Multi-Omics, an Integrated Approach to Identify Novel Blood Biomarkers of Alzheimer's Disease

Integrative approach of omics and imaging data to discover new insights for understanding brain diseases

Treatments that can completely resolve brain diseases have yet to be discovered. Omics is a novel technology that allows researchers to understand the molecular pathways underlying brain diseases. Multiple omics, including genomics, transcriptomics and proteomics, and brain imaging technologies, such as MRI, PET and EEG, have contributed to brain disease-related therapeutic target detection. However, new treatment discovery remains challenging. We focused on establishing brain multi-molecular maps using an integrative approach of omics and imaging to provide insights into brain disease diagnosis and treatment. This approach requires precise data collection using omics and imaging technologies, data processing and normalization. Incorporating a brain molecular map with the advanced technologies through artificial intelligence will help establish a system for brain disease diagnosis and treatment through regulation at the molecular level.

Proteomics profiling of extracellular vesicle for identification of potential biomarkers in Alzheimer's disease: A comprehensive review

The intricate origins and diverse symptoms of Alzheimer's disease (AD) pose significant challenges for both diagnosis and treatment. Exosomes and microvesicles, which carry disease-specific cargo from a variety of central nervous system cell types, have emerged as promising reservoirs of biomarkers for AD. Research on the screening of possible biomarkers in Alzheimer's disease using proteomic profiling of EVs is systematically reviewed in this comprehensive review. We highlight key methodologies employed in EV isolation, characterization, and proteomic analysis, elucidating their advantages and limitations. Furthermore, we summarize the evolving landscape of EV-associated biomarkers implicated in AD pathogenesis, including proteins involved in amyloid-beta metabolism, tau phosphorylation, neuroinflammation, synaptic dysfunction, and neuronal injury. The literature review highlights the necessity for robust validation strategies and standardized protocols to effectively transition EV-based biomarkers into clinical use. In the concluding section, this review delves into potential future avenues and technological advancements pivotal in crafting EV-derived biomarkers applicable to AD diagnostics and prognostics. This review contributes to our comprehension of AD pathology and the advancement of precision medicine in neurodegenerative diseases, hinting at a promising era in AD precision medicine.

Neuronal expression of human amyloid-β and Tau drives global phenotypic and multi-omic changes in C. elegans

Alzheimer's disease (AD) and Alzheimer's related diseases (ADRD) are prevalent age-related neurodegenerative disorders characterized by the accumulation of amyloid-β (Aβ) plaques and Tau neurofibrillary tangles. The nematode Caenorhabditis elegan s ( C. elegans ) serves as an invaluable model organism in diseases of old age-due to its rapid aging. Here we performed an unbiased systems analysis of a C. elegans strain expressing both Aβ and Tau proteins within neurons. We set out to determine if there was a phenotypic interaction between Aβ and Tau. In addition, we were interested in determining the temporal order of the phenotypic and multi-omic (geromic) outcomes. At an early stage of adulthood, we observed reproductive impairments and mitochondrial dysfunction consistent with disruptions in mRNA transcript abundance, protein solubility, and metabolite levels. Notably, the expression of these neurotoxic proteins exhibited a synergistic effect, leading to accelerated aging. Our findings shed light on the close relationship between normal aging and ADRD. Specifically, we demonstrate alterations to metabolic functions preceding age-related neurotoxicity, offering a resource for the development of new therapeutic strategies.

Development of quantitative detection methods for four Alzheimer's disease specific biomarker panels using electrochemical immunosensors based on enzyme immunoassay

Accurate and timely diagnosis of Alzheimer's disease (AD) is necessary to maximize the effectiveness of treatment and using biomarkers for diagnosis is attracting attention as a minimally invasive method with few side effects. Electrochemical immunosensor (EI) is a method that is in the spotlight in the medical and bioanalytical fields due to its portability and field usability. Here, we quantified four AD specific biomarkers using EIs based on enzyme immunoassay. We selected and developed quantitative methods for the biomarkers using screen-printed gold electrodes. For three biomarkers, quantification was performed using competition immunoassays in which antigen-antibody premix mixtures were applied to antigen-immobilized electrodes and the limit of detection (LOD) values were secured, 1.20 ng/ml, 1.30 ng/ml, and 1.74 ng/ml, respectively. For the other, a sandwich immunoassay using antibody pair was selected for quantification and LOD was also achieved as 0.077 ng/ml. All four biomarkers in buffer samples were successfully quantified and reliable R2 values were obtained, and reliable calibration curves were secured for three biomarkers in spiked human serum samples. The immunosensors developed and will be optimized are expected to be used in various fields, including detection of biomarkers for not only AD but also related diseases.

Paired plasma lipidomics and proteomics analysis in the conversion from mild cognitive impairment to Alzheimer's disease

BACKGROUND

Alzheimer's disease (AD) is a neurodegenerative condition for which there is currently no available medication that can stop its progression. Previous studies suggest that mild cognitive impairment (MCI) is a phase that precedes the disease. Therefore, a better understanding of the molecular mechanisms behind MCI conversion to AD is needed.

