s2fieldsofstudy: Biology Psychology

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Quantitative genomics of locomotor behavior in Drosophila melanogaster

The locomotor behavior of Drosophila melanogaster was quantified in a large population of inbred lines derived from a single natural population, showing that many pleiotropic genes show correlated transcriptional responses to multiple behaviors.

Background
Locomotion is required for localization of food and mates, escape from predators, defense of territory, and response to stress, and is, therefore, an integral component of most animal behaviors. In humans, Parkinson's disease, Huntington's disease, activity disorders and depression are associated with deficits in locomotion. Thus, understanding the genetic architecture of locomotor behavior is important from the dual perspectives of evolutionary biology and human health.
Locomotion is a complex behavior, with variation in nature attributable to multiple interacting quantitative trait loci (QTL) with individually small effects, whose expression is sensitive to the environment [1]. Dissecting the genetic architecture of complex behavior is greatly facilitated in model organisms, such as Drosophila melanogaster, where one can assess the effects of mutations to infer what genes are required for the manifestation of the behavior, and map QTL affecting naturally occurring variation with high resolution [2]. General features of the genetic architecture of complex behaviors are likely to be recapitulated across diverse taxa. Basic biological processes, including the development of the nervous system, are evolutionarily conserved between flies and mammals [3]. Thus, orthologues of genes affecting Drosophila locomotion may well be relevant in humans. For example, Parkinson's disease is associated with progressive degeneration of nigrostriatal dopaminergic neurons [4,5], and dopamine has also been implicated in locomotion of mice [6] and Drosophila [1,[7][8][9][10][11][12].
Several studies reveal the underlying genetic complexity of locomotor behavior in Drosophila. The neurotransmitters serotonin (5-hydroxytryptamine) [13], octopamine (the invertebrate homolog of noradrenaline) [14], and γ-aminobutyric acid [15] affect Drosophila locomotion; as do genes required for the proper neuroanatomical development of the mushroom bodies and components of the central complex, brain regions required for normal locomotion [16][17][18][19][20][21]. Recently, we developed a high-throughput assay to quantify the 'locomotor reactivity' component of locomotor behavior (measured by the level of activity immediately following a mechanical disturbance), and used this to map QTL segregating between two inbred lines that had significantly different levels of locomotor reactivity [1]. We identified 13 positional candidate genes corresponding to the QTL. Three of these genes were known to affect adult locomotion; six had mutant phenotypes consistent with an involvement in regulating locomotion, although effects on locomotor behavior were not quantified previously; and the remaining four genes, all encoding RNA polymerase II transcription factors implicated in nervous system development, were novel candidate genes affecting locomotor behavior. This study highlights the power of using natural allelic variants to study complex behavior [22], but was limited to identifying genes segregating in the two parental lines used, which represent a restricted sample of alleles segregating in a natural population.
An alternative strategy to discover genes affecting complex behaviors is to combine artificial selection for divergent phenotypes with whole genome expression profiling [23][24][25][26][27][28]. The rationale of this approach is that genes exhibiting consistent changes in expression as a correlated response to selection are candidate genes affecting the selected trait. This strategy has two advantages compared to traditional QTL mapping paradigms and unbiased screens for mutations affecting behavioral traits. First, initiating artificial selection from a large base population recently derived from nature ensures that a larger and more representative sample of alleles affect-ing segregating variation in behavior is included than in QTL mapping studies utilizing two parental lines. Second, assessing the behavioral effects of mutations in candidate genes whose expression is co-regulated in the genetically divergent lines is more efficient than unbiased mutational screens for identifying genes affecting the trait of interest [23,26,27]. Here, we have combined this strategy with classical quantitative genetic analysis to further understand the genetic architecture of locomotor reactivity. We created artificial selection lines from a genetically heterogeneous background and selected for 25 generations to derive replicate lines with increased and decreased levels of locomotor reactivity, as well as unselected control lines. We also measured locomotor reactivity in a population of 340 inbred lines derived from the same natural population. We then used whole genome expression profiling to quantify the suite of genes that were differentially expressed between the selection lines. Functional tests of mutations in ten of the differentially expressed genes identified seven novel candidate genes affecting locomotor behavior.

Natural genetic variation in locomotor reactivity
We quantified the magnitude of variation in locomotor activity among a panel of 340 inbred lines derived from the Raleigh natural population. We observed substantial naturally segregating variation in locomotor reactivity behavior (F 326,25736 = 41.84, P < 0.0001; Figure 1). The estimate of broad-sense heritability (H 2 ) of locomotor reactivity in this population was high: H 2 = 0.519. The line by sex interaction term was not significant (F 339,25736 = 0.11, P = 1.0000), indicating that magnitude and/or rank order of the sexual dimorphism does not vary among the lines in this population. The correlation in locomotor reactivity between the sexes (r GS = 0.973 ± 0.015) was correspondingly high and positive, and not significantly different from unity.

Response to artificial selection for locomotor reactivity
We derived a heterogeneous base population from isofemale lines sampled from the Raleigh natural population, and used artificial selection to create replicate genetically divergent lines with high (H) and low (L) activity levels, as well as replicate unselected (control, C) lines. At generation 25, the H and L lines diverged by 27.6 seconds, or 61% of the total 45 s assay period (Figure 2a).
We estimated realized heritability (h 2 ± standard error of the regression coefficient) of locomotor reactivity from the regressions of the cumulated response on cumulated selection differential [29]. Heritability estimates from the divergence between H and L lines over 25 generations were h 2 = 0.147 ± 0.008 (P < 0.0001) for replicate 1 and h 2 = 0.170 ± 0.010 (P < 0.0001) for replicate 2 (Figure 2b). The selection response was asymmetrical, largely due to low selection differentials in the H lines. Estimates of realized heritability for each of the selection lines (estimated as deviations from the contemporaneous control) were h 2 = 0.030 ± 0.036 (P = 0.43) and h 2 = 0.074 ± 0.0265 (P = 0.01) for H line replicates 1 and 2, respectively; and h 2 = 0.181 ± 0.0093 (P < 0.0001) and h 2 = 0.201 ± 0.011 (P < 0.0001) for L line replicates 1 and 2, respectively. There was no inbreeding depression for locomotor reactivity: the regression of locomotor behavior in the control lines over 25 generations was b = 0.0006 ± 0.053 (P = 0.98) and b = -0.012 ± 0.044 (P = 0.78) for C line replicates 1 and 2, respectively.

Correlated phenotypic response to selection for locomotor reactivity
We evaluated whether the response to selection was specific for locomotor activity in response to a mechanical stress, or if other traits involved in stress response or behaviors that have a locomotor component were also affected. We did not observe significant differences among the selection lines for starvation resistance (F 2,3 = 1.22, P = 0.41; Figure 3a), chill coma recovery (F 2,3 = 0.13, P = 0.89; Figure 3b), ethanol sensitivity (F 2,3 = 0.73, P = 0.55; Figure 3c), or copulation latency (F 2,3 = 4.21, P = 0.13; Figure 3d). These results suggest that the response to selection is specific for locomotor reactivity, and not a general behavioral response; that is, the slowly reacting low activity lines are not generally 'sick'.
We assessed whether selection for increased and decreased locomotor reactivity early in life affected locomotion at later ages -that is, whether selection affected the typical senescent decline in locomotor behavior with age [30]. We repeated our assay of locomotor reactivity on the selection lines each week until the flies were eight weeks old. We found that by week 4 (F 2,3 = 8.76, P = 0.05; Figure 3e) the H and C lines no longer differed, and by week 6 (F 2,3 = 3.33, P = 0.18; Figure 3e), none of the lines differed from one another. Thus, the selection response was specific for genes affecting locomotor reactivity of young animals. We infer from this result that either there is little genetic variation for locomotor reactivity in aged flies, or that such variation is genetically uncoupled from that which affects locomotion of young flies.

Transcriptional response to selection for locomotor reactivity
We assessed transcript abundance in the H, L, and C selection lines using Affymetrix high density oligonucleotide whole genome microarrays, for flies of the same age and physiological state as selected individuals. The raw microarray data are given in Additional data file 1, and have been deposited in the GEO database [31] under series record GSE5956 [32]. We used factorial ANOVA to quantify statistically significant differences in transcript level for each probe set on the array. Using a false discovery rate [33] of Q < 0.001, we found 8,766 probe sets were significant for the main effect of sex, 1,825 were significant for the main effect of line, and 42 were significant for the line × sex interaction (Additional data file 2). All Frequency distribution of locomotor reactivity scores (in seconds) among inbred lines derived from the Raleigh population   Phenotypic response to selection for locomotor reactivity   AB 42 probe sets that were significant for the interaction term were also significant for the main effect of line.
We used ANOVA contrast statements on the 1,825 probe sets with differences in transcript abundance between selection lines to detect probe sets that were consistently up-or downregulated in replicate lines [25,27]. We found 1,790 probe sets (9.5%) that differed between the selection lines when pooled across replicates (Additional data file 3). The pattern of the transcriptional response to selection was complex, and fell into four categories: H ≥ C ≥ L (H > L, 486 probe sets); H ≤ C ≤ L (H < L, 686 probe sets); H ≤ C ≥ L (379 probe sets); and H ≥ C ≤ L (239 probe sets). The first two categories can readily be interpreted as linear relationships between transcript abundance and complex trait phenotype, while for the latter two categories the relationship is quadratic, with the most extreme expression values in the C lines. There are two possible explanations for the apparently non-linear patterns of transcriptional response to selection. First, probe sets in the third category could represent cases in which H and L alleles respond to selection, but harbor polymorphisms in the probes used to interrogate expression levels, thus yielding reduced levels of expression relative to the control. Second, the nonlinear patterns could be attributable to changes in expression as a consequence of reduced fitness of the selection lines rel-ative to the control. Although there was a widespread transcriptional response to selection for locomotor reactivity, the magnitude of the changes of transcript abundance was not great, with the vast majority much less than two-fold (Additional data file 3, Figure 4).
The probe sets with altered transcript abundance between the selection lines fell into all major biological process and molecular function Gene Ontology (GO) categories (Additional data file 4). We assessed which categories were represented more frequently than expected by chance, based on representation on the microarray, since the over-represented GO categories are likely to contain probe sets for which transcript abundance has responded to artificial selection. Highlights of the transcriptional response to artificial selection for locomotor reactivity are given in Table 1; the complete list of significantly over-represented categories is given in Additional data file 5. The greatest enrichment in the biological process categories were for genes affecting lipid, cellular lipid, steroid and general metabolism, responses to biotic, abiotic, and chemical stimuli, and defense response and responses to toxins and stress. The molecular function categories of catalytic, monooxygenase and oxidoreductase activity were highly enriched, as were the cellular component categories of vesicular, cell and membrane fractions and microsome. These   classifications reflect the striking over-representation of genes in the cytochrome P-450 and Glutathione S tranferase gene families, genes affecting lipid metabolism, and genes encoding immune/defense molecules.

