scholarly journals The comprehensive transcriptional analysis in Caenorhabditis elegans by integrating ChIP-seq and gene expression data

2014 ◽  
Vol 96 ◽  
Author(s):  
KAN HE ◽  
JIAOFANG SHAO ◽  
ZHONGYING ZHAO ◽  
DAHAI LIU
Keyword(s):  
Gene Expression ◽  
Transcriptional Regulation ◽  
Caenorhabditis Elegans ◽  
Gene Expression Data ◽  
Target Genes ◽  
Model Systems ◽  
Transcriptional Analysis ◽  
Expression Data ◽  
Tissue Specific ◽  
C Elegans

SummaryThe fundamental step of learning transcriptional regulation mechanism is to identify the target genes regulated by transcription factors (TFs). Despite numerous target genes identified by chromatin immunopre-cipitation followed by high-throughput sequencing technology (ChIP-seq) assays, it is not possible to infer function from binding alone in vivo. This is equally true in one of the best model systems, the nematode Caenorhabditis elegans (C. elegans), where regulation often occurs through diverse TF binding features of transcriptional networks identified in modENCODE. Here, we integrated ten ChIP-seq datasets with genome-wide expression data derived from tiling arrays, involved in six TFs (HLH-1, ELT-3, PQM-1, SKN-1, CEH-14 and LIN-11) with tissue-specific and four TFs (CEH-30, LIN-13, LIN-15B and MEP-1) with broad expression patterns. In common, TF bindings within 3 kb upstream of or within its target gene for these ten studies showed significantly elevated level of expression as opposed to that of non-target controls, indicated that these sites may be more likely to be functional through up-regulating its target genes. Intriguingly, expression of the target genes out of 5 kb upstream of their transcription start site also showed high levels, which was consistent with the results of following network component analysis. Our study has identified similar transcriptional regulation mechanisms of tissue-specific or broad expression TFs in C. elegans using ChIP-seq and gene expression data. It may also provide a novel insight into the mechanism of transcriptional regulation not only for simple organisms but also for more complex species.

scholarly journals Synaptic polarity and sign-balance prediction using gene expression data in the Caenorhabditis elegans chemical synapse neuronal connectome network

2020 ◽  
Author(s):  
Bánk G. Fenyves ◽  
Gábor S. Szilágyi ◽  
Zsolt Vassy ◽  
Csaba Sőti ◽  
Péter Csermely
Keyword(s):  
Gene Expression ◽  
Caenorhabditis Elegans ◽  
Gene Expression Data ◽  
Temporal Dynamics ◽  
Receptor Gene ◽  
Inhibitory Synapses ◽  
Directed Network ◽  
Chemical Synapse ◽  
Expression Data ◽  
C Elegans

AbstractGraph theoretical analyses of nervous systems usually omit the aspect of connection polarity, due to data insufficiency. The chemical synapse network of Caenorhabditis elegans is a well-reconstructed directed network, but the signs of its connections are yet to be elucidated. Here, we present the gene expression-based sign prediction of the C. elegans connectome, incorporating presynaptic neurotransmitter and postsynaptic receptor gene expression data (3,638 connections and 20,589 synapses total). We made successful predictions for more than two-thirds of all chemical synapses and determined a ratio of excitatory-inhibitory (E:I) interneuronal ionotropic chemical connections close to 4:1 which was found similar to that observed in many real-world networks. Our open source tool (http://EleganSign.linkgroup.hu) is simple but efficient in predicting polarities by integrating neuronal connectome and gene expression data.Author SummaryThe fundamental way neurons communicate is by activating or inhibiting each other via synapses. The balance between the two is crucial for the optimal functioning of a nervous system. However, whole-brain synaptic polarity information is unavailable for any species and experimental validation is challenging. The roundworm Caenorhabditis elegans possesses a fully mapped connectome with a comprehensive gene expression profile of its 302 neurons. Based on the consideration that the polarity of a synapse must be determined by the neurotransmitter(s) expressed in the presynaptic neuron and the receptors expressed in the postsynaptic neuron, we conceptualized and created a tool that predicts synaptic polarities based on connectivity and gene expression information. We were able to show for the first time that the ratio of excitatory and inhibitory synapses in C. elegans is around 4 to 1 which is in line with the balance observed in many natural systems. Our method opens a way to include spatial and temporal dynamics of synaptic polarity that would add a new dimension of plasticity in the excitatory:inhibitory balance. Our tool is freely available to be used on any network accompanied by any expression atlas.

