Alzheimer's disease in the omics era

Single-cell and spatial omics: exploring hypothalamic heterogeneity

Elucidating the complex dynamic cellular organization in the hypothalamus is critical for understanding its role in coordinating fundamental body functions. Over the past decade, single-cell and spatial omics technologies have significantly evolved, overcoming initial technical challenges in capturing and analyzing individual cells. These high-throughput omics technologies now offer a remarkable opportunity to comprehend the complex spatiotemporal patterns of transcriptional diversity and cell-type characteristics across the entire hypothalamus. Current single-cell and single-nucleus RNA sequencing methods comprehensively quantify gene expression by exploring distinct phenotypes across various subregions of the hypothalamus. However, single-cell/single-nucleus RNA sequencing requires isolating the cell/nuclei from the tissue, potentially resulting in the loss of spatial information concerning neuronal networks. Spatial transcriptomics methods, by bypassing the cell dissociation, can elucidate the intricate spatial organization of neural networks through their imaging and sequencing technologies. In this review, we highlight the applicative value of single-cell and spatial transcriptomics in exploring the complex molecular-genetic diversity of hypothalamic cell types, driven by recent high-throughput achievements.

Exploration of plasma biomarkers for Alzheimer's disease by targeted lipid metabolomics based on nuclear magnetic resonance (NMR) spectroscopy

Alzheimer's disease (AD) is the most common cause of dementia, but the disease lacks convenient and cost-effective alternative biomarkers currently. We utilized targeted lipid metabolomics based on nuclear magnetic resonance (NMR) spectroscopy to identify plasma biomarkers in AD patients. Our study was a cross-sectional study that enrolled 58 AD patients and 40 matched health controls (HCs). Firstly, we identified plasma lipid metabolites that were significantly different between the two groups based on P < 0.05 and variable importance in the projection (VIP) > 1. Then we examined the correlation between the lipid metabolites and cognitive function using partial correlation analysis and assessed the diagnostic ability of the lipid metabolites using receiver operating characteristic (ROC) curves. Seventeen lipoproteins showed significant differences between AD patients and HCs among 114 lipid metabolites. All 17 lipoproteins were subtypes of low-density lipoprotein (LDL). Among them, LDL-3 particle number, LDL-3 apolipoprotein-B, LDL-3 phospholipids, LDL free cholesterol and LDL phospholipids were significantly correlated with cognitive function. The ROC curves showed that LDL-2 triglycerides (TG) and LDL-3 TG could significantly distinguish AD patients from HCs, with the area under the curve (AUC) above 0.7. In addition, we explored a strategy of combined diagnosis that significantly improved the diagnostic efficacy for AD (AUC = 0.879). Our study provides insight into the lipoprotein alterations associated with AD and potential biomarkers for its diagnosis and cognitive function assessment.

The early diagnosis of Alzheimer's disease: Blood-based panel biomarker discovery by proteomics and metabolomics

Diagnosis and prediction of Alzheimer's disease (AD) are increasingly pressing in the early stage of the disease because the biomarker-targeted therapies may be most effective. Diagnosis of AD largely depends on the clinical symptoms of AD. Currently, cerebrospinal fluid biomarkers and neuroimaging techniques are considered for clinical detection and diagnosis. However, these clinical diagnosis results could provide indications of the middle and/or late stages of AD rather than the early stage, and another limitation is the complexity attached to limited access, cost, and perceived invasiveness. Therefore, the prediction of AD still poses immense challenges, and the development of novel biomarkers is needed for early diagnosis and urgent intervention before the onset of obvious phenotypes of AD. Blood-based biomarkers may enable earlier diagnose and aid detection and prognosis for AD because various substances in the blood are vulnerable to AD pathophysiology. The application of a systematic biological paradigm based on high-throughput techniques has demonstrated accurate alterations of molecular levels during AD onset processes, such as protein levels and metabolite levels, which may facilitate the identification of AD at an early stage. Notably, proteomics and metabolomics have been used to identify candidate biomarkers in blood for AD diagnosis. This review summarizes data on potential blood-based biomarkers identified by proteomics and metabolomics that are closest to clinical implementation and discusses the current challenges and the future work of blood-based candidates to achieve the aim of early screening for AD. We also provide an overview of early diagnosis, drug target discovery and even promising therapeutic approaches for AD.

