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NF2 (Neurofibromine 2, merlin) is frequently inactivated in cancer, where its NF2 tumor suppressor functionality is tightly coupled to protein conformation. How NF2 conformation is regulated and how NF2 conformation influences tumor suppressor activity is a largely open question. Here we systematically characterized three NF2 conformation-dependent protein interactions utilizing deep mutational scanning interaction perturbation analyses. We identified two regions in NF2 with clustered mutations which affected conformation dependent protein interactions. NF2 variants in the F3 subdomain and the α3H helix region substantially modulated NF2 conformation and homomerization. Mutations in the F3 subdomain altered proliferation in three cell lines and matched patterns of disease mutations in neurofibromatosis. This study highlights the power of systematic mutational interaction perturbation analysis to identify missense variants impacting NF2 conformation and provides insight into NF2 tumor suppressor function.

Colorectal cancer (CRC) is currently the second most common cause of cancer related mortality in the world. One of the hallmarks of CRC is its complex mutational landscape that contributes to the derivation of neoantigens which aid the immunosurveillance of the cancer. Immunopeptides play an essential role in adaptive immunity by activating and ensuring T-cell specificity. Mass spectrometry (MS) is currently the only technology that can measure and identify the immunopeptide profiles of biological samples at a large scale, however, MS-based studies are frequently limited by sample input and poor scalability. Here, we introduce a semi-automated workflow requiring low sample input to robustly identify immunopeptides and apply it to a cohort of fresh frozen, high quality CRC samples (Indivumed) for immunopeptide profiling and neoantigen identification. We initially optimized a workflow that allowed the native lysis and sequential immunoprecipitation of class-I and class-II immunopeptides while ensuring scalability and reproducibility. The immunopeptides were thereafter subjected to our True Discovery FAIMS-DIA LC-MS/MS platform which we supported through a specific DDA library and searched the resulting data with Spectronaut (Biognosys) by applying a 1% FDR threshold. We utilized data from WGS on both tumor and adjacent normal tissue for the calling of high-confidence somatic variations (Indivumed) and the definition of the resulting neoantigens. We characterized 131,578 unique immunopeptides from 15 mg of fresh-frozen tumor tissue across 20 patients with CRC lesions that mapped to 12,488 genes. On average we identified 9,767 class-I and 16,445 class-II immunopeptides from all patients indicating significant inter-patient heterogeneity. Overall, we found the identification numbers of class-II immunopeptides to be more variable than that of class-I which might be linked to immune infiltration levels. From our pool of identified immunopeptides, we were able to detect 16 class-I and 29 class-II neoantigens, covering 87% of the microsatellite instability-high (MSI-H) samples in the cohort. Coverage of both classes was essential to increase neoantigen discovery as 53% MSI-H samples had neoantigens mapping to a single class. Interestingly we also detected a significant correlation between the mutational burden and the number of detected neoantigens in our patient cohort suggesting that the genetic set up of the cancer might influence the repertoire of presented neoantigens thereby controlling immunosurveillance. In summary, we have established a scalable and efficient pipeline for cell line and tissue immunopeptidomics for both class-I and II immunopeptides. Our pipeline generates high-quality identifications and can be deployed to help shed light on immunopeptidomics heterogeneity through large-scale profiling of patients as exemplified in the case of MSI-H CRC. Citation Format: Ilja E. Shapiro, Luca Räss, Christopher R. Below, Marco Tognetti, Tikira Temu, Oliver M. Bernhardt, Roland Bruderer, Jonathan Woodsmith, Lukas Reiter. Complementarity of class I and II neoantigen mapping in MSI-high colorectal cancer in needle biopsy size tissue samples [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 2991.

