scholarly journals Genetic buffering and potentiation in metabolism

2019 ◽  
Author(s):  
Juan F. Poyatos
Keyword(s):  
Growth Rate ◽  
Regulatory Network ◽  
Metabolic Inhibitors ◽  
Cancer Therapies ◽  
Specific Enzyme ◽  
Glycolysis Pathway ◽  
Gene Regulatory ◽  
Genetic Buffering ◽  
The Impact ◽  
Genetic Context

AbstractCells adjust their metabolism in response to mutations, but how this reprogramming depends on the genetic context is not well known. Specifically, the absence of individual enzymes can affect reprogramming and thus the impact of mutations in cell growth. Here, we examine this issue with an in silico model of Saccharomyces cerevisiae’s metabolism. By quantifying the variability in the growth rate of 10000 different mutant metabolisms that accumulated changes in their reaction fluxes, in the presence, or absence, of a specific enzyme, we distinguish a subset of modifier genes serving as buffers or potentiators of variability. We notice that the most potent modifiers refer to the glycolysis pathway and that, more broadly, they show strong pleiotropy and epistasis. Moreover, the evidence that this subset depends on the specific growing condition strengthens its systemic underpinning, a feature only observed before in a simple model of a gene-regulatory network. Some of these enzymes also modulate the effect that biochemical noise and environmental fluctuations produce in growth. Thus, the reorganization of metabolism triggered by mutations has not only direct physiological implications but also changes the influence that other mutations have on growth. This is a general result with implications in the development of cancer therapies based on metabolic inhibitors.

scholarly journals The Impact of Gene Expression Variation on the Robustness and Evolvability of a Developmental Gene Regulatory Network

PLoS Biology ◽  
2013 ◽  
Vol 11 (10) ◽  
pp. e1001696 ◽  
Author(s):  
David A. Garfield ◽  
Daniel E. Runcie ◽  
Courtney C. Babbitt ◽  
Ralph Haygood ◽  
William J. Nielsen ◽  
...  
Keyword(s):  
Gene Expression ◽  
Gene Regulatory Network ◽  
Regulatory Network ◽  
Gene Expression Variation ◽  
Developmental Gene ◽  
Expression Variation ◽  
Gene Regulatory ◽  
The Impact

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 Gene regulatory network analysis of Trichoderma reesei grown on glucose-lactose mixtures suggests links between development and cellulase production

2020 ◽  
Author(s):  
Aurélie Pirayre ◽  
Laurent Duval ◽  
Corinne Blugeon ◽  
Cyril Firmo ◽  
Sandrine Perrin ◽  
...  
Keyword(s):  
Transcription Factors ◽  
Gene Regulatory Network ◽  
Trichoderma Reesei ◽  
Regulatory Network ◽  
Cellulase Production ◽  
Biofuel Production ◽  
Batch Mode ◽  
Lactose Concentration ◽  
Gene Regulatory ◽  
The Impact

Abstract Background: The degradation of cellulose and hemicellulose molecules into simpler sugars such as glucose is part of the second generation biofuel production process. Hydrolysis of lignocellulosic substrates is usually performed by enzymes produced and secreted by the fungus Trichoderma reesei . Studies identifying transcription factors involved in the regulation of cellulase production have been conducted but no overview of the whole regulation network is available. A transcriptomic approach with mixtures of glucose and lactose, used as a substrate for cellulase induction, was used to help us decipher missing parts in the network.Results: Experimental results confirmed the impact of sugar mixture on the enzymatic cocktail composition. The transcriptomic study shows a temporal regulation of the main transcription factors and a lactose concentration impact on the transcriptional profile. A gene regulatory network built using the BRANE Cut software reveals three sub-networks related to i) a positive correlation between lactose concentration and cellulase production, ii) a particular dependence of the lactose onto the β-glucosidase regulation and iii) a negative regulation of the development process and growth.Conclusions: This work is the first investigating a transcriptomic study regarding the effects of pure and mixed carbon sources in a fed-batch mode. Our study expose a co-orchestration of xyr1 , clr2 and ace3 for cellulase and hemicellulase induction and production, a fine regulation of the β-glucosidase and a decrease of growth in favor of cellulase production. These conclusions provide us with potential targets for further genetic engineering leading to better cellulase-producing strains.

scholarly journals Loricarioid catfish evolved skin denticles that recapitulate teeth at the structural, developmental, and genetic levels

2021 ◽  
Author(s):  
Carlos Javier Rivera-Rivera ◽  
Nadezhda I Guevara-Delgadillo ◽  
Ilham A Bahechar ◽  
Claire A Shea ◽  
Juan I Montoya-Burgos
Keyword(s):  
Regulatory Network ◽  
Fish Scales ◽  
Gene Expression Analyses ◽  
Initial Evolution ◽  
Gene Regulatory ◽  
Underlying Processes ◽  
Genetic Context ◽  
Shed Light ◽  
Key Innovations