ApoE: The Non-Protagonist Actor in Neurological Diseases

BACKGROUND

Apolipoprotein E (APOE = gene, ApoE = protein) is a glycoprotein involved in the biological process of lipid transportation and metabolism, contributing to lipid homeostasis. APOE has been extensively studied for its correlation with neurodegenerative diseases, in particular Alzheimer's disease (AD), where the possession of the epsilon 4 (E4) allele is established as a risk factor for developing AD in non-familiar sporadic forms. Recently, evidence suggests a broad involvement of E4 also in other neurological conditions, where it has been shown to be a predictive marker for worse clinical outcomes in Parkinson's disease (PD), brain trauma, and disturbances of consciousness. The mechanisms underlying these associations are complex and involve amyloid-β (Aβ) peptide accumulation and neuroinflammation, although many others have yet to be identified.

OBJECTIVES

The aim of this review is to overview the current knowledge on ApoE as a non-protagonist actor in processes underlying neurodegenerative diseases and its clinical significance in AD, PD, acquired brain trauma, and Disorders of Consciousness (DoC). Ethical implications of genetic testing for APOE variants and information disclosure will also be briefly discussed.

Analysis of Brain Protein Stability Changes in a Mouse Model of Alzheimer's Disease

The stability of proteins from rates of oxidation (SPROX), thermal proteome profiling (TPP), and limited proteolysis (LiP) techniques were used to profile the stability of ∼2500 proteins in hippocampus tissue cell lysates from 2- and 8-months-old wild-type (C57BL/6J; n = 7) and transgenic (5XFAD; n = 7) mice with five Alzheimer's disease (AD)-linked mutations. Approximately 200-500 protein hits with AD-related stability changes were detected by each technique at each age point. The hit overlap from technique to technique was low, and all of the techniques generated protein hits that were more numerous and largely different from those identified in protein expression level analyses, which were also performed here. The hit proteins identified by each technique were enriched in a number of the same pathways and biological processes, many with known connections to AD. The protein stability hits included 25 high-value conformation biomarkers with AD-related stability changes detected using at least 2 techniques at both age points. Also discovered were subunit- and age-specific AD-related stability changes in the proteasome, which had reduced function at both age points. The different folding stability profiles of the proteasome at the two age points are consistent with a different mechanism for proteasome dysfunction at the early and late stages of AD.

Biobanking, digital health and privacy: the choices of 1410 volunteers and neurological patients regarding limitations on use of data and biological samples, return of results and sharing

BACKGROUND

The growing diffusion of artificial intelligence, data science and digital health has highlighted the role of collection of data and biological samples, thus raising legal and ethical concerns regarding its use and dissemination. Further, the expansion of biobanking, from the basic collection of frozen specimens to the virtual biobanks of specimens and associated data that exist today, has given a revolutionary potential on healthcare systems, particularly in the field of neurological diseases, due to the inaccessibility of central nervous system and the need of non-invasive investigation approaches. Informed Consent (IC) is considered mandatory in all research studies and specimen collections, and must specifically take into account the ethical respect to the individuals to whom the used biological material and data belong.

METHODS

We evaluated the attitudes of patients with neurological diseases (NP) and healthy volunteers (HV) towards the donation of biological samples to a biobank for future research studies on neurological diseases, and limitations on the use of data, related to the requirements set by the General Data Protection Regulation (GDPR). The study involved a total of 1454 subjects, including 502 HVs and 952 NPs, recruited at Santa Lucia Foundation IRCCS, Rome, from 2020 to 2024.

RESULTS

We found that (i) almost all subjects agreed with the participation in biobanking (ii) and authorization to genetic studies (HV = 99.1%; NP = 98.3%); Regarding the return of results, (iii) we found a statistically significant difference between NP and HV, the latter preferring not to be informed of potential results (HV = 43%; NP = 11.3%; p < 0.0001); (iv) a small number limited the sharing inside European Union (EU) (HV = 4.6%; NP = 6.6%), whereas patients were more likely to refuse transfer outside EU (HV = 7.4%; NP = 10.7% p = 0.05); (v) nearly all patients agreed with the use of additional health data from EMR for research purposes (98.9%).

CONCLUSIONS

Consent for the donation of material for research purposes is crucial for biobanking and biomedical research studies that use biological material of human origin. Here, we have shown that choices regarding participation in a neurological biobank can be different between HVs and NPs, even if the benefit for research and scientific progress is recognized. NP have a strong interest in being informed of possible results but limit sharing of samples, highlighting a perception of greater individual or relative benefit, while HV prefer a wide dissemination and sharing of data but not to have the return of the results, favoring a possible benefit for society and knowledge. The results underline the need to carefully manage biological material and data collected in biobanks, in compliance with the GDPR and the specific requests of donors.