scholarly journals Synaptic polarity and sign-balance prediction using gene expression data in the Caenorhabditis elegans chemical synapse neuronal connectome network

2020 ◽  
Vol 16 (12) ◽  
pp. e1007974
Author(s):  
Bánk G. Fenyves ◽  
Gábor S. Szilágyi ◽  
Zsolt Vassy ◽  
Csaba Sőti ◽  
Peter Csermely
Keyword(s):  
Gene Expression ◽  
Caenorhabditis Elegans ◽  
Gene Expression Data ◽  
Receptor Gene ◽  
Directed Network ◽  
Chemical Synapse ◽  
Expression Data ◽  
Chemical Synapses ◽  
Theoretical Analyses ◽  
Receptor Gene Expression

Graph theoretical analyses of nervous systems usually omit the aspect of connection polarity, due to data insufficiency. The chemical synapse network of Caenorhabditis elegans is a well-reconstructed directed network, but the signs of its connections are yet to be elucidated. Here, we present the gene expression-based sign prediction of the ionotropic chemical synapse connectome of C. elegans (3,638 connections and 20,589 synapses total), incorporating available presynaptic neurotransmitter and postsynaptic receptor gene expression data for three major neurotransmitter systems. We made predictions for more than two-thirds of these chemical synapses and observed an excitatory-inhibitory (E:I) ratio close to 4:1 which was found similar to that observed in many real-world networks. Our open source tool (http://EleganSign.linkgroup.hu) is simple but efficient in predicting polarities by integrating neuronal connectome and gene expression data.

scholarly journals New strains for tissue-specific RNAi studies in Caenorhabditis elegans

2018 ◽  
Author(s):  
Jason S. Watts ◽  
Henry F. Harrison ◽  
Shizue Omi ◽  
Quentin Guenthers ◽  
James Dalelio ◽  
...  
Keyword(s):  
Fatty Acid ◽  
Caenorhabditis Elegans ◽  
Gene Function ◽  
Target Genes ◽  
Resistant Mutant ◽  
Knockout Mutant ◽  
Tissue Specific ◽  
Double Stranded Rna ◽  
C Elegans ◽  
Insight Into

AbstractRNA interference is a powerful tool for dissecting gene function. In Caenorhabditis elegans, ingestion of double stranded RNA causes strong, systemic knockdown of target genes. Further insight into gene function can be revealed by tissue-specific RNAi techniques. Currently available tissue-specific C. elegans strains rely on rescue of RNAi function in a desired tissue or cell in an otherwise RNAi deficient genetic background. We attempted to assess the contribution of specific tissues to polyunsaturated fatty acid (PUFA) synthesis using currently available tissue-specific RNAi strains. We discovered that rde-1 (ne219), a commonly used RNAi-resistant mutant strain, retains considerable RNAi capacity against RNAi directed at PUFA synthesis genes. By measuring changes in the fatty acid products of the desaturase enzymes that synthesize PUFAs, we found that the before mentioned strain, rde-1 (ne219) and the reported germline only RNAi strain, rrf-1 (pk1417) are not appropriate genetic backgrounds for tissue-specific RNAi experiments. However, the knockout mutant rde-1 (ne300) was strongly resistant to dsRNA induced RNAi, and thus is more appropriate for construction of a robust tissue-specific RNAi strains. Using newly constructed strains in the rde-1(null) background, we found considerable desaturase activity in intestinal, epidermal, and germline tissues, but not in muscle. The RNAi-specific strains reported in this study will be useful tools for C. elegans researchers studying a variety of biological processes.

