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While considerable knowledge exists about the enzymes pivotal for C₄photosynthesis, much less is known about thecis-regulation important for specifying their expression in distinct cell types. Here, we use single-cell-indexed ATAC-seq to identify cell-type-specific accessible chromatin regions (ACRs) associated with C₄enzymes for five different grass species. This study spans four C₄species, covering three distinct photosynthetic subtypes:Zea maysandSorghum bicolor(NADP-dependent malic enzyme),Panicum miliaceum(NAD-dependent malic enzyme),Urochloa fusca(phosphoenolpyruvate carboxykinase), along with the C₃outgroupOryza sativa. We studied thecis-regulatory landscape of enzymes essential across all C₄species and those unique to C₄subtypes, measuring cell-type-specific biases for C₄enzymes using chromatin accessibility data. Integrating these data with phylogenetics revealed diverse co-option of gene family members between species, showcasing the various paths of C₄evolution. Besides promoter proximal ACRs, we found that, on average, C₄genes have two to three distal cell-type-specific ACRs, highlighting the complexity and divergent nature of C₄evolution. Examining the evolutionary history of these cell-type-specific ACRs revealed a spectrum of conserved and novel ACRs, even among closely related species, indicating ongoing evolution ofcis-regulation at these C₄loci. This study illuminates the dynamic and complex nature ofcis-regulatory elements evolution in C₄photosynthesis, particularly highlighting the intricatecis-regulatory evolution of key loci. Our findings offer a valuable resource for future investigations, potentially aiding in the optimization of C₃crop performance under changing climatic conditions. 

Abstract Advances in genome sequencing and annotation have eased the difficulty of identifying new gene sequences. Predicting the functions of these newly identified genes remains challenging. Genes descended from a common ancestral sequence are likely to have common functions. As a result, homology is widely used for gene function prediction. This means functional annotation errors also propagate from one species to another. Several approaches based on machine learning classification algorithms were evaluated for their ability to accurately predict gene function from non‐homology gene features. Among the eight supervised classification algorithms evaluated, random‐forest‐based prediction consistently provided the most accurate gene function prediction. Non‐homology‐based functional annotation provides complementary strengths to homology‐based annotation, with higher average performance in Biological Process GO terms, the domain where homology‐based functional annotation performs the worst, and weaker performance in Molecular Function GO terms, the domain where the accuracy of homology‐based functional annotation is highest. GO prediction models trained with homology‐based annotations were able to successfully predict annotations from a manually curated “gold standard” GO annotation set. Non‐homology‐based functional annotation based on machine learning may ultimately prove useful both as a method to assign predicted functions to orphan genes which lack functionally characterized homologs, and to identify and correct functional annotation errors which were propagated through homology‐based functional annotations.