Systematic In Vivo Analysis of the Intrinsic Determinants of Amyloid β Pathogenicity

1A wide range of proteins has been found to convert into extracellular amyloid fibrils, or amyloid-like intracellular inclusions, under physiological conditions. Such proteins have largely been identified through their association with disease, although a number have been found to have beneficial physiological functions in organisms including, amongst others, bacteria, yeast, and humans. Indeed, the ability to aggregate and assemble into amyloid-like fibrils has emerged as a common, and perhaps fundamental, property of polypeptide chains. This discovery has stimulated extensive biophysical and mutational analysis of the underlying molecular determinants of amyloid fibril formation. These studies have resulted in the derivation of general models, based on physicochemical parameters, that both rationalise and predict the propensity of proteins to convert from their soluble forms into intractable amyloid aggregates in vitro.

2The misfolding and aggregation of proteins in vivo, however, differ from similar processes taking place under in vitro experimental conditions, in that they occur in complex cellular environments containing a host of factors that are known to modulate protein aggregation and protect against any subsequent toxicity. This difference between in vitro and in vivo experimental conditions represents a significant barrier to the development of a molecular understanding of protein aggregation in living systems and its consequences for disease. In this paper we describe the results of an approach designed to bridge this divide by expressing a range of mutational variants of Aβ42 in a Drosophila model of Alzheimer disease and correlating their influence on the longevity and behaviour of the flies with their underlying physicochemical characteristics.

3The expression of the Aβ42 peptide (coupled to a secretion signal peptide) in the central nervous system of Drosophila melanogaster results in both intracellular and extracellular deposition of Aβ42, along with neuronal dysfunction, revealed by abnormal locomotor behaviour and reduced longevity. Learning and memory deficits are also observed in flies expressing Aβ42 and to a lesser extent in those expressing Aβ40. Importantly, the severity of the cognitive deficits is closely correlated with the magnitude of the locomotor and longevity phenotypes. Our system, as with other recently developed invertebrate models of neurodegenerative disease, therefore produces clear, quantitative phenotypes that allow rapid and statistically robust assessments of the effects of mutations. Using an algorithm described previously we computed the intrinsic aggregation propensities (Z agg) of all 798 possible single point mutations of the Aβ42 peptide and also of the more toxic E22G Aβ42 peptide. A total of 17 mutational variants, with a wide range of aggregation propensities (Table 1), were then expressed throughout the central nervous system of Drosophila melanogaster, and their effects were compared to those of wild-type (WT) and E22G Aβ42 expression. The longevity of multiple lines of flies (n = 4–6 independent lines) for each variant was compared to that of flies expressing the WT or E22G Aβ42 peptide. This pooling of data from multiple independent lines for each Aβ42 mutant studied serves as a control for the potential variation in expression levels between transgene insertion sites. In addition, the locomotor ability of a representative selection of the Aβ42-variant-expressing flies was assessed to provide a measure of the early effects of the peptides on neuronal dysfunction. Examples of the results of this analysis are shown for four of the variants studied (Figure 1).

4Flies expressing the WT Aβ42 peptide have a median survival of 24 ± 1 d; flies expressing the E22G Aβ42 peptide associated with familial Alzheimer disease have a median survival of only 8 ± 1 d. In contrast, some of the peptide variants are less harmful. For example, flies expressing F20E Aβ42 have a median survival of 29 ± 1 d (Figure 1A), and flies expressing I31E/E22G Aβ42 peptide have a median survival of 27 ± 1 d (Figure 1B), representing substantial increases in longevity compared to WT and E22G Aβ42 flies. Furthermore, the longevity of these variants is comparable to that of flies expressing the Aβ40 peptide (median survival = 30 ± 1 d; Figure S1A and S1B), which has been previously shown to be non-toxic when expressed both in transgenic flies and in transgenic mice. F20E Aβ42 and I31E/E22G Aβ42 flies also have very significantly improved locomotor ability compared to WT and E22G Aβ42 flies (Figure 1C and 1D; Videos S1 and S2) and are comparable in locomotor performance to flies expressing the Aβ40 peptide (Figure S1C and S1D). We also analysed a range of Aβ42 variants that were more harmful than the WT peptide; for example, flies expressing the E11G or M35F variants of the Aβ42 peptide have significantly shorter lifespans than WT Aβ42 flies (median survival = 21 ± 1 and 15 ± 1 d, respectively; Figure 1E and 1F).

