Determining protein complex connectivity using a probabilistic deletion network derived from quantitative proteomics

RebL1 is required for macronuclear structure stability and gametogenesis in Tetrahymena thermophila

Histone modification and nucleosome assembly play important roles in chromatin-related processes. Histone chaperones form different complexes and coordinate histone transportation and assembly. Various histone chaperone complexes have been identified in different organisms. The ciliate protozoa (ciliates) have various chromatin structures and different nuclear morphology. However, histone chaperone components and functions of different subunits remain unclear in ciliates. Tetrahymema thermophila contains a transcriptionally active macronucleus (MAC) and a transcriptionally inactive micronucleus (MIC) which exhibit multiple replication and various chromatin remodeling progresses during vegetative growth and sexual developmental stages. Here, we found histone chaperone RebL1 not only localized evenly in the transcriptionally active MAC but also dynamically changed in the MIC during vegetative growth and sexual developmental stages. REBL1 knockdown inhibited cellular proliferation. The macronuclear morphology became bigger in growing mutants. The abnormal macronuclear structure also occurred in the starvation stage. Furthermore, micronuclear meiosis was disturbed during sexual development, leading to a failure to generate new gametic nuclei. RebL1 potentially interacted with various factors involved in histone-modifying complexes and chromatin remodeling complexes in different developmental stages. REBL1 knockdown affected expression levels of the genes involved in chromatin organization and transcription. Taken together, RebL1 plays a vital role in maintaining macronuclear structure stability and gametogenesis in T. thermophila.

Supplementary Information

The online version contains supplementary material available at 10.1007/s42995-024-00219-z.

Meta-analysis of dispensable essential genes and their interactions with bypass suppressors

Genes have been historically classified as essential or non-essential based on their requirement for viability. However, genomic mutations can sometimes bypass the requirement for an essential gene, challenging the binary classification of gene essentiality. Such dispensable essential genes represent a valuable model for understanding the incomplete penetrance of loss-of-function mutations often observed in natural populations. Here, we compiled data from multiple studies on essential gene dispensability in Saccharomyces cerevisiae to comprehensively characterize these genes. In analyses spanning different evolutionary timescales, dispensable essential genes exhibited distinct phylogenetic properties compared with other essential and non-essential genes. Integration of interactions with suppressor genes that can bypass the gene essentiality revealed the high functional modularity of the bypass suppression network. Furthermore, dispensable essential and bypass suppressor gene pairs reflected simultaneous changes in the mutational landscape of S. cerevisiae strains. Importantly, species in which dispensable essential genes were non-essential tended to carry bypass suppressor mutations in their genomes. Overall, our study offers a comprehensive view of dispensable essential genes and illustrates how their interactions with bypass suppressors reflect evolutionary outcomes.

Cryo-EM structure of the Saccharomyces cerevisiae Rpd3L histone deacetylase complex

The Rpd3L histone deacetylase (HDAC) complex is an ancient 12-subunit complex conserved in a broad range of eukaryotes that performs localized deacetylation at or near sites of recruitment by DNA-bound factors. Here we describe the cryo-EM structure of this prototypical HDAC complex that is characterized by as many as seven subunits performing scaffolding roles for the tight integration of the only catalytic subunit, Rpd3. The principal scaffolding protein, Sin3, along with Rpd3 and the histone chaperone, Ume1, are present in two copies, with each copy organized into separate lobes of an asymmetric dimeric molecular assembly. The active site of one Rpd3 is completely occluded by a leucine side chain of Rxt2, while the tips of the two lobes and the more peripherally associated subunits exhibit varying levels of flexibility and positional disorder. The structure reveals unexpected structural homology/analogy between unrelated subunits in the fungal and mammalian complexes and provides a foundation for deeper interrogations of structure, biology, and mechanism of these complexes, as well as for the discovery of HDAC complex-specific inhibitors.

