A New in Vivo Cross-linking Mass Spectrometry Platform to Define Protein–Protein Interactions in Living Cells

Protein–protein interactions (PPIs) are fundamental to the structure and function of protein complexes. Resolving the physical contacts between proteins as they occur in cells is critical to uncovering the molecular details underlying various cellular activities. To advance the study of PPIs in living cells, we have developed a new in vivo cross-linking mass spectrometry platform that couples a novel membrane-permeable, enrichable, and MS-cleavable cross-linker with multistage tandem mass spectrometry. This strategy permits the effective capture, enrichment, and identification of in vivo cross-linked products from mammalian cells and thus enables the determination of protein interaction interfaces. The utility of the developed method has been demonstrated by profiling PPIs in mammalian cells at the proteome scale and the targeted protein complex level. Our work represents a general approach for studying in vivo PPIs and provides a solid foundation for future studies toward the complete mapping of PPI networks in living systems.Protein–protein interactions (PPIs)¹ play a key role in defining protein functions in biological systems. Aberrant PPIs can have drastic effects on biochemical activities essential to cell homeostasis, growth, and proliferation, and thereby lead to various human diseases (1). Consequently, PPI interfaces have been recognized as a new paradigm for drug development. Therefore, mapping PPIs and their interaction interfaces in living cells is critical not only for a comprehensive understanding of protein function and regulation, but also for describing the molecular mechanisms underlying human pathologies and identifying potential targets for better therapeutics.Several strategies exist for identifying and mapping PPIs, including yeast two-hybrid, protein microarray, and affinity purification mass spectrometry (AP-MS) (2–5). Thanks to new developments in sample preparation strategies, mass spectrometry technologies, and bioinformatics tools, AP-MS has become a powerful and preferred method for studying PPIs at the systems level (6–9). Unlike other approaches, AP-MS experiments allow the capture of protein interactions directly from their natural cellular environment, thus better retaining native protein structures and biologically relevant interactions. In addition, a broader scope of PPI networks can be obtained with greater sensitivity, accuracy, versatility, and speed. Despite the success of this very promising technique, AP-MS experiments can lead to the loss of weak/transient interactions and/or the reorganization of protein interactions during biochemical manipulation under native purification conditions. To circumvent these problems, in vivo chemical cross-linking has been successfully employed to stabilize protein interactions in native cells or tissues prior to cell lysis (10–16). The resulting covalent bonds formed between interacting partners allow affinity purification under stringent and fully denaturing conditions, consequently reducing nonspecific background while preserving stable and weak/transient interactions (12–16). Subsequent mass spectrometric analysis can reveal not only the identities of interacting proteins, but also cross-linked amino acid residues. The latter provides direct molecular evidence describing the physical contacts between and within proteins (17). This information can be used for computational modeling to establish structural topologies of proteins and protein complexes (17–22), as well as for generating experimentally derived protein interaction network topology maps (23, 24). Thus, cross-linking mass spectrometry (XL-MS) strategies represent a powerful and emergent technology that possesses unparalleled capabilities for studying PPIs.Despite their great potential, current XL-MS studies that have aimed to identify cross-linked peptides have been mostly limited to in vitro cross-linking experiments, with few successfully identifying protein interaction interfaces in living cells (24, 25). This is largely because XL-MS studies remain challenging due to the inherent difficulty in the effective MS detection and accurate identification of cross-linked peptides, as well as in unambiguous assignment of cross-linked residues. In general, cross-linked products are heterogeneous and low in abundance relative to non-cross-linked products. In addition, their MS fragmentation is too complex to be interpreted using conventional database searching tools (17, 26). It is noted that almost all of the current in vivo PPI studies utilize formaldehyde cross-linking because of its membrane permeability and fast kinetics (10–16). However, in comparison to the most commonly used amine reactive NHS ester cross-linkers, identification of formaldehyde cross-linked peptides is even more challenging because of its promiscuous nonspecific reactivity and extremely short spacer length (27). Therefore, further developments in reagents and methods are urgently needed to enable simple MS detection and effective identification of in vivo cross-linked products, and thus allow the mapping of authentic protein contact sites as established in cells, especially for protein complexes.Various efforts have been made to address the limitations of XL-MS studies, resulting in new developments in bioinformatics tools for improved data interpretation (28–32) and new designs of cross-linking reagents for enhanced MS analysis of cross-linked peptides (24, 33–39). Among these approaches, the development of new cross-linking reagents holds great promise for mapping PPIs on the systems level. One class of cross-linking reagents containing an enrichment handle have been shown to allow selective isolation of cross-linked products from complex mixtures, boosting their detectability by MS (33–35, 40–42). A second class of cross-linkers containing MS-cleavable bonds have proven to be effective in facilitating the unambiguous identification of cross-linked peptides (36–39, 43, 44), as the resulting cross-linked products can be identified based on their characteristic and simplified fragmentation behavior during MS analysis. Therefore, an ideal cross-linking reagent would possess the combined features of both classes of cross-linkers. To advance the study of in vivo PPIs, we have developed a new XL-MS platform based on a novel membrane-permeable, enrichable, and MS-cleavable cross-linker, Azide-A-DSBSO (azide-tagged, acid-cleavable disuccinimidyl bis-sulfoxide), and multistage tandem mass spectrometry (MSⁿ). This new XL-MS strategy has been successfully employed to map in vivo PPIs from mammalian cells at both the proteome scale and the targeted protein complex level.     

