Distribution of events of positive selection and population differentiation in a metabolic pathway: the case of asparagine N-glycosylation

Abstract

Background: Asparagine N-Glycosylation is one of the most important forms of protein post-translational modification in eukaryotes. This metabolic pathway can be subdivided into two parts: an upstream sub-pathway required for achieving proper folding for most of the proteins synthesized in the secretory pathway, and a downstream sub-pathway required to give variability to trans-membrane proteins, and involved in adaptation to the environment and innate immunity. Here we analyze the nucleotide variability of the genes of this pathway in human populations, identifying which genes show greater population differentiation and which genes show signatures of recent positive selection. We also compare how these signals are distributed between the upstream and the downstream parts of the pathway, with the aim of exploring how forces of population differentiation and positive selection vary among genes involved in the same metabolic pathway but subject to different functional constraints.

Results: Our results show that genes in the downstream part of the pathway are more likely to show a signature of population differentiation, while events of positive selection are equally distributed among the two parts of the pathway. Moreover, events of positive selection are frequent on genes that are known to be at bifurcation points, and that are identified as being in key position by a network-level analysis such as MGAT3 and GCS1.

Conclusions: These findings indicate that the upstream part of the Asparagine N-Glycosylation pathway has lower diversity among populations, while the downstream part is freer to tolerate diversity among populations. Moreover, the distribution of signatures of population differentiation and positive selection can change between parts of a pathway, especially between parts that are exposed to different functional constraints. Our results support the hypothesis that genes involved in constitutive processes can be expected to show lower population differentiation, while genes involved in traits related to the environment should show higher variability. Taken together, this work broadens our knowledge on how events of population differentiation and of positive selection are distributed among different parts of a metabolic pathway.

Figure 1

Overview of the Asparagine N-Glycosylation pathway. The Quality Control Cycle (also known as Calnexin/Calreticulin Cycle), which divides the pathway into two parts, is shown as an octagon. Genes classified as ‘upstream’ in the analysis are in blue; genes classified as ‘downstream’ are in green. Genes in gray have been excluded from the network analysis (see methods).

Figure 2

Distribution of F_STvalues per SNP on the nearby chromosomal regions for a set of 10 genes (shown here as example) in the Asparagine N-Glycosylation pathway that show a signature of population differentiation in at least one continental group. The same representation but for all the genes in the pathway is presented as Supplementary Figure S2. Points in darker gray represent SNPs inside the gene. A blue smoothing line (calculated with the Loess function) is shown to facilitate the reading. Note that although 800 kb regions are plotted here, only the SNPs within 100 kb upstream and downstream of the gene have been included in the analysis.

Figure 3

Distribution of iHS values per SNP on a the nearby chromosomal regions for a set of 15 genes (shown here as example) in the Asparagine N-Glycosylation pathway that show a signature of positive selection in at least one continental group. The same representation but for all the genes in the pathway is presented as Supplementary Figure S3. Points in darker gray represent SNPs inside the gene. A blue smoothing line (calculated with the Loess function) is shown to facilitate the reading. Note that although 800 kb regions are plotted here, only the SNPs within 100 kb upstream and downstream of the gene have been included in the analysis.

Figure 4

Box plot of network centrality parameters between genes that have empirical significant population differentiation (S) and genes showing no significant population differentiation (NS). Comparisons for (D) Eccentricity and (E) Node Degree were significant at p < 0.05.