Genotype-phenotype modeling of light ecotypes in Prochlorococcus reveals genomic signatures of ecotypic divergence
Evan Brenner^, Charmie Vang^, Connah Johnson, Janani Ravi*. bioRxiv 2025. doi: 10.1101/2025.10.13.678797
^Co-primary authors. *Corresponding author: janani.ravi@cuanschutz.edu
Prochlorococcus species are the most abundant marine photosynthetic bacteria. Despite broadly shared phenotypic traits and marine habitats, they exhibit remarkable genomic diversity. We ask what genomic signatures underlie its ecotypic divergence into high- and low-light adapted lineages, and whether these signatures can still be recovered from incomplete assemblies. From ~1,000 publicly available Prochlorococcus genomes, we focused on those with information on their light adaptation ecotype (high-light/low-light), phylogenetic clades, and depth of isolation. Across these divisions, we calculated average nucleotide identity and constructed pangenomes to assess cyanobacterial core genes vs. those that separate ecotypes. Despite scant conservation, we observe a sharp taxon separation by light ecotypes. Classical machine learning models trained to predict ecotype achieve near-perfect binary classification accuracy even when predicting on partial genomes (Matthews Correlation Coefficient = 0.86 – 1.00), while regression models trained to predict the depth of isolation performed poorly, with high root mean square error values (37.6 – 42.0m). For ecotype prediction, we analyzed top gene features across model runs and classes; these features included photosynthesis-associated genes and pathways, as well as many novel markers of unknown function. When separating ecotypes further by previously described phylogenetic clades, genomic content and composition show even clearer separation among clades, supporting the taxonomic breadth of the Prochlorococcus collective. These results emphasize the genomic specialization underlying ecotypic divergence and support the utility of ML approaches for cyanobacterial ecotype prediction from metagenomic data. Expanded sampling will yield novel clade-specific biology. All data, models, and results are available on GitHub: https://github.com/JRaviLab/cyano_adaptation.
This project uses public data from the cyanobacterial genus Prochlorococcus to build classical machine learning models that use Prochlorococcus gene content to predict the isolate-level light adaptation ecotype (high-light, HL; low-light, LL) or the depth of isolation in meters. The scripts in this directory process data from NCBI's GenBank and metadata from NCBI and the DOE Joint Genome Institute. The processed data are used for machine learning, statistical summary, and plot generation in R.
Each script in this workflow is numbered to run sequentially and reproduce the analysis reported in our manuscript. Each script contains a header description block that describes its purpose, function, inputs, and outputs. Sbatch files (e.g., 03b) are provided as examples for running these more intensive compute tasks on SLURM-based HPC clusters.
Cyano_adaptation is made freely available under the terms of Simplified BSD license.
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This project was partially supported by the University of Colorado Anschutz (CUA), the Northwest Biopreparedness Research Virtual Environment project (NW-BRaVE), and CUA under subcontract to PNNL as part of NW-BRaVE. This project also used resources on the project award (Enhancing biopreparedness through a model system to understand the molecular mechanisms that lead to pathogenesis and disease transmission) from the Environmental Molecular Sciences Laboratory, a DOE Office of Science User Facility sponsored by the Biological and Environmental Research program under Contract No. DE-AC05-76RL01830, and the University of Colorado Boulder's high-performance computing resource, Alpine. Alpine is jointly funded by the University of Colorado Boulder, the University of Colorado Anschutz, Colorado State University, and the National Science Foundation (award 2201538). Support from CUA came from start-up funds from the University of Colorado Anschutz, awarded to JR. Support for NW-BRaVE came from the Department of Energy, Office of Science, Biological and Environmental Research program FWP 81832. Pacific Northwest National Laboratory is a multi-program national laboratory operated by Battelle for the DOE under Contract DE-AC05-76RL01830.