Opposing Roles of FACT for Euchromatin and Heterochromatin in Yeast

DNA is stored in the nucleus of a cell in a folded state; however, only the necessary genetic information is extracted from the required group of genes. The key to extracting genetic information is chromatin ambivalence. Depending on the chromosomal region, chromatin is characterized into low-density "euchromatin" and high-density "heterochromatin", with various factors being involved in its regulation. Here, we focus on chromatin regulation and gene expression by the yeast FACT complex, which functions in both euchromatin and heterochromatin. FACT is known as a histone H2A/H2B chaperone and was initially reported as an elongation factor associated with RNA polymerase II. In budding yeast, FACT activates promoter chromatin by interacting with the transcriptional activators SBF/MBF via the regulation of G1/S cell cycle genes. In fission yeast, FACT plays an important role in the formation of higher-order chromatin structures and transcriptional repression by binding to Swi6, an HP1 family protein, at heterochromatin. This FACT property, which refers to the alternate chromatin-regulation depending on the binding partner, is an interesting phenomenon. Further analysis of nucleosome regulation within heterochromatin is expected in future studies.

The Fng3 ING protein regulates H3 acetylation and H4 deacetylation by interacting with two distinct histone-modifying complexes

The steady-state level of histone acetylation is maintained by histone acetyltransferase (HAT) and histone deacetylase (HDAC) complexes. INhibitor of Growth (ING) proteins are key components of the HAT or HDAC complexes but their relationship with other components and roles in phytopathogenic fungi are not well-characterized. Here, the FNG3 ING gene was functionally characterized in the wheat head blight fungus Fusarium graminearum. Deletion of FNG3 results in defects in fungal development and pathogenesis. Unlike other ING proteins that are specifically associated with distinct complexes, Fng3 was associated with both NuA3 HAT and FgRpd3 HDAC complexes to regulate H3 acetylation and H4 deacetylation. Whereas FgNto1 mediates the FgSas3-Fng3 interaction in the NuA3 complex, Fng3 interacted with the C-terminal region of FgRpd3 that is present in Rpd3 orthologs from filamentous fungi but absent in yeast Rpd3. The intrinsically disordered regions in the C-terminal tail of FgRpd3 underwent phase separation, which was important for its interaction with Fng3. Furthermore, the ING domain of Fng3 is responsible for its specificities in protein-protein interactions and functions. Taken together, Fng3 is involved in the dynamic regulation of histone acetylation by interacting with two histone modification complexes, and is important for fungal development and pathogenicity.

The Rpd3-Complex Regulates Expression of Multiple Cell Surface Recycling Factors in Yeast

Intracellular trafficking pathways control residency and bioactivity of integral membrane proteins at the cell surface. Upon internalisation, surface cargo proteins can be delivered back to the plasma membrane via endosomal recycling pathways. Recycling is thought to be controlled at the metabolic and transcriptional level, but such mechanisms are not fully understood. In yeast, recycling of surface proteins can be triggered by cargo deubiquitination and a series of molecular factors have been implicated in this trafficking. In this study, we follow up on the observation that many subunits of the Rpd3 lysine deacetylase complex are required for recycling. We validate ten Rpd3-complex subunits in recycling using two distinct assays and developed tools to quantify both. Fluorescently labelled Rpd3 localises to the nucleus and complements recycling defects, which we hypothesised were mediated by modulated expression of Rpd3 target gene(s). Bioinformatics implicated 32 candidates that function downstream of Rpd3, which were over-expressed and assessed for capacity to suppress recycling defects of rpd3∆ cells. This effort yielded three hits: Sit4, Dit1 and Ldb7, which were validated with a lipid dye recycling assay. Additionally, the essential phosphatidylinositol-4-kinase Pik1 was shown to have a role in recycling. We propose recycling is governed by Rpd3 at the transcriptional level via multiple downstream target genes.

Generating topological protein interaction scores and data visualization with TopS

Detecting subnetworks in large networks is of great interest. Recently, we developed a topological score framework for the analysis of protein interaction networks and implemented it as a web application, called TopS. Given a multivariate data presented as a matrix, TopS generates topological scores between any column and row in the matrix aiming to identify overwhelming preference interactions. This information can be further used into visualization tools such as clusters and networks to investigate how networks benefit from these interactions. We present a web tool called TopS that aims to have an intuitive user interface. Users can upload data from a simple delimited CSV file that can be created in a spreadsheet program. As an output, user is provided with a scoring matrix as tab-delimited file that can be interchanged with other software, heatmap and clustering figures in pdf format. Here we demonstrate the current capabilities of TopS using an existing dataset generated for the study of the human Sin3 chromatin remodeling complex.

