Affinity Purification Followed By Mass Spectrometry Research Articles

In vivo cross-linking-based affinity purification and mass spectrometry for targeting intracellular protein-protein interactions

Comprehensive interactome analysis of targeted proteins is important to understand how proteins work together in regulating functions. Commonly, affinity purification followed by mass spectrometry (AP-MS) has been recognized as the most often used technique for studying protein-protein interactions (PPIs). However, some proteins with weak interactions, which are responsible for key roles in regulation, are easily broken during cell lysis and purification through an AP approach. Herein, we have developed an approach termed in vivo cross-linking-based affinity purification and mass spectrometry (ICAP-MS). By this method, in vivo cross-linking was introduced to covalently fix intracellular PPIs in their functional states to assure all PPIs could be integrally maintained during cell disruption. In addition, the chemically cleavable crosslinkers which were employed enabled unbinding of PPIs for in-depth identification of components within the interactome and biological analysis, while allowing binding of PPIs for cross-linking-mass spectrometry (CXMS)-based direct interaction determination. Multi-level information on targeted PPIs network can be obtained by ICAP-MS, including composition of interacting proteins, as well as direct interacting partners and binding sites. As a proof of concept, the interactome of MAPK3 from 293A cells was profiled with 6.15-fold improvement in identification than by conventional AP-MS. Meanwhile, 184 cross-link site pairs of these PPIs were experimentally identified by CXMS. Furthermore, ICAP-MS was applied in the temporal profiling of MAPK3 interactions under activation by cAMP-mediated pathway. The regulatory manner of MAPK pathways was presented through the quantitative changes of MAPK3 and its interacting proteins at different time points after activation. Therefore, all reported results demonstrated that the ICAP-MS approach may provide comprehensive information on interactome of targeted protein for functional exploration.

Proteomic analysis identifies novel binding partners of BAP1.

BRCA1-associated protein 1 (BAP1) is a tumor suppressor and its loss can result in mesothelioma, uveal and cutaneous melanoma, clear cell renal cell carcinoma and bladder cancer. BAP1 is a deubiquitinating enzyme of the UCH class that has been implicated in various cellular processes like cell growth, cell cycle progression, ferroptosis, DNA damage response and ER metabolic stress response. ASXL proteins activate BAP1 by forming the polycomb repressive deubiquitinase (PR-DUB) complex which acts on H2AK119ub1. Besides the ASXL proteins, BAP1 is known to interact with an established set of additional proteins. Here, we identify novel BAP1 interacting proteins in the cytoplasm by expressing GFP-tagged BAP1 in an endogenous BAP1 deficient cell line using affinity purification followed by mass spectrometry (AP-MS) analysis. Among these novel interacting proteins are Histone acetyltransferase 1 (HAT1) and all subunits of the heptameric coat protein complex I (COPI) that is involved in vesicle formation and protein cargo binding and sorting. We validate that the HAT1 and COPI interactions occur at endogenous levels but find that this interaction with COPI is not mediated through the C-terminal KxKxx cargo sorting signals of the COPI complex.

Stable and EGF-Induced Temporal Interactome Profiling of CBL and CBLB Highlights Their Signaling Complex Diversity.

The epidermal growth factor receptor (EGFR) signal modulates cell proliferation, migration, and survival. Aberrant activation of EGFR constitutes the major cause of various cancers. Receptor ubiquitination and degradation mediated by CBL proteins play negative regulatory roles and control the intensity and duration of the signaling. With the construction of stable cell lines inducibly expressing FLAG-tagged CBL or CBLB, we identified 102 and 82 stable interacting proteins of CBL and CBLB, respectively, through the affinity purification followed by mass spectrometry (AP-MS) approach. Time-resolved profiling at six different time points combined with functional annotations of the temporal interactomes provides insights into the dynamic assembly of signal proteins upon EGFR signaling activation. Comparison between the interactomes of CBL and CBLB indicates their redundant but also complementary functions. Importantly, we validated the stable association of EPS15L1 and ITSN2 and temporal association of TNK2 to both CBL and CBLB through biochemical assays. Collectively, these results offer a useful resource for CBL and CBLB interactomes and highlight their prominent and diverse roles in the EGFR signaling network.