scholarly journals Single-copy Knock-In Loci for Defined Gene Expression in C. elegans

2018 ◽  
Author(s):  
Carlos G Silva-Garcia ◽  
Caroline Heintz ◽  
Sneha Dutta ◽  
Nicole M Clark ◽  
Anne Lanjuin ◽  
...  
Keyword(s):  
Gene Expression ◽  
Caenorhabditis Elegans ◽  
Cdna Sequence ◽  
Single Copy ◽  
Tissue Specific ◽  
C Elegans

We have generated a single-copy knock-in loci for defined gene expression (SKI LODGE) system to insert any cDNA by CRISPR/Cas9 at defined safe harbors in the Caenorhabditis elegans genome. Utilizing a single crRNA guide, which also acts as a Co-CRISPR enrichment marker, any cDNA sequence can be introduced as a single integrated copy, regulated by different tissue-specific promoters. The SKI LODGE system provides a fast, economical and effective approach for generating single-copy non-silenced transgenes in C. elegans.

scholarly journals Inferring TF activities and activity regulators from gene expression data with constraints from TF perturbation data

2020 ◽  
Author(s):  
Cynthia Ma ◽  
Michael R. Brent
Keyword(s):  
Gene Expression ◽  
Gene Expression Data ◽  
Large Scale ◽  
Target Genes ◽  
Activity Levels ◽  
Expression Data ◽  
Performance Constraints ◽  
Perturbation Data ◽  
Carry Over ◽  
Insight Into

ABSTRACTBackgroundThe activity of a transcription factor (TF) in a sample of cells is the extent to which it is exerting its regulatory potential. Many methods of inferring TF activity from gene expression data have been described, but due to the lack of appropriate large-scale datasets, systematic and objective validation has not been possible until now.ResultsUsing a new dataset, we systematically evaluate and optimize the approach to TF activity inference in which a gene expression matrix is factored into a condition-independent matrix of control strengths and a condition-dependent matrix of TF activity levels. These approaches require a TF network map, which specifies the target genes of each TF, as input. We evaluate different approaches to building the network map and deriving constraints on the matrices. We find that such constraints are essential for good performance. Constraints can be obtained from expression data in which the activities of individual TFs have been perturbed, and we find that such data are both necessary and sufficient for obtaining good performance. Remaining uncertainty about whether a TF activates or represses a target is a major source of error. To a considerable extent, control strengths inferred using expression data from one growth condition carry over to other conditions. As a result, the control strength matrices derived here can be used for other applications. Finally, we apply these methods to gain insight into the upstream factors that regulate the activities of four yeast TFs: Gcr2, Gln3, Gcn4, and Msn2. Evaluation code and data available at https://github.com/BrentLab/TFA-evaluationConclusionsWhen a high-quality network map, constraints, and perturbation-response data are available, inferring TF activity levels by factoring gene expression matrices is effective. Furthermore, it provides insight into regulators of TF activity.

scholarly journals dynDeepDRIM: a dynamic deep learning model to infer direct regulatory interactions using single cell time-course gene expression data

2021 ◽  
Author(s):  
Yu Xu ◽  
Jiaxing Chen ◽  
Aiping Lyu ◽  
William K Cheung ◽  
Lu Zhang
Keyword(s):  
Gene Expression ◽  
Single Cell ◽  
Gene Expression Data ◽  
Time Course ◽  
Target Genes ◽  
Neighborhood Context ◽  
Specific Gene ◽  
Expression Data ◽  
Regulatory Interactions ◽  
Gene Functions

Time-course single-cell RNA sequencing (scRNA-seq) data have been widely applied to reconstruct the cell-type-specific gene regulatory networks by exploring the dynamic changes of gene expression between transcription factors (TFs) and their target genes. The existing algorithms were commonly designed to analyze bulk gene expression data and could not deal with the dropouts and cell heterogeneity in scRNA-seq data. In this paper, we developed dynDeepDRIM that represents gene pair joint expression as images and considers the neighborhood context to eliminate the transitive interactions. dynDeepDRIM integrated the primary image, neighbor images with time-course into a four-dimensional tensor and trained a convolutional neural network to predict the direct regulatory interactions between TFs and genes. We evaluated the performance of dynDeepDRIM on five time-course gene expression datasets. dynDeepDRIM outperformed the state-of-the-art methods for predicting TF-gene direct interactions and gene functions. We also observed gene functions could be better performed if more neighbor images were involved.

