Robust Sampling of Defective Pathways in Alzheimer's Disease. Implications in Drug Repositioning

Associating brain imaging phenotypes and genetic risk factors via a hypergraph based netNMF method

Abstract

Alzheimer's disease (AD) is a severe neurodegenerative disease for which there is currently no effective treatment. Mild cognitive impairment (MCI) is an early disease that may progress to AD. The effective diagnosis of AD and MCI in the early stage has important clinical significance.

Methods

To this end, this paper proposed a hypergraph-based netNMF (HG-netNMF) algorithm for integrating structural magnetic resonance imaging (sMRI) of AD and MCI with corresponding gene expression profiles.

Results

Hypergraph regularization assumes that regions of interest (ROIs) and genes were located on a non-linear low-dimensional manifold and can capture the inherent prevalence of two modalities of data and mined high-order correlation features of the two data. Further, this paper used the HG-netNMF algorithm to construct a brain structure connection network and a protein interaction network (PPI) with potential role relationships, mine the risk (ROI) and key genes of both, and conduct a series of bioinformatics analyses.

Conclusion

Finally, this paper used the risk ROI and key genes of the AD and MCI groups to construct diagnostic models. The AUC of the AD group and MCI group were 0.8 and 0.797, respectively.

1-L Transcription in Alzheimer's Disease

Alzheimer’s disease is a very complex disease and better explanations and models are needed to understand how neurons are affected and microglia are activated. A new model of Alzheimer’s disease is presented here, the β-amyloid peptide is considered an important RNA recognition/binding peptide. 1-L transcription revealed compatible sequences with AAUAAA (PAS signal) and UUUC (class III ARE rich in U) in the Aβ peptide, supporting the peptide–RNA regulatory model. When a hypothetical model of fibril selection with the prionic character of amyloid assemblies is added to the peptide-RNA regulatory model, the downregulation of the PI3K-Akt pathway and the upregulation of the PLC-IP3 pathway are well explained. The model explains why neurons are less protected from inflammation and why microglia are activated; why mitochondria are destabilized; why the autophagic flux is destabilized; and why the post-transcriptional attenuation of the axonal signal “noise” is interrupted. For example, the model suggests that Aβ peptide may post-transcriptionally control ELAVL2 (ELAV-like RNA binding protein 2) and DCP2 (decapping mRNA protein 2), which are known to regulate RNA processing, transport, and stability.

Decoding the Role of Astrocytes in the Entorhinal Cortex in Alzheimer’s Disease Using High-Dimensional Single-Nucleus RNA Sequencing Data and Next-Generation Knowledge Discovery Methodologies: Focus on Drugs and Natural Product Remedies for Dementia

Introduction: Alzheimer’s disease (AD) is a major cause of the development of cognitive decline and dementia. AD and associated dementias (ADRD) are the major contributors to the enormous burden of morbidity and mortality worldwide. To date, there are no robust therapies to alleviate or cure this debilitating disease. Most drug treatments focus on restoring the normal function of neurons and the cells that cause inflammation, such as microglia in the brain. However, the role of astrocytes, the brain’s housekeeping cells, in the development of AD and the initiation of dementia is still not well understood.

Objective: To decipher the role of astrocytes in the entorhinal cortex of AD patients using single nuclear RNA sequencing (snRNASeq) datasets from the Single Cell RNA-seq Database for Alzheimer’s Disease (scREAD). The datasets were originally derived from astrocytes, isolated from the entorhinal cortex of AD brain and healthy brain to decipher disease-specific signaling pathways as well as drugs and natural products that reverse AD-specific signatures in astrocytes.

Methods: We used snRNASeq datasets from the scREAD database originally derived from astrocytes isolated from the entorhinal cortex of AD and healthy brains from the Gene Expression Omnibus (GEO) (GSE138852 and GSE147528) and analyzed them using next-generation knowledge discovery (NGKD) platforms. scREAD is a user-friendly open-source interface available at https://bmbls.bmi.osumc.edu/scread/that enables more discovery-oriented strategies. snRNASeq data and metadata can also be visualized and downloaded via an interactive web application at adsn.ddnetbio.com. Differentially expressed genes (DEGs) for each snRNASeq dataset were analyzed using iPathwayGuide to compare and derive disease-specific pathways, gene ontologies, and in silico predictions of drugs and natural products that regulate AD -specific signatures in astrocytes. In addition, DEGs were analyzed using the L1000FWD and L1000CDS2 signature search programming interfaces (APIs) to identify additional drugs and natural products that mimic or reverse AD-specific gene signatures in astrocytes.