5Quantitative analysis of all 17 Aβ variants studied reveals a highly statistically significant correlation between the propensity of a variant to aggregate (Z agg) and its effect on the survival of the flies (S tox) (Figure 2A; r = 0.75, p = 0.001). A significant correlation is also observed when we analyse the relationship between the predicted aggregation propensity (Z agg) of a representative selection of Aβ variants and their effects on mobility or locomotor performance (M tox) (Figure 2B; r = 0.65, p = 0.009). We have also verified that correlations exist between the measured aggregation rates (K agg) and both S tox and M tox for a representative selection of the Aβ42 variants, as we would expect from our predictions (Figure 3).

6Whilst our analysis reveals a significant relationship between the aggregation propensity of Aβ42 and its effects on neuronal integrity in vivo, it has also uncovered a small number of variants that do not conform to this trend, most notably the I31E/E22G Aβ42 peptide. In order to determine the significance of such divergent behaviour for the origins of Aβ42 pathogenicity, we selected one peptide whose effects on the longevity and mobility of the flies is well predicted by its Z agg (F20E) and one whose effects did not correlate with its Z agg (I31E/E22G) and performed further analysis of their aggregation in vitro and in vivo.

7The F20E mutation is predicted to reduce significantly the propensity of the Aβ42 peptide to aggregate (Table 1). Indeed, when we measure the rate of aggregation using thioflavin T fluorescence we find that F20E Aβ42 does aggregate significantly more slowly in vitro than the WT Aβ42 peptide (t 1/2 = 44 and 11 min, respectively; Figure 4A), in good accord with our predictions.

8The in vivo aggregation of the F20E Aβ42 peptide is also significantly reduced compared to that of the WT Aβ42 peptide. Anti-Aβ42 immunohistochemistry using a C-terminal-specific antibody that binds an epitope (Aβ residues 35–42) that does not include the residues being studied here, reveals progressive accumulation of Aβ42 in the brains of WT-Aβ42-expressing flies from 10 d of age, with extensive deposition being evident by day 20 (Figure 4B). In contrast to this behaviour, flies expressing F20E Aβ42 show no signs of Aβ42 deposition at day 20 (Figure 4C). Quantitative reverse transcription polymerase chain reaction (qRT-PCR) analysis of Aβ42 transcription levels was also carried out on WT Aβ42 and two independent lines of F20E Aβ42 fly brains to ensure that the reduced deposition and toxicity of the F20E Aβ42 peptide was not due to coincidentally lower transcription levels. In fact, the F20E Aβ42 transgene was transcribed at slightly higher levels than WT Aβ42 (Figure S2) in both lines tested.

9That the F20E Aβ42 peptide does not form in vivo deposits, despite being able to form amyloid fibrils in vitro (albeit significantly more slowly than WT Aβ42) suggests that the F20E mutation reduces the aggregation propensity of Aβ42 sufficiently to allow cellular clearance mechanisms such as proteases (e.g., neprilysin) to prevent its accumulation in vivo. We conclude, therefore, that the increased longevity and locomotor performance of F20E Aβ42 flies are indeed attributable to a measurable reduction in the aggregation propensity of this peptide in vivo, as predicted by our analysis.

10In the case of the I31E/E22G Aβ42 variant there appears to be no correlation between its predicted aggregation propensity (which is very similar to that of the highly pathogenic E22G Aβ42 peptide; Table 1) and its effects on longevity and locomotor behaviour in the fly (Figure 1B and 1D). However, studies of the I31E/E22G and E22G Aβ42 peptides in vitro show that, as predicted by our algorithm, they aggregate at very similar rates (t 1/2 = 7 and 4 min, respectively; Figure 4D). Furthermore, anti-Aβ42 immunohistochemistry reveals similar levels of deposition in the brains of both E22G- and I31E/E22G-Aβ42-expressing flies at 8 d of age (Figure 4E and 4F) that cannot be accounted for by variations in transcription level as measured by qRT-PCR (Figure S2). Together these observations confirm that our predictions of aggregation propensity are accurate for these peptides in vivo as well as in vitro. To determine the consequences of peptide deposition on the integrity of the brain, we looked for the presence of vacuoles, which are a well-documented sign of neurodegeneration. Despite comparable levels of deposition, the vacuoles seen in the brains of E22G-Aβ42-expressing flies are entirely absent from the brains of I31E/E22G-Aβ42-expressing flies. In this case, therefore, the relationship between the presence of Aβ42 deposits and the functional and anatomical integrity of the brain does not appear to hold.

