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ABSTRACT A complete picture of how signaling pathways lead to multicellularity is largely unknown. Previously, we generated mutations in a protein prenylation enzyme, GGB, and showed that it is essential for maintaining multicellularity in the moss Physcomitrium patens. Here, we show that ROP GTPases act as downstream factors that are prenylated by GGB and themselves play an important role in the multicellularity of P. patens. We also show that the loss of multicellularity caused by the suppression of GGB or ROP GTPases is due to uncoordinated cell expansion, defects in cell wall integrity and the disturbance of the directional control of cell plate orientation. Expressing prenylatable ROP in the ggb mutant not only rescues multicellularity in protonemata but also results in development of gametophores. Although the prenylation of ROP is important for multicellularity, a higher threshold of active ROP is required for gametophore development. Thus, our results suggest that ROP activation via prenylation by GGB is a key process at both cell and tissue levels, facilitating the developmental transition from one dimension to two dimensions and to three dimensions in P. patens. 

Brain age (BA), distinct from chronological age (CA), can be estimated from MRIs to evaluate neuroanatomic aging in cognitively normal (CN) individuals. BA, however, is a cross-sectional measure that summarizes cumulative neuroanatomic aging since birth. Thus, it conveys poorly recent or contemporaneous aging trends, which can be better quantified by the (temporal) pace P of brain aging. Many approaches to map P, however, rely on quantifying DNA methylation in whole-blood cells, which the blood–brain barrier separates from neural brain cells. We introduce a three-dimensional convolutional neural network (3D-CNN) to estimate P noninvasively from longitudinal MRI. Our longitudinal model (LM) is trained on MRIs from 2,055 CN adults, validated in 1,304 CN adults, and further applied to an independent cohort of 104 CN adults and 140 patients with Alzheimer’s disease (AD). In its test set, the LM computes P with a mean absolute error (MAE) of 0.16 y (7% mean error). This significantly outperforms the most accurate cross-sectional model, whose MAE of 1.85 y has 83% error. By synergizing the LM with an interpretable CNN saliency approach, we map anatomic variations in regional brain aging rates that differ according to sex, decade of life, and neurocognitive status. LM estimates of P are significantly associated with changes in cognitive functioning across domains. This underscores the LM’s ability to estimate P in a way that captures the relationship between neuroanatomic and neurocognitive aging. This research complements existing strategies for AD risk assessment that estimate individuals’ rates of adverse cognitive change with age.