Echinoderms are an exceptional group of bilaterians that develop pentameral adult symmetry from a bilaterally symmetric larva. However, the genetic basis in evolution and development of this unique transformation remains to be clarified. Here we report newly sequenced genomes, developmental transcriptomes, and proteomes of diverse echinoderms including the green sea urchin (L. variegatus), a sea cucumber (A. japonicus), and with particular emphasis on a sister group of the earliest-diverged echinoderms, the feather star (A. japonica). We learned that the last common ancestor of echinoderms retained a well-organized Hox cluster reminiscent of the hemichordate, and had gene sets involved in endoskeleton development. Further, unlike in other animal groups, the most conserved developmental stages were not at the body plan establishing phase, and genes normally involved in bilaterality appear to function in pentameric axis development. These results enhance our understanding of the divergence of protostomes and deuterostomes almost 500 Mya.

Species retaining ancestral features, such as species called living fossils, are often regarded as less derived than their sister groups, but such discussions are usually based on qualitative enumeration of conserved traits. This approach creates a major barrier, especially when quantifying the degree of phenotypic evolution or degree of derivedness, since it focuses only on commonly shared traits, and newly acquired or lost traits are often overlooked. To provide a potential solution to this problem, especially for inter-species comparison of gene expression profiles, we propose a new method named “derivedness index” to quantify the degree of derivedness. In contrast to the conservation-based approach, which deals with expressions of commonly shared genes among species being compared, the derivedness index also considers those that were potentially lost or duplicated during evolution. By applying our method, we found that the gene expression profiles of penta-radial phases in echinoderm tended to be more highly derived than those of the bilateral phase. However, our results suggest that echinoderms may not have experienced much larger modifications to their developmental systems than chordates, at least at the transcriptomic level. In vertebrates, we found that the mid-embryonic and organogenesis stages were generally less derived than the earlier or later stages, indicating that the conserved phylotypic period is also less derived. We also found genes that potentially explain less derivedness, such as Hox genes. Finally, we highlight technical concerns that may influence the measured transcriptomic derivedness, such as read depth and library preparation protocols, for further improvement of our method through future studies. We anticipate that this index will serve as a quantitative guide in the search for constrained developmental phases or processes.

While the concept of “evolutionary conservation” has enabled biologists to explain many ancestral features and traits, it has also frequently been misused to evaluate the degree of changes from a common ancestor, or “derivedness”. We propose that the distinction of these two concepts allows us to properly understand phenotypic and organismal evolution. From a methodological aspect, “conservation” mainly considers genes or traits which species have in common, while “derivedness” additionally covers those that are not commonly shared, such as novel or lost traits and genes to evaluate changes from the time of divergence from a common ancestor. Due to these differences, while conservation-oriented methods are effective in identifying ancestral features, they may be prone to underestimating the overall changes accumulated during the evolution of certain lineages. Herein, we describe our recently developed method, “transcriptomic derivedness index”, for estimating the phenotypic derivedness of embryos with a molecular approach using the whole-embryonic transcriptome as a phenotype. Although echinoderms are often considered as highly derived species, our analyses with this method showed that their embryos, at least at the transcriptomic level, may not be much more derived than those of chordates. We anticipate that the future development of derivedness-oriented methods could provide quantitative indicators for finding highly/lowly evolvable traits.

Organismal evolution is a process of discovering better‐fitting phenotypes through trial and error across generations. This iterative process resembles learning processes, an analogy recognized since the 1950s. Recognizing this parallel suggests that evolutionary biology and machine learning can mutually benefit from each other; however, ample opportunities for research into their corresponding concepts remain. In this review, we aim to enhance predictive capabilities and theoretical developments in both fields by exploring their conceptual parallels through specific examples that have emerged from recent advances. We focus on the importance of moving beyond predictions by machine learning approaches for specific cases, but instead advocate for interpretable machine learning approaches for discovering common laws for predicting evolutionary outcomes. This approach seeks to establish a theoretical framework that can transform evolutionary science into a field enriched with predictive theory while also inspiring new modeling and algorithmic strategies in machine learning.