Functional tests of candidate genes
To assess the extent to which transcript profiling of divergent selection lines accurately predicts genes that directly affect the selected trait, we evaluated the locomotor reactivity of *Number of genes in the annotation category. † Number of genes in the annotation category/total number of significant genes. ‡ P value from a modified Fisher exact test for enrichment of genes in an annotation category. The cross-classified factors in the 2 × 2 contingency tables are genes in the annotation category versus not in the annotation category, and significant genes versus all genes on the array. lines containing P-element insertional mutations in ten candidate genes that were implicated by the analysis of differential transcript abundance. All of the P-element insertions were derived in a common isogenic background, and are viable and fertile as homozygotes [34,35]. The P-elements are inserted either in the coding region or approximately 100 bp upstream of the start of transcription of each candidate gene.
The candidate genes are involved in diverse biological processes, including signal transduction (tartan, center divider), neurotransmitter secretion (Amphiphysin, Cysteine string protein), nervous system and muscle development (muscleblind), chromosome segregation (nebbish), and copulation (ken and barbie). Three of the mutations are in computationally predicted genes (CG33523, CG31145, and CG10990). Six of the mutations exhibited significant differences in locomotor reactivity from the co-isogenic control line, after Bonfer-roni correction for multiple tests (Table 2, Figure 5). In addition, Amphiphysin was formally significant (F 1,112 = 5.66, P = 0.019), but not at the conservative Bonferroni threshold of P = 0.005. There was no clear relationship between the pattern of transcriptional response to selection of the candidate genes and the results of the functional tests. The significant genes belonged to categories 1 (H > L, CG33523 and Amphiphysin), 2 (H < L, ken and barbie and nebbish) and 3 (H ≤ C ≥ L, muscleblind, Cysteine string protein and CG10990) (Additional data file 3). All of the non-significant candidate genes belonged to category 1. From these data, we infer that transcripts in category 3 do not solely represent instances of changes in expression as a consequence of reduced fitness of the selection lines relative to the control, as in this case one would not expect the genes to affect the selected trait.
Mean locomotor reactivity scores (seconds) of lines containing P-element insertional mutations in candidate genes Figure 5 Mean locomotor reactivity scores (seconds) of lines containing P-element insertional mutations in candidate genes. The blue bar denotes the Canton S B co-isogenic control line; the red bars indicate the mutant lines. The red asterisk represents mutants that are significantly different from the control line with P values that exceed Bonferroni correction for multiple testing (P = 0.005), and the black asterisk represents mutants for which P < 0.05, but do not surpass the conservative Bonferroni correction. Mutations in each significant gene had lower levels of locomotor reactivity than the control line. Of these genes, four have been previously implicated to affect activity: muscleblind mutants are paralytic [36]; Amphiphysin [37] and Cysteine string protein [38] mutants are sluggish; and nebbish mutants are not well coordinated [39].

Discussion
Genetic architecture of locomotor reactivity D. melanogaster exhibits a strong response to artificial selection for high and low levels of locomotor reactivity. The heritability of locomotor reactivity is fairly high for a behavioral trait (approximately 0.16). However, the genetic response to selection, as inferred from the realized heritability, was asymmetrical. Responses were much greater in the direction of decreased locomotor reactivity (heritabilities approximately 0.20) than for increased activity. Asymmetrical responses to selection are often observed for traits that are major components of fitness [29,40]. However, in this case we cannot rule out a more trivial explanation: the attenuated selection differential in the H lines. The highly reactive individuals remained active for the majority of the 45 s assay period. Indeed, we recorded the locomotor reactivity of flies from the high selection lines for assay periods of one to five minutes, and found that most flies were active throughout the assay period regardless of the duration of the assay (data not shown).
The phenotypic response to selection appears to be specific for locomotor reactivity. In particular, we did not observe correlated responses to selection for locomotor reactivity for responses to different stressors, nor for other traits involving locomotion.
Since the broad sense heritability estimated from the variation among inbred lines (H 2 = 0.52) greatly exceeds the narrow sense heritability estimated from response to selection (h 2 = 0.16), we infer that considerable non-additive genetic variance due to dominance and/or epistasis affects natural variation for this trait. We estimate the additive genetic variance (V A ) as V A = h 2 V P = 3.74, where h 2 is the narrow sense heritability from divergent response to artificial selection, averaged over both replicate lines, and V P is the total phenotypic variance for the first 10 generations averaged over all 6 selection lines (V P = 23.58). If only additive genetic variance affected locomotor reactivity, we would predict the total genetic variance among the inbred lines to be 2FV A = 7.48, for an expected F = 1 after 20 generations of full sib inbreeding [29]. In contrast, the estimate of the total genetic variance among the inbred lines was V G = 28.14. The difference, therefore, must be due to dominance and/or epistasis.

Transciptional response to selection for locomotor behavior
We found a large transcriptional response to selection for locomotor reactivity, with changes in expression of nearly 1,800 probe sets (approximately 9.5% of the genome) between the selection lines, using a stringent false discovery rate of 0.001. Previously, we selected replicate lines for increased and decreased copulation latency [25] and increased and decreased aggressive behavior [27]; both sets of selection lines were derived from the same initial heterogeneous base population that was used in this study. We found that the transcript abundance of over 3,700 probe sets evolved as a correlated response to selection for copulation latency [25], and over 1,500 probe sets evolved as a correlated response to selection for divergent aggressive behavior [27]. These results are in contrast to analyses of transcriptional response to selection for geotaxis behavior [23] and aggressive behavior [26], in which approximately 200 genes were inferred to exhibit differences in expression between the selection lines. The discrepancy is likely to be attributable to differences in the base population used to initiate selection. In this study, and others [25,27], the base population was derived from 60 isofemale lines recently collected from nature, while the other studies [23,26] used lines derived from severely restricted genetic bases. Classical quantitative genetics theory shows that the magnitude of direct and correlated responses to selection is directly proportional to the effective population size [41].
The large number of genes exhibiting changes in transcript abundance among replicate selection lines implies that genes affecting complex behaviors are highly pleiotropic: if approximately 10% of the genome affects any one trait, the same genes must affect multiple traits. Thus, genes affecting behavior are also likely to be involved in neurogenesis, metabolism, development and general cellular processes, and many of the same genes may affect multiple behaviors. Indeed, 1,638 probe sets that were significant at a false discovery rate of Q < 0.001 were common between the selection lines for locomotor reactivity (this study), aggression [27], and copulation latency [25] that were all initiated from the same base population (Additional data file 6, Figure 6). We assessed whether we observed more common differentially regulated probe sets than expected by chance using χ 2 tests. We found 908 probe sets in common between copulation latency and locomotor reactivity (χ 1 2 = 862, P << 0.0001), 474 probe sets in common between aggressive behavior and locomotor reactivity (χ 1 2 = 731, P << 0.0001), and 878 probe sets in common between copulation latency and aggressive behavior (χ 1 2 = 1,076, P << 0.0001). The transcript abundance of 311 genes (Additional data file 7) was altered as a correlated response to selection for all three behaviors (χ 1 2 = 2,736, P << 0.0001).
The magnitude of the overlapping correlated transcriptional responses to selection for three different behaviors is astonishing. However, the patterns of the transcriptional responses to selection for the three behaviors were not correlated. We computed the correlations of the difference in mean expression between the H and L selection lines (or fast and slow lines for the case of mating behavior) for common probe sets that had significant H ≠ L contrast statements for all pairwise combinations of behaviors. The data set consisted of 655 probe sets for the comparison of copulation latency and locomotor reactivity, 580 probe sets for the comparison of mating and aggressive behavior, and 270 probe sets for the comparison of locomotion and aggressive behavior. The correlations of the mean difference in expression between H and L lines were: r = 0.059 ± 0.060 between mating and locomotor behavior; r = 0.025 ± 0.012 between mating and aggressive behavior; and r = 0.034 ± 0.081 between aggressive and locomotor behavior (Figure 7). Similarly, there is no association between the four categories of patterns of transcriptional response to selection for the 311 genes in common to all three behaviors (data not shown). Thus, it is not likely that the common probe sets are associated with the locomotor component of the behaviors, or with the reductions in fitness that typically occur as an unwanted side-effect of response to artificial selection, since in these cases one would expect correlated patterns of expression differences between selection lines. However, both the specific phenotypic responses to selection for the three behaviors [25,27] (this study) and the lack of correlation of patterns of transcript abundance of common coregulated genes suggest that multiple alleles at pleiotropic genes with independent effects on different behaviors segregate in natural populations. This inference is bolstered by a recent observation that independent molecular polymorphisms at Catecholamines up are associated with locomotor behavior, longevity and sensory bristle number in Drosophila [12].
The probe sets with altered transcriptional responses to selection that were in common to at least two behaviors were enriched for genes in the cytochrome P-450 and Glutathione S tranferase gene families, genes affecting lipid metabolism, genes encoding immune/defense molecules, and genes affecting circadian rhythm and sleep (data not shown). Pleiotropic effects of genes affecting circadian rhythm, cytochrome P450s and glutathione S transferases on multiple behaviors in Drosophila have been implicated by other studies. Expression levels of Pigment dispersing factor and cryptochrome, both of which affect circadian rhythm, were up-regulated in lines selected for positive geotaxis, and confirmed to affect geotaxis behavior in functional tests [23]. Similarly, one member of the cytochrome P450 gene family was recently shown to affect aggressive behavior [26], and another member of this family was down-regulated in lines derived from nature with high male reproductive success [42]. The expression of many cytochrome P450 family members was altered in mated relative to virgin females [43]. Glutathione trans-Numbers of probe sets that were significantly (Q < 0.001) differentially expressed between replicate selection lines selected for locomotor reactivity, copulation latency, and aggressive behavior Figure 6 Numbers of probe sets that were significantly (Q < 0.001) differentially expressed between replicate selection lines selected for locomotor reactivity, copulation latency, and aggressive behavior. All selection lines were derived from the same base population.  ferase genes are thought to be involved in resistance to oxidative stress [44]. Glutathione transferase genes were also overrepresented in a whole genome expression study comparing natural lines with high and low levels of male reproductive success [42]. Two of these genes, GstE5 and GstE1, were among those that were differentially expressed between lines selected for locomotor behavior (this study), aggressive behavior [27] and mating behavior [25]. Functional evidence linking glutathione transferase activity to locomotor behavior comes from the observation that the locomotor defect of Drosophila parkin mutants is enhanced by loss-of-function mutants of GstS1 and reduced when GstS1 is over-expressed [45].
The transcriptional response to selection that we observed is attributable to genes that have causally responded to selection, and genes that are co-regulated by these genes.
Since the transcriptional response to single mutations with subtle phenotypic effects can involve over 100 co-regulated genes [46], the number of selected loci causing the changes in transcript abundance between the selection lines could be rather large. It will be necessary to map the QTL causing divergence between the H and L lines in order to elucidate causal versus consequential transcriptional responses and correlated responses to selection.

