Genetic Evidence for Protective Effects of Angiotensin-Converting Enzyme Against Alzheimer Disease But Not Other Neurodegenerative Diseases in European Populations

Employing Informatics Strategies in Alzheimer's Disease Research: A Review from Genetics, Multiomics, and Biomarkers to Clinical Outcomes

Alzheimer's disease (AD) is a critical national concern, affecting 5.8 million people and costing more than $250 billion annually. However, there is no available cure. Thus, effective strategies are in urgent need to discover AD biomarkers for disease early detection and drug development. In this review, we study AD from a biomedical data scientist perspective to discuss the four fundamental components in AD research: genetics (G), molecular multiomics (M), multimodal imaging biomarkers (B), and clinical outcomes (O) (collectively referred to as the GMBO framework). We provide a comprehensive review of common statistical and informatics methodologies for each component within the GMBO framework, accompanied by the major findings from landmark AD studies. Our review highlights the potential of multimodal biobank data in addressing key challenges in AD, such as early diagnosis, disease heterogeneity, and therapeutic development. We identify major hurdles in AD research, including data scarcity and complexity, and advocate for enhanced collaboration, data harmonization, and advanced modeling techniques. This review aims to be an essential guide for understanding current biomedical data science strategies in AD research, emphasizing the need for integrated, multidisciplinary approaches to advance our understanding and management of AD.

Classical and nonclassical effects of angiotensin-converting enzyme: How increased ACE enhances myeloid immune function

As part of the classical renin-angiotensin system, the peptidase angiotensin-converting enzyme (ACE) makes angiotensin II which has myriad effects on systemic cardiovascular function, inflammation, and cellular proliferation. Less well known is that macrophages and neutrophils make ACE in response to immune activation which has marked effects on myeloid cell function independent of angiotensin II. Here, we discuss both classical (angiotensin) and nonclassical functions of ACE and highlight mice called ACE 10/10 in which genetic manipulation increases ACE expression by macrophages and makes these mice much more resistant to models of tumors, infection, atherosclerosis, and Alzheimer's disease. In another model called NeuACE mice, neutrophils make increased ACE and these mice are much more resistant to infection. In contrast, ACE inhibitors reduce neutrophil killing of bacteria in mice and humans. Increased expression of ACE induces a marked increase in macrophage oxidative metabolism, particularly mitochondrial oxidation of lipids, secondary to increased peroxisome proliferator-activated receptor α expression, and results in increased myeloid cell ATP. ACE present in sperm has a similar metabolic effect, and the lack of ACE activity in these cells reduces both sperm motility and fertilization capacity. These nonclassical effects of ACE are not due to the actions of angiotensin II but to an unknown molecule, probably a peptide, that triggers a profound change in myeloid cell metabolism and function. Purifying and characterizing this peptide could offer a new treatment for several diseases and prove potentially lucrative.

Optimising the use of electronic medical records for large scale research in psychiatry

The explosion and abundance of digital data could facilitate large-scale research for psychiatry and mental health. Research using so-called "real world data"-such as electronic medical/health records-can be resource-efficient, facilitate rapid hypothesis generation and testing, complement existing evidence (e.g. from trials and evidence-synthesis) and may enable a route to translate evidence into clinically effective, outcomes-driven care for patient populations that may be under-represented. However, the interpretation and processing of real-world data sources is complex because the clinically important 'signal' is often contained in both structured and unstructured (narrative or "free-text") data. Techniques for extracting meaningful information (signal) from unstructured text exist and have advanced the re-use of routinely collected clinical data, but these techniques require cautious evaluation. In this paper, we survey the opportunities, risks and progress made in the use of electronic medical record (real-world) data for psychiatric research.

Angiotensin Converting Enzyme (ACE) expression in microglia reduces amyloid β deposition and neurodegeneration by increasing SYK signaling and endolysosomal trafficking

Genome-wide association studies (GWAS) have identified many gene polymorphisms associated with an increased risk of developing Late Onset Alzheimer's Disease (LOAD). Many of these LOAD risk-associated alleles alter disease pathogenesis by influencing microglia innate immune responses and lipid metabolism. Angiotensin Converting Enzyme (ACE), a GWAS LOAD risk-associated gene best known for its role in regulating systemic blood pressure, also enhances innate immunity and lipid processing in peripheral myeloid cells, but a role for ACE in modulating the function of myeloid-derived microglia remains unexplored. Using novel mice engineered to express ACE in microglia and CNS associated macrophages (CAMs), we find that ACE expression in microglia reduces Aβ plaque load, preserves vulnerable neurons and excitatory synapses, and greatly reduces learning and memory abnormalities in the 5xFAD amyloid mouse model of Alzheimer's Disease (AD). ACE-expressing microglia show enhanced Aβ phagocytosis and endolysosomal trafficking, increased clustering around amyloid plaques, and increased SYK tyrosine kinase activation downstream of the major Aβ receptors, TREM2 and CLEC7A. Single microglia sequencing and digital spatial profiling identifies downstream SYK signaling modules that are expressed by ACE expression in microglia that mediate endolysosomal biogenesis and trafficking, mTOR and PI3K/AKT signaling, and increased oxidative phosphorylation, while gene silencing or pharmacologic inhibition of SYK activity in ACE-expressing microglia abrogates the potentiated Aβ engulfment and endolysosomal trafficking. These findings establish a role for ACE in enhancing microglial immune function and they identify a potential use for ACE-expressing microglia as a cell-based therapy to augment endogenous microglial responses to Aβ in AD.

