RNxQuest: An Extension to the xQuest Pipeline Enabling Analysis of Protein–RNA Cross-Linking/Mass Spectrometry Data

Cross-linking and mass spectrometry (XL-MS) workflows are increasingly popular techniques for generating low-resolution structural information about interacting biomolecules. xQuest is an established software package for analysis of protein–protein XL-MS data, supporting stable isotope-labeled cross-linking reagents. Resultant paired peaks in mass spectra aid sensitivity and specificity of data analysis. The recently developed cross-linking of isotope-labeled RNA and mass spectrometry (CLIR-MS) approach extends the XL-MS concept to protein–RNA interactions, also employing isotope-labeled cross-link (XL) species to facilitate data analysis. Data from CLIR-MS experiments are broadly compatible with core xQuest functionality, but the required analysis approach for this novel data type presents several technical challenges not optimally served by the original xQuest package. Here we introduce RNxQuest, a Python package extension for xQuest, which automates the analysis approach required for CLIR-MS data, providing bespoke, state-of-the-art processing and visualization functionality for this novel data type. Using functions included with RNxQuest, we evaluate three false discovery rate control approaches for CLIR-MS data. We demonstrate the versatility of the RNxQuest-enabled data analysis pipeline by also reanalyzing published protein–RNA XL-MS data sets that lack isotope-labeled RNA. This study demonstrates that RNxQuest provides a sensitive and specific data analysis pipeline for detection of isotope-labeled XLs in protein–RNA XL-MS experiments

First Community-Wide, Comparative Cross-Linking Mass Spectrometry Study

The number of publications in the field of chemical cross-linking combined with mass spectrometry (XL-MS) to derive constraints for protein three-dimensional structure modeling and to probe protein-protein interactions has increased during the last years. As the technique is now becoming routine for in vitro and in vivo applications in proteomics and structural biology there is a pressing need to define protocols as well as data analysis and reporting formats. Such consensus formats should become accepted in the field and be shown to lead to reproducible results. This first, community-based harmonization study on XL-MS is based on the results of 32 groups participating worldwide. The aim of this paper is to summarize the status quo of XL-MS and to compare and evaluate existing cross-linking strategies. Our study therefore builds the framework for establishing best practice guidelines to conduct cross-linking experiments, perform data analysis, and define reporting formats with the ultimate goal of assisting scientists to generate accurate and reproducible XL-MS results

Genetic and lifestyle risk factors for MRI-defined brain infarcts in a population-based setting.

OBJECTIVE: To explore genetic and lifestyle risk factors of MRI-defined brain infarcts (BI) in large population-based cohorts. METHODS: We performed meta-analyses of genome-wide association studies (GWAS) and examined associations of vascular risk factors and their genetic risk scores (GRS) with MRI-defined BI and a subset of BI, namely, small subcortical BI (SSBI), in 18 population-based cohorts (n = 20,949) from 5 ethnicities (3,726 with BI, 2,021 with SSBI). Top loci were followed up in 7 population-based cohorts (n = 6,862; 1,483 with BI, 630 with SBBI), and we tested associations with related phenotypes including ischemic stroke and pathologically defined BI. RESULTS: The mean prevalence was 17.7% for BI and 10.5% for SSBI, steeply rising after age 65. Two loci showed genome-wide significant association with BI: FBN2, p = 1.77 × 10-8; and LINC00539/ZDHHC20, p = 5.82 × 10-9. Both have been associated with blood pressure (BP)-related phenotypes, but did not replicate in the smaller follow-up sample or show associations with related phenotypes. Age- and sex-adjusted associations with BI and SSBI were observed for BP traits (p value for BI, p [BI] = 9.38 × 10-25; p [SSBI] = 5.23 × 10-14 for hypertension), smoking (p [BI] = 4.4 × 10-10; p [SSBI] = 1.2 × 10-4), diabetes (p [BI] = 1.7 × 10-8; p [SSBI] = 2.8 × 10-3), previous cardiovascular disease (p [BI] = 1.0 × 10-18; p [SSBI] = 2.3 × 10-7), stroke (p [BI] = 3.9 × 10-69; p [SSBI] = 3.2 × 10-24), and MRI-defined white matter hyperintensity burden (p [BI] = 1.43 × 10-157; p [SSBI] = 3.16 × 10-106), but not with body mass index or cholesterol. GRS of BP traits were associated with BI and SSBI (p ≤ 0.0022), without indication of directional pleiotropy. CONCLUSION: In this multiethnic GWAS meta-analysis, including over 20,000 population-based participants, we identified genetic risk loci for BI requiring validation once additional large datasets become available. High BP, including genetically determined, was the most significant modifiable, causal risk factor for BI

