Peptide Level Turnover Measurements Enable the Study of Proteoform Dynamics

Cellular control of protein levels: A systems biology perspective

How cells regulate protein levels is a central question of biology. Over the past decades, molecular biology research has provided profound insights into the mechanisms and the molecular machinery governing each step of the gene expression process, from transcription to protein degradation. Recent advances in transcriptomics and proteomics have complemented our understanding of these fundamental cellular processes with a quantitative, systems-level perspective. Multi-omic studies revealed significant quantitative, kinetic and functional differences between the genome, transcriptome and proteome. While protein levels often correlate with mRNA levels, quantitative investigations have demonstrated a substantial impact of translation and protein degradation on protein expression control. In addition, protein-level regulation appears to play a crucial role in buffering protein abundances against undesirable mRNA expression variation. These findings have practical implications for many fields, including gene function prediction and precision medicine.

Global, site-resolved analysis of ubiquitylation occupancy and turnover rate reveals systems properties

Ubiquitylation regulates most proteins and biological processes in a eukaryotic cell. However, the site-specific occupancy (stoichiometry) and turnover rate of ubiquitylation have not been quantified. Here we present an integrated picture of the global ubiquitylation site occupancy and half-life. Ubiquitylation site occupancy spans over four orders of magnitude, but the median ubiquitylation site occupancy is three orders of magnitude lower than that of phosphorylation. The occupancy, turnover rate, and regulation of sites by proteasome inhibitors are strongly interrelated, and these attributes distinguish sites involved in proteasomal degradation and cellular signaling. Sites in structured protein regions exhibit longer half-lives and stronger upregulation by proteasome inhibitors than sites in unstructured regions. Importantly, we discovered a surveillance mechanism that rapidly and site-indiscriminately deubiquitylates all ubiquitin-specific E1 and E2 enzymes, protecting them against accumulation of bystander ubiquitylation. The work provides a systems-scale, quantitative view of ubiquitylation properties and reveals general principles of ubiquitylation-dependent governance.

Nuclear release of eIF1 globally increases stringency of start-codon selection to preserve mitotic arrest physiology

Regulated start-codon selection has the potential to reshape the proteome through the differential production of uORFs, canonical proteins, and alternative translational isoforms. However, conditions under which start-codon selection is altered remain poorly defined. Here, using transcriptome-wide translation initiation site profiling, we reveal a global increase in the stringency of start-codon selection during mammalian mitosis. Low-efficiency initiation sites are preferentially repressed in mitosis, resulting in pervasive changes in the translation of thousands of start sites and their corresponding protein products. This increased stringency of start-codon selection during mitosis results from increased interactions between the key regulator of start-codon selection, eIF1, and the 40S ribosome. We find that increased eIF1-40S ribosome interactions during mitosis are mediated by the release of a nuclear pool of eIF1 upon nuclear envelope breakdown. Selectively depleting the nuclear pool of eIF1 eliminates the changes to translational stringency during mitosis, resulting in altered mitotic proteome composition. In addition, preventing mitotic translational rewiring results in substantially increased cell death and decreased mitotic slippage following treatment with anti-mitotic chemotherapeutics. Thus, cells globally control translation initiation stringency with critical roles during the mammalian cell cycle to preserve mitotic cell physiology.

Systematic analysis of proteome turnover in an organoid model of pancreatic cancer by dSILO

The role of protein turnover in pancreatic ductal adenocarcinoma (PDA) metastasis has not been previously investigated. We introduce dynamic stable-isotope labeling of organoids (dSILO): a dynamic SILAC derivative that combines a pulse of isotopically labeled amino acids with isobaric tandem mass-tag (TMT) labeling to measure proteome-wide protein turnover rates in organoids. We applied it to a PDA model and discovered that metastatic organoids exhibit an accelerated global proteome turnover compared to primary tumor organoids. Globally, most turnover changes are not reflected at the level of protein abundance. Interestingly, the group of proteins that show the highest turnover increase in metastatic PDA compared to tumor is involved in mitochondrial respiration. This indicates that metastatic PDA may adopt alternative respiratory chain functionality that is controlled by the rate at which proteins are turned over. Collectively, our analysis of proteome turnover in PDA organoids offers insights into the mechanisms underlying PDA metastasis.

Maximum entropy determination of mammalian proteome dynamics

Full understanding of proteostasis and energy utilization in cells will require knowledge of the fraction of cell proteins being degraded with different half-lives and their rates of synthesis. We therefore developed a method to determine such information that combines mathematical analysis of protein degradation kinetics obtained in pulse-chase experiments with Bayesian data fitting using the maximum entropy principle. This approach will enable rapid analyses of whole-cell protein dynamics in different cell types, physiological states, and neurodegenerative disease. Using it, we obtained surprising insights about protein stabilities in cultured cells normally and upon activation of proteolysis by mTOR inhibition and increasing cAMP or cGMP. It revealed that >90% of protein content in dividing mammalian cell lines is long-lived, with half-lives of 24 to 200 h, and therefore comprises much of the proteins in daughter cells. The well-studied short-lived proteins (half-lives < 10 h) together comprise <2% of cell protein mass, but surprisingly account for 10 to 20% of measurable newly synthesized protein mass. Evolution thus appears to have minimized intracellular proteolysis except to rapidly eliminate misfolded and regulatory proteins.

Dual regulation of SLC25A39 by AFG3L2 and iron controls mitochondrial glutathione homeostasis

Organelle transporters define metabolic compartmentalization, and how this metabolite transport process can be modulated is poorly explored. Here, we discovered that human SLC25A39, a mitochondrial transporter critical for mitochondrial glutathione uptake, is a short-lived protein under dual regulation at the protein level. Co-immunoprecipitation mass spectrometry and CRISPR knockout (KO) in mammalian cells identified that mitochondrial m-AAA protease AFG3L2 is responsible for degrading SLC25A39 through the matrix loop 1. SLC25A39 senses mitochondrial iron-sulfur cluster using four matrix cysteine residues and inhibits its degradation. SLC25A39 protein regulation is robust in developing and mature neurons. This dual transporter regulation, by protein quality control and metabolic sensing, allows modulating mitochondrial glutathione level in response to iron homeostasis, opening avenues for exploring regulation of metabolic compartmentalization. Neuronal SLC25A39 regulation connects mitochondrial protein quality control, glutathione, and iron homeostasis, which were previously unrelated biochemical features in neurodegeneration.

Solid-Phase Compatible Silane-Based Cleavable Linker Enables Custom Isobaric Quantitative Chemoproteomics

Mass spectrometry-based chemoproteomics has emerged as an enabling technology for functional biology and drug discovery. To address limitations of established chemoproteomics workflows, including cumbersome reagent synthesis and low throughput sample preparation, here, we established the silane-based cleavable isotopically labeled proteomics (sCIP) method. The sCIP method is enabled by a high yielding and scalable route to dialkoxydiphenylsilane fluorenylmethyloxycarbonyl (DADPS-Fmoc)-protected amino acid building blocks, which enable the facile synthesis of customizable, isotopically labeled, and chemically cleavable biotin capture reagents. sCIP is compatible with both MS1- and MS2-based quantitation, and the sCIP-MS2 method is distinguished by its click-assembled isobaric tags in which the reporter group is encoded in the sCIP capture reagent and balancer in the pan cysteine-reactive probe. The sCIP-MS2 workflow streamlines sample preparation with early stage isobaric labeling and sample pooling, allowing for high coverage and increased sample throughput via customized low cost six-plex sample multiplexing. When paired with a custom FragPipe data analysis workflow and applied to cysteine-reactive fragment screens, sCIP proteomics revealed established and unprecedented cysteine-ligand pairs, including the discovery that mitochondrial uncoupling agent FCCP acts as a covalent-reversible cysteine-reactive electrophile.