Neuropsychiatric symptoms in cognitive decline and Alzheimer's disease: biomarker discovery using plasma proteomics

BACKGROUND AND OBJECTIVES

Neuropsychiatric symptoms (NPS) are common in older people with cognitive impairment and Alzheimer's disease (AD). No biomarkers to detect the related pathology or predict the clinical evolution of NPS are available yet. This study aimed to identify plasma proteins that may serve as biomarkers for NPS and NPS-related clinical disease progression.

METHODS

A panel of 190 plasma proteins was quantified using Luminex xMAP in the Alzheimer's Disease Neuroimaging Initiative cohort. NPS and cognitive performance were assessed at baseline and after 1 and 2 years. Logistic regression, receiver operating characteristic analysis and cross-validation were used to address the relations of interest.

RESULTS

A total of 507 participants with mild cognitive impairment (n=396) or mild AD dementia (n=111) were considered. Selected plasma proteins improved the prediction of NPS (area under the curve (AUC) from 0.61 to 0.76, p<0.001) and future NPS (AUC from 0.63 to 0.80, p<0.001) when added to a reference model. Distinct protein panels were identified for single symptoms. Among the selected proteins, ANGT, CCL1 and IL3 were associated with NPS at all three time points while CCL1, serum glutamic oxaloacetic transaminase and complement factor H were also associated with cognitive decline. The associations were independent of the presence of cerebral AD pathology as assessed using cerebrospinal fluid biomarkers.

CONCLUSIONS

Plasma proteins are associated with NPS and improve prediction of future NPS.

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.

Understanding machine learning applications in dementia research and clinical practice: a review for biomedical scientists and clinicians

Several (inter)national longitudinal dementia observational datasets encompassing demographic information, neuroimaging, biomarkers, neuropsychological evaluations, and muti-omics data, have ushered in a new era of potential for integrating machine learning (ML) into dementia research and clinical practice. ML, with its proficiency in handling multi-modal and high-dimensional data, has emerged as an innovative technique to facilitate early diagnosis, differential diagnosis, and to predict onset and progression of mild cognitive impairment and dementia. In this review, we evaluate current and potential applications of ML, including its history in dementia research, how it compares to traditional statistics, the types of datasets it uses and the general workflow. Moreover, we identify the technical barriers and challenges of ML implementations in clinical practice. Overall, this review provides a comprehensive understanding of ML with non-technical explanations for broader accessibility to biomedical scientists and clinicians.

Artificial Intelligence in Point-of-Care Biosensing: Challenges and Opportunities

The integration of artificial intelligence (AI) into point-of-care (POC) biosensing has the potential to revolutionize diagnostic methodologies by offering rapid, accurate, and accessible health assessment directly at the patient level. This review paper explores the transformative impact of AI technologies on POC biosensing, emphasizing recent computational advancements, ongoing challenges, and future prospects in the field. We provide an overview of core biosensing technologies and their use at the POC, highlighting ongoing issues and challenges that may be solved with AI. We follow with an overview of AI methodologies that can be applied to biosensing, including machine learning algorithms, neural networks, and data processing frameworks that facilitate real-time analytical decision-making. We explore the applications of AI at each stage of the biosensor development process, highlighting the diverse opportunities beyond simple data analysis procedures. We include a thorough analysis of outstanding challenges in the field of AI-assisted biosensing, focusing on the technical and ethical challenges regarding the widespread adoption of these technologies, such as data security, algorithmic bias, and regulatory compliance. Through this review, we aim to emphasize the role of AI in advancing POC biosensing and inform researchers, clinicians, and policymakers about the potential of these technologies in reshaping global healthcare landscapes.

Post-stroke cognitive impairment: exploring molecular mechanisms and omics biomarkers for early identification and intervention

