Characterizing Protein–Protein Interactions Using Mass Spectrometry: Challenges and Opportunities

Advances in crosslinking chemistry and proximity-enabled strategies: deciphering protein complexes and interactions

Mass spectrometry, coupled with innovative crosslinking techniques to decode protein conformations and interactions through uninterrupted signal connections, has undergone remarkable progress in recent years. It is crucial to develop selective crosslinking reagents that minimally disrupt protein structure and dynamics, providing insights into protein network regulation and biological functions. Compared to traditional crosslinkers, new bifunctional chemical crosslinkers exhibit high selectivity and specificity in connecting proximal amino acid residues, resulting in stable molecular crosslinked products. The conjugation with specific amino acid residues like lysine, cysteine, arginine and tyrosine expands the XL-MS toolbox, enabling more precise modeling of target substrates and leading to improved data quality and reliability. Another emerging crosslinking method utilizes unnatural amino acids (UAAs) derived from proximity-enabled reactivity with specific amino acids or sulfur-fluoride exchange (SuFEx) reactions with nucleophilic residues. These UAAs are genetically encoded into proteins for the formation of specific covalent bonds. This technique combines the benefits of genetic encoding for live cell compatibility with chemical crosslinking, providing a valuable method for capturing transient and weak protein-protein interactions (PPIs) for mapping PPI coordinates and improving the pharmacological properties of proteins. With continued advancements in technology and applications, crosslinking mass spectrometry is poised to play an increasingly significant role in guiding our understanding of protein dynamics and function in the future.

Massively parallel measurement of protein-protein interactions by sequencing using MP3-seq

Protein-protein interactions (PPIs) regulate many cellular processes and engineered PPIs have cell and gene therapy applications. Here, we introduce massively parallel PPI measurement by sequencing (MP3-seq), an easy-to-use and highly scalable yeast two-hybrid approach for measuring PPIs. In MP3-seq, DNA barcodes are associated with specific protein pairs and barcode enrichment can be read by sequencing to provide a direct measure of interaction strength. We show that MP3-seq is highly quantitative and scales to over 100,000 interactions. We apply MP3-seq to characterize interactions between families of rationally designed heterodimers and to investigate elements conferring specificity to coiled-coil interactions. Lastly, we predict coiled heterodimer structures using AlphaFold-Multimer (AF-M) and train linear models on physics-based energy terms to predict MP3-seq values. We find that AF-M-based models could be valuable for prescreening interactions but experimentally measuring interactions remains necessary to rank their strengths quantitatively.

Comprehensive Overview of Bottom-Up Proteomics Using Mass Spectrometry

Proteomics is the large scale study of protein structure and function from biological systems through protein identification and quantification. "Shotgun proteomics" or "bottom-up proteomics" is the prevailing strategy, in which proteins are hydrolyzed into peptides that are analyzed by mass spectrometry. Proteomics studies can be applied to diverse studies ranging from simple protein identification to studies of proteoforms, protein-protein interactions, protein structural alterations, absolute and relative protein quantification, post-translational modifications, and protein stability. To enable this range of different experiments, there are diverse strategies for proteome analysis. The nuances of how proteomic workflows differ may be challenging to understand for new practitioners. Here, we provide a comprehensive overview of different proteomics methods. We cover from biochemistry basics and protein extraction to biological interpretation and orthogonal validation. We expect this Review will serve as a handbook for researchers who are new to the field of bottom-up proteomics.

Bacterial growth-mediated systems remodelling of Nicotiana benthamiana defines unique signatures of target protein production in molecular pharming

The need for therapeutics to treat a plethora of medical conditions and diseases is on the rise and the demand for alternative approaches to mammalian-based production systems is increasing. Plant-based strategies provide a safe and effective alternative to produce biological drugs but have yet to enter mainstream manufacturing at a competitive level. Limitations associated with batch consistency and target protein production levels are present; however, strategies to overcome these challenges are underway. In this study, we apply state-of-the-art mass spectrometry-based proteomics to define proteome remodelling of the plant following agroinfiltration with bacteria grown under shake flask or bioreactor conditions. We observed distinct signatures of bacterial protein production corresponding to the different growth conditions that directly influence the plant defence responses and target protein production on a temporal axis. Our integration of proteomic profiling with small molecule detection and quantification reveals the fluctuation of secondary metabolite production over time to provide new insight into the complexities of dual system modulation in molecular pharming. Our findings suggest that bioreactor bacterial growth may promote evasion of early plant defence responses towards Agrobacterium tumefaciens (updated nomenclature to Rhizobium radiobacter). Furthermore, we uncover and explore specific targets for genetic manipulation to suppress host defences and increase recombinant protein production in molecular pharming.

The small CRL4CSA ubiquitin ligase component DDA1 regulates transcription-coupled repair dynamics

Transcription-blocking DNA lesions are specifically targeted by transcription-coupled nucleotide excision repair (TC-NER), which removes a broad spectrum of DNA lesions to preserve transcriptional output and thereby cellular homeostasis to counteract aging. TC-NER is initiated by the stalling of RNA polymerase II at DNA lesions, which triggers the assembly of the TC-NER-specific proteins CSA, CSB and UVSSA. CSA, a WD40-repeat containing protein, is the substrate receptor subunit of a cullin-RING ubiquitin ligase complex composed of DDB1, CUL4A/B and RBX1 (CRL4CSA). Although ubiquitination of several TC-NER proteins by CRL4CSA has been reported, it is still unknown how this complex is regulated. To unravel the dynamic molecular interactions and the regulation of this complex, we apply a single-step protein-complex isolation coupled to mass spectrometry analysis and identified DDA1 as a CSA interacting protein. Cryo-EM analysis shows that DDA1 is an integral component of the CRL4CSA complex. Functional analysis reveals that DDA1 coordinates ubiquitination dynamics during TC-NER and is required for efficient turnover and progression of this process.

Method for B Cell Receptor Enrichment in Malignant B Cells

B cells are central to the adaptive immune response and provide long-lasting immunity after infection. B cell activation is mediated by the surface membrane-bound B cell receptor (BCR) following recognition of a specific antigen. The BCR has been challenging to analyse using mass spectrometry (MS) due to the difficulty of isolating and enriching this membrane-bound protein complex. There are approximately 120,000 BCRs on the B cell surface; however, depending on the B cell activation state, there may be hundreds-of-millions to billions of proteins in a B cell. Consequently, advanced proteomic techniques such as MS workflows that use purified proteins to yield structural and protein-interaction information have not been published for the BCR complex. This paper describes a method for enriching the BCR complex that is MS-compatible. The method involves a Protein G pull down on agarose beads using an intermediary antibody to each of the BCR complex subcomponents (CD79a, CD79b, and membrane immunoglobulin). The enrichment process is shown to pull down the entire BCR complex and has the advantage of being readily compatible with further proteomic study including MS analysis. Using intermediary antibodies has the potential to enrich all isotypes of the BCR, unlike previous methods described in the literature that use protein G-coated beads to directly pull down the membrane IgG (mIgG) but cannot be used for other mIg isotypes.