Rpd3L HDAC links H3K4me3 to transcriptional repression memory

Transcriptional memory is critical for the faster reactivation of necessary genes upon environmental changes and requires that the genes were previously in an active state. However, whether transcriptional repression also displays ‘memory’ of the prior transcriptionally inactive state remains unknown. In this study, we show that transcriptional repression of ∼540 genes in yeast occurs much more rapidly if the genes have been previously repressed during carbon source shifts. This novel transcriptional response has been termed transcriptional repression memory (TREM). Interestingly, Rpd3L histone deacetylase (HDAC), targeted to active promoters induces TREM. Mutants for Rpd3L exhibit increased acetylation at active promoters and delay TREM significantly. Surprisingly, the interaction between H3K4me3 and Rpd3L via the Pho23 PHD finger is critical to promote histone deacetylation and TREM by Rpd3L. Therefore, we propose that an active mark, H3K4me3 enriched at active promoters, instructs Rpd3L HDAC to induce histone deacetylation and TREM.

Identification of Topological Network Modules in Perturbed Protein Interaction Networks

Biological networks consist of functional modules, however detecting and characterizing such modules in networks remains challenging. Perturbing networks is one strategy for identifying modules. Here we used an advanced mathematical approach named topological data analysis (TDA) to interrogate two perturbed networks. In one, we disrupted the S. cerevisiae INO80 protein interaction network by isolating complexes after protein complex components were deleted from the genome. In the second, we reanalyzed previously published data demonstrating the disruption of the human Sin3 network with a histone deacetylase inhibitor. Here we show that disrupted networks contained topological network modules (TNMs) with shared properties that mapped onto distinct locations in networks. We define TMNs as proteins that occupy close network positions depending on their coordinates in a topological space. TNMs provide new insight into networks by capturing proteins from different categories including proteins within a complex, proteins with shared biological functions, and proteins disrupted across networks.

Proteomic and Genomic Analyses of the Rvb1 and Rvb2 Interaction Network upon Deletion of R2TP Complex Components

The highly conserved yeast R2TP complex, consisting of Rvb1, Rvb2, Pih1, and Tah1, participates in diverse cellular processes ranging from assembly of protein complexes to apoptosis. Rvb1 and Rvb2 are closely related proteins belonging to the AAA+ superfamily and are essential for cell survival. Although Rvbs have been shown to be associated with various protein complexes including the Ino80 and Swr1chromatin remodeling complexes, we performed a systematic quantitative proteomic analysis of their associated proteins and identified two additional complexes that associate with Rvb1 and Rvb2: the chaperonin-containing T-complex and the 19S regulatory particle of the proteasome complex. We also analyzed Rvb1 and Rvb2 purified from yeast strains devoid of PIH1 and TAH1. These analyses revealed that both Rvb1 and Rvb2 still associated with Hsp90 and were highly enriched with RNA polymerase II complex components. Our analyses also revealed that both Rvb1 and Rvb2 were recruited to the Ino80 and Swr1 chromatin remodeling complexes even in the absence of Pih1 and Tah1 proteins. Using further biochemical analysis, we showed that Rvb1 and Rvb2 directly interacted with Hsp90 as well as with the RNA polymerase II complex. RNA-Seq analysis of the deletion strains compared with the wild-type strains revealed an up-regulation of ribosome biogenesis and ribonucleoprotein complex biogenesis genes, down-regulation of response to abiotic stimulus genes, and down-regulation of response to temperature stimulus genes. A Gene Ontology analysis of the 80 proteins whose protein associations were altered in the PIH1 or TAH1 deletion strains found ribonucleoprotein complex proteins to be the most enriched category. This suggests an important function of the R2TP complex in ribonucleoprotein complex biogenesis at both the proteomic and genomic levels. Finally, these results demonstrate that deletion network analyses can provide novel insights into cellular systems.

