In-Cell NMR of Intrinsically Disordered Proteins in Mammalian Cells

Cell-to-cell transmitted alpha-synuclein recapitulates experimental Parkinson's disease

Parkinson's disease is characterized by a progressive accumulation of alpha-Synuclein (αSyn) neuronal inclusions called Lewy bodies in the nervous system. Lewy bodies can arise from the cell-to-cell propagation of αSyn, which can occur via sequential steps of secretion and uptake. Here, by fusing a removable short signal peptide to the N-terminus of αSyn, we developed a novel mouse model with enhanced αSyn secretion and cell-to-cell transmission. Expression of the secreted αSyn in the mouse brain was under the control of a novel hybrid promoter in combination with adeno-associated virus serotype 9 (AAV9). This combination of promoter and viral vector induced a robust expression in neurons but not in the glia of injected mice. Biochemical characterization of the secreted αSyn revealed that, in cultured cells, this protein is released to the extracellular milieu via conventional secretion. The released αSyn is then internalized and processed by acceptor cells via the endosome-lysosome pathway indicating that the secreted αSyn is cell-to-cell transmitted. The secreted αSyn is aggregation-prone and amyloidogenic, and when expressed in the brain of wild-type non-transgenic mice, it induces a Parkinson's disease-like phenotype that includes a robust αSyn pathology in the substantia nigra, neuronal loss, neuroinflammation, and motor deficits, all the key features of experimental animal models of Parkinson's disease. In summary, a novel animal model of Parkinson's disease based on enhanced cell-to-cell transmission of αSyn was developed. The neuron-produced cell-to-cell transmitted αSyn triggers all phenotypic features of experimental Parkinson's disease in mice.

Multi-Dimensional Structure and Dynamics Landscape of Proteins in Mammalian Cells Revealed by In-Cell NMR

Governing function, half-life and subcellular localization, the 3D structure and dynamics of proteins are in nature constantly changing in a tightly regulated manner to fulfill the physiological and adaptive requirements of the cells. To find evidence for this hypothesis, we applied in-cell NMR to three folded model proteins and propose that the splitting of cross peaks constitutes an atomic fingerprint of distinct structural states that arise from multiple target binding co-existing inside mammalian cells. These structural states change upon protein loss of function or subcellular localisation into distinct cell compartments. In addition to peak splitting, we observed NMR signal intensity attenuations indicative of transient interactions with other molecules and dynamics on the microsecond to millisecond time scale.

Protein structure determination in human cells by in-cell NMR and a reporter system to optimize protein delivery or transexpression

Most experimental methods for structural biology proceed in vitro and therefore the contribution of the intracellular environment on protein structure and dynamics is absent. Studying proteins at atomic resolution in living mammalian cells has been elusive due to the lack of methodologies. In-cell nuclear magnetic resonance spectroscopy (in-cell NMR) is an emerging technique with the power to do so. Here, we improved current methods of in-cell NMR by the development of a reporter system that allows monitoring the delivery of exogenous proteins into mammalian cells, a process that we called here "transexpression". The reporter system was used to develop an efficient protocol for in-cell NMR which enables spectral acquisition with higher quality for both disordered and folded proteins. With this method, the 3D atomic resolution structure of the model protein GB1 in human cells was determined with a backbone root-mean-square deviation (RMSD) of 1.1 Å.

Challenges in describing the conformation and dynamics of proteins with ambiguous behavior

Traditionally, our understanding of how proteins operate and how evolution shapes them is based on two main data sources: the overall protein fold and the protein amino acid sequence. However, a significant part of the proteome shows highly dynamic and/or structurally ambiguous behavior, which cannot be correctly represented by the traditional fixed set of static coordinates. Representing such protein behaviors remains challenging and necessarily involves a complex interpretation of conformational states, including probabilistic descriptions. Relating protein dynamics and multiple conformations to their function as well as their physiological context (e.g., post-translational modifications and subcellular localization), therefore, remains elusive for much of the proteome, with studies to investigate the effect of protein dynamics relying heavily on computational models. We here investigate the possibility of delineating three classes of protein conformational behavior: order, disorder, and ambiguity. These definitions are explored based on three different datasets, using interpretable machine learning from a set of features, from AlphaFold2 to sequence-based predictions, to understand the overlap and differences between these datasets. This forms the basis for a discussion on the current limitations in describing the behavior of dynamic and ambiguous proteins.