Characterize direct protein interactions with enrichable, cleavable and latent bioreactive unnatural amino acids

Latent bioreactive unnatural amino acids (Uaas) have been widely used in the development of covalent drugs and identification of protein interactors, such as proteins, DNA, RNA and carbohydrates. However, it is challenging to perform high-throughput identification of Uaa cross-linking products due to the complexities of protein samples and the data analysis processes. Enrichable Uaas can effectively reduce the complexities of protein samples and simplify data analysis, but few cross-linked peptides were identified from mammalian cell samples with these Uaas. Here we develop an enrichable and multiple amino acids reactive Uaa, eFSY, and demonstrate that eFSY is MS cleavable when eFSY-Lys and eFSY-His are the cross-linking products. An identification software, AixUaa is developed to decipher eFSY mass cleavable data. We systematically identify direct interactomes of Thioredoxin 1 (Trx1) and Selenoprotein M (SELM) with eFSY and AixUaa.

Unveiling the power of proteomics in advancing tropical animal health and production

Proteomics, the large-scale study of proteins in biological systems has emerged as a pivotal tool in the field of animal and veterinary sciences, mainly for investigating local and rustic breeds. Proteomics provides valuable insights into biological processes underlying animal growth, reproduction, health, and disease. In this review, we highlight the key proteomics technologies, methodologies, and their applications in domestic animals, particularly in the tropical context. We also discuss advances in proteomics research, including integration of multi-omics data, single-cell proteomics, and proteogenomics, all of which are promising for improving animal health, adaptation, welfare, and productivity. However, proteomics research in domestic animals faces challenges, such as sample preparation variation, data quality control, privacy and ethical considerations relating to animal welfare. We also provide recommendations for overcoming these challenges, emphasizing the importance of following best practices in sample preparation, data quality control, and ethical compliance. We therefore aim for this review to harness the full potential of proteomics in advancing our understanding of animal biology and ultimately improve animal health and productivity in local breeds of diverse animal species in a tropical context.

Harnessing the predicted maize pan-interactome for putative gene function prediction and prioritization of candidate genes for important traits

The recent assembly and annotation of the 26 maize nested association mapping population founder inbreds have enabled large-scale pan-genomic comparative studies. These studies have expanded our understanding of agronomically important traits by integrating pan-transcriptomic data with trait-specific gene candidates from previous association mapping results. In contrast to the availability of pan-transcriptomic data, obtaining reliable protein-protein interaction (PPI) data has remained a challenge due to its high cost and complexity. We generated predicted PPI networks for each of the 26 genomes using the established STRING database. The individual genome-interactomes were then integrated to generate core- and pan-interactomes. We deployed the PPI clustering algorithm ClusterONE to identify numerous PPI clusters that were functionally annotated using gene ontology (GO) functional enrichment, demonstrating a diverse range of enriched GO terms across different clusters. Additional cluster annotations were generated by integrating gene coexpression data and gene description annotations, providing additional useful information. We show that the functionally annotated PPI clusters establish a useful framework for protein function prediction and prioritization of candidate genes of interest. Our study not only provides a comprehensive resource of predicted PPI networks for 26 maize genomes but also offers annotated interactome clusters for predicting protein functions and prioritizing gene candidates. The source code for the Python implementation of the analysis workflow and a standalone web application for accessing the analysis results are available at https://github.com/eporetsky/PanPPI.

Micropeptides: potential treatment strategies for cancer

Some noncoding RNAs (ncRNAs) carry open reading frames (ORFs) that can be translated into micropeptides, although noncoding RNAs (ncRNAs) have been previously assumed to constitute a class of RNA transcripts without coding capacity. Furthermore, recent studies have revealed that ncRNA-derived micropeptides exhibit regulatory functions in the development of many tumours. Although some of these micropeptides inhibit tumour growth, others promote it. Understanding the role of ncRNA-encoded micropeptides in cancer poses new challenges for cancer research, but also offers promising prospects for cancer therapy. In this review, we summarize the types of ncRNAs that can encode micropeptides, highlighting recent technical developments that have made it easier to research micropeptides, such as ribosome analysis, mass spectrometry, bioinformatics methods, and CRISPR/Cas9. Furthermore, based on the distribution of micropeptides in different subcellular locations, we explain the biological functions of micropeptides in different human cancers and discuss their underestimated potential as diagnostic biomarkers and anticancer therapeutic targets in clinical applications, information that may contribute to the discovery and development of new micropeptide-based tools for early diagnosis and anticancer drug development.

Pathogenic mutations of human phosphorylation sites affect protein-protein interactions

Despite their lack of a defined 3D structure, intrinsically disordered regions (IDRs) of proteins play important biological roles. Many IDRs contain short linear motifs (SLiMs) that mediate protein-protein interactions (PPIs), which can be regulated by post-translational modifications like phosphorylation. 20% of pathogenic missense mutations are found in IDRs, and understanding how such mutations affect PPIs is essential for unraveling disease mechanisms. Here, we employ peptide-based interaction proteomics to investigate 36 disease-associated mutations affecting phosphorylation sites. Our results unveil significant differences in interactomes between phosphorylated and non-phosphorylated peptides, often due to disrupted phosphorylation-dependent SLiMs. We focused on a mutation of a serine phosphorylation site in the transcription factor GATAD1, which causes dilated cardiomyopathy. We find that this phosphorylation site mediates interaction with 14-3-3 family proteins. Follow-up experiments reveal the structural basis of this interaction and suggest that 14-3-3 binding affects GATAD1 nucleocytoplasmic transport by masking a nuclear localisation signal. Our results demonstrate that pathogenic mutations of human phosphorylation sites can significantly impact protein-protein interactions, offering insights into potential molecular mechanisms underlying pathogenesis.

Arginine (315) is required for the PLIN2-CGI-58 interface and plays a functional role in regulating nascent LDs formation in bovine adipocytes

Perilipin-2 (PLIN2) can anchor to lipid droplets (LDs) and play a crucial role in regulating nascent LDs formation. Bimolecular fluorescence complementation (BiFC) and flow cytometry were examined to verify the PLIN2-CGI-58 interaction efficiency in bovine adipocytes. GST-Pulldown assay was used to detect the key site arginine315 function in PLIN2-CGI-58 interaction. Experiments were also examined to research these mutations function of PLIN2 in LDs formation during adipocytes differentiation, LDs were measured after staining by BODIPY, lipogenesis-related genes were also detected. Results showed that Leucine (L371A, L311A) and glycine (G369A, G376A) mutations reduced interaction efficiencies. Serine (S367A) mutations enhanced the interaction efficiency. Arginine (R315A) mutations resulted in loss of fluorescence in the cytoplasm and disrupted the interaction with CGI-58, as verified by pulldown assay. R315W mutations resulted in a significant increase in the number of LDs compared with wild-type (WT) PLIN2 or the R315A mutations. Lipogenesis-related genes were either up- or downregulated when mutated PLIN2 interacted with CGI-58. Arginine315 in PLIN2 is required for the PLIN2-CGI-58 interface and could regulate nascent LD formation and lipogenesis. This study is the first to study amino acids on the PLIN2 interface during interaction with CGI-58 in bovine and highlight the role played by PLIN2 in the regulation of bovine adipocyte lipogenesis.

