Mapping the Structural Topology of the Yeast 19S Proteasomal Regulatory Particle Using Chemical Cross-linking and Probabilistic Modeling

Structural characterization of proteasome complexes is an essential step toward understanding the ubiquitin-proteasome system. Currently, high resolution structures are not available for the 26S proteasome holocomplex as well as its subcomplex, the 19S regulatory particle (RP). Here we have employed a novel integrated strategy combining chemical cross-linking with multistage tandem mass spectrometry to define the proximity of subunits within the yeast 19S RP to elucidate its topology. This has resulted in the identification of 174 cross-linked peptides of the yeast 19S RP, representing 43 unique lysine-lysine linkages within 24 nonredundant pair-wise subunit interactions. To map the spatial organization of the 19S RP, we have developed and utilized a rigorous probabilistic framework to derive maximum likelihood (ML) topologies based on cross-linked peptides determined from our analysis. Probabilistic modeling of the yeast 19S AAA-ATPase ring (i.e., Rpt1–6) has produced an ML topology that is in excellent agreement with known topologies of its orthologs. In addition, similar analysis was carried out on the 19S lid subcomplex, whose predicted ML topology corroborates recently reported electron microscopy studies. Together, we have demonstrated the effectiveness and potential of probabilistic modeling for unraveling topologies of protein complexes using cross-linking data. This report describes the first study of the 19S RP topology using a new integrated strategy combining chemical cross-linking, mass spectrometry, and probabilistic modeling. Our results have provided a solid foundation to advance our understanding of the 19S RP architecture at peptide level resolution. Furthermore, our methodology developed here is a valuable proteomic tool that can be generalized for elucidating the structures of protein complexes.Basic cellular homeostasis depends on the regulated protein degradation and turnover by the ubiquitin-proteasome system (1, 2). Central to this pathway is the 26S proteasome complex, which is responsible for ubiquitin/ATP-dependent protein degradation (3–5). The 26S holocomplex is a megadalton-sized protein assembly consisting of the 20S catalytic core particle (CP)¹ and the 19S regulatory particle (RP). The eukaryotic 20S CP is composed of two copies of 14 nonidentical subunits (α_1–7 and β_1–7) arranged into four stacked heptameric rings in an order of α₇β₇β₇α₇. The crystal structure and topology of the highly ordered 20S CP has been resolved and is evolutionarily conserved (6). Although α subunits of the 20S CP are essential for the assembly of the complex and its interactions with the regulatory complex, three catalytic β subunits (β1, β2, and β5) harbor various catalytic activities responsible for regulated proteasomal degradation. The 19S RP is composed of 19 subunits, which forms two subcomplexes, the base consisting of six related AAA-ATPase (Rpt1–6) and four non-ATPase (Rpn1, Rpn2, Rpn10, and Rpn13) subunits and the lid containing nine non-ATPase subunits (Rpn3, Rpn5–9, Rpn11, Rpn12, and Rpn15/Sem1) (7, 8). In comparison with the 20S core, the function and structure of the 19S RP is much less well understood. Nevertheless, it is believed that the 19S RP is involved in multiple functions including recognition of polyubiquitinated substrates (9, 10), cleavage of the polyubiquitin chains to recycle ubiquitin (11), unfolding of substrates, assisting in opening the gate of the 20S chamber, and subsequently translocating the unfolded substrates into the catalytic chamber (4, 12–14). The six AAA-ATPase subunits (Rpt1–6), which directly interact with the 20S α-ring, function as a molecular chaperone responsible for protein unfolding and are involved in substrate translocation and modulating gating of the CP (5, 15). Although detailed functions for most of the 19S non-ATPase subunits remain elusive, Rpn11 is known to carry an Mpr1p and Pad1p N-termini (MPN) domain, which harbors an essential deubiquitination activity responsible for cleaving polyubiquitin chains from proteasomal substrates (11, 16). In addition, two proteasome subunits, Rpn10 and Rpn13, have been identified as ubiquitin receptors, which are important in docking ubiquitinated substrates to the proteasome for degradation (4). Moreover, the two largest proteasome subunits, Rpn1 and Rpn2, interact with a variety of proteins including ubiquitin receptors and deubiquitinases and thus may function as scaffolding proteins to assist proteasomal degradation. Thus far, no atomic resolution structures are available for either the 19S RP or the 26S holocomplex. New insights of the overall topology of the 19S RP will illuminate protein interactions within, thus providing evidence for its otherwise unknown functions.Although many studies have been performed to characterize the 19S structure utilizing various techniques including cryo-EM (17, 18) and native mass spectrometry (19), details on spatial interfaces and subunit interconnectivity of the 19S RP remain to be unraveled. During the course of our study, the rough topology of the 19S RP was determined by cryo-EM alone (20) or coupled with other approaches (21); nevertheless more detailed information at the peptide or atomic level is still required. In addition to technological limitations in current approaches, the highly dynamic and heterogeneous nature of the 19S RP may attribute to the difficulty in obtaining its high resolution structure. In recent years, chemical cross-linking coupled with mass spectrometry (XL-MS) has become an attractive alternative for structure analysis of proteins and protein complexes (22, 23). The ability of XL-MS to identify interaction interfaces between proteins allows us to define low resolution protein topology. In addition to protein interaction networks and the site of protein interactions at binding interfaces, cross-linking analysis can reveal information about the spatial distance between cross-linked amino acids on the surface of folded proteins. Although such knowledge only reveals the maximum distance given by the length of the cross-linker and can be influenced by protein conformational flexibility, it can be used as the distance constraint for molecular modeling of protein folds and complex topologies, i.e., the arrangement of the constituents of a complex in space. A recent study by Chen et al. (24) on yeast RNA polymerase II (RNAPII) complex has exemplified the power of XL-MS in elucidating the architecture of large multisubunit complexes. Although effective, cross-linking studies have been challenging because of the low abundance of cross-linked products and the inherent complexity of sequencing interlinked peptides by MS for unambiguous identification. To facilitate MS detection and identification of cross-linked products, we have recently developed a novel homobifunctional amine reactive, low energy MS-cleavable cross-linker, disuccinimidyl sulfoxide (DSSO), and successfully applied it to cross-link the yeast 20S proteasome for rapid, accurate, and simplified determination of protein interaction interfaces within the complex (25). The unique functionality of our cross-linking reagent and specialized bioinformatics tools significantly increase our confidence and speed in the identification of cross-linked products when compared with cross-linking studies using traditional noncleavable reagents. Current cross-linking studies have been focused on protein complexes with known crystal structures, but topological structures of protein complexes based primarily on cross-linking data have not yet been reported. This is due to the lack of computational tools that use cross-linking data to deduce the spatial organization of subunits in a given complex. To define the architecture of the yeast 19S RP, we have characterized the proximity and interconnectivity of the subunits by employing our newly developed cross-linking strategy. The resulting cross-linking information serves as a basis for a rigorous probabilistic analysis to obtain the maximum likelihood (ML) topology. This strategy is developed by first analyzing our cross-linking data for the 19S six-member AAA-ATPase base ring, as the topology ordering of yeast orthologs has been recently determined (14, 26–28). The effectiveness of this new probabilistic platform is supported by the agreement between our derived ML topology of the AAA-ATPase base ring and previous reports. When the same probabilistic approach is applied to the 19S lid subcomplex, the resulting topology is also in agreement with recently proposed models (20, 21). This work represents the first application of probabilistic modeling of protein complexes based solely on cross-link data, establishing a new workflow for future structural analysis of large protein complexes using XL-MS.     