The first vertebrate mineralized skeleton was an external bony armor coated with dental structures. The subsequent emergence of a mineralized endoskeleton and of teeth are considered key innovations in the diversification of vertebrates. Although time clouds our understanding of the initial evolution of these mineralized structures, recent re-emergences may shed light on the underlying processes. Loricarioid catfishes are a lineage that, much like the ancestral vertebrates, bear denticle-clad bony armor from head to tail. Loricarioid denticles (LDs) and oral teeth are very similar in superstructure. We show here that other extra-oral dental structures are found as ancestral characters only in lineages that are distantly related to loricarioids such as sharks or coelacanth, indicating that LDs have independently re-emerged in loricarioid catfishes. We investigate whether the similarities between LDs and teeth extend to their developmental and genetic context, and how their development compares to that of other vertebrate integument structures. Our detailed study of the development of LDs, and gene expression analyses through in situ hybridization confirm that all 12 genes from the tooth-forming gene regulatory network (oGRN) are expressed in developing LDs in a similar way as they are expressed in developing teeth. We then compare the developmental, structural, and genetic aspects of LD and teeth with that of other integument appendages such as fish scales, shark dermal denticles, feathers and hairs. We find that LDs share all developmental cues with teeth and, to a lesser extent, with the other vertebrate integument structures. Taken together, our results indicate that denticles have re-emerged on the trunk of loricarioid catfishes through the ectopic co-option of the oGRN rather than the resurrection of an ancestral trunk-specific denticle genetic pathway.

scholarly journals Glucose-lactose mixture feeds in industry-like conditions: a gene regulatory network analysis on the hyperproducing Trichoderma reesei strain Rut-C30

2020 ◽  
Author(s):  
Aurélie Pirayre ◽  
Laurent Duval ◽  
Corinne Blugeon ◽  
Cyril Firmo ◽  
Sandrine Perrin ◽  
...  
Keyword(s):  
Transcription Factors ◽  
Gene Regulatory Network ◽  
Trichoderma Reesei ◽  
Regulatory Network ◽  
Cellulase Production ◽  
Biofuel Production ◽  
Lactose Concentration ◽  
Gene Regulatory ◽  
The Impact ◽  
Rut C30

Abstract Background: The degradation of cellulose and hemicellulose molecules into simpler sugars such as glucose is part of the second generation biofuel production process. Hydrolysis of lignocellulosic substrates is usually performed by enzymes produced and secreted by the fungus Trichoderma reesei . Studies identifying transcription factors involved in the regulation of cellulase production have been conducted but no overview of the whole regulation network is available. A transcriptomic approach with mixtures of glucose and lactose, used as a substrate for cellulase induction, was used to help us decipher missing parts in the network of T. reesei Rut-C30.Results: Experimental results on the Rut-C30 hyperproducing strain confirmed the impact of sugar mixtures on the enzymatic cocktail composition. The transcriptomic study shows a temporal regulation of the main transcription factors and a lactose concentration impact on the transcriptional profile. A gene regulatory network built using BRANE Cut software reveals three sub-networks related to iq a positive correlation between lactose concentration and cellulase production, iiq a particular dependence of the lactose onto the β-glucosidase regulation and iiiq a negative regulation of the development process and growth.Conclusions: This work is the first investigating a transcriptomic study regarding the effects of pure and mixed carbon sources in a fed-batch mode. Our study expose a co-orchestration of xyr1 , clr2 and ace3 for cellulase and hemicellulase induction and production, a fine regulation of the β-glucosidase and a decrease of growth in favor of cellulase production. These conclusions provide us with potential targets for further genetic engineering leading to bettercellulase-producing strains in industry-like conditions.

scholarly journals Glucose-lactose mixture feeds in industry-like conditions: a gene regulatory network analysis on the hyperproducing Trichoderma reesei strain Rut-C30

2020 ◽  
Author(s):  
Aurélie Pirayre ◽  
Laurent Duval ◽  
Corinne Blugeon ◽  
Cyril Firmo ◽  
Sandrine Perrin ◽  
...  
Keyword(s):  
Transcription Factors ◽  
Gene Regulatory Network ◽  
Trichoderma Reesei ◽  
Regulatory Network ◽  
Cellulase Production ◽  
Biofuel Production ◽  
Batch Mode ◽  
Lactose Concentration ◽  
Gene Regulatory ◽  
The Impact