Inclusion of Porous Graphitic Carbon Chromatography Yields Greater Protein Identification and Compartment and Process Coverage and Enables More Reflective Protein-Level Label-Free Quantitation

The ubiquity of mass spectrometry-based bottom-up proteomic analyses as a component of biological investigation mandates the validation of methodologies that increase acquisition efficiency, improve sample coverage, and enhance profiling depth. Chromatographic separation is often ignored as an area of potential improvement, with most analyses relying on traditional reversed-phase liquid chromatography (RPLC); this consistent reliance on a single chromatographic paradigm fundamentally limits our view of the observable proteome. Herein, we build upon early reports and validate porous graphitic carbon chromatography (PGC) as a facile means to substantially enhance proteomic coverage without changes to sample preparation, instrument configuration, or acquisition methods. Analysis of offline fractionated cell line digests using both separations revealed an increase in peptide and protein identifications by 43% and 24%, respectively. Increased identifications provided more comprehensive coverage of cellular components and biological processes independent of protein abundance, highlighting the substantial quantity of proteomic information that may go undetected in standard analyses. We further utilize these data to reveal that label-free quantitative analyses using RPLC separations alone may not be reflective of actual protein constituency. Together, these data highlight the value and comprehension offered through PGC-MS proteomic analyses. RAW proteomic data have been uploaded to the MassIVE repository with the primary accession code MSV000091495.

Live cell transcription-coupled nucleotide excision repair dynamics revisited

Transcription-blocking lesions are specifically targeted by transcription-coupled nucleotide excision repair (TC-NER), which prevents DNA damage-induced cellular toxicity and maintains proper transcriptional processes. TC-NER is initiated by the stalling of RNA polymerase II (RNAPII), which triggers the assembly of TC-NER-specific proteins, namely CSB, CSA and UVSSA, which collectively control and drive TC-NER progression. Previous research has revealed molecular functions for these proteins, however, exact mechanisms governing the initiation and regulation of TC-NER, particularly at low UV doses have remained elusive, partly due to technical constraints. In this study, we employ knock-in cell lines designed to target the endogenous CSB gene locus with mClover, a GFP variant. Through live cell imaging, we uncover the intricate molecular dynamics of CSB in response to physiologically relevant UV doses. We showed that the DNA damage-induced association of CSB with chromatin is tightly regulated by the CSA-containing ubiquitin-ligase CRL complex (CRL4CSA). Combining the CSB-mClover knock-in cell line with SILAC-based GFP-mediated complex isolation and mass-spectrometry-based proteomics, revealed novel putative CSB interactors as well as discernible variations in complex composition during distinct stages of TC-NER progression. Our work not only provides molecular insight into TC-NER, but also illustrates the versatility of endogenously tagging fluorescent and affinity tags.

EGFR-T790M Mutation-Derived Interactome Rerouted EGFR Translocation Contributing to Gefitinib Resistance in Non-Small Cell Lung Cancer

Secondary mutation, T790M, conferring tyrosine kinase inhibitors (TKIs) resistance beyond oncogenic epidermal growth factor receptor (EGFR) mutations presents a challenging unmet need. Although TKI-resistant mechanisms are intensively investigated, the underlying responses of cancer cells adapting drug perturbation are largely unknown. To illuminate the molecular basis linking acquired mutation to TKI resistance, affinity purification coupled mass spectrometry was adopted to dissect EGFR interactome in TKI-sensitive and TKI-resistant non-small cell lung cancer cells. The analysis revealed TKI-resistant EGFR-mutant interactome allocated in diverse subcellular distribution and enriched in endocytic trafficking, in which gefitinib intervention activated autophagy-mediated EGFR degradation and thus autophagy inhibition elevated gefitinib susceptibility. Alternatively, gefitinib prompted TKI-sensitive EGFR translocating toward cell periphery through Rab7 ubiquitination which may favor efficacy to TKIs suppression. This study revealed that T790M mutation rewired EGFR interactome that guided EGFR to autophagy-mediated degradation to escape treatment, suggesting that combination therapy with TKI and autophagy inhibitor may overcome acquired resistance in non-small cell lung cancer.

Massively parallel protein-protein interaction measurement by sequencing (MP3-seq) enables rapid screening of protein heterodimers

Protein-protein interactions (PPIs) regulate many cellular processes, and engineered PPIs have cell and gene therapy applications. Here we introduce massively parallel protein-protein interaction measurement by sequencing (MP3-seq), an easy-to-use and highly scalable yeast-two-hybrid approach for measuring PPIs. In MP3-seq, DNA barcodes are associated with specific protein pairs, and barcode enrichment can be read by sequencing to provide a direct measure of interaction strength. We show that MP3-seq is highly quantitative and scales to over 100,000 interactions. We apply MP3-seq to characterize interactions between families of rationally designed heterodimers and to investigate elements conferring specificity to coiled-coil interactions. Finally, we predict coiled heterodimer structures using AlphaFold-Multimer (AF-M) and train linear models on physics simulation energy terms to predict MP3-seq values. We find that AF-M and AF-M complex prediction-based models could be valuable for pre-screening interactions, but that measuring interactions experimentally remains necessary to rank their strengths quantitatively.

Molecular complex detection in protein interaction networks through reinforcement learning

BACKGROUND

Proteins often assemble into higher-order complexes to perform their biological functions. Such protein-protein interactions (PPI) are often experimentally measured for pairs of proteins and summarized in a weighted PPI network, to which community detection algorithms can be applied to define the various higher-order protein complexes. Current methods include unsupervised and supervised approaches, often assuming that protein complexes manifest only as dense subgraphs. Utilizing supervised approaches, the focus is not on how to find them in a network, but only on learning which subgraphs correspond to complexes, currently solved using heuristics. However, learning to walk trajectories on a network to identify protein complexes leads naturally to a reinforcement learning (RL) approach, a strategy not extensively explored for community detection. Here, we develop and evaluate a reinforcement learning pipeline for community detection on weighted protein-protein interaction networks to detect new protein complexes. The algorithm is trained to calculate the value of different subgraphs encountered while walking on the network to reconstruct known complexes. A distributed prediction algorithm then scales the RL pipeline to search for novel protein complexes on large PPI networks.

RESULTS

The reinforcement learning pipeline is applied to a human PPI network consisting of 8k proteins and 60k PPI, which results in 1,157 protein complexes. The method demonstrated competitive accuracy with improved speed compared to previous algorithms. We highlight protein complexes such as C4orf19, C18orf21, and KIAA1522 which are currently minimally characterized. Additionally, the results suggest TMC04 be a putative additional subunit of the KICSTOR complex and confirm the involvement of C15orf41 in a higher-order complex with HIRA, CDAN1, ASF1A, and by 3D structural modeling.

CONCLUSIONS

Reinforcement learning offers several distinct advantages for community detection, including scalability and knowledge of the walk trajectories defining those communities. Applied to currently available human protein interaction networks, this method had comparable accuracy with other algorithms and notable savings in computational time, and in turn, led to clear predictions of protein function and interactions for several uncharacterized human proteins.

