Prediction of plant organismal complexity based on transcription factor annotation: an AI approach
How morphological complexity evolves is still enigmatic. While there is evidence in algae and plants as well as animals that diversification of the repertoire of transcription factors (TF) is causative for evolution of organismal complexity, there are many examples from lineages that follow their own way of complexity evolution, for example by expansion of particular families. For land plants,…
The complexity of plant organisms, particularly their morphological evolution, remains a mystery. While evidence exists in algae, plants, and animals that an increase in transcription factors (TF) leads to diversification and complexity, some lineages have evolved complexity differently, such as by expanding specific families of TF.
For land plants, a correlation has been established between the size of the TF complement and the number of cell types (serving as a proxy for morphological complexity). Additionally, certain families of TF have been identified as potential drivers of complexity evolution.
In this study, the dataset of cell type numbers was expanded from 12 to 82 proteomes, and a four-class body plan scheme was introduced. The researchers found that the total TF complement correlates with the number of cell types in Archaeplastida, a group of primary plastid-bearing plants and algae. Using the TabPFN algorithm, which employs prior-data fitted networks for binary and four-class body plan classification, the study discovered that TabPFN can accurately predict morphological complexity based on an organism's gene space.
This approach enables the determination of organismal complexity solely through the study of an organism's genes.
The findings confirm that plant morphological evolution is driven by the gain and expansion of TF families.
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