11This observation is reminiscent of the finding that there are cases in which the presence of Aβ plaques in the brains of elderly humans, and indeed in transgenic mouse models of Alzheimer disease, does not correlate with cognitive ability. It has been proposed, in explanation of this finding, that the neuronal dysfunction and degeneration historically attributed to the presence of Aβ amyloid fibrils in the brains of patients with Alzheimer disease may in fact be caused by the concomitant presence of prefibrillar aggregates. With this in mind, the unexpected in vivo effects of variants such as the I31E/E22G Aβ42 peptide prompted us to develop a second algorithm (see Materials and Methods) by analysing a set of data for which the rates of formation of protofibrils containing β-sheet structure have been reported. This algorithm is able to predict the propensity of other polypeptides to form protofibrils. Whilst there are a few Aβ42 variants (including I31E/E22G Aβ42) whose global aggregation propensities (Zagg) do not correlate well with their in vivo effects on neuronal dysfunction (Figure 2), we find that the predicted propensities of these variants to form protofibrillar aggregates (Z tox) correlate very strongly with their in vivo effects (S tox, r = 0.83, p < 0.00001; M tox, r = 0.75, p = 0.001; Figure 5).

12We propose, therefore, that the effects of all Aβ42 variants in the flies can be directly attributed to their effects on the intrinsic propensities to form deleterious protofibrillar aggregates. It is extremely interesting in this regard that a comparison, using electron microscopy, of the morphology of E22G and I31E/E22G Aβ42 aggregates formed under identical conditions reveals the presence of a significant quantity of protofibrils in the former and only well-defined fibrils in the latter (Figure S4). Furthermore, we propose that it is possible to predict accurately in silico the in vivo effects of the Aβ42 peptide from a knowledge only of the intrinsic physicochemical properties of its constituent amino acids. We believe that this approach to understanding the determinants of protein misfolding in vivo will be applicable to many other diseases as we have demonstrated previously that the physicochemical parameters that determine the aggregation propensity of Aβ also determine the aggregation behaviour of a wide range of both disease- and non-disease-related proteins.

13It is also remarkable that, despite the fact that the intrinsic aggregation propensities of typical protein sequences vary by at least five orders of magnitude, we have been able to achieve profound alterations in the pathogenic effects of Aβ42 by increasing or decreasing its propensity to aggregate by less than 15%. This result suggests that proteins implicated in misfolding diseases are likely to be extremely close to the limit of their solubility under normal physiological conditions, and consequently the small alterations in their concentration, environment, or sequence, such as occur with genetic mutations or with increasing age, are likely to be the fundamental origin of these highly debilitating and increasingly common conditions.

14In conclusion, we have presented accurate, quantitative measurements of the relationships between the manifestations of neuronal dysfunction in a complex organism, such as locomotor deficits and reduced lifespan, and the fundamental physicochemical factors that determine the propensity of the Aβ42 peptide to aggregate into protofibrils. These results provide compelling evidence that, despite the presence within the cell of multiple regulatory mechanisms such as molecular chaperones and degradation systems, it is the intrinsic, sequence-dependent propensity of the Aβ42 peptide to aggregate to form protofibrillar aggregates that is the primary determinant of its pathological behaviour in living systems.

15Mutant Aβ42 expression constructs were produced by site-directed mutagenesis of the WT Aβ42 sequence in the pMT vector (Invitrogen) and were subcloned into the pUAST vector. Transgenic Drosophila expressing the desired Aβ42 variants were generated according to the procedures described by Crowther et al..

16All survival assays were carried out as described previously. Survival curves were calculated using Kaplan–Meier statistics, and differences between them analysed using the log rank method. All survival times in the text are given as median ± standard error of the median. For previously characterised control lines expressing either WT or E22G Aβ42, the survival of one representative line was measured. For each novel mutational variant of Aβ42, between four and six independent lines were analysed (n = 100 for each line) in order to control for variability in expression levels between individual lines due to the varying location of transgene insertion. The effect of a mutational variant on survival (S tox) was calculated by comparing the survival time of the flies in which it was expressed (S mut) to the survival of Aβ40-expressing flies (S max) used as a negative control in the same experiment: S tox = (S max − S mut)/S max.