Analysis of Free Energy Signals Arising from Nucleotide Hybridization Between rRNA and mRNA Sequences during Translation in Eubacteria

A decoding algorithm is tested that mechanistically models the progressive alignments that arise as the mRNA moves past the rRNA tail during translation elongation. Each of these alignments provides an opportunity for hybridization between the single-stranded, -terminal nucleotides of the 16S rRNA and the spatially accessible window of mRNA sequence, from which a free energy value can be calculated. Using this algorithm we show that a periodic, energetic pattern of frequency 1/3 is revealed. This periodic signal exists in the majority of coding regions of eubacterial genes, but not in the non-coding regions encoding the 16S and 23S rRNAs. Signal analysis reveals that the population of coding regions of each bacterial species has a mean phase that is correlated in a statistically significant way with species () content. These results suggest that the periodic signal could function as a synchronization signal for the maintenance of reading frame and that codon usage provides a mechanism for manipulation of signal phase.[1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32]     

Deletion of the Chloride Transporter Slc26a7 Causes Distal Renal Tubular Acidosis and Impairs Gastric Acid Secretion

SLC26A7 (human)/Slc26a7 (mouse) is a recently identified chloride-base exchanger and/or chloride transporter that is expressed on the basolateral membrane of acid-secreting cells in the renal outer medullary collecting duct (OMCD) and in gastric parietal cells. Here, we show that mice with genetic deletion of Slc26a7 expression develop distal renal tubular acidosis, as manifested by metabolic acidosis and alkaline urine pH. In the kidney, basolateral Cl⁻/HCO₃⁻ exchange activity in acid-secreting intercalated cells in the OMCD was significantly decreased in hypertonic medium (a normal milieu for the medulla) but was reduced only mildly in isotonic medium. Changing from a hypertonic to isotonic medium (relative hypotonicity) decreased the membrane abundance of Slc26a7 in kidney cells in vivo and in vitro. In the stomach, stimulated acid secretion was significantly impaired in isolated gastric mucosa and in the intact organ. We propose that SLC26A7 dysfunction should be investigated as a potential cause of unexplained distal renal tubular acidosis or decreased gastric acid secretion in humans.The collecting duct segment of the distal kidney nephron plays a major role in systemic acid base homeostasis by acid secretion and bicarbonate absorption. The acid secretion occurs via H⁺-ATPase and H-K-ATPase into the lumen and bicarbonate is absorbed via basolateral Cl⁻/HCO₃⁻ exchangers (1–4). The tubules, which are located within the outer medullary region of the kidney collecting duct (OMCD),² have the highest rate of acid secretion among the distal tubule segments and are therefore essential to the maintenance of acid base balance (2).The gastric parietal cell is the site of generation of acid and bicarbonate through the action of cytosolic carbonic anhydrase II (5, 6). The intracellular acid is secreted into the lumen via gastric H-K-ATPase, which works in conjunction with a chloride channel and a K⁺ recycling pathway (7–10). The intracellular bicarbonate is transported to the blood via basolateral Cl⁻/HCO₃⁻ exchangers (11–14).SLC26 (human)/Slc26 (mouse) isoforms are members of a conserved family of anion transporters that display tissue-specific patterns of expression in epithelial cells (15–24). Several SLC26 members can function as chloride/bicarbonate exchangers. These include SLC26A3 (DRA), SLC26A4 (pendrin), SLC26A6 (PAT1 or CFEX), SLC26A7, and SLC26A9 (25–31). SLC26A7 and SLC26A9 can also function as chloride channels (32–34).SLC26A7/Slc26a7 is predominantly expressed in the kidney and stomach (28, 29). In the kidney, Slc26a7 co-localizes with AE1, a well-known Cl⁻/HCO₃⁻ exchanger, on the basolateral membrane of (acid-secreting) A-intercalated cells in OMCD cells (29, 35, 36) (supplemental Fig. 1). In the stomach, Slc26a7 co-localizes with AE2, a major Cl⁻/HCO₃⁻ exchanger, on the basolateral membrane of acid secreting parietal cells (28). To address the physiological function of Slc26a7 in the intact mouse, we have generated Slc26a7 ko mice. We report here that Slc26a7 ko mice exhibit distal renal tubular acidosis and impaired gastric acidification in the absence of morphological abnormalities in kidney or stomach.     