Sip1, a Novel RS Domain-Containing Protein Essential for Pre-mRNA Splicing

Previous studies have shown that protein-protein interactions among splicing factors may play an important role in pre-mRNA splicing. We report here identification and functional characterization of a new splicing factor, Sip1 (SC35-interacting protein 1). Sip1 was initially identified by virtue of its interaction with SC35, a splicing factor of the SR family. Sip1 interacts with not only several SR proteins but also with U1-70K and U2AF65, proteins associated with 5′ and 3′ splice sites, respectively. The predicted Sip1 sequence contains an arginine-serine-rich (RS) domain but does not have any known RNA-binding motifs, indicating that it is not a member of the SR family. Sip1 also contains a region with weak sequence similarity to the Drosophila splicing regulator suppressor of white apricot (SWAP). An essential role for Sip1 in pre-mRNA splicing was suggested by the observation that anti-Sip1 antibodies depleted splicing activity from HeLa nuclear extract. Purified recombinant Sip1 protein, but not other RS domain-containing proteins such as SC35, ASF/SF2, and U2AF65, restored the splicing activity of the Sip1-immunodepleted extract. Addition of U2AF65 protein further enhanced the splicing reconstitution by the Sip1 protein. Deficiency in the formation of both A and B splicing complexes in the Sip1-depleted nuclear extract indicates an important role of Sip1 in spliceosome assembly. Together, these results demonstrate that Sip1 is a novel RS domain-containing protein required for pre-mRNA splicing and that the functional role of Sip1 in splicing is distinct from those of known RS domain-containing splicing factors.Pre-mRNA splicing takes place in spliceosomes, the large RNA-protein complexes containing pre-mRNA, U1, U2, U4/6, and U5 small nuclear ribonucleoprotein particles (snRNPs), and a large number of accessory protein factors (for reviews, see references 21, 22, 37, 44, and 48). It is increasingly clear that the protein factors are important for pre-mRNA splicing and that studies of these factors are essential for further understanding of molecular mechanisms of pre-mRNA splicing.Most mammalian splicing factors have been identified by biochemical fractionation and purification (3, 15, 19, 31–36, 45, 69–71, 73), by using antibodies recognizing splicing factors (8, 9, 16, 17, 61, 66, 67, 74), and by sequence homology (25, 52, 74).Splicing factors containing arginine-serine-rich (RS) domains have emerged as important players in pre-mRNA splicing. These include members of the SR family, both subunits of U2 auxiliary factor (U2AF), and the U1 snRNP protein U1-70K (for reviews, see references 18, 41, and 59). Drosophila alternative splicing regulators transformer (Tra), transformer 2 (Tra2), and suppressor of white apricot (SWAP) also contain RS domains (20, 40, 42). RS domains in these proteins play important roles in pre-mRNA splicing (7, 71, 75), in nuclear localization of these splicing proteins (23, 40), and in protein-RNA interactions (56, 60, 64). Previous studies by us and others have demonstrated that one mechanism whereby SR proteins function in splicing is to mediate specific protein-protein interactions among spliceosomal components and between general splicing factors and alternative splicing regulators (1, 1a, 6, 10, 27, 63, 74, 77). Such protein-protein interactions may play critical roles in splice site recognition and association (for reviews, see references 4, 18, 37, 41, 47 and 59). Specific interactions among the splicing factors also suggest that it is possible to identify new splicing factors by their interactions with known splicing factors.Here we report identification of a new splicing factor, Sip1, by its interaction with the essential splicing factor SC35. The predicted Sip1 protein sequence contains an RS domain and a region with sequence similarity to the Drosophila splicing regulator, SWAP. We have expressed and purified recombinant Sip1 protein and raised polyclonal antibodies against the recombinant Sip1 protein. The anti-Sip1 antibodies specifically recognize a protein migrating at a molecular mass of approximately 210 kDa in HeLa nuclear extract. The anti-Sip1 antibodies sufficiently deplete Sip1 protein from the nuclear extract, and the Sip1-depleted extract is inactive in pre-mRNA splicing. Addition of recombinant Sip1 protein can partially restore splicing activity to the Sip1-depleted nuclear extract, indicating an essential role of Sip1 in pre-mRNA splicing. Other RS domain-containing proteins, including SC35, ASF/SF2, and U2AF65, cannot substitute for Sip1 in reconstituting splicing activity of the Sip1-depleted nuclear extract. However, addition of U2AF65 further increases splicing activity of Sip1-reconstituted nuclear extract, suggesting that there may be a functional interaction between Sip1 and U2AF65 in nuclear extract.     