Incorporating biological and clinical insights into variant choice for Mendelian randomisation: examples and principles

Mendelian randomisation is an accessible and valuable epidemiological approach to provide insight into the causal nature of relationships between risk factor exposures and disease outcomes. However, if performed without critical thought, we may simply have replaced one set of implausible assumptions (no unmeasured confounding or reverse causation) with another set of implausible assumptions (no pleiotropy or other instrument invalidity). The most critical decision to avoid pleiotropy is which genetic variants to use as instrumental variables. Two broad strategies for instrument selection are a biologically motivated strategy and a genome-wide strategy; in general, a biologically motivated strategy is preferred. In this review, we discuss various ways of implementing a biologically motivated selection strategy: using variants in a coding gene region for the exposure or a gene region that encodes a regulator of exposure levels, using a positive control variable and using a biomarker as the exposure rather than its behavioural proxy. In some cases, a genome-wide analysis can provide important complementary evidence, even when its reliability is questionable. In other cases, a biologically-motivated analysis may not be possible. The choice of genetic variants must be informed by biological and functional considerations where possible, requiring collaboration to combine biological and clinical insights with appropriate statistical methodology.

Evaluating the association between genetically proxied ACE inhibition and dementias

INTRODUCTION

Angiotensin-converting enzyme (ACE) has been implicated in the metabolism of amyloid beta; however, the causal effect of ACE inhibition on risk of Alzheimer's disease (AD) dementia and other common dementias is largely unknown.

METHODS

We examined the causal association of genetically proxied ACE inhibition with four types of dementias using a two-sample Mendelian randomization (MR) approach.

RESULTS

Genetically proxied ACE inhibition was associated with increased risk of AD dementia (odds ratio per one standard deviation reduction in serum ACE [95% confidence interval]; 1.07 [1.04-1.10], P = 5 × 10-07 ) and frontotemporal dementia (1.16 [1.04-1.29], P = 0.01) but not with Lewy body dementia or vascular dementia (P > 0.05). These findings were independently replicated and remained consistent in sensitivity analyses.

DISCUSSION

This comprehensive MR study provided genetic evidence for an association between ACE inhibition and the risk for AD and frontotemporal dementias. These results should encourage further studies of the neurocognitive effects of ACE inhibition.

HIGHLIGHTS

This study evaluated genetically proxied angiotensin-converting enzyme (ACE) inhibition association with dementias. The results suggest an association between ACE inhibition and Alzheimer's disease. The results suggest an association between ACE inhibition and frontotemporal dementia. Those associations can be interpreted as potentially causal.

Using genetic association data to guide drug discovery and development: Review of methods and applications

Evidence on the validity of drug targets from randomized trials is reliable but typically expensive and slow to obtain. In contrast, evidence from conventional observational epidemiological studies is less reliable because of the potential for bias from confounding and reverse causation. Mendelian randomization is a quasi-experimental approach analogous to a randomized trial that exploits naturally occurring randomization in the transmission of genetic variants. In Mendelian randomization, genetic variants that can be regarded as proxies for an intervention on the proposed drug target are leveraged as instrumental variables to investigate potential effects on biomarkers and disease outcomes in large-scale observational datasets. This approach can be implemented rapidly for a range of drug targets to provide evidence on their effects and thus inform on their priority for further investigation. In this review, we present statistical methods and their applications to showcase the diverse opportunities for applying Mendelian randomization in guiding clinical development efforts, thus enabling interventions to target the right mechanism in the right population group at the right time. These methods can inform investigators on the mechanisms underlying drug effects, their related biomarkers, implications for the timing of interventions, and the population subgroups that stand to gain the most benefit. Most methods can be implemented with publicly available data on summarized genetic associations with traits and diseases, meaning that the only major limitations to their usage are the availability of appropriately powered studies for the exposure and outcome and the existence of a suitable genetic proxy for the proposed intervention.

The Pharmacorank Search Tool for the Retrieval of Prioritized Protein Drug Targets and Drug Repositioning Candidates According to Selected Diseases

We present the Pharmacorank search tool as an objective means to obtain prioritized protein drug targets and their associated medications according to user-selected diseases. This tool could be used to obtain prioritized protein targets for the creation of novel medications or to predict novel indications for medications that already exist. To prioritize the proteins associated with each disease, a gene similarity profiling method based on protein functions is implemented. The priority scores of the proteins are found to correlate well with the likelihoods that the associated medications are clinically relevant in the disease's treatment. When the protein priority scores are plotted against the percentage of protein targets that are known to bind medications currently indicated to treat the disease, which we termed the pertinency score, a strong correlation was observed. The correlation coefficient was found to be 0.9978 when using a weighted second-order polynomial fit. As the highly predictive fit was made using a broad range of diseases, we were able to identify a general threshold for the pertinency score as a starting point for considering drug repositioning candidates. Several repositioning candidates are described for proteins that have high predicated pertinency scores, and these provide illustrative examples of the applications of the tool. We also describe focused reviews of repositioning candidates for Alzheimer's disease. Via the tool's URL, https://protein.som.geisinger.edu/Pharmacorank/, an open online interface is provided for interactive use; and there is a site for programmatic access.