Finding meaning in chaos: a selection signature for functional interactions and its use in molecular biology

A primary goal of biomedical research is to elucidate molecular mechanisms, particularly those responsible for human traits, either normal or pathological. Yet achieving this goal is difficult if not impossible when the traits of interest lack tractable models and so cannot be dissected through time-honoured approaches like forward genetics or reconstitution. Arguably, no biological problem has hindered scientific progress more than this: the inability to dissect a trait's mechanism without a tractable likeness of the trait. At root, forward genetics and reconstitution are powerful approaches because they assay for specific molecular functions. Here, we discuss an alternative way to uncover important mechanistic interactions, namely, to assay for positive natural selection. If an interaction has been selected for, then it must perform an important function, a function that significantly promotes reproductive success. Accordingly, selection is a consequence and indicator of function, and uncovering multimolecular selection will reveal important functional interactions. We propose a selection signature for interactions and review recent selection-based approaches through which to dissect traits that are not inherently tractable. The review includes proof-of-principle studies in which important interactions were uncovered by screening for selection. In sum, screens for selection appear feasible when screens for specific functions are not. Selection screens thus constitute a novel tool through which to reveal the mechanisms that shape the fates of organisms.

ZNF330/NOA36 interacts with HSPA1 and HSPA8 and modulates cell cycle and proliferation in response to heat shock in HEK293 cells

BACKGROUND

The human genome contains nearly 20.000 protein-coding genes, but there are still more than 6,000 proteins poorly characterized. Among them, ZNF330/NOA36 stand out because it is a highly evolutionarily conserved nucleolar zinc-finger protein found in the genome of ancient animal phyla like sponges or cnidarians, up to humans. Firstly described as a human autoantigen, NOA36 is expressed in all tissues and human cell lines, and it has been related to apoptosis in human cells as well as in muscle morphogenesis and hematopoiesis in Drosophila. Nevertheless, further research is required to better understand the roles of this highly conserved protein.

RESULTS

Here, we have investigated possible interactors of human ZNF330/NOA36 through affinity-purification mass spectrometry (AP-MS). Among them, NOA36 interaction with HSPA1 and HSPA8 heat shock proteins was disclosed and further validated by co-immunoprecipitation. Also, "Enhancer of Rudimentary Homolog" (ERH), a protein involved in cell cycle regulation, was detected in the AP-MS approach. Furthermore, we developed a NOA36 knockout cell line using CRISPR/Cas9n in HEK293, and we found that the cell cycle profile was modified, and proliferation decreased after heat shock in the knocked-out cells. These differences were not due to a different expression of the HSPs genes detected in the AP-MS after inducing stress.

CONCLUSIONS

Our results indicate that NOA36 is necessary for proliferation recovery in response to thermal stress to achieve a regular cell cycle profile, likely by interaction with HSPA1 and HSPA8. Further studies would be required to disclose the relevance of NOA36-EHR interaction in this context.

mTORC2 interactome and localization determine aggressiveness of high-grade glioma cells through association with gelsolin

mTOR complex 2 (mTORC2) has been implicated as a key regulator of glioblastoma cell migration. However, the roles of mTORC2 in the migrational control process have not been entirely elucidated. Here, we elaborate that active mTORC2 is crucial for GBM cell motility. Inhibition of mTORC2 impaired cell movement and negatively affected microfilament and microtubule functions. We also aimed to characterize important players involved in the regulation of cell migration and other mTORC2-mediated cellular processes in GBM cells. Therefore, we quantitatively characterized the alteration of the mTORC2 interactome under selective conditions using affinity purification-mass spectrometry in glioblastoma. We demonstrated that changes in cell migration ability specifically altered mTORC2-associated proteins. GSN was identified as one of the most dynamic proteins. The mTORC2-GSN linkage was mostly highlighted in high-grade glioma cells, connecting functional mTORC2 to multiple proteins responsible for directional cell movement in GBM. Loss of GSN disconnected mTORC2 from numerous cytoskeletal proteins and affected the membrane localization of mTORC2. In addition, we reported 86 stable mTORC2-interacting proteins involved in diverse molecular functions, predominantly cytoskeletal remodeling, in GBM. Our findings might help expand future opportunities for predicting the highly migratory phenotype of brain cancers in clinical investigations.

Assessment of community efforts to advance network-based prediction of protein-protein interactions

Comprehensive understanding of the human protein-protein interaction (PPI) network, aka the human interactome, can provide important insights into the molecular mechanisms of complex biological processes and diseases. Despite the remarkable experimental efforts undertaken to date to determine the structure of the human interactome, many PPIs remain unmapped. Computational approaches, especially network-based methods, can facilitate the identification of previously uncharacterized PPIs. Many such methods have been proposed. Yet, a systematic evaluation of existing network-based methods in predicting PPIs is still lacking. Here, we report community efforts initiated by the International Network Medicine Consortium to benchmark the ability of 26 representative network-based methods to predict PPIs across six different interactomes of four different organisms: A. thaliana, C. elegans, S. cerevisiae, and H. sapiens. Through extensive computational and experimental validations, we found that advanced similarity-based methods, which leverage the underlying network characteristics of PPIs, show superior performance over other general link prediction methods in the interactomes we considered.

Systematic characterization of extracellular glycoproteins using mass spectrometry

Surface and secreted glycoproteins are essential to cells and regulate many extracellular events. Because of the diversity of glycans, the low abundance of many glycoproteins, and the complexity of biological samples, a system-wide investigation of extracellular glycoproteins is a daunting task. With the development of modern mass spectrometry (MS)-based proteomics, comprehensive analysis of different protein modifications including glycosylation has advanced dramatically. This review focuses on the investigation of extracellular glycoproteins using MS-based proteomics. We first discuss the methods for selectively enriching surface glycoproteins and investigating protein interactions on the cell surface, followed by the application of MS-based proteomics for surface glycoprotein dynamics analysis and biomarker discovery. We then summarize the methods to comprehensively study secreted glycoproteins by integrating various enrichment approaches with MS-based proteomics and their applications for global analysis of secreted glycoproteins in different biological samples. Collectively, MS significantly expands our knowledge of extracellular glycoproteins and enables us to identify extracellular glycoproteins as potential biomarkers for disease detection and drug targets for disease treatment.

Exploring Protein Interactome Data with IPinquiry: Statistical Analysis and Data Visualization by Spectral Counts

Immunoprecipitation mass spectrometry (IP-MS) is a popular method for the identification of protein-protein interactions. This approach is particularly powerful when information is collected without a priori knowledge and has been successively used as a first key step for the elucidation of many complex protein networks. IP-MS consists in the affinity purification of a protein of interest and of its interacting proteins followed by protein identification and quantification by mass spectrometry analysis. We developed an R package, named IPinquiry, dedicated to IP-MS analysis and based on the spectral count quantification method. The main purpose of this package is to provide a simple R pipeline with a limited number of processing steps to facilitate data exploration for biologists. This package allows to perform differential analysis of protein accumulation between two groups of IP experiments, to retrieve protein annotations, to export results, and to create different types of graphics. Here we describe the step-by-step procedure for an interactome analysis using IPinquiry from data loading to result export and plot production.

