Alain Laederach

Common genetic variation in the HLA region is associated with late-onset sporadic Parkinson's disease

Parkinsons disease is a common disorder that leads to motor and cognitive disability. We performed a genome-wide association study of 2,000 individuals with Parkinsons disease (cases) and 1,986 unaffected controls from the NeuroGenetics Research Consortium (NGRC). We confirmed associations with SNCA and MAPT, replicated an association with GAK (using data from the NGRC and a previous study, P = 3.2 × 10−9) and detected a new association with the HLA region (using data from the NGRC only, P = 2.9 × 10−8), which replicated in two datasets (meta-analysis P = 1.9 × 10−10). The HLA association was uniform across all genetic and environmental risk strata and was strong in sporadic (P = 5.5 × 10−10) and late-onset (P = 2.4 × 10−8) disease. The association peak we found was at rs3129882, a noncoding variant in HLA-DRA. Two studies have previously suggested that rs3129882 influences expression of HLA-DR and HLA-DQ. The brains of individuals with Parkinsons disease show upregulation of DR antigens and the presence of DR-positive reactive microglia, and nonsteroidal anti-inflammatory drugs reduce Parkinsons disease risk. The genetic association with HLA supports the involvement of the immune system in Parkinsons disease and offers new targets for drug development.

Coarse-grained modeling of large RNA molecules with knowledge-based potentials and structural filters

Understanding the function of complex RNA molecules depends critically on understanding their structure. However, creating three-dimensional (3D) structural models of RNA remains a significant challenge. We present a protocol (the nucleic acid simulation tool [NAST]) for RNA modeling that uses an RNA-specific knowledge-based potential in a coarse-grained molecular dynamics engine to generate plausible 3D structures. We demonstrate NASTs capabilities by using only secondary structure and tertiary contact predictions to generate, cluster, and rank structures. Representative structures in the best ranking clusters averaged 8.0 +/- 0.3 A and 16.3 +/- 1.0 A RMSD for the yeast phenylalanine tRNA and the P4-P6 domain of the Tetrahymena thermophila group I intron, respectively. The coarse-grained resolution allows us to model large molecules such as the 158-residue P4-P6 or the 388-residue T. thermophila group I intron. One advantage of NAST is the ability to rank clusters of structurally similar decoys based on their compatibility with experimental data. We successfully used ideal small-angle X-ray scattering data and both ideal and experimental solvent accessibility data to select the best cluster of structures for both tRNA and P4-P6. Finally, we used NAST to build in missing loops in the crystal structures of the Azoarcus and Twort ribozymes, and to incorporate crystallographic data into the Michel-Westhof model of the T. thermophila group I intron, creating an integrated model of the entire molecule. Our software package is freely available at https://simtk.org/home/nast.

Disease-associated mutations that alter the RNA structural ensemble.

Genome-wide association studies (GWAS) often identify disease-associated mutations in intergenic and non-coding regions of the genome. Given the high percentage of the human genome that is transcribed, we postulate that for some observed associations the disease phenotype is caused by a structural rearrangement in a regulatory region of the RNA transcript. To identify such mutations, we have performed a genome-wide analysis of all known disease-associated Single Nucleotide Polymorphisms (SNPs) from the Human Gene Mutation Database (HGMD) that map to the untranslated regions (UTRs) of a gene. Rather than using minimum free energy approaches (e.g. mFold), we use a partition function calculation that takes into consideration the ensemble of possible RNA conformations for a given sequence. We identified in the human genome disease-associated SNPs that significantly alter the global conformation of the UTR to which they map. For six disease-states (Hyperferritinemia Cataract Syndrome, β-Thalassemia, Cartilage-Hair Hypoplasia, Retinoblastoma, Chronic Obstructive Pulmonary Disease (COPD), and Hypertension), we identified multiple SNPs in UTRs that alter the mRNA structural ensemble of the associated genes. Using a Boltzmann sampling procedure for sub-optimal RNA structures, we are able to characterize and visualize the nature of the conformational changes induced by the disease-associated mutations in the structural ensemble. We observe in several cases (specifically the 5′ UTRs of FTL and RB1) SNP–induced conformational changes analogous to those observed in bacterial regulatory Riboswitches when specific ligands bind. We propose that the UTR and SNP combinations we identify constitute a “RiboSNitch,” that is a regulatory RNA in which a specific SNP has a structural consequence that results in a disease phenotype. Our SNPfold algorithm can help identify RiboSNitches by leveraging GWAS data and an analysis of the mRNA structural ensemble.

