Crosstalk between glucocorticoid and mineralocorticoid receptors boosts glucocorticoid-induced killing of multiple myeloma cells.

The glucocorticoid receptor (GR) is a crucial drug target in multiple myeloma as its activation with glucocorticoids effectively triggers myeloma cell death. However, as high-dose glucocorticoids are also associated with deleterious side effects, novel approaches are urgently needed to improve GR action in myeloma. Here, we reveal a functional crosstalk between GR and the mineralocorticoid receptor (MR) that plays a role in improved myeloma cell killing. We show that the GR agonist dexamethasone (Dex) downregulates MR levels in a GR-dependent way in myeloma cells. Co-treatment of Dex with the MR antagonist spironolactone (Spi) enhances Dex-induced cell killing in primary, newly diagnosed GC-sensitive myeloma cells. In a relapsed GC-resistant setting, Spi alone induces distinct myeloma cell killing. On a mechanistic level, we find that a GR-MR crosstalk likely arises from an endogenous interaction between GR and MR in myeloma cells. Quantitative dimerization assays show that Spi reduces Dex-induced GR-MR heterodimerization and completely abolishes Dex-induced MR-MR homodimerization, while leaving GR-GR homodimerization intact. Unbiased transcriptomics analyses reveal that c-myc and many of its target genes are downregulated most by combined Dex-Spi treatment. Proteomics analyses further identify that several metabolic hallmarks are modulated most by this combination treatment. Finally, we identified a subset of Dex-Spi downregulated genes and proteins that may predict prognosis in the CoMMpass myeloma patient cohort. Our study demonstrates that GR-MR crosstalk is therapeutically relevant in myeloma as it provides novel strategies for glucocorticoid-based dose-reduction.

Limited Evidence for Protein Products of Noncoding Transcripts in the HEK293T Cellular Cytosol.

Ribosome profiling has revealed translation outside canonical coding sequences, including translation of short upstream ORFs, long noncoding RNAs, overlapping ORFs, ORFs in UTRs, or ORFs in alternative reading frames. Studies combining mass spectrometry, ribosome profiling, and CRISPR-based screens showed that hundreds of ORFs derived from noncoding transcripts produce (micro)proteins, whereas other studies failed to find evidence for such types of noncanonical translation products. Here, we attempted to discover translation products from noncoding regions by strongly reducing the complexity of the sample prior to mass spectrometric analysis. We used an extended database as the search space and applied stringent filtering of the identified peptides to find evidence for novel translation events. We show that, theoretically our strategy facilitates the detection of translation events of transcripts from noncoding regions but experimentally only find 19 peptides that might originate from such translation events. Finally, Virotrap-based interactome analysis of two N-terminal proteoforms originating from noncoding regions showed the functional potential of these novel proteins.

Mass spectrometry and the cellular surfaceome.

The collection of exposed plasma membrane proteins, collectively termed the surfaceome, is involved in multiple vital cellular processes, such as the communication of cells with their surroundings and the regulation of transport across the lipid bilayer. The surfaceome also plays key roles in the immune system by recognizing and presenting antigens, with its possible malfunctioning linked to disease. Surface proteins have long been explored as potential cell markers, disease biomarkers, and therapeutic drug targets. Despite its importance, a detailed study of the surfaceome continues to pose major challenges for mass spectrometry-driven proteomics due to the inherent biophysical characteristics of surface proteins. Their inefficient extraction from hydrophobic membranes to an aqueous medium and their lower abundance compared to intracellular proteins hamper the analysis of surface proteins, which are therefore usually underrepresented in proteomic datasets. To tackle such problems, several innovative analytical methodologies have been developed. This review aims at providing an extensive overview of the different methods for surfaceome analysis, with respective considerations for downstream mass spectrometry-based proteomics.

Site-specific methylation of 18S ribosomal RNA by SNORD42A is required for acute myeloid leukemia cell proliferation.

Noncoding RNAs, including small nucleolar RNAs (snoRNAs), play important roles in leukemogenesis, but the relevant mechanisms remain incompletely understood. We performed snoRNA-focused CRISPR-Cas9 knockout library screenings that targeted the entire snoRNAnome and corresponding host genes. The C/D box containing SNORD42A was identified as an essential modulator for acute myeloid leukemia (AML) cell survival and proliferation in multiple human leukemia cell lines. In line, SNORD42A was consistently expressed at higher levels in primary AML patient samples than in CD34+ progenitors, monocytes, and granulocytes. Functionally, knockout of SNORD42A reduced colony formation capability and inhibited proliferation. The SNORD42A acts as a C/D box snoRNA and directs 2'-O-methylation at uridine 116 of 18S ribosomal RNA (rRNA). Deletion of SNORD42A decreased 18S-U116 2'-O-methylation, which was associated with a specific decrease in the translation of ribosomal proteins. In line, the cell size of SNORD42A deletion carrying leukemia cells was decreased. Taken together, these findings establish that high-level expression of SNORD42A with concomitant U116 18S rRNA 2'-O-methylation is essential for leukemia cell growth and survival.