A Pipeline for Determining Protein–Protein Interactions and Proximities in the Cellular Milieu

It remains extraordinarily challenging to elucidate endogenous protein-protein interactions and proximities within the cellular milieu. The dynamic nature and the large range of affinities of these interactions augment the difficulty of this undertaking. Among the most useful tools for extracting such information are those based on affinity capture of target bait proteins in combination with mass spectrometric readout of the co-isolated species. Although highly enabling, the utility of affinity-based methods is generally limited by difficulties in distinguishing specific from nonspecific interactors, preserving and isolating all unique interactions including those that are weak, transient, or rapidly exchanging, and differentiating proximal interactions from those that are more distal. Here, we have devised and optimized a set of methods to address these challenges. The resulting pipeline involves flash-freezing cells in liquid nitrogen to preserve the cellular environment at the moment of freezing; cryomilling to fracture the frozen cells into intact micron chunks to allow for rapid access of a chemical reagent and to stabilize the intact endogenous subcellular assemblies and interactors upon thawing; and utilizing the high reactivity of glutaraldehyde to achieve sufficiently rapid stabilization at low temperatures to preserve native cellular interactions. In the course of this work, we determined that relatively low molar ratios of glutaraldehyde to reactive amines within the cellular milieu were sufficient to preserve even labile and transient interactions. This mild treatment enables efficient and rapid affinity capture of the protein assemblies of interest under nondenaturing conditions, followed by bottom-up MS to identify and quantify the protein constituents. For convenience, we have termed this approach Stabilized Affinity Capture Mass Spectrometry. Here, we demonstrate that Stabilized Affinity Capture Mass Spectrometry allows us to stabilize and elucidate local, distant, and transient protein interactions within complex cellular milieux, many of which are not observed in the absence of chemical stabilization.Insights into many cellular processes require detailed information about interactions between the participating proteins. However, the analysis of such interactions can be challenging because of the often-diverse physicochemical properties and the abundances of the constituent proteins, as well as the sometimes wide range of affinities and complex dynamics of the interactions. One of the key challenges has been acquiring information concerning transient, low affinity interactions in highly complex cellular milieux (3, 4).Methods that allow elucidation of such information include co-localization microscopy (5), fluorescence protein Förster resonance energy transfer (4), immunoelectron microscopy (5), yeast two-hybrid (6), and affinity capture (7, 8). Among these, affinity capture (AC)¹ has the unique potential to detect all specific in vivo interactions simultaneously, including those that interact both directly and indirectly. In recent times, the efficacy of such affinity isolation experiments has been greatly enhanced through the use of sensitive modern mass spectrometric protein identification techniques (9). Nevertheless, AC suffers from several shortcomings. These include the problem of 1) distinguishing specific from nonspecific interactors (10, 11); 2) preserving and isolating all unique interactions including those that are weak and/or transient, as well as those that exchange rapidly (10, 12, 13); and 3) differentiating proximal from more distant interactions (14).We describe here an approach to address these issues, which makes use of chemical stabilization of protein assemblies in the complex cellular milieu prior to AC. Chemical stabilization is an emerging technique for stabilizing and elucidating protein associations both in vitro (15–20) and in vivo (3, 12, 14, 21–29), with mass spectrometric (MS) readout of the AC proteins and their connectivities. Such chemical stabilization methods are indeed well-established and are often used in electron microscopy for preserving complexes and subcellular structures both in the cellular milieu (3) and in purified complexes (30, 31), wherein the most reliable, stable, and established stabilization reagents is glutaraldehyde. Recently, glutaraldehyde has been applied in the “GraFix” protocol in which purified protein complexes are subjected to centrifugation through a density gradient that also contains a gradient of glutaraldehyde (30, 31), allowing for optimal stabilization of authentic complexes and minimization of nonspecific associations and aggregation. GraFix has also been combined with mass spectrometry on purified complexes bound to EM grids to obtain a compositional analysis of the complexes (32), thereby raising the possibility that glutaraldehyde can be successfully utilized in conjunction with AC in complex cellular milieux directly.In this work, we present a robust pipeline for determining specific protein-protein interactions and proximities from cellular milieux. The first steps of the pipeline involve the well-established techniques of flash freezing the cells of interest in liquid nitrogen and cryomilling, which have been known for over a decade (33, 34) to preserve the cellular environment, as well as having shown outstanding performance when used in analysis of macromolecular interactions in yeast (35–39), bacterial (40, 41), trypanosome (42), mouse (43), and human (44–47) systems. The resulting frozen powder, composed of intact micron chunks of cells that have great surface area and outstanding solvent accessibility, is well suited for rapid low temperature chemical stabilization using glutaraldehyde. We selected glutaraldehyde for our procedure based on the fact that it is a very reactive stabilizing reagent, even at lower temperatures, and because it has already been shown to stabilize enzymes in their functional state (48–50). We employed highly efficient, rapid, single stage affinity capture (36, 51) for isolation and bottom-up MS for analysis of the macromolecular assemblies of interest (52–54). For convenience, we have termed this approach Stabilized Affinity-Capture Mass Spectrometry (SAC-MS).     