Algorithms for Finding Small Attractors in Boolean Networks

A Boolean network is a model used to study the interactions between different genes in genetic regulatory networks. In this paper, we present several algorithms using gene ordering and feedback vertex sets to identify singleton attractors and small attractors in Boolean networks. We analyze the average case time complexities of some of the proposed algorithms. For instance, it is shown that the outdegree-based ordering algorithm for finding singleton attractors works in time for , which is much faster than the naive time algorithm, where is the number of genes and is the maximum indegree. We performed extensive computational experiments on these algorithms, which resulted in good agreement with theoretical results. In contrast, we give a simple and complete proof for showing that finding an attractor with the shortest period is NP-hard.[1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32]     

Dbf4-Cdc7 Phosphorylation of Mcm2 Is Required for Cell Growth

The Dbf4-Cdc7 kinase (DDK) is required for the activation of the origins of replication, and DDK phosphorylates Mcm2 in vitro. We find that budding yeast Cdc7 alone exists in solution as a weakly active multimer. Dbf4 forms a likely heterodimer with Cdc7, and this species phosphorylates Mcm2 with substantially higher specific activity. Dbf4 alone binds tightly to Mcm2, whereas Cdc7 alone binds weakly to Mcm2, suggesting that Dbf4 recruits Cdc7 to phosphorylate Mcm2. DDK phosphorylates two serine residues of Mcm2 near the N terminus of the protein, Ser-164 and Ser-170. Expression of mcm2-S170A is lethal to yeast cells that lack endogenous MCM2 (mcm2Δ); however, this lethality is rescued in cells harboring the DDK bypass mutant mcm5-bob1. We conclude that DDK phosphorylation of Mcm2 is required for cell growth.The Cdc7 protein kinase is required throughout the yeast S phase to activate origins (1, 2). The S phase cyclin-dependent kinase also activates yeast origins of replication (3–5). It has been proposed that Dbf4 activates Cdc7 kinase in S phase, and that Dbf4 interaction with Cdc7 is essential for Cdc7 kinase activity (6). However, it is not known how Dbf4-Cdc7 (DDK)² acts during S phase to trigger the initiation of DNA replication. DDK has homologs in other eukaryotic species, and the role of Cdc7 in activation of replication origins during S phase may be conserved (7–10).The Mcm2-7 complex functions with Cdc45 and GINS to unwind DNA at a replication fork (11–15). A mutation of MCM5 (mcm5-bob1) bypasses the cellular requirements for DBF4 and CDC7 (16), suggesting a critical physiologic interaction between Dbf4-Cdc7 and Mcm proteins. DDK phosphorylates Mcm2 in vitro with proteins purified from budding yeast (17, 18) or human cells (19). Furthermore, there are mutants of MCM2 that show synthetic lethality with DBF4 mutants (6, 17), suggesting a biologically relevant interaction between DBF4 and MCM2. Nevertheless, the physiologic role of DDK phosphorylation of Mcm2 is a matter of dispute. In human cells, replacement of MCM2 DDK-phosphoacceptor residues with alanines inhibits DNA replication, suggesting that Dbf4-Cdc7 phosphorylation of Mcm2 in humans is important for DNA replication (20). In contrast, mutation of putative DDK phosphorylation sites at the N terminus of Schizosaccharomyces pombe Mcm2 results in viable cells, suggesting that phosphorylation of S. pombe Mcm2 by DDK is not critical for cell growth (10).In budding yeast, Cdc7 is present at high levels in G₁ and S phase, whereas Dbf4 levels peak in S phase (18, 21, 22). Furthermore, budding yeast DDK binds to chromatin during S phase (6), and it has been shown that Dbf4 is required for Cdc7 binding to chromatin in budding yeast (23, 24), fission yeast (25), and Xenopus (9). Human and fission yeast Cdc7 are inert on their own (7, 8), but Dbf4-Cdc7 is active in phosphorylating Mcm proteins in budding yeast (6, 26), fission yeast (7), and human (8, 10). Based on these data, it has been proposed that Dbf4 activates Cdc7 kinase in S phase and that Dbf4 interaction with Cdc7 is essential for Cdc7 kinase activity (6, 9, 18, 21–24). However, a mechanistic analysis of how Dbf4 activates Cdc7 has not yet been accomplished. For example, the multimeric state of the active Dbf4-Cdc7 complex is currently disputed. A heterodimer of fission yeast Cdc7 (Hsk1) in complex with fission yeast Dbf4 (Dfp1) can phosphorylate Mcm2 (7). However, in budding yeast, oligomers of Cdc7 exist in the cell (27), and Dbf4-Cdc7 exists as oligomers of 180 and 300 kDa (27).DDK phosphorylates the N termini of human Mcm2 (19, 20, 28), human Mcm4 (10), budding yeast Mcm4 (26), and fission yeast Mcm6 (10). Although the sequences of the Mcm N termini are poorly conserved, the DDK sites identified in each study have neighboring acidic residues. The residues of budding yeast Mcm2 that are phosphorylated by DDK have not yet been identified.In this study, we find that budding yeast Cdc7 is weakly active as a multimer in phosphorylating Mcm2. However, a low molecular weight form of Dbf4-Cdc7, likely a heterodimer, has a higher specific activity for phosphorylation of Mcm2. Dbf4 or DDK, but not Cdc7, binds tightly to Mcm2, suggesting that Dbf4 recruits Cdc7 to Mcm2. DDK phosphorylates two serine residues of Mcm2, Ser-164 and Ser-170, in an acidic region of the protein. Mutation of Ser-170 is lethal to yeast cells, but this phenotype is rescued by the DDK bypass mutant mcm5-bob1. We conclude that DDK phosphorylation of Ser-170 of Mcm2 is required for budding yeast growth.     