Abstract 223: Using physical, genetic and integrated cancer cell maps to investigate head and neck cancer and breast cancer

Abstract The Cancer Genome Atlas and similar projects have now analyzed over 10,000 tumor genomes, providing a catalog of the gene mutations, copy number variants and other genetic alterations that are present cancer. In many cases though, it remains unclear which are the key driver mutations or dependencies in a given cancer and how these influence pathogenesis and response to therapy. Although tumors of similar types and clinical outcomes can have patterns of mutations that are strikingly different, it is becoming apparent that these mutations recurrently hijack the same hallmark molecular pathways and networks. For this reason, cancer research and treatment is increasingly dependent on knowledge of biological networks of multiple types, including physical interactions among proteins and both synthetic-lethal and epistatic interactions among genes. Founded in 2015 by labs at UC San Diego and UCSF, the mission of the Cancer Cell Map Initiative (CCMI) is to enable a new era of cancer discovery and treatment based on the complete elucidation of the molecular networks underlying cancer. We believe that this information will be critical for developing computational models of cancer cells that will enable both basic research and clinical decision-making. Our initial research efforts are focused on head and neck cancer and breast cancer but we believe that our approach is widely applicable. Here, I will discuss results from three of the CCMI's main research efforts. One major focus is using affinity purification followed by mass spectrometry (AP/MS) to map protein-protein interactions of frequently mutated genes in multiple, relevant cell culture models. These efforts for approximately 40 genes in both head and neck cancer and breast cancer have largely been completed leading to a range of validation and follow-up studies. To complement these context-specific structural maps, a second main research effort is to generate functional maps by measuring genetic interactions using CRISPR/Cas9 in many of the same cell culture models. Many of these screens have now been completed with data analysis ongoing. Along with these two experimental efforts, a third focus has been to use bioinformatics approaches to assemble a hierarchical map of biological subsystems in cancer, to identify which systems in which cohorts are mutated more often than expected by chance and to explore these potentially driver systems in more detail through a range of experimental studies. To share these and other network maps, the CCMI's bioinformatics core has created the network data exchange (NDEx, ndexbio.org), an open-source framework where scientists and organizations can share, store, manipulate and publish biological network knowledge. Citation Format: Jason F. Kreisberg, Fan Zheng, Samson Fong, Brent M. Kuenzi, Kyle Ford, Danielle Swaney, Minkyu Kim, Jisoo Park, Zhiyong Wang, Dexter Pratt, Natalia Jura, Silvio Gutkind, Prashant Mali, Nevan Krogan, Trey Ideker. Using physical, genetic and integrated cancer cell maps to investigate head and neck cancer and breast cancer [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 223.

Identification of Tomato Proteins That Interact With Replication Initiator Protein (Rep) of the Geminivirus TYLCV

Geminiviruses are plant-infecting DNA viruses that reshape the intracellular environment of their host in order to create favorable conditions for viral replication and propagation. Viral manipulation is largely mediated via interactions between viral and host proteins. Identification of this protein network helps us to understand how these viruses manipulate their host and therefore provides us potentially with novel leads for resistance against this class of pathogens, as genetic variation in the corresponding plant genes could subvert viral manipulation. Different studies have already yielded a list of host proteins that interact with one of the geminiviral proteins. Here, we use affinity purification followed by mass spectrometry (AP-MS) to further expand this list of interacting proteins, focusing on an important host (tomato) and the Replication initiator protein (Rep, AL1, C1) from Tomato yellow leaf curl virus (TYLCV). Rep is the only geminiviral protein proven to be essential for geminiviral replication and it forms an integral part of viral replisomes, a protein complex that consists of plant and viral proteins that allows for viral DNA replication. Using AP-MS, fifty-four ‘high confidence’ tomato proteins were identified that specifically co-purified with Rep. For two of them, an unknown EWS-like RNA-binding protein (called Geminivirus Rep interacting EWS-like protein 1 or GRIEP1) and an isoform of the THO complex subunit 4A (ALY1), we were able to confirm this interaction with Rep in planta using a second method, bimolecular fluorescence complementation (BiFC). The THO subunit 4 is part of the THO/TREX (TRanscription-EXport) complex, which controls RNA splicing and nuclear export of mRNA to the cytoplasm and is also connected to plant disease resistance. This work represents the first step towards characterization of novel host factors with a putative role in the life cycle of TYLCV and possibly other geminiviruses.

