Defining Distinct RNA-Protein Interactomes of SARS-CoV-2 Genomic and Subgenomic RNAs.

Host RNA binding proteins recognize viral RNA and play key roles in virus replication and antiviral mechanisms. SARS-CoV-2 generates a series of tiered subgenomic RNAs (sgRNAs), each encoding distinct viral protein(s) that regulate different aspects of viral replication. Here, for the first time, we demonstrate the successful isolation of SARS-CoV-2 genomic RNA and three distinct sgRNAs (N, S, and ORF8) from a single population of infected cells and characterize their protein interactomes. Over 500 protein interactors (including 260 previously unknown) were identified as associated with one or more target RNA. These included protein interactors unique to a single RNA pool and others present in multiple pools, highlighting our ability to discriminate between distinct viral RNA interactomes despite high sequence similarity. Individual interactomes indicated viral associations with cell response pathways, including regulation of cytoplasmic ribonucleoprotein granules and posttranscriptional gene silencing. We tested the significance of three protein interactors in these pathways (APOBEC3F, PPP1CC, and MSI2) using siRNA knockdowns, with several knockdowns affecting viral gene expression, most consistently PPP1CC. This study describes a new technology for high-resolution studies of SARS-CoV-2 RNA regulation and reveals a wealth of new viral RNA-associated host factors of potential functional significance to infection.

Elucidating the RNA-Protein Interactomes of Target RNAs in Tissue.

RNA-protein interactions are key to many aspects of cellular homeostasis and their identification is important to understanding cellular function. Multiple strategies have been developed for the RNA-centric characterization of RNA-protein complexes. However, these studies have all been done in immortalized cell lines that do not capture the complexity of heterogeneous tissue samples. Here, we develop hybridization purification of RNA-protein complexes followed by mass spectrometry (HyPR-MS) for use in tissue samples. We isolated both polyadenylated RNA and the specific long noncoding RNA MALAT1 and characterized their protein interactomes. These results demonstrate the feasibility of HyPR-MS in tissue for the multiplexed characterization of specific RNA-protein complexes.

Discovery of Dehydroamino Acid Residues in the Capsid and Matrix Structural Proteins of HIV-1.

Human immunodeficiency virus type 1 (HIV-1) remains a deadly infectious disease despite existing antiretroviral therapies. A comprehensive understanding of the specific mechanisms of viral infectivity remains elusive and currently limits the development of new and effective therapies. Through in-depth proteomic analysis of HIV-1 virions, we discovered the novel post-translational modification of highly conserved residues within the viral matrix and capsid proteins to the dehydroamino acids, dehydroalanine and dehydrobutyrine. We further confirmed their presence by labeling the reactive alkene, characteristic of dehydroamino acids, with glutathione via Michael addition. Dehydroamino acids are rare, understudied, and have been observed mainly in select bacterial and fungal species. Until now, they have not been observed in HIV proteins. We hypothesize that these residues are important in viral particle maturation and could provide valuable insight into HIV infectivity mechanisms.

MetaNetwork Enhances Biological Insights from Quantitative Proteomics Differences by Combining Clustering and Enrichment Analyses.

Interpreting proteomics data remains challenging due to the large number of proteins that are quantified by modern mass spectrometry methods. Weighted gene correlation network analysis (WGCNA) can identify groups of biologically related proteins using only protein intensity values by constructing protein correlation networks. However, WGCNA is not widespread in proteomic analyses due to challenges in implementing workflows. To facilitate the adoption of WGCNA by the proteomics field, we created MetaNetwork, an open-source, R-based application to perform sophisticated WGCNA workflows with no coding skill requirements for the end user. We demonstrate MetaNetwork's utility by employing it to identify groups of proteins associated with prostate cancer from a proteomic analysis of tumor and adjacent normal tissue samples. We found a decrease in cytoskeleton-related protein expression, a known hallmark of prostate tumors. We further identified changes in module eigenproteins indicative of dysregulation in protein translation and trafficking pathways. These results demonstrate the value of using MetaNetwork to improve the biological interpretation of quantitative proteomics experiments with 15 or more samples.