Results: We found that PI3K/AKT signaling, Wnt signaling, neuroactive ligand-receptor interaction pathways, neurodegeneration pathways, etc. were significantly impaired in astrocytes from the entorhinal cortex of AD patients. Biological processes such as glutamate receptor signaling pathway, regulation of synapse organization, cell-cell adhesion via plasma membrane adhesion molecules, and chylomicrons were negatively enriched in the astrocytes from the entorhinal cortex of AD patients. Gene sets involved in cellular components such as postsynaptic membrane, synaptic membrane, postsynapse, and synapse part were negatively enriched (p < 0.01). Moreover, molecular functions such as glutamate receptor activity, neurotransmitter receptor activity, and extracellular ligand-gated ion channels were negatively regulated in the astrocytes of the entorhinal cortex of AD patients (p < 0.01). Moreover, the application of NGKD platforms revealed that antirheumatic drugs, vitamin-E, emetine, narciclasine, cephaeline, trichostatin A, withaferin A, dasatinib, etc. can potentially reverse gene signatures associated with AD.

Conclusions: The present study highlights an innovative approach to use NGKD platforms to find unique disease-associated signaling pathways and specific synthetic drugs and natural products that can potentially reverse AD and ADRD-associated gene signatures.

Single-Cell RNA-Seq Analysis of Olfactory Mucosal Cells of Alzheimer’s Disease Patients

Olfaction is orchestrated by olfactory mucosal cells located in the upper nasal cavity. Olfactory dysfunction manifests early in several neurodegenerative disorders including Alzheimer’s disease, however, disease-related alterations to the olfactory mucosal cells remain poorly described. The aim of this study was to evaluate the olfactory mucosa differences between cognitively healthy individuals and Alzheimer’s disease patients. We report increased amyloid-beta secretion in Alzheimer’s disease olfactory mucosal cells and detail cell-type-specific gene expression patterns, unveiling 240 differentially expressed disease-associated genes compared to the cognitively healthy controls, and five distinct cell populations. Overall, alterations of RNA and protein metabolism, inflammatory processes, and signal transduction were observed in multiple cell populations, suggesting their role in Alzheimer’s disease-related olfactory mucosa pathophysiology. Furthermore, the single-cell RNA-sequencing proposed alterations in gene expression of mitochondrially located genes in AD OM cells, which were verified by functional assays, demonstrating altered mitochondrial respiration and a reduction of ATP production. Our results reveal disease-related changes of olfactory mucosal cells in Alzheimer’s disease and demonstrate the utility of single-cell RNA sequencing data for investigating molecular and cellular mechanisms associated with the disease.

The Road to Personalized Medicine in Alzheimer’s Disease: The Use of Artificial Intelligence

Dementia remains an extremely prevalent syndrome among older people and represents a major cause of disability and dependency. Alzheimer’s disease (AD) accounts for the majority of dementia cases and stands as the most common neurodegenerative disease. Since age is the major risk factor for AD, the increase in lifespan not only represents a rise in the prevalence but also adds complexity to the diagnosis. Moreover, the lack of disease-modifying therapies highlights another constraint. A shift from a curative to a preventive approach is imminent and we are moving towards the application of personalized medicine where we can shape the best clinical intervention for an individual patient at a given point. This new step in medicine requires the most recent tools and analysis of enormous amounts of data where the application of artificial intelligence (AI) plays a critical role on the depiction of disease–patient dynamics, crucial in reaching early/optimal diagnosis, monitoring and intervention. Predictive models and algorithms are the key elements in this innovative field. In this review, we present an overview of relevant topics regarding the application of AI in AD, detailing the algorithms and their applications in the fields of drug discovery, and biomarkers.

Pan-Tissue Aging Clock Genes That Have Intimate Connections with the Immune System and Age-Related Disease

In our recent transcriptomic meta-analysis, we used random forest machine learning to accurately predict age in human blood, bone, brain, heart, and retina tissues given gene inputs. Although each tissue-specific model utilized a unique number of genes for age prediction, we found that the following six genes were prioritized in all five tissues: CHI3L2, CIDEC, FCGR3A, RPS4Y1, SLC11A1, and VTCN1. Since being selected for age prediction in multiple tissues is unique, we decided to explore these pan-tissue clock genes in greater detail. In the present study, we began by performing over-representation and network topology-based enrichment analyses in the Gene Ontology Biological Process database. These analyses revealed that the immunological terms "response to protozoan," "immune response," and "positive regulation of immune system process" were significantly enriched by these clock inputs. Expression analyses in mouse and human tissues identified that these inputs are frequently upregulated or downregulated with age. A detailed literature search showed that all six genes had noteworthy connections to age-related disease. For example, mice deficient in Cidec are protected against various metabolic defects, while suppressing VTCN1 inhibits age-related cancers in mouse models. Using a large multitissue transcriptomic dataset, we additionally generate a novel, minimalistic aging clock that can predict human age using just these six genes as inputs. Taken all together, these six genes are connected to diverse aspects of aging.