Omics Assisted N-terminal Proteoform and Protein Expression Profiling On Methionine Aminopeptidase 1 (MetAP1) Deletion.

Excision of the N-terminal initiator methionine (iMet) residue from nascent peptide chains is an essential and omnipresent protein modification carried out by methionine aminopeptidases (MetAPs) that accounts for a major source of N-terminal proteoform diversity. Although MetAP2 is known to be implicated in processes such as angiogenesis and proliferation in mammals, the physiological role of MetAP1 is much less clear. In this report we studied the omics-wide effects of human MetAP1 deletion and general MetAP inhibition. The levels of iMet retention are inversely correlated with cellular proliferation rates. Further, despite the increased MetAP2 expression on MetAP1 deletion, MetAP2 was unable to restore processing of Met-Ser-, Met-Pro-, and Met-Ala- starting N termini as inferred from the iMet retention profiles observed, indicating a higher activity of MetAP1 over these N termini. Proteome and transcriptome expression profiling point to differential expression of proteins implicated in lipid metabolism, cytoskeleton organization, cell proliferation and protein synthesis upon perturbation of MetAP activity.

eIF1 modulates the recognition of suboptimal translation initiation sites and steers gene expression via uORFs.

Alternative translation initiation mechanisms such as leaky scanning and reinitiation potentiate the polycistronic nature of human transcripts. By allowing for reprogrammed translation, these mechanisms can mediate biological responses to stimuli. We combined proteomics with ribosome profiling and mRNA sequencing to identify the biological targets of translation control triggered by the eukaryotic translation initiation factor 1 (eIF1), a protein implicated in the stringency of start codon selection. We quantified expression changes of over 4000 proteins and 10 000 actively translated transcripts, leading to the identification of 245 transcripts undergoing translational control mediated by upstream open reading frames (uORFs) upon eIF1 deprivation. Here, the stringency of start codon selection and preference for an optimal nucleotide context were largely diminished leading to translational upregulation of uORFs with suboptimal start. Interestingly, genes affected by eIF1 deprivation were implicated in energy production and sensing of metabolic stress.

Leveraging a self-cleaving peptide for tailored control in proximity labeling proteomics.

Protein-protein interactions play an important biological role in every aspect of cellular homeostasis and functioning. Proximity labeling mass spectrometry-based proteomics overcomes challenges typically associated with other methods and has quickly become the current state of the art in the field. Nevertheless, tight control of proximity-labeling enzymatic activity and expression levels is crucial to accurately identify protein interactors. Here, we leverage a T2A self-cleaving peptide and a non-cleaving mutant to accommodate the protein of interest in the experimental and control TurboID setup. To allow easy and streamlined plasmid assembly, we built a Golden Gate modular cloning system to generate plasmids for transient expression and stable integration. To highlight our T2A Split/link design, we applied it to identify protein interactions of the glucocorticoid receptor and severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) nucleocapsid and non-structural protein 7 (NSP7) proteins by TurboID proximity labeling. Our results demonstrate that our T2A split/link provides an opportune control that builds upon previously established control requirements in the field.

Crosstalk interactions between transcription factors ERRα and PPARα assist PPARα-mediated gene expression.

OBJECTIVE: The peroxisome proliferator-activated receptor α (PPARα) is a transcription factor driving target genes involved in fatty acid ß-oxidation. To what extent various PPARα interacting proteins may assist its function as a transcription factor is incompletely understood. An ORFeome-wide unbiased mammalian protein-protein interaction trap (MAPPIT) using PPARα as bait revealed a PPARα-ligand-dependent interaction with the orphan nuclear receptor estrogen-related receptor α (ERRα). The goal of this study was to characterize the nature of the interaction in depth and to explore whether it was of physiological relevance. METHODS: We used orthogonal protein-protein interaction assays and pharmacological inhibitors of ERRα in various systems to confirm a functional interaction and study the impact of crosstalk mechanisms. To characterize the interaction surfaces and contact points we applied a random mutagenesis screen and structural overlays. We pinpointed the extent of reciprocal ligand effects of both nuclear receptors via coregulator peptide recruitment assays. On PPARα targets revealed from a genome-wide transcriptome analysis, we performed an ERRα chromatin immunoprecipitation analysis on both fast and fed mouse livers. RESULTS: Random mutagenesis scanning of PPARα's ligand-binding domain and coregulator profiling experiments supported the involvement of (a) bridging coregulator(s), while recapitulation of the interaction in vitro indicated the possibility of a trimeric interaction with RXRα. The PPARα·ERRα interaction depends on 3 C-terminal residues within helix 12 of ERRα and is strengthened by both PGC1α and serum deprivation. Pharmacological inhibition of ERRα decreased the interaction of ERRα to ligand-activated PPARα and revealed a transcriptome in line with enhanced mRNA expression of prototypical PPARα target genes, suggesting a role for ERRα as a transcriptional repressor. Strikingly, on other PPARα targets, including the isolated PDK4 enhancer, ERRα behaved oppositely. Chromatin immunoprecipitation analyses demonstrate a PPARα ligand-dependent ERRα recruitment onto chromatin at PPARα-binding regions, which is lost following ERRα inhibition in fed mouse livers. CONCLUSIONS: Our data support the coexistence of multiple layers of transcriptional crosstalk mechanisms between PPARα and ERRα, which may serve to finetune the activity of PPARα as a nutrient-sensing transcription factor.

