Glycoproteomic landscape and structural dynamics of TIM family immune checkpoints enabled by mucinase SmE.

Mucin-domain glycoproteins are densely O-glycosylated and play critical roles in a host of biological functions. In particular, the T cell immunoglobulin and mucin-domain containing family of proteins (TIM-1, -3, -4) decorate immune cells and act as key regulators in cellular immunity. However, their dense O-glycosylation remains enigmatic, primarily due to the challenges associated with studying mucin domains. Here, we demonstrate that the mucinase SmE has a unique ability to cleave at residues bearing very complex glycans. SmE enables improved mass spectrometric analysis of several mucins, including the entire TIM family. With this information in-hand, we perform molecular dynamics (MD) simulations of TIM-3 and -4 to understand how glycosylation affects structural features of these proteins. Finally, we use these models to investigate the functional relevance of glycosylation for TIM-3 function and ligand binding. Overall, we present a powerful workflow to better understand the detailed molecular structures and functions of the mucinome.

Glycoproteomic landscape and structural dynamics of TIM family immune checkpoints enabled by mucinase SmE.

Mucin-domain glycoproteins are densely O-glycosylated and play critical roles in a host of biological functions. In particular, the T cell immunoglobulin and mucin-domain containing family of proteins (TIM-1, -3, -4) decorate immune cells and act as key checkpoint inhibitors in cancer. However, their dense O-glycosylation remains enigmatic both in terms of glycoproteomic landscape and structural dynamics, primarily due to the challenges associated with studying mucin domains. Here, we present a mucinase (SmE) and demonstrate its ability to selectively cleave along the mucin glycoprotein backbone, similar to others of its kind. Unlike other mucinases, though, SmE harbors the unique ability to cleave at residues bearing extremely complex glycans which enabled improved mass spectrometric analysis of several mucins, including the entire TIM family. With this information in-hand, we performed molecular dynamics (MD) simulations of TIM-3 and -4 to demonstrate how glycosylation affects structural features of these proteins. Overall, we present a powerful workflow to better understand the detailed molecular structures of the mucinome.

Orthographic Depth May Influence the Degree of Severity of Maze Learning Performance in Children at Risk for Reading Disorder.

Reading disability (RD), which affects between 5 and 17% of the population worldwide, is the most prevalent form of learning disability, and is associated with underactivation of a universal reading network in children. However, recent research suggests there are differences in learning rates on cognitive predictors of reading performance, as well as differences in activation patterns within the reading neural network, based on orthographic depth (i.e., transparent/shallow vs. deep/opaque orthographies) in children with RD. Recently, we showed that native English-speaking children with RD exhibit impaired performance on a maze learning task that taps into the same neural networks that are activated during reading. In addition, we demonstrated that genetic risk for RD strengthens the relationship between reading impairment and maze learning performance. However, it is unclear whether the results from these studies can be broadly applied to children from other language orthographies. In this study, we examined whether low reading skill was associated with poor maze learning performance in native English-speaking and native German-speaking children, and the influence of genetic risk for RD on cognition and behavior. In addition, we investigated the link between genetic risk and performance on this task in an orthographically diverse sample of children attending an English-speaking international school in Germany. The results from our data suggest that children with low reading skill, or with a genetic risk for reading impairment, exhibit impaired performance on the maze learning task, regardless of orthographic depth. However, these data also suggest that orthographic depth influences the degree of impairment on this task. The maze learning task requires the involvement of various cognitive processes and neural networks that underlie reading, but is not influenced by potential differences in reading experience due to lack of text or oral reporting. As a fully automated tool, it does not require specialized training to administer, and current results suggest it may be a practicable screening tool for early identification of reading impairment across orthographies.