Abstracts (first author)


Entamoeba varieties use biochemical signaling and behavioral aggregation to discriminate between members of distinctive strains

Author(s): Espinosa A, Paz-y-Miño-C G


Evolutionary processes in which selection acted continuously and cumulatively on ancestors of Entamoeba populations gave rise to chemical and behavioral signals that allowed individuals to discriminate non-population members and, gradually, to the emergence of new lineages. The concept of ‘species recognition’ at the unicellular level might be artificial and inadequate to define signaling in single-cell natural populations. Aggregative behavior could be explored in a nonsocial protist to define discrimination cues among/between natural varieties. We demonstrate that by color tagging and pair-mix-culturing six Entamoeba varieties, the difficulty of discerning among apparently similar taxa can be resolved. When grown together with different amoeba strains, free-living/opportunistic (E. moshkovskii Laredo), commensal (E. moshkovskii Snake) or parasitic (E. invadens IP-1, E. invadens VK-1:NS, E. terrapinae, E. histolytica) trophozoites aggregate only with members of their own lineage. Clusters of trophozoites from each amoeba show distinctive rate of aggregation, density of cells per cluster, and distance between clusters. By using these behavioral cues, and identifying the genes involved in cell-signaling for cluster formation, distinctive amoeba taxa can be characterized quantitatively; we postulate that not only Entamoeba varieties, but apparent taxa crypticity in other protists, can be resolved by examining the natural ability of unicellular eukaryotes to discriminate between members and non-members of a lineage. Thus, phylogenetic relations among protists, which are usually determined by morphology and molecular techniques (the latter often confounded by horizontal gene transfer), could be further understood by incorporating behavior into the evolutionary analysis of this complex group of organisms.

Abstracts (coauthor)


Protein evolution is not a random process. We use slot-machine probabilities and ion channels, in an inquiry-based learning scenario, to show biological directionality on molecular change. The slot-machine represents the cellular chemical apparatus, product itself of Darwinian evolution, required to generate, step by step, each of the nucleotides coding for an amino acid of a model protein. Teachers and students can access the Jackprot Simulation and run statistical analysis of protein evolution by cutting and pasting nucleotide sequences obtained from the WWW. The Jackprot generates statistics on nucleotide evolution under selection (observed vs. expected values) and at random (without selection). We will use the following example when explaining hands-on how to use the Jackprot: Because ion channels reside in the lipid bilayer of cell membranes, their residue location must be in balance with the membrane’s hydrophobic/philic nature; a selective ‘pore’ for ion passage is located within the hydrophobic region. We will contrast the random generation of DNA sequence for KcsA, a bacterial two-transmembrane-domain (2TM) potassium channel, from Streptomyces lividans, with an under-selection scenario, the ‘Jackprot,’ which predicts much faster evolution than chance. We will distribute guidelines on how to use the online interface The Jackprot Simulation (JAVA APPLET Version 1.0) to model a numerical interaction between mutation rate and natural selection during the scenario of polypeptide evolution. Winning the ‘Jackprot,’ or highest-fitness complete-peptide sequence, requires cumulative smaller ‘wins’ (rewarded by selection) at the first, second and third positions in each of the 161 KcsA codons (‘jackdons’ that led to ‘jackacids’ that led to the ‘Jackprot’). The ‘Jackprot,’ as didactic tool, helps students understand how mutation rate coupled with natural selection suffice to explain the evolution of specialized, complex proteins. Student learning data will be shared.


Chairman: Octávio S. Paulo
Tel: 00 351 217500614 direct
Tel: 00 351 217500000 ext22359
Fax: 00 351 217500028
email: mail@eseb2013.com


XIV Congress of the European Society for Evolutionary Biology

Organization Team
Department of Animal Biology (DBA)
Faculty of Sciences of the University of Lisbon
P-1749-016 Lisbon


Computational Biology & Population Genomics Group