Post-stroke cognitive impairment (PSCI) is a major stroke consequence that has a severe impact on patients' quality of life and survival rate. For this reason, it is especially crucial to identify and intervene early in high-risk groups during the acute phase of stroke. Currently, there are no reliable and efficient techniques for the early diagnosis, appropriate evaluation, or prognostication of PSCI. Instead, plenty of biomarkers in stroke patients have progressively been linked to cognitive impairment in recent years. High-throughput omics techniques that generate large amounts of data and process it to a high quality have been used to screen and identify biomarkers of PSCI in order to investigate the molecular mechanisms of the disease. These techniques include metabolomics, which explores dynamic changes in the organism, gut microbiomics, which studies host-microbe interactions, genomics, which elucidates deeper disease mechanisms, transcriptomics and proteomics, which describe gene expression and regulation. We looked through electronic databases like PubMed, the Cochrane Library, Embase, Web of Science, and common databases for each omics to find biomarkers that might be connected to the pathophysiology of PSCI. As all, we found 34 studies: 14 in the field of metabolomics, 5 in the field of gut microbiomics, 5 in the field of genomics, 4 in the field of transcriptomics, and 7 in the field of proteomics. We discovered that neuroinflammation, oxidative stress, and atherosclerosis may be the primary causes of PSCI development, and that metabolomics may play a role in the molecular mechanisms of PSCI. In this study, we summarized the existing issues across omics technologies and discuss the latest discoveries of PSCI biomarkers in the context of omics, with the goal of investigating the molecular causes of post-stroke cognitive impairment. We also discuss the potential therapeutic utility of omics platforms for PSCI mechanisms, diagnosis, and intervention in order to promote the area's advancement towards precision PSCI treatment.

Human brain glycoform coregulation network and glycan modification alterations in Alzheimer's disease

Despite the importance of protein glycosylation to brain health, current knowledge of glycosylated proteoforms or glycoforms in human brain and their alterations in Alzheimer's disease (AD) is limited. Here, we report a proteome-wide glycoform profiling study of human AD and control brains using intact glycopeptide-based quantitative glycoproteomics coupled with systems biology. Our study identified more than 10,000 human brain N-glycoforms from nearly 1200 glycoproteins and uncovered disease signatures of altered glycoforms and glycan modifications, including reduced sialylation and N-glycan branching and elongation as well as elevated mannosylation and N-glycan truncation in AD. Network analyses revealed a higher-order organization of brain glycoproteome into networks of coregulated glycoforms and glycans and discovered glycoform and glycan modules associated with AD clinical phenotype, amyloid-β accumulation, and tau pathology. Our findings provide valuable insights into disease pathogenesis and a rich resource of glycoform and glycan changes in AD and pave the way forward for developing glycosylation-based therapies and biomarkers for AD.

Using blood transcriptome analysis for Alzheimer's disease diagnosis and patient stratification

INTRODUCTION

Blood protein biomarkers demonstrate potential for Alzheimer's disease (AD) diagnosis. Limited studies examine the molecular changes in AD blood cells.

METHODS

Bulk RNA-sequencing of blood cells was performed on AD patients of Chinese descent (n = 214 and 26 in the discovery and validation cohorts, respectively) with normal controls (n = 208 and 38 in the discovery and validation cohorts, respectively). Weighted gene co-expression network analysis (WGCNA) and deconvolution analysis identified AD-associated gene modules and blood cell types. Regression and unsupervised clustering analysis identified AD-associated genes, gene modules, cell types, and established AD classification models.

RESULTS

WGCNA on differentially expressed genes revealed 15 gene modules, with 6 accurately classifying AD (areas under the receiver operating characteristics curve [auROCs] > 0.90). These modules stratified AD patients into subgroups with distinct disease states. Cell-type deconvolution analysis identified specific blood cell types potentially associated with AD pathogenesis.

DISCUSSION

This study highlights the potential of blood transcriptome for AD diagnosis, patient stratification, and mechanistic studies.

HIGHLIGHTS

We comprehensively analyze the blood transcriptomes of a well-characterized Alzheimer's disease cohort to identify genes, gene modules, pathways, and specific blood cells associated with the disease. Blood transcriptome analysis accurately classifies and stratifies patients with Alzheimer's disease, with some gene modules achieving classification accuracy comparable to that of the plasma ATN biomarkers. Immune-associated pathways and immune cells, such as neutrophils, have potential roles in the pathogenesis and progression of Alzheimer's disease.

Brain high-throughput multi-omics data reveal molecular heterogeneity in Alzheimer's disease

Unbiased data-driven omic approaches are revealing the molecular heterogeneity of Alzheimer disease. Here, we used machine learning approaches to integrate high-throughput transcriptomic, proteomic, metabolomic, and lipidomic profiles with clinical and neuropathological data from multiple human AD cohorts. We discovered 4 unique multimodal molecular profiles, one of them showing signs of poor cognitive function, a faster pace of disease progression, shorter survival with the disease, severe neurodegeneration and astrogliosis, and reduced levels of metabolomic profiles. We found this molecular profile to be present in multiple affected cortical regions associated with higher Braak tau scores and significant dysregulation of synapse-related genes, endocytosis, phagosome, and mTOR signaling pathways altered in AD early and late stages. AD cross-omics data integration with transcriptomic data from an SNCA mouse model revealed an overlapping signature. Furthermore, we leveraged single-nuclei RNA-seq data to identify distinct cell-types that most likely mediate molecular profiles. Lastly, we identified that the multimodal clusters uncovered cerebrospinal fluid biomarkers poised to monitor AD progression and possibly cognition. Our cross-omics analyses provide novel critical molecular insights into AD.