Fused in sarcoma regulates DNA replication timing and kinetics.

Fused in sarcoma (FUS) encodes an RNA-binding protein with diverse roles in transcriptional activation and RNA splicing. While oncogenic fusions of FUS and transcription factor DNA-binding domains are associated with soft tissue sarcomas, dominant mutations in FUS can cause amyotrophic lateral sclerosis. FUS has also been implicated in genome maintenance. However, the underlying mechanisms of its actions in genome stability are unknown. Here, we applied gene editing, functional reconstitution, and integrated proteomics and transcriptomics to illuminate roles for FUS in DNA replication and repair. Consistent with a supportive role in DNA double-strand break repair, FUS-deficient cells exhibited subtle alterations in the recruitment and retention of double-strand break-associated factors, including 53BP1 and BRCA1. FUS-/- cells also exhibited reduced proliferative potential that correlated with reduced speed of replication fork progression, diminished loading of prereplication complexes, enhanced micronucleus formation, and attenuated expression and splicing of S-phase-associated genes. Finally, FUS-deficient cells exhibited genome-wide alterations in DNA replication timing that were reversed upon re-expression of FUS complementary DNA. We also showed that FUS-dependent replication domains were enriched in transcriptionally active chromatin and that FUS was required for the timely replication of transcriptionally active DNA. These findings suggest that alterations in DNA replication kinetics and programming contribute to genome instability and functional defects in FUS-deficient cells.

Towards an Ideal In Cell Hybridization-Based Strategy to Discover Protein Interactomes of Selected RNA Molecules.

RNA-binding proteins are crucial to the function of coding and non-coding RNAs. The disruption of RNA-protein interactions is involved in many different pathological states. Several computational and experimental strategies have been developed to identify protein binders of selected RNA molecules. Amongst these, 'in cell' hybridization methods represent the gold standard in the field because they are designed to reveal the proteins bound to specific RNAs in a cellular context. Here, we compare the technical features of different 'in cell' hybridization approaches with a focus on their advantages, limitations, and current and potential future applications.

Comprehensive in vivo identification of the c-Myc mRNA protein interactome using HyPR-MS.

Proteins bind mRNA through their entire life cycle from transcription to degradation. We analyzed c-Myc mRNA protein interactors in vivo using the HyPR-MS method to capture the crosslinked mRNA by hybridization and then analyzed the bound proteins using mass spectrometry proteomics. Using HyPR-MS, 229 c-Myc mRNA-binding proteins were identified, confirming previously proposed interactors, suggesting new interactors, and providing information related to the roles and pathways known to involve c-Myc. We performed structural and functional analysis of these proteins and validated our findings with a combination of RIP-qPCR experiments, in vitro results released in past studies, publicly available RIP- and eCLIP-seq data, and results from software tools for predicting RNA-protein interactions.

Canonical and Noncanonical Actions of Arabidopsis Histone Deacetylases in Ribosomal RNA Processing.

Ribosome biogenesis is a fundamental process required for all cellular activities. Histone deacetylases play critical roles in many biological processes including transcriptional repression and rDNA silencing. However, their function in pre-rRNA processing remains poorly understood. Here, we discovered a previously uncharacterized role of Arabidopsis thaliana histone deacetylase HD2C in pre-rRNA processing via both canonical and noncanonical manners. HD2C interacts with another histone deacetylase HD2B and forms homo- and/or hetero-oligomers in the nucleolus. Depletion of HD2C and HD2B induces a ribosome-biogenesis deficient phenotype and aberrant accumulation of 18S pre-rRNA intermediates. Our genome-wide analysis revealed that HD2C binds and represses the expression of key genes involved in ribosome biogenesis. Using RNA immunoprecipitation and sequencing, we further uncovered a noncanonical mechanism of HD2C directly associating with pre-rRNA and small nucleolar RNAs to regulate rRNA methylation. Together, this study reveals a multifaceted role of HD2C in ribosome biogenesis and provides mechanistic insights into how histone deacetylases modulate rRNA maturation at the transcriptional and posttranscriptional levels.

