Distinct ultrastructural phenotypes of glial and neuronal alpha-synuclein inclusions in multiple system atrophy.

Multiple System Atrophy is characterized pathologically by the accumulation of alpha-synuclein (aSyn) into glial cytoplasmic inclusions (GCIs). The mechanism underlying the formation of GCIs is not well understood. In this study, correlative light and electron microscopy was employed to investigate aSyn pathology in the substantia nigra and putamen of post-mortem multiple system atrophy brain donors. Three distinct types of aSyn immuno-positive inclusions were identified in oligodendrocytes, neurons and dark cells presumed to be dark microglia. Oligodendrocytes contained fibrillar GCIs that were consistently enriched with lysosomes and peroxisomes, supporting the involvement of the autophagy pathway in aSyn aggregation in multiple system atrophy. Neuronal cytoplasmic inclusions exhibited ultrastructural heterogeneity resembling both fibrillar and membranous inclusions, linking multiple systems atrophy and Parkinson's disease. The novel aSyn pathology identified in the dark cells, displayed GCI-like fibrils or non-GCI-like ultrastructures suggesting various stages of aSyn accumulation in these cells. The observation of GCI-like fibrils within dark cells suggests these cells may be an important contributor to the origin or spread of pathological aSyn in multiple system atrophy. Our results suggest a complex interplay between multiple cell types that may underlie the formation of aSyn pathology in multiple system atrophy brain and highlight the need for further investigation into cell-specific disease pathologies in multiple system atrophy.

Structure-function analysis of the cyclic ß-1,2-glucan synthase from Agrobacterium tumefaciens.

The synthesis of complex sugars is a key aspect of microbial biology. Cyclic ß-1,2-glucan (CßG) is a circular polysaccharide critical for host interactions of many bacteria, including major pathogens of humans (Brucella) and plants (Agrobacterium). CßG is produced by the cyclic glucan synthase (Cgs), a multi-domain membrane protein. So far, its structure as well as the mechanism underlining the synthesis have not been clarified. Here we use cryo-electron microscopy (cryo-EM) and functional approaches to study Cgs from A. tumefaciens. We determine the structure of this complex protein machinery and clarify key aspects of CßG synthesis, revealing a distinct mechanism that uses a tyrosine-linked oligosaccharide intermediate in cycles of polymerization and processing of the glucan chain. Our research opens possibilities for combating pathogens that rely on polysaccharide virulence factors and may lead to synthetic biology approaches for producing complex cyclic sugars.

Automated fiducial-based alignment of cryo-electron tomography tilt series in Dynamo.

With the advent of modern technologies for cryo-electron tomography (cryo-ET), high-quality tilt series are more rapidly acquired than processed and analyzed. Thus, a robust and fast-automated alignment for batch processing in cryo-ET is needed. While different software packages have made available several approaches for automated marker-based alignment of tilt series, manual user intervention remains necessary for many datasets, thus preventing high-throughput tomography. We have developed a MATLAB-based framework integrated into the Dynamo software package for automatic detection of fiducial markers that generates a robust alignment model with minimal input parameters. This approach allows high-throughput, unsupervised volume reconstruction. This new module extends Dynamo with a large repertory of tools for tomographic alignment and reconstruction, as well as specific visualization browsers to rapidly assess the biological relevance of the dataset. Our approach has been successfully tested on a broad range of datasets that include diverse biological samples and cryo-ET modalities.

Computational drug development for membrane protein targets.

The application of computational biology in drug development for membrane protein targets has experienced a boost from recent developments in deep learning-driven structure prediction, increased speed and resolution of structure elucidation, machine learning structure-based design and the evaluation of big data. Recent protein structure predictions based on machine learning tools have delivered surprisingly reliable results for water-soluble and membrane proteins but have limitations for development of drugs that target membrane proteins. Structural transitions of membrane proteins have a central role during transmembrane signaling and are often influenced by therapeutic compounds. Resolving the structural and functional basis of dynamic transmembrane signaling networks, especially within the native membrane or cellular environment, remains a central challenge for drug development. Tackling this challenge will require an interplay between experimental and computational tools, such as super-resolution optical microscopy for quantification of the molecular interactions of cellular signaling networks and their modulation by potential drugs, cryo-electron microscopy for determination of the structural transitions of proteins in native cell membranes and entire cells, and computational tools for data analysis and prediction of the structure and function of cellular signaling networks, as well as generation of promising drug candidates.

Hierarchical Protofilament Intertwining Rules the Formation of Mixed-Curvature Amyloid Polymorphs.

Amyloid polymorphism is a hallmark of almost all amyloid species, yet the mechanisms underlying the formation of amyloid polymorphs and their complex architectures remain elusive. Commonly, two main mesoscopic topologies are found in amyloid polymorphs characterized by non-zero Gaussian and mean curvatures: twisted ribbons and helical fibrils, respectively. Here, a rich heterogeneity of configurations is demonstrated on insulin amyloid fibrils, where protofilament packing can occur, besides the common polymorphs, also in a combined mode forming mixed-curvature polymorphs. Through AFM statistical analysis, an extended array of heterogeneous architectures that are rationalized by mesoscopic theoretical arguments are identified. Notably, an unusual fibrillization pathway is also unraveled toward mixed-curvature polymorphs via the widespread recruitment and intertwining of protofilaments and protofibrils. The results present an original view of amyloid polymorphism and advance the fundamental understanding of the fibrillization mechanism from single protofilaments into mature amyloid fibrils.

Rapid iPSC inclusionopathy models shed light on formation, consequence, and molecular subtype of α-synuclein inclusions.

The heterogeneity of protein-rich inclusions and its significance in neurodegeneration is poorly understood. Standard patient-derived iPSC models develop inclusions neither reproducibly nor in a reasonable time frame. Here, we developed screenable iPSC "inclusionopathy" models utilizing piggyBac or targeted transgenes to rapidly induce CNS cells that express aggregation-prone proteins at brain-like levels. Inclusions and their effects on cell survival were trackable at single-inclusion resolution. Exemplar cortical neuron α-synuclein inclusionopathy models were engineered through transgenic expression of α-synuclein mutant forms or exogenous seeding with fibrils. We identified multiple inclusion classes, including neuroprotective p62-positive inclusions versus dynamic and neurotoxic lipid-rich inclusions, both identified in patient brains. Fusion events between these inclusion subtypes altered neuronal survival. Proteome-scale α-synuclein genetic- and physical-interaction screens pinpointed candidate RNA-processing and actin-cytoskeleton-modulator proteins like RhoA whose sequestration into inclusions could enhance toxicity. These tractable CNS models should prove useful in functional genomic analysis and drug development for proteinopathies.