Transient Oxidative Stress Damages Mitochondrial Machinery Inducing Persistent ��-Cell Dysfunction

Transient exposure of β-cells to oxidative stress interrupts the transduction of signals normally coupling glucose metabolism to insulin secretion. We investigated putative persistence of effects induced by one transient oxidative stress (200 μm H₂O₂, 10 min) on insulin secreting cells following recovery periods of days and weeks. Three days after oxidative stress INS-1E cells and rat islets exhibited persistent dysfunction. In particular, the secretory response to 15 mm glucose was reduced by 40% in INS-1E cells stressed 3 days before compared with naïve cells. Compared with non-stressed INS-1E cells, we observed reduced oxygen consumption (−43%) and impaired glucose-induced ATP generation (−46%). These parameters correlated with increased mitochondrial reactive oxygen species formation (+60%) accompanied with down-regulation of subunits of the respiratory chain and decreased expression of genes responsible for mitochondrial biogenesis (TFAM, −24%; PGC-1α, −67%). Three weeks after single oxidative stress, both mitochondrial respiration and secretory responses were recovered. Moreover, such recovered INS-1E cells exhibited partial resistance to a second transient oxidative stress and up-regulation of UCP2 (+78%) compared with naïve cells. In conclusion, one acute oxidative stress induces β-cell dysfunction lasting over days, explained by persistent damages in mitochondrial components.Pancreatic β-cells are poised to sense blood glucose to regulate insulin exocytosis and thereby glucose homeostasis. The conversion from metabolic signals to secretory responses is mediated through mitochondrial metabolism (1). Failure of the insulin secreting β-cells, a common characteristic of both type 1 and type 2 diabetes, derives from various origins, among them mitochondrial impairment secondary to oxidative stress is a proposed mechanism (2).Oxidative stress is characterized by a persistent imbalance between excessive production of reactive oxygen species (ROS)³ and limited antioxidant defenses. Examples of ROS include superoxide (O₂^−̇), hydroxyl radical (OH^⋅), and hydrogen peroxide (H₂O₂). Superoxide can be converted to less reactive H₂O₂ by superoxide dismutase (SOD) and then to oxygen and water by catalase (CAT), glutathione peroxidase (GPx), and peroxiredoxin, which constitute antioxidant defenses. Increased oxidative stress and free radical damages have been proposed to participate in the diabetic state (3). In type 1 diabetes, ROS are implicated in β-cell dysfunction caused by autoimmune reactions and inflammatory cytokines (4). In the context of type 2 diabetes, excessive ROS could promote deficient insulin synthesis (5, 6) and apoptotic pathways in β-cells (5, 7). Of note, ROS fluctuations may also contribute to physiological control of cell functions (8), including the control of insulin secretion (9). It should also be stressed that metabolism of physiological nutrient increases ROS without causing deleterious effects on cell function. However, uncontrolled increase of oxidants, or reduction of their detoxification, leads to free radical-mediated chain reactions ultimately triggering pathogenic events. Pancreatic β-cells are relatively weak in expressing free radical-quenching enzymes SOD, CAT, and GPx (10, 11), rendering those cells particularly susceptible to oxidative attacks (12). Mitochondria are not only the main source of cellular oxidants, they are also the primary target of ROS (13, 14).Mitochondria are essential for pancreatic β-cell function, and damages to these organelles are well known to markedly alter glucose-stimulated insulin secretion (15). The mitochondrial genome constitutes one of the targets, encoding for 13 polypeptides essential for the integrity of electron transport chain (16). Damages to mitochondrial DNA (mtDNA) induce mutations that in turn may favor ROS generation, although the contribution of mtDNA mutations to ROS generation remains unclear. We previously reported that patient-derived mitochondrial A3243G mutation, causing mitochondrial inherited diabetes, is responsible for defective mitochondrial metabolism associated with elevated ROS levels and reduced antioxidant enzyme expression (17). On the other hand, mtDNA mutator mice exhibit accelerated aging without changes in superoxide levels in embryonic fibroblasts (18), showing that ROS generation can be dissociated from mtDNA mutations.In humans, mitochondrial defects typically appear with aging (19), accompanied by sustained ROS generation and progressive oxidant-induced damages (20). In support of this “mitochondrial theory of aging” (21), accumulating evidence shows that in older individuals mitochondria are altered, both morphologically and functionally (22). These age-related mitochondrial changes are foreseen to play a role in the late onset diabetes. In a rat model of intrauterine growth retardation, a vicious cycle between accumulation of mtDNA mutations and elevation of ROS production has been associated to β-cell abnormalities and the onset of type 2 diabetes in adulthood (23). Similarly, mitochondrion-derived ROS impair β-cell function in the Zucker diabetic fatty rat (24). Altogether, these observations point to ROS action as a triggering event inducing mitochondrial dysfunction and ultimately resulting in the loss of the secretory response in β-cells (14).In vitro, oxidative stress applied to β-cells rapidly interrupts the transduction of signals normally coupling glucose metabolism to insulin secretion (12, 25). Specifically, we reported that INS-1E β-cells and rat islets subjected to a 10-min H₂O₂ exposure exhibit impaired secretory response associated with mitochondrial dysfunction appearing already during the first minutes of oxidative stress (12). In the context of the mitochondrial theory of aging (21, 26), it is important to know whether transient exposure to H₂O₂ could possibly induce persistent modifications of mitochondrial function. Cells surviving an oxidative stress might carry defects leading to progressive loss of β-cell function. In the present study, we asked the simple but unanswered question if a short transient oxidative stress could induce durable alterations of the mitochondria and thereby chronically impair β-cell function. INS-1E β-cells and rat islets were transiently exposed to H₂O₂ for 10 min and analyzed after days and weeks of standard tissue culture.     