OPENPichia: licence-free Komagataella phaffii chassis strains and toolkit for protein expression.

The industrial yeast Komagataella phaffii (formerly named Pichia pastoris) is commonly used to synthesize recombinant proteins, many of which are used as human therapeutics or in food. However, the basic strain, named NRRL Y-11430, from which all commercial hosts are derived, is not available without restrictions on its use. Comparative genome sequencing leaves little doubt that NRRL Y-11430 is derived from a K. phaffii type strain deposited in the UC Davis Phaff Yeast Strain Collection in 1954. We analysed four equivalent type strains in several culture collections and identified the NCYC 2543 strain, from which we started to develop an open-access Pichia chassis strain that anyone can use to produce recombinant proteins to industry standards. NRRL Y-11430 is readily transformable, which we found to be due to a HOC1 open-reading-frame truncation that alters cell-wall mannan. We introduced the HOC1 open-reading-frame truncation into NCYC 2543, which increased the transformability and improved secretion of some but not all of our tested proteins. We provide our genome-sequenced type strain, the hoc1tr derivative that we named OPENPichia as well as a synthetic, modular expression vector toolkit under liberal end-user distribution licences as an unencumbered OPENPichia resource for the microbial biotechnology community.

Integrating artificial intelligence-based epitope prediction in a SARS-CoV-2 antibody discovery pipeline: caution is warranted.

BACKGROUND: SARS-CoV-2-neutralizing antibodies (nABs) showed great promise in the early phases of the COVID-19 pandemic. The emergence of resistant strains, however, quickly rendered the majority of clinically approved nABs ineffective. This underscored the imperative to develop nAB cocktails targeting non-overlapping epitopes. METHODS: Undertaking a nAB discovery program, we employed a classical workflow, while integrating artificial intelligence (AI)-based prediction to select non-competing nABs very early in the pipeline. We identified and in vivo validated (in female Syrian hamsters) two highly potent nABs. FINDINGS: Despite the promising results, in depth cryo-EM structural analysis demonstrated that the AI-based prediction employed with the intention to ensure non-overlapping epitopes was inaccurate. The two nABs in fact bound to the same receptor-binding epitope in a remarkably similar manner. INTERPRETATION: Our findings indicate that, even in the Alphafold era, AI-based predictions of paratope-epitope interactions are rough and experimental validation of epitopes remains an essential cornerstone of a successful nAB lead selection. FUNDING: Full list of funders is provided at the end of the manuscript.

N-terminal proteoforms may engage in different protein complexes.

Alternative translation initiation and alternative splicing may give rise to N-terminal proteoforms, proteins that differ at their N-terminus compared with their canonical counterparts. Such proteoforms can have altered localizations, stabilities, and functions. Although proteoforms generated from splice variants can be engaged in different protein complexes, it remained to be studied to what extent this applies to N-terminal proteoforms. To address this, we mapped the interactomes of several pairs of N-terminal proteoforms and their canonical counterparts. First, we generated a catalogue of N-terminal proteoforms found in the HEK293T cellular cytosol from which 22 pairs were selected for interactome profiling. In addition, we provide evidence for the expression of several N-terminal proteoforms, identified in our catalogue, across different human tissues, as well as tissue-specific expression, highlighting their biological relevance. Protein-protein interaction profiling revealed that the overlap of the interactomes for both proteoforms is generally high, showing their functional relation. We also showed that N-terminal proteoforms can be engaged in new interactions and/or lose several interactions compared with their canonical counterparts, thus further expanding the functional diversity of proteomes.

A Dynamic rRNA Ribomethylome Drives Stemness in Acute Myeloid Leukemia.