Protein kinases play an important role in cellular signaling pathways and their dysregulation leads to multiple diseases, making kinases prime drug targets. While more than 500 human protein kinases are known to collectively mediate phosphorylation of over 290,000 S/T/Y sites, the activities have been characterized only for a minor, intensively studied subset. To systematically address this discrepancy, we developed a human kinase array in Saccharomyces cerevisiae as a simple readout tool to systematically assess kinase activities. For this array, we expressed 266 human kinases in four different Saccharomyces cerevisiae strains and profiled ectopic growth as a proxy for kinase activity across 33 conditions. More than half of the kinases showed an activity-dependent phenotype across many conditions and in more than one strain. We then employed the kinase array to identify the kinase(s) that can modulate protein-protein-interactions (PPIs). Two characterized, phosphorylation-dependent PPIs with unknown kinase-substrate relationships were analyzed in a phospho-yeast two-hybrid assay. CK2α1 and SGK2 kinases can abrogate the interaction between the spliceosomal proteins AAR2 and PRPF8 and NEK6 kinase was found to mediate the estrogen receptor (ERα) interaction with 14-3-3 proteins. The human kinase yeast array can thus be used for a variety of kinase activity-dependent readouts.

NRF2 (NFE2L2) is a cytoprotective transcription factor associated with >60 human diseases, adverse drug reactions and therapeutic resistance. To provide insight into the complex regulation of NRF2 responses, 1962 predicted NRF2-partner interactions were systematically tested to generate an experimentally defined high-density human NRF2 interactome. Verification and conditional stratification of 46 new NRF2 partners was achieved by co-immunoprecipitation and the novel integration of quantitative data from dual luminescence-based co-immunoprecipitation (DULIP) assays and live-cell fluorescence cross-correlation spectroscopy (FCCS). The functional impact of new partners was then assessed in genetically edited loss-of-function (NRF2−/−) and disease-related gain-of-function (NRF2T80K and KEAP1−/−) cell-lines. Of the new partners investigated >77% (17/22) modified NRF2 responses, including partners that only exhibited effects under disease-related conditions. This experimentally defined binary NRF2 interactome provides a new vision of the complex molecular networks that govern the modulation and consequence of NRF2 activity in health and disease., Graphical abstract Image 1, Highlights • 1,962 predicted NRF2 partners were tested and 46 new binary partners were identified. • 77% of tested new NRF2 partners modulate ARE-mediated gene expression. • Some novel partners only exhibit functional effects under disease-like conditions. • Quantitative evidence that KEAP1 can modulate ARE-transactivation independently of NRF2. • New NRF2 binding partners reveal 130 novel disease gene associations.

Dynamic trafficking of G protein-coupled receptors (GPCRs) out of cilia is mediated by the BBSome. In concert with its membrane recruitment factor, the small GTPase ARL6/BBS3, the BBSome ferries GPCRs across the transition zone, a diffusion barrier at the base of cilia. Here, we present the near-atomic structures of the BBSome by itself and in complex with ARL6GTP, and we describe the changes in BBSome conformation induced by ARL6GTP binding. Modeling the interactions of the BBSome with membranes and the GPCR Smoothened (SMO) reveals that SMO, and likely also other GPCR cargoes, must release their amphipathic helix 8 from the membrane to be recognized by the BBSome.

Author(s): Bahl, Kriti; Yang, Shuang; Chou, Hui-Ting; Woodsmith, Jonathan; Stelzl, Ulrich; Walz, Thomas; Nachury, Maxence | Abstract: ABSTRACT The BBSome is a complex of eight Bardet-Biedl Syndrome (BBS) proteins that removes signaling receptors from cilia. The GTPase ARL6/BBS3 recruits the BBSome to the ciliary membrane where the BBSome–ARL6 GTP complex ferries G protein-coupled receptors (GPCRs) across the transition zone, a diffusion barrier at the base of cilia. Here, we find that the BBSome undergoes a conformational change upon recruitment to membranes by ARL6 GTP . Modeling the binding of the BBSome to membranes and to the GPCR Smoothened (SMO) reveals that the amphipathic helix 8 of SMO must be released from the membrane for SMO to be recognized by the BBSome. Underscoring the functional importance of amphipathic helix extraction in TZ crossing, we find that exchanging the amphipathic helix of ARL6 for one that embeds deeper into the membrane blocks BBSome-mediated exit of GPCRs from cilia. We propose that the rigid curvature and dense lipid packing of the transition zone reject asymmetric insertions in the inner leaflet and that the BBSome licenses transition zone crossing by extracting bulky amphipathic helices from the inner leaflet.