The Wavelet-Based Cluster Analysis for Temporal Gene Expression Data

A variety of high-throughput methods have made it possible to generate detailed temporal expression data for a single gene or large numbers of genes. Common methods for analysis of these large data sets can be problematic. One challenge is the comparison of temporal expression data obtained from different growth conditions where the patterns of expression may be shifted in time. We propose the use of wavelet analysis to transform the data obtained under different growth conditions to permit comparison of expression patterns from experiments that have time shifts or delays. We demonstrate this approach using detailed temporal data for a single bacterial gene obtained under 72 different growth conditions. This general strategy can be applied in the analysis of data sets of thousands of genes under different conditions.[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]     

Paradoxical Condensation of Copper with Elevated ��-Amyloid in Lipid Rafts under Cellular Copper Deficiency Conditions: IMPLICATIONS FOR ALZHEIMER DISEASE*

Redox-active copper is implicated in the pathogenesis of Alzheimer disease (AD), β-amyloid peptide (Aβ) aggregation, and amyloid formation. Aβ·copper complexes have been identified in AD and catalytically oxidize cholesterol and lipid to generate H₂O₂ and lipid peroxides. The site and mechanism of this abnormality is not known. Growing evidence suggests that amyloidogenic processing of the β-amyloid precursor protein (APP) occurs in lipid rafts, membrane microdomains enriched in cholesterol. β- and γ-secretases, and Aβ have been identified in lipid rafts in cultured cells, human and rodent brains, but the role of copper in lipid raft amyloidogenic processing is presently unknown. In this study, we found that copper modulates flotillin-2 association with cholesterol-rich lipid raft domains, and consequently Aβ synthesis is attenuated via copper-mediated inhibition of APP endocytosis. We also found that total cellular copper is associated inversely with lipid raft copper levels, so that under intracellular copper deficiency conditions, Aβ·copper complexes are more likely to form. This explains the paradoxical hypermetallation of Aβ with copper under tissue copper deficiency conditions in AD.Imbalance of metal ions has been recognized as one of the key factors in the pathogenesis of Alzheimer disease (AD).² Aberrant interactions between copper or zinc with the β-amyloid peptide (Aβ) released into the glutamatergic synaptic cleft vicinity could result in the formation of toxic Aβ oligomers and aggregation into plaques characteristic of AD brains (reviewed in Ref. 1). Copper, iron, and zinc are highly concentrated in extracellular plaques (2, 3), and yet brain tissues from AD (4–6) and human β-amyloid precursor protein (APP) transgenic mice (7–10) are paradoxically copper deficient compared with age-matched controls. Elevation of intracellular copper levels by genetic, dietary, and pharmacological manipulations in both AD transgenic animal and cell culture models is able to attenuate Aβ production (7, 9, 11–15). However, the underlying mechanism is at present unclear.Abnormal cholesterol metabolism is also a contributing factor in the pathogenesis of AD. Hypercholesterolemia increases the risk of developing AD-like pathology in a transgenic mouse model (16). Epidemiological and animal model studies show that a hypercholesterolemic diet is associated with Aβ accumulation and accelerated cognitive decline, both of which are further aggravated by high dietary copper (17, 18). In contrast, biochemical depletion of cholesterol using statins, inhibitors of 3-hydroxy-3-methyglutaryl coenzyme A reductase, and methyl-β-cyclodextrin, a cholesterol sequestering agent, inhibit Aβ production in animal and cell culture models (19–25).Cholesterol is enriched in lipid rafts, membrane microdomains implicated in Aβ generation from APP cleavage by β- and γ-secretases. Recruitment of BACE1 (β-secretase) into lipid rafts increases the production of sAPP_β and Aβ (23, 26). The β-secretase-cleaved APP C-terminal fragment (β-CTF), and γ-secretase, a multiprotein complex composed of presenilin (PS1 or PS2), nicastrin (Nct), PEN-2 and APH-1, colocalize to lipid rafts (27). The accumulation of Aβ in lipid rafts isolated from AD and APP transgenic mice brains (28) provided further evidence that cholesterol plays a role in APP processing and Aβ generation.Currently, copper and cholesterol have been reported to modulate APP processing independently. However, evidence indicates that, despite tissue copper deficiency, Aβ·Cu²⁺ complexes form in AD that catalytically oxidize cholesterol and lipid to generate H₂O₂ and lipid peroxides (e.g. hydroxynonenal and malondialdehyde), which contribute to oxidative damage observed in AD (29–35). The underlying mechanism leading to the formation of pathological Aβ·Cu²⁺ complexes is unknown. In this study, we show that copper alters the structure of lipid rafts, and attenuates Aβ synthesis in lipid rafts by inhibition of APP endocytosis. We also identify a paradoxical inverse relationship between total cellular copper levels and copper distribution to lipid rafts, which appear to possess a privileged pool of copper where Aβ is more likely to interact with Cu²⁺ under copper-deficiency conditions to form Aβ·Cu²⁺ complexes. These data provide a novel mechanism by which cellular copper deficiency in AD could foster an environment for potentially adverse interactions between Aβ, copper, and cholesterol in lipid rafts.     

