Contribution of Gene Expression to Metabolic Fluxes in Hypermetabolic Livers Induced Through Burn Injury and Polymicrobial Sepsis in Rats

Banta, Scott; Vemula, Murali; Yokoyama, Toshiharu; Jayaraman, Arul; Berthiaume, François; Yarmush, Martin L.

doi:10.1097/01253092-200603001-00230

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“…For example, rules for transcriptional regulation and physical interactions can be integrated into the current FBA framework to improve its accuracy [52]. In addition, mapping between individual alleles and metabolic flux reduction is a complex process and difficult to measure experimentally [53]. It is noteworthy that in our simulations we have uniformly evaluated fitness consequence based on the percentage of WT flux attainable in a specific background.…”

Section: Discussionmentioning

confidence: 99%

“…Enzymes may be involved in multiple reactions (i.e., pleiotropy). Although we often have Boolean rules describing the relationship between genes in an enzyme complex, it is currently extremely difficult to ascertain the exact contribution of each enzyme to each reaction [53]. Choosing the simplest unbiased approach, we used gene-reaction mapping and uniformly constrained the flux through each reaction associated to the gene being mutated.…”

Section: Flux Balance Analysismentioning

confidence: 99%

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Dynamic epistasis for different alleles of the same gene

Xu¹,

Barker

Gu³

2012

Proc. Natl. Acad. Sci. U.S.A.

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Epistasis refers to the phenomenon in which phenotypic consequences caused by mutation of one gene depend on one or more mutations at another gene. Epistasis is critical for understanding many genetic and evolutionary processes, including pathway organization, evolution of sexual reproduction, mutational load, ploidy, genomic complexity, speciation, and the origin of life. Nevertheless, current understandings for the genome-wide distribution of epistasis are mostly inferred from interactions among one mutant type per gene, whereas how epistatic interaction partners change dynamically for different mutant alleles of the same gene is largely unknown. Here we address this issue by combining predictions from flux balance analysis and data from a recently published high-throughput experiment. Our results show that different alleles can epistatically interact with very different gene sets. Furthermore, between two random mutant alleles of the same gene, the chance for the allele with more severe mutational consequence to develop a higher percentage of negative epistasis than the other allele is 50∼70% in eukaryotic organisms, but only 20∼30% in bacteria and archaea. We developed a population genetics model that predicts that the observed distribution for the sign of epistasis can speed up the process of purging deleterious mutations in eukaryotic organisms. Our results indicate that epistasis among genes can be dynamically rewired at the genome level, and call on future efforts to revisit theories that can integrate epistatic dynamics among genes in biological systems.E pistasis between two deleterious mutations is positive when a double mutant causes a weaker mutational defect than predicted from individual deleterious mutations, and is negative when the double mutant causes a larger defect (1, 2). In a population with sexual reproduction, positive epistasis alleviates the total harm when multiple deleterious mutations combine together and thus reduces the effectiveness of natural selection in removing these deleterious mutations, whereas negative epistasis can lower average mutational load by efficiently purging deleterious mutants (3). As a consequence, selective elimination of deleterious mutations would be especially effective if negative epistasis is prevalent. It is important to understand the distribution of epistasis among mutations, which plays a central role in genetics and theoretical descriptions for many evolutionary processes (1, 2).Tremendous efforts have been put into genome-wide measurements for the sign and magnitude of epistasis among different genes in various species (4-15). A series of high-throughput experimental platforms have been developed, such as synthetic genetic array (SGA) (4, 5), diploid-based synthetic lethality analysis with microarrays (6, 7), synthetic dosage-suppression and lethality screen (8-10), and epistatic miniarray profiles (11-13). The epistatic relations in these experiments were mostly measured based on one mutant type (deletion mutant) per gene. Few studies constructed ...

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Section: Discussionmentioning

confidence: 99%

Section: Flux Balance Analysismentioning

confidence: 99%