnt signaling on EMT in this context. Our regulatory network construction process can be applied beyond EMT to illuminate the behavior of any biological phenomenon occurring in a specific context, allowing better identification of therapeutic targets and further research directions.

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 Role of PIF4 and FLC in controlling flowering time under daily variable temperature profile

2020 ◽  
Author(s):  
Jutapak Jenkitkonchai ◽  
Poppy Marriott ◽  
Weibing Yang ◽  
Napaporn Sriden ◽  
Jae-Hoon Jung ◽  
...  
Keyword(s):  
Flowering Time ◽  
Molecular Mechanisms ◽  
Transcriptional Regulatory Network ◽  
Variable Temperature ◽  
Loss Of Function ◽  
Environmental Signals ◽  
Regulatory Interactions ◽  
Flowering Genes ◽  
Gene Regulatory ◽  
Flowering Locus

ABSTRACTInitiation of flowering is a crucial developmental event that requires both internal and environmental signals to determine when floral transition should occur to maximize reproductive success. Ambient temperature is one of the key environmental signals that highly influence flowering time, not only seasonally but also in the context of drastic temperature fluctuation due to global warming. Molecular mechanisms of how high or low constant temperatures affect the flowering time have been largely characterized in the model plant Arabidopsis thaliana; however, the effect of natural daily variable temperature outside laboratories is only partly explored. Several groups of flowering genes have been shown to play important roles in temperature responses, including two temperature-responsive transcription factors (TFs), namely PHYTOCHROME INTERACTING FACTOR 4 (PIF4) and FLOWERING LOCUS C (FLC), that act antagonistically to regulate flowering time by activating or repressing floral integrator FLOWERING LOCUS T (FT). In this study, we have demonstrated that the daily variable temperature (VAR) causes early flowering in both natural accessions Col-0, C24 and their late flowering hybrid C24xCol, which carries both functional floral repressor FLC and its activator FRIGIDA (FRI), as compared to a constant temperature (CON). The loss-of-function mutation of PIF4 exhibits later flowering in VAR, suggesting that PIF4 at least in part, contributes to acceleration of flowering in response to the daily variable temperature. We find that VAR increases PIF4 transcription at the end of the day when temperature peaks at 32 °C. The FT transcription is also elevated in VAR, as compared to CON, in agreement with earlier flowering observed in VAR. In addition, VAR causes a decrease in FLC transcription in 4-week-old plants, and we further show that overexpression of PIF4 can reduce FLC transcription, suggesting that PIF4 might also regulate FT indirectly through the repression of FLC. To further conceptualize an overall model of gene regulatory mechanisms involving PIF4 and FLC in controlling flowering in response to temperature changes, we construct a co-expression – transcriptional regulatory network by combining publicly available transcriptomic data and gene regulatory interactions of our flowering genes of interest and their partners. The network model reveals the conserved and tissue-specific regulatory functions of 62 flowering-time-relating genes, namely PIF4, PIF5, FLC, ELF3 and their immediate neighboring genes, which can be useful for confirming and predicting the functions and regulatory interactions between the key flowering genes.

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