Metronomic dosing of ovarian cancer cells with the ATR inhibitor AZD6738 leads to loss of CDC25A expression and resistance to ATRi treatment

OBJECTIVE

ATR kinase inhibitors promote cell killing by inducing replication stress and through potentiation of genotoxic agents in gynecologic cancer cells. To explore mechanisms of acquired resistance to ATRi in ovarian cancer, we characterized ATRi-resistant ovarian cancer cells generated by metronomic dosing with the clinical ATR inhibitor AZD6738.

METHODS

ATRi-resistant ovarian cancer cells (OVCAR3 and OV90) were generated by dosing with AZD6738 and assessed for sensitivity to Chk1i (LY2603618), PARPi (Olaparib) and combination with cisplatin or a CDK4/6 inhibitor (Palbociclib). Models were characterized by diverse methods including silencing CDC25A in OV90 cells and assessing impact on ATRi response. Serum proteomic analysis of ATRi-resistant OV90 xenografts was performed to identify circulating biomarker candidates of ATRi-resistance.

RESULTS

AZD6738-resistant cell lines are refractory to LY2603618, but not to Olaparib or combinations with cisplatin. Cell cycle analyses showed ATRi-resistant cells exhibit G1/S arrest following AZD6738 treatment. Accordingly, combination with Palbociclib confers resistance to AZD6738. AZD6738-resistant cells exhibit altered abundances of G1/S phase regulatory proteins, including loss of CDC25A in AZD6738-resistant OV90 cells. Silencing of CDC25A in OV90 cells confers resistance to AZD6738. Serum proteomics from AZD6738-resistant OV90 xenografts identified Vitamin D-Binding Protein (GC), Apolipoprotein E (APOE) and A1 (APOA1) as significantly elevated in AZD6738-resistant backgrounds.

CONCLUSIONS

We show that metronomic dosing of ovarian cancer cells with AZD6738 results in resistance to ATR/ Chk1 inhibitors, that loss of CDC25A expression represents a mechanism of resistance to ATRi treatment in ovarian cancer cells and identify several circulating biomarker candidates of CDC25A low, AZD6738-resistant ovarian cancer cells.

Determining DNA-Protein Binding Affinities and Specificities from Crude Lysates Using a Combined SILAC/TMT Labeling Strategy

In recent years, quantitative mass spectrometry-based interaction proteomics technology has proven very useful in identifying specific DNA-protein interactions using single pull-downs from crude lysates. Here, we applied a SILAC/TMT-based higher-order multiplexing approach to develop an interaction proteomics workflow called Protein-nucleic acid Affinity and Specificity quantification by MAss spectrometry in Nuclear extracts or PASMAN. In PASMAN, DNA pull-downs using a concentration range of specific and control DNA baits are performed in SILAC-labeled nuclear extracts. MS1-based quantification to determine specific DNA-protein interactions is then combined with sequential TMT-based quantification of fragmented SILAC peptides, allowing the generation of Hill-like curves and determination of apparent binding affinities. We benchmarked PASMAN using the SP/KLF motif and further applied it to gain insights into two CGCG-containing consensus DNA motifs. These motifs are recognized by two BEN domain-containing proteins, BANP and BEND3, which we find to interact with these motifs with distinct affinities. Finally, we profiled the BEND3 proximal proteome, revealing the NuRD complex as the major BEND3 proximal protein complex in vivo. In summary, PASMAN represents, to our knowledge, the first higher-order multiplexing-based interaction proteomics method that can be used to decipher specific DNA-protein interactions and their apparent affinities in various biological and pathological contexts.

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.

THRONCAT: metabolic labeling of newly synthesized proteins using a bioorthogonal threonine analog

Profiling the nascent cellular proteome and capturing early proteomic changes in response to external stimuli provides valuable insights into cellular physiology. Existing metabolic protein labeling approaches based on bioorthogonal methionine- or puromycin analogs allow for the selective visualization and enrichment of newly synthesized proteins. However, their applications are limited as they often require methionine-free conditions, auxotrophic cells and/or are toxic to cells. Here, we introduce THRONCAT, a threonine-derived non-canonical amino acid tagging method based on the bioorthogonal threonine analog β-ethynylserine (βES) that enables efficient labeling of the nascent proteome in complete growth media within minutes. We use THRONCAT for the visualization and enrichment of nascent proteins in bacteria, mammalian cells and Drosophila melanogaster. We profile immediate proteome dynamics of B-cells in response to B-cell receptor activation simply by adding βES to the culture medium, demonstrating the ease-of-use of the method and its potential to address diverse biological questions. In addition, using a Drosophila model of Charcot-Marie-Tooth peripheral neuropathy, we show that THRONCAT enables visualization and quantification of relative protein synthesis rates in specific cell types in vivo.

Quantifying label enrichment from two mass isotopomers increases proteome coverage for in vivo protein turnover using heavy water metabolic labeling

Heavy water metabolic labeling followed by liquid chromatography coupled with mass spectrometry is a powerful high throughput technique for measuring the turnover rates of individual proteins in vivo. The turnover rate is obtained from the exponential decay modeling of the depletion of the monoisotopic relative isotope abundance. We provide theoretical formulas for the time course dynamics of six mass isotopomers and use the formulas to introduce a method that utilizes partial isotope profiles, only two mass isotopomers, to compute protein turnover rate. The use of partial isotope profiles alleviates the interferences from co-eluting contaminants in complex proteome mixtures and improves the accuracy of the estimation of label enrichment. In five different datasets, the technique consistently doubles the number of peptides with high goodness-of-fit characteristics of the turnover rate model. We also introduce a software tool, d2ome+, which automates the protein turnover estimation from partial isotope profiles.

eIF4A1-dependent mRNAs employ purine-rich 5'UTR sequences to activate localised eIF4A1-unwinding through eIF4A1-multimerisation to facilitate translation

Altered eIF4A1 activity promotes translation of highly structured, eIF4A1-dependent oncogene mRNAs at root of oncogenic translational programmes. It remains unclear how these mRNAs recruit and activate eIF4A1 unwinding specifically to facilitate their preferential translation. Here, we show that single-stranded RNA sequence motifs specifically activate eIF4A1 unwinding allowing local RNA structural rearrangement and translation of eIF4A1-dependent mRNAs in cells. Our data demonstrate that eIF4A1-dependent mRNAs contain AG-rich motifs within their 5'UTR which specifically activate eIF4A1 unwinding of local RNA structure to facilitate translation. This mode of eIF4A1 regulation is used by mRNAs encoding components of mTORC-signalling and cell cycle progression, and renders these mRNAs particularly sensitive to eIF4A1-inhibition. Mechanistically, we show that binding of eIF4A1 to AG-rich sequences leads to multimerization of eIF4A1 with eIF4A1 subunits performing distinct enzymatic activities. Our structural data suggest that RNA-binding of multimeric eIF4A1 induces conformational changes in the RNA resulting in an optimal positioning of eIF4A1 proximal to the RNA duplex enabling efficient unwinding. Our data proposes a model in which AG-motifs in the 5'UTR of eIF4A1-dependent mRNAs specifically activate eIF4A1, enabling assembly of the helicase-competent multimeric eIF4A1 complex, and positioning these complexes proximal to stable localised RNA structure allowing ribosomal subunit scanning.