SUMOylome Profiling Reveals a Diverse Array of Nuclear Targets Modified by the SUMO Ligase SIZ1 during Heat Stress.

The posttranslational addition of small ubiquitin-like modifier (SUMO) is an essential protein modification in plants that provides protection against numerous environmental challenges. Ligation is accomplished by a small set of SUMO ligases, with the SAP-MIZ domain-containing SIZ1 and METHYL METHANESULFONATE-SENSITIVE21 (MMS21) ligases having critical roles in stress protection and DNA endoreduplication/repair, respectively. To help identify their corresponding targets in Arabidopsis thaliana, we used siz1 and mms21 mutants for proteomic analyses of SUMOylated proteins enriched via an engineered SUMO1 isoform suitable for mass spectrometric studies. Through multiple data sets from seedlings grown at normal temperatures or exposed to heat stress, we identified over 1000 SUMO targets, most of which are nuclear localized. Whereas no targets could be assigned to MMS21, suggesting that it modifies only a few low abundance proteins, numerous targets could be assigned to SIZ1, including major transcription factors, coactivators/repressors, and chromatin modifiers connected to abiotic and biotic stress defense, some of which associate into multisubunit regulatory complexes. SIZ1 itself is also a target, but studies with mutants protected from SUMOylation failed to uncover a regulatory role. The catalog of SIZ1 substrates indicates that SUMOylation by this ligase provides stress protection by modifying a large array of key nuclear regulators.

A Bayesian Null Interval Hypothesis Test Controls False Discovery Rates and Improves Sensitivity in Label-Free Quantitative Proteomics.

Statistical significance tests are a common feature in quantitative proteomics workflows. The Student's t-test is widely used to compute the statistical significance of a protein's change between two groups of samples. However, the t-test's null hypothesis asserts that the difference in means between two groups is exactly zero, often marking small but uninteresting fold-changes as statistically significant. Compensations to address this issue are widely used in quantitative proteomics, but we suggest that a replacement of the t-test with a Bayesian approach offers a better path forward. In this article, we describe a Bayesian hypothesis test in which the null hypothesis is an interval rather than a single point at zero; the width of the interval is estimated from population statistics. The improved sensitivity of the method substantially increases the number of truly changing proteins detected in two benchmark data sets (ProteomeXchange identifiers PXD005590 and PXD016470). The method has been implemented within FlashLFQ, an open-source software program that quantifies bottom-up proteomics search results obtained from any search tool. FlashLFQ is rapid, sensitive, and accurate and is available both as an easy-to-use graphical user interface (Windows) and as a command-line tool (Windows/Linux/OSX).

Improving Proteoform Identifications in Complex Systems Through Integration of Bottom-Up and Top-Down Data.

Cellular functions are performed by a vast and diverse set of proteoforms. Proteoforms are the specific forms of proteins produced as a result of genetic variations, RNA splicing, and post-translational modifications (PTMs). Top-down mass spectrometric analysis of intact proteins enables proteoform identification, including proteoforms derived from sequence cleavage events or harboring multiple PTMs. In contrast, bottom-up proteomics identifies peptides, which necessitates protein inference and does not yield proteoform identifications. We seek here to exploit the synergies between these two data types to improve the quality and depth of the overall proteomic analysis. To this end, we automated the large-scale integration of results from multiprotease bottom-up and top-down analyses in the software program Proteoform Suite and applied it to the analysis of proteoforms from the human Jurkat T lymphocyte cell line. We implemented the recently developed proteoform-level classification scheme for top-down tandem mass spectrometry (MS/MS) identifications in Proteoform Suite, which enables users to observe the level and type of ambiguity for each proteoform identification, including which of the ambiguous proteoform identifications are supported by bottom-up-level evidence. We used Proteoform Suite to find instances where top-down identifications aid in protein inference from bottom-up analysis and conversely where bottom-up peptide identifications aid in proteoform PTM localization. We also show the use of bottom-up data to infer proteoform candidates potentially present in the sample, allowing confirmation of such proteoform candidates by intact-mass analysis of MS1 spectra. The implementation of these capabilities in the freely available software program Proteoform Suite enables users to integrate large-scale top-down and bottom-up data sets and to utilize the synergies between them to improve and extend the proteomic analysis.

