Tissue-Specific Transcriptome for Poeciliopsis prolifica Reveals Evidence for Genetic Adaptation Related to the Evolution of a Placental Fish

Nathaniel K. Jue, Robert J. Foley, David N. Reznick, Rachel J. O'Neill, Michael J. O'Neill

Research output: Contribution to journalArticlepeer-review


The evolution of the placenta is an excellent model to examine the evolutionary processes underlying adaptive complexity due to the recent, independent derivation of placentation in divergent animal lineages. In fishes, the family Poeciliidae offers the opportunity to study placental evolution with respect to variation in degree of post-fertilization maternal provisioning among closely related sister species. In this study, we present a detailed examination of a new reference transcriptome sequence for the live-bearing, matrotrophic fish, Poeciliopsis prolifica, from multiple-tissue RNA-seq data. We describe the genetic components active in liver, brain, late-stage embryo, and the maternal placental/ovarian complex, as well as associated patterns of positive selection in a suite of orthologous genes found in fishes. Results indicate the expression of many signaling transcripts, “non coding” sequences and repetitive elements in the maternal placental/ovarian complex. Moreover, patterns of positive selection in protein sequence evolution were found associated with live-bearing fishes, generally, and the placental P. prolifica, specifically, that appear independent of the general live-bearer lifestyle. Much of the observed patterns of gene expression and positive selection are congruent with the evolution of placentation in fish functionally converging with mammalian placental evolution and with the patterns of rapid evolution facilitated by the teleost-specific whole genome duplication event.
Original languageAmerican English
JournalG3: Genes, Genomes, Genetics
StatePublished - Jul 1 2018
Externally publishedYes


  • fish
  • gene expression
  • placenta
  • positive selection
  • transcriptome


  • Genetics
  • Biology
  • Genomics

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