Using protein-per-mRNA differences among human tissues in codon optimization

BACKGROUND

Codon usage and nucleotide composition of coding sequences have profound effects on protein expression. However, while it is recognized that different tissues have distinct tRNA profiles and codon usages in their transcriptomes, the effect of tissue-specific codon optimality on protein synthesis remains elusive.

RESULTS

We leverage existing state-of-the-art transcriptomics and proteomics datasets from the GTEx project and the Human Protein Atlas to compute the protein-to-mRNA ratios of 36 human tissues. Using this as a proxy of translational efficiency, we build a machine learning model that identifies codons enriched or depleted in specific tissues. We detect two clusters of tissues with an opposite pattern of codon preferences. We then use these identified patterns for the development of CUSTOM, a codon optimizer algorithm which suggests a synonymous codon design in order to optimize protein production in a tissue-specific manner. In human cell-line models, we provide evidence that codon optimization should take into account particularities of the translational machinery of the tissues in which the target proteins are expressed and that our approach can design genes with tissue-optimized expression profiles.

CONCLUSIONS

We provide proof-of-concept evidence that codon preferences exist in tissue-specific protein synthesis and demonstrate its application to synthetic gene design. We show that CUSTOM can be of benefit in biological and biotechnological applications, such as in the design of tissue-targeted therapies and vaccines.

UHMK1 is a novel splicing regulatory kinase

The U2AF Homology Motif Kinase 1 (UHMK1) is the only kinase that contains the U2AF homology motif, a common protein interaction domain among splicing factors. Through this motif, UHMK1 interacts with the splicing factors SF1 and SF3B1, known to participate in the 3' splice site recognition during the early steps of spliceosome assembly. Although UHMK1 phosphorylates these splicing factors in vitro, the involvement of UHMK1 in RNA processing has not previously been demonstrated. Here, we identify novel putative substrates of this kinase and evaluate UHMK1 contribution to overall gene expression and splicing, by integrating global phosphoproteomics, RNA-seq, and bioinformatics approaches. Upon UHMK1 modulation, 163 unique phosphosites were differentially phosphorylated in 117 proteins, of which 106 are novel potential substrates of this kinase. Gene Ontology analysis showed enrichment of terms previously associated with UHMK1 function, such as mRNA splicing, cell cycle, cell division, and microtubule organization. The majority of the annotated RNA-related proteins are components of the spliceosome but are also involved in several steps of gene expression. Comprehensive analysis of splicing showed that UHMK1 affected over 270 alternative splicing events. Moreover, splicing reporter assay further supported UHMK1 function on splicing. Overall, RNA-seq data demonstrated that UHMK1 knockdown had a minor impact on transcript expression and pointed to UHMK1 function in epithelial-mesenchymal transition. Functional assays demonstrated that UHMK1 modulation affects proliferation, colony formation, and migration. Taken together, our data implicate UHMK1 as a splicing regulatory kinase, connecting protein regulation through phosphorylation and gene expression in key cellular processes.

Toward a hypothesis-free understanding of how phosphorylation dynamically impacts protein turnover

The turnover measurement of proteins and proteoforms has been largely facilitated by workflows coupling metabolic labeling with mass spectrometry (MS), including dynamic stable isotope labeling by amino acids in cell culture (dynamic SILAC) or pulsed SILAC (pSILAC). Very recent studies including ours have integrated themeasurement of post-translational modifications (PTMs) at the proteome level (i.e., phosphoproteomics) with pSILAC experiments in steady state systems, exploring the link between PTMs and turnover at the proteome-scale. An open question in the field is how to exactly interpret these complex datasets in a biological perspective. Here, we present a novel pSILAC phosphoproteomic dataset which was obtained during a dynamic process of cell starvation using data-independent acquisition MS (DIA-MS). To provide an unbiased "hypothesis-free" analysis framework, we developed a strategy to interrogate how phosphorylation dynamically impacts protein turnover across the time series data. With this strategy, we discovered a complex relationship between phosphorylation and protein turnover that was previously underexplored. Our results further revealed a link between phosphorylation stoichiometry with the turnover of phosphorylated peptidoforms. Moreover, our results suggested that phosphoproteomic turnover diversity cannot directly explain the abundance regulation of phosphorylation during cell starvation, underscoring the importance of future studies addressing PTM site-resolved protein turnover.

An E3 ligase network engages GCN1 to promote the degradation of translation factors on stalled ribosomes

Ribosomes frequently stall during mRNA translation, resulting in the context-dependent activation of quality control pathways to maintain proteostasis. However, surveillance mechanisms that specifically respond to stalled ribosomes with an occluded A site have not been identified. We discovered that the elongation factor-1α (eEF1A) inhibitor, ternatin-4, triggers the ubiquitination and degradation of eEF1A on stalled ribosomes. Using a chemical genetic approach, we unveiled a signaling network comprising two E3 ligases, RNF14 and RNF25, which are required for eEF1A degradation. Quantitative proteomics revealed the RNF14 and RNF25-dependent ubiquitination of eEF1A and a discrete set of ribosomal proteins. The ribosome collision sensor GCN1 plays an essential role by engaging RNF14, which directly ubiquitinates eEF1A. The site-specific, RNF25-dependent ubiquitination of the ribosomal protein RPS27A/eS31 provides a second essential signaling input. Our findings illuminate a ubiquitin signaling network that monitors the ribosomal A site and promotes the degradation of stalled translation factors, including eEF1A and the termination factor eRF1.

Current Role of Modern Chromatography with Mass Spectrometry and Nuclear Magnetic Resonance Spectroscopy in the Investigation of Biomarkers of Endometriosis

Endometriosis has a wide range of clinical manifestations, and the disease course is unpredictable, making the diagnosis a challenging task. Despite significant advances in the pathophysiology of endometriosis and various proposed theories, the exact etiology is not fully understood and is still unknown. The most commonly used biomarker of endometriosis is CA-125, however, it is nonspecific and is applied for cancers diagnosis. Therefore, the development of reliable noninvasive diagnostic tests for the early diagnosis of endometriosis remains one of the top priorities. Omics technologies are very promising approaches for constructing diagnostic models and biomarker discovery. Their use can greatly facilitate the study of such a complex disease as endometriosis. Nowadays, powerful analytical platforms commonly used in omics, such as gas and liquid chromatography with mass spectrometry and nuclear magnetic resonance (NMR) spectroscopy, have proven to be a promising tools for biomarker discovery. The aim of this review is to summarize the various features of the analytical approaches, practical challenges and features of gas and liquid chromatography with MS and NMR spectroscopy (including sample processing protocols, technological advancements, and methodology) used for profiling of metabolites, lipids, peptides and proteins in physiological fluids and tissues from patients with endometriosis. In addition, this report devotes special attention to the issue of how comprehensive analyses of these profiles can effectively contribute to the study of endometriosis. The search query included reports published between 2012 and 2022 years in PubMed, Web-of-Science, SCOPUS, Science Direct.