Predicting Physical Interactions between Protein Complexes

Protein complexes enact most biochemical functions in the cell. Dynamic interactions between protein complexes are frequent in many cellular processes. As they are often of a transient nature, they may be difficult to detect using current genome-wide screens. Here, we describe a method to computationally predict physical interactions between protein complexes, applied to both humans and yeast. We integrated manually curated protein complexes and physical protein interaction networks, and we designed a statistical method to identify pairs of protein complexes where the number of protein interactions between a complex pair is due to an actual physical interaction between the complexes. An evaluation against manually curated physical complex-complex interactions in yeast revealed that 50% of these interactions could be predicted in this manner. A community network analysis of the highest scoring pairs revealed a biologically sensible organization of physical complex-complex interactions in the cell. Such analyses of proteomes may serve as a guide to the discovery of novel functional cellular relationships.Protein complexes are central to nearly all biochemical processes in the cell (1). In physiologically relevant states, their protein members assemble with varying degrees of stability, over time and under different cellular conditions, to carry out specific cellular functions (1). Although it is a dynamic and tightly regulated process, there is much evidence to support the notion that protein complex assembly results in discrete signaling macromolecules (2). According to the modular organization of molecular networks of the cell (3), protein complexes cooperate in functional networks through dynamic physical interactions with other macromolecules, including other protein complexes (4–6). These physical interactions between pairs of protein complexes may form the backbone of cellular processes (7), such as the recruitment of complexes by other complexes to sites of genome reorganization or in signaling networks. In this study, we attempted to predict these physical interactions between all pairs of known protein complexes, using the manually curated protein complex databases in CORUM and CYC2008 for humans and yeast, respectively.The physical protein interactions that may occur between pairs of complexes have been reported to be more transient, compared with the combination of both permanent and transient interactions that occur within complexes (8). Indeed, the very stability of protein interactions within a protein complex lies between the two extremes of either transient or permanent states (9). Consequently, the experimental identification in a genome-wide manner of the physical interactions between pairs of complexes is very difficult. This challenge has recently been addressed (7, 10) by experiments where the weak interactions were preserved during affinity purifications, followed by inference of the less stable interactions of proteins with the core proteins within the complex. Guided by a computational method to predict the list of protein members in the complexes (10), this allowed a screen of putative inter-complex relationships from human cell lines (7). This adds to the many landmark developments in recent years to characterize protein complexes in a genome-wide manner (7, 11–13). However, in these experiments it is not always easy to infer accurately what constitutes the protein members of a protein complex. Because of various experimental limitations (14) and the dynamic nature of complex assembly in the cell (15), the protein members of the complexes must be predicted from thousands of purification measurements (10–12, 16). As a result, there are surprisingly large differences in the protein complexes inferred in these studies, depending on the algorithm used (17, 18). Hence, the inference of protein complexes from genome-wide screens (11, 12) is likely to contain significant noise from false-positives resulting from methodological