Stroke genetics informs drug discovery and risk prediction across ancestries

Previous genome-wide association studies (GWASs) of stroke — the second leading cause of death worldwide — were conducted predominantly in populations of European ancestry1,2. Here, in cross-ancestry GWAS meta-analyses of 110,182 patients who have had a stroke (five ancestries, 33% non-European) and 1,503,898 control individuals, we identify association signals for stroke and its subtypes at 89 (61 new) independent loci: 60 in primary inverse-variance-weighted analyses and 29 in secondary meta-regression and multitrait analyses. On the basis of internal cross-ancestry validation and an independent follow-up in 89,084 additional cases of stroke (30% non-European) and 1,013,843 control individuals, 87% of the primary stroke risk loci and 60% of the secondary stroke risk loci were replicated (P < 0.05). Effect sizes were highly correlated across ancestries. Cross-ancestry fine-mapping, in silico mutagenesis analysis3, and transcriptome-wide and proteome-wide association analyses revealed putative causal genes (such as SH3PXD2A and FURIN) and variants (such as at GRK5 and NOS3). Using a three-pronged approach4, we provide genetic evidence for putative drug effects, highlighting F11, KLKB1, PROC, GP1BA, LAMC2 and VCAM1 as possible targets, with drugs already under investigation for stroke for F11 and PROC. A polygenic score integrating cross-ancestry and ancestry-specific stroke GWASs with vascular-risk factor GWASs (integrative polygenic scores) strongly predicted ischaemic stroke in populations of European, East Asian and African ancestry5. Stroke genetic risk scores were predictive of ischaemic stroke independent of clinical risk factors in 52,600 clinical-trial participants with cardiometabolic disease. Our results provide insights to inform biology, reveal potential drug targets and derive genetic risk prediction tools across ancestries

RNxQuest: An Extension to the xQuest Pipeline Enabling Analysis of Protein-RNA Cross-Linking/Mass Spectrometry Data

Cross-linking and mass spectrometry (XL-MS) workflows are increasingly popular techniques for generating low-resolution structural information about interacting biomolecules. xQuest is an established software package for analysis of protein-protein XL-MS data, supporting stable isotope-labeled cross-linking reagents. Resultant paired peaks in mass spectra aid sensitivity and specificity of data analysis. The recently developed cross-linking of isotope-labeled RNA and mass spectrometry (CLIR-MS) approach extends the XL-MS concept to protein-RNA interactions, also employing isotope-labeled cross-link (XL) species to facilitate data analysis. Data from CLIR-MS experiments are broadly compatible with core xQuest functionality, but the required analysis approach for this novel data type presents several technical challenges not optimally served by the original xQuest package. Here we introduce RNxQuest, a Python package extension for xQuest, which automates the analysis approach required for CLIR-MS data, providing bespoke, state-of-the-art processing and visualization functionality for this novel data type. Using functions included with RNxQuest, we evaluate three false discovery rate control approaches for CLIR-MS data. We demonstrate the versatility of the RNxQuest-enabled data analysis pipeline by also reanalyzing published protein-RNA XL-MS data sets that lack isotope-labeled RNA. This study demonstrates that RNxQuest provides a sensitive and specific data analysis pipeline for detection of isotope-labeled XLs in protein-RNA XL-MS experiments.ISSN:1535-3893ISSN:1535-390

Cross-linking and mass spectrometry as a tool for studying the structural biology of ribonucleoproteins

Cross-linking and mass spectrometry (XL-MS) workflows represent an increasingly popular technique for low-resolution structural studies of macromolecular complexes. Cross-linking reactions take place in the solution state, capturing contact sites between components of a complex that represent the native, functionally relevant structure. Protein-protein XL-MS protocols are widely adopted, providing precise localization of cross-linking sites to single amino acid positions within a pair of cross-linked peptides. In contrast, protein-RNA XL-MS workflows are evolving rapidly and differ in their ability to localize interaction regions within the RNA sequence. Here, we review protein-protein and protein-RNA XL-MS workflows, and discuss their applications in studies of protein-RNA complexes. The examples highlight the complementary value of XL-MS in structural studies of protein-RNA complexes, where more established high-resolution techniques might be unable to produce conclusive data.ISSN:0969-2126ISSN:1878-418

Nucleotide-amino acid π-stacking interactions initiate photo cross-linking in RNA-protein complexes

Photo-induced cross-linking is a mainstay technique to characterize RNA-protein interactions. However, UV-induced cross-linking between RNA and proteins at “zero-distance” is poorly understood. Here, we investigate cross-linking of the RBFOX alternative splicing factor with its hepta-ribonucleotide binding element as a model system. We examine the influence of nucleobase, nucleotide position and amino acid composition using CLIR-MS technology (crosslinking-of-isotope-labelled-RNA-and-tandem-mass-spectrometry), that locates cross-links on RNA and protein with site-specific resolution. Surprisingly, cross-linking occurs only at nucleotides that are π-stacked to phenylalanines. Notably, this π-stacking interaction is also necessary for the amino-acids flanking phenylalanines to partake in UV-cross-linking. We confirmed these observations in several published datasets where cross-linking sites could be mapped to a high resolution structure. We hypothesize that π-stacking to aromatic amino acids activates cross-linking in RNA-protein complexes, whereafter nucleotide and peptide radicals recombine. These findings will facilitate interpretation of cross-linking data from structural studies and from genome-wide datasets generated using CLIP (cross-linking-and-immunoprecipitation) methods