Logistics of neuronal protein turnover: Numbers and mechanisms

Research in the past twenty years or so has revealed that neurons synthesize and degrade proteins at their synapses to enable synaptic proteome remodelling on demand and in real-time. Here we provide a quantitative overview of the decentralized neuronal protein-turnover logistics. We first analyse the huge neuronal protein turnover demand that arises from subcellular compartments outside the cell body, followed by an overview of key quantities and modulation strategies in neuronal protein turnover logistics. In the end, we briefly review recent progress in neuronal local protein synthesis and summarize diverse protein-degradation mechanisms that are found near synapses.

An Anticancer Rhenium Tricarbonyl Targets Fe-S Cluster Biogenesis in Ovarian Cancer Cells

Target identification remains a critical challenge in inorganic drug discovery to deconvolute potential polypharmacology. Herein, we describe an improved approach to prioritize candidate protein targets based on a combination of dose-dependent chemoproteomics and treatment effects in living cancer cells for the rhenium tricarbonyl compound TRIP. Chemoproteomics revealed 89 distinct dose-dependent targets with concentrations of competitive saturation between 0.1 and 32 μM despite the broad proteotoxic effects of TRIP. Target-response networks revealed two highly probable targets of which the Fe-S cluster biogenesis factor NUBP2 was competitively saturated by free TRIP at nanomolar concentrations. Importantly, TRIP treatment led to a down-regulation of Fe-S cluster containing proteins and upregulated ferritin. Fe-S cluster depletion was further verified by assessing mitochondrial bioenergetics. Consequently, TRIP emerges as a first-in-class modulator of the scaffold protein NUBP2, which disturbs Fe-S cluster biogenesis at sub-cytotoxic concentrations in ovarian cancer cells.

Machine Learning Modeling of Protein-intrinsic Features Predicts Tractability of Targeted Protein Degradation

Targeted protein degradation (TPD) has rapidly emerged as a therapeutic modality to eliminate previously undruggable proteins by repurposing the cell's endogenous protein degradation machinery. However, the susceptibility of proteins for targeting by TPD approaches, termed "degradability", is largely unknown. Here, we developed a machine learning model, model-free analysis of protein degradability (MAPD), to predict degradability from features intrinsic to protein targets. MAPD shows accurate performance in predicting kinases that are degradable by TPD compounds [with an area under the precision-recall curve (AUPRC) of 0.759 and an area under the receiver operating characteristic curve (AUROC) of 0.775] and is likely generalizable to independent non-kinase proteins. We found five features with statistical significance to achieve optimal prediction, with ubiquitination potential being the most predictive. By structural modeling, we found that E2-accessible ubiquitination sites, but not lysine residues in general, are particularly associated with kinase degradability. Finally, we extended MAPD predictions to the entire proteome to find 964 disease-causing proteins (including proteins encoded by 278 cancer genes) that may be tractable to TPD drug development.

Experimental reproducibility limits the correlation between mRNA and protein abundances in tumor proteomic profiles

Large-scale studies of human proteomes have revealed only a moderate correlation between mRNA and protein abundances. It is unclear to what extent this moderate correlation reflects post-transcriptional regulation and to what extent it reflects measurement error. Here, by analyzing replicate profiles of tumors and cell lines, we show that there is considerable variation in the reproducibility of measurements of transcripts and proteins from individual genes. Proteins with more reproducible measurements tend to have a higher mRNA-protein correlation, suggesting that measurement reproducibility accounts for a substantial fraction of the unexplained variation between mRNA and protein abundances. The reproducibility of individual proteins is somewhat consistent across studies, and we exploit this to develop an aggregate reproducibility score that explains a substantial amount of the variation in mRNA-protein correlations across multiple studies. Finally, we show that pathways previously reported to have a higher-than-average mRNA-protein correlation may simply contain members that can be more reproducibly quantified.

Delineation of proteome changes driven by cell size and growth rate

Increasing cell size drives changes to the proteome, which affects cell physiology. As cell size increases, some proteins become more concentrated while others are diluted. As a result, the state of the cell changes continuously with increasing size. In addition to these proteomic changes, large cells have a lower growth rate (protein synthesis rate per unit volume). That both the cell’s proteome and growth rate change with cell size suggests they may be interdependent. To test this, we used quantitative mass spectrometry to measure how the proteome changes in response to the mTOR inhibitor rapamycin, which decreases the cellular growth rate and has only a minimal effect on cell size. We found that large cell size and mTOR inhibition, both of which lower the growth rate of a cell, remodel the proteome in similar ways. This suggests that many of the effects of cell size are mediated by the size-dependent slowdown of the cellular growth rate. For example, the previously reported size-dependent expression of some senescence markers could reflect a cell’s declining growth rate rather than its size per se. In contrast, histones and other chromatin components are diluted in large cells independently of the growth rate, likely so that they remain in proportion with the genome. Finally, size-dependent changes to the cell’s growth rate and proteome composition are still apparent in cells continually exposed to a saturating dose of rapamycin, which indicates that cell size can affect the proteome independently of mTORC1 signaling. Taken together, our results clarify the dependencies between cell size, growth, mTOR activity, and the proteome remodeling that ultimately controls many aspects of cell physiology.

Increasing cell size remodels the proteome and promotes senescence

Cell size is tightly controlled in healthy tissues, but it is unclear how deviations in cell size affect cell physiology. To address this, we measured how the cell's proteome changes with increasing cell size. Size-dependent protein concentration changes are widespread and predicted by subcellular localization, size-dependent mRNA concentrations, and protein turnover. As proliferating cells grow larger, concentration changes typically associated with cellular senescence are increasingly pronounced, suggesting that large size may be a cause rather than just a consequence of cell senescence. Consistent with this hypothesis, larger cells are prone to replicative, DNA-damage-induced, and CDK4/6i-induced senescence. Size-dependent changes to the proteome, including those associated with senescence, are not observed when an increase in cell size is accompanied by an increase in ploidy. Together, our findings show how cell size could impact many aspects of cell physiology by remodeling the proteome and provide a rationale for cell size control and polyploidization.