Candidate genes for locomotor behavior
Regardless of whether or not the observed changes in gene expression are causally associated with genetic divergence in locomotor behavior between the selection lines, the genes exhibiting altered expression levels are candidate genes affecting locomotor behavior. We quantified locomotor reactivity for ten mutations in candidate genes that were generated in a common co-isogenic background, and identified seven genes with mutational effects on locomotor reactivity, six of which are significant using a conservative Bonferroni correction for multiple tests. Locomotor defects have been described previously for mutations of four of these genes. muscleblind encodes a protein with a zinc-finger domain involved in muscle development, and mutants are paralytic [36]. The mutant allele of muscleblind that had reduced locomotor reactivity in this study was also associated with increased aggressive behavior [27], consistent with the above inference regarding non-directional allelic effects of pleiotropic genes on behavioral traits. Cysteine string protein is involved in stress response and neurotransmitter secretion, and mutants are sluggish and hypoactive [38]. nebbish is involved in mitotic chromosome segregation, and mutants in this gene have been characterized as not well coordinated [39]. Amphiphysin, which did not surpass the conservative estimate of significance, but is formally significant at the P < 0.05 significance level, is involved in neurotransmitter secretion and regulation of muscle contraction, and mutants have been shown to be flightless and hypoactive [37]. The remaining three genes, CG10990, CG33523, and ken and barbie are novel genes affecting locomotor behavior. CG10990 and CG33523 are computationally predicted genes with unknown functions, and ken and barbie is involved in copulation and insemination [47].
The success of these functional tests validates using expression profiling on genetically divergent lines in directed mutagenesis screens to identify genes affecting complex traits. This strategy is complementary to traditional strategies and cannot supplant them, since many key genes will not be detected as differentially expressed. Specifically, we will not detect genes that are differentially expressed at a different developmental period or if the magnitude of the difference in transcript abundance is too small to be detected, or genes affecting the trait that are not regulated at the level of transcription. Of the 82 genes with known effects on some aspect of locomotor behavior, 20 were not present on the microarray, and eight did not give a statistically significant signal (that is, were called absent). Of the 54 remaining genes, 16 were statistically differentially expressed between the selection lines at nominally significant P values that did not meet our conservative false discovery rate criterion, and 13 genes (cacophony, Drop, homer, Shaker, TBP-related factor,turtle, Casein kinase IIα, Clock, cycle, discs overgrown, cAMP-dependent protein kinase1, shaggy and timeless) were differentially expressed at Q < 0.001 (Additional data file 8). We identified 13 positional candidate genes affecting locomotor reactivity in our previous QTL mapping study [1]. Of these genes, only Drop and turtle showed significant differences in transcript abundance between the selected lines. These results highlight the utility of applying multiple complementary approaches to understand the genetic basis of complex traits.
Many of the genes with mutational effects on locomotor behavior are evolutionarily conserved and have human orthologs (Table 2). For example, muscleblind is orthologous to Muscleblind-like Protein 1, which encodes a triplet repeat expansion, similar to the one that causes muscular dystrophy [48]. It is thus possible that the genes and pathways affecting locomotion in Drosophila will elucidate corresponding mechanisms in other organisms, including humans.

Locomotor reactivity assay
Locomotor reactivity was assessed as described previously [1,12]. Briefly, we placed single 3-7 day old adult flies, collected under CO 2 exposure, into vials containing 5 ml of standard cornmeal/agar/molasses media, and left them over-night to acclimate to their new environment. To quantify locomotor reactivity, we subjected each fly to a mechanical disturbance by tapping the vial twice against a table, and recorded the amount of time the fly is active in the 45 seconds immediately following the disturbance using a stopwatch to record movement, while a timer counts down the 45 s assay period. The measure of locomotor reactivity is a score ranging from 0 s to 45 s, denoting the total amount of activity during the assay period.

Quantitative genetic analysis
Mixed model factorial analysis of variance (ANOVA) was used to partition variance in locomotor activity among the inbred lines, according to the model: where μ is the overall mean, B is the random effect of block, S is the fixed main effect of sex, Line(Block) is the random main effect of line within each block, Sex × Line(Block) is the random effect of the sex by line interaction within each block, Rep(Line × Sex × Block) is the random effect of replicate, and E is the within-vial variance. Parentheses indicate nested effects. The total genotypic variance among lines was estimated as: where σ L(B) 2 is the among-line variance component and σ LS(B) 2 is the variance attributable to the L ×S interaction. The total phenotypic variance was estimated as: where σ E 2 is the environmental variance component. We estimated broad sense heritabilities as: We also ran reduced analyses for each sex separately. The genetic correlation (r GS ) across sexes was calculated as: where σ 2 L is the among-line variance component from the analysis pooled across both sexes, and σ LM and σ LF are the square roots of the among-line variance components from the analyses of each sex separately. All statistical analyses were performed using SAS procedures (SAS Institute, Cary, NC, USA).

Artificial selection for locomotor reactivity
The base population was generated from 60 isofemale lines established from flies collected in Raleigh, NC in 1999. The isofemale lines were crossed in a round robin design (line 1 Ǩ × line 2 ǩ, line 2 Ǩ × line 3 ǩ, line 60 Ǩ × line 1 ǩ). Single fertilized females from each cross were placed in each of two culture bottles. The two initial bottles were split into three replicate bottles each that were arbitrarily assigned into H, L, and C groups. The six bottles were maintained by random mating for four generations to allow initial linkage disequilibrium to decay. After four generations of random mating, the following generation (G1) and all subsequent generations repeated the same procedure: 50 virgin females and males were scored from each line (H, L, and C), and the 20 most active males and females from the H lines and the 20 least active males and females from the L lines were selected as parents for the next generation. The first 20 males and females scored from the C lines were used as the C line parents. Selection was continued for 25 generations.
Estimates of realized heritability (h 2 ) were calculated by regression of the cumulative selection response (ΣR) on the cumulative selection differential (ΣS) [29].

Correlated responses to selection
Starvation resistance was assessed as previously described [49]. Single sex groups of ten two-day-old flies were placed in vials containing non-nutritive medium (1.5% agar and 5 ml water). Survival was scored every 8 h until all flies were dead. This assay was conducted for generations 23-25, with five replicate measurements per line per sex per generation.
Chill-coma recovery was quantified as previously described [50]. We transferred 25 3-to 7-day-old flies per line per sex per generation without anesthesia into an empty vial and placed them on ice for 3 h. The flies were then transferred to room temperature, and the recovery time was recorded as the length of time necessary for an individual to right itself and stand on its legs. The assay was performed at generations 23-25.
Ethanol sensitivity was measured using an inebriometer [51]. We aspirated 50-60 same-sex flies per line per generation into a glass column with mesh partitions, which was filled with saturated ethanol vapors. The flies lose postural control due to ethanol exposure and fall down the partitions to the bottom of the column, where they were collected at one minute intervals. The elution time was recorded as the meas-ure of ethanol sensitivity. This assay was conducted for generations 23-25.
Copulation latency was scored as previously described [25]. For each selection line per generation, 20 pairs of 3-to 7-dayold virgin flies were aspirated into vials containing approximately 3 ml standard culture medium. The score recorded for a pair was the number of minutes from introduction to the vial until initiation of copulation. Assays were performed at generations 23-25.

Statistical analysis of correlated responses
Differences between the selection lines for the correlated traits were assessed using a nested mixed model analysis of variance (ANOVA): where Y is the phenotypic score, μ is the overall mean, Selection is the fixed effect of the selection treatment (high activity, control, or low activity), Line(Selection) is the random effect of the replicate within each selection group, Sex is the fixed effect of sex, Gen is the fixed effect of generation, and ε is the error variance. A significant Selection term is indicative of a correlated response in the trait being tested to selection for locomotor behavior. Behavioral locomotor senescence was analyzed in the same manner, with the replacement of the Week term instead of Gen term.