scholarly journals Reconstructing Generalized Logical Networks of Transcriptional Regulation in Mouse Brain from Temporal Gene Expression Data

2009 ◽  
Vol 2009 ◽  
pp. 1-13 ◽  
Author(s):  
Mingzhou (Joe) Song ◽  
Chris K. Lewis ◽  
Eric R. Lance ◽  
Elissa J. Chesler ◽  
Roumyana Kirova Yordanova ◽  
...  
Keyword(s):  
Gene Expression ◽  
Transcriptional Regulation ◽  
Mouse Brain ◽  
Gene Expression Data ◽  
Expression Data ◽  
Temporal Gene Expression

METABOLIC RECONSTRUCTION FOR DEVELOPING A TISSUE-SPECIFIC MODEL FOR CATTLE GLYCOLYSIS USING COBRA

2017 ◽  
Vol 25 (02) ◽  
pp. 231-246
Author(s):  
DO GYUN KIM ◽  
WANG-HEE LEE ◽  
SUNG-WON SEO ◽  
HYE-SUN PARK
Keyword(s):  
Gene Expression ◽  
Mammary Gland ◽  
Epithelial Cells ◽  
Gene Expression Data ◽  
Specific Model ◽  
Expression Data ◽  
Bovine Mammary Gland ◽  
Tissue Specific ◽  
Metabolic Reactions ◽  
Mammary Gland Epithelial Cells

This study aims at developing a tissue-specific model for glycolysis in bovine mammary gland epithelial cells by incorporating gene expression data into metabolic reactions. Two types of data sets were embedded in the COnstraint-Based Reconstruction Analysis (COBRA) toolbox: metabolic reactions that overlay bovine genetic information into human glycolysis data from a public database and gene expression data of cattle acquired from lab experiments. As a result, we successfully generated a tissue-specific model of bovine glycolysis in bovine mammary gland epithelial cells, providing information on expression of metabolic pathways and gene annotations still required for curation.

scholarly journals XGRN: Reconstruction of Biological Networks Based on Boosted Trees Regression

Computation ◽  
2021 ◽  
Vol 9 (4) ◽  
pp. 48
Author(s):  
Georgios N. Dimitrakopoulos
Keyword(s):  
Gene Expression ◽  
Regression Model ◽  
Gene Expression Data ◽  
Biological Networks ◽  
High Performance ◽  
Regulatory Networks ◽  
Target Genes ◽  
Biological Information ◽  
Expression Data ◽  
Gene Regulatory

In Systems Biology, the complex relationships between different entities in the cells are modeled and analyzed using networks. Towards this aim, a rich variety of gene regulatory network (GRN) inference algorithms has been developed in recent years. However, most algorithms rely solely on gene expression data to reconstruct the network. Due to possible expression profile similarity, predictions can contain connections between biologically unrelated genes. Therefore, previously known biological information should also be considered by computational methods to obtain more consistent results, such as experimentally validated interactions between transcription factors and target genes. In this work, we propose XGBoost for gene regulatory networks (XGRN), a supervised algorithm, which combines gene expression data with previously known interactions for GRN inference. The key idea of our method is to train a regression model for each known interaction of the network and then utilize this model to predict new interactions. The regression is performed by XGBoost, a state-of-the-art algorithm using an ensemble of decision trees. In detail, XGRN learns a regression model based on gene expression of the two interactors and then provides predictions using as input the gene expression of other candidate interactors. Application on benchmark datasets and a real large single-cell RNA-Seq experiment resulted in high performance compared to other unsupervised and supervised methods, demonstrating the ability of XGRN to provide reliable predictions.

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