Low complexity regions in mammalian proteins are associated with low protein abundance and high transcript abundance

Low Complexity Regions (LCRs) are present in a surprisingly large number of eukaryotic proteins. These highly repetitive and compositionally biased sequences are often structurally disordered, bind promiscuously, and evolve rapidly. Frequently studied in terms of evolutionary dynamics, little is known about how LCRs affect the expression of the proteins which contain them. It would be expected that rapidly evolving LCRs are unlikely to be tolerated in strongly conserved, highly abundant proteins, leading to lower overall abundance in proteins which contain LCRs. To test this hypothesis and examine the associations of protein abundance and transcript abundance with the presence of LCRs, we have integrated high-throughput data from across mammals. We have found that LCRs are indeed associated with reduced protein abundance, but are also associated with elevated transcript abundance. These associations are qualitatively consistent across 12 human tissues and nine mammalian species. The differential impacts of LCRs on abundance at the protein and transcript level are not explained by differences in either protein degradation rates or the inefficiency of translation for LCR containing proteins. We suggest that rapidly evolving LCRs are a source of selective pressure on the regulatory mechanisms which maintain steady-state protein abundance levels.

Evaluation of machine learning models on protein level inference from prioritized RNA features

The parallel measurement of transcriptome and proteome revealed unmatched profiles. Since proteomic analysis is more expensive and challenging than transcriptomic analysis, the question of how to use messenger RNA (mRNA) expression data to predict protein level is extremely important. Here, we comprehensively evaluated 13 machine learning models on inferring protein expression levels using RNA expression profile. A total of 20 proteogenomic datasets from three mainstream proteomic platforms with >2500 samples of 13 human tissues were collected for model evaluation. Our results highlighted that the appropriate feature selection methods combined with classical machine learning models could achieve excellent predictive performance. The voting ensemble model outperformed other candidate models across datasets. Adding the mRNA proxy model to the regression model further improved the prediction performance. The dataset and gene characteristics could affect the prediction performance. Finally, we applied the model to the brain transcriptome of cerebral cortex regions to infer the protein profile for better understanding the functional characteristics of the brain regions. This benchmarking work not only provides useful hints on the inherent correlation between transcriptome and proteome, but also has practical value of the transcriptome-based prediction of protein expression levels.

Protein degradation on the global scale

Protein degradation occurs through proteasomal, endosomal, and lysosomal pathways. Technological advancements have allowed for the determination of protein copy numbers and turnover rates on a global scale, which has provided an overview of trends and rules governing protein degradation. Sharper chemical and gene-editing tools have enabled the specific perturbation of each degradation pathway, whose effects on protein dynamics can now be comprehensively analyzed. We review major studies and innovation in this field and discuss the interdependence between the major pathways of protein degradation.

Protein turnover models for LC-MS data of heavy water metabolic labeling

Protein turnover is vital for cellular functioning and is often associated with the pathophysiology of a variety of diseases. Metabolic labeling with heavy water followed by liquid chromatography coupled to mass spectrometry is a powerful tool to study in vivo protein turnover in high throughput and large scale. Heavy water is a cost-effective and easy to use labeling agent. It labels all nonessential amino acids. Due to its toxicity in high concentrations (20% or higher), small enrichments (8% or smaller) of heavy water are used with most organisms. The low concentration results in incomplete labeling of peptides/proteins. Therefore, the data processing is more challenging and requires accurate quantification of labeled and unlabeled forms of a peptide from overlapping mass isotopomer distributions. The work describes the bioinformatics aspects of the analysis of heavy water labeled mass spectral data, available software tools and current challenges and opportunities.

Linking post-translational modifications and protein turnover by site-resolved protein turnover profiling

Proteome-wide measurements of protein turnover have largely ignored the impact of post-translational modifications (PTMs). To address this gap, we employ stable isotope labeling and mass spectrometry to measure the turnover of >120,000 peptidoforms including >33,000 phosphorylated, acetylated, and ubiquitinated peptides for >9,000 native proteins. This site-resolved protein turnover (SPOT) profiling discloses global and site-specific differences in turnover associated with the presence or absence of PTMs. While causal relationships may not always be immediately apparent, we speculate that PTMs with diverging turnover may distinguish states of differential protein stability, structure, localization, enzymatic activity, or protein-protein interactions. We show examples of how the turnover data may give insights into unknown functions of PTMs and provide a freely accessible online tool that allows interrogation and visualisation of all turnover data. The SPOT methodology is applicable to many cell types and modifications, offering the potential to prioritize PTMs for future functional investigations.

Post-translational modifications (PTMs) can regulate cellular protein function but their global impact on protein turnover is largely unknown. Here, the authors develop proteomic workflows to profile PTM-resolved protein turnover and analyze the effects of phosphorylation, acetylation and ubiquitination.

Designing HDAC-PROTACs: lessons learned so far

Proteolysis-targeting chimeras (PROTACs) are a powerful tool to hijack the endogenous ubiquitin-proteasome system (UPS) and to degrade the intracellular proteins of therapeutic importance. Recently, two heterobifunctional degraders targeting hormone receptors headed into Phase II clinical trials. Compared to traditional drug design and common modes of action, the PROTAC approach offers new opportunities for the drug research field. Histone deacetylase inhibitors (HDACi) are well-established drugs for the treatment of hematological malignancies. The integration of HDAC binding motifs in PROTACs explores the possibility of targeted, chemical HDAC degradation. This review provides an overview and a perspective about the key steps in the structure development of HDAC-PROTACs. In particular, the influence of the three canonical PROTAC elements on HDAC-PROTAC efficacy and selectivity are discussed, the HDACi, the linker and the E3 ligase ligand.

Dichotomous responses to chronic fetal hypoxia lead to a predetermined aging phenotype

Hypoxia-induced intrauterine growth restriction increases the risk for cardiovascular, renal and other chronic diseases in adults, representing thus a major public health problem. Still, not much is known about the fetal mechanisms that predispose these individuals to disease. Using a previously validated mouse model of fetal hypoxia and bottom-up proteomics we characterize the response of the fetal kidney to chronic hypoxic stress. Fetal kidneys exhibit a dichotomous response to chronic hypoxia, comprising on the one hand cellular adaptations that promote survival (glycolysis, autophagy, and reduced DNA and protein synthesis), but on the other processes that induce a senescence-like phenotype (infiltration of inflammatory cells, DNA damage, and reduced proliferation). Importantly, chronic hypoxia also reduces the expression of the anti-aging proteins klotho and Sirt6, a mechanism that is evolutionary conserved between mice and humans. Taken together, we uncover that predetermined aging during fetal development is a key event in chronic hypoxia, establishing a solid foundation for Barker's hypothesis of fetal programming of adult diseases. This phenotype is associated with a characteristic biomarker profile in tissue and serum samples, exploitable for detecting and targeting accelerated aging in chronic hypoxic human diseases.

Quantitative high-confidence human mitochondrial proteome and its dynamics in cellular context

Mitochondria are key organelles for cellular energetics, metabolism, signaling, and quality control and have been linked to various diseases. Different views exist on the composition of the human mitochondrial proteome. We classified >8,000 proteins in mitochondrial preparations of human cells and defined a mitochondrial high-confidence proteome of >1,100 proteins (MitoCoP). We identified interactors of translocases, respiratory chain, and ATP synthase assembly factors. The abundance of MitoCoP proteins covers six orders of magnitude and amounts to 7% of the cellular proteome with the chaperones HSP60-HSP10 being the most abundant mitochondrial proteins. MitoCoP dynamics spans three orders of magnitudes, with half-lives from hours to months, and suggests a rapid regulation of biosynthesis and assembly processes. 460 MitoCoP genes are linked to human diseases with a strong prevalence for the central nervous system and metabolism. MitoCoP will provide a high-confidence resource for placing dynamics, functions, and dysfunctions of mitochondria into the cellular context.