METHOD

Here, we propose a machine learning-based approach to detect the key metabolites and proteins involved in MCI progression to AD using data from the European Medical Information Framework for Alzheimer's Disease Multimodal Biomarker Discovery Study. Proteins and metabolites were evaluated separately in multiclass models (controls, MCI and AD) and together in MCI conversion models (MCI stable vs converter). Only features selected as relevant by 3/4 algorithms proposed were kept for downstream analysis.

RESULTS

Multiclass models of metabolites highlighted nine features further validated in an independent cohort (0.726 mean balanced accuracy). Among these features, one metabolite, oleamide, was selected by all the algorithms. Further in-vitro experiments in rodents showed that disease-associated microglia excreted oleamide in vesicles. Multiclass models of proteins stood out with nine features, validated in an independent cohort (0.720 mean balanced accuracy). However, none of the proteins was selected by all the algorithms. Besides, to distinguish between MCI stable and converters, 14 key features were selected (0.872 AUC), including tTau, alpha-synuclein (SNCA), junctophilin-3 (JPH3), properdin (CFP) and peptidase inhibitor 15 (PI15) among others.

CONCLUSIONS

This omics integration approach highlighted a set of molecules associated with MCI conversion important in neuronal and glia inflammation pathways.

Multi-Omic Blood Biomarkers as Dynamic Risk Predictors in Late-Onset Alzheimer's Disease

Late-onset Alzheimer's disease is the leading cause of dementia worldwide, accounting for a growing burden of morbidity and mortality. Diagnosing Alzheimer's disease before symptoms are established is clinically challenging, but would provide therapeutic windows for disease-modifying interventions. Blood biomarkers, including genetics, proteins and metabolites, are emerging as powerful predictors of Alzheimer's disease at various timepoints within the disease course, including at the preclinical stage. In this review, we discuss recent advances in such blood biomarkers for determining disease risk. We highlight how leveraging polygenic risk scores, based on genome-wide association studies, can help stratify individuals along their risk profile. We summarize studies analyzing protein biomarkers, as well as report on recent proteomic- and metabolomic-based prediction models. Finally, we discuss how a combination of multi-omic blood biomarkers can potentially be used in memory clinics for diagnosis and to assess the dynamic risk an individual has for developing Alzheimer's disease dementia.

Inflammation and the pathological progression of Alzheimer's disease are associated with low circulating choline levels

Deficiency of dietary choline, an essential nutrient, is observed worldwide, with ~ 90% of Americans being deficient. Previous work highlights a relationship between decreased choline intake and an increased risk for cognitive decline and Alzheimer's disease (AD). The associations between blood circulating choline and the pathological progression in both mild cognitive impairment (MCI) and AD remain unknown. Here, we examined these associations in a cohort of patients with MCI with presence of either sparse or high neuritic plaque density and Braak stage and a second cohort with either moderate AD (moderate to frequent neuritic plaques, Braak stage = IV) or severe AD (frequent neuritic plaques, Braak stage = VI), compared to age-matched controls. Metabolomic analysis was performed on serum from the AD cohort. We then assessed the effects of dietary choline deficiency (Ch-) in 3xTg-AD mice and choline supplementation (Ch+) in APP/PS1 mice, two rodent models of AD. The levels of circulating choline were reduced while pro-inflammatory cytokine TNFα was elevated in serum of both MCI sparse and high pathology cases. Reduced choline and elevated TNFα correlated with higher neuritic plaque density and Braak stage. In AD patients, we found reductions in choline, its derivative acetylcholine (ACh), and elevated TNFα. Choline and ACh levels were negatively correlated with neuritic plaque load, Braak stage, and TNFα, but positively correlated with MMSE, and brain weight. Metabolites L-Valine, 4-Hydroxyphenylpyruvic, Methylmalonic, and Ferulic acids were significantly associated with circuiting choline levels. In 3xTg-AD mice, the Ch- diet increased amyloid-β levels and tau phosphorylation in cortical tissue, and TNFα in both blood and cortical tissue, paralleling the severe human-AD profile. Conversely, the Ch+ diet increased choline and ACh while reducing amyloid-β and TNFα levels in brains of APP/PS1 mice. Collectively, low circulating choline is associated with AD-neuropathological progression, illustrating the importance of adequate dietary choline intake to offset disease.