Homodimeric PHD Domain-containing Rco1 Subunit Constitutes a Critical Interaction Hub within the Rpd3S Histone Deacetylase Complex

Recognition of histone post-translational modifications is pivotal for directing chromatin-modifying enzymes to specific genomic regions and regulating their activities. Emerging evidence suggests that other structural features of nucleosomes also contribute to precise targeting of downstream chromatin complexes, such as linker DNA, the histone globular domain, and nucleosome spacing. However, how chromatin complexes coordinate individual interactions to achieve high affinity and specificity remains unclear. The Rpd3S histone deacetylase utilizes the chromodomain-containing Eaf3 subunit and the PHD domain-containing Rco1 subunit to recognize nucleosomes that are methylated at lysine 36 of histone H3 (H3K36me). We showed previously that the binding of Eaf3 to H3K36me can be allosterically activated by Rco1. To investigate how this chromatin recognition module is regulated in the context of the Rpd3S complex, we first determined the subunit interaction network of Rpd3S. Interestingly, we found that Rpd3S contains two copies of the essential subunit Rco1, and both copies of Rco1 are required for full functionality of Rpd3S. Our functional dissection of Rco1 revealed that besides its known chromatin-recognition interfaces, other regions of Rco1 are also critical for Rpd3S to recognize its nucleosomal substrates and functionin vivo. This unexpected result uncovered an important and understudied aspect of chromatin recognition. It suggests that precisely reading modified chromatin may not only need the combined actions of reader domains but also require an internal signaling circuit that coordinates the individual actions in a productive way.

Suberoylanilide hydroxamic acid (SAHA)-induced dynamics of a human histone deacetylase protein interaction network

Histone deacetylases (HDACs) are targets for cancer therapy. Suberoylanilide hydroxamic acid (SAHA) is an HDAC inhibitor approved by the U.S. Food and Drug Administration for the treatment of cutaneous T-cell lymphoma. To obtain a better mechanistic understanding of the Sin3/HDAC complex in cancer, we extended its protein-protein interaction network and identified a mutually exclusive pair within the complex. We then assessed the effects of SAHA on the disruption of the complex network through six homologous baits. SAHA perturbs multiple protein interactions and therefore compromises the composition of large parts of the Sin3/HDAC network. A comparison of the effect of SAHA treatment on gene expression in breast cancer cells to a knockdown of the ING2 subunit indicated that a portion of the anticancer effects of SAHA may be attributed to the disruption of ING2's association with the complex. Our dynamic protein interaction network resource provides novel insights into the molecular mechanism of SAHA action and demonstrates the potential for drugs to rewire networks.

Bypassing the requirement for an essential MYST acetyltransferase

Histone acetylation is a key regulatory feature for chromatin that is established by opposing enzymatic activities of lysine acetyltransferases (KATs/HATs) and deacetylases (KDACs/HDACs). Esa1, like its human homolog Tip60, is an essential MYST family enzyme that acetylates histones H4 and H2A and other nonhistone substrates. Here we report that the essential requirement for ESA1 in Saccharomyces cerevisiae can be bypassed upon loss of Sds3, a noncatalytic subunit of the Rpd3L deacetylase complex. By studying the esa1∆ sds3∆ strain, we conclude that the essential function of Esa1 is in promoting the cellular balance of acetylation. We demonstrate this by fine-tuning acetylation through modulation of HDACs and the histone tails themselves. Functional interactions between Esa1 and HDACs of class I, class II, and the Sirtuin family define specific roles of these opposing activities in cellular viability, fitness, and response to stress. The fact that both increased and decreased expression of the ESA1 homolog TIP60 has cancer associations in humans underscores just how important the balance of its activity is likely to be for human well-being.

Quantitative proteomics demonstrates that the RNA polymerase II subunits Rpb4 and Rpb7 dissociate during transcriptional elongation

Eukaryotic RNA polymerase II (RNAPII) is a 12-subunit enzyme that is responsible for the transcription of messenger RNA. Two of the subunits of RNA polymerase II, Rpb4 and Rpb7, have been shown to dissociate from the enzyme under a number of specific laboratory conditions. However, a biological context for the dissociation of Rpb4 and Rpb7 has not been identified. We have found that Rpb4/7 dissociate from RNAPII upon interaction with specific transcriptional elongation-associated proteins that are recruited to the hyperphosphorylated form of the C-terminal domain. However, the dissociation of Rpb4/7 is likely short lived because a significant level of free Rpb4/7 was not detected by quantitative proteomic analyses. In addition, we have found that RNAPII that is isolated through Rpb7 is depleted in serine 2 C-terminal domain phosphorylation. In contrast to previous reports, these data indicate that Rpb4/7 are dispensable during specific stages of transcriptional elongation in Saccharomyces cerevisiae.