3542 Background: Right (R) vs left (L) sided colorectal cancers are clinically distinguishable based on prognosis and response to certain therapies, but as of yet, limited data have emerged to explain these differences. The science of molecular testing has evolved rapidly. Enabled by improved technologies and computing power, it is now feasible to obtain to systematic multi-omic datasets covering DNA, RNA, proteins, phospho-proteins and metabolomics on large numbers of patients. Multi-omic analysis can further define disease specific subgroups but pre-analytic quality of the tissues (ischemia time) and comparison to normal tissue controls is paramount to optimize results. Methods: Following informed consent, 450 colorectal cancer primary tumors and paired normal tissues were collected following an SOP to minimize ischemia time, and were analyzed using comprehensive genomics, transcriptomics, proteomics, phosphoproteomics, morphology and annual clinical information. Right (C18.0,2,3) and left (C18.6,7) CRC tumors, normal tissue were compared using machine learning tools to unravel the molecular mechanisms that underpin these clinically distinguishable phenotypes as well as correlating with known genomic metrics such MSI and KRAS mutation status. Results: Through leveraging the tumor and paired normal patient samples, systematic differences between left and right tumor samples were observed including specific molecular events associated with these anatomical differences. The detailed results will be presented at the meeting. Conclusions: Progress in precision medicine requires the inclusion of multi-omics which in turn requires changes to our current SOPs of tissue collection. The ability to define molecular distinctions such as between R and L colon cancer will permit the rapid discovery of clinically useful prognostic and predictive markers, dramatically adding to our fundamental understanding to colon cancer biology. Future work will focus on the discovery of novel targets and signatures, creating innovative tools that depict multi-omic results for clinicians.

e15536 Background: The rise of precision oncology therapeutics requires deep understanding of the molecular mechanisms implicated in cancer biology. Colorectal cancer (CRC) is one of the first solid tumors to be molecularly characterized by defined genes and pathways. Advances in tumor profiling have revealed a profound molecular heterogeneity in CRC leading to the definition of several consensus molecular subtypes (CMS). However, this molecular heterogeneity is still largely defined on the genomic and transcriptomics level. To complement the understanding of genetically defined molecular subgroups, we performed large-scale deep proteomic and phospho-proteomic profiling of CRC patient biopsies and adjacent healthy control tissue, which has enabled to explore the phenotype and obtain more functional insights in cancer biology. Methods: Sample processing from 5-10 mg of tissue per sample was performed using a liquid handling robot. Phospho-peptide enrichment was carried out with a Kingfisher Flex device and MagReSyn Ti-IMAC magnetic beads. Data-Independent Acquisition (DIA) LC-MS/MS was performed on multiple platforms consisting of a Thermo Scientific Q Exactive HF-X mass spectrometer coupled to a Waters M-Class LC. Chromatography was operating at 5 µL/min, and separation was achieved using 45 min (whole proteome) and 60 min (phospho-proteome) gradients. Results: Indivumed has built IndivuType, the world’s first multi-omics database for individualized cancer therapy, analyzing the highest quality cancer biospecimens to generate the most comprehensive dataset, including genomics, transcriptomics, proteomics, and clinical outcome information. Enabled by the DIA technology, a mass spectrometric method developed by Biognosys that obtains peptide fragmentation data in a highly parallelized way with high sensitivity, more than 7,000 proteins in the whole proteome and 20,000 phospho-peptides in the phospho-proteome workflow were profiled across more than 900 resected tissue samples of various CMS of CRC. The resulting proteome and phospho-proteome data were integrated into the IndivuType database and cross-analyzed with genomic and transcriptomic markers. Through this combined analysis, novel insights in clinically relevant signaling pathways in CRC subtypes were revealed. Conclusions: The deep phenotypic profiling of cancer samples, using next generation proteomics and phospho-proteomics, has enabled us to go beyond the genomic level in the characterization of tumor molecular heterogeneity. This multi-omics approach provides a solid foundation to advance the understanding of cancer biology, unravel key molecular events, and support the identification of novel therapeutic targets for precision medicine in CRC.

High-density interaction mapping of mitochondrial proteins provides clues to molecular mechanisms implicated in the progression of neurological disorders.

