scholarly journals Populations of genetic circuits are unable to find the fittest solution in a multilevel genotype–phenotype map

2020 ◽  
Vol 17 (167) ◽  
pp. 20190843 ◽  
Author(s):  
Pablo Catalán ◽  
Susanna Manrubia ◽  
José A. Cuesta
Keyword(s):  
Regulatory Networks ◽  
Expression Patterns ◽  
Genetic Circuits ◽  
Temporal Expression ◽  
Target Pattern ◽  
Temporal Gene Expression ◽  
Genotype Space ◽  
Great Relevance ◽  
Spatio Temporal ◽  
The Relationship

The evolution of gene regulatory networks (GRNs) is of great relevance for both evolutionary and synthetic biology. Understanding the relationship between GRN structure and its function can allow us to understand the selective pressures that have shaped a given circuit. This is especially relevant when considering spatio-temporal expression patterns, where GRN models have been shown to be extremely robust and evolvable. However, previous models that studied GRN evolution did not include the evolution of protein and genetic elements that underlie GRN architecture. Here we use toy LIFE, a multilevel genotype–phenotype map, to show that not all GRNs are equally likely in genotype space and that evolution is biased to find the most common GRNs. toy LIFE rules create Boolean GRNs that, embedded in a one-dimensional tissue, develop a variety of spatio-temporal gene expression patterns. Populations of toy LIFE organisms choose the most common GRN out of a set of equally fit alternatives and, most importantly, fail to find a target pattern when it is very rare in genotype space. Indeed, we show that the probability of finding the fittest phenotype increases dramatically with its abundance in genotype space. This phenotypic bias represents a mechanism that can prevent the fixation in the population of the fittest phenotype, one that is inherent to the structure of genotype space and the genotype–phenotype map.

scholarly journals Populations of genetic circuits are unable to find the fittest solution in a multilevel genotype-phenotype map

2019 ◽  
Author(s):  
Pablo Catalán ◽  
Susanna Manrubia ◽  
José A. Cuesta
Keyword(s):  
Regulatory Networks ◽  
Expression Patterns ◽  
Genetic Circuits ◽  
Target Pattern ◽  
Selective Pressures ◽  
Spatiotemporal Expression ◽  
Genotype Space ◽  
Great Relevance ◽  
Gene Regulatory ◽  
The Relationship

AbstractThe evolution of gene regulatory networks (GRNs) is of great relevance for both evolutionary and synthetic biology. Understanding the relationship between GRN structure and its function can allow us to understand the selective pressures that have shaped a given circuit. This is especially relevant when considering spatiotemporal expression patterns, where GRN models have been shown to be extremely robust and evolvable. However, previous models that studied GRN evolution did not include the evolution of protein and genetic elements that underlie GRN architecture. Here we use toyLIFE, a multilevel genotype-phenotype map, to show that not all GRNs are equally likely in genotype space and that evolution is biased to find the most common GRNs. toyLIFE rules create Boolean GRNs that, embedded in a one-dimensional tissue, develop a variety of spatiotemporal gene expression patterns. Populations of toyLIFE organisms choose the most common GRN out of a set of equally fit alternatives and, most importantly, fail to find a target pattern when it is very rare in genotype space. Indeed, we show that the probability of finding the fittest phenotype increases dramatically with its abundance in genotype space. This phenotypic bias represents a mechanism that can prevent the fixation in the population of the fittest phenotype, one that is inherent to the structure of genotype space and the genotype-phenotype map.

scholarly journals Cloning, Characterization, and Expression Features of Chicken CDS2 Splicing Variants

2020 ◽  
Author(s):  
Yuanyuan Xu ◽  
Shuping Zhang ◽  
Yujun Guo ◽  
Wen Chen ◽  
Yanqun Huang
Keyword(s):  
Adipose Tissue ◽  
Real Time ◽  
Real Time Pcr ◽  
Expression Patterns ◽  
Mrna Levels ◽  
Exogenous Insulin ◽  
Quantitative Real Time Pcr ◽  
Temporal Expression ◽  
Spatio Temporal ◽  
The Brain