Translational bioinformatics and data science for biomarker discovery in mental health: an analytical review

Translational bioinformatics and data science play a crucial role in biomarker discovery as it enables translational research and helps to bridge the gap between the bench research and the bedside clinical applications. Thanks to newer and faster molecular profiling technologies and reducing costs, there are many opportunities for researchers to explore the molecular and physiological mechanisms of diseases. Biomarker discovery enables researchers to better characterize patients, enables early detection and intervention/prevention and predicts treatment responses. Due to increasing prevalence and rising treatment costs, mental health (MH) disorders have become an important venue for biomarker discovery with the goal of improved patient diagnostics, treatment and care. Exploration of underlying biological mechanisms is the key to the understanding of pathogenesis and pathophysiology of MH disorders. In an effort to better understand the underlying mechanisms of MH disorders, we reviewed the major accomplishments in the MH space from a bioinformatics and data science perspective, summarized existing knowledge derived from molecular and cellular data and described challenges and areas of opportunities in this space.

Explainable variational autoencoder (E-VAE) model using genome-wide SNPs to predict dementia

OBJECTIVE

Alzheimer's disease (AD) and AD related dementias (ADRD) are complex multifactorial neurodegenerative diseases. The associations between genetic variants obtained from genome wide association studies (GWAS) are the most widely available and well documented variants associated with ADRD. Application of deep learning methods to analyze large scale GWAS data may be a powerful approach to elucidate the biological mechanisms in ADRD compared to penalized regression models that may lead to over-fitting.

METHODS

We developed a deep learning frame work explainable variational autoencoder (E-VAE) classifier model using genotype (GWAS SNPs = 5474) data from 2714 study participants in the Health and Retirement Study (HRS) to classify ADRD. We validated the generalizability of this model among 234 participants in the Religious Orders Study and Memory and Aging Project (ROSMAP). Utilizing a linear decoder approach we have extracted the weights associated with latent features for biological interpretation.

RESULTS

We obtained a predictive accuracy of 0.71 (95 % CI [0.59, 0.84]) with an AUC of 0.69 in the HRS test dataset and got an accuracy of 0.62 (95 % CI [0.56, 0.68]) with an AUC of 0.63 in the ROSMAP dataset.

CONCLUSION

This is the first study showing the generalizability of a deep learning prediction model for dementia using genetic variants in an independent cohort. The latent features identified using E-VAE can help us understand the biology of AD/ ADRD and better characterize disease status.

Human brain glycoform co-regulation network and glycan modification alterations in Alzheimer's disease

Despite the importance of protein glycosylation to brain health, current knowledge of glycosylated proteoforms or glycoforms in human brain and their alterations in Alzheimer's disease (AD) is limited. Here, we present a new paradigm of proteome-wide glycoform profiling study of human AD and control brains using intact glycopeptide-based quantitative glycoproteomics coupled with systems biology. Our study identified over 10,000 human brain N-glycoforms from nearly 1200 glycoproteins and uncovered disease signatures of altered glycoforms and glycan modifications, including reduced sialylation and N-glycan branching as well as elevated mannosylation and N-glycan truncation in AD. Network analyses revealed a higher-order organization of brain glycoproteome into networks of co-regulated glycoforms and glycans and discovered glycoform and glycan modules associated with AD clinical phenotype, amyloid-β accumulation, and tau pathology. Our findings provide novel insights and a rich resource of glycoform and glycan changes in AD and pave the way forward for developing glycosylation-based therapies and biomarkers for AD.

The increment of annexin V-positive microvesicles versus annexin V-negative microvesicles in CSF of an animal model of Alzheimer's disease

OBJECTIVE

Extracellular microvesicles (MVs) as a specific signaling molecule have received much attention in nervous system studies. Alterations in the tissue redox status in pathological conditions, such as Alzheimer's disease (AD), facilitate the translocation of cell membrane phosphatidylserine to the outer leaflet and lead to the MVs shedding. Annexin V binds with high affinity to phosphatidylserine. Some arguments exist about whether Annexin V-negative MVs should be considered in pathological conditions.