AbstractBackgroundThe degradation of cellulose and hemicellulose molecules into simpler sugars such as glucose is part of the second generation biofuel production process. Hydrolysis of lignocellulosic substrates is usually performed by enzymes produced and secreted by the fungus Trichoderma reesei. Studies identifying transcription factors involved in the regulation of cellulase production have been conducted but no overview of the whole regulation network is available. A transcriptomic approach with mixtures of glucose and lactose, used as a substrate for cellulase induction, was used to help us decipher missing parts in the network.ResultsExperimental results confirmed the impact of sugar mixture on the enzymatic cocktail composition. The transcriptomic study shows a temporal regulation of the main transcription factors and a lactose concentration impact on the transcriptional profile. A gene regulatory network (GRN) built using the BRANE Cut software reveals three sub-networks related to i a positive correlation between lactose concentration and cellulase production, ii a particular dependence of the lactose onto the β-glucosidase regulation and iii a negative regulation of the development process and growth.ConclusionsThis work is the first investigating a transcriptomic study regarding the effects of pure and mixed carbon sources in a fed-batch mode. Our study expose a co-orchestration of xyr1, clr2 and ace3 for cellulase and hemicellulase induction and production, a fine regulation of the β-glucosidase and a decrease of growth in favor of cellulase production. These conclusions provide us with potential targets for further genetic engineering leading to better cellulase-producing strains.

scholarly journals Glucose-lactose mixture feeds in industry-like conditions: a gene regulatory network analysis on the hyperproducing Trichoderma reesei strain Rut-C30

BMC Genomics ◽  
2020 ◽  
Vol 21 (1) ◽  
Author(s):  
Aurélie Pirayre ◽  
Laurent Duval ◽  
Corinne Blugeon ◽  
Cyril Firmo ◽  
Sandrine Perrin ◽  
...  
Keyword(s):  
Transcription Factors ◽  
Gene Regulatory Network ◽  
Trichoderma Reesei ◽  
Regulatory Network ◽  
Cellulase Production ◽  
Biofuel Production ◽  
Lactose Concentration ◽  
Gene Regulatory ◽  
The Impact ◽  
Rut C30

Abstract Background The degradation of cellulose and hemicellulose molecules into simpler sugars such as glucose is part of the second generation biofuel production process. Hydrolysis of lignocellulosic substrates is usually performed by enzymes produced and secreted by the fungus Trichoderma reesei. Studies identifying transcription factors involved in the regulation of cellulase production have been conducted but no overview of the whole regulation network is available. A transcriptomic approach with mixtures of glucose and lactose, used as a substrate for cellulase induction, was used to help us decipher missing parts in the network of T. reesei Rut-C30. Results Experimental results on the Rut-C30 hyperproducing strain confirmed the impact of sugar mixtures on the enzymatic cocktail composition. The transcriptomic study shows a temporal regulation of the main transcription factors and a lactose concentration impact on the transcriptional profile. A gene regulatory network built using BRANE Cut software reveals three sub-networks related to i) a positive correlation between lactose concentration and cellulase production, ii) a particular dependence of the lactose onto the β-glucosidase regulation and iii) a negative regulation of the development process and growth. Conclusions This work is the first investigating a transcriptomic study regarding the effects of pure and mixed carbon sources in a fed-batch mode. Our study expose a co-orchestration of xyr1, clr2 and ace3 for cellulase and hemicellulase induction and production, a fine regulation of the β-glucosidase and a decrease of growth in favor of cellulase production. These conclusions provide us with potential targets for further genetic engineering leading to better cellulase-producing strains in industry-like conditions.

scholarly journals Glucose-lactose mixture feeds in industry-like conditions: a gene regulatory network analysis on the hyperproducing Trichoderma reesei strain Rut-C30

2020 ◽  
Author(s):  
Aurélie Pirayre ◽  
Laurent Duval ◽  
Corinne Blugeon ◽  
Cyril Firmo ◽  
Sandrine Perrin ◽  
...  
Keyword(s):  
Transcription Factors ◽  
Gene Regulatory Network ◽  
Trichoderma Reesei ◽  
Regulatory Network ◽  
Cellulase Production ◽  
Biofuel Production ◽  
Lactose Concentration ◽  
Gene Regulatory ◽  
The Impact ◽  
Rut C30

