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Evolution of a genome-encoded bias in amino acid biosynthetic pathways is a potential indicator of amino acid dynamics in the environment.

Fasani RA, Savageau MA - Mol. Biol. Evol. (2014)

Bottom Line: Our mathematical analysis and computational results indicate that there are two distinctly different outcomes: Partial recovery to a new steady state, or full system failure.We test these implications by analyzing the proteomes of over 1,800 sequenced microbes, which reveals statistically significant evidence of low cognate bias, a genetic trait that would avoid the biosynthetic quandary.Furthermore, these results suggest that the pattern of cognate bias, which is readily derived by genome sequencing, may provide evolutionary clues to an organism's natural environment.

View Article: PubMed Central - PubMed

Affiliation: Department of Biomedical Engineering and Microbiology Graduate Group, University of California, Davis.

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Model of amino acid biosynthesis and regulation during starvation. (A) Model illustration of amino acid biosynthesis and regulation with an external supply. NA, nucleic acid precursors; mRNA, messenger RNA for the enzymes of the amino acid biosynthetic pathway; AA1–20, free amino acid; AAi, free cognate amino acid. (B) Abstract model of species concentrations and interactions used here. X1, mRNA; X2, critical enzyme of the amino acid biosynthetic pathway; X3, free cognate amino acid; X4, nucleic acid precursors; X5, protein precursors; X6, cognate amino acid precursors; X7, external cognate amino acid; X8, total cellular mRNA.
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msu225-F3: Model of amino acid biosynthesis and regulation during starvation. (A) Model illustration of amino acid biosynthesis and regulation with an external supply. NA, nucleic acid precursors; mRNA, messenger RNA for the enzymes of the amino acid biosynthetic pathway; AA1–20, free amino acid; AAi, free cognate amino acid. (B) Abstract model of species concentrations and interactions used here. X1, mRNA; X2, critical enzyme of the amino acid biosynthetic pathway; X3, free cognate amino acid; X4, nucleic acid precursors; X5, protein precursors; X6, cognate amino acid precursors; X7, external cognate amino acid; X8, total cellular mRNA.

Mentions: Mathematical models of amino acid biosynthetic systems have been developed in the past, in many cases for specific systems, such as Trp biosynthesis in E. coli (Xiu et al. 2002; Alves and Savageau 2005; Elf and Ehrenberg 2005). Many of these models tend to be complex with idiosyncratic features that do not readily generalize to other systems, as discussed above for our submodel. For instance, the pathways may have different numbers of enzymes with very different kinetic properties. Rather, we require relatively simple models that nevertheless retain the essential generic character of amino acid biosynthetic systems, can be readily analyzed to make testable predictions, and can be used to elucidate general design principles. For example, models of inducible and repressible pathways, very similar to the one developed below, were used to make predictions regarding the coupling of expression in elementary gene circuits; the resulting predictions were confirmed experimentally in over 50 specific cases and the predicted coupling rules are now established as a general design principle (Hlavacek and Savageau 1996, 1997; Wall et al. 2003, 2004). Figure 3 depicts our model of amino acid biosynthesis, one that includes the transcription and translation of enzymes in the biosynthetic pathway, as well as the synthesis of the cognate amino acid. Feedback repression of the biosynthetic enzymes, which is a prominent control mechanism in bacteria (Neidhardt et al. 1996), is also included, as is the ability to import amino acid from the external environment. As was shown in the previous section, the rate of translation of the biosynthetic enzymes depends on the concentration of the free cognate amino acid, which is also depleted by cellular demand.Fig. 3.


Evolution of a genome-encoded bias in amino acid biosynthetic pathways is a potential indicator of amino acid dynamics in the environment.

Fasani RA, Savageau MA - Mol. Biol. Evol. (2014)

Model of amino acid biosynthesis and regulation during starvation. (A) Model illustration of amino acid biosynthesis and regulation with an external supply. NA, nucleic acid precursors; mRNA, messenger RNA for the enzymes of the amino acid biosynthetic pathway; AA1–20, free amino acid; AAi, free cognate amino acid. (B) Abstract model of species concentrations and interactions used here. X1, mRNA; X2, critical enzyme of the amino acid biosynthetic pathway; X3, free cognate amino acid; X4, nucleic acid precursors; X5, protein precursors; X6, cognate amino acid precursors; X7, external cognate amino acid; X8, total cellular mRNA.
© Copyright Policy - creative-commons
Related In: Results  -  Collection

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Show All Figures
getmorefigures.php?uid=PMC4209129&req=5

msu225-F3: Model of amino acid biosynthesis and regulation during starvation. (A) Model illustration of amino acid biosynthesis and regulation with an external supply. NA, nucleic acid precursors; mRNA, messenger RNA for the enzymes of the amino acid biosynthetic pathway; AA1–20, free amino acid; AAi, free cognate amino acid. (B) Abstract model of species concentrations and interactions used here. X1, mRNA; X2, critical enzyme of the amino acid biosynthetic pathway; X3, free cognate amino acid; X4, nucleic acid precursors; X5, protein precursors; X6, cognate amino acid precursors; X7, external cognate amino acid; X8, total cellular mRNA.
Mentions: Mathematical models of amino acid biosynthetic systems have been developed in the past, in many cases for specific systems, such as Trp biosynthesis in E. coli (Xiu et al. 2002; Alves and Savageau 2005; Elf and Ehrenberg 2005). Many of these models tend to be complex with idiosyncratic features that do not readily generalize to other systems, as discussed above for our submodel. For instance, the pathways may have different numbers of enzymes with very different kinetic properties. Rather, we require relatively simple models that nevertheless retain the essential generic character of amino acid biosynthetic systems, can be readily analyzed to make testable predictions, and can be used to elucidate general design principles. For example, models of inducible and repressible pathways, very similar to the one developed below, were used to make predictions regarding the coupling of expression in elementary gene circuits; the resulting predictions were confirmed experimentally in over 50 specific cases and the predicted coupling rules are now established as a general design principle (Hlavacek and Savageau 1996, 1997; Wall et al. 2003, 2004). Figure 3 depicts our model of amino acid biosynthesis, one that includes the transcription and translation of enzymes in the biosynthetic pathway, as well as the synthesis of the cognate amino acid. Feedback repression of the biosynthetic enzymes, which is a prominent control mechanism in bacteria (Neidhardt et al. 1996), is also included, as is the ability to import amino acid from the external environment. As was shown in the previous section, the rate of translation of the biosynthetic enzymes depends on the concentration of the free cognate amino acid, which is also depleted by cellular demand.Fig. 3.

Bottom Line: Our mathematical analysis and computational results indicate that there are two distinctly different outcomes: Partial recovery to a new steady state, or full system failure.We test these implications by analyzing the proteomes of over 1,800 sequenced microbes, which reveals statistically significant evidence of low cognate bias, a genetic trait that would avoid the biosynthetic quandary.Furthermore, these results suggest that the pattern of cognate bias, which is readily derived by genome sequencing, may provide evolutionary clues to an organism's natural environment.

View Article: PubMed Central - PubMed

Affiliation: Department of Biomedical Engineering and Microbiology Graduate Group, University of California, Davis.

Show MeSH
Related in: MedlinePlus