MATERIAL AND METHOD

We compared the kinetics of two phenotypes of Annexin V-positive and Annexin V-negative MVs in the cerebrospinal fluid (CSF) of amyloid-β (Aβ)-treated male Wistar rats with flow cytometry technique. The Aβ was injected bilaterally into the cerebral ventricles. Thioflavin T staining was used to confirm the presence of hippocampal Aβ fibrils two weeks post-Aβ injection. Levels of hippocampal interleukin-1β were assessed as an inflammatory index. The CSF malondialdehyde (MDA) concentration was determined. The cognitive impairment and anxiety behaviors were assessed by object recognition and elevated plus maze tests, respectively.

RESULTS

Elevation of MDA levels and a significant rise in the scoring of IL-1β staining were found in the Aβ group. The Aβ induced anxiogenic behavior, impaired novel object recognition memory, and increased the CSF levels of the total number of MVs. The number of Annexin V-positive MVs was significantly higher than Annexin V-negative MVs in all groups.

CONCLUSION

Data showed that Annexin V-positive MVs potentially have a significant contribution to the pathophysiology of the Aβ-induced cognitive impairment. To catch a clear image of microvesicle production in pathological conditions, both phenotypes of Annexin V-positive and Annexin V-negative MVs should be analyzed and reported.

Application of Multiple Omics to Understand Postoperative Delirium Pathophysiology in Humans

Delirium, an acute change in cognition, is common, morbid, and costly, particularly among hospitalized older adults. Despite growing knowledge of its epidemiology, far less is known about delirium pathophysiology. Initial work understanding delirium pathogenesis has focused on assaying single or a limited subset of molecules or genetic loci. Recent technological advances at the forefront of biomarker and drug target discovery have facilitated application of multiple "omics" approaches aimed to provide a more complete understanding of complex disease processes such as delirium. At its basic level, "omics" involves comparison of genes (genomics, epigenomics), transcripts (transcriptomics), proteins (proteomics), metabolites (metabolomics), or lipids (lipidomics) in biological fluids or tissues obtained from patients who have a certain condition (i.e., delirium) and those who do not. Multi-omics analyses of these various types of molecules combined with machine learning and systems biology enable the discovery of biomarkers, biological pathways, and predictors of delirium, thus elucidating its pathophysiology. This review provides an overview of the most recent omics techniques, their current impact on identifying delirium biomarkers, and future potential in enhancing our understanding of delirium pathogenesis. We summarize challenges in identification of specific biomarkers of delirium and, more importantly, in discovering the mechanisms underlying delirium pathophysiology. Based on mounting evidence, we highlight a heightened inflammatory response as one common pathway in delirium risk and progression, and we suggest other promising biological mechanisms that have recently emerged. Advanced multiple omics approaches coupled with bioinformatics methodologies have great promise to yield important discoveries that will advance delirium research.

Analysis of differentially expressed proteins after EHP-infection and characterization of caspase 3 protein in the whiteleg shrimp (Litopenaeus vannamei)

Whiteleg shrimp (Litopenaeus vannamei) is the most important species of shrimp farmed worldwide in terms of its economic value. Enterocytozoon hepatopenaei (EHP) infects the hepatopancreas, resulting in the hepatopancreatic microsporidiosis (HPM) of the host, which causes slow growth of the shrimp and poses a threat to the farming industry. In this study, differentially expressed proteins (DEPs) between EHP-infected and uninfected shrimp were investigated through proteomics sequencing. A total of 9908 peptides and 2092 proteins were identified. A total of 69 DEPs were identified in the hepatopancreas (HP), of which, 28 were upregulated and 41 were downregulated. Our results showed that the differences among the level of multiple proteins involved in the apoptosis were significant after the EHP infection, which indicated that the apoptosis pathway was activated in whiteleg shrimp. In addition, expression leve of caspase 3 gene were identified related to the EHP infection. Furthermore, predictions of spatial structure, analysis of phylogeny and chromosome-level linearity of the caspase 3 protein were performed as well. In conclusion, a relatively complete proteomic data set of hepatopancreas tissues in whiteleg shrimp were established in this study. Findings about genes involved in the apoptosis here will provide a further understanding of the molecular mechanism of EHP infection in the internal immunity of whiteleg shrimp.

Combined metabolic activators improve cognitive functions in Alzheimer's disease patients: a randomised, double-blinded, placebo-controlled phase-II trial

BACKGROUND

Alzheimer's disease (AD) is associated with metabolic abnormalities linked to critical elements of neurodegeneration. We recently administered combined metabolic activators (CMA) to the AD rat model and observed that CMA improves the AD-associated histological parameters in the animals. CMA promotes mitochondrial fatty acid uptake from the cytosol, facilitates fatty acid oxidation in the mitochondria, and alleviates oxidative stress.