Detection of Recombinant Proteins SOX2 and OCT4 Interacting in HEK293T Cells Using Real-Time Quantitative PCR

In vivo biotinylation using wild-type and mutants of biotin ligases is now widely applied for the study of cellular proteomes. The commercial availability of kits for the highly efficient purification of biotinylated proteins and their excellent compatibility with LC-MS/MS protocols are the main reasons for the choice of biotin ligases. Since they are all enzymes, however, just a very low expression in cells is required for experiments. Therefore, it can be difficult to perform the quantifications of these enzymes in various samples. Traditional methods, such as western blotting, are not always fit for the detection of the expression levels. Therefore, real-time qRT-PCR, a technology that is more sensitive, was used in this study to quantify the expression of BirA fusions. Using this method, we detected high expression levels of BirA fusions in models of interactions of pluripotency transcription factors to carry out their relative quantification. We also found the absence of the competing endogenous proteins SOX2 and OCT4, as well as no cross-reactivity between BAP/BirA and the endogenous biotinylation system in HEK293T cells. Thus, these data indicated that the high level of biotinylation is due to the in vivo interaction of BAP-X and BirA-Y (X,Y = SOX2, OCT4) in the cell rather than their random collision, a big difference in the expression level of BirA fusions across samples or endogenous biotinylation.

The Hypolipidemic Effect of Hawthorn Leaf Flavonoids through Modulating Lipid Metabolism and Gut Microbiota in Hyperlipidemic Rats

Objective. The purpose of this study was to explore the potential mechanisms of the lipid-regulating effects and the effect on modulating the gut microbiota of hawthorn leaf flavonoids (HLF) in the high-fat diet-induced hyperlipidemic rats. Methods. The hypolipidemic effect of HLF was investigated in the high-fat diet-induced hyperlipidemic rats. The action targets of HLF in the treatment of hyperlipidemia were predicted by network pharmacology and KEGG enrichment bubble diagram, which were verified by the test of western blotting. Meanwhile, we used 16S rRNA sequencing to evaluate the effects of HLF on the microbes. Results. The results of animal experiments showed that HLF could reduce the body weight and regulate the levels of serum lipid in high-fat diet (HFD) rats. Meanwhile, for the related targets of cholesterol metabolism, HLF could significantly upregulate the expression of LDLR, NR1H3, and ABCG5/ABCG8; reduce the expression of PCSK9; and increase the level of CYP7A1 in the intestinal tissue, whereas cholesterol biosynthetic protein expressions including HMGCR and SCAP were lowered by HLF. In addition, HLF increased the activities of plasma SOD, CAT, and GSH-Px and decreased the levels of Casp 1, NLRP3, IL-1β, IL-18, and TNF-α, improving the degree of hepatocyte steatosis and inflammatory infiltration of rats. Notably, HLF significantly regulated the relative abundance of major bacteria such as g_Lactobacillus, g_Anaerostipes, g_[Eubacterium]_hallii_group, g_Fusicatenibacter, g_Akkermansia, and g_Collinsella. Synchronously, we found that HLF could regulate the disorder of plasma HEPC and TFR levels caused by HFD. Conclusion. This study demonstrates that HLF can regulate metabolic hyperlipidemia syndromes and modulate the relative abundance of major bacteria, which illustrated that it might be associated with the modulation of gut microbiota composition and metabolites.

Compound Interaction Screen on a Photoactivatable Cellulose Membrane (CISCM) Identifies Drug Targets

Identifying the protein targets of drugs is an important but tedious process. Existing proteomic approaches enable unbiased target identification but lack the throughput needed to screen larger compound libraries. Here, we present a compound interaction screen on a photoactivatable cellulose membrane (CISCM) that enables target identification of several drugs in parallel. To this end, we use diazirine-based undirected photoaffinity labeling (PAL) to immobilize compounds on cellulose membranes. Functionalized membranes are then incubated with protein extract and specific targets are identified via quantitative affinity purification and mass spectrometry. CISCM reliably identifies known targets of natural products in less than three hours of analysis time per compound. In summary, we show that combining undirected photoimmobilization of compounds on cellulose with quantitative interaction proteomics provides an efficient means to identify the targets of natural products.

Intramolecular covalent bonds in Gram-positive bacterial surface proteins

Gram-positive bacteria experience considerable mechanical perturbation when adhering to host surfaces during colonization and infection. They have evolved various adhesion proteins that are mechanically robust to ensure strong surface adhesion. Recently, it was discovered that these adhesion proteins contain rare, extra intramolecular covalent bonds that stabilize protein structures and participate in surface bonding. These intramolecular covalent bonds include isopeptides, thioesters, and ester bonds, which often form spontaneously without the need for additional enzymes. With the development of single-molecule force spectroscopy techniques, the detailed mechanical roles of these intramolecular covalent bonds have been revealed. In this review, we summarize the recent advances in this area of research, focusing on the link between the mechanical stability and function of these covalent bonds in Gram-positive bacterial surface proteins. We also highlight the potential impact of these discoveries on the development of novel antibiotics and chemical biology tools.

Profiling protein interactions by purification with capillary monolithic affinity column in combination with label-free quantitative proteomics

An approach for profiling protein-protein interactions by using affinity purification with capillary monolithic immobilized metal affinity chromatography column (cm-IMAC) in combination with label free quantitative proteomics was described in the present work. The cm-IMAC columns were prepared in a single step by copolymerization of the function monomer, namely (S)-2,2'-((1-carboxy-5-(pent‑4-enamido)pentyl)azanediyl)diacetic acid which provide a nitrilotriacetate (NTA) moiety to form chelated complexation with Ni (II) ions, inside the fused silica capillaries. The His₆-tagged bait protein can be easily immobilized on the cm-IMAC columns through the formation of chelating complexation with the NTA-Ni (II) functional groups of the matrix. The cm-IMAC columns were used to explore protein-protein interactions (PPIs) on a proteomic scale when combined with label-free proteomics. A known interaction pair of proteins, namely NDP52 (amino acid sequence 10-126) and NAP1 (33-75) as well as Bcl-2 family proteins were used for proof of concept. New interactors of Bcl-X_L were identified and validated by co-immunoprecipitation.

Protein interaction networks in neurodegenerative diseases: from physiological function to aggregation

The accumulation of protein inclusions is linked to many neurodegenerative diseases that typically develop in older individuals, due to a combination of genetic and environmental factors. In rare familial neurodegenerative disorders, genes encoding for aggregation-prone proteins are often mutated. While the underlying mechanism leading to these diseases still remains to be fully elucidated, efforts in the past 20 years revealed a vast network of protein–protein interactions that play a major role in regulating the aggregation of key proteins associated with neurodegeneration. Misfolded proteins that can oligomerize and form insoluble aggregates associate with molecular chaperones and other elements of the proteolytic machineries that are the frontline workers attempting to protect the cells by promoting clearance and preventing aggregation. Proteins that are normally bound to aggregation-prone proteins can become sequestered and mislocalized in protein inclusions, leading to their loss of function. In contrast, mutations, posttranslational modifications, or misfolding of aggregation-prone proteins can lead to gain of function by inducing novel or altered protein interactions, which in turn can impact numerous essential cellular processes and organelles, such as vesicle trafficking and the mitochondria. This review examines our current knowledge of protein–protein interactions involving several key aggregation-prone proteins that are associated with Alzheimer’s disease, Parkinson’s disease, Huntington’s disease, or amyotrophic lateral sclerosis. We aim to provide an overview of the protein interaction networks that play a central role in driving or mitigating inclusion formation, while highlighting some of the key proteomic studies that helped to uncover the extent of these networks.