High-throughput single-nucleotide structural mapping by capillary automated footprinting analysis

The use of capillary electrophoresis with fluorescently labeled nucleic acids revolutionized DNA sequencing, effectively fueling the genomic revolution. We present an application of this technology for the high-throughput structural analysis of nucleic acids by chemical and enzymatic mapping (‘footprinting’). We achieve the throughput and data quality necessary for genomic-scale structural analysis by combining fluorophore labeling of nucleic acids with novel quantitation algorithms. We implemented these algorithms in the CAFA (capillary automated footprinting analysis) open-source software that is downloadable gratis from https://simtk.org/home/cafa. The accuracy, throughput and reproducibility of CAFA analysis are demonstrated using hydroxyl radical footprinting of RNA. The versatility of CAFA is illustrated by dimethyl sulfate mapping of RNA secondary structure and DNase I mapping of a protein binding to a specific sequence of DNA. Our experimental and computational approach facilitates the acquisition of high-throughput chemical probing data for solution structural analysis of nucleic acids.

Structural inference of native and partially folded RNA by high-throughput contact mapping

The biological behaviors of ribozymes, riboswitches, and numerous other functional RNA molecules are critically dependent on their tertiary folding and their ability to sample multiple functional states. The conformational heterogeneity and partially folded nature of most of these states has rendered their characterization by high-resolution structural approaches difficult or even intractable. Here we introduce a method to rapidly infer the tertiary helical arrangements of large RNA molecules in their native and non-native solution states. Multiplexed hydroxyl radical (·OH) cleavage analysis (MOHCA) enables the high-throughput detection of numerous pairs of contacting residues via random incorporation of radical cleavage agents followed by two-dimensional gel electrophoresis. We validated this technology by recapitulating the unfolded and native states of a well studied model RNA, the P4–P6 domain of the Tetrahymena ribozyme, at subhelical resolution. We then applied MOHCA to a recently discovered third state of the P4–P6 RNA that is stabilized by high concentrations of monovalent salt and whose partial order precludes conventional techniques for structure determination. The three-dimensional portrait of a compact, non-native RNA state reveals a well ordered subset of native tertiary contacts, in contrast to the dynamic but otherwise similar molten globule states of proteins. With its applicability to nearly any solution state, we expect MOHCA to be a powerful tool for illuminating the many functional structures of large RNA molecules and RNA/protein complexes.

Semiautomated and rapid quantification of nucleic acid footprinting and structure mapping experiments

We have developed protocols for rapidly quantifying the band intensities from nucleic acid chemical mapping gels at single-nucleotide resolution. These protocols are implemented in the software SAFA (semi-automated footprinting analysis) that can be downloaded without charge from http://safa.stanford.edu. The protocols implemented in SAFA have five steps: (i) lane identification, (ii) gel rectification, (iii) band assignment, (iv) model fitting and (v) band-intensity normalization. SAFA enables the rapid quantitation of gel images containing thousands of discrete bands, thereby eliminating a bottleneck to the analysis of chemical mapping experiments. An experienced user of the software can quantify a gel image in ∼20 min. Although SAFA was developed to analyze hydroxyl radical (·OH) footprints, it effectively quantifies the gel images obtained with other types of chemical mapping probes. We also present a series of tutorial movies that illustrate the best practices and different steps in the SAFA analysis as a supplement to this protocol.