The N-Terminal CCHC Zinc Finger Motif Mediates Homodimerization of Transcription Factor BCL11B.

The BCL11B gene encodes a Krüppel-like, sequence-specific zinc finger (ZF) transcription factor that acts as either a repressor or an activator, depending on its posttranslational modifications. The importance of BCL11B in numerous biological processes in multiple organs has been well established in mouse knockout models. The phenotype of the first de novo monoallelic germ line missense mutation in the BCL11B gene (encoding N441K) strongly implies that the mutant protein acts in a dominant-negative manner by neutralizing the unaffected protein through the formation of a nonfunctional dimer. Using a Förster resonance energy transfer-assisted fluorescence-activated cell sorting (FACS-FRET) assay and affinity purification followed by mass spectrometry (AP-MS), we show that the N-terminal CCHC zinc finger motif is necessary and sufficient for the formation of the BCL11B dimer. Mutation of the CCHC ZF in BCL11B abolishes its transcription-regulatory activity. In addition, unlike wild-type BCL11B, this mutant is incapable of inducing cell cycle arrest and protecting against DNA damage-driven apoptosis. Our results confirm the BCL11B dimerization hypothesis and prove its importance for BCL11B function. By mapping the relevant regions to the CCHC domain, we describe a previously unidentified mechanism of transcription factor homodimerization.

A tissue-specific protein purification approach in Caenorhabditis elegans identifies novel interaction partners of DLG-1/Discs large.

BackgroundAffinity purification followed by mass spectrometry (AP/MS) is a widely used approach to identify protein interactions and complexes. In multicellular organisms, the accurate identification of protein complexes by AP/MS is complicated by the potential heterogeneity of complexes in different tissues. Here, we present an in vivo biotinylation-based approach for the tissue-specific purification of protein complexes from Caenorhabditis elegans. Tissue-specific biotinylation is achieved by the expression in select tissues of the bacterial biotin ligase BirA, which biotinylates proteins tagged with the Avi peptide.ResultsWe generated N- and C-terminal tags combining GFP with the Avi peptide sequence, as well as four BirA driver lines expressing BirA ubiquitously and specifically in the seam and hyp7 epidermal cells, intestine, or neurons. We validated the ability of our approach to identify bona fide protein interactions by identifying the known LGL-1 interaction partners PAR-6 and PKC-3. Purification of the Discs large protein DLG-1 identified several candidate interaction partners, including the AAA-type ATPase ATAD-3 and the uncharacterized protein MAPH-1.1. We have identified the domains that mediate the DLG-1/ATAD-3 interaction, and show that this interaction contributes to C. elegans development. MAPH-1.1 co-purified specifically with DLG-1 purified from neurons, and shared limited homology with the microtubule-associated protein MAP1A, a known neuronal interaction partner of mammalian DLG4/PSD95. A CRISPR/Cas9-engineered GFP::MAPH-1.1 fusion was broadly expressed and co-localized with microtubules.ConclusionsThe method we present here is able to purify protein complexes from specific tissues. We uncovered a series of DLG-1 interactors, and conclude that ATAD-3 is a biologically relevant interaction partner of DLG-1. Finally, we conclude that MAPH-1.1 is a microtubule-associated protein of the MAP1 family and a candidate neuron-specific interaction partner of DLG-1.Electronic supplementary materialThe online version of this article (doi:10.1186/s12915-016-0286-x) contains supplementary material, which is available to authorized users.

A Scalable Genome-Editing-Based Approach for Mapping Multiprotein Complexes in Human Cells.

Conventional affinity purification followed by mass spectrometry (AP-MS) analysis is a broadly applicable method used to decipher molecular interaction networks and infer protein function. However, it is sensitive to perturbations induced by ectopically overexpressed target proteins and does not reflect multilevel physiological regulation in response to diverse stimuli. Here, we developed an interface between genome editing and proteomics to isolate native protein complexes produced from their natural genomic contexts. We used CRISPR/Cas9 and TAL effector nucleases (TALENs) to tag endogenous genes and purified several DNA repair and chromatin-modifying holoenzymes to near homogeneity. We uncovered subunits and interactions among well-characterized complexes and report the isolation of MCM8/9, highlighting the efficiency and robustness of the approach. These methods improve and simplify both small- and large-scale explorations of protein interactions as well as the study of biochemical activities and structure-function relationships.