Proteomic approaches to the study of papillomavirus-host interactions

The identification of interactions between viral and host cellular proteins has provided major insights into papillomavirus research, and these interactions are especially relevant to the role of papillomaviruses in the cancers with which they are associated. Recent advances in mass spectrometry technology and data processing now allow the systematic identification of such interactions. This has led to an improved understanding of the different pathologies associated with the many papillomavirus types, and the diverse nature of these viruses is reflected in the spectrum of interactions with host proteins. Here we review a history of proteomic approaches, particularly as applied to the papillomaviruses, and summarize current techniques. Current proteomic studies on the papillomaviruses use yeast-two-hybrid or affinity purification-mass spectrometry approaches. We detail the advantages and disadvantages of each and describe current examples of papillomavirus proteomic studies, with a particular focus on the HPV E6 and E7 oncoproteins.

Computational and informatics strategies for identification of specific protein interaction partners in affinity purification mass spectrometry experiments

Analysis of protein interaction networks and protein complexes using affinity purification and mass spectrometry (AP/MS) is among most commonly used and successful applications of proteomics technologies. One of the foremost challenges of AP/MS data is a large number of false-positive protein interactions present in unfiltered data sets. Here we review computational and informatics strategies for detecting specific protein interaction partners in AP/MS experiments, with a focus on incomplete (as opposite to genome wide) interactome mapping studies. These strategies range from standard statistical approaches, to empirical scoring schemes optimized for a particular type of data, to advanced computational frameworks. The common denominator among these methods is the use of label-free quantitative information such as spectral counts or integrated peptide intensities that can be extracted from AP/MS data. We also discuss related issues such as combining multiple biological or technical replicates, and dealing with data generated using different tagging strategies. Computational approaches for benchmarking of scoring methods are discussed, and the need for generation of reference AP/MS data sets is highlighted. Finally, we discuss the possibility of more extended modeling of experimental AP/MS data, including integration with external information such as protein interaction predictions based on functional genomics data.

Exploring the yeast acetylome using functional genomics

Lysine acetylation is a dynamic posttranslational modification with a well-defined role in regulating histones. The impact of acetylation on other cellular functions remains relatively uncharacterized. We explored the budding yeast acetylome with a functional genomics approach, assessing the effects of gene overexpression in the absence of lysine deacetylases (KDACs). We generated a network of 463 synthetic dosage lethal (SDL) interactions involving class I and II KDACs, revealing many cellular pathways regulated by different KDACs. A biochemical survey of genes interacting with the KDAC RPD3 identified 72 proteins acetylated in vivo. In-depth analysis of one of these proteins, Swi4, revealed a role for acetylation in G1-specific gene expression. Acetylation of Swi4 regulates interaction with its partner Swi6, both components of the SBF transcription factor. This study expands our view of the yeast acetylome, demonstrates the utility of functional genomic screens for exploring enzymatic pathways, and provides functional information that can be mined for future studies.

Beyond hairballs: The use of quantitative mass spectrometry data to understand protein-protein interactions

The past 10 years have witnessed a dramatic proliferation in the availability of protein interaction data. However, for interaction mapping based on affinity purification coupled with mass spectrometry (AP-MS), there is a wealth of information present in the datasets that often goes unrecorded in public repositories, and as such remains largely unexplored. Further, how this type of data is represented and used by bioinformaticians has not been well established. Here, we point out some common mistakes in how AP-MS data are handled, and describe how protein complex organization and interaction dynamics can be inferred using quantitative AP-MS approaches.