Abstract Background: The degradation of cellulose and hemicellulose molecules into simpler sugars such as glucose is part of the second generation biofuel production process. Hydrolysis of lignocellulosic substrates is usually performed by enzymes produced and secreted by the fungus Trichoderma reesei. Studies identifying transcription factors involved in the regulation of cellulase production have been conducted but no overview of the whole regulation network is available. A transcriptomic approach with mixtures of glucose and lactose, used as a substrate for cellulase induction, was used to help us decipher missing parts in the network of T. reesei Rut-C30. Results: Experimental results on the Rut-C30 hyperproducing strain confirmed the impact of sugar mixtures on the enzymatic cocktail composition. The transcriptomic study shows a temporal regulation of the main transcription factors and a lactose concentration impact on the transcriptional profile. A gene regulatory network built using BRANE Cut software reveals three sub-networks related to i) a positive correlation between lactose concentration and cellulase production, ii) a particular dependence of the lactose onto the β-glucosidase regulation and iii) a negative regulation of the development process and growth. Conclusions: This work is the first investigating a transcriptomic study regarding the effects of pure and mixed carbon sources in a fed-batch mode. Our study expose a co-orchestration of xyr1, clr2 and ace3 for cellulase and hemicellulase induction and production, a fine regulation of the β-glucosidase and a decrease of growth in favor of cellulase production. These conclusions provide us with potential targets for further genetic engineering leading to better cellulase-producing strains in industry-like conditions.

scholarly journals Local genetic context shapes the function of a gene regulatory network

2021 ◽  
Author(s):  
Anna Nagy-Staroń ◽  
Kathrin Tomasek ◽  
Caroline Caruso Carter ◽  
Elisabeth Sonnleitner ◽  
Bor Kavčič ◽  
...  
Keyword(s):  
Gene Regulatory Network ◽  
Gene Regulatory Networks ◽  
Regulatory Network ◽  
Regulatory Networks ◽  
Molecular Mechanisms ◽  
Transcriptional Unit ◽  
Regulatory Sequences ◽  
Gene Regulatory ◽  
Context Shapes ◽  
Genetic Context

Gene expression levels are influenced by multiple coexisting molecular mechanisms. Some of these interactions, such as those of transcription factors and promoters have been studied extensively. However, predicting phenotypes of gene regulatory networks remains a major challenge. Here, we use a well-defined synthetic gene regulatory network to study how network phenotypes depend on local genetic context, i.e. the genetic neighborhood of a transcription factor and its relative position. We show that one gene regulatory network with fixed topology can display not only quantitatively but also qualitatively different phenotypes, depending solely on the local genetic context of its components. Our results demonstrate that changes in local genetic context can place a single transcriptional unit within two separate regulons without the need for complex regulatory sequences. We propose that relative order of individual transcriptional units, with its potential for combinatorial complexity, plays an important role in shaping phenotypes of gene regulatory networks.

scholarly journals Identification of new therapeutic targets in CRLF2-overexpressing B-ALL through discovery of TF-gene regulatory interactions

2019 ◽  
Author(s):  
Sana Badri ◽  
Beth Carella ◽  
Priscillia Lhoumaud ◽  
Dayanne M. Castro ◽  
Claudia Skok Gibbs ◽  
...  
Keyword(s):  
Regulatory Network ◽  
Therapeutic Targets ◽  
Genetic Alterations ◽  
Patient Specific ◽  
Regulatory Interactions ◽  
Transcriptional Regulatory ◽  
Gene Regulatory ◽  
New Therapeutic Targets ◽  
Tf Gene ◽  
The Impact

ABSTRACTAlthough genetic alterations are initial drivers of disease, aberrantly activated transcriptional regulatory programs are often responsible for the maintenance and progression of cancer. CRLF2-overexpression in B-ALL patients leads to activation of JAK-STAT, PI3K and ERK/MAPK signaling pathways and is associated with poor outcome. Although inhibitors of these pathways are available, there remains the issue of treatment-associated toxicities, thus it is important to identify new therapeutic targets. Using a network inference approach, we reconstructed a B-ALL specific transcriptional regulatory network to evaluate the impact of CRLF2-overexpression on downstream regulatory interactions.Comparing RNA-seq from CRLF2-High and other B-ALL patients (CRLF2-Low), we defined a CRLF2-High gene signature. Patient-specific chromatin accessibility was interrogated to identify altered putative regulatory elements that could be linked to transcriptional changes. To delineate these regulatory interactions, a B-ALL cancer-specific regulatory network was inferred using 868 B-ALL patient samples from the NCI TARGET database coupled with priors generated from ATAC-seq peak TF-motif analysis. CRISPRi, siRNA knockdown and ChIP-seq of nine TFs involved in the inferred network were analyzed to validate predicted TF-gene regulatory interactions.In this study, a B-ALL specific regulatory network was constructed using ATAC-seq derived priors. Inferred interactions were used to identify differential patient-specific transcription factor activities predicted to control CRLF2-High deregulated genes, thereby enabling identification of new potential therapeutic targets.

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