Abstract Background: The CDS gene encodes the CDP-diacylglycerol synthase enzyme that catalyzes the formation of CDP-diacylglycerol (CDP-DAG) from phosphatidic acid. At present, there are no reports of CDS2 in birds. Here, we identified chicken CDS2 transcripts by combining conventional RT- PCR amplification, 5' RACE (Fig. 1A), and 3' RACE, explored the spatio-temporal expression profiles of total CDS2 and the longest transcript variant CDS2-4, and investigated the effect of exogenous insulin on total the mRNA level of CDS2 by quantitative real-time PCR. Results: Four transcripts of chicken CDS2 (CDS2-1, -2, -3, and -4) were identified, which were alternatively spliced at the 3′-untranslated region (UTR). CDS2 was widely expressed in all tissues examined and the longest variant CDS2-4 was the major transcript. Both total CDS2 and CDS2-4 were prominently expressed in adipose tissue and the heart, and exhibited low expression in the liver and pectoralis of 49 day-old chickens. Quantitative real-time PCR revealed that total CDS2 and CDS2-4 had different spatio-temporal expression patterns in chicken. Total CDS2 exhibited a similar temporal expression tendency with a high level in the later period of incubation (embryonic day 19 [E19] or 1-day-old) in the brain, liver, and pectoralis. While CDS2-4 presented a distinct temporal expression pattern in these tissues, CDS2-4 levels peaked at 21 days in the brain and pectoralis, while liver CDS2-4 mRNA levels were highest at the early stage of hatching (E10). Total CDS2 (P < 0.001) and CDS2-4 (P = 0.0090) mRNA levels in the liver were differentially regulated throughout development of the chicken. Exogenous insulin significantly downregulated the level of total CDS2 at 240 min in the pectoralis of Silky chickens (P < 0.01). Total CDS2 levels in the liver of Silky chickens were higher than that of the broiler in the basal state and after insulin stimulation. Conclusion: Chicken CDS2 has multiple transcripts with variation at the 3′-UTR, which was prominently expressed in adipose tissue. Total CDS2 and CDS2-4 presented distinct spatio-temporal expression patterns, and they were differentially regulated with age in liver. Insulin could regulate chicken CDS2 levels in a breed- and tissue-specific manner.

scholarly journals Regulation of spatial and temporal gene expression in an animal germline

2018 ◽  
Author(s):  
Asija Diag ◽  
Marcel Schilling ◽  
Filippos Klironomos ◽  
Salah Ayoub ◽  
Nikolaus Rajewsky
Keyword(s):  
Gene Expression ◽  
Regulatory Mechanisms ◽  
Rna Expression ◽  
Temporal Expression ◽  
Temporal Gene Expression ◽  
Cell Proliferation And Differentiation ◽  
Proliferation And Differentiation ◽  
Gene Regulatory ◽  
Spatio Temporal

SUMMARYIn animal germlines, regulation of cell proliferation and differentiation is particularly important but poorly understood. Here, using a cryo-cut approach, we mapped RNA expression along the Caenorhabditis elegans germline and, using mutants, dissected gene regulatory mechanisms that control spatio-temporal expression. We detected, at near single-cell resolution, > 10,000 mRNAs, > 300 miRNAs and numerous novel miRNAs. Most RNAs were organized in distinct spatial patterns. Germline-specific miRNAs and their targets were co-localized. Moreover, we observed differential 3’ UTR isoform usage for hundreds of mRNAs. In tumorous gld-2 gld-1 mutants, gene expression was strongly perturbed. In particular, differential 3’ UTR usage was significantly impaired. We propose that PIE-1, a transcriptional repressor, functions to maintain spatial gene expression. Our data also suggest that cpsf-4 and fipp-1 control differential 3’ UTR usage for hundreds of genes. Finally, we constructed a “virtual gonad” enabling “virtual in situ hybridizations” and access to all data (https://shiny.mdc-berlin.de/spacegerm/).

scholarly journals Spatio-temporal Expression Patterns of Gelatinolytic Activity in Mouse Secondary Palates.