An atlas of protein turnover rates in mouse tissues

Protein turnover is critical to cellular physiology as well as to the growth and maintenance of tissues. The unique synthesis and degradation rates of each protein help to define tissue phenotype, and knowledge of tissue- and protein-specific half-lives is directly relevant to protein-related drug development as well as the administration of medical therapies. Using stable isotope labeling and mass spectrometry, we determine the in vivo turnover rates of thousands of proteins-including those of the extracellular matrix-in a set of biologically important mouse tissues. We additionally develop a data visualization platform, named ApplE Turnover, that enables facile searching for any protein of interest in a tissue of interest and then displays its half-life, confidence interval, and supporting measurements. This extensive dataset and the corresponding visualization software provide a reference to guide future studies of mammalian protein turnover in response to physiologic perturbation, disease, or therapeutic intervention.

Selectivity aspects of activity-based (chemical) probes

Selective chemical modulators are ideal tools to study the function of a protein. Yet, the poor ligandability of many proteins has hampered the development of specific chemical probes for numerous protein classes. Tools, such as covalent inhibitors and activity-based protein profiling, have enhanced our understanding of thus-far difficult-to-target proteins and have enabled correct assessment of the selectivity of small-molecule modulators. This also requires deeper knowledge of compound and target site reactivity, evaluation of binding to noncovalent targets and protein turnover. The availability of highly selective chemical probes, the evolution of activity-based probes, and the development of profiling methods will open a new era of drugging the undruggable proteome.

Chemo-proteomics exploration of HDAC degradability by small molecule degraders

Targeted protein degradation refers to the use of small molecules that recruit a ubiquitin ligase to a target protein for ubiquitination and subsequent proteasome-dependent degradation. While degraders have been developed for many targets, key questions regarding degrader development and the consequences of acute pharmacological degradation remain, specifically for targets that exist in obligate multi-protein complexes. Here, we synthesize a pan-histone deacetylase (HDAC) degrader library for the chemo-proteomic exploration of acute degradation of a key class of chromatin-modifying enzymes. Using chemo-proteomics, we not only map the degradability of the zinc-dependent HDAC family identifying leads for targeting HDACs 1-8 and 10 but also explore important aspects of degrading epigenetic enzymes. We discover cell line-driven target specificity and that HDAC degradation often results in collateral loss of HDAC-containing repressive complexes. These findings potentially offer a new mechanism toward controlling chromatin structure, and our resource will facilitate accelerated degrader design and development for HDACs.

The San Diego Nathan Shock Center: tackling the heterogeneity of aging

Understanding basic mechanisms of aging holds great promise for developing interventions that prevent or delay many age-related declines and diseases simultaneously to increase human healthspan. However, a major confounding factor in aging research is the heterogeneity of the aging process itself. At the organismal level, it is clear that chronological age does not always predict biological age or susceptibility to frailty or pathology. While genetics and environment are major factors driving variable rates of aging, additional complexity arises because different organs, tissues, and cell types are intrinsically heterogeneous and exhibit different aging trajectories normally or in response to the stresses of the aging process (e.g., damage accumulation). Tackling the heterogeneity of aging requires new and specialized tools (e.g., single-cell analyses, mass spectrometry-based approaches, and advanced imaging) to identify novel signatures of aging across scales. Cutting-edge computational approaches are then needed to integrate these disparate datasets and elucidate network interactions between known aging hallmarks. There is also a need for improved, human cell-based models of aging to ensure that basic research findings are relevant to human aging and healthspan interventions. The San Diego Nathan Shock Center (SD-NSC) provides access to cutting-edge scientific resources to facilitate the study of the heterogeneity of aging in general and to promote the use of novel human cell models of aging. The center also has a robust Research Development Core that funds pilot projects on the heterogeneity of aging and organizes innovative training activities, including workshops and a personalized mentoring program, to help investigators new to the aging field succeed. Finally, the SD-NSC participates in outreach activities to educate the general community about the importance of aging research and promote the need for basic biology of aging research in particular.

A strategy to assess the cellular activity of E3 ligase components against neo-substrates using electrophilic probes

While there are hundreds of predicted E3 ligases, characterizing their applications for targeted protein degradation has proved challenging. Here, we report a chemical biology approach to evaluate the ability of modified recombinant E3 ligase components to support neo-substrate degradation. Bypassing the need for specific E3 ligase binders, we use maleimide-thiol chemistry for covalent functionalization followed by E3 electroporation (COFFEE) in live cells. We demonstrate that electroporated recombinant von Hippel-Lindau (VHL) protein, covalently functionalized at its ligandable cysteine with JQ1 or dasatinib, induces degradation of BRD4 or tyrosine kinases, respectively. Furthermore, by applying COFFEE to SPSB2, a Cullin-RING ligase 5 receptor, as well as to SKP1, the adaptor protein for Cullin-RING ligase 1 F box (SCF) complexes, we validate this method as a powerful approach to define the activity of previously uncharacterized ubiquitin ligase components, and provide further evidence that not only E3 ligase receptors but also adaptors can be directly hijacked for neo-substrate degradation.

Time-Resolved Proteome Analysis of Listeria monocytogenes during Infection Reveals the Role of the AAA+ Chaperone ClpC for Host Cell Adaptation

The cellular proteome comprises all proteins expressed at a given time and defines an organism's phenotype under specific growth conditions. The proteome is shaped and remodeled by both protein synthesis and protein degradation. Here, we developed a new method which combines metabolic and chemical isobaric peptide labeling to simultaneously determine the time-resolved protein decay and de novo synthesis in an intracellular human pathogen. We showcase this method by investigating the Listeria monocytogenes proteome in the presence and absence of the AAA+ chaperone protein ClpC. ClpC associates with the peptidase ClpP to form an ATP-dependent protease complex and has been shown to play a role in virulence development in L. monocytogenes. However, the mechanism by which ClpC is involved in the survival and proliferation of intracellular L. monocytogenes remains elusive. Employing this new method, we observed extensive proteome remodeling in L. monocytogenes upon interaction with the host, supporting the hypothesis that ClpC-dependent protein degradation is required to initiate bacterial adaptation mechanisms. We identified more than 100 putative ClpC target proteins through their stabilization in a clpC deletion strain. Beyond the identification of direct targets, we also observed indirect effects of the clpC deletion on the protein abundance in diverse cellular and metabolic pathways, such as iron acquisition and flagellar assembly. Overall, our data highlight the crucial role of ClpC for L. monocytogenes adaptation to the host environment through proteome remodeling. IMPORTANCE Survival and proliferation of pathogenic bacteria inside the host depend on their ability to adapt to the changing environment. Profiling the underlying changes on the bacterial proteome level during the infection process is important to gain a better understanding of the pathogenesis and the host-dependent adaptation processes. The cellular protein abundance is governed by the interplay between protein synthesis and decay. The direct readout of these events during infection can be accomplished using pulsed stable-isotope labeling by amino acids in cell culture (SILAC). Combining this approach with tandem-mass-tag (TMT) labeling enabled multiplexed and time-resolved bacterial proteome quantification during infection. Here, we applied this integrated approach to investigate protein turnover during the temporal progression of adaptation of the human pathogen L. monocytogenes to its host on a system-wide scale. Our experimental approach can easily be transferred to probe the proteome remodeling in other bacteria under a variety of perturbations.