Proteomic analysis of affinity-purified 26S proteasomes identifies a suite of assembly chaperones in Arabidopsis.

The 26S proteasome is an essential protease that selectively eliminates dysfunctional and short-lived regulatory proteins in eukaryotes. To define the composition of this proteolytic machine in plants, we tagged either the core protease (CP) or the regulatory particle (RP) sub-complexes in Arabidopsis to enable rapid affinity purification followed by mass spectrometric analysis. Studies on proteasomes enriched from whole seedlings, with or without ATP needed to maintain the holo-proteasome complex, identified all known proteasome subunits but failed to detect isoform preferences, suggesting that Arabidopsis does not construct distinct proteasome sub-types. We also detected a suite of proteasome-interacting proteins, including likely orthologs of the yeast and mammalian chaperones Pba1, Pba2, Pba3, and Pba4 that assist in CP assembly; Ump1 that helps connect CP half-barrels; Nas2, Nas6, and Hsm3 that assist in RP assembly; and Ecm29 that promotes CP-RP association. Proteasomes from seedlings exposed to the proteasome inhibitor MG132 accumulated assembly intermediates, reflecting partially built proteasome sub-complexes associated with assembly chaperones, and the CP capped with the PA200/Blm10 regulator. Genetic analyses of Arabidopsis UMP1 revealed that, unlike in yeast, this chaperone is essential, with mutants lacking the major UMP1a and UMP1b isoforms displaying a strong gametophytic defect. Single ump1 mutants were hypersensitive to conditions that induce proteotoxic, salt and osmotic stress, and also accumulated several proteasome assembly intermediates, consistent with its importance for CP construction. Insights into the chaperones reported here should enable study of the assembly events that generate the 26S holo-proteasome in Arabidopsis from the collection of 64 or more subunits.

Mutation-dependent aggregation and toxicity in a Drosophila model for UBQLN2-associated ALS.

Members of the conserved ubiquilin (UBQLN) family of ubiquitin (Ub) chaperones harbor an antipodal UBL (Ub-like)-UBA (Ub-associated) domain arrangement and participate in proteasome and autophagosome-mediated protein degradation. Mutations in a proline-rich-repeat region (PRR) of UBQLN2 cause amyotrophic lateral sclerosis (ALS)/frontotemporal dementia (FTD); however, neither the normal functions of the PRR nor impacts of ALS-associated mutations within it are well understood. In this study, we show that ALS mutations perturb UBQLN2 solubility and folding in a mutation-specific manner. Biochemical impacts of ALS mutations were additive, transferable to UBQLN1, and resulted in enhanced Ub association. A Drosophila melanogaster model for UBQLN2-associated ALS revealed that both wild-type and ALS-mutant UBQLN2 alleles disrupted Ub homeostasis; however, UBQLN2ALS mutants exhibited age-dependent aggregation and caused toxicity phenotypes beyond those seen for wild-type UBQLN2. Although UBQLN2 toxicity was not correlated with aggregation in the compound eye, aggregation-prone UBQLN2 mutants elicited climbing defects and neuromuscular junctions (NMJ) abnormalities when expressed in neurons. An UBA domain mutation that abolished Ub binding also diminished UBQLN2 toxicity, implicating Ub binding in the underlying pathomechanism. We propose that ALS-associated mutations in UBQLN2 disrupt folding and that both aggregated species and soluble oligomers instigate neuron autonomous toxicity through interference with Ub homeostasis.

