Building a pipeline to discover and validate novel therapeutic targets and lead compounds for Alzheimer's disease

Abstract

Cognitive decline, Alzheimer's disease (AD) and other causes are major public health problems worldwide. With changing demographics, the number of persons with dementia will increase rapidly. The treatment and prevention of AD and other dementias, therefore, is an urgent unmet need. There have been considerable advances in understanding the biology of many age-related disorders that cause dementia. Gains in understanding AD have led to the development of ante-mortem biomarkers of traditional neuropathology and the conduct of several phase III interventions in the amyloid-β cascade early in the disease process. Many other intervention strategies are in various stages of development. However, efforts to date have met with limited success. A recent National Institute on Aging Research Summit led to a number of requests for applications. One was to establish multi-disciplinary teams of investigators who use systems biology approaches and stem cell technology to identify a new generation of AD targets. We were recently awarded one of three such grants to build a pipeline that integrates epidemiology, systems biology, and stem cell technology to discover and validate novel therapeutic targets and lead compounds for AD treatment and prevention. Here we describe the two cohorts that provide the data and biospecimens being exploited for our pipeline and describe the available unique datasets. Second, we present evidence in support of a chronic disease model of AD that informs our choice of phenotypes as the target outcome. Third, we provide an overview of our approach. Finally, we present the details of our planned drug discovery pipeline.

Keywords: Alzheimer's disease; RNAi; Small molecule screen; Systems biology; Targeted proteomics.

Figure 1. Relations between AD phenotypes of cognitive decline, clinical diagnosis and AD pathology

Figure 1A [upper left]: Cognitive decline in participants with and without clinical AD dementia diagnosis. Light green lines are repeated raw cognitive scores for up to 15 years from 50 randomly selected participants who never received a diagnosis of AD dementia. The light pink lines are repeated raw cognitive scores for up to 15 years from 50 randomly selected participants who received a diagnosis of incident AD dementia. The dark green line is the linear mixed model derived mean trajectory for an average participant (i.e. female at mean age and mean level of education) who never received a diagnosis of AD dementia, and the red line is the change-point mixed model derived mean trajectory for an average participant who developed AD dementia. The inflection point was fixed at the time of clinical diagnosis of AD dementia. The figure illustrates that cognitive decline is detectable over multiple years prior to the AD diagnosis and the increase in the rate of decline following diagnosis. It also nicely illustrates the person-specific differences in rates of cognitive decline among persons who did and did not develop AD dementia. Figure 1B [upper right]: Similar to Figure 1A but for incident MCI. Light blue lines are the repeated raw cognitive scores for up to 15 years from 50 randomly selected participants who never received a diagnosis of MCI. The gray lines are raw cognitive scores for up to 15 years from 50 randomly selected participants who received a diagnosis of incident MCI. The dark blue line is the linear mixed model derived mean trajectory for an average participant (i.e. female at mean age and mean level of education) who never developed MCI, and the black line is the change-point mixed model derived mean trajectory for an average participant who developed incident MCI. The inflection point was fixed at the time of first MCI diagnosis. The figure illustrates that cognitive decline is detectable over multiple years prior to the MCI diagnosis and that there is an increase in the rate of decline following diagnosis. It also nicely illustrates the person-specific differences in rates of cognitive decline among persons who did and did not develop MCI. Figure 1C [lower left]: Continuum in the relation of AD pathology to cognition proximate to death. On the horizontal axis are quantitative measures of AD pathology. On the vertical axis are scores of global cognitive function proximate to death. Light blue dots represent participants without dementia or MCI at death. The light green dots are participants with MCI at death, and the pink dots represent participants with AD dementia at death. The blue, green, and red lines represent the linear regression lines for average participants (i.e. female at mean age and mean level of education) among those without dementia or MCI, MCI, and AD dementia respectfully, adjusted for age, sex, and education. The figure illustrates the point that the relation between cognition and pathology is only slightly steeper in those with AD dementia. It also nicely illustrates the relatively poor correspondence between the burden of the classic pathologic indices of AD and the level of cognition proximate to death. Figure 1D [lower right]: Continuum in the relation of AD pathology to change in cognition over time. On the horizontal axis are quantitative measures of AD pathology. On the vertical axis are model derived estimates for personal specific slope of decline in cognition, from a mixed model controlling for age, sex, and education. Light blue dots represent participants without dementia or MCI at death. The light green dots are participants with MCI at death, and the pink dots represent participants with AD dementia death. The blue, green, and red lines represent the linear regression lines for average participants (i.e. female at mean age and mean level of education) among those without dementia or MCI, MCI, and AD dementia respectfully. The figure illustrates the relation between the rate of cognitive decline and pathology, which is only slightly steeper in those with AD dementia. It also nicely illustrates the relatively poor correspondence between the burden of the classic pathologic indices of AD and rate of cognition decline over multiple years prior to death.

Figure 2. Outline of the AD drug discovery pipeline

Component 1: System biology component. This component employs systems biology to integrate multiple types of “omics” data [upper middle] generated from frozen human DLPFC [upper left] to identify AD molecular networks and nodes, and nominate > 300 genes, and therefore proteins, to move to components 2 and 3. A representative result of a network analysis [upper right] that uncovered a molecular pathway linking known AD susceptibility genes with AD pathology is shown in the upper right panel. Component 2: Targeted proteomics component. This component uses LC-SRM/MS to quantify > 300 proteins from frozen human DLPFC from the same subjects used in component 1 to ensure that the targeted protein is translated and that its level is related our AD endophenotypes. A cartoon of the MS intensity as a function of LC elution time results in detection of different proteoforms (protein fragments) and can be used to generate relative protein abundance with high specificity and fidelity [middle left]. Component 3: Functional validation component [middle right]. This component uses RNAi to knock down and overexpress each of the genes identified in component 1 in iPSC-derived neurons and/or astrocytes to confirm the identified pathways, address the directionality of relationships in these networks, and enrich the information that we use to identify the key network nodes. The picture shows iPSC-derived neurons in vitro (stained for neuronal marker TUJ1 in green). Component 4: Small molecule screen component [bottom]. This component will use high throughput small molecular screening of the same iPSC-derived neurons and astrocytes used in component 3 to identify compounds affecting the selected target genes. It will use the Broad Institute's Therapeutics Platform.

Figure 3. Illustration of the systems biology component

Our approach leverages different genomic features (e.g., genetic variation, CpG, H3K9Ac peak, miRNA) to be integrated with mRNA expression and our AD endophenotypes. Notably, all of the data are from frozen human DLPFC of the same participants [far left]. A representative genome-wide association SNP scan [top] that was used to identify novel variants associated with AD pathology such as SNPs in the amyloid precursor protein (APP) locus. Circos plot [upper middle] presenting the results of a genome-wide DNA methylation data with mRNA confirmation data to identify convergence of associations across multiple genomic layers to identify novel CpG dinucleotides at which the level of methylation is associated with AD pathology. Analysis of miRNA data [lower middle] reveals a network of up-regulated and down-regulated miRNAs in relation to four different AD pathologic traits. Representative tracks of ChIP-seq data [bottom] in the clusterin locus that illustrate the ongoing H3K9 chip-seq scan being used to identify novel acetylation peaks associated with AD pathology . Results from the different analyses are integrated using mRNA expression [right]: the panel shows the integration of the effects of two types of genomic features (DNA methylation and miRNA) that are identified as regulators of mRNA expression. These regulators are circled in the network diagram. The heatmap presents subjects in columns and genes in rows: genes are either up-regulated or down-regulated in a given subject.