Reverse transcription and real-time PCR (RT-qPCR) has been widely used for rapid quantification of relative gene expression. To offset technical confounding variations, stably-expressed internal reference genes are measured simultaneously along with target genes for data normalization. Statistic methods have been developed for reference validation; however normalization of RT-qPCR data still remains arbitrary due to pre-experimental determination of particular reference genes. To establish a method for determination of the most stable normalizing factor (NF) across samples for robust data normalization, we measured the expression of 20 candidate reference genes and 7 target genes in 15 Drosophila head cDNA samples using RT-qPCR. The 20 reference genes exhibit sample-specific variation in their expression stability. Unexpectedly the NF variation across samples does not exhibit a continuous decrease with pairwise inclusion of more reference genes, suggesting that either too few or too many reference genes may detriment the robustness of data normalization. The optimal number of reference genes predicted by the minimal and most stable NF variation differs greatly from 1 to more than 10 based on particular sample sets. We also found that GstD1, InR and Hsp70 expression exhibits an age-dependent increase in fly heads; however their relative expression levels are significantly affected by NF using different numbers of reference genes. Due to highly dependent on actual data, RT-qPCR reference genes thus have to be validated and selected at post-experimental data analysis stage rather than by pre-experimental determination.

The mechanism of widespread neuronal death occurring in Alzheimer's disease (AD) remains enigmatic even after extensive investigation during the last two decades. Amyloid beta 42 peptide (Aβ1–42) is believed to play a causative role in the development of AD. Here we expressed human Aβ1–42 and amyloid beta 40 (Aβ1–40) in Drosophila neurons. Aβ1–42 but not Aβ1–40 causes an extensive accumulation of autophagic vesicles that become increasingly dysfunctional with age. Aβ1–42-induced impairment of the degradative function, as well as the structural integrity, of post-lysosomal autophagic vesicles triggers a neurodegenerative cascade that can be enhanced by autophagy activation or partially rescued by autophagy inhibition. Compromise and leakage from post-lysosomal vesicles result in cytosolic acidification, additional damage to membranes and organelles, and erosive destruction of cytoplasm leading to eventual neuron death. Neuronal autophagy initially appears to play a pro-survival role that changes in an age-dependent way to a pro-death role in the context of Aβ1–42 expression. Our in vivo observations provide a mechanistic understanding for the differential neurotoxicity of Aβ1–42 and Aβ1–40, and reveal an Aβ1–42-induced death execution pathway mediated by an age-dependent autophagic-lysosomal injury.

Aging is known to be the most prominent risk factor for Alzheimer's disease (AD); however, the underlying mechanism linking brain aging with AD pathogenesis remains unknown. The expression of human amyloid beta 42 peptide (Aβ₁₋₄₂), but not Aβ₁₋₄₀ in Drosophila brain induces an early onset and progressive autophagy-lysosomal neuropathology. Here we show that the natural process of brain aging also accompanies a chronic and late-onset deterioration of neuronal autophagy-lysosomal system. This process is characterized by accumulation of dysfunctional autophagy-lysosomal vesicles, a compromise of these vesicles leading to damage of intracellular membranes and organelles, necrotic-like intraneuronal destruction and neurodegeneration. In addition, conditional activation of neuronal autophagy in young animals is protective while late activation is deleterious for survival. Intriguingly, conditional Aβ₁₋₄₂ expression limited to young animals exacerbates the aging process to a greater extent than Aβ₁₋₄₂ expression in old animals. These data suggest that the neuronal autophagy-lysosomal system may shift from a functional and protective state to a pathological and deleterious state either during brain aging or via Aβ₁₋₄₂ neurotoxicity. A chronic deterioration of the neuronal autophagy-lysosomal system is likely to be a key event in transitioning from normal brain aging to pathological aging leading to Alzheimer's neurodegeneration.

