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. 2020 Feb 13;8:e8546.
doi: 10.7717/peerj.8546. eCollection 2020.

Proteomic Similarity of the Littorinid Snails in the Evolutionary Context

Free PMC article

Proteomic Similarity of the Littorinid Snails in the Evolutionary Context

Arina L Maltseva et al. PeerJ. .
Free PMC article


Background: The introduction of DNA-based molecular markers made a revolution in biological systematics. However, in cases of very recent divergence events, the neutral divergence may be too slow, and the analysis of adaptive part of the genome is more informative to reconstruct the recent evolutionary history of young species. The advantage of proteomics is its ability to reflect the biochemical machinery of life. It may help both to identify rapidly evolving genes and to interpret their functions.

Methods: Here we applied a comparative gel-based proteomic analysis to several species from the gastropod family Littorinidae. Proteomes were clustered to assess differences related to species, geographic location, sex and body part, using data on presence/absence of proteins in samples and data on protein occurrence frequency in samples of different species. Cluster support was assessed using multiscale bootstrap resampling and the stability of clustering-using cluster-wise index of cluster stability. Taxon-specific protein markers were derived using IndVal method. Proteomic trees were compared to consensus phylogenetic tree (based on neutral genetic markers) using estimates of the Robinson-Foulds distance, the Fowlkes-Mallows index and cophenetic correlation.

Results: Overall, the DNA-based phylogenetic tree and the proteomic similarity tree had consistent topologies. Further, we observed some interesting deviations of the proteomic littorinid tree from the neutral expectations. (1) There were signs of molecular parallelism in two Littoraria species that phylogenetically are quite distant, but live in similar habitats. (2) Proteome divergence was unexpectedly high between very closely related Littorina fabalis and L. obtusata, possibly reflecting their ecology-driven divergence. (3) Conservative house-keeping proteins were usually identified as markers for cryptic species groups ("saxatilis" and "obtusata" groups in the Littorina genus) and for genera (Littoraria and Echinolittorina species pairs), while metabolic enzymes and stress-related proteins (both potentially adaptively important) were often identified as markers supporting species branches. (4) In all five Littorina species British populations were separated from the European mainland populations, possibly reflecting their recent phylogeographic history. Altogether our study shows that proteomic data, when interpreted in the context of DNA-based phylogeny, can bring additional information on the evolutionary history of species.

Keywords: Cryptic species; Ecological divergence; IndVal; Littorinidae; Outliers; Phylogenetic markers; Phylogeny; Proteomics; Taxon-specific proteomic markers; Taxonomy.

Conflict of interest statement

The authors declare that they have no competing interests.


Figure 1
Figure 1. Interspecies relations within the family Littorinidae.
(A) Dendrogram of consensus species proteomes obtained via neighbor joining based on Jaccard dissimilarities of protein occurrence frequency in samples of different species. The bootstrap support values are shown. (B) The molecular phylogeny tree obtained via Bayesian inference using concatenated partial gene sequences from 28S rRNA, 12S rRNA and cytochrome oxidase C subunit I (COI). Support values are posterior probabilities. Prior to comparison, the both trees (A) and (B) were made ultrametric using non-negative least squares. Robinson–Foulds distance between unrooted trees was RF = 2 (normalized RF = 0.143). The cophenetic correlation between trees (A) and (B) is CC = 0,801; between raw NJ and Bayesian trees is 0.798 (С) Fowlkes–Mallows index comparing dendrograms (A) and (B). Black line with dots shows the change of the compositional similarity of clusters (Bk) with the number of clusters (k). Dashed line indicates Bk values under a null hypothesis of insignificant similarity of cluster’ composition in the trees under comparison). Red line depicts threshold values for rejection of the null hypothesis. (D) Matrices of cophenetic distances for the proteomic and DNA-based trees expressed as a percentage of the total tree length. L. lit Littorina (Littorina) littorea, L. obt Littorina (Neritrema) obtusata, L. fab Littorina (Neritrema) fabalis, L. sax Littorina (Neritrema) saxatilis, L. arc Littorina (Neritrema) arcana, L. com Littorina (Neritrema) compressa, L. ard Littoraria ardouiniana, L. mel Littoraria melanostoma, E. mil Echinolittorina millegrana, E. mar Echinolittorina marisrubri.
Figure 2
Figure 2. Dendrogram of proteome UPGMA clustering from samples of the 10 Littorinidae species.
Clustering was produced using unweighted pair group method with arithmetic mean (UPGMA) algorithm based on Jaccard dissimilarity coefficients for the data on presence/absence of proteins in the samples. Sample labels indicate species (L.arc: Littorina (Neritrema) arcana; L.comp: Littorina (Neritrema) compressa; L.sax: Littorina (Neritrema) saxatilis; L.obt: Littorina (Neritrema) obtusata; L.fab: Littorina (Neritrema) fabalis; L.lit: Littorina (Littorina) littorea; L.ard: Littoraria ardouiniana; L.mel: L. melanostoma; E.mar: Echinolittorina marisrubri; E. millegrana), location (Ru: White Sea, Russia; Fr: English Channel, France; UK: Atlantic coast, Scotland; No: Barents Sea, Norway; Cn: East-China Sea, Hong Kong; Il: Israel), sex (f: female; m: male) and body part (so: foot + head parts; pe: penis). The approximately unbiased bootstrap support values are shown. (neighbor joining-based clustering is presented in File S2).

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Grant support

This research was funded by Russian Foundation for Basic Research Grants Number 18-54-20001 and 18-34-00873 and by St. Petersburg State University Grant Number 0.40.491.2017. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

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