METHODS

Here, we designed a randomised, double-blinded, placebo-controlled phase-II clinical trial and studied the effect of CMA administration on the global metabolism of AD patients. One-dose CMA included 12.35 g L-serine (61.75%), 1 g nicotinamide riboside (5%), 2.55 g N-acetyl-L-cysteine (12.75%), and 3.73 g L-carnitine tartrate (18.65%). AD patients received one dose of CMA or placebo daily during the first 28 days and twice daily between day 28 and day 84. The primary endpoint was the difference in the cognitive function and daily living activity scores between the placebo and the treatment arms. The secondary aim of this study was to evaluate the safety and tolerability of CMA. A comprehensive plasma metabolome and proteome analysis was also performed to evaluate the efficacy of the CMA in AD patients.

RESULTS

We showed a significant decrease of AD Assessment Scale-cognitive subscale (ADAS-Cog) score on day 84 vs day 0 (P = 0.00001, 29% improvement) in the CMA group. Moreover, there was a significant decline (P = 0.0073) in ADAS-Cog scores (improvement of cognitive functions) in the CMA compared to the placebo group in patients with higher ADAS-Cog scores. Improved cognitive functions in AD patients were supported by the relevant alterations in the hippocampal volumes and cortical thickness based on imaging analysis. Moreover, the plasma levels of proteins and metabolites associated with NAD + and glutathione metabolism were significantly improved after CMA treatment.

CONCLUSION

Our results indicate that treatment of AD patients with CMA can lead to enhanced cognitive functions and improved clinical parameters associated with phenomics, metabolomics, proteomics and imaging analysis. Trial registration ClinicalTrials.gov NCT04044131 Registered 17 July 2019, https://clinicaltrials.gov/ct2/show/NCT04044131.

Molecular insights into sex-specific metabolic alterations in Alzheimer's mouse brain using multi-omics approach

BACKGROUND

Alzheimer's disease (AD) is a progressive neurodegenerative disorder that is characterized by altered cellular metabolism in the brain. Several of these alterations have been found to be exacerbated in females, known to be disproportionately affected by AD. We aimed to unravel metabolic alterations in AD at the metabolic pathway level and evaluate whether they are sex-specific through integrative metabolomic, lipidomic, and proteomic analysis of mouse brain tissue.

METHODS

We analyzed male and female triple-transgenic mouse whole brain tissue by untargeted mass spectrometry-based methods to obtain a molecular signature consisting of polar metabolite, complex lipid, and protein data. These data were analyzed using multi-omics factor analysis. Pathway-level alterations were identified through joint pathway enrichment analysis or by separately evaluating lipid ontology and known proteins related to lipid metabolism.

RESULTS

Our analysis revealed significant AD-associated and in part sex-specific alterations across the molecular signature. Sex-dependent alterations were identified in GABA synthesis, arginine biosynthesis, and in alanine, aspartate, and glutamate metabolism. AD-associated alterations involving lipids were also found in the fatty acid elongation pathway and lysophospholipid metabolism, with a significant sex-specific effect for the latter.

CONCLUSIONS

Through multi-omics analysis, we report AD-associated and sex-specific metabolic alterations in the AD brain involving lysophospholipid and amino acid metabolism. These findings contribute to the characterization of the AD phenotype at the molecular level while considering the effect of sex, an overlooked yet determinant metabolic variable.

Multiomics and artificial intelligence enabled peripheral blood-based prediction of amnestic mild cognitive impairment

BACKGROUND

Since dementia is preventable with early interventions, biomarkers that assist in diagnosing early stages of dementia, such as mild cognitive impairment (MCI), are urgently needed.

METHODS

Multiomics analysis of amnestic MCI (aMCI) peripheral blood (n = 25) was performed covering the transcriptome, microRNA, proteome, and metabolome. Validation analysis for microRNAs was conducted in an independent cohort (n = 12). Artificial intelligence was used to identify the most important features for predicting aMCI.

FINDINGS

We found that hsa-miR-4455 is the best biomarker in all omics analyses. The diagnostic index taking a ratio of hsa-miR-4455 to hsa-let-7b-3p predicted aMCI patients against healthy subjects with 97% overall accuracy. An integrated review of multiomics data suggested that a subset of T cells and the GCN (general control nonderepressible) pathway are associated with aMCI.

