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Evolution after whole-genome duplication: a network perspective.

Zhu Y, Lin Z, Nakhleh L - G3 (Bethesda) (2013)

Bottom Line: We find that molecular interactions involving WGD genes evolve at rates that are three orders of magnitude slower than the rates of evolution of the corresponding sequences.Further epistasis analysis of WGD pairs categorized by their inferred evolutionary fates demonstrated the utility of these techniques.Finally, we find that WGD pairs and other pairs of paralogous genes of small-scale duplication origin share similar properties, giving good support for generalizing our results from WGD pairs to evolution after gene duplication in general.

View Article: PubMed Central - PubMed

Affiliation: Department of Computer Science, Rice University, Houston, Texas 77005.

ABSTRACT
Gene duplication plays an important role in the evolution of genomes and interactomes. Elucidating how evolution after gene duplication interplays at the sequence and network level is of great interest. In this work, we analyze a data set of gene pairs that arose through whole-genome duplication (WGD) in yeast. All these pairs have the same duplication time, making them ideal for evolutionary investigation. We investigated the interplay between evolution after WGD at the sequence and network levels and correlated these two levels of divergence with gene expression and fitness data. We find that molecular interactions involving WGD genes evolve at rates that are three orders of magnitude slower than the rates of evolution of the corresponding sequences. Furthermore, we find that divergence of WGD pairs correlates strongly with gene expression and fitness data. Because of the role of gene duplication in determining redundancy in biological systems and particularly at the network level, we investigated the role of interaction networks in elucidating the evolutionary fate of duplicated genes. We find that gene neighborhoods in interaction networks provide a mechanism for inferring these fates, and we developed an algorithm for achieving this task. Further epistasis analysis of WGD pairs categorized by their inferred evolutionary fates demonstrated the utility of these techniques. Finally, we find that WGD pairs and other pairs of paralogous genes of small-scale duplication origin share similar properties, giving good support for generalizing our results from WGD pairs to evolution after gene duplication in general.

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The average (left) and maximum (right) shared neighborhood sizes of WGD pairs as functions of the divergence between the pair’s sequences. The normalized sequence distance is d(g1, g2)/L(g1, g2). The red curves are the results of fitting data to Equation (2). Estimated Lμℓ 0.9261 for the average neighborhood size case and 0.9533 for the maximum neighborhood size case. Estimated Lμa is about 0.0001 in both cases.
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fig5: The average (left) and maximum (right) shared neighborhood sizes of WGD pairs as functions of the divergence between the pair’s sequences. The normalized sequence distance is d(g1, g2)/L(g1, g2). The red curves are the results of fitting data to Equation (2). Estimated Lμℓ 0.9261 for the average neighborhood size case and 0.9533 for the maximum neighborhood size case. Estimated Lμa is about 0.0001 in both cases.

Mentions: Replacing d with Lp in the aforementioned formula, we obtain shT(g1, g2) = a(1 − μℓ)Lp + Lp ⋅ μa. When μℓ is very small, we have (1 − μℓ)L ∼1 − Lμℓ. Thus, we obtainshT(g1,g2)=a(1−Lμℓ)p+p⋅Lμa.(2)As we are interested in obtaining estimates of μℓ and μa from WGD pairs, we fit the function in Equation (2) to data obtained from WGD pairs from S. cerevisiae that are the result of the WGD event that occurred in yeast approximately 100 million years ago. Because different WGD pairs with the same sequence divergence have different shared neighborhood sizes, we considered both the average and maximum shared neighborhood sizes for given sequence divergence values. Figure 5 shows the results with the function fitting.


Evolution after whole-genome duplication: a network perspective.

Zhu Y, Lin Z, Nakhleh L - G3 (Bethesda) (2013)

The average (left) and maximum (right) shared neighborhood sizes of WGD pairs as functions of the divergence between the pair’s sequences. The normalized sequence distance is d(g1, g2)/L(g1, g2). The red curves are the results of fitting data to Equation (2). Estimated Lμℓ 0.9261 for the average neighborhood size case and 0.9533 for the maximum neighborhood size case. Estimated Lμa is about 0.0001 in both cases.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC3815064&req=5

fig5: The average (left) and maximum (right) shared neighborhood sizes of WGD pairs as functions of the divergence between the pair’s sequences. The normalized sequence distance is d(g1, g2)/L(g1, g2). The red curves are the results of fitting data to Equation (2). Estimated Lμℓ 0.9261 for the average neighborhood size case and 0.9533 for the maximum neighborhood size case. Estimated Lμa is about 0.0001 in both cases.
Mentions: Replacing d with Lp in the aforementioned formula, we obtain shT(g1, g2) = a(1 − μℓ)Lp + Lp ⋅ μa. When μℓ is very small, we have (1 − μℓ)L ∼1 − Lμℓ. Thus, we obtainshT(g1,g2)=a(1−Lμℓ)p+p⋅Lμa.(2)As we are interested in obtaining estimates of μℓ and μa from WGD pairs, we fit the function in Equation (2) to data obtained from WGD pairs from S. cerevisiae that are the result of the WGD event that occurred in yeast approximately 100 million years ago. Because different WGD pairs with the same sequence divergence have different shared neighborhood sizes, we considered both the average and maximum shared neighborhood sizes for given sequence divergence values. Figure 5 shows the results with the function fitting.

Bottom Line: We find that molecular interactions involving WGD genes evolve at rates that are three orders of magnitude slower than the rates of evolution of the corresponding sequences.Further epistasis analysis of WGD pairs categorized by their inferred evolutionary fates demonstrated the utility of these techniques.Finally, we find that WGD pairs and other pairs of paralogous genes of small-scale duplication origin share similar properties, giving good support for generalizing our results from WGD pairs to evolution after gene duplication in general.

View Article: PubMed Central - PubMed

Affiliation: Department of Computer Science, Rice University, Houston, Texas 77005.

ABSTRACT
Gene duplication plays an important role in the evolution of genomes and interactomes. Elucidating how evolution after gene duplication interplays at the sequence and network level is of great interest. In this work, we analyze a data set of gene pairs that arose through whole-genome duplication (WGD) in yeast. All these pairs have the same duplication time, making them ideal for evolutionary investigation. We investigated the interplay between evolution after WGD at the sequence and network levels and correlated these two levels of divergence with gene expression and fitness data. We find that molecular interactions involving WGD genes evolve at rates that are three orders of magnitude slower than the rates of evolution of the corresponding sequences. Furthermore, we find that divergence of WGD pairs correlates strongly with gene expression and fitness data. Because of the role of gene duplication in determining redundancy in biological systems and particularly at the network level, we investigated the role of interaction networks in elucidating the evolutionary fate of duplicated genes. We find that gene neighborhoods in interaction networks provide a mechanism for inferring these fates, and we developed an algorithm for achieving this task. Further epistasis analysis of WGD pairs categorized by their inferred evolutionary fates demonstrated the utility of these techniques. Finally, we find that WGD pairs and other pairs of paralogous genes of small-scale duplication origin share similar properties, giving good support for generalizing our results from WGD pairs to evolution after gene duplication in general.

Show MeSH