Cancer is a highly heterogeneous disease, both intra- and inter-individually consisting of complex phenotypes and systems biology. Although genomic data has contributed greatly towards the identification of cancer-specific mutations and the progress of precision medicine, genomic alterations are only one of several important biological drivers of cancer. Furthermore, single-layer omics represent only a small piece of the cancer biology puzzle and provide only partial clues to connecting genotype with phenotype. A more integrated approach is urgently needed to unravel the underpinnings of molecular signatures and the phenotypic manifestation of cancer hallmarks. We have developed IndivuType, a knowledge and discovery platform that combines genomics, transcriptomics, and proteomics datasets to identify putative therapeutic targets in a colorectal cancer (CRC) cohort of 500 patients. We identified hundreds of proteins dysregulated in CRC that are dependent on the genetic background of the patient. Further, we characterized these proteins using clinical parameters from the IndivuType database to prioritize targets for precision medicine approaches. This project highlights the utility of moving beyond solely genomics approaches to better understand the molecular mechanisms underpinning cancer. Citation Format: Margarita Krawczyk, Jonathan Woodsmith, Marcel Nutsoa, Thomas Corwin, Silvia von der Heyde, Vincent Piras, Mark Whittaker, Alex Hergovich, Peter Frommolt, Hartmut Juhl. Proteogenomic characterization of colorectal cancer using the IndivuType multiomics database [abstract]. In: Proceedings of the Annual Meeting of the American Association for Cancer Research 2020; 2020 Apr 27-28 and Jun 22-24. Philadelphia (PA): AACR; Cancer Res 2020;80(16 Suppl):Abstract nr 6587.

e16063 Background: Cancer is a highly heterogeneous disease, both intra- and inter-individually consisting of complex phenotypes and systems biology. Although genomic data has contributed greatly towards the identification of cancer-specific mutations and the progress of precision medicine, genomic alterations are only one of several important biological drivers of cancer. Furthermore, single-layer omics represent only a small piece of the cancer biology puzzle and provide only partial clues to connecting genotype with clinically relevant phenotypic data. A more integrated approach is urgently needed to unravel the underpinnings of molecular signatures and the phenotypic manifestation of cancer hallmarks. Methods: Here we characterize a colorectal cancer (CRC) cohort of 500 patients across multiple distinct omic data types. Across this CRC cohort, we defined clinically relevant whole genome sequencing based metrics such as micro-satellite-instability (MSI) status, and furthermore investigate gene expression at the transcript level using RNA-Seq, as well as at the proteomic level using tandem mass spectrometry. We further characterized a subgroup of 100 of these patients through 16s rRNA sequencing to identify associated microbiome profiles. Results: We combined these analyses with comprehensive clinical data to observe the impact of ascertained molecular signatures on the CRC patient cohort. Here, we report how patient survival correlates both with specific molecular events across individual omic data types, as well as with combined multi-omic analyses. Conclusions: This project highlights the utility of integrating multiple distinct data types to obtain a more comprehensive overview of the molecular mechanisms underpinning colo-rectal cancer. Furthermore, through combining identified aberrant molecular mechanisms with clinical reports, multi-omic data can be prioritized through their impact on patient cohort survival.

representative structures of all domain models that were used when modeling the domains of the BBsome. Structures are built using Rosetta abinitio, or Rosetta CM.

Extensively modifiable arrays of clustered arginines in a large set of human proteins function as regulatory protein interaction platforms. Quantitative immunoprecipitation assays defined two distinct cumulative binding mechanisms operating across an array of 18 methyl-arginine motifs in SYNCRIP., Systematic analysis of human arginine methylation identifies two distinct signaling modes; either isolated modifications akin to canonical post-translational modification regulation, or clustered arrays within disordered protein sequence. Hundreds of proteins contain these methyl-arginine arrays and are more prone to accumulate mutations and more tightly expression-regulated than dispersed methylation targets. Arginines within an array in the highly methylated RNA-binding protein synaptotagmin binding cytoplasmic RNA interacting protein (SYNCRIP) were experimentally shown to function in concert, providing a tunable protein interaction interface. Quantitative immunoprecipitation assays defined two distinct cumulative binding mechanisms operating across 18 proximal arginine–glycine (RG) motifs in SYNCRIP. Functional binding to the methyltransferase PRMT1 was promoted by continual arginine stretches, whereas interaction with the methyl-binding protein SMN1 was arginine content–dependent irrespective of linear position within the unstructured region. This study highlights how highly repetitive modifiable amino acid arrays in low structural complexity regions can provide regulatory platforms, with SYNCRIP as an extreme example how arginine methylation leverages these disordered sequences to mediate cellular interactions.