Whole genome expression profiling
At generation 25, two replicates of 12 3-to 7-day-old virgin males and females were collected from each selection line, at the same time of day that the behavioral assays were performed. Total RNA was extracted from the 24 samples (6 lines × 2 sexes × 2 replicates) using the Trizol reagent (Gibco BRL, Gaithersburg, MD, USA). Biotinylated cRNA probes were hybridized to high density Drosophila GeneChip 2.0 oligonucleotide microarrays (Affymetrix, Inc., Santa Clara, CA, USA)) and visualized with a streptavidin-phycoerythrin conjugate, as described in the Affymetrix GeneChip Expression Analysis Technical Manual (2000), using internal references for quantification. The quantitative estimate of expression of each probe set is the Signal (Sig) metric, as described in the Affymetrix Microarray Suite, Version 5.0.

Microarray data analysis
The 18,800 probe sets on the Affymetrix Drosophila Gene-Chip 2.0 are represented by 14 perfect-match (PM) and 14 mismatch (MM) pairs. The Sig metric is computed using the weighted log(PM -MM) intensity for each probe set, and was scaled to a median intensity of 500. A detection call of present, absent, or marginal is also reported for each probe set. We excluded probe sets with more than half of the samples called absent from the analysis, leaving 11,656 probe sets. This filter retained sex-specific transcripts but eliminated probe sets with very low and/or variable expression levels [25]. On the remaining probe sets, we conducted two-way fixed effect ANOVAs of the Signal metric, using the following model: where Sex and Line are the fixed effects of sex and selection line and ε is the variance between replicate arrays. We corrected the P-values computed in these ANOVAs for multiple tests using a stringent false-discovery rate criterion [33] of Q < 0.001. We used contrast statements [25,27] to assess whether expression levels of probe sets with L and/or S × L terms at or below the Q = 0.001 threshold were significantly different between selection groups (H, C, and L) at the P < 0.05 level, both within each sex and pooled across sexes. GO categories were annotated using DAVID [52] ( Table 1) and Affymetrix [53] and FlyBase [54] compilations (Additional data files 4 and 5).

Functional tests of mutations in candidate genes
We tested whether mutations in ten of the candidate genes with altered transcript abundance between the selection lines affected locomotor behavior. The mutations were homozygous P{GT1} elements inserted within the candidate genes, and all were generated in a common co-isogenic background (Canton S, B background) [35]. Locomotor reactivity was assessed for all mutant lines using the same assay as the selection lines, but using a 30 s assay period. The ten mutant lines were tested in two blocks, with ten flies/sex/line/block. We also assessed 20 flies per sex of the co-isogenic control line (B) in each block. We used the following ANOVA model to determine whether the locomotor reactivity of the mutant lines differed significantly from the control: where Y is the phenotypic score, μ is the overall mean, Line is the fixed effect of the genotype (mutant or control), Block is the random effect of the replicate, Sex is the fixed effect of sex, Line×Sex (fixed), Line×Block (random), Block×Sex (random), and Line×Sex×Block (block) are the interaction terms, and ε is the variance within lines. A significant Line term suggests that the mutant is significantly different from the control.

Additional data files
The following additional data are available with the online version of this paper. Additional data file contains raw microarray data. Additional data file 2 gives P and Q values for all probe sets with significant (Q < 0.001) expression differences between selection lines. Additional data file 3 gives P values of significant contrasts of mean expression differences between selection lines. Additional data file 4 gives GO categories of differentially expressed genes. Additional data file 5 shows the GO categories that are over-represented by differentially expressed genes. Additional data file 6 lists probe sets common to multiple behaviors. Additional data file 7 gives P values of significant contrasts for probe sets that are differentially expressed between lines independently selected for locomotor, aggressive and mating behavior. Additional data file 8 shows known genes affecting locomotor behavior.


extfieldsofstudy: Medicine Chemistry

s2fieldsofstudy: Biology Chemistry

text:
Receptor-Drug Interaction: Europium Employment for Studying the Biochemical Pathway of G-Protein-Coupled Receptor Activation

In medicinal chemistry field, the biochemical pathways, involved in 7-transmembrane domains G-protein coupled receptors (GPCRs) activation, are commonly studied to establish the activity of ligands towards GPCRs. The most studied steps are the measurement of activated GTP-α subunit and stimulated intracellular cAMP. At the present, many researchers defined agonist or antagonist activity of potential GPCRs drugs employing [35S]GTPγS or [3H]cAMP as probes. Recently, the corresponding lanthanide labels Eu-GTP and Eu-cAMP as alternative to radiochemicals have been developed because they are highly sensitive, easy to automate, easily synthesized, they display a much longer shelf-life and they can be used in multilabel experiments. In the present review, the receptor-drug interaction by europium employment for studying the biochemical pathway of GPCR activation has been focused. Moreover, comparative studies between lanthanide label probes and the corresponding radiolabeled compounds have been carried out.

INTRODUCTION
Luminescence is defined as emitted radiation by a chemical species, molecule, or atom. It occurs when an electron returns to the electronic ground state from an excited state, loosing its excess energy as a photon. Electronic states of organic molecules can be grouped into singlet and triplet state. The singlet state is characterized by the presence of all the electrons with their spins paired, while the triplet state displays one set of the electron spin unpaired (see Figure 1).
The phosphorescence and fluorescence light emissions are the main phenomena belonging to luminescence (see Figure 2).
Phosphorescence occurs when the excited electron in the singlet state undergoes intersystem crossing to a metastable triplet state and then to the electronic ground state with the emission of a photon. Since this phenomenon originates from the lowest triplet state, it shows a decay time approximately equal to the lifetime of the triplet state (from 10 −4 to 10 seconds). Therefore, phosphorescence is often characterized by an afterglow which is not observed for fluorescence.
Fluorescence occurs when the molecule returns to the electronic ground state from the excited singlet state by emission of a photon and the lifetime of an excited singlet state is approximately from 10 −9 to 10 −7 seconds. To date, many biological and medical applications are based on fluorescence properties of pharmacological tools. These compounds permit to visualize single step activated in cell biochemical pathways, to estimate changes in functional and structural property cells, and to appreciate the modification of their molecular complexes involved in biological systems.
Indeed, the fluorescence spectroscopy is one of the most important applications for monitoring the molecular interactions at the single molecule level and now widely used in biological research.
In particular, the receptor-ligand interaction study, which represents the starting point of drug discovery in medicinal chemistry field, could be better performed by  At the present, this study is carried out employing radioligands that display some limitations due to the safety, the storage, the handling, and the disposal of radioactive materials; other limitations are due to the difficulties to perform analysis in living cells. The development of lanthanide-based assays to assess receptor-ligand interactions is improving the characterization and the evaluation of potential new drugs and pharmacological tools discovery [1][2][3].
Lanthanide labels are an alternative to radiochemicals because they are highly sensitive, easy to automate, easily synthesized, they display a much longer shelf life, and can be used in multilabel experiments.
In the present review, the receptor-drug interaction by europium employment for studying the biochemical pathway of GPCR activation will be focused.
The lanthanide series comprises the 15 elements with atomic number 57 through 71 as depicted in Figure 3. The complexes of some lanthanides such as Samarium (Sm 3+ ), Europium (Eu 3+ ), Terbium (Tb 3+ ), and Dysprosium (Dy 3+ ) are known to be luminescent; their ions emit fluorescent light of specific wavelengths when coordinated to specific ligands. The lanthanide ions in solutions or in complex possess luminescence properties because of the transitions within 4fshell. The excitation of lanthanide ions occurs at the expense of the intramolecular energy transfer from excited organic molecule to lanthanide ion. The sensitization of luminescence of lanthanide ions in complexes with organic ligand allows their application as luminescence probes to establish the structure and the properties of biological objects.
The major advantages of lanthanide labels are (i) ultrasensitive and specific signal, (ii) low background, (iii) amenability to automation, as well as (iv) stability and safety.
The specific signal from the lanthanide is due to the long lifetime of the excited state that can be temporally separated from the nonspecific signal [1][2][3][4][5]. Typical lifetimes of background fluorescence from plates, reagents, or cells are ranging from picoseconds to microseconds [6,7] while lanthanide lifetimes are from 0.2 to 1.5 milliseconds. Thus, the excited state of lanthanides is long lived. This gives the advantage of being able to excite the label, to delay measurement of the emission signal until the background fluorescence has completely decayed, and then to collect the specific emission signal from the lanthanide. This delay period leads to an ultrasensitive and specific signal which can be time averaged.
Lanthanides excitation occurs in the ultraviolet region while the emission is in the visible spectrum as depicted in Table 1.
The emitted light is at a longer wavelength (lower energy) than the absorbed light since some of the energy is lost because of the vibrations; the difference in wavelenght between positions of the band of the excitation and emission is termed Stokes' shift as depicted in Figure 4. The large Stokes' shift (greater than 200 nm) of the lanthanide ions contributes to the low background signal since there is minimal crosstalk between excitation and emission signals. In addition, the emission peak is very sharp allowing tight limits for the excitation filter sets. These features make this method amenable to use with multiple labels, since Eu, Sm, Dy, and Tb have different excitation and emission profiles and different decay times. Hence, multiple assays can be performed in a single well, thus greatly reducing the number of time and reagents needed. Finally, not less important is the safety of fluorescent probes instead of radiolabeled probes. Indeed, fluorescent probes do not have the drawbacks of radioactivity such as the production, delivery, and disposal of the radioactive materials; the relatively short shelf life of some radionuclides; and the long signal acquisition times required to reach the desired sensitivity.
To detect lanthanides by time-resolved fluorescence (TR-F) in biological assays, a sensitization is necessary. For this purpose, organic chromophores are covalently attached to the lanthanide chelate. The chromophore acts as an antenna which absorbs light. This energy is transferred to the lanthanide excited state and emitted as a fluorescent signal. Since water molecules will deactivate the lanthanide ion, an ideal chelator will saturate the coordination shell of the lanthanide to prevent water from binding.
The lanthanide chelators are divided into two groups: photoactive and nonphotoactive subclasses. A photoactive chelator is useful because firstly, it provides the attachment of the chromophore to the lanthanide facilitating energy transfer; secondly, it protects the lanthanide from coordination with water, and it permits the attachment of other reactive groups [8]. An alternative approach is to use nonphotoactive Colabufo Nicola Antonio et al.     chelators which should be stable, hydrophilic, and capable of releasing the lanthanide after the addition of an enhancement solution because in this case the fluorescent chelator is contained within this solution.
The most common chelators used in monitoring ligand-receptor interactions are Eu-chelates of diethylenetriaminetetraacetic acid (DTTA) and diethylenetriaminepentaacetic acid (DTPA). These chelates, used for protein labeling, bear an isothiocyanate group which reacts with the εamino lysine residues. As depicted in Figure 5, DTTA and DTPA form a stable complex with Eu 3+ by their four and five carboxylic acid groups, respectively [9].
In most assays, to obtain a measurable TRF signal, the lanthanide must be released from the nonphotoactive chelator and must be transferred to a fluorescent chelator contained within the enhancement solution.