Predictive value of serum proteomic biomarkers for noise-induced hearing loss

Early detection of noise-induced hearing loss (NIHL) in patients with long-term noise exposure is vital for improving public health and reducing social burden. However, at present, the diagnosis of NIHL mainly depends on audiometric testing, and the primary test is pure-tone audiometry. Moreover, testing requires professional operators and complex equipment; thus, NIHL is often diagnosed at a later disease stage. Using a liquid chromatography-tandem mass spectrometry (LC-MS/MS)-based proteomic approach, we identified 9 differentially expressed proteins (DEPs), namely, 6 upregulated serum proteins and 3 downregulated serum proteins, in samples from 50 ground crew personnel working at an air force station. Then, according to the results, we predicted that caldesmon (CALD1), myocilin (MYOC), zyxin (ZYX), creatine kinase M-type (CKM), insulin-like growth factor-binding protein 2 (IGFBP2), complement factor H-related protein 4 (CFHR4), prenylcysteine oxidase 1 (PCYOX1), heat shock cognate 71 kDa protein (HSPA8), and immunoglobulin lambda variable 3-21 (IGLV3-21) were associated with NIHL. We selected these DEPs as variables to perform logistic regression. Finally, a logistic regression model was constructed based on IGFBP2, ZYX, CKM, and CFHR4. The area under the curve was 0.894 (95% CI = 0.812 to 0.977). These findings suggested that IGFBP2, ZYX, CKM, and CFHR4 in serum are differentially expressed in NIHL patients and have the potential to be biomarkers for predicting the risk for NIHL. Further experiments in mice showed that ZYX and IGFBP2 in the cochlear were increased after noise exposure. ZYX and IGFBP2 may be involved in the occurrence and development of NIHL.

Integrative metabolomics science in Alzheimer's disease: Relevance and future perspectives

Alzheimer's disease (AD) is determined by various pathophysiological mechanisms starting 10-25 years before the onset of clinical symptoms. As multiple functionally interconnected molecular/cellular pathways appear disrupted in AD, the exploitation of high-throughput unbiased omics sciences is critical to elucidating the precise pathogenesis of AD. Among different omics, metabolomics is a fast-growing discipline allowing for the simultaneous detection and quantification of hundreds/thousands of perturbed metabolites in tissues or biofluids, reproducing the fluctuations of multiple networks affected by a disease. Here, we seek to critically depict the main metabolomics methodologies with the aim of identifying new potential AD biomarkers and further elucidating AD pathophysiological mechanisms. From a systems biology perspective, as metabolic alterations can occur before the development of clinical signs, metabolomics - coupled with existing accessible biomarkers used for AD screening and diagnosis - can support early disease diagnosis and help develop individualized treatment plans. Presently, the majority of metabolomic analyses emphasized that lipid metabolism is the most consistently altered pathway in AD pathogenesis. The possibility that metabolomics may reveal crucial steps in AD pathogenesis is undermined by the difficulty in discriminating between the causal or epiphenomenal or compensatory nature of metabolic findings.

Untangling Alzheimer's disease with spatial multi-omics: a brief review

Alzheimer's disease (AD) is the most common form of neurological dementia, specified by extracellular β-amyloid plaque deposition, neurofibrillary tangles, and cognitive impairment. AD-associated pathologies like cerebral amyloid angiopathy (CAA) are also affiliated with cognitive impairment and have overlapping molecular drivers, including amyloid buildup. Discerning the complexity of these neurological disorders remains a significant challenge, and the spatiomolecular relationships between pathogenic features of AD and AD-associated pathologies remain poorly understood. This review highlights recent developments in spatial omics, including profiling and molecular imaging methods, and how they are applied to AD. These emerging technologies aim to characterize the relationship between how specific cell types and tissue features are organized in combination with mapping molecular distributions to provide a systems biology view of the tissue microenvironment around these neuropathologies. As spatial omics methods achieve greater resolution and improved molecular coverage, they are enabling deeper characterization of the molecular drivers of AD, leading to new possibilities for the prediction, diagnosis, and mitigation of this debilitating disease.

Low circulating choline, a modifiable dietary factor, is associated with the pathological progression and metabolome dysfunction in Alzheimer's disease

Most Americans (∼90%) are deficient in dietary choline, an essential nutrient. Associations between circulating choline and pathological progression in Alzheimer's disease (AD) remain unknown. Here, we examined these associations and performed a metabolomic analysis in blood serum from severe AD, moderate AD, and healthy controls. Additionally, to gain mechanistic insight, we assessed the effects of dietary choline deficiency (Ch-) in 3xTg-AD mice and choline supplementation (Ch+) in APP/PS1 mice. In humans, we found AD-associated reductions in choline, it's derivative acetylcholine (ACh), and elevated pro-inflammatory cytokine TNFα. Choline and ACh were negatively correlated with Plaque density, Braak stage, and TNFα, but positively correlated with MMSE and brain weight. Metabolites L-Valine, 4-Hydroxyphenylpyruvic, Methylmalonic, and Ferulic acids were associated with choline levels. In mice, Ch-paralleled AD severe, but Ch+ was protective. In conclusion, low circulating choline is associated with AD-neuropathological progression, illustrating the importance of dietary choline consumption to offset disease.