INTERPRETATION

The multiomics approach has enabled aMCI biomarkers with high specificity and illuminated the accompanying changes in peripheral blood. Future large-scale studies are necessary to validate candidate biomarkers for clinical use.

The promise of multi-omics approaches to discover biological alterations with clinical relevance in Alzheimer's disease

Beyond the core features of Alzheimer's disease (AD) pathology, i.e. amyloid pathology, tau-related neurodegeneration and microglia response, multiple other molecular alterations and pathway dysregulations have been observed in AD. Their inter-individual variations, complex interactions and relevance for clinical manifestation and disease progression remain poorly understood, however. Heterogeneity at both pathophysiological and clinical levels complicates diagnosis, prognosis, treatment and drug design and testing. High-throughput "omics" comprise unbiased and untargeted data-driven methods which allow the exploration of a wide spectrum of disease-related changes at different endophenotype levels without focussing a priori on specific molecular pathways or molecules. Crucially, new methodological and statistical advances now allow for the integrative analysis of data resulting from multiple and different omics methods. These multi-omics approaches offer the unique advantage of providing a more comprehensive characterisation of the AD endophenotype and to capture molecular signatures and interactions spanning various biological levels. These new insights can then help decipher disease mechanisms more deeply. In this review, we describe the different multi-omics tools and approaches currently available and how they have been applied in AD research so far. We discuss how multi-omics can be used to explore molecular alterations related to core features of the AD pathologies and how they interact with comorbid pathological alterations. We further discuss whether the identified pathophysiological changes are relevant for the clinical manifestation of AD, in terms of both cognitive impairment and neuropsychiatric symptoms, and for clinical disease progression over time. Finally, we address the opportunities for multi-omics approaches to help discover novel biomarkers for diagnosis and monitoring of relevant pathophysiological processes, along with personalised intervention strategies in AD.

An integrative machine-learning meta-analysis of high-throughput omics data identifies age-specific hallmarks of Alzheimer's disease

Alzheimer's disease (AD) is an incredibly complex and presently incurable age-related brain disorder. To better understand this debilitating disease, we collated and performed a meta-analysis on publicly available RNA-Seq, microarray, proteomics, and microRNA samples derived from AD patients and non-AD controls. 4089 samples originating from brain tissues and blood remained after applying quality filters. Since disease progression in AD correlates with age, we stratified this large dataset into three different age groups: < 75 years, 75-84 years, and ≥ 85 years. The RNA-Seq, microarray, and proteomics datasets were then combined into different integrated datasets. Ensemble machine learning was employed to identify genes and proteins that can accurately classify samples as either AD or control. These predictive inputs were then subjected to network-based enrichment analyses. The ability of genes/proteins associated with different pathways in the Molecular Signatures Database to diagnose AD was also tested. We separately identified microRNAs that can be used to make an AD diagnosis and subjected the predicted gene targets of the most predictive microRNAs to an enrichment analysis. The following key themes emerged from our machine learning and bioinformatics analyses: cell death, cellular senescence, energy metabolism, genomic integrity, glia, immune system, metal ion homeostasis, oxidative stress, proteostasis, and synaptic function. Many of the results demonstrated unique age-specificity. For example, terms highlighting cellular senescence only emerged in the earliest and intermediate age ranges while the majority of results relevant to cell death appeared in the youngest patients. Existing literature corroborates the importance of these hallmarks in AD.

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

The metabolomic and proteomic basis of mild cognitive impairment (MCI) and Alzheimer's disease (AD) is poorly understood, and the relationships between systemic abnormalities in metabolism and AD/MCI pathogenesis is unclear. This study compared the metabolomic and proteomic signature of plasma from cognitively normal (CN) and dementia patients diagnosed with MCI or AD, to identify specific cellular pathways and new biomarkers altered with the progression of the disease. We analysed 80 plasma samples from individuals with MCI or AD, as well as age- and gender-matched CN individuals, by utilising mass spectrometry methods and data analyses that included combined pathway analysis and model predictions. Several proteins clearly identified AD from the MCI and CN groups and included plasma actins, mannan-binding lectin serine protease 1, serum amyloid A2, fibronectin and extracellular matrix protein 1 and Keratin 9. The integrated pathway analysis showed various metabolic pathways were affected in AD, such as the arginine, alanine, aspartate, glutamate and pyruvate metabolism pathways. Therefore, our multi-omics approach identified novel plasma biomarkers for the MCI and AD groups, identified changes in metabolic processes, and may form