LANTHANIDES APPLICATIONS
The lanthanide chelates have found applications in biomedical assays starting from radiotherapy based on some samarium isotopes, nuclear magnetic imaging, specific cleavage of DNA or RNA, and for sensing of various analytes and conditions. Europium chelates have been used for sensing several determinations, such as, pH [10], temperature [11], Diethylenetriaminepentaacetic acid (DTPA) (b) Figure 5: Eu 3+ stable complexes with DTTA and DTPA. light doses, phosphate with unsaturated europium chelate [12], glucose by time-resolving imaging [13], catalase by using tetracycline as enhancing ligand [14], neurotoxic agents for bioterror monitoring [15], and anesthetic agents using a dried strip reagent [16].
The photoluminescence of lanthanides is largely applied in bioanalytical assays especially in the field of clinical immunodiagnostic and for evaluating receptor-ligand interactions.
Two of the most frequently applied technologies for these purposes are the dissociation-enhanced Lanthanide Fluorescent ImmunoAssay (DELFIA), a heterogeneous assay technology based on fluorescence enhancement from PerkinElmer and homogeneous Time Resolved-Fluorescence Resonance Energy Transfer (TR-FRET) (TRACE) from Brahms.
DELFIA has been used for the analysis of different biochemical pathways by monitoring second messengers (calcium, cAMP) for the study of G-protein and kinases activation and for cell viability determination.

EVALUATION OF BIOLOGICAL PATHWAYS INDUCED BY RECEPTOR-DRUG INTERACTION
The interaction between G-protein-coupled receptors (GPCRs) with drugs activates specific cell pathways. GPCRs, Colabufo Nicola Antonio et al. also known as seven transmembrane receptors (7-TM receptors), are a large family of eukaryotic transmembrane receptors that activate several pathways and are involved in many diseases. GPCRs are characterized by three subunits α, β, and γ; and the α subunit, in the nonactivated state, binds GDP. GPCR stimulation by agonists leads to the dissociation from the α subunit of the GDP and to its replacement with GTP. This GTP-G α complex detaches from the βγ subunit and both of these complexes can lead to downstream signaling. These activities are modulated in cells by GTPase activity that allows the G-protein subunit to return to its inactivated GDP form, hydrolyzing the GTP as depicted in Figure 6. The first biochemical step is monitored by quantifying the amount of GTP associated with the membrane. To date, the amount of GTP produced by GPCR agonist activation has been evaluated by binding experiment using [ 35 S]GTPγS. Nowadays, several research groups start to use fluorescent probes to phase out radioactivity-based methods.
DELFIA GTP-binding assay is directed to the measurement of GPCRs activation in membrane preparations employing a nonhydrolyzable GTP-Eu-label. This method is a time-resolved fluorometric assay based on GDP-GTP exchange on Gα subunit followed by agonists GPCR activation.
These results suggest that the Eu-GTP binding assay is a reasonable alternative to the traditional [ 35 S]GTPγS binding assay, and that lanthanides are replacing radiolabeled traditional methods to characterize the biological pathways linked to receptor-drug interactions.
For a more wide application of Eu-GTP assay, this method should be studied on several GPCRs subtypes.
Eu-GTP and [ 35 S]GTPγS assays are usually used on cloned cell lines and this is an advantage with respect to the use of animal tissues. The employment of cell lines in some cases constitutes a limit because of the low receptor expression and/or the presence of other GPCR system quenching the signal. More specific and amplified signals (see Figure 7) can be obtained detecting cyclic AMP (cAMP) or kinases on living cells.
cAMP is an important second messenger mediating several physiological responses of neurotransmitters, hormones, and drugs. cAMP is formed by ATP, and its intracellular concentration is regulated by two membrane-bound enzymes: adenylate cyclase (AC) and phosphodiesterase. The cAMP concentration in cells is stimulated by the activation of adenilate cyclase, responsible for the ATP conversion into cAMP upon ligand binding to GPCR (see Figure 8). The first method directly measuring the cAMP levels is based on radioisotopes using scintillation proximity assay (SPA) tecnology [21][22][23][24]. An alternative method is DELFIA. This assay is intended for the quantitative determination of cAMP in cell-culture samples. This method is a solid-phase timeresolved fluoroimmunoassay based on the competition between europium-labeled cAMP and cAMP of the samples for the binding sites of cAMP-specific polyclonal antibodies from rabbit. A second antibody, directed against rabbit IgG, is coated to the solid phase, allowing the separation of the antibody-bound and the free antigen. The addition of an enhancement solution to each sample permit the dissiociation of the europium ions from the labeled antigen into solution, where they form highly fluorescent chelates with the components of enhancement solution (see Figure 8). The fluorescence detected is inversely proportional to the amount of cAMP in the sample.
Eu-cAMP constitutes a suitable alternative to the common radiolabeled method, [ 3 H] cAMP, commonly used to evaluate the accumulation of cAMP in cell line overexpressing β 3 -AR.
These results suggest that also in this case, the Eu-cAMP binding assay is a reasonable alternative to the traditional radiolabeled binding assay.
Moreover, fluorescent probes have also been developed to study the activity of some protein kinases. The protein kinases are a class of enzymes classified as PKCα, PKCβ, and PKCγ, each having a specific function. These enzymes remove a phosphate group from ATP and covalently attach it to one of the three aminoacids having a free hydroxyl group (serine, threonine, and tyrosine) chemically modifying other    Never In Mitosis Arrest-(NIMA-) related kinase 2, also known as Nek2, is a serine/threonine kinase required for centrosome splitting and bipolar spindle formation during mitosis. Nek2 phosphorylates a large centrosomal linker protein called c-NAP1 (centrosomal Nek2-associated protein 1), which subsequently triggers the release and separation of duplicated centrosomes [27]. Nek2 has been demonstrated to be concentrated primarily in the centrosomes of rapidly proliferating cells. High expression of Nek2 compared to normal tissue has been observed in lung, colon, and breast carcinomas as well as B-cell lymphomas. Downregulation of Nek2 and overexpression of a kinase-defective dominant negative enzyme result in (i) lack of centrosome separation, (ii) defective spindle formation, (iii) increased apoptosis, and (iv) decreased cell proliferation. Thus, targeting Nek2 in tumour cells by a small molecule inhibitor may have the same phenotypic consequences. For this kinase, two different DELFIA assays have been developed. One method uses a peptide identified within c-NAP1, the other one employs Nek2 enzyme as a substrate to monitor autophosphorylation.
DELFIA resulted in a useful assay to monitor changes in kinases phosphorylation instead of the traditional measure of radioactive phosphate incorporation or the use of phosphokinase antibodies (ELISA and Western blot). For example, the insulin receptor (IR) is a tyrosine kinase composed of two extracellular α subunits and two transmembrane β subunits. Insulin action starts by its binding to the α subunits of the IR, and causes conformational changes that lead to autophosphorylation of the β subunits and activation of the receptor tyrosine kinase [28]. Moreover, DELFIA assay can be easily adapted to monitor changes in the phosphorylation status of other cellular proteins.
IKK complex consists of two kinases, IKKα and IKKβ, and of the regulatory nonenzymatic scaffold protein IKKγ also known as NEMO (NF-κB essential modifier) [29][30][31][32][33]. IκB phosphorylation by IKK results in the degradation of IκB, allowing NF-κB to translocate to the nucleus and activate transcription of a variety of genes. Many studies have indicated that activity of IKKβ is directly related to TNFα activation, whereas IKKα is critical for the development of the skin and skeleton during embryogenesis. To elucidate the mechanisms by which NF-κB is activated, it is important to have an effective assay to examine different pathways. Traditionally, IKK activity has been measured by a radioactive kinase assay utilizing [ 33 P]ATP or [ 32 P]ATP as a donor. For monitoring substrates phosphorylation, DELFIA resulted in sensitive and efficient nonradioactive assays to detect multiple kinase activities simultaneously, including IKK [34].