Multipattern Consensus Regions in Multiple Aligned Protein Sequences and Their Segmentation

Decomposing a biological sequence into its functional regions is an important prerequisite to understand the molecule. Using the multiple alignments of the sequences, we evaluate a segmentation based on the type of statistical variation pattern from each of the aligned sites. To describe such a more general pattern, we introduce multipattern consensus regions as segmented regions based on conserved as well as interdependent patterns. Thus the proposed consensus region considers patterns that are statistically significant and extends a local neighborhood. To show its relevance in protein sequence analysis, a cancer suppressor gene called p53 is examined. The results show significant associations between the detected regions and tendency of mutations, location on the 3D structure, and cancer hereditable factors that can be inferred from human twin studies.[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]     

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]     

Increased Lipid Accumulation in the Chlamydomonas reinhardtii sta7-10 Starchless Isoamylase Mutant and Increased Carbohydrate Synthesis in Complemented Strains

The accumulation of bioenergy carriers was assessed in two starchless mutants of Chlamydomonas reinhardtii (the sta6 [ADP-glucose pyrophosphorylase] and sta7-10 [isoamylase] mutants), a control strain (CC124), and two complemented strains of the sta7-10 mutant. The results indicate that the genetic blockage of starch synthesis in the sta6 and sta7-10 mutants increases the accumulation of lipids on a cellular basis during nitrogen deprivation relative to that in the CC124 control as determined by conversion to fatty acid methyl esters. However, this increased level of lipid accumulation is energetically insufficient to completely offset the loss of cellular starch that is synthesized by CC124 during nitrogen deprivation. We therefore investigated acetate utilization and O₂ evolution to obtain further insights into the physiological adjustments utilized by the two starchless mutants in the absence of starch synthesis. The results demonstrate that both starchless mutants metabolize less acetate and have more severely attenuated levels of photosynthetic O₂ evolution than CC124, indicating that a decrease in overall anabolic processes is a significant physiological response in the starchless mutants during nitrogen deprivation. Interestingly, two independent sta7-10:STA7 complemented strains exhibited significantly greater quantities of cellular starch and lipid than CC124 during acclimation to nitrogen deprivation. Moreover, the complemented strains synthesized significant quantities of starch even when cultured in nutrient-replete medium.Microalgae are able to efficiently convert sunlight, water, and CO₂ into a variety of products suitable for renewable energy applications, including H₂, carbohydrates, and lipids (11, 12, 16, 21, 38, 41, 44). The unicellular green alga Chlamydomonas reinhardtii has emerged as a model organism for studying algal physiology, photosynthesis, metabolism, nutrient stress, and the synthesis of bioenergy carriers (12, 15, 19, 24, 32). During acclimation to nitrogen deprivation, C. reinhardtii cells accumulate significant quantities of starch and form lipid bodies (4, 5, 8, 26, 28, 30, 34, 43, 46, 48). Despite the significance of these products in algal physiology and in biofuels applications, the metabolic, enzymatic, and regulatory mechanisms controlling the partitioning of metabolites into these distinct carbon stores in algae are poorly understood. Several C. reinhardtii starch mutants with various phenotypic changes in starch content and structure have been isolated (2,–4). Two of these, the sta6 and sta7 mutants, contain single-gene disruptions that result in “starchless” phenotypes with severely attenuated levels of starch granule accumulation (2, 4, 34, 39, 40, 48).The disrupted loci in the two isolated starchless mutants are distinct and each mutant has a unique phenotype (7, 40). In the sta6 mutant, the small, catalytic subunit of ADP-glucose pyrophosphorylase (AGPase-SS) is disrupted (2, 4, 48), and this mutant accumulates less than 1% of the starch observed in wild-type (WT) cells under conditions of nitrogen deprivation. The sta7 mutant contains a disrupted isoamylase gene (7, 8, 10, 39, 40) and also has severely attenuated levels of starch, but it accumulates a soluble glycogen-like product (4, 9). In this study, we conducted an examination of the unique physiological acclimations that are utilized by these mutants to adapt to the loss of starch synthesis. As the genetic lesions in these two mutants are distinct and block starch synthesis via two very different mechanisms, we investigated the physiological consequences of starch inhibition in both of these mutants from a holistic bioenergy perspective, which included photosynthetic parameters and the overall yields of lipids and carbohydrates, the two primary bioenergy carriers in C. reinhardtii. Specifically, we examined whether the inability to synthesize starch would result in the accumulation of additional lipid, alter cellular growth or cell size, affect acetate utilization, and/or influence photosynthetic O₂ evolution. Our data indicate that both the sta6 (BAFJ5) and sta7 (sta7-10) mutants accumulate more lipid than the CC124 control during nitrogen deprivation. However, the additional lipid does not completely offset the loss of starch synthesis from a complete energetic perspective. Increased lipid accumulation during nitrogen stress has also been reported for a variety of starch mutants in recent papers (26, 27, 46). A significant feature in both of the starchless mutants studied here is that O₂ evolution and acetate utilization are diminished during nitrogen stress, which is undesirable from an overall bioenergy perspective. Remarkably, complementation of sta7-10 with genomic DNA encoding the wild-type isoamylase gene resulted in cells that were larger than those of the sta6, sta7-10, and CC124 strains, exhibited the highest total lipid levels during nitrogen deprivation, and overaccumulated starch even in nutrient-replete medium.     