The identification of genomic variants in healthy and diseased individuals continues to rapidly outpace our ability to functionally annotate these variants. Techniques that both systematically assay the functional consequences of nucleotide-resolution variation and can scale to hundreds of genes are urgently required. We designed a sensitive yeast two-hybrid-based 'off switch' for positive selection of interaction-disruptive variants from complex genetic libraries. Combined with massively parallel programmed mutagenesis and a sequencing readout, this method enables systematic profiling of protein-interaction determinants at amino-acid resolution. We defined >1,000 interaction-disrupting amino acid mutations across eight subunits of the BBSome, the major human cilia protein complex associated with the pleiotropic genetic disorder Bardet–Biedl syndrome. These high-resolution interaction-perturbation profiles provide a framework for interpreting patient-derived mutations across the entire protein complex and thus highlight how the impact of disease variation on interactome networks can be systematically assessed.

The unique membrane composition of cilia is maintained by a diffusion barrier at the transition zone that is breached when the BBSome escorts signaling receptors out of cilia. Understanding how the BBSome removes proteins from cilia has been hampered by a lack of structural information. Here, we present a nearly complete Cα model of BBSome purified from cow retina. The model is based on a single-particle cryo-electron microscopy density map at 4.9-Å resolution that was interpreted with the help of comprehensive Rosetta-based structural modeling constrained by crosslinking mass spectrometry data. We find that BBSome subunits have a very high degree of interconnectivity, explaining the obligate nature of the complex. Furthermore, like other coat adaptors, the BBSome exists in an autoinhibited state in solution and must thus undergo a conformational change upon recruitment to membranes by the small GTPase ARL6/BBS3. Our model provides the first detailed view of the machinery enabling ciliary exit.

Normal cellular functioning is maintained by macromolecular machines that control both core and specialized molecular tasks. These machines are in large part multi-subunit protein complexes that undergo regulation at multiple levels, from expression of requisite components to a vast array of post-translational modifications (PTMs). PTMs such as phosphorylation, ubiquitination, and acetylation currently number more than 200,000 in the human proteome and function within all molecular pathways. Here we provide a framework for systematically studying these PTMs in the context of global protein-protein interaction networks. This analytical framework allows insight into which functions specific PTMs tend to cluster in, and furthermore which complexes either single or multiple PTM signaling pathways converge on.

Normal cellular functioning is maintained by macromolecular machines that control both core and specialized molecular tasks. These machines are in large part multi-subunit protein complexes that undergo regulation at multiple levels, from expression of requisite components to a vast array of post-translational modifications (PTMs). PTMs such as phosphorylation, ubiquitination, and acetylation currently number more than 200,000 in the human proteome and function within all molecular pathways. Here we provide a framework for systematically studying these PTMs in the context of global protein-protein interaction networks. This analytical framework allows insight into which functions specific PTMs tend to cluster in, and furthermore which complexes either single or multiple PTM signaling pathways converge on.

At least 46 interactome studies, broad at proteome scale or biologically more focused, have together mapped about 75,000 human protein-protein interactions (PPIs). Many of the studies addressed local interactome data paucity analyzing specific homeostatic and regulatory systems, with recent focus demonstrating the involvement of post-translational protein modification (PTM) enzyme families in a wide range of cellular functions. These datasets provided insight into binding mechanisms, the dynamic modularity of complexes or delineated combinatorial enzymatic cascades. Furthermore, the combined study of PPI and PTM dynamics has begun to reveal conditional rewiring of molecular networks through PTM-mediated recognition events. Taken together these studies highlight the utility of local and global interaction networks to functionally prioritize the many changing PTMs mapped in human cells.

