Ecology

Data collection
We hypothesized that the evolution of underground species affected protein networks in a unique manner in which various types of protein domains served as building blocks of protein evolution. To study the evolution of protein networks, we collected genomic, proteomic, and protein domain classification data, namely, fully sequenced genomes with coding sequences and annotated proteomes, together with protein ortholog assignments, from 32 species living in three broad ecological niches, namely subterranean, fossorial, and aboveground (Table 1, and listed in Materials and Methods). We first sought overall statistics regarding the number of proteins and the number of corresponding orthologous protein families. Overall PPI statistics were calculated, including those predicting PPIs in organisms for which experimentally verified PPI data are missing. We used the KEGG orthologs (KO) group of orthologous proteins in KEGG (Kyoto Encyclopaedia of Genes and Genomes)17 to reproduce gain and loss of protein domains in orthologous proteins. We collected 1,350,898 proteins from the studied organisms that belong to 624,787 KO groups (10,314 are unique ortholog groups). The matching number of interactors and networks for every organism were exhaustively calculated for all these proteins (Fig. 1). We found that 361,615 of the 1,350,898 proteins are distributed among 5,879,879 (predicted and real) PPIs. The mean number of interactors per protein within each habitat, namely, aboveground (A), fossorial (F), and subterranean (S) were 32.07, 32.48, and 32.67, respectively (see details in the supplementary results and in Tables S1–S3). This shows that the number of interactors per protein is similar for organisms from different ecologies.
Table 1 All organisms included in the PASTORAL database, with a complete number of proteins in the corresponding proteome.
Full size table

Figure 1

The study overview. Fully sequenced genomes with coding sequences and annotated proteomes were collected from 32 species living in three broad ecological niches: subterranean, fossorial, and aboveground. For collected proteins (1,350,898), protein domains, protein disordered regions, and KEGG orthologous annotation (624,787) were predicted using the Pfam search tool53 along with HMMER60 , IUPred2A44, and the KEGG database17, respectively. Next, 5,879,879 PPIs were evaluated using our previously developed ChiPPI tool15. Briefly, ChiPPI uses a domain-domain co-occurrence table. When a certain domain is missing, ChiPPI evaluates the corresponding missing interactors in the PPI network15, based on real PPI data (363,816) as obtained from BioGrid (release 3.4.163)16. Finally, PPI data are organized in PASTORAL, a dedicated database.

Full size image

Additional analysis of PPI features for orthologous proteins (516 KOs) common to all organisms were similar across ecologies. These features included the number of interactors, the number of PPIs, and global/individual clustering coefficients (supplementary results, Figures S1, S2, Table S4). Thus, we studied PPI properties of genes encoding products related to stresses that differ across the ecologies considered, such as hypoxia. Our findings confirm our hypothesis that the design principles of the evolution of underground species involve various types of protein domains serving as building blocks of protein evolution.
Analysis of the PPIs of stress-response proteins cluster organisms according to habitat
To examine how organisms might have adapted to the various stresses in each habitat, we analyzed mutations and changes in the PPIs encoded by stress response genes. Heat-shock, hypoxia, and circadian stresses differ considerably between aboveground and underground environments, and are likely to drive evolutionary selection of proteins that provide optimal function in each niche1,9. We assumed that organisms subject to a shared ecological experience would face similar environmental stresses. PPI networks of stress-related proteins would thus be expected to differ substantially according to ecology.
To test our hypothesis, we performed clustering analysis of all the organisms included in our study, based on mutations and PPI network features, and compared the results for each classification. Such analysis included all orthologous stress-response, hypoxia, heat-shock, and circadian stress proteins (Table 1). In total, 85,173 PPIs related to stress-response proteins were found to be distributed among 1,103 proteins. These comprised of 730 heat shock proteins in 71,940 PPIs, 254 hypoxia-related proteins in 10,256 PPIs, and 119 circadian proteins in 2,977 PPIs (Table 1, Tables S1–S7). All orthologous stress-response genes (KO groups) were obtained by querying the KEGG database with the terms “heat-shock”, “hypoxia”, and “circadian” terms. The results are listed in Table 2, while the corresponding lists of proteins are found in Tables S5, S6 and S7, respectively.
Table 2 KEGG Orthologs: Heat-shock (upper), hypoxia-related (middle) and circadian (bottom) proteins.
Full size table

Next, we performed clustering analysis based on sequence mutations and PPI features for the full set of heat-shock, hypoxia, and circadian stress proteins (Table 2). Remarkably, proteins related to hypoxia, heat-shock, and circadian stresses in the 32 organisms studied did not all cluster according to shared ecology based on sequence mutations (Fig. 2A) but significantly did so on the basis of “PPI network clustering coefficient” (Fig. 2B–D; p value (AU)  More

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