Mapping the protein interaction network of the human COP9 signalosome complex using a label-free QTAX strategy

The COP9 signalosome (CSN) is a multi-subunit protein complex that performs critical roles in controlling diverse cellular and developmental processes. Aberrant regulation of the CSN complex has been shown to lead to tumorigenesis. Despite its biological significance, our current knowledge of the function and regulation of the CSN complex is very limited. To explore CSN biology, we have developed and employed a new version of the tag team-based QTAX strategy (quantitative analysis of tandem affinity purified in vivo cross-linked (X) protein complexes) by incorporating a label-free MS method for quantitation. Coupled with protein interaction network analysis, this strategy produced a comprehensive and detailed assessment of the protein interaction network of the human CSN complex. In total, we quantitatively characterized 825 putative CSN-interacting proteins, with 270 classified as core interactors (captured by all three bait purifications). Biochemical validation further confirms the validity of selected identified interactors. This work presents the most complete analysis of the CSN interaction network to date, providing an inclusive set of physical interaction data consistent with physiological roles for the CSN. Moreover, the methodology described here is a general proteomic tool for the comprehensive study of protein interaction networks.

Characterization of a highly conserved histone related protein, Ydl156w, and its functional associations using quantitative proteomic analyses

A significant challenge in biology is to functionally annotate novel and uncharacterized proteins. Several approaches are available for deducing the function of proteins in silico based upon sequence homology and physical or genetic interaction, yet this approach is limited to proteins with well-characterized domains, paralogs and/or orthologs in other species, as well as on the availability of suitable large-scale data sets. Here, we present a quantitative proteomics approach extending the protein network of core histones H2A, H2B, H3, and H4 in Saccharomyces cerevisiae, among which a novel associated protein, the previously uncharacterized Ydl156w, was identified. In order to predict the role of Ydl156w, we designed and applied integrative bioinformatics, quantitative proteomics and biochemistry approaches aiming to infer its function. Reciprocal analysis of Ydl156w protein interactions demonstrated a strong association with all four histones and also to proteins strongly associated with histones including Rim1, Rfa2 and 3, Yku70, and Yku80. Through a subsequent combination of the focused quantitative proteomics experiments with available large-scale genetic interaction data and Gene Ontology functional associations, we provided sufficient evidence to associate Ydl156w with multiple processes including chromatin remodeling, transcription and DNA repair/replication. To gain deeper insights into the role of Ydl156w in histone biology we investigated the effect of the genetic deletion of ydl156w on H4 associated proteins, which lead to a dramatic decrease in the association of H4 with RNA polymerase III proteins. The implication of a role for Ydl156w in RNA Polymerase III mediated transcription was consequently verified by RNA-Seq experiments. Finally, using these approaches we generated a refined network of Ydl156w-associated proteins.

Unraveling tobacco BY-2 protein complexes with BN PAGE/LC-MS/MS and clustering methods

To understand physiological processes, insight into protein complexes is very important. Through a combination of blue native gel electrophoresis and LC-MS/MS, we were able to isolate protein complexes and identify their potential subunits from Nicotiana tabacum cv. Bright Yellow-2. For this purpose, a bioanalytical approach was used that works without a priori knowledge of the interacting proteins. Different clustering methods (e.g., k-means and hierarchical clustering) and a biclustering approach were evaluated according to their ability to group proteins by their migration profile and to correlate the proteins to a specific complex. The biclustering approach was identified as a very powerful tool for the exploration of protein complexes of whole cell lysates since it allows for the promiscuous nature of proteins. Furthermore, it searches for associations between proteins that co-occur frequently throughout the BN gel, which increases the confidence of the putative associations between co-migrating proteins. The statistical significance and biological relevance of the profile clusters were verified using functional gene ontology annotation. The proof of concept for identifying protein complexes by our BN PAGE/LC-MS/MS approach is provided through the analysis of known protein complexes. Both well characterized long-lived protein complexes as well as potential temporary sequential multi-enzyme complexes were characterized.

Combinatorial depletion analysis to assemble the network architecture of the SAGA and ADA chromatin remodeling complexes

Despite the availability of several large-scale proteomics studies aiming to identify protein interactions on a global scale, little is known about how proteins interact and are organized within macromolecular complexes. Here, we describe a technique that consists of a combination of biochemistry approaches, quantitative proteomics and computational methods using wild-type and deletion strains to investigate the organization of proteins within macromolecular protein complexes. We applied this technique to determine the organization of two well-studied complexes, Spt-Ada-Gcn5 histone acetyltransferase (SAGA) and ADA, for which no comprehensive high-resolution structures exist. This approach revealed that SAGA/ADA is composed of five distinct functional modules, which can persist separately. Furthermore, we identified a novel subunit of the ADA complex, termed Ahc2, and characterized Sgf29 as an ADA family protein present in all Gcn5 histone acetyltransferase complexes. Finally, we propose a model for the architecture of the SAGA and ADA complexes, which predicts novel functional associations within the SAGA complex and provides mechanistic insights into phenotypical observations in SAGA mutants.

