Pathway-based analysis of genome-wide siRNA screens reveals the regulatory landscape of APP processing

Abstract

The progressive aggregation of Amyloid-β (Aβ) in the brain is a major trait of Alzheimer's Disease (AD). Aβ is produced as a result of proteolytic processing of the β-amyloid precursor protein (APP). Processing of APP is mediated by multiple enzymes, resulting in the production of distinct peptide products: the non-amyloidogenic peptide sAPPα and the amyloidogenic peptides sAPPβ, Aβ40, and Aβ42. Using a pathway-based approach, we analyzed a large-scale siRNA screen that measured the production of different APP proteolytic products. Our analysis identified many of the biological processes/pathways that are known to regulate APP processing and have been implicated in AD pathogenesis, as well as revealing novel regulatory mechanisms. Furthermore, we also demonstrate that some of these processes differentially regulate APP processing, with some mechanisms favouring production of certain peptide species over others. For example, synaptic transmission having a bias towards regulating Aβ40 production over Aβ42 as well as processes involved in insulin and pancreatic biology having a bias for sAPPβ production over sAPPα. In addition, some of the pathways identified as regulators of APP processing contain genes (CLU, BIN1, CR1, PICALM, TREM2, SORL1, MEF2C, DSG2, EPH1A) recently implicated with AD through genome wide association studies (GWAS) and associated meta-analysis. In addition, we provide supporting evidence and a deeper mechanistic understanding of the role of diabetes in AD. The identification of these processes/pathways, their differential impact on APP processing, and their relationships to each other, provide a comprehensive systems biology view of the "regulatory landscape" of APP.

Fig 1. Identification of pathways that regulate APP processing (Aβ42).

A. By combining the P-value and PI score, we identified pathways/processes that, when knocked down, significantly affect the readout in question. Depicted here are the results for Aβ42 readout. Each circle represents a process/pathway set and the size of the circle corresponds to the number of genes, measured in the screen, that comprise each pathway. Colors correspond to the database from which the pathway/process set was derived. Y-axis represents the likelihood of a pathway of a given size to have the corresponding net or absolute PI score by chance. Black dotted line corresponds to p-value = 0.01 or -log₁₀(p-value) = 2. One of the most significant sets was the AD pathway as defined by KEGG (red arrow). This pathway contains γ-secretase, β secretase, and other enzymes known to either cleave APP or degrade Aβ42. B. Clustering of candidate pathways/processes based on gene overlap. The overlap between two pathways/processes is determined by the ratio of the overlap of the smaller with the larger set to the size of the smaller set (see materials and methods). Clusters (black boxes) of highly overlapping pathways/processes were identified using hierarchical clustering. Cluster 4 contains the AD pathway. This type of representation also allows for the identification of interplay across the different pathways/processes. For example, the red-dashed squares indicate overlap between sets in Cluster 3 (inflammation and cell adhesion) with genes in Cluster 6 (mRNA processing, translation, and transcription). The table captures each cluster which consists of pathways/processes that share similar overlapping patterns. Several of these pathways/processes have been implicated in modulating γ-secretase activity, have been implicated in AD pathogenesis, and/or are under consideration as strategies for the treatment and prevention of AD [1,3,7,24].

Fig 2. Differential effects of pathways on different readouts.

Not all pathways, if knocked down by siRNA, affect biological endpoints in the same manner. A. The dendrogram on the left represents hierarchical clustering of pathways across different readouts using their Net PI score. Each row corresponds to a pathway. Blue: negative PI score (readout decreased). Red: positive PI score (readout increased). B. Individual pathway/process profiles across the readouts for each cluster. This representation allows one to identify pathways/processes that may have favourable profiles (lower net levels of amyloidogenic peptides), such as Cluster 2 and Cluster 6, and those with undesirable profiles (greater net levels of amyloidogenic peptides), such as Cluster 10. Cluster 2 and Cluster 6 show reduction in the amyloidogenic peptides Aβ40, Aβ42, and sAPPβ, with increases in sAPPα (β-secretase-inhibition profile) and no net decrease in viability. Conversely, Cluster 10 pathways have strong net decreases in viability and net increases in amyloidogenic peptides, and hence could be potentially considered pathological.

Fig 3. Pathways/processes that differentially regulate Aβ42 vs. Aβ40 production.

A. Scatter plot of -log(-P-values) for Net PI scores of pathways/processes for Aβ42 against that for Aβ40. Each circle represents a pathway/process. The size of the circle corresponds to the number of genes in the set. The color corresponds to the database source from which the pathway/process was derived. As expected, most pathways and processes that regulate Aβ40 also regulate Aβ42 production. However, there are some “modulator” pathways that are significant for one readout but not the other. Red square: Aβ42-regulating pathways. Blue square: Aβ40-specific pathways.

Fig 4. “Maturity onset diabetes of the young” pathway (KEGG) [26].

This pathway was found to be a significant regulator of sAPPβ. Proteins/genes are coloured based on their corresponding Z* values for sAPPα (A), and sAPPβ (B). Genes do not behave equally across the different readouts. For example, knock-down of NKX2–2 (black circle), which is a homeobox transcription factor, results in a significant decrease of sAPPβ (Z* = –12.3) but increases sAPPα (Z* = 2.4). Hence, the mechanism by which this pathway would favour the production of sAPPβ over sAPPα could potentially be mediated by this transcription factor. C. “Maturity onset diabetes of the young (MODY) (KEGG)” and "Alzheimer's disease" pathways (KEGG database). The network illustrates how proteins from these two pathways interact with/regulate each other. D. Two potential mechanisms by which sAPPβ levels can be lowered. One hypothetical mechanism could be via NKX2–2 regulation of APP processing via an insulin-mediated pathway. Knock-down of NKX2–2 would result in increased insulin levels leading to inhibition of caspase 3 activation and hence decreased cleavage of APP by caspase 3 at the BACE1 cleavage site[–71]. Increased insulin levels have been associated with decreases of intracellular accumulation of Aβ levels, and caspase 3 has been shown to regulate APP processing via BACE1-related mechanisms [–73]. Knock-down of caspase 3 in this study reduces sAPPβ levels. Although the insulin gene was not included in the screen, the knock-downs of NKX-2 and caspase 3 are consistent with known biology (i.e. reduction in levels of sAPPβ. An alternative hypothesis could be via HNF4A, a transcription factor previously characterized as binding to the BACE promoter [74]. Genes/proteins in the network are coloured by their corresponding sAPPβ Z* values.

Fig 5. Pathway/process context matters.

Not all pathways/processes that contain genes with extreme values are significant suggesting that the approach may be resistant to to outliers. For example, ITGB3 and APP are clear outliers with Z* scores of 7.18 and -7.1, respectively but not all of their corresponding pathways/processes were found to be significant regulators of Aβ42. Each circle corresponds to pathway/process and the size corresponds to the number of genes in that pathway/process. Y-axis represents the likelihood of a pathway of a given size to have the corresponding net or absolute PI score by chance. Black dotted line corresponds to p-value = 0.01 or -log₁₀(p-value) = 2 and the x-axis corresponds to the either Net or ABS PI score based on the Aβ42 readout.