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Bommareddy, V.

Publications and source records attributed to Bommareddy, V..

2 recordsLinked to original sources

Functional classification of tauopathy strains reveals the role of protofilament core residues.

Distinct tau amyloid assemblies underlie diverse tauopathies but defy rapid classification. Cell and animal experiments indicate tau functions as a prion, as different strains propagated in cells cause unique, transmissible neuropathology after inoculation. Strain amplification requires compatibility of the monomer and amyloid template. We used cryo-EM to study one cell-based YFP-tagged strain, resolving its amyloid nature. We then used sequential alanine (Ala) substitution (scan) within tau repeat domain (RD) to measure incorporation to pre-existing tau RD-YFP aggregates. This robustly discriminated strains, defining sequences critical for monomer incorporation. We then created 3R/4R or 4R WT RD (aa 246-408) biosensors. Ala scan of recombinant tau seeds with the Alzheimers Disease fold matched that of AD homogenate. We scanned 22 brain lysates comprising 4 tauopathies. This clustered cases by neuropathological syndrome, revealed the role of amino acids in protofilament folds, and allowed strain discrimination based on amino acid requirements for prion replication. TeaserDiscrimination of tau strains based on the relative contribution of each amino acid to templated propagation of the amyloid.

neuroscience↗

Network of hotspot interactions cluster tau amyloid folds

Cryogenic electron microscopy has revealed unprecedented molecular insight into the conformation of {beta}-sheet-rich protein amyloids linked to neurodegenerative diseases. It remains unknown how a protein can adopt a diversity of folds and form multiple distinct fibrillar structures. Here we develop an in silico alanine scan method to estimate the relative energetic contribution of each amino acid in an amyloid assembly. We apply our method to twenty-seven ex vivo and in vitro fibril structural polymorphs of the microtubule-associated protein tau. We uncover networks of energetically important interactions involving amyloid-forming motifs that stabilize the different fibril folds. We test our predictions in cellular and in vitro aggregation assays. Using a machine learning approach, we classify the structures based on residue energetics to identify distinguishing and unifying features. Our energetic profiling suggests that minimal sequence elements that control the stability of tau fibrils, allowing future design of protein sequences that fold into unique structures.

neuroscience↗