Energy barriers, pathways, and dynamics during folding of large, multidomain RNAs

Large, multidomain RNA molecules are generally thought to fold following multiple pathways down rugged landscapes populated with intermediates and traps. A challenge to understanding RNA folding reactions is the complex relationships that exist between the structure of the RNA and its folding landscape. The identification of intermediate species that populate folding landscapes and characterization of elements of their structures are the key components to solving the RNA folding problem. This review explores recent studies that characterize the dominant pathways by which RNA folds, structural and dynamic features of intermediates that populate the folding landscape, and the energy barriers that separate the distinct steps of the folding process.

Distinct contribution of electrostatics, initial conformational ensemble, and macromolecular stability in RNA folding

We distinguish the contribution of the electrostatic environment, initial conformational ensemble, and macromolecular stability on the folding mechanism of a large RNA using a combination of time-resolved “Fast Fenton” hydroxyl radical footprinting and exhaustive kinetic modeling. This integrated approach allows us to define the folding landscape of the L-21 Tetrahymena thermophila group I intron structurally and kinetically from its earliest steps with unprecedented accuracy. Distinct parallel pathways leading the RNA to its native form upon its Mg2+-induced folding are observed. The structures of the intermediates populating the pathways are not affected by variation of the concentration and type of background monovalent ions (electrostatic environment) but are altered by a mutation that destabilizes one domain of the ribozyme. Experiments starting from different conformational ensembles but folding under identical conditions show that whereas the electrostatic environment modulates molecular flux through different pathways, the initial conformational ensemble determines the partitioning of the flux. This study showcases a robust approach for the development of kinetic models from collections of local structural probes.

Modeling protein recognition of carbohydrates

We have a limited understanding of the details of molecular recognition of carbohydrates by proteins, which is critical to a multitude of biological processes. Furthermore, carbohydrate‐modifying proteins such as glycosyl hydrolases and phosphorylases are of growing importance as potential drug targets. Interactions between proteins and carbohydrates have complex thermodynamics, and in general the specific positioning of only a few hydroxyl groups determines their binding affinities. A thorough understanding of both carbohydrate and protein structures is thus essential to predict these interactions. An atomic‐level view of carbohydrate recognition through structures of carbohydrate‐active enzymes complexed with transition‐state inhibitors reveals some of the distinctive molecular features unique to protein–carbohydrate complexes. However, the inherent flexibility of carbohydrates and their often water‐mediated hydrogen bonding to proteins makes simulation of their complexes difficult. Nonetheless, recent developments such as the parameterization of specific force fields and docking scoring functions have greatly improved our ability to predict protein–carbohydrate interactions. We review protein–carbohydrate complexes having defined molecular requirements for specific carbohydrate recognition by proteins, providing an overview of the different computational techniques available to model them. Proteins 2005.

Competing modes of self-association in the regulatory domains of Bruton's tyrosine kinase: Intramolecular contact versus asymmetric homodimerization

A nuclear magnetic resonance (NMR) investigation of a fragment of the nonreceptor Tec family tyrosine kinase Btk has revealed an intricate set of coupled monomer‐dimer equilibria. The Btk fragment studied contains two consecutive proline‐rich motifs followed by a single Src homology 3 (SH3) domain. We provide evidence for an asymmetric homodimer in which the amino‐terminal proline sequence of one monomer contacts the opposite SH3 binding pocket, whereas the carboxy‐terminal proline sequence of the other monomer is engaged by the second SH3 domain across the dimer interface. We show that the asymmetric homodimer structure is mimicked by a heterodimer formed in an equimolar mixture of complimentary mutants: one carrying mutations in the amino‐terminal proline stretch; the other, in the carboxy‐terminal proline motif. Moreover, a monomeric species characterized by an intramolecular complex between the amino‐terminal proline motif and the SH3 domain predominates at low concentration. Association constants were determined for each of the competing equilibria by NMR titration. The similarity of the determined Ka values reveals a delicate balance between the alternative conformational states available to Btk. Thus, changes in the local concentration of Btk itself, or co‐localization with exogenous signaling molecules that have high affinity for either proline sequence or the SH3 domain, can significantly alter species composition and regulate Btk kinase activity.