CONCLUSIONS
Lanthanide labels constitute a perspective in medicinal chemistry field for studying the activity of potential new drugs towards GPCRs. To date, few results relating to Eu-GTP and Eu-cAMP and comparable to the corresponding radiolabeled probes are available. With respect to radiolabeled method, these new tools display several advantages such as the endocellular pathways investigation in living cells, the high sensitivity, the long shelf life, the amenability to automation, the safety, and last but not least, they represent the novel green biochemistry. At the present, it is important to investigate Eu-GTP and Eu-cAMP impact in several GPCRs in order to better define the potentials of lanthanide labels in medicinal chemistry field.


extfieldsofstudy: Biology Medicine

text:
Active γ-Secretase Complexes Contain Only One of Each Component*

γ-Secretase is an intramembrane aspartyl protease complex that cleaves type I integral membrane proteins, including the amyloid β-protein precursor and the Notch receptor, and is composed of presenilin, Pen-2, nicastrin, and Aph-1. Although all four of these membrane proteins are essential for assembly and activity, the stoichiometry of the complex is unknown, with the number of presenilin molecules present being especially controversial. Here we analyze functional γ-secretase complexes, isolated by immunoprecipitation from solubilized membrane fractions and able to produce amyloid β-peptides and amyloid β-protein precursor intracellular domain. We show that the active isolated protease contains only one presenilin per complex, which excludes certain models of the active site that require aspartate dyads formed between two presenilin molecules. We also quantified components in the isolated complexes by Western blot using protein standards and found that the amounts of Pen-2 and nicastrin were the same as that of presenilin. Moreover, we found that one Aph-1 was not co-immunoprecipitated with another in active complexes, evidence that Aph-1 is likewise present as a monomer. Taken together, these results demonstrate that the stoichiometry of γ-components presenilin:Pen-2:nicastrin:Aph-1 is 1:1:1:1.

The ␥-secretase complex hydrolyzes within the transmembrane region of type I integral membrane proteins, including the ␤-amyloid precursor protein (APP) 2 and the Notch recep-tor, and plays critical roles in biology and human disease. This enzyme is composed of four membrane proteins, presenilin (PS), Pen-2, nicastrin (NCT), and Aph-1 (1), which are necessary and sufficient for protease activity. PS is the catalytic component of ␥-secretase, with two conserved transmembrane aspartates that form the active site (2). More than 100 missense mutations in PS are associated with early onset, familial Alzheimer disease (FAD), and these mutants shift the cleavage sites within APP to alter the lengths of the products, the amyloid ␤-peptide (A␤) (3), and the APP intracellular domain (AICD) (4). NCT is involved in the recognition of substrates, interacting with their luminal/extracellular N termini (5), whereas Aph-1 is thought to be a scaffold for assembly of the complex (6). Pen-2 triggers the endoproteolysis of PS into N-terminal and C-terminal fragments (NTF and CTF) as part of the maturation and activation of the protease complex (7) and stabilizes the catalytic subunit PS NTF/CTF after endoproteolysis (8).
Although the essential components of ␥-secretase were all known by 2003, the stoichiometry of the complex remains unknown. Some evidence suggests that two PS molecules reside in a single protease complex, leading to the suggestion that a PS dimer is at the catalytic core of ␥-secretase and/or that substrates are cleaved at the dimer interface (9 -11). Recently reported low resolution electron microscopy structures of ␥-secretase (at 15 and 48 Å (Refs. 12 and 13, respectively)) could not distinguish between the one-PS and two-PS models. Native gel electrophoresis, glycerol velocity gradient centrifugation, and size exclusion chromatography experiments have given variable results, none of which have clarified this issue (14 -18). The distant presenilin homolog signal peptide peptidase (19) can apparently form stable dimers (20), although in our hands, purified, active, bacterially expressed signal peptide peptidase is not observed as a dimer (48). Although the question of PS representation in the complex is unclear, that of other members of the complex has been studied even less. Here we address the stoichiometry of active ␥-secretase by co-immunoprecipitation (co-IP) and Western blot methods and show that the complex contains only one of each component. How these find-ings relate to results from native gel electrophoresis analysis and the critical importance of detergents is also discussed.

MATERIALS AND METHODS
DNA Constructs and Cell Lines-The cDNA of His-FLAG-⌬E9 was inserted at the restriction enzyme sites between KpnI and NotI into pcDNA6/V5-His (Invitrogen). This vector was transfected into ␥-30 cells (17) with Lipofectamine 2000 (Invitrogen), and the transfected cells were selected with blasticidin S (Invitrogen). The cDNA of N-terminally Myc-or HAtagged PS1 was inserted at the restriction enzyme sites between HindIII and XhoI into pMX-IZ or pMX-IB, in which the green fluorescent protein gene of pMX-GFP (21) is replaced with a resistance gene of zeocin or blasticidin, respectively. These vectors were transfected into Plat-E cells (22) with Effectene (Qiagen), and the resultant retroviruses in the condition media were collected by filtration and centrifugation and frozen as a viral stock. PS double knock-out (DKO) cells (23,24) were infected with these viruses supplemented with 4 g/ml of polybrene (Sigma), and the infected cells were selected with zeocin and blasticidin. The cDNA of C-terminally HA-or FLAG-tagged Aph-1 was inserted at the restriction enzyme sites between HindIII and EcoRI into pcDNA3.1(ϩ) or pcDNA6/V5-His, respectively. These vectors were transfected into Chinese hamster ovary (CHO) cells with Lipofectamine LTX (Invitrogen), and the transfected cells were selected with zeocin and blasticidin. All of the cell lines were maintained in Dulbecco's modified Eagle's medium supplemented with fetal bovine serum and penicillin-streptomycin.
Preparation of Detergent-solubilized Membrane Fractions and ␥-Secretase Assays-The cells in 15 cm dishes were collected and resuspended in homogenization buffer (50 mM Hepes, pH 7.0, 250 mM sucrose, 5 mM EDTA) containing complete protease inhibitor mixture (Roche Applied Science). The resuspended cells were collapsed by passing once through a French press at 1,000 p.s.i., and the cell debris and nuclei were removed at 3,000 ϫ g for 10 min. The supernatants were further centrifuged at 100,000 ϫ g for 1 h, and the resultant pellets were washed with 0.1 M sodium bicarbonate (pH 11.4) and then centrifuged again. The membrane pellets were solubilized with 1% digitonin or CHAPSO in homogenization buffer for 90 min on ice and then centrifuged at 100,000 ϫ g for 1 h. The protein concentration of the supernatant was determined with BCA protein assay reagent (Pierce). CHAPSO-solubilized membrane fractions were diluted to 0.25% and incubated with C100FLAG (25) at 37°C for 2 h. The reaction was stopped by adding 2ϫ SDS sample buffer containing 5% 2-mercaptoethanol. The samples were normalized for protein levels in the solubilized membrane and subjected to SDS-PAGE (16% Tricine gel (Invitrogen)) and Western blot, with A␤ and AICD detected with mouse monoclonal antibody 6E10 (Covance Research Products) and M2 (Sigma) antibodies, respectively.
IP of Active ␥-Secretase-1% CHAPSO-solubilized membrane fractions of DKO cells expressing Myc-wt PS1 and HA-wt PS1 were prepared to 0.25% CHAPSO and 0.02% Nonidet P-40. The prepared membrane fractions were incubated at 37°C for 30 min in the presence of Me 2 SO as a control vehicle, C100FLAG, 10 M of 31C (25), 10 M of compound 11 (34), or all of them. After incubation, ␥-secretase was immunoprecipitated overnight with anti-Myc or anti-HA antibody. The resultant pellets were washed with 0.25% CHAPSO and 0.02% Nonidet P-40 three times and subjected to Western blot as described above. To label ␥-secretase with a photoaffinity reagent, solubilized membrane fractions of 0.25% CHAPSO and 0.02% Nonidet P-40 were incubated with 500 nM of biotinylated inhibitor photoprobe III-63 (35) in the presence of Me 2 SO or 10 M of parent inhibitor 31C and then irradiated for 30 min at 350 nm. Irradiated samples were immunoprecipitated overnight with anti-Myc or anti-HA antibody, and the resultant pellets were washed and subjected to Western blot. The labeled PS was detected with rabbit polyclonal anti-biotin antibody (Bethyl).
Quantification of ␥-Components-␥-Secretase was immunoprecipitated from solubilized membrane fractions of DKO cells expressing Myc-and HA-tagged wt PS1 or HEK293 cells expressing endogenous PS1 with mouse monoclonal anti-Myc antibody or rat monoclonal PS1 antibody (Millipore). The immunoprecipitated Myc-PS1 was subjected to SDS-PAGE (10 -20% Tricine gel (Invitrogen)) and Western blot and then quantified with rabbit polyclonal anti-Myc antibody (Sigma) by using N-terminally Myc-tagged ubiquitin (Boston Biochem) to generate a standard curve. To quantify the immunoprecipitated wt PS1 from HEK293 cells, the different amounts of quantified Myc-PS1 were further used as a standard curve and detected with AB14. To quantify co-immunoprecipitated endogenous Pen-2, different amounts of FLAG-Pen-2 immunoprecipitated from digitonin solubilized membrane fractions of S-1 cells (38) were quantified by using FLAG-tagged ubiquitin (Boston Biochem) as a standard curve with M2 antibody. The various quantified FLAG-Pen-2 samples were next used as standards to quantify endogenous Pen-2 with rabbit polyclonal ab24743 (Abcam). To quantify co-immunoprecipitated endogenous NCT, different amounts of NCT-GST immunoprecipitated from solubilized membrane fractions of S-1 cells (38) were quantified by using GST-E1 (Boston Biochem) to generate a standard curve with rabbit polyclonal GST antibody (Invitrogen). The quantified NCT-GST samples were next used as standards to quantify endogenous NCT with rabbit polyclonal anti-NCT. Software, ImageJ, was used to quantify the intensity of immunoblot signals.