ABHD17 regulation of plasma membrane palmitoylation and N-Ras-dependent cancer growth

Multiple Ras proteins, including N-Ras, depend on a palmitoylation/depalmitoylation cycle to regulate their subcellular trafficking and oncogenicity. General lipase inhibitors such as Palmostatin M (Palm M) block N-Ras depalmitoylation, but lack specificity and target several enzymes displaying depalmitoylase activity. Here, we describe ABD957, a potent and selective covalent inhibitor of the ABHD17 family of depalmitoylases, and show that this compound impairs N-Ras depalmitoylation in human acute myeloid leukemia (AML) cells. ABD957 produced partial effects on N-Ras palmitoylation compared with Palm M, but was much more selective across the proteome, reflecting a plasma membrane-delineated action on dynamically palmitoylated proteins. Finally, ABD957 impaired N-Ras signaling and the growth of NRAS-mutant AML cells in a manner that synergizes with MAP kinase kinase (MEK) inhibition. Our findings uncover a surprisingly restricted role for ABHD17 enzymes as regulators of the N-Ras palmitoylation cycle and suggest that ABHD17 inhibitors may have value as targeted therapies for NRAS-mutant cancers.

A proximity-dependent biotinylation map of a human cell

Compartmentalization is a defining characteristic of eukaryotic cells, and partitions distinct biochemical processes into discrete subcellular locations. Microscopy¹ and biochemical fractionation coupled with mass spectrometry^2-4 have defined the proteomes of a variety of different organelles, but many intracellular compartments have remained refractory to such approaches. Proximity-dependent biotinylation techniques such as BioID provide an alternative approach to define the composition of cellular compartments in living cells^5-7. Here we present a BioID-based map of a human cell on the basis of 192 subcellular markers, and define the intracellular locations of 4,145 unique proteins in HEK293 cells. Our localization predictions exceed the specificity of previous approaches, and enabled the discovery of proteins at the interface between the mitochondrial outer membrane and the endoplasmic reticulum that are crucial for mitochondrial homeostasis. On the basis of this dataset, we created humancellmap.org as a community resource that provides online tools for localization analysis of user BioID data, and demonstrate how this resource can be used to understand BioID results better.

A patient-based medaka alg2 mutant as a model for hypo-N-glycosylation

Defects in the evolutionarily conserved protein-glycosylation machinery during embryonic development are often fatal. Consequently, congenital disorders of glycosylation (CDG) in human are rare. We modelled a putative hypomorphic mutation described in an alpha-1,3/1,6-mannosyltransferase (ALG2) index patient (ALG2-CDG) to address the developmental consequences in the teleost medaka (Oryzias latipes). We observed specific, multisystemic, late-onset phenotypes, closely resembling the patient's syndrome, prominently in the facial skeleton and in neuronal tissue. Molecularly, we detected reduced levels of N-glycans in medaka and in the patient's fibroblasts. This hypo-N-glycosylation prominently affected protein abundance. Proteins of the basic glycosylation and glycoprotein-processing machinery were over-represented in a compensatory response, highlighting the regulatory topology of the network. Proteins of the retinal phototransduction machinery, conversely, were massively under-represented in the alg2 model. These deficiencies relate to a specific failure to maintain rod photoreceptors, resulting in retinitis pigmentosa characterized by the progressive loss of these photoreceptors. Our work has explored only the tip of the iceberg of N-glycosylation-sensitive proteins, the function of which specifically impacts on cells, tissues and organs. Taking advantage of the well-described human mutation has allowed the complex interplay of N-glycosylated proteins and their contribution to development and disease to be addressed.

Subcellular proteomics

The eukaryotic cell is compartmentalized into subcellular niches, including membrane-bound and membrane-less organelles. Proteins localize to these niches to fulfil their function, enabling discreet biological processes to occur in synchrony. Dynamic movement of proteins between niches is essential for cellular processes such as signalling, growth, proliferation, motility and programmed cell death, and mutations causing aberrant protein localization are associated with a wide range of diseases. Determining the location of proteins in different cell states and cell types and how proteins relocalize following perturbation is important for understanding their functions, related cellular processes and pathologies associated with their mislocalization. In this Primer, we cover the major spatial proteomics methods for determining the location, distribution and abundance of proteins within subcellular structures. These technologies include fluorescent imaging, protein proximity labelling, organelle purification and cell-wide biochemical fractionation. We describe their workflows, data outputs and applications in exploring different cell biological scenarios, and discuss their main limitations. Finally, we describe emerging technologies and identify areas that require technological innovation to allow better characterization of the spatial proteome.

Exploring the dermotoxicity of the mycotoxin deoxynivalenol: combined morphologic and proteomic profiling of human epidermal cells reveals alteration of lipid biosynthesis machinery and membrane structural integrity relevant for skin barrier function

Deoxynivalenol (vomitoxin, DON) is a secondary metabolite produced by Fusarium spp. fungi and it is one of the most prevalent mycotoxins worldwide. Crop infestation results not only in food and feed contamination, but also in direct dermal exposure, especially during harvest and food processing. To investigate the potential dermotoxicity of DON, epidermoid squamous cell carcinoma cells A431 were compared to primary human neonatal keratinocytes (HEKn) cells via proteome/phosphoproteome profiling. In A431 cells, 10 µM DON significantly down-regulated ribosomal proteins, as well as mitochondrial respiratory chain elements (OXPHOS regulation) and transport proteins (TOMM22; TOMM40; TOMM70A). Mitochondrial impairment was reflected in altered metabolic competence, apparently combined with interference of the lipid biosynthesis machinery. Functional effects on the cell membrane were confirmed by live cell imaging and membrane fluidity assays (0.1–10 µM DON). Moreover, a common denominator for both A431 and HEKn cells was a significant downregulation of the squalene synthase (FDFT1). In sum, proteome alterations could be traced back to the transcription factor Klf4, a crucial regulator of skin barrier function. Overall, these results describe decisive molecular events sustaining the capability of DON to impair skin barrier function. Proteome data generated in the study are fully accessible via ProteomeXchange with the accession numbers PXD011474 and PXD013613.