Characterization of In Vivo Protein Complexes via Chemical Cross-Linking and Mass Spectrometry

Cells perform various functions by proteins via protein complexes. Characterization of protein complexes is critical to understanding their biological and clinical significance and has been one of the major efforts of functional proteomics. To date, most protein complexes are characterized by the in vitro system from protein extracts after the cells or tissues are lysed, and it has been challenging to determine which of these protein complexes are formed in intact cells. Herein, we report an approach to preserve protein complexes using in vivo cross-linking, followed by size exclusion chromatography and data-independent acquisition mass spectrometry. This approach enables the characterization of in vivo protein complexes from cells or tissues, which allows the determination of protein complexes in clinical research. More importantly, the described approach can identify protein complexes that are not detected by the in vitro system, which provide unique protein function information.

Diverse Roles of TRPV4 in Macrophages: A Need for Unbiased Profiling

Transient receptor potential vanilloid 4 (TRPV4) is a non-selective mechanosensitive ion channel expressed by various macrophage populations. Recent reports have characterized the role of TRPV4 in shaping the activity and phenotype of macrophages to influence the innate immune response to pathogen exposure and inflammation. TRPV4 has been studied extensively in the context of inflammation and inflammatory pain. Although TRPV4 activity has been generally described as pro-inflammatory, emerging evidence suggests a more complex role where this channel may also contribute to anti-inflammatory activities. However, detailed understanding of how TRPV4 may influence the initiation, maintenance, and resolution of inflammatory disease remains limited. This review highlights recent insights into the cellular processes through which TRPV4 contributes to pathological conditions and immune processes, with a focus on macrophage biology. The potential use of high-throughput and omics methods as an unbiased approach for studying the functional outcomes of TRPV4 activation is also discussed.

A Practical Guide to Small Protein Discovery and Characterization Using Mass Spectrometry

Small proteins of up to ∼50 amino acids are an abundant class of biomolecules across all domains of life. Yet due to the challenges inherent in their size, they are often missed in genome annotations, and are difficult to identify and characterize using standard experimental approaches. Consequently, we still know few small proteins even in well-studied prokaryotic model organisms. Mass spectrometry (MS) has great potential for the discovery, validation, and functional characterization of small proteins. However, standard MS approaches are poorly suited to the identification of both known and novel small proteins due to limitations at each step of a typical proteomics workflow, i.e., sample preparation, protease digestion, liquid chromatography, MS data acquisition, and data analysis. Here, we outline the major MS-based workflows and bioinformatic pipelines used for small protein discovery and validation. Special emphasis is placed on highlighting the adjustments required to improve detection and data quality for small proteins. We discuss both the unbiased detection of small proteins and the targeted analysis of small proteins of interest. Finally, we provide guidelines to prioritize novel small proteins, and an outlook on methods with particular potential to further improve comprehensive discovery and characterization of small proteins.

Comprehensive Profiling of Mammalian Tribbles Interactomes Implicates TRIB3 in Gene Repression

The three human Tribbles (TRIB) pseudokinases have been implicated in a plethora of signaling and metabolic processes linked to cancer initiation and progression and can potentially be used as biomarkers of disease and prognosis. While their modes of action reported so far center around protein-protein interactions, the comprehensive profiling of TRIB interactomes has not been reported yet. Here, we have developed a robust mass spectrometry (MS)-based proteomics approach to characterize Tribbles' interactomes and report a comprehensive assessment and comparison of the TRIB1, -2 and -3 interactomes, as well as domain-specific interactions for TRIB3. Interestingly, TRIB3, which is predominantly localized in the nucleus, interacts with multiple transcriptional regulators, including proteins involved in gene repression. Indeed, we found that TRIB3 repressed gene transcription when tethered to DNA in breast cancer cells. Taken together, our comprehensive proteomic assessment reveals previously unknown interacting partners and functions of Tribbles proteins that expand our understanding of this family of proteins. In addition, our findings show that MS-based proteomics provides a powerful tool to unravel novel pseudokinase biology.

Protein-protein interactions: Methods, databases, and applications in virus-host study

Almost all the cellular processes in a living system are controlled by proteins: They regulate gene expression, catalyze chemical reactions, transport small molecules across membranes, and transmit signal across membranes. Even, a viral infection is often initiated through virus-host protein interactions. Protein-protein interactions (PPIs) are the physical contacts between two or more proteins and they represent complex biological functions. Nowadays, PPIs have been used to construct PPI networks to study complex pathways for revealing the functions of unknown proteins. Scientists have used PPIs to find the molecular basis of certain diseases and also some potential drug targets. In this review, we will discuss how PPI networks are essential to understand the molecular basis of virus-host relationships and several databases which are dedicated to virus-host interaction studies. Here, we present a short but comprehensive review on PPIs, including the experimental and computational methods of finding PPIs, the databases dedicated to virus-host PPIs, and the associated various applications in protein interaction networks of some lethal viruses with their hosts.

Human bronchial-pulmonary proteomics in coronavirus disease 2019 (COVID-19) pandemic: applications and implications

INTRODUCTION

The outbreak of the newly discovered human coronavirus, severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), has disrupted the normal life of almost every civilization worldwide. Studies have shown that the coronavirus disease 2019 (COVID-19) caused by the SARS-CoV-2 can affect multiple human organs and physiological systems, but the respiratory system remains the primary location for viral infection.

AREAS COVERED

We summarize how omics technologies are used in SARS-CoV-2 research and specifically review the current knowledge of COVID-19 from the aspect of human bronchial-pulmonary proteomics. Also, knowledge gaps in COVID-19 that can be fulfilled by proteomics are discussed.

EXPERT OPINION

Overall, human bronchial-pulmonary proteomics plays an important role in revealing the dynamics, functions, tropism, and pathogenicity of SARS-CoV-2, which is crucial for COVID-19 biomarker and therapeutic target discoveries. To more fully understand the impact of COVID-19, research from various angles using multi-omics approaches should also be conducted on the lungs as well as other organs.

Off-the-shelf proximity biotinylation for interaction proteomics

Proximity biotinylation workflows typically require CRISPR-based genetic manipulation of target cells. To overcome this bottleneck, we fused the TurboID proximity biotinylation enzyme to Protein A. Upon target cell permeabilization, the ProtA-Turbo enzyme can be targeted to proteins or post-translational modifications of interest using bait-specific antibodies. Addition of biotin then triggers bait-proximal protein biotinylation. Biotinylated proteins can subsequently be enriched from crude lysates and identified by mass spectrometry. We demonstrate this workflow by targeting Emerin, H3K9me3 and BRG1. Amongst the main findings, our experiments reveal that the essential protein FLYWCH1 interacts with a subset of H3K9me3-marked (peri)centromeres in human cells. The ProtA-Turbo enzyme represents an off-the-shelf proximity biotinylation enzyme that facilitates proximity biotinylation experiments in primary cells and can be used to understand how proteins cooperate in vivo and how this contributes to cellular homeostasis and disease.