uncertainty (9). This noise would in turn cause ambiguity when attempting to predict, genome-wide, interactions that may occur between protein complexes. One solution to this problem, as applied in this study, is the use of comprehensive databases of the so-called “gold standard” community definitions of protein complexes (19–22). In these resources, critical reading of the scientific literature by trained experts leads to definitions of the lists of protein members that are experimentally verified to form complexes. Each of these manually curated protein complexes are assigned functional annotations and a unique identifier. It is our assumption that this approach will allow for a more accurate resolution of the physical interactions between protein complexes.Based on this reasoning, we utilized all protein complex pairs from 1216 human protein complexes in CORUM (21) and 471 in the yeast CYC2008 databases (22, 23), and we attempted to predict physical interactions between them.To this end, we integrated only binary physical protein interactions that were experimentally verified and supported by Medline references, from the iRefIndex database (24, 25), and we developed a statistical method that compared the number of observed physical protein interactions between pairs of protein complexes versus the number of protein interactions expected to be present in pairs of randomized protein complexes. The highest scoring predicted pairs formed a network that was analyzed to identify communities of physically interacting protein complexes. Such higher order perspectives of cellular proteomes may aid discovery of novel functional relationships and lead to an improved understanding of cellular behavior.One recent study utilized manually curated protein complexes-complex interactions in yeast (23) as part of a machine learning strategy to identify complex-complex interactions. However, they added to the training data complex pairs enriched with protein interactions under the assumption that these were likely to contain complex-complex interactions but without a clear statistical argument to assess the reliability of these. Our aim has been to provide a more rigorous statistical approach applied to yeast and human, in which the main confounding factors, protein degrees and protein similarities within the complexes, have been taken into account.We used only the manually curated yeast complex-complex interactions from Ref. 23 as the reference set to evaluate our method after verifying with the authors that the manual curation had not been guided by enrichment in the protein network. Of these interactions, we predicted half at a 10% false discovery rate. Thus, although improvements in data as well as methods are still required for a more complete prediction of complex-complex interactions, a fair portion of these interactions can be reliably predicted now by using our method.     

Splitting the BLOSUM Score into Numbers of Biological Significance

Mathematical tools developed in the context of Shannon information theory were used to analyze the meaning of the BLOSUM score, which was split into three components termed as the BLOSUM spectrum (or BLOSpectrum). These relate respectively to the sequence convergence (the stochastic similarity of the two protein sequences), to the background frequency divergence (typicality of the amino acid probability distribution in each sequence), and to the target frequency divergence (compliance of the amino acid variations between the two sequences to the protein model implicit in the BLOCKS database). This treatment sharpens the protein sequence comparison, providing a rationale for the biological significance of the obtained score, and helps to identify weakly related sequences. Moreover, the BLOSpectrum can guide the choice of the most appropriate scoring matrix, tailoring it to the evolutionary divergence associated with the two sequences, or indicate if a compositionally adjusted matrix could perform better.[1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29]     