Affinity‐purification coupled to mass spectrometry: Basic principles and strategies

Identifying the interactions established by a protein of interest can be a critical step in understanding its function. This is especially true when an unknown protein of interest is demonstrated to physically interact with proteins of known function. While many techniques have been developed to characterize protein-protein interactions, one strategy that has gained considerable momentum over the past decade for identification and quantification of protein-protein interactions, is affinity-purification followed by mass spectrometry (AP-MS). Here, we briefly review the basic principles used in affinity-purification coupled to mass spectrometry, with an emphasis on tools (both biochemical and computational), which enable the discovery and reporting of high quality protein-protein interactions.

Analysis of protein complexes through model‐based biclustering of label‐free quantitative AP‐MS data

Affinity purification followed by mass spectrometry (AP-MS) has become a common approach for identifying protein–protein interactions (PPIs) and complexes. However, data analysis and visualization often rely on generic approaches that do not take advantage of the quantitative nature of AP-MS. We present a novel computational method, nested clustering, for biclustering of label-free quantitative AP-MS data. Our approach forms bait clusters based on the similarity of quantitative interaction profiles and identifies submatrices of prey proteins showing consistent quantitative association within bait clusters. In doing so, nested clustering effectively addresses the problem of overrepresentation of interactions involving baits proteins as compared with proteins only identified as preys. The method does not require specification of the number of bait clusters, which is an advantage against existing model-based clustering methods. We illustrate the performance of the algorithm using two published intermediate scale human PPI data sets, which are representative of the AP-MS data generated from mammalian cells. We also discuss general challenges of analyzing and interpreting clustering results in the context of AP-MS data.

A Novel Scoring Approach for Protein Co-Purification Data Reveals High Interaction Specificity

Large-scale protein interaction networks (PINs) have typically been discerned using affinity purification followed by mass spectrometry (AP/MS) and yeast two-hybrid (Y2H) techniques. It is generally recognized that Y2H screens detect direct binary interactions while the AP/MS method captures co-complex associations; however, the latter technique is known to yield prevalent false positives arising from a number of effects, including abundance. We describe a novel approach to compute the propensity for two proteins to co-purify in an AP/MS data set, thereby allowing us to assess the detected level of interaction specificity by analyzing the corresponding distribution of interaction scores. We find that two recent AP/MS data sets of yeast contain enrichments of specific, or high-scoring, associations as compared to commensurate random profiles, and that curated, direct physical interactions in two prominent data bases have consistently high scores. Our scored interaction data sets are generally more comprehensive than those of previous studies when compared against four diverse, high-quality reference sets. Furthermore, we find that our scored data sets are more enriched with curated, direct physical associations than Y2H sets. A high-confidence protein interaction network (PIN) derived from the AP/MS data is revealed to be highly modular, and we show that this topology is not the result of misrepresenting indirect associations as direct interactions. In fact, we propose that the modularity in Y2H data sets may be underrepresented, as they contain indirect associations that are significantly enriched with false negatives. The AP/MS PIN is also found to contain significant assortative mixing; however, in line with a previous study we confirm that Y2H interaction data show weak disassortativeness, thus revealing more clearly the distinctive natures of the interaction detection methods. We expect that our scored yeast data sets are ideal for further biological discovery and that our scoring system will prove useful for other AP/MS data sets.

High-Quality Binary Protein Interaction Map of the Yeast Interactome Network

Current yeast interactome network maps contain several hundred molecular complexes with limited and somewhat controversial representation of direct binary interactions. We carried out a comparative quality assessment of current yeast interactome data sets, demonstrating that high-throughput yeast two-hybrid (Y2H) screening provides high-quality binary interaction information. Because a large fraction of the yeast binary interactome remains to be mapped, we developed an empirically controlled mapping framework to produce a "second-generation" high-quality, high-throughput Y2H data set covering approximately 20% of all yeast binary interactions. Both Y2H and affinity purification followed by mass spectrometry (AP/MS) data are of equally high quality but of a fundamentally different and complementary nature, resulting in networks with different topological and biological properties. Compared to co-complex interactome models, this binary map is enriched for transient signaling interactions and intercomplex connections with a highly significant clustering between essential proteins. Rather than correlating with essentiality, protein connectivity correlates with genetic pleiotropy.