2001 ◽  
Vol 6 (1) ◽  
pp. 37-41
Author(s):  
Yuji Taya ◽  
Yoshihito Shimazu ◽  
Yuuichi Soeno ◽  
Kaori Sato ◽  
Hisao Yagishita ◽  
...  
Keyword(s):  
Expression Patterns ◽  
Gelatinolytic Activity ◽  
Temporal Expression ◽  
Spatio Temporal

scholarly journals Quantitative Amplification of Cleaved Ends (qACE) to assay miRNA-directed target cleavage

F1000Research ◽  
2014 ◽  
Vol 3 ◽  
pp. 240 ◽  
Author(s):  
Suresh Damodaran ◽  
Sajag Adhikari ◽  
Marie Turner ◽  
Senthil Subramanian
Keyword(s):  
Target Genes ◽  
Expression Patterns ◽  
Mirna Regulation ◽  
Temporal Expression ◽  
Specific Expression ◽  
Rna Molecules ◽  
Specific Target Gene ◽  
Pcr Method ◽  
Spatio Temporal ◽  
Fusion Primer

microRNA (miRNA) regulation is crucial to achieve precise spatio-temporal expression patterns of their target genes. This makes it crucial to determine the levels of cleavage of a particular target mRNA in different tissues and under different conditions. We developed a quantitative PCR method “quantitative Amplification of Cleaved Ends (qACE)” to assay levels of specific cleavage products in order to determine the extent of miRNA regulation for a specific target gene. qACE uses cDNA generated from adapter-ligated RNA molecules and relies on a carefully designed fusion primer that spans the adapter-cleaved RNA junction in qPCR to specifically amplify and quantify cleaved products. The levels of full-length transcripts can also be assayed in the same cDNA preparation using primers that span across the miRNA cleavage site. We used qACE to demonstrate that soybean roots over-expressing miR164 had increased levels of target cleavage and that miRNA deficient Arabidopsis thaliana hen1-1 mutants had reduced levels of target cleavage. We used qACE to discover that differential cleavage by miR164 in nodule vs. adjacent root tissue contributed to nodule-specific expression of NAC1 transcription factors in soybean. These experiments show that qACE can be used to discover and demonstrate differential cleavage by miRNAs to achieve specific spatio-temporal expression of target genes in plants.

scholarly journals BEST: a web server for brain expression Spatio-temporal pattern analysis

2019 ◽  
Vol 20 (1) ◽  
Author(s):  
Liyuan Guo ◽  
Wei Lin ◽  
Yidan Zhang ◽  
Wenhan Li ◽  
Jing Wang
Keyword(s):  
Gene Expression ◽  
Expression Pattern ◽  
Temporal Pattern ◽  
Pattern Analysis ◽  
Expression Patterns ◽  
Temporal Expression ◽  
Expression Pattern Analysis ◽  
Brain Expression ◽  
Spatio Temporal ◽  
Temporal Expression Pattern

Abstract Background Dysregulated gene expression patterns have been reported in several mental disorders. Limited by the difficulty of obtaining samples, psychiatric molecular mechanism research still relies heavily on clues from genetics studies. By using reference data from brain expression studies, multiple types of comprehensive gene expression pattern analysis have been performed on psychiatric genetic results. These systems-level spatial-temporal expression pattern analyses provided evidence on specific brain regions, developmental stages and molecular pathways that are possibly involved in psychiatric pathophysiology. At present, there is no online tool for such systematic analysis, which hinders the applications of analysis by non-informatics researchers such as experimental biologists and clinical molecular biologists. Results We developed the BEST web server to support Brain Expression Spatio-Temporal pattern analysis. There are three highlighted features of BEST: 1) visualization: it generates user-friendly visual results that are easy to interpret, including heatmaps, Venn diagrams, gene co-expression networks and cluster-based Manhattan gene plots; these results illustrate the complex spatio-temporal expression patterns, including expression quantification and correlation between genes; 2) integration: it provides comprehensive human brain spatio-temporal expression patterns by integrating data from currently available databases; 3) multi-dimensionality: it analyses input genes as both a whole set and several subsets (clusters) which are enriched according to co-expression patterns, and it also presents the correlation between genetic and expression data. Conclusions To the best of our knowledge, BEST is the first data tool to support comprehensive human brain spatial-temporal expression pattern analysis. It helps to bridge disease-related genetic studies and mechanism studies, provides clues for key gene and molecular system identification, and supports the analysis of disease sensitive brain region and age stages. BEST is freely available at http://best.psych.ac.cn.

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