Photosystem II Complex in Vivo Is a Monomer

Photosystem II (PS II) complexes are membrane protein complexes that are composed of >20 distinct subunit proteins. Similar to many other membrane protein complexes, two PS II complexes are believed to form a homo-dimer whose molecular mass is ∼650 kDa. Contrary to this well known concept, we propose that the functional form of PS II in vivo is a monomer, based on the following observations. Deprivation of lipids caused the conversion of PS II from a monomeric form to a dimeric form. Only a monomeric PS II was detected in solubilized cyanobacterial and red algal thylakoids using blue-native polyacrylamide gel electrophoresis. Furthermore, energy transfer between PS II units, which was observed in the purified dimeric PS II, was not detected in vivo. Our proposal will lead to a re-evaluation of many crystallographic models of membrane protein complexes in terms of their oligomerization status.Photosystem II (PS II)3 complexes convert solar energy to biological redox energy. Through this reaction process, water molecules are oxidized and molecular oxygen is released as a byproduct (reviewed in Ref. 1), which is the only source of molecular oxygen upon which all aerobic organisms on earth rely. PS II core complexes are membrane protein complexes that are composed of >20 distinct subunit proteins and many functional cofactors, including chlorophylls (Chls), carotenoids, plastoquinone, and metal ions (2–5). Similar to many other membrane protein complexes (6–10), two PS II core complexes are believed to associate together to form a homo-dimer with a molecular mass of ∼650 kDa, as shown by crystallographic models (2, 3, 5).The PS II complex turns over dynamically, although it is quite an integrated complex; our current understanding is that the PS II complex that is damaged by high light is disintegrated into a monomeric form and is further dissociated to replace a degraded D1 protein with a de novo synthesized D1 (reviewed in Refs. 11 and 12). After the replacement, the PS II complex is integrated into a functional form as a dimer. It is supposed that PS II subunit proteins such as PsbI (13) or PsbTc (14) participate in the formation of the PS II dimer.Crystallographic models of PS II have enabled the determination of the accurate molecular architecture of PS II complexes, all of which are in a dimeric form. The most recent crystallographic model of the PS II dimer at 3.0-Å resolution revealed the presence of six detergent molecules located at the interface of the two monomers (5). Small structural fluctuations during the purification process might allow the invasion of those detergent molecules. However, it is also probable that the PS II complexes exist in the form of a monomer in vivo and the two distinct monomers become a dimer during the purification step incorporating detergents between their interfaces. This idea led us to investigate the actual form of PS II in vivo. Contrary to the above well known dimeric model of a functional PS II core complex, here we show that the PS II core complex functions and exists in a monomeric form in vivo.     

