QIAD assay for quantitating a compound’s
Strong evidence exists for a central role of amyloid β-protein (Aβ) oligomers in the pathogenesis of Alzheimer’s disease. We have developed a fast, reliable and robust in vitro assay, termed QIAD, to quantify the effect of any compound on the Aβ aggregate size distribution. Applying QIAD, we studied the effect of homotaurine, scyllo-inositol, EGCG, the benzofuran derivative KMS88009, ZAβ3W, the D-enantiomeric peptide D3 and its tandem version D3D3 on Aβ aggregation. The predictive power of the assay for in vivo efficacy is demonstrated by comparing the oligomer elimination efficiency of D3 and D3D3 with their treatment effects in animal models of Alzheimer´s disease.
Alzheimer’s disease (AD) is a progressive neurodegenerative disorder with like Agilent N9912A Battery, Agilent N9915A Battery, Agilent N9916A Battery, Agilent N9917A Battery, Agilent N9927A Battery, Agilent N9937A Battery, Anritsu MT9082A2 Battery, Anritsu MT9082A8 Battery, Anritsu MT9082C9 Battery, Anritsu MT9082A2 Battery, Anritsu MT9080 Battery, Anritsu MT9080D Battery, which is the most common cause of dementia. Growing evidence exists that instead of amyloid β-protein (Aβ) monomers or fibrils, small and diffusible Aβ oligomers seem to be decisive for disease development and progression1. Thus, the potency to eliminate Aβ oligomers is one of the most desirable criteria for the selection of agents as lead compounds for drug development towards AD treatment2. Any screening for oligomer eliminating compounds requires a well characterized target. Therefore, new methods for the preparation, purification and quantification of specific Aβ oligomers, which are representative for the toxic oligomers involved in AD pathogenesis, are urgently needed in AD drug development. Quantitative assessment of Aβ assembly size-distributions is difficult, because the heterogeneity of in vitro obtained Aβ assemblies3 impedes most of the standard analytical methods.
We have designed an assay for the quantitative determination of interference with Aβ aggregate size distribution (QIAD). The QIAD assay, based on a combination of density gradient ultracentrifugation (DGC) and reversed phase high performance liquid chromatography (RP-HPLC), permits the quantitative analysis of the impact of any compound on Aβ aggregation. We applied QIAD on several compounds, i.e., homotaurine, scyllo-inositol, EGCG, the benzofuran derivative KMS88009, ZAβ3W, the D-enantiomeric peptide D3, and its tandem version D3D3. Comparison of QIAD data obtained for D3 and D3D3 with the treatment effects in AD animal models demonstrates the predictive power of the assay for in vivo efficacy.
The QIAD assay consists of the following steps (Fig. 1): i) preparation of Aβ(1-42) assemblies containing the target aggregate species by Aβ(1-42) pre-incubation; ii) incubation with and without the agent of interest; iii) separation of Aβ(1–42) assemblies by DGC and subsequent fractionation; and iv) determination of the total Aβ(1–42) amount in each fraction, e.g. by integrating the Aβ(1–42) absorption signal during RP-HPLC analysis. The essential requirement for the quantitative analysis of different Aβ(1–42) aggregates is total disassembly of all different Aβ(1–42) assemblies. We were able to achieve this by the harsh conditions during the RP-HPLC runs (presence of acetonitrile and column heating to 80 °C) converting all Aβ assemblies into a single species with the same retention time. Thus, the integrated peak area of this peak correlates with total Aβ(1–42) amount (Fig. 2a). The obtained results indicate that under these conditions the integrated peak areas were independent from Aβ(1–42) incubation time and thus independent from its aggregation state. Prior to RP-HPLC DGC allows matrix-free separation and fractionation of different Aβ(1–42) assemblies according to their sedimentation coefficients, which are dependent on particle size and shape4,5. Alternatively the Aβ(1–42) aggregate size distribution could have been studied by a combination of SEC (Size Exclusion Chromatography) and MALS (Multi-Angle Light Scattering) detection. Such approach seems to be more convenient since it would take less time for a single sample and the direct outcome would be a distribution of molecular masses instead of sedimentation coefficients. Nevertheless, DGC was superior to SEC/MALS with regard to the recovery rates of Aβ(1–42). Possible interactions of Aβ(1–42) with the column matrix and the necessity of either an online filtration or centrifugation step prior to chromatography might be responsible for material losses. Aβ(1–42) fibrils, huge aggregates or huge complexes (Aβ/ligand) could not pass through in the AD research field prevalent SEC columns with a void volume of 75 or 200 kDa. For DGC the whole sample can be loaded onto the gradient regardless of the aggregation state of the sample. Additionally, the fact, that for each sample a fresh gradient is prepared, while SEC columns have to be reused, contributes clearly to the very good reproducibility of the analysis. We used steps (i) to (iii) previously to investigate the effect of agent candidates on size distribution of Aβ(1–42) aggregates formed in vitro, although only in a qualitative or semi-quantitative way6. Here, for the first time, we add the fully quantitative analysis of a compound’s impact on the Aβ(1–42) aggregate size distribution and call it QIAD assay (Fig. 2b,c). The principle of the QIAD assay can easily be transferred to measure the quantitative interference on aggregates consisting of aggregation-prone peptides or proteins other than Aβ. Thus, to avoid any confusion with QIAD variants yet to come, we have termed the Aβ-specific QIAD assay “Aβ-QIAD”.