Interactomes of SARS-CoV-2 and human coronaviruses reveal host factors potentially affecting pathogenesis

Host-virus protein-protein interactions play key roles in the life cycle of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). We conducted a comprehensive interactome study between the virus and host cells using tandem affinity purification and proximity-labeling strategies and identified 437 human proteins as the high-confidence interacting proteins. Further characterization of these interactions and comparison to other large-scale study of cellular responses to SARS-CoV-2 infection elucidated how distinct SARS-CoV-2 viral proteins participate in its life cycle. With these data mining, we discovered potential drug targets for the treatment of COVID-19. The interactomes of two key SARS-CoV-2-encoded viral proteins, NSP1 and N, were compared with the interactomes of their counterparts in other human coronaviruses. These comparisons not only revealed common host pathways these viruses manipulate for their survival, but also showed divergent protein-protein interactions that may explain differences in disease pathology. This comprehensive interactome of SARS-CoV-2 provides valuable resources for the understanding and treating of this disease.

Protein-fragment complementation assays for large-scale analysis of protein-protein interactions

Protein-protein interactions (PPIs) orchestrate nearly all biological processes. They are also considered attractive drug targets for treating many human diseases, including cancers and neurodegenerative disorders. Protein-fragment complementation assays (PCAs) provide a direct and straightforward way to study PPIs in living cells or multicellular organisms. Importantly, PCAs can be used to detect the interaction of proteins expressed at endogenous levels in their native cellular environment. In this review, we present the principle of PCAs and discuss some of their advantages and limitations. We describe their application in large-scale experiments to investigate PPI networks and to screen or profile PPI targeting compounds.

Disease-specific interactome alterations via epichaperomics: the case for Alzheimer's disease

The increasingly appreciated prevalence of complicated stressor-to-phenotype associations in human disease requires a greater understanding of how specific stressors affect systems or interactome properties. Many currently untreatable diseases arise due to variations in, and through a combination of, multiple stressors of genetic, epigenetic, and environmental nature. Unfortunately, how such stressors lead to a specific disease phenotype or inflict a vulnerability to some cells and tissues but not others remains largely unknown and unsatisfactorily addressed. Analysis of cell- and tissue-specific interactome networks may shed light on organization of biological systems and subsequently to disease vulnerabilities. However, deriving human interactomes across different cell and disease contexts remains a challenge. To this end, this opinion article links stressor-induced protein interactome network perturbations to the formation of pathologic scaffolds termed epichaperomes, revealing a viable and reproducible experimental solution to obtaining rigorous context-dependent interactomes. This article presents our views on how a specialized 'omics platform called epichaperomics may complement and enhance the currently available conventional approaches and aid the scientific community in defining, understanding, and ultimately controlling interactome networks of complex diseases such as Alzheimer's disease. Ultimately, this approach may aid the transition from a limited single-alteration perspective in disease to a comprehensive network-based mindset, which we posit will result in precision medicine paradigms for disease diagnosis and treatment.

A New Sequential Forward Feature Selection (SFFS) Algorithm for Mining Best Topological and Biological Features to Predict Protein Complexes from Protein-Protein Interaction Networks (PPINs)

Protein-protein interaction plays an important role in the understanding of biological processes in the body. A network of dynamic protein complexes within a cell that regulates most biological processes is known as a protein-protein interaction network (PPIN). Complex prediction from PPINs is a challenging task. Most of the previous computation approaches mine cliques, stars, linear and hybrid structures as complexes from PPINs by considering topological features and fewer of them focus on important biological information contained within protein amino acid sequence. In this study, we have computed a wide variety of topological features and integrate them with biological features computed from protein amino acid sequence such as bag of words, physicochemical and spectral domain features. We propose a new Sequential Forward Feature Selection (SFFS) algorithm, i.e., random forest-based Boruta feature selection for selecting the best features from computed large feature set. Decision tree, linear discriminant analysis and gradient boosting classifiers are used as learners. We have conducted experiments by considering two reference protein complex datasets of yeast, i.e., CYC2008 and MIPS. Human and mouse complex information is taken from CORUM 3.0 dataset. Protein interaction information is extracted from the database of interacting proteins (DIP). Our proposed SFFS, i.e., random forest-based Brouta feature selection in combination with decision trees, linear discriminant analysis and Gradient Boosting Classifiers outperforms other state of art algorithms by achieving precision, recall and F-measure rates, i.e. 94.58%, 94.92% and 94.45% for MIPS, 96.31%, 93.55% and 96.02% for CYC2008, 98.84%, 98.00%, 98.87 % for CORUM humans and 96.60%, 96.70%, 96.32% for CORUM mouse dataset complexes, respectively.

Microfluidics for Peptidomics, Proteomics, and Cell Analysis

Microfluidics is the advanced microtechnology of fluid manipulation in channels with at least one dimension in the range of 1-100 microns. Microfluidic technology offers a growing number of tools for manipulating small volumes of fluid to control chemical, biological, and physical processes relevant to separation, analysis, and detection. Currently, microfluidic devices play an important role in many biological, chemical, physical, biotechnological and engineering applications. There are numerous ways to fabricate the necessary microchannels and integrate them into microfluidic platforms. In peptidomics and proteomics, microfluidics is often used in combination with mass spectrometric (MS) analysis. This review provides an overview of using microfluidic systems for peptidomics, proteomics and cell analysis. The application of microfluidics in combination with MS detection and other novel techniques to answer clinical questions is also discussed in the context of disease diagnosis and therapy. Recent developments and applications of capillary and microchip (electro)separation methods in proteomic and peptidomic analysis are summarized. The state of the art of microchip platforms for cell sorting and single-cell analysis is also discussed. Advances in detection methods are reported, and new applications in proteomics and peptidomics, quality control of peptide and protein pharmaceuticals, analysis of proteins and peptides in biomatrices and determination of their physicochemical parameters are highlighted.

The role of micropeptides in biology

Micropeptides are small polypeptides coded by small open-reading frames. Progress in computational biology and the analyses of large-scale transcriptomes and proteomes have revealed that mammalian genomes produce a large number of transcripts encoding micropeptides. Many of these have been previously annotated as long noncoding RNAs. The role of micropeptides in cellular homeostasis maintenance has been demonstrated. This review discusses different types of micropeptides as well as methods to identify them, such as computational approaches, ribosome profiling, and mass spectrometry.