The development and regulation of malignant self-renewal remain unresolved issues. Here, we provide biochemical, genetic, and functional evidence that dynamics in ribosomal RNA (rRNA) 2'-O-methylation regulate leukemia stem cell (LSC) activity in vivo. A comprehensive analysis of the rRNA 2'-O-methylation landscape of 94 patients with acute myeloid leukemia (AML) revealed dynamic 2'-O-methylation specifically at exterior sites of ribosomes. The rRNA 2'-O-methylation pattern is closely associated with AML development stage and LSC gene expression signature. Forced expression of the 2'-O-methyltransferase fibrillarin (FBL) induced an AML stem cell phenotype and enabled engraftment of non-LSC leukemia cells in NSG mice. Enhanced 2'-O-methylation redirected the ribosome translation program toward amino acid transporter mRNAs enriched in optimal codons and subsequently increased intracellular amino acid levels. Methylation at the single site 18S-guanosine 1447 was instrumental for LSC activity. Collectively, our work demonstrates that dynamic 2'-O-methylation at specific sites on rRNAs shifts translational preferences and controls AML LSC self-renewal. SIGNIFICANCE: We establish the complete rRNA 2'-O-methylation landscape in human AML. Plasticity of rRNA 2'-O-methylation shifts protein translation toward an LSC phenotype. This dynamic process constitutes a novel concept of how cancers reprogram cell fate and function. This article is highlighted in the In This Issue feature, p. 247.

Orthogonal proteomics methods to unravel the HOTAIR interactome.

Accumulating evidence highlights the role of long non-coding RNAs (lncRNAs) in cellular homeostasis, and their dysregulation in disease settings. Most lncRNAs function by interacting with proteins or protein complexes. While several orthogonal methods have been developed to identify these proteins, each method has its inherent strengths and limitations. Here, we combine two RNA-centric methods ChIRP-MS and RNA-BioID to obtain a comprehensive list of proteins that interact with the well-known lncRNA HOTAIR. Overexpression of HOTAIR has been associated with a metastasis-promoting phenotype in various cancers. Although HOTAIR is known to bind with PRC2 and LSD1 protein complexes, only very limited unbiased comprehensive approaches to map its interactome have been performed. Both ChIRP-MS and RNA-BioID data sets show an association of HOTAIR with mitoribosomes, suggesting that HOTAIR has functions independent of its (post-)transcriptional mode-of-action.

An affinity-enhanced, broadly neutralizing heavy chain-only antibody protects against SARS-CoV-2 infection in animal models.

Broadly neutralizing antibodies are an important treatment for individuals with coronavirus disease 2019 (COVID-19) caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Antibody-based therapeutics are also essential for pandemic preparedness against future Sarbecovirus outbreaks. Camelid-derived single domain antibodies (VHHs) exhibit potent antimicrobial activity and are being developed as SARS-CoV-2­neutralizing antibody-like therapeutics. Here, we identified VHHs that neutralize both SARS-CoV-1 and SARS-CoV-2, including now circulating variants. We observed that the VHHs bound to a highly conserved epitope in the receptor binding domain of the viral spike protein that is difficult to access for human antibodies. Structure-guided molecular modeling, combined with rapid yeast-based prototyping, resulted in an affinity enhanced VHH-human immunoglobulin G1 Fc fusion molecule with subnanomolar neutralizing activity. This VHH-Fc fusion protein, produced in and purified from cultured Chinese hamster ovary cells, controlled SARS-CoV-2 replication in prophylactic and therapeutic settings in mice expressing human angiotensin converting enzyme 2 and in hamsters infected with SARS-CoV-2. These data led to affinity-enhanced selection of the VHH, XVR011, a stable anti­COVID-19 biologic that is now being evaluated in the clinic.

Bacterial riboproteogenomics: the era of N-terminal proteoform existence revealed.

With the rapid increase in the number of sequenced prokaryotic genomes, relying on automated gene annotation became a necessity. Multiple lines of evidence, however, suggest that current bacterial genome annotations may contain inconsistencies and are incomplete, even for so-called well-annotated genomes. We here discuss underexplored sources of protein diversity and new methodologies for high-throughput genome reannotation. The expression of multiple molecular forms of proteins (proteoforms) from a single gene, particularly driven by alternative translation initiation, is gaining interest as a prominent contributor to bacterial protein diversity. In consequence, riboproteogenomic pipelines were proposed to comprehensively capture proteoform expression in prokaryotes by the complementary use of (positional) proteomics and the direct readout of translated genomic regions using ribosome profiling. To complement these discoveries, tailored strategies are required for the functional characterization of newly discovered bacterial proteoforms.