Nicholson, D. | bioRxiv Science

bioRxiv2021

'Lose-to-gain' adaptation to genome decayin the structure of the smallest eukaryotic ribosomes

AO_SCPLOWBSTRACTC_SCPLOWThe evolution of microbial parasites involves the interplay of two opposing forces. On the one hand, the pressure to survive drives parasites to improve through Darwinian natural selection. On the other, frequent genetic drifts result in genome decay, an evolutionary process in which an ever-increasing burden of deleterious mutations leads to gene loss and gradual genome reduction. Here, seeking to understand how this interplay occurs at the scale of individual macromolecules, we describe cryo-EM and evolutionary analyses of ribosomes from Encephalitozoon cuniculi, a eukaryote with one of the most reduced genomes in nature. We show that E. cuniculi ribosomes, the smallest eukaryotic cytoplasmic ribosomes to be structurally characterized, employ unparalleled structural innovations that allow extreme rRNA reduction without loss of ribosome integrity. These innovations include the evolution of previously unknown rRNA features such as molten rRNA linkers and bulgeless rRNA. Furthermore, we show that E. cuniculi ribosomes withstand the loss of rRNA and protein segments by evolving a unique ability to effectively trap small molecules and use them as ribosomal building-blocks and structural mimics of degenerated rRNA and protein segments. Overall, our work reveals a recurrent evolutionary pattern, which we term "lose-to-gain" evolution, where it is only through the loss of rRNA and protein segments that E. cuniculi ribosomes evolve their major innovations. Our study shows that the molecular structures of intracellular parasites long viewed as reduced, degenerated, and suffering from various debilitating mutations instead possess an array of systematically overlooked and extraordinary structural features. These features allow them to not only adapt to molecular reduction but evolve new activities that parasites can possibly use to their advantage.

evolutionary biology↗

bioRxiv2020

Neural network models of object recognition can also account for visual search behavior.

To find an object we are looking for, we must recognize it. Prevailing models of visual search neglect recognition, focusing instead on selective attention mechanisms. These models account for performance limitations that participants exhibit when searching highly simplified stimuli often used in laboratory tasks. However, it is unclear how to apply these models to complex natural images of real-world objects. Deep neural networks (DNN) can be applied to any image, and recently have emerged as state-of-the-art models of object recognition in the primate ventral visual pathway. Using these DNN models, we ask whether object recognition explains limitations on performance across visual search tasks. First, we show that DNNs exhibit a hallmark effect seen when participants search simplified stimuli. Further experiments show this effect results from optimizing for object recognition: DNNs trained from randomly-initialized weights do not exhibit the same performance limitations. Next, we test DNN models of object recognition with natural images, using a dataset where each image has a visual search difficulty score, derived from human reaction times. We find DNN accuracy is inversely correlated with visual search difficulty score. Our findings suggest that to a large extent visual search performance is explained by object recognition.

animal behavior and cognition↗