HDA9-PWR-HOS15 Is a Core Histone Deacetylase Complex Regulating Transcription and Development.

Histone deacetylases remove acetyl groups from histone proteins and play important roles in many genomic processes. How histone deacetylases perform specialized molecular and biological functions in plants is poorly understood. Here, we identify HIGH EXPRESSION OF OSMOTICALLY RESPONSIVE GENES 15 (HOS15) as a core member of the Arabidopsis (Arabidopsis thaliana) HISTONE DEACETYLASE9-POWERDRESS (HDA9-PWR) complex. HOS15 immunoprecipitates with both HDA9 and PWR. Mutation of HOS15 induces histone hyperacetylation and methylation changes similar to hda9 and pwr mutants. HOS15, HDA9, and PWR are coexpressed in all organs, and mutant combinations display remarkable phenotypic resemblance and nonadditivity for organogenesis and developmental phase transitions. Ninety percent of HOS15-regulated genes are also controlled by HDA9 and PWR HDA9 binds to and directly represses 92 genes, many of which are responsive to biotic and abiotic stimuli, including a family of ethylene response factor genes. Additionally, HOS15 regulates HDA9 nuclear accumulation and chromatin association. Collectively, this study establishes that HOS15 forms a core complex with HDA9 and PWR to control gene expression and plant development.

Constructing Human Proteoform Families Using Intact-Mass and Top-Down Proteomics with a Multi-Protease Global Post-Translational Modification Discovery Database.

Complex human biomolecular processes are made possible by the diversity of human proteoforms. Constructing proteoform families, groups of proteoforms derived from the same gene, is one way to represent this diversity. Comprehensive, high-confidence identification of human proteoforms remains a central challenge in mass spectrometry-based proteomics. We have previously reported a strategy for proteoform identification using intact-mass measurements, and we have since improved that strategy by mass calibration based on search results, the use of a global post-translational modification discovery database, and the integration of top-down proteomics results with intact-mass analysis. In the present study, we combine these strategies for enhanced proteoform identification in total cell lysate from the Jurkat human T lymphocyte cell line. We collected, processed, and integrated three types of proteomics data (NeuCode-labeled intact-mass, label-free top-down, and multi-protease bottom-up) to maximize the number of confident proteoform identifications. The integrated analysis revealed 5950 unique experimentally observed proteoforms, which were assembled into 848 proteoform families. Twenty percent of the observed proteoforms were confidently identified at a 3.9% false discovery rate, representing 1207 unique proteoforms derived from 484 genes.

Identification of MS-Cleavable and Noncleavable Chemically Cross-Linked Peptides with MetaMorpheus.

Protein chemical cross-linking combined with mass spectrometry has become an important technique for the analysis of protein structure and protein-protein interactions. A variety of cross-linkers are well developed, but reliable, rapid, and user-friendly tools for large-scale analysis of cross-linked proteins are still in need. Here we report MetaMorpheusXL, a new search module within the MetaMorpheus software suite that identifies both MS-cleavable and noncleavable cross-linked peptides in MS data. MetaMorpheusXL identifies MS-cleavable cross-linked peptides with an ion-indexing algorithm, which enables an efficient large database search. The identification does not require the presence of signature fragment ions, an advantage compared with similar programs such as XlinkX. One complication associated with the need for signature ions from cleavable cross-linkers such as DSSO (disuccinimidyl sulfoxide) is the requirement for multiple fragmentation types and energy combinations, which is not necessary for MetaMorpheusXL. The ability to perform proteome-wide analysis is another advantage of MetaMorpheusXL compared with programs such as MeroX and DXMSMS. MetaMorpheusXL is also faster than other currently available MS-cleavable cross-link search software programs. It is imbedded in MetaMorpheus, an open-source and freely available software suite that provides a reliable, fast, user-friendly graphical user interface that is readily accessible to researchers.