Single Nucleotide Resolution RNA-Protein Cross-Linking Mass Spectrometry: A Simple Extension of the CLIR-MS Workflow

RNA-protein interactions mediate many intracellular processes. CLIR-MS (cross-linking of isotope-labeled RNA and tandem mass spectrometry) allows the identification of RNA-protein interaction sites at single nucleotide/amino acid resolution in a single experiment. Using isotopically labeled RNA segments for UV-light-induced cross-linking generates characteristic isotope patterns that constrain the sequence database searches, increasing spatial resolution. Whereas the use of segmentally isotopically labeled RNA is effective, it is technically involved and not applicable in some settings, e.g., in cell or tissue samples. Here we introduce an extension of the CLIR-MS workflow that uses unlabeled RNA during cross-linking and subsequently adds an isotopic label during sample preparation for MS analysis. After RNase and protease digests of a cross-linked complex, the nucleic acid part of a peptide-RNA conjugate is labeled using the enzyme T4 polynucleotide kinase and a 1:1 mixture of heavy O-18(4)-gamma-ATP and light ATP. In this simple, one-step reaction, three heavy oxygen atoms are transferred from the gamma-phosphate to the 5'-end of the RNA, introducing an isotopic shift of 6.01 Da that is detectable by mass spectrometry. We applied this approach to the RNA recognition motif (RRM) of the protein FOX1 in complex with its cognate binding substrate, FOX-binding element (FBE) RNA. We also labeled a single phosphate within an RNA and unambiguously determined the cross-linking site of the FOX1-RRM binding to FBE at single residue resolution on the RNA and protein level and used differential ATP labeling for relative quantification based on isotope dilution. Data are available via ProteomeXchange with the identifier PXD024010.ISSN:1520-6882ISSN:0003-270

Nucleotide-amino acid π-stacking interactions initiate photo cross-linking in RNA-protein complexes

Photo-induced cross-linking is a mainstay technique to characterize RNA-protein interactions. However, UV-induced cross-linking between RNA and proteins at “zero-distance” is poorly understood. Here, we investigate cross-linking of the RBFOX alternative splicing factor with its hepta-ribonucleotide binding element as a model system. We examine the influence of nucleobase, nucleotide position and amino acid composition using CLIR-MS technology (crosslinking-of-isotope-labelled-RNA-and-tandem-mass-spectrometry), that locates cross-links on RNA and protein with site-specific resolution. Surprisingly, cross-linking occurs only at nucleotides that are π-stacked to phenylalanines. Notably, this π-stacking interaction is also necessary for the amino-acids flanking phenylalanines to partake in UV-cross-linking. We confirmed these observations in several published datasets where cross-linking sites could be mapped to a high resolution structure. We hypothesize that π-stacking to aromatic amino acids activates cross-linking in RNA-protein complexes, whereafter nucleotide and peptide radicals recombine. These findings will facilitate interpretation of cross-linking data from structural studies and from genome-wide datasets generated using CLIP (cross-linking-and -immunoprecipitation) methods.ISSN:2041-172

Sequence-specific RNA recognition by an RGG motif connects U1 and U2 snRNP for spliceosome assembly

In mammals, the structural basis for the interaction between U1 and U2 small nuclear ribonucleoproteins (snRNPs) during the early steps of splicing is still elusive. The binding of the ubiquitin-like (UBL) domain of SF3A1 to the stem-loop 4 of U1 snRNP (U1-SL4) contributes to this interaction. Here, we determined the 3D structure of the complex between the UBL of SF3A1 and U1-SL4 RNA. Our crystallography, NMR spectroscopy, and cross-linking mass spectrometry data show that SF3A1-UBL recognizes, sequence specifically, the GCG/CGC RNA stem and the apical UUCG tetraloop of U1-SL4. In vitro and in vivo mutational analyses support the observed intermolecular contacts and demonstrate that the carboxyl-terminal arginine-glycine-glycine-arginine (RGGR) motif of SF3A1-UBL binds sequence specifically by inserting into the RNA major groove. Thus, the characterization of the SF3A1-UBL/U1-SL4 complex expands the repertoire of RNA binding domains and reveals the capacity of RGG/RG motifs to bind RNA in a sequencespecific manner.ISSN:0027-8424ISSN:1091-649