RESULTS
To determine whether two PS molecules are present in a single ␥-secretase complex, we stably expressed two different and distinquishable forms of PS1 in cells. One FAD mutant form of PS1, ⌬E9 (deletion of exon 9), does not undergo endoproteolysis into NTF and CTF and can be readily discriminated against wt PS1 by Western blot (39). We transfected His-FLAG-⌬E9 (or its aspartyl mutants TM6DC and TM7DC, in which the active site aspartates of transmembrane domains 6 and 7, respectively, are mutated to cysteine) into ␥-30 cells. ␥-30 cells are CHO cells that stably express human proteins APP, wt PS1, FLAG-Pen-2, and Aph-1a-HA (17). Thus, the transfected ␥-30 cells stably express both His-FLAG-⌬E9 and untagged wt PS1 (␥-30 ϩ ⌬E9 cells).
The membrane fractions of these cells were solubilized with the zwitterionic detergent CHAPSO, and IPs were performed with anti-His antibodies. After washing the resultant pellets, we analyzed PS1 and other members of the complexes by Western blot. Although the parent cell line ␥-30 does not express any His-tagged proteins, wt PS1 and NCT were pulled down with anti-His antibody and protein G beads (supplemental Fig. S1A), as was active ␥-secretase (supplemental Fig. S1B), suggesting that ␥-secretase in CHAPSO-solubilized membrane fractions strongly binds to beads nonspecifically and that CHAPSO, commonly used in the study of ␥-secretase, is not an appropriate detergent for evaluating the stoichiometry of the complex.
We then identified digitonin as a detergent that eliminates the nonspecific binding but conserves activity. When HAtagged Aph-1a of ␥-30 cells was immunoprecipitated with anti-HA antibody, all components of the complex were coimmunoprecipitated, whereas anti-His antibody did not pull down any (Fig. 1A, lanes 2 and 3; a small amount of Aph-1a-HA is brought down, probably because of the highly hydrophobic nature of this protein). On the other hand, when His-tagged ⌬E9 was immunoprecipitated from ␥-30 ϩ ⌬E9 cell membranes with anti-His antibody, all of the other components co-immunoprecipitated, but wt PS1 (as NTF and CTF) did not (Fig. 1A, lane 5). Catalytically inactive PS ⌬E9 mutants TM6DC and TM7DC likewise assembled into full, mature complexes pulled down by anti-His antibodies but without wt PS1 NTF or CTF co-immunoprecipitating ( Fig. 1A, lanes 7 and 9).
The A␤ species so produced were immunoprecipitated with 6E10 and analyzed by MALDI-TOF MS. Whereas the A␤ species produced by wt PS1 ranged from A␤38 to A␤45, ⌬E9 produced mainly A␤43 with small portions of A␤45 and A␤46 (Fig.  1D), indicating that the upper band contains species of A␤43 and longer. ⌬E9 can produce small amounts of shorter A␤ species (see Fig. 3B, lane 8); however, overall activity was much lower than wt PS. These observations are consistent with previous reports that FAD PS mutants, including ⌬E9, cleave substrates less efficiently and generate longer forms of A␤ (40,41). Importantly, ⌬E9-containing ␥-secretase complexes were proteolytically active upon co-IP, with no wt PS1 NTF and CTF present.
We confirmed the above findings by carrying out the reverse experiment, pulling down wt PS1 using a different antibody, B17, which recognizes wt PS1 but not ⌬E9 (26). Although wt PS1 CTF was detected with B17, ⌬E9 was not, even when concentrated by IP ( Fig. 2A, bottom panel). IP of ␥-complexes con-taining wt PS1 with this antibody did not bring down any ⌬E9 (Fig. 2B), indicating that none of the isolated complexes contained PS-PS dimers of wt PS1 and ⌬E9. In digitonin-solubilized membrane fractions, blue native gel showed full ␥-complexes containing PS1 and NCT with mobilities of ϳ500 kDa (Fig. 2C, lanes 1-4), although note that ␥-complexes in CHAPSO-solubilized membrane fractions have slower mobilities (lanes 5-6). These observations are consistent with previous reports (16,18); however, the apparent large size is not due to the presence of two or more PS molecules/complex. One is sufficient for an active ␥-secretase complex.
These findings were elicited by using wt PS1 and ⌬E9; however, these two PS variants may not interact because of their differences. wt PS1 undergoes endoproteolysis, and ⌬E9 does not. To further investigate the possibility of a PS-PS interaction within ␥-complexes, we co-expressed Myc-tagged PS1 and HAtagged PS1 in PS1/2 DKO cells and performed co-IP experiments (Fig. 3A). Digitonin-solubilized membranes from these PS-expressing cells showed the same size of ␥-secretase by blue native PAGE as shown in Fig. 2C (supplemental Fig. S2A). IP with anti-Myc antibodies brought down ␥-secretase activity (Fig. 3B). A small amount of background activity was brought down with beads alone (i.e. without antibody; e.g. lane 3). Immunoprecipitated ␥-secretase containing wt PS1 produced A␤ and AICD (lanes 4 and 6), whereas isolated complexes containing ⌬E9 again possessed low activity and produced longer A␤ species (lane 8), similar to that shown in Fig. 1B, indicating that these immunoprecipitated ␥-complexes maintain their integrity and protease activity. Co-IPs with anti-HA antibody brought down activity as well (data not shown).
We next examined the co-IPs isolated with each antibody. When Myc-tagged wt PS1 or ⌬E9 was immunoprecipitated with anti-Myc antibody (Fig. 3C), HA-tagged wtPS1 or ⌬E9 did not co-IP (cf. lanes 5 versus 6, lanes 8 versus 9, and lanes 11 versus 12, middle panel), whereas mature, glycosylated NCT (the form found in active protease complexes) did (bottom panel). Likewise, when HA-tagged wt PS1 or ⌬E9 was immunoprecipitated with anti-HA antibody (Fig. 3D), Myc-tagged wt PS1 or ⌬E9 did not co-IP (cf. lanes 5 versus 6, lanes 8 versus 9, and lanes 11 versus 12, top panel), although mature NCT did (bottom panel). These results demonstrate that active ␥-secretase can be isolated in the absence of interactions between two wt PS molecules or between two ⌬E9 molecules, each with different N-terminal tags.
binding of ␥-secretase and that ␥-secretase was intact, of similar size by BN-PAGE (data not shown), and active under these conditions (Fig. 4A, lanes 4 and 8). We incubated the solubilized membranes for 30 min with substrates (C100FLAG) or inhibitors (31C, a transition state analog inhibitor (25), or Compound 11, a helical peptide inhibitor (34)), and then immunoprecipitated ␥-complexes with anti-Myc antibody. After IP, although Myc-tagged PS1 and NCT were co-immunoprecipitated (Fig. 4B, top and bottom panels), HA-tagged PS1 showed as background levels (middle panel, background lane 7, beads alone), indicating that HA-tagged PS was not co-immunoprecipitated with Myc-tagged PS1 in the presence of substrates or inhibitors. Likewise, Myc-tagged PS1 was not co-immunoprecipitated with HA-tagged PS1 (data not shown). Substrates or inhibitors might have been washed out from the co-immunoprecipitated ␥-secretase in the above experiments. To avoid this, a photoreactive transition state analog inhibitor III-63, a biotinylated probe that covalently binds to the active site of ␥-secretase (35), was immobilized to ␥-secretase prior to co-IP. When ␥-secretase bound to III-63 was immunoprecipitated with anti-Myc antibody, NCT was co-immunoprecipitated as well (Fig. 4C, bottom panel); however, HA-tagged PS1 was not co-immunoprecipitated (second panel). PS was photolabeled (third panel, lane 3), and this labeling was blocked by transition state analog inhibitor 31C, the parent compound of III-63 (lane 2), suggesting that ␥-secretase is active and specifically binds to the inhibitor. Similar results were obtained upon co-IP with anti-HA antibodies (data not shown). These results further confirm that the isolated, active ␥-secretase contains only one PS molecule/complex.
Although the isolated active ␥-secretase complex contains only one PS molecule, it nevertheless appears relatively large by native gel analysis (supplemental Fig. S2A), raising the question about the stoichiometry of the other components, NCT, Pen-2, and Aph-1. To address this issue, we set out to quantify complex members by Western blot analysis. Endogenous NCT, Pen-2, and Aph-1 in DKO cells transfected with wt PS1 could be detected with antibodies for each of these components (Fig.  5A, lane 6). Because one of the wt PS1 molecules transfected in DKO cells is N-terminally Myc-tagged, we first quantified wt PS1 by generating a standard curve with known amounts of commercially available Myc-tagged ubiquitin (Fig. 5B, lanes  4 -8). PS1 NTF and holoprotein that had been immunoprecipitated with active ␥-secretase using anti-Myc antibody were quantified (Fig. 5, B and E, and Table 1), with 0.24 pmol of PS1 NTF and 0.12 pmol of holo-PS brought down. Closely similar results were obtained with a different anti-Myc antibody (data not shown). The possibility remains that epitope accessibility differs between the Myc-tagged ubiquitin and Myc-tagged PS1. Nevertheless, The N terminus of PS1 is not needed for ␥-secretase assembly and activity, which is the reason that epitope tags are typically fused there. Thus, the N-terminal epitope is expected to be freely available for antibody binding. An additional concern is unequal transfer efficiencies between the small Myc-ubiquitin and the larger PS1 NTF and holoprotein. Care must be taken in the interpretation of these quantification results, although the quantification of other members of the complex (see below) suggest that our results are reasonably accurate.
We next set out to quantify co-immunoprecipitated Pen-2; however, endogenous Pen-2 of DKO cells has no epitope tag. Thus, we first quantified varied amounts of FLAG-tagged Pen-2 (immunoprecipitated with M2 anti-FLAG antibodies) from membrane fractions of S-1 cells, CHO cells that stably express wt PS1, FLAG-Pen-2, NCT-GST, and Aph-1a-HA (38), using FLAG-tagged ubiquitin to generate the standard curve (Fig. 5C,  top panel). These FLAG-Pen-2 standards were then used to quantify endogenous Pen-2 of DKO cells that had co-immunoprecipitated with PS (Fig. 5C, bottom panel). The amount of Pen-2 was virtually identical to that of PS-NTF (Fig. 5E), suggesting that Pen-2 is the last member added to the complex and promotes the endoproteolysis of PS as previously reported (7).
We also quantified endogenous NCT of DKO cells that coimmunoprecipitated with PS using the same method. Various amounts of NCT-GST, co-immunoprecipitated with FLAG-Pen-2 from S-1 cells using M2 antibody, were quantified using recombinant GST-E1 to generate the standard curve (Fig. 5D,  top panel). The NCT-GST standards were then used to quantify the total amount of endogenous NCT (mature and immature) that co-immunoprecipitated with PS (Fig. 5D,  bottom panel). The amount of endogenous NCT, 0.34 pmol, was virtually identical to the sum total of PS-NTF and holoprotein (Fig. 5E) FIGURE 2. wt PS1 is not co-immunoprecipitated with ⌬E9. A, digitoninsolubilized membrane fractions were immunoprecipitated with anti-His antibody and subjected to Western blot. PS1 was detected with AB14, and only wt PS1 was detected with B17. Lysate indicates solubilized membrane fractions, and Ϫ or ϩ indicates with protein G beads alone (no antibody) or with both antibody and protein G beads, respectively. B, wt PS was immunoprecipitated with B17 and subjected to Western blot. ⌬E9 was not co-immunoprecipitated with wt PS1. C, digitonin-and CHAPSO-solubilized membrane fractions were subjected to 3-12% blue native gel analysis followed by Western blot. PS1 and NCT were detected with AB14 and anti-NCT antibodies, respectively.
to Pen-2 and before PS undergoes endoproteolysis (42). Similar results were also obtained using different anti-FLAG and anti-GST antibodies (data not shown). IP of active endogenous ␥-secretase from membrane fractions of HEK293 cells gave very similar results ( Table 1), suggesting that these quantification methods do not vary with cell type. We next set out to quantify endogenous Aph-1a; however, we could not quantify Aph-1a-HA from S-1 cells by Western blot using HA-tagged ubiquitin as a standard because different anti-HA antibodies gave radically different results (data not shown). These discrepancies may be due to the position of the HA epitope tag; HA is fused to the N terminus of commercially available ubiquitin, whereas this epitope is fused to the Aph-1 C terminus. We therefore performed Aph-1 co-IP experiments, similar to those carried out for PS in Figs. 1-3. A series of HA-tagged Aph-1 isoforms (Aph-1aL-HA, Aph-1-aS-HA, and Aph-1b-HA) were each co-  expressed with Aph-1aL-FLAG in the CHO cells. All of these Aph-1 isoforms were incorporated into full ␥-complexes (supplemental Fig. S2B). HA-tagged Aph-1 was immunoprecipitated with anti-HA antibody-conjugated agarose beads (Fig. 6A, top panel, lanes 4,  6, and 8). Although PS1 NTF co-immunoprecipitated with each Aph-1-HA isoform (Fig. 6A, bottom panel, lanes 4, 6, and 8), the very small amount of Aph-1aL-FLAG pulled down by anti-HA beads (Fig. 6A, middle panel) was the same level as background seen with anti-His agarose beads (data not shown). The lack of Aph1-Aph1 interaction was confirmed using an Aph-1aL specific antibody: endogenous Aph-1aS did not co-IP with Aph-1aL from membrane fractions of MEFs that were wild type (32), PS DKO cells or NCT knock-out (5) (Fig. 6B, top right panel,  lanes 3, 6, and 9, respectively). Nevertheless, wt MEF showed co-IP of PS1 NTF (middle right panel, lane 3) and mature NCT (bottom right panel, lane 3) and displayed the high molecular size of full protease complexes on the blue native gel (Fig. 6B,   left panel). These observations were consistent with previous reports (33,43). We likewise confirmed that endogenous Pen-2 or endogenous NCT from membrane fractions of S-1 cells did not co-immunoprecipitate with FLAG-Pen-2 or NCT-GST using M2 antibody or GST antibody, respectively (data not shown). Taken together, these results demonstrate that the stoichiometry of the components of isolated, active ␥-secretase complexes is 1:1:1:1 for PS:Pen-2:NCT:Aph-1.