HyPR-MS for Multiplexed Discovery of MALAT1, NEAT1, and NORAD lncRNA Protein Interactomes.

RNA-protein interactions are integral to the regulation of gene expression. RNAs have diverse functions and the protein interactomes of individual RNAs vary temporally, spatially, and with physiological context. These factors make the global acquisition of individual RNA-protein interactomes an essential endeavor. Although techniques have been reported for discovery of the protein interactomes of specific RNAs they are largely laborious, costly, and accomplished singly in individual experiments. We developed HyPR-MS for the discovery and analysis of the protein interactomes of multiple RNAs in a single experiment while also reducing design time and improving efficiencies. Presented here is the application of HyPR-MS to simultaneously and selectively isolate the interactomes of lncRNAs MALAT1, NEAT1, and NORAD. Our analysis features the proteins that potentially contribute to both known and previously undiscovered roles of each lncRNA. This platform provides a powerful new multiplexing tool for the efficient and cost-effective elucidation of specific RNA-protein interactomes.

Identification and Quantification of Murine Mitochondrial Proteoforms Using an Integrated Top-Down and Intact-Mass Strategy.

The development of effective strategies for the comprehensive identification and quantification of proteoforms in complex systems is a critical challenge in proteomics. Proteoforms, the specific molecular forms in which proteins are present in biological systems, are the key effectors of biological function. Thus, knowledge of proteoform identities and abundances is essential to unraveling the mechanisms that underlie protein function. We recently reported a strategy that integrates conventional top-down mass spectrometry with intact-mass determinations for enhanced proteoform identifications and the elucidation of proteoform families and applied it to the analysis of yeast cell lysate. In the present work, we extend this strategy to enable quantification of proteoforms, and we examine changes in the abundance of murine mitochondrial proteoforms upon differentiation of mouse myoblasts to myotubes. The integrated top-down and intact-mass strategy provided an increase of ∼37% in the number of identified proteoforms compared to top-down alone, which is in agreement with our previous work in yeast; 1779 unique proteoforms were identified using the integrated strategy compared to 1301 using top-down analysis alone. Quantitative comparison of proteoform differences between the myoblast and myotube cell types showed 129 observed proteoforms exhibiting statistically significant abundance changes (fold change >2 and false discovery rate <5%).

Proteoform Suite: Software for Constructing, Quantifying, and Visualizing Proteoform Families.

We present an open-source, interactive program named Proteoform Suite that uses proteoform mass and intensity measurements from complex biological samples to identify and quantify proteoforms. It constructs families of proteoforms derived from the same gene, assesses proteoform function using gene ontology (GO) analysis, and enables visualization of quantified proteoform families and their changes. It is applied here to reveal systemic proteoform variations in the yeast response to salt stress.

Expanding Proteoform Identifications in Top-Down Proteomic Analyses by Constructing Proteoform Families.

In top-down proteomics, intact proteins are analyzed by tandem mass spectrometry and proteoforms, which are defined forms of a protein with specific sequences of amino acids and localized post-translational modifications, are identified using precursor mass and fragmentation data. Many proteoforms that are detected in the precursor scan (MS1) are not selected for fragmentation by the instrument and therefore remain unidentified in typical top-down proteomic workflows. Our laboratory has developed the open source software program Proteoform Suite to analyze MS1-only intact proteoform data. Here, we have adapted it to provide identifications of proteoform masses in precursor MS1 spectra of top-down data, supplementing the top-down identifications obtained using the MS2 fragmentation data. Proteoform Suite performs mass calibration using high-scoring top-down identifications and identifies additional proteoforms using calibrated, accurate intact masses. Proteoform families, the set of proteoforms from a given gene, are constructed and visualized from proteoforms identified by both top-down and intact-mass analyses. Using this strategy, we constructed proteoform families and identified 1861 proteoforms in yeast lysate, yielding an approximately 40% increase over the original 1291 proteoform identifications observed using traditional top-down analysis alone.