An Improved Calibration Approach for Traveling Wave Ion Mobility Spectrometry: Robust, High-Precision Collision Cross Sections

The combination of ion-mobility (IM) separation with mass spectrometry (MS) has impacted global measurement efforts in areas ranging from food analysis to drug discovery. Reasons for the broad adoption of IM-MS include its significantly increased peak capacity, duty-cycle, and ability to reconstruct fragmentation data in parallel, all of which greatly enable the analyses of complex mixtures. More fundamentally, however, measurements of ion-gas molecule collision cross sections (CCSs) are used to support compound identification and quantitation efforts as well as study the structures of large biomolecules. As the first commercialized form of IM-MS, Traveling Wave Ion Mobility (TWIM) devices are operated at low pressures (∼3 mbar) and voltages, are relatively short (∼25 cm), and separate ions on a timescale of tens of milliseconds. These qualities make TWIM ideally suited for hybridization with MS. Owing to the complicated motion of ions in TWIM devices, however, IM transit times must be calibrated to enable CCS measurements. Applicability of these calibrations has hitherto been restricted to primarily singly charged small molecules and some classes of large, multiply charged ions under a significantly narrower range of instrument conditions. Here, we introduce and extensively characterize a dramatically improved TWIM calibration methodology. Using over 2500 experimental TWIM data sets, covering ions that span over 3.5 orders of magnitude of molecular mass, we demonstrate robust calibrations for a significantly expanded range of instrument conditions, thereby opening up new analytical application areas and enabling the expansion of high-precision CCS measurements for both existing and next-generation TWIM instrumentation.

Chromatin Proteomics to Study Epigenetics - Challenges and Opportunities

Regulation of gene expression is essential for the functioning of all eukaryotic organisms. Understanding gene expression regulation requires determining which proteins interact with regulatory elements in chromatin. MS-based analysis of chromatin has emerged as a powerful tool to identify proteins associated with gene regulation, as it allows studying protein function and protein complex formation in their in vivo chromatin-bound context. Total chromatin isolated from cells can be directly analyzed using MS or further fractionated into transcriptionally active and inactive chromatin prior to MS-based analysis. Newly formed chromatin that is assembled during DNA replication can also be specifically isolated and analyzed. Furthermore, capturing specific chromatin domains facilitates the identification of previously unknown transcription factors interacting with these domains. Finally, in recent years, advances have been made toward identifying proteins that interact with a single genomic locus of interest. In this review, we highlight the power of chromatin proteomics approaches and how these provide complementary alternatives compared with conventional affinity purification methods. Furthermore, we discuss the biochemical challenges that should be addressed to consolidate and expand the role of chromatin proteomics as a key technology in the context of gene expression regulation and epigenetics research in health and disease.

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An overview of proteomics methods to study chromatin and gene regulation.

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Strength and limitations of the different approaches are highlighted.

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An outlook on the outstanding challenges for chromatin proteomics.

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Future directions for chromatin proteomics are discussed.

Mass spectrometry-based protein-protein interaction networks for the study of human diseases

A better understanding of the molecular mechanisms underlying disease is key for expediting the development of novel therapeutic interventions. Disease mechanisms are often mediated by interactions between proteins. Insights into the physical rewiring of protein-protein interactions in response to mutations, pathological conditions, or pathogen infection can advance our understanding of disease etiology, progression, and pathogenesis and can lead to the identification of potential druggable targets. Advances in quantitative mass spectrometry (MS)-based approaches have allowed unbiased mapping of these disease-mediated changes in protein-protein interactions on a global scale. Here, we review MS techniques that have been instrumental for the identification of protein-protein interactions at a system-level, and we discuss the challenges associated with these methodologies as well as novel MS advancements that aim to address these challenges. An overview of examples from diverse disease contexts illustrates the potential of MS-based protein-protein interaction mapping approaches for revealing disease mechanisms, pinpointing new therapeutic targets, and eventually moving toward personalized applications.

Direct measurement of protein-protein interactions by FLIM-FRET at UV laser-induced DNA damage sites in living cells

Protein-protein interactions are essential to ensure timely and precise recruitment of chromatin remodellers and repair factors to DNA damage sites. Conventional analyses of protein-protein interactions at a population level may mask the complexity of interaction dynamics, highlighting the need for a method that enables quantification of DNA damage-dependent interactions at a single-cell level. To this end, we integrated a pulsed UV laser on a confocal fluorescence lifetime imaging (FLIM) microscope to induce localized DNA damage. To quantify protein-protein interactions in live cells, we measured Förster resonance energy transfer (FRET) between mEGFP- and mCherry-tagged proteins, based on the fluorescence lifetime reduction of the mEGFP donor protein. The UV-FLIM-FRET system offers a unique combination of real-time and single-cell quantification of DNA damage-dependent interactions, and can distinguish between direct protein-protein interactions, as opposed to those mediated by chromatin proximity. Using the UV-FLIM-FRET system, we show the dynamic changes in the interaction between poly(ADP-ribose) polymerase 1, amplified in liver cancer 1, X-ray repair cross-complementing protein 1 and tripartite motif containing 33 after DNA damage. This new set-up complements the toolset for studying DNA damage response by providing single-cell quantitative and dynamic information about protein-protein interactions at DNA damage sites.

Mutant thermal proteome profiling for characterization of missense protein variants and their associated phenotypes within the proteome

Temperature-sensitive (TS) missense mutants have been foundational for characterization of essential gene function. However, an unbiased approach for analysis of biochemical and biophysical changes in TS missense mutants within the context of their functional proteomes is lacking. We applied MS-based thermal proteome profiling (TPP) to investigate the proteome-wide effects of missense mutations in an application that we refer to as mutant thermal proteome profiling (mTPP). This study characterized global impacts of temperature sensitivity-inducing missense mutations in two different subunits of the 26S proteasome. The majority of alterations identified by RNA-Seq and global proteomics were similar between the mutants, which could suggest that a similar functional disruption is occurring in both missense variants. Results from mTPP, however, provide unique insights into the mechanisms that contribute to the TS phenotype in each mutant, revealing distinct changes that were not obtained using only steady-state transcriptome and proteome analyses. Computationally, multisite λ-dynamics simulations add clear support for mTPP experimental findings. This work shows that mTPP is a precise approach to measure changes in missense mutant-containing proteomes without the requirement for large amounts of starting material, specific antibodies against proteins of interest, and/or genetic manipulation of the biological system. Although experiments were performed under permissive conditions, mTPP provided insights into the underlying protein stability changes that cause dramatic cellular phenotypes observed at nonpermissive temperatures. Overall, mTPP provides unique mechanistic insights into missense mutation dysfunction and connection of genotype to phenotype in a rapid, nonbiased fashion.

Approaches to Investigating the Protein Interactome of PTEN

The tumor suppressor phosphatase and tensin homologue (PTEN) is a redox-sensitive dual specificity phosphatase with an essential role in the negative regulation of the PI3K-AKT signaling pathway, affecting metabolic and cell survival processes. PTEN is commonly mutated in cancer, and dysregulation in the metabolism of PIP₃ is implicated in other diseases such as diabetes. PTEN interactors are responsible for some functional roles of PTEN beyond the negative regulation of the PI3K pathway and are thus of great importance in cell biology. Both high-data content proteomics-based approaches and low-data content PPI approaches have been used to investigate the interactome of PTEN and elucidate further functions of PTEN. While low-data content approaches rely on co-immunoprecipitation and Western blotting, and as such require previously generated hypotheses, high-data content approaches such as affinity pull-down proteomic assays or the yeast 2-hybrid system are hypothesis generating. This review provides an overview of the PTEN interactome, including redox effects, and critically appraises the methods and results of high-data content investigations into the global interactome of PTEN. The biological significance of findings from recent studies is discussed and illustrates the breadth of cellular functions of PTEN that can be discovered by these approaches.