17The locomotor ability of the flies was assessed in a 45-s negative geotaxis assay. Flies were placed in a plastic 25-ml pipette and knocked to the bottom of the pipette. The number reaching the top of the pipette (above the 25-ml line) and the number remaining at the bottom (below the 2-ml line) after 45 s was measured. The mobility index was calculated as (n top− n bottom + n total)/2n total. Two representative lines were tested for each novel mutant Aβ42 and one line for each previously characterised control (WT Aβ42 and E22G Aβ42). Three independent groups of 15 flies each were tested three times at each time point for each line. Differences between genotypes were analysed by ANOVA. The effect of each mutational variant on locomotor performance (M tox) was calculated by fitting the decline in mobility index over time to a straight line and then estimating the time at which each mutant line of flies had declined to a mobility index of 0.5.

18Immunohistochemistry analysis was performed as described previously on single representative lines for each genotype using the G2–11 anti-Aβ42 antibody (The Genetics Company). Representative lines of F20E- and I31E/E22G-Aβ42-expressing flies were chosen to have median survivals within 1 d of the combined median survival determined for each genotype.

19The propensity to form amyloid aggregates (Z agg) was calculated using an approach described previously. Briefly, for a given protein, Z agg is obtained by averaging the propensities that are above zero in the aggregation profile. All the propensities are normalised into a variable that has an average of zero and a standard deviation that equals one (the normalisation is made using the propensities of a set of random sequences). In a profile there can be residues with a propensity larger than one, but these peaks are usually sparse and their contribution is diluted upon averaging. Consequently, sequences with an overall Z agg score larger than one are very rare. In order to calculate the propensity for forming protofibrillar aggregates (Z tox), we developed a method based on an equation containing the same physicochemical contributions used to calculate the propensity for fibrillar aggregation, but with specific weights determined using a set of experimentally determined protofibrillar aggregation rates for the protein acylphosphatase. A Web server for calculating Z agg and Z tox is available at http://rd.plos.org/10.1371_journal.pbio.0050290_01.

20All peptides were dissolved in trifluoroacetic acid and sonicated for 30 s on ice. The trifluoroacetic acid was removed by lyophilization and the peptides were then dissolved in 1,1,1,3,3,3-hexafluoro-2-propanol and divided into aliquots that were dried by rotary evaporation at room temperature. The amount of peptide in the aliquots was determined by quantitative amino acid analysis.

21The peptides were dissolved at a concentration of 30 μM in 50 mM NaH2PO4 (pH 7.4) and incubated at 29 °C with continuous agitation. At regular time intervals, 5 μl of the peptide solution was removed and added to 100 μl of 20 μM thioflavin T in 50 mM Gly-NaOH (pH 8.5). Fluorescence intensity was measured at 440 nm excitation and 480 nm emission using BMG FLUOstar OPTIMA. The rate of aggregation (k) was determined by fitting the plot of fluorescence intensity versus time to a single exponential function y = q + Ae(−kt), and t 1/2 was calculated using t 1/2 = ln2/k.

22Twenty flies expressing each variant of Aβ42 were collected at day 0 (i.e., on the day of eclosion) for each transgenic line to be analysed. The flies were then anaesthetised and decapitated, and the heads were collected and snap frozen in liquid N2. Total RNA was extracted from the fly heads using the Qiagen RNeasy mini kit with on-column genomic DNA digestion using DNAse 1. The concentration of total RNA purified for each line was measured using a NanoDrop spectrophotometer. One microgram of RNA was then subjected to reverse transcription using the Promega Reverse Transcription System with oligo dT primers. qRT-PCR was performed using a BioRad iCycler and Absolute QPCR SYBR Green Fluorescein Mix (ABgene). Each sample was analysed in triplicate and with both target gene (Aβ42) and control gene (RP49) primers in parallel. The primers for the Aβ42 PCR were directed to the 5′ end of the signal secretion peptide sequence and the 3′ end of the Aβ coding sequence: forward, GCATTCGTGAATTCATGGCGAGCAAAGT; reverse, TACTTCTAGATCCTCGAGTTACGCAATCAC. The RP49 primers were designed across an intron to avoid amplifying any residual genomic DNA contamination: forward, ATGACCATCCGCCCAGCATCAGG; reverse, ATCTCGCCGCAGTAAACG. Relative expression levels were calculated using the Livak method.