Building protein-protein interaction networks with proteomics and informatics tools

The systematic characterization of the whole interactomes of different model organisms has revealed that the eukaryotic proteome is highly interconnected. Therefore, biological research is progressively shifting away from classical approaches that focus only on a few proteins toward whole protein interaction networks to describe the relationship of proteins in biological processes. In this minireview, we survey the most common methods for the systematic identification of protein interactions and exemplify different strategies for the generation of protein interaction networks. In particular, we will focus on the recent development of protein interaction networks derived from quantitative proteomics data sets.

Unraveling the dynamics of protein interactions with quantitative mass spectrometry

Knowledge of structure and dynamics of proteins and protein complexes is important to unveil the molecular basis and mechanisms involved in most biological processes. Protein complex dynamics can be defined as the changes in the composition of a protein complex during a cellular process. Protein dynamics can be defined as conformational changes in a protein during enzyme activation, for example, when a protein binds to a ligand or when a protein binds to another protein. Mass spectrometry (MS) combined with affinity purification has become the analytical tool of choice for mapping protein-protein interaction networks and the recent developments in the quantitative proteomics field has made it possible to identify dynamically interacting proteins. Furthermore, hydrogen/deuterium exchange MS is emerging as a powerful technique to study structure and conformational dynamics of proteins or protein assemblies in solution. Methods have been developed and applied for the identification of transient and/or weak dynamic interaction partners and for the analysis of conformational dynamics of proteins or protein complexes. This review is an overview of existing and recent developments in studying the overall dynamics of in vivo protein interaction networks and protein complexes using MS-based methods.

Construction of protein interaction networks based on the label-free quantitative proteomics

Multiprotein complexes are essential building blocks for many cellular processes in an organism. Taking the process of transcription as an example, the interplay of several chromatin-remodeling complexes is responsible for the tight regulation of gene expression. Knowing how those proteins associate into protein complexes not only helps to improve our understanding of these cellular processes, but can also lead to the discovery of the function of novel interacting proteins. Given the large number of proteins with little to no functional annotation throughout many organisms, including human, the identification and characterization of protein complexes has grown into a major focus of network biology. Toward this goal, we have developed several computational approaches based upon label-free quantitative proteomics approaches for the analysis of protein complexes and protein interaction networks. Here, we describe the computational approaches used to build probabilistic protein interaction networks, which are detailed in this chapter using the example of complexes involved in chromatin remodeling and transcription.

Highly reproducible label free quantitative proteomic analysis of RNA polymerase complexes

The use of quantitative proteomics methods to study protein complexes has the potential to provide in-depth information on the abundance of different protein components as well as their modification state in various cellular conditions. To interrogate protein complex quantitation using shotgun proteomic methods, we have focused on the analysis of protein complexes using label-free multidimensional protein identification technology and studied the reproducibility of biological replicates. For these studies, we focused on three highly related and essential multi-protein enzymes, RNA polymerase I, II, and III from Saccharomyces cerevisiae. We found that label-free quantitation using spectral counting is highly reproducible at the protein and peptide level when analyzing RNA polymerase I, II, and III. In addition, we show that peptide sampling does not follow a random sampling model, and we show the need for advanced computational models to predict peptide detection probabilities. In order to address these issues, we used the APEX protocol to model the expected peptide detectability based on whole cell lysate acquired using the same multidimensional protein identification technology analysis used for the protein complexes. Neither method was able to predict the peptide sampling levels that we observed using replicate multidimensional protein identification technology analyses. In addition to the analysis of the RNA polymerase complexes, our analysis provides quantitative information about several RNAP associated proteins including the RNAPII elongation factor complexes DSIF and TFIIF. Our data shows that DSIF and TFIIF are the most highly enriched RNAP accessory factors in Rpb3-TAP purifications and demonstrate our ability to measure low level associated protein abundance across biological replicates. In addition, our quantitative data supports a model in which DSIF and TFIIF interact with RNAPII in a dynamic fashion in agreement with previously published reports.