Structural Characterization of Clusterin-Chaperone Client Protein Complexes

Clusterin (CLU) is a potent extracellular chaperone that inhibits protein aggregation and precipitation otherwise caused by physical or chemical stresses (e.g. heat, reduction). This action involves CLU forming soluble high molecular weight (HMW) complexes with the client protein. Other than their unquantified large size, the physical characteristics of these complexes were previously unknown. In this study, HMW CLU-citrate synthase (CS), HMW CLU-fibrinogen (FGN), and HMW CLU-glutathione S-transferase (GST) complexes were generated in vitro, and their structures studied using size exclusion chromatography (SEC), ELISA, SDS-PAGE, dynamic light scattering (DLS), bisANS fluorescence, and circular dichroism spectrophotometry (CD). Densitometry of Coomassie Blue-stained SDS-PAGE gels indicated that all three HMW CLU-client protein complexes had an approximate mass ratio of 1:2 (CLU:client protein). SEC indicated that all three clients formed complexes with CLU ≥ 4 × 10⁷ Da; however, DLS estimated HMW CLU-FGN to have a diameter of 108.57 ± 18.09 nm, while HMW CLU-CS and HMW CLU-GST were smaller with estimated diameters of 51.06 ± 6.87 nm and 52.61 ± 7.71 nm, respectively. Measurements of bisANS fluorescence suggest that the chaperone action of CLU involves preventing the exposure to aqueous solvent of hydrophobic regions that are normally exposed by the client protein during heat-induced unfolding. CD analysis indicated that, depending on the individual client protein, CLU may interact with a variety of intermediates on protein unfolding pathways with different amounts of native secondary structure. In vivo, soluble complexes like those studied here are likely to serve as vehicles to dispose of otherwise dangerous aggregation-prone misfolded extracellular proteins.Controlled unfolding is important in many biological processes including protein translocation, degradation by proteases, and regulation of enzyme activity. Uncontrolled unfolding and the consequent accumulation of insoluble protein aggregates is implicated in the pathology of many diseases including Alzheimer disease and type II diabetes and is promoted by various stresses such as oxidative stress (1), shear stress (2), and thermal stress (3). Cells have extensive quality control mechanisms to ensure that intracellular proteins are maintained predominantly in their native conformations. Molecular chaperones are known to play a central role in these systems by targeting unfolded proteins for refolding or degradation (4–7). However, little is known about the existence of corresponding systems for protein folding quality control in the extracellular environment (8).A large number of alternative functions have been proposed for clusterin (CLU),⁴ nevertheless, the potent chaperone activity of this protein (9–13) and its constitutive presence in many biological fluids suggests that it is likely to be important in extracellular protein folding quality control. Recently haptoglobin (14) and α₂-macroglobulin (15, 16) have also been identified as extracellular chaperones. All three proteins exhibit small heat shock protein (sHsp)-like activity, preferentially binding to stressed client proteins to prevent their precipitation in an ATP-independent manner (9, 11, 14, 16). When acting alone, extracellular chaperones lack refolding activity; however it has been shown that CLU can hold partially unfolded proteins in a state competent for refolding by Hsc70 (11).CLU is found associated with extracellular protein deposits in numerous diseases including drusen in age-related macular degeneration (17), renal immunoglobulin deposits in kidney disease (18), Lewy bodies in Parkinson disease (19), prion deposits in Creutzfeldt-Jakob disease (20), and amyloid plaques in Alzheimer disease (21). Knock-out studies have shown that CLU-deficient mice accumulate insoluble protein deposits in the kidneys and develop progressive glomerulopathy (22). These findings suggest a role for CLU in the clearance of extracellular misfolded proteins; however, the mechanism by which this may occur has yet to be determined.Currently, little is known about the physical characteristics of the soluble complexes formed during the interaction of CLU with chaperone client proteins (9–12). This is the first study to investigate the physical properties of CLU-client protein complexes. The present study provides new insights into the properties of complexes formed in vitro between CLU and citrate synthase (CS), fibrinogen (FGN), and glutathione S-transferase (GST).     

A Small Conserved Domain in the Yeast Spa2p Is Necessary and Sufficient for Its Polarized Localization