The macroautophagy (autophagy) pathway is thought to be involved in a variety of neurodegenerative diseases, including Alzheimer disease (AD). It is not clear however, if autophagy plays a causative role, a protective role or is a consequence of the disease process itself. Using a Drosophila model of neuron-limited expression of AD-associated amyloid beta (Abeta) peptides, we have demonstrated an autophagy-mediated neurodegenerative cascade that is initiated by Abeta(1-42) and enhanced by aging. Our results suggest a central role for the autophagy pathway in AD type neurodegeneration and a new framework to understand seemingly unrelated AD phenotypes.

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Abnormal accumulation of Aβ (amyloid β) within AEL (autophagy–endosomal–lysosomal) vesicles is a prominent neuropathological feature of AD (Alzheimer's disease), but the mechanism of accumulation within vesicles is not clear. We express secretory forms of human Aβ1–40 or Aβ1–42 in Drosophila neurons and observe preferential localization of Aβ1–42 within AEL vesicles. In young animals, Aβ1–42 appears to associate with plasma membrane, whereas Aβ1–40 does not, suggesting that recycling endocytosis may underlie its routing to AEL vesicles. Aβ1–40, in contrast, appears to partially localize in extracellular spaces in whole brain and is preferentially secreted by cultured neurons. As animals become older, AEL vesicles become dysfunctional, enlarge and their turnover appears delayed. Genetic inhibition of AEL function results in decreased Aβ1–42 accumulation. In samples from older animals, Aβ1–42 is broadly distributed within neurons, but only the Aβ1–42 within dysfunctional AEL vesicles appears to be in an amyloid-like state. Moreover, the Aβ1–42-containing AEL vesicles share properties with AD-like extracellular plaques. They appear to be able to relocate to extracellular spaces either as a consequence of age-dependent neurodegeneration or a non-neurodegenerative separation from host neurons by plasma membrane infolding. We propose that dysfunctional AEL vesicles may thus be the source of amyloid-like plaque accumulation in Aβ1–42-expressing Drosophila with potential relevance for AD.

Reverse transcription quantitative real-time PCR (RT-qPCR) is a key method for measurement of relative gene expression. Analysis of RT-qPCR data requires many iterative computations for data normalization and analytical optimization. Currently no computer program for RT-qPCR data analysis is suitable for analytical optimization and user-controllable customization based on data quality, experimental design as well as specific research aims. Here I introduce an all-in-one computer program, SASqPCR, for robust and rapid analysis of RT-qPCR data in SAS. This program has multiple macros for assessment of PCR efficiencies, validation of reference genes, optimization of data normalizers, normalization of confounding variations across samples, and statistical comparison of target gene expression in parallel samples. Users can simply change the macro variables to test various analytical strategies, optimize results and customize the analytical processes. In addition, it is highly automatic and functionally extendable. Thus users are the actual decision-makers controlling RT-qPCR data analyses. SASqPCR and its tutorial are freely available at http://code.google.com/p/sasqpcr/downloads/list.

Reverse transcription quantitative real-time polymerase chain reaction (RT-qPCR) uses threshold cycles (Ct values) for measuring relative gene expression. Ct values are signal-to-noise data composed of target gene expression and multiple sources of confounding variations. Data analysis is to minimize technical noises, evaluate biological variances, and estimate treatment-attributable expression changes of particular genes. However, this function is not sufficiently fulfilled in current analytic methods. An important but unrecognizable problem is that Ct values from all biological replicates and technical repeats are pooled across genes and treatment types. This violates the sample-specific association between target and reference genes, leading to inefficient removal of technical noises. To resolve this problem, here we propose to separate Ct values into replicate-specific data subsets and iteratively analyze expression ratios for individual data subsets. The individual expression ratios, rather than the raw Ct values, are pooled to determine the final expression change. The variances of all biological replicates and technical repeats across all target and reference genes are summed up. Our results from example data demonstrate that this separated method can substantially minimize RT-qPCR variance compared with the traditional methods using pooled Ct profiles. This analytic strategy is more effective in control of technical noises and improves the fidelity of RT-qPCR quantification.