Translational remodeling by RNA-binding proteins and noncoding RNAs

Responsible for generating the proteome that controls phenotype, translation is the ultimate convergence point for myriad upstream signals that influence gene expression. System-wide adaptive translational reprogramming has recently emerged as a pillar of cellular adaptation. As classic regulators of mRNA stability and translation efficiency, foundational studies established the concept of collaboration and competition between RNA-binding proteins (RBPs) and noncoding RNAs (ncRNAs) on individual mRNAs. Fresh conceptual innovations now highlight stress-activated, evolutionarily conserved RBP networks and ncRNAs that increase the translation efficiency of populations of transcripts encoding proteins that participate in a common cellular process. The discovery of post-transcriptional functions for long noncoding RNAs (lncRNAs) was particularly intriguing given their cell-type-specificity and historical definition as nuclear-functioning epigenetic regulators. The convergence of RBPs, lncRNAs, and microRNAs on functionally related mRNAs to enable adaptive protein synthesis is a newer biological paradigm that highlights their role as "translatome (protein output) remodelers" and reinvigorates the paradigm of "RNA operons." Together, these concepts modernize our understanding of cellular stress adaptation and strategies for therapeutic development. This article is categorized under: RNA Interactions with Proteins and Other Molecules > Protein-RNA Interactions: Functional Implications Translation > Translation Regulation Regulatory RNAs/RNAi/Riboswitches > Regulatory RNAs.

The Vital Role of Proteomics in Characterizing Novel Protein Degraders

Mass spectrometry-based proteomics profiling is a discovery tool that enables researchers to understand the mechanisms of action of drug candidates. When applied to proteolysis targeting chimeras (PROTACs) such approaches provide unbiased perspectives of the binding, degradation selectivity, and mechanism related to efficacy and safety. Specifically, global profiling experiments can identify direct degradation events and assess downstream pathway modulation that may result from degradation or off-target inhibition. Targeted proteomics approaches can be used to quantify the levels of relevant E3 ligases and the protein of interest in cell lines and tissues of interest, which can inform the line of sight and provide insights on possible safety liabilities early in the project. Furthermore, proteomics approaches can be applied to understand protein turnover and resynthesis rates and inform on target tractability, as well as pharmacokinetics/pharmacodynamics understanding. In this perspective, we survey the literature around the impact of mass spectrometry-based proteomics in the development of PROTACs and present our envisioned proteomics cascade for supporting targeted protein degradation projects.

Improved Proteomics-Based Drug Mechanism-of-Action Studies Using 16-Plex Isobaric Mass Tags

Multiplexed quantitative proteomics enabled complex workflows to study the mechanisms by which small molecule drugs interact with the proteome such as thermal proteome profiling (TPP) or multiplexed proteome dynamics profiling (mPDP). TPP measures changes in protein thermal stability in response to drug treatment and thus informs on direct targets and downstream regulation events, while the mPDP approach enables the discovery of regulated protein synthesis and degradation events caused by small molecules and other perturbations. The isobaric mass tags available for multiplexed proteomics have thus far limited the efficiency and sensitivity by which such experiments could be performed. Here we evaluate a recent generation of 16-plex isobaric mass tags and demonstrate the sensitive and time efficient identification of Staurosporine targets in HepG2 cell extracts by recording full thermal denaturation/aggregation profiles of vehicle and compound treated samples in a single mass spectrometry experiment. In 2D-TPP experiments, isothermal titration over seven concentrations per temperature enabled comprehensive selectivity profiling of Staurosporine with EC₅₀ values for kinase targets tightly matching to the kinobeads gold standard assay. Finally, we demonstrate time and condition-based multiplexing of dynamic SILAC labeling experiments to delineate proteome-wide effects of the molecular glue Indisulam on synthesis and degradation rates.

The IMiD target CRBN determines HSP90 activity toward transmembrane proteins essential in multiple myeloma

The complex architecture of transmembrane proteins requires quality control (QC) of folding, membrane positioning, and trafficking as prerequisites for cellular homeostasis and intercellular communication. However, it has remained unclear whether transmembrane protein-specific QC hubs exist. Here we identify cereblon (CRBN), the target of immunomodulatory drugs (IMiDs), as a co-chaperone that specifically determines chaperone activity of HSP90 toward transmembrane proteins by means of counteracting AHA1. This function is abrogated by IMiDs, which disrupt the interaction of CRBN with HSP90. Among the multiple transmembrane protein clients of CRBN-AHA1-HSP90 revealed by cell surface proteomics, we identify the amino acid transporter LAT1/CD98hc as a determinant of IMiD activity in multiple myeloma (MM) and present an Anticalin-based CD98hc radiopharmaceutical for MM radio-theranostics. These data establish the CRBN-AHA1-HSP90 axis in the biogenesis of transmembrane proteins, link IMiD activity to tumor metabolism, and nominate CD98hc and LAT1 as attractive diagnostic and therapeutic targets in MM.

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CRBN functions as a transmembrane protein-specific co-chaperone of HSP90

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Disruption of CRBN-HSP90 interaction determines the anti-tumor activity of IMiDs

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The CD98hc/LAT1 complex is a central target of IMiDs in multiple myeloma

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CD98hc-Anticalin is a theranostic tool in multiple myeloma

Isoform-resolved correlation analysis between mRNA abundance regulation and protein level degradation

Profiling of biological relationships between different molecular layers dissects regulatory mechanisms that ultimately determine cellular function. To thoroughly assess the role of protein post‐translational turnover, we devised a strategy combining pulse stable isotope‐labeled amino acids in cells (pSILAC), data‐independent acquisition mass spectrometry (DIA‐MS), and a novel data analysis framework that resolves protein degradation rate on the level of mRNA alternative splicing isoforms and isoform groups. We demonstrated our approach by the genome‐wide correlation analysis between mRNA amounts and protein degradation across different strains of HeLa cells that harbor a high grade of gene dosage variation. The dataset revealed that specific biological processes, cellular organelles, spatial compartments of organelles, and individual protein isoforms of the same genes could have distinctive degradation rate. The protein degradation diversity thus dissects the corresponding buffering or concerting protein turnover control across cancer cell lines. The data further indicate that specific mRNA splicing events such as intron retention significantly impact the protein abundance levels. Our findings support the tight association between transcriptome variability and proteostasis and provide a methodological foundation for studying functional protein degradation.

Data-independent acquisition-based proteome and phosphoproteome profiling across six melanoma cell lines reveals determinants of proteotypes

Human cancer cell lines are widely used in pharmacological and systems biological studies. The rapid documentation of the steady-state gene expression landscape of the cells used in a particular experiment may help to improve the reproducibility of scientific research. Here we applied a data-independent acquisition mass spectrometry (DIA-MS) method, coupled with a peptide spectral-library-free data analysis workflow, to measure both the proteome and phosphoproteome of a melanoma cell line panel with different metastatic properties. For each cell line, the single-shot DIA-MS detected 8100 proteins and almost 40 000 phosphopeptides in the respective measurements of two hours. Benchmarking the DIA-MS data towards the RNA-seq data and tandem mass tag (TMT)-MS results from the same set of cell lines demonstrated comparable qualitative coverage and quantitative reproducibility. Our data confirmed the high but complex mRNA-protein and protein-phospsite correlations. The results successfully established DIA-MS as a strong and competitive proteotyping approach for cell lines. The data further showed that all subunits of the glycosylphosphatidylinositol (GPI)-anchor transamidase complex were overexpressed in metastatic melanoma cells and identified altered phosphoprotein modules such as the BAF complex and mRNA splicing between metastatic and primary cells. This study provides a high-quality resource for calibrating DIA-MS performance, benchmarking DIA bioinformatic algorithms, and exploring the metastatic proteotypes in melanoma cells.