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Worth, R.

Publications and source records attributed to Worth, R..

2 recordsLinked to original sources

Mutations in two SARS-CoV-2 variants of concern reflect two distinct strategies of antibody escape

Understanding the factors that contribute to antibody escape of SARS-CoV-2 and its variants is key for the development of drugs and vaccines that provide broad protection against a variety of virus variants. Using microfluidic diffusional sizing, we determined the dissociation constant (KD) for the interaction between receptor binding domains (RBDs) of SARS-CoV-2 in its original version (WT) as well as alpha and beta variants with the host-cell receptor angiotensin converting enzyme 2 (ACE2). For RBD-alpha, the ACE2-binding affinity was increased by a factor of ten when compared with RBD-WT, while ACE2-binding of RBD-beta was largely unaffected. However, when challenged with a neutralizing antibody that binds to both RBD-WT and RBD-alpha with low nanomolar KD values, RBD-beta displayed no binding, suggesting a substantial epitope change. In SARS-CoV-2 convalescent sera, RBD-binding antibodies showed low nanomolar affinities to both wild-type and variant RBD proteins--strikingly, the concentration of antibodies binding to RBD-beta was half that of RBD-WT and RBD-alpha, again indicating considerable epitope changes in the beta variant. Our data therefore suggests that one factor contributing to the higher transmissibility and antibody evasion of SARS-CoV-2 alpha and beta is a larger fraction of viruses that can form a complex with ACE2. However, the two variants employ different mechanisms to achieve this goal. While SARS-CoV-2 alpha RBD binds with greater affinity to ACE2 and is thus more difficult to displace from the receptor by neutralizing antibodies, RBD-beta is less accessible to antibodies due to epitope changes which increases the chances of ACE2-binding and infection.

biochemistry↗

Connecting Concepts in the Brain: Mapping Cortical Representations of Semantic Relations

In the brain, the semantic system is thought to store concepts. However, little is known about how it connects different concepts and infers semantic relations. To address this question, we collected hours of functional magnetic resonance imaging data from human subjects listening to natural stories. We developed a predictive model of the voxel-wise response and further applied it to thousands of new words. Our results suggest that both semantic categories and relations are represented by spatially overlapping cortical patterns, instead of anatomically segregated regions. Semantic relations that reflect conceptual progression from concreteness to abstractness are represented by cortical patterns of activation in the default mode network and deactivation in the frontoparietal attention network. We conclude that the human brain uses distributed networks to encode not only concepts but also relationships between concepts. In particular, the default mode network plays a central role in semantic processing for abstraction of concepts.

neuroscience↗