Driving biochemical discovery with quantitative proteomics

Proteomic analysis of biological samples plays an increasing role in modern research. Although the application of proteomics technologies varies across many disciplines, proteomics largely is a tool for discovery that then leads to novel hypotheses. In recent years, new methods and technologies have been developed and applied in many areas of proteomics, and there is a strong push towards using proteomics in a quantitative manner. Indeed, mass spectrometry-based, quantitative proteomics approaches have been applied to great success in a variety of biochemical studies. In particular, the use of quantitative proteomics provides new insights into protein complexes and post-translational modifications and leads to the generation of novel insights into these important biochemical systems.

Relative, label-free protein quantitation: spectral counting error statistics from nine replicate MudPIT samples

Nine replicate samples of peptides from soybean leaves, each spiked with a different concentration of bovine apotransferrin peptides, were analyzed on a mass spectrometer using multidimensional protein identification technology (MudPIT). Proteins were detected from the peptide tandem mass spectra, and the numbers of spectra were statistically evaluated for variation between samples. The results corroborate prior knowledge that combining spectra from replicate samples increases the number of identifiable proteins and that a summed spectral count for a protein increases linearly with increasing molar amounts of protein. Furthermore, statistical analysis of spectral counts for proteins in two- and three-way comparisons between replicates and combined replicates revealed little significant variation arising from run-to-run differences or data-dependent instrument ion sampling that might falsely suggest differential protein accumulation. In these experiments, spectral counting was enabled by PANORAMICS, probability-based software that predicts proteins detected by sets of observed peptides. Three alternative approaches to counting spectra were also evaluated by comparison. As the counting thresholds were changed from weaker to more stringent, the accuracy of ratio determination also changed. These results suggest that thresholds for counting can be empirically set to improve relative quantitation. All together, the data confirm the accuracy and reliability of label-free spectral counting in the relative, quantitative analysis of proteins between samples.

Refinements to label free proteome quantitation: how to deal with peptides shared by multiple proteins

Quantitative shotgun proteomics is dependent on the detection, identification, and quantitative analysis of peptides. An issue arises with peptides that are shared between multiple proteins. What protein did they originate from and how should these shared peptides be used in a quantitative proteomics workflow? To systematically evaluate shared peptides in label-free quantitative proteomics, we devised a well-defined protein sample consisting of known concentrations of six albumins from different species, which we added to a highly complex yeast lysate. We used the spectral counts based normalized spectral abundance factor (NSAF) as the starting point for our analysis and compared an exhaustive list of possible combinations of parameters to determine what was the optimal approach for dealing with shared peptides and shared spectral counts. We showed that distributing shared spectral counts based on the number of unique spectral counts led to the most accurate and reproducible results.

Profiling of protein interaction networks of protein complexes using affinity purification and quantitative mass spectrometry

Protein-protein interactions are important for nearly all biological processes, and it is known that aberrant protein-protein interactions can lead to human disease and cancer. Recent evidence has suggested that protein interaction interfaces describe a new class of attractive targets for drug development. Full characterization of protein interaction networks of protein complexes and their dynamics in response to various cellular cues will provide essential information for us to understand how protein complexes work together in cells to maintain cell viability and normal homeostasis. Affinity purification coupled with quantitative mass spectrometry has become the primary method for studying in vivo protein interactions of protein complexes and whole organism proteomes. Recent developments in sample preparation and affinity purification strategies allow the capture, identification, and quantification of protein interactions of protein complexes that are stable, dynamic, transient, and/or weak. Current efforts have mainly focused on generating reliable, reproducible, and high confidence protein interaction data sets for functional characterization. The availability of increasing amounts of information on protein interactions in eukaryotic systems and new bioinformatics tools allow functional analysis of quantitative protein interaction data to unravel the biological significance of the identified protein interactions. Existing studies in this area have laid a solid foundation toward generating a complete map of in vivo protein interaction networks of protein complexes in cells or tissues.