SPA2 encodes a yeast protein that is one of the first proteins to localize to sites of polarized growth, such as the shmoo tip and the incipient bud. The dynamics and requirements for Spa2p localization in living cells are examined using Spa2p green fluorescent protein fusions. Spa2p localizes to one edge of unbudded cells and subsequently is observable in the bud tip. Finally, during cytokinesis Spa2p is present as a ring at the mother–daughter bud neck. The bud emergence mutants bem1 and bem2 and mutants defective in the septins do not affect Spa2p localization to the bud tip. Strikingly, a small domain of Spa2p comprised of 150 amino acids is necessary and sufficient for localization to sites of polarized growth. This localization domain and the amino terminus of Spa2p are essential for its function in mating. Searching the yeast genome database revealed a previously uncharacterized protein which we name, Sph1p (Spa2p homolog), with significant homology to the localization domain and amino terminus of Spa2p. This protein also localizes to sites of polarized growth in budding and mating cells. SPH1, which is similar to SPA2, is required for bipolar budding and plays a role in shmoo formation. Overexpression of either Spa2p or Sph1p can block the localization of either protein fused to green fluorescent protein, suggesting that both Spa2p and Sph1p bind to and are localized by the same component. The identification of a 150–amino acid domain necessary and sufficient for localization of Spa2p to sites of polarized growth and the existence of this domain in another yeast protein Sph1p suggest that the early localization of these proteins may be mediated by a receptor that recognizes this small domain.Polarized cell growth and division are essential cellular processes that play a crucial role in the development of eukaryotic organisms. Cell fate can be determined by cell asymmetry during cell division (Horvitz and Herskowitz, 1992; Cohen and Hyman, 1994; Rhyu and Knoblich, 1995). Consequently, the molecules involved in the generation and maintenance of cell asymmetry are important in the process of cell fate determination. Polarized growth can occur in response to external signals such as growth towards a nutrient (Rodriguez-Boulan and Nelson, 1989; Eaton and Simons, 1995) or hormone (Jackson and Hartwell, 1990a , b ; Segall, 1993; Keynes and Cook, 1995) and in response to internal signals as in Caenorhabditis elegans (Goldstein et al., 1993; Kimble, 1994; Priess, 1994) and Drosophila melanogaster (St Johnston and Nusslein-Volhard, 1992; Anderson, 1995) early development. Saccharomyces cerevisiae undergo polarized growth towards an external cue during mating and to an internal cue during budding. Polarization towards a mating partner (shmoo formation) and towards a new bud site requires a number of proteins (Chenevert, 1994; Chant, 1996; Drubin and Nelson, 1996). Many of these proteins are necessary for both processes and are localized to sites of polarized growth, identified by the insertion of new cell wall material (Tkacz and Lampen, 1972; Farkas et al., 1974; Lew and Reed, 1993) to the shmoo tip, bud tip, and mother–daughter bud neck. In yeast, proteins localized to growth sites include cytoskeletal proteins (Adams and Pringle, 1984; Kilmartin and Adams, 1984; Ford, S.K., and J.R. Pringle. 1986. Yeast. 2:S114; Drubin et al., 1988; Snyder, 1989; Snyder et al., 1991; Amatruda and Cooper, 1992; Lew and Reed, 1993; Waddle et al., 1996), neck filament components (septins) (Byers and Goetsch, 1976; Kim et al., 1991; Ford and Pringle, 1991; Haarer and Pringle, 1987; Longtine et al., 1996), motor proteins (Lillie and Brown, 1994), G-proteins (Ziman, 1993; Yamochi et al., 1994; Qadota et al., 1996), and two membrane proteins (Halme et al., 1996; Roemer et al., 1996; Qadota et al., 1996). Septins, actin, and actin-associated proteins localize early in the cell cycle, before a bud or shmoo tip is recognizable. How this group of proteins is localized to and maintained at sites of cell growth remains unclear.Spa2p is one of the first proteins involved in bud formation to localize to the incipient bud site before a bud is recognizable (Snyder, 1989; Snyder et al., 1991; Chant, 1996). Spa2p has been localized to where a new bud will form at approximately the same time as actin patches concentrate at this region (Snyder et al., 1991). An understanding of how Spa2p localizes to incipient bud sites will shed light on the very early stages of cell polarization. Later in the cell cycle, Spa2p is also found at the mother–daughter bud neck in cells undergoing cytokinesis. Spa2p, a nonessential protein, has been shown to be involved in bud site selection (Snyder, 1989; Zahner et al., 1996), shmoo formation (Gehrung and Snyder, 1990), and mating (Gehrung and Snyder, 1990; Chenevert et al., 1994; Yorihuzi and Ohsumi, 1994; Dorer et al., 1995). Genetic studies also suggest that Spa2p has a role in cytokinesis (Flescher et al., 1993), yet little is known about how this protein is localized to sites of polarized growth.We have used Spa2p green fluorescent protein (GFP)¹ fusions to investigate the early localization of Spa2p to sites of polarized growth in living cells. Our results demonstrate that a small domain of ∼150 amino acids of this large 1,466-residue protein is sufficient for targeting to sites of polarized growth and is necessary for Spa2p function. Furthermore, we have identified and characterized a novel yeast protein, Sph1p, which has homology to both the Spa2p amino terminus and the Spa2p localization domain. Sph1p localizes to similar regions of polarized growth and sph1 mutants have similar phenotypes as spa2 mutants.