DISCUSSION
Although members of the ␥secretase complex were identified several years ago, the stoichiometry has remained unknown and, in the case of PS, controversial. Some reports provide evidence for two or more PS members per complex (9 -11, 44); however, co-IP from commonly used CHAPSO-solubilized membrane fractions may lead to experimental artifacts (supplemental Fig. S1), because the components of ␥-secretase are hydrophobic membrane proteins that can bind nonspecifically to agarose or Sepharose beads. Digitonin minimizes this nonspecific binding to negligible levels and conserves the integrity and activity of ␥-secretase. In our hands, active ␥-secretase contains only one molecule of PS per complex, a finding that rules out certain models for the protease active site that involve two pairs of aspartates (e.g. one pair responsible for the cutting at the ␥ site and the other at the ⑀ site) or one pair formed in trans at a PS dimer interface (10,11). Another group has recently come to a similar conclusion (49).
Our findings also do not support the suggestion that one PS can affect another PS, for instance, that one FAD mutant PS molecule can alter the site of proteolysis by wt PS (9). Pull-down of FAD mutant ␥-secretase complexes, containing only ⌬E9 PS1, shift the production of A␤ to longer forms, although the overall activity is substantially reduced. The presence of the FAD mutant PS1 did not cause the wt enzyme to produce longer A␤ peptides (Fig. 3B). How then do FAD mutations in PS1 both increase the ratio of A␤42 to A␤40 (gain of function) and decrease overall protease activity (loss of function)? Recent findings suggest that the protease cleaves from the ⑀ site in the APP transmembrane domain (to produce A␤48 or A␤49 peptides) and then trims every 3-4 residues (reviewed in Ref. 50). Thus, mutations that reduce the proteolytic efficiency of the enzyme would lead to less antibody but with an increase in the proportion of longer antibody peptides.
Although we find that the four essential components of ␥-secretase are represented only once per complex, we cannot fully rule out the possibility that in cells the complexes assemble into larger aggregates (e.g. two 1:1:1:1 complexes associated The results are representative of three independent experiments. C, co-immunoprecipitated endogenous Pen-2 was quantified with ab24743 by using various amounts of FLAG-Pen-2 (S1-M2-IP) and FLAG-tagged ubiquitin as standard curves. The band above the endogenous Pen-2 band in WT1-3 lanes is nonspecific. D, co-immunoprecipitated endogenous NCT was quantified with anti-NCT antibody by using various amounts of NCT-GST (S1-M2-IP) and GST-tagged E1 as standard curves. E, quantification results show the stoichiometry of PS1-NTF, PS1-NTF, holo-PS1, Pen-2, and NCT, respectively (see also Table 1). The error bars represent the standard deviation (n ϭ 3). with one another), because such assemblies might be interrupted by solubilization with detergent. Nevertheless, isolated ␥-secretase remains proteolytically active; any interaction between one ␥-secretase complex and another is apparently very weak and not necessary for recognition and cleavage of substrates. The size of the ␥-secretase complex has likewise been controversial. Our study shows that all four components are represented only once, and adding together the molecular weights of the individual subunits gives ϳ220 kDa. However, the size of the complex as estimated by blue native gel appears to be ϳ500 kDa; thus, we tested whether other proteins (e.g. substrates, modifiers) were isolated with the ␥-complexes. By native gel, APP appeared as a smear from ϳ300 to 800 kDa, but membrane fractions of PS DKO cells showed the same smear of APP in the absence of ␥-complexes (Fig. 7C). Also, Tmp21, recently reported as a regulator of ␥-secretase (45), did not co-migrate with ␥-complexes, appearing instead at ϳ200 kDa (Fig. 7D). Mobilities may differ with detergents (Fig. 2C, lanes 1  and 5, and Fig. 7), and comparisons of a hydrophobic membrane protein complex with soluble protein molecular weight standards may not be appropriate. Moreover, we found that different commercial standard marker kits showed different mobilities for certain marker proteins of comparable size (data not shown). Although we cannot rule out the presence of other possible modifier proteins and substrates that would bring the size up to ϳ500 kDa, we suggest that the interpretation of results from blue native gel analysis of membrane protein complexes can be fraught with difficulties. In contrast, glycerol velocity gradient centrifugation suggested a size of ϳ150 -250 kDa for solubilized endogenous ␥-secretase (i.e. not overexpressed) (14,46), which includes the size predicted by adding up the molecular weights of the four individual components. In our hands, glycerol velocity gradient analysis indeed showed a smaller size (ϳ200 -250 kDa) than observed by blue native gels (ϳ500 kDa) (supplemental Fig. 2C). Markers may also run differently in this method as well, and interpretations must be made with care, but our results combined with those in previous reports demonstrate the limitations of these methods of size determination for the ␥-secretase complex. Most recently, the size of the purified enzyme complex has been determined to be ϳ220 kDa by scanning transmission electron microscopy, or STEM. 3 STEM does not involve protein mobility and provides further support for our findings on the stoichiometry.
The present study shows the stoichiometry of ␥-components, with the amount of Pen-2 equal that of PS-NTF and the amount of NCT equal to that of the sum total of PS-NTF and holo-PS (Fig. 5). This quantification result is consistent with the current model for maturation of the ␥-secretase complex, in which an initially formed NCT⅐Aph-1 complex binds to holo-PS, followed by Pen-2 binding to this NCT⅐Aph-1⅐holo-PS complex to trigger PS endoproteolysis into NTF and CTF (7,47). In certain cells (wild type or transfected), Pen-2 is the limiting factor, with excess premature NCT⅐Aph-1⅐holo-PS complexes building up as a result. In conclusion, our results dem-3 H. Li   onstrate that having one of each of the four subunits is sufficient for ␥-secretase complexes to bind and cleave substrates. These observations exclude certain models for ␥-secretase assembly and active site composition and should aid interpretation of results from structural studies of the protease complex.