Subcomplexes of PA700, the 19 S Regulator of the 26 S Proteasome, Reveal Relative Roles of AAA Subunits in 26 S Proteasome Assembly and Activation and ATPase Activity

We have identified, purified, and characterized three subcomplexes of PA700, the 19 S regulatory complex of the 26 S proteasome. These subcomplexes (denoted PS-1, PS-2, and PS-3) collectively account for all subunits present in purified PA700 but contain no overlapping components or significant levels of non-PA700 proteins. Each subcomplex contained two of the six AAA subunits (Rpt1–6) that form the binding interface of PA700 with the 20 S proteasome, the protease component of the 26 S proteasome. Unlike intact PA700, no individual PA700 subcomplex displayed ATPase activity or proteasome activating activity. However, both activities were manifested by ATP-dependent in vitro reconstitution of PA700 from the subcomplexes. We exploited functional reconstitution to define and distinguish roles of different PA700 subunits in PA700 function by selective alteration of subunits within individual subcomplexes prior to reconstitution. Carboxypeptidase treatment of either PS-2 or PS-3, subcomplexes containing specific Rpt subunits previously shown to have important roles in 26 S proteasome assembly and activation, inhibited these processes but did not affect PA700 reconstitution or ATPase activity. Thus, the intact C termini of both subunits are required for 26 S proteasome assembly and activation but not for PA700 reconstitution. Surprisingly, carboxypeptidase treatment of PS-1 also inhibited 26 S proteasome assembly and activation upon reconstitution with untreated PS-2 and PS-3. These results suggest a previously unidentified role for other PA700 subunits in 26 S proteasome assembly and activation. Our results reveal relative structural and functional relationships among the AAA subunits of PA700 and new insights about mechanisms of 26 S proteasome assembly and activation.The 26 S proteasome is a 2,500,000-Da protease complex that degrades polyubiquitylated proteins by an ATP-dependent mechanism (1, 2). The biochemical processes required for this function are divided between two subcomplexes that compose the holoenzyme (3, 4). The first, called 20 S proteasome or core particle, is a 700,000-Da complex that catalyzes peptide bond hydrolysis (5). The second, called PA700 or 19 S regulatory particle, is a 700,000-Da complex that mediates multiple aspects of proteasome function related to initial binding and subsequent delivery of substrates to the catalytic sites of the 20 S proteasome (6). The 20 S proteasome is composed of 28 subunits representing the products of 14 genes arranged in four axially stacked heteroheptameric rings (7, 8). Each of the two center β rings contains three different protease subunits that utilize N-terminal threonine residues as catalytic nucleophiles (5, 8, 9). These residues line an interior lumen formed by the stacked rings and thus are sequestered from interaction with substrates by a shell of 20 S proteasome subunits.PA700 is composed of 20 different subunits. Six of these subunits, termed Rpt1–6, are AAA² (ATPases Associated with various cellular Activities) family members that confer ATPase activity to the complex and mediate energy-dependent proteolysis by the 26 S proteasome (2, 10). 26 S proteasome assembly from PA700 and 20 S proteasome requires ATP binding to Rpt subunits (11–15). Binding of PA700 to the 20 S proteasome occurs at an axial interface between a heterohexameric ring of the PA700 Rpt subunits and the heteroheptameric outer ring of α-type 20 S proteasome subunits (16). Substrates enter the proteasome through a pore in the center of the α subunit ring that is reversibly gated by conformationally variable N-terminal residues of certain α subunits in response to PA700 binding (12, 17–19). Although the degradation of polyubiquitylated proteins requires additional ATP hydrolysis-dependent actions by PA700, the assembled 26 S proteasome displays greatly increased rates of energy-independent degradation of short peptides by virtue of their increased access to catalytic sites via diffusion through the open pore (15, 18, 20).Recently, specific interactions between Rpt and α subunits that determine PA700-20 S proteasome binding and gate opening have been defined. These findings established nonequivalent roles among the six different Rpt subunits for these processes (12, 19). For example, carboxypeptidase A treatment of PA700 selectively cleaves the C termini of two Rpt subunits (Rpt2 and Rpt5) and renders PA700 incompetent for proteasome binding and activation (19). Remarkably, short peptides corresponding to the C terminus of either Rpt2 or Rpt5, but none of the other Rpt subunits, were sufficient to bind to the 20 S proteasome and activate peptide substrate hydrolysis by inducing gate opening (12, 15, 18). The C-terminal peptides of Rpt2 and Rpt5 appear to bind to different and distinct sites on the proteasome and produce additive effects on rates of peptide substrate hydrolysis, suggesting that pore size or another feature of gating can be variably modulated (19). These various results, however, do not specify whether the action of one or the other or both C-terminal peptides is essential for function of intact PA700.In addition to its role in activation, PA700 plays other essential roles in 26 S proteasome function related to substrate selection and processing. For example, PA700 captures polyubiquitylated proteins via multiple subunits that bind polyubiquitin chains (21–23). Moreover, to ensure translocation of the bound ubiquitylated protein through the narrow opened substrate access pore for proteolysis, PA700 destabilizes the tertiary structure of the protein via chaperone-like activity and removes polyubiquitin chains via deubiquitylating activities of several different subunits (24–30). These various functions appear to be highly coordinated and may be mechanistically linked to one another and to the hydrolysis of ATP by Rpt subunits during substrate processing.Despite support for this general model of PA700 action, there is a lack of detailed knowledge about how PA700 subunits are structurally organized and functionally linked. Previously, we identified and characterized a subcomplex of PA700 called “modulator” that contained two ATPase subunits, Rpt4 and Rpt5, and one non-ATPase subunit, p27 (31). Although this protein was identified by an assay that measured increased PA700-dependent proteasome activation, the mechanistic basis of this effect was not clear. Moreover, the modulator lacked detectable ATPase activity and proteasome activating activity. The latter feature is surprising in retrospect because of the newly identified capacity of Rpt5 to activate the proteasome directly (12, 19). This disparity suggests that specific interactions among multiple PA700 subunits determine the manifestation and regulation of various activities.This study extends our recent findings regarding relative roles of Rpt subunits in the regulation of proteasome function. It also provides new insights and significance to older work that identified and characterized the modulator as a subcomplex of PA700. Our findings unite two different lines of investigation to offer new information about the structure, function, and regulation of 26 S proteasome. They also offer insights about alternative models for assembly of PA700 and 26 S proteasome in intact cells.     