To accelerate high-density interactome mapping, we developed a yeast two-hybrid interaction screening approach involving short-read second-generation sequencing (Y2H-seq) with improved sensitivity and a quantitative scoring readout allowing rapid interaction validation. We applied Y2H-seq to investigate enzymes involved in protein methylation, a largely unexplored post-translational modification. The reported network of 523 interactions involving 22 methyltransferases or demethylases is comprehensively annotated and validated through coimmunoprecipitation experiments and defines previously undiscovered cellular roles of nonhistone protein methylation.

In eukaryotic cells the stability and function of many proteins are regulated by the addition of ubiquitin or ubiquitin-like peptides. This process is dependent upon the sequential action of an E1-activating enzyme, an E2-conjugating enzyme, and an E3 ligase. Different combinations of these proteins confer substrate specificity and the form of protein modification. However, combinatorial preferences within ubiquitination networks remain unclear. In this study, yeast two-hybrid (Y2H) screens were combined with true homology modeling methods to generate a high-density map of human E2/E3-RING interactions. These data include 535 experimentally defined novel E2/E3-RING interactions and >1300 E2/E3-RING pairs with more favorable predicted free-energy values than the canonical UBE2L3–CBL complex. The significance of Y2H predictions was assessed by both mutagenesis and functional assays. Significantly, 74/80 (>92%) of Y2H predicted complexes were disrupted by point mutations that inhibit verified E2/E3-RING interactions, and a ∼93% correlation was observed between Y2H data and the functional activity of E2/E3-RING complexes in vitro. Analysis of the high-density human E2/E3-RING network reveals complex combinatorial interactions and a strong potential for functional redundancy, especially within E2 families that have undergone evolutionary expansion. Finally, a one-step extended human E2/E3-RING network, containing 2644 proteins and 5087 edges, was assembled to provide a resource for future functional investigations.

The intracellular activation of trypsinogen, which is both pH- and calcium-dependent, is an important early step in the development of acute pancreatitis. The cellular compartment in which trypsinogen activation occurs currently is unknown. We therefore investigated the site of intracellular trypsinogen activation by using an established cellular model of acute pancreatitis: supramaximal stimulation of pancreatic acinar cells with cholecystokinin. We used fluorescent dextrans as fluid phase tracers and observed the cholecystokinin-elicited formation and translocation of large endocytic vacuoles. The fluorescent probe rhodamine 110 bis-(CBZ- l -isoleucyl- l -prolyl- l -arginine amide) dihydrochloride (BZiPAR) was used to detect trypsinogen activation. Fluid phase tracers were colocalized with cleaved BZiPAR, indicating that trypsinogen activation occurred within endocytic vacuoles. The development of BZiPAR fluorescence was inhibited by the trypsin inhibitor benzamidine. Fluorescein dextran and Oregon Green 488 BAPTA-5N were used to measure endosomal pH and calcium, respectively. The pH in endocytic vacuoles was 5.9 ± 0.1, and the calcium ion concentration was 37 ± 11 μM. The caged calcium probe o -nitrophenyl EGTA and UV uncaging were used to increase calcium in endocytic vacuoles. This increase of calcium caused by calcium uncaging was followed by recovery to the prestimulated level within ≈100 s. We propose that the initiation of acute pancreatitis depends on endocytic vacuole formation and trypsinogen activation in this compartment.

Ubiquitination controls the stability or function of many human proteins, thereby regulating a wide range of physiological processes. In most cases the combinatorial pattern of protein interactions that facilitate substrate recognition or modification remain unclear. Moreover, the efficiency of ubiquitination reactions can be altered by the formation of homo- and heterotypic E3-RING complexes. To establish the prevalence and nature of binary E3-RING/E3-RING interactions systematic yeast two-hybrid screens were performed to test 7269 potential interactions between 124 human E3-RING proteins. These studies identified 228 dimeric interactions between 100 E3-RINGs, of which 205 were novel. Complementary co-immunoprecipitation studies were performed to test predicted network interactions, showing a high correlation (64%) with primary yeast two-hybrid data. This data was integrated with known E3-RING interactions, tissue expression profiles and proteomic ubiquitination datasets to facilitate identification of subnetworks in which E3-RING dimerization events have the potential to alter network structure. These results reveal a widespread yet selective pattern of E3-RING dimerization events, which have the potential to confer further combinatorial complexity within human ubiquitination cascades.