Characterization of the ATP-dependent Sphingosine 1-Phosphate Transporter in Rat Erythrocytes

Sphingosine 1-phosphate (S1P) is a bioactive lipid signal transmitter present in blood. Blood plasma S1P is supplied from erythrocytes and plays an important role in lymphocyte egress from lymphoid organs. However, the S1P export mechanism from erythrocytes to blood plasma is not well defined. To elucidate the mechanism of S1P export from erythrocytes, we performed the enzymatic characterization of S1P transporter in rat erythrocytes. Rat erythrocytes constitutively released S1P without any stimulus. The S1P release was reduced by an ABCA1 transporter inhibitor, glyburide, but not by a multidrug resistance-associated protein inhibitor, MK571, or a multidrug resistance protein inhibitor, cyclosporine A. Furthermore, we measured S1P transport activity using rat erythrocyte inside-out membrane vesicles (IOVs). Although the effective S1P transport into IOVs was observed in the presence of ATP, this activity was also supported by dATP and adenosine 5′-(β,γ-imido)triphosphate. The rate of S1P transport increased depending on S1P concentration, with an apparent K_m value of 21 μm. Two phosphorylated sphingolipids, dihydrosphingosine 1-phosphate and ceramide 1-phosphate, did not inhibit S1P transport. Similar to the intact erythrocytes, the uptake of S1P into IOVs was inhibited by glyburide and vanadate but not by the other ABC transporter inhibitors. These results suggest that S1P is exported from the erythrocytes by a novel ATP-dependent transporter.Sphingosine 1-phosphate (S1P),² a bioactive lipid molecule present in the blood, plays an important role in diverse cellular responses, such as migration, proliferation, and differentiation (1, 2). These processes are triggered by the binding of S1P to its specific receptors (3), of which five subtypes (S1P₁-S1P₅) have been identified in endothelial and immune cells (4). Studies using S1P₁ receptor-deficient mice showed abnormalities in lymphocyte egress from lymph nodes, spleen, and thymus (5, 6). Whereas blood plasma contains a basal level of S1P from the nanomolar to the micromolar range (7–12), lymphoid tissues maintain a low S1P environment through the activity of S1P lyase (13). It has been proposed that a higher concentration of S1P in the blood plasma than in the lymphoid organs establishes an essential gradient along which lymphocytes expressing the S1P₁ receptor on cell surfaces migrate (2, 5, 6, 13–15).The source of plasma S1P remains unclear despite its importance in the cellular responses of endothelial cells and lymphocytes. Unlike most cells, blood cells, astrocytes, and vascular endothelial cells are reported to release S1P (8, 16–18). These cells contain sphingosine kinase, which synthesizes S1P through the phosphorylation of sphingosine (16, 18, 19). Whereas platelets and mast cells release S1P in a stimulus-dependent manner (17, 20), erythrocytes, neutrophils, and mononuclear cells release S1P in a stimulus-independent manner (16). The roles of S1P derived from erythrocytes, the most abundant of these blood cells, have not been elucidated. However, recent reports suggest that S1P released from erythrocytes is a major source of plasma S1P (7, 9) and promotes lymphocyte egress to blood (9).Previously, we showed that S1P is released from rat platelets upon stimulation by thrombin or Ca²⁺ (21). We proposed that an ATP-dependent transporter plays a key role in S1P release from platelets (21). However, the detailed mechanism of S1P release is unclear because there is no way to assay the transport of S1P across the membrane. In this study we compared the properties of S1P release from erythrocytes with that of platelets and showed that S1P release from erythrocytes does not require any stimuli. We then established an assay to measure the ATP-dependent S1P uptake into inside-out membrane vesicles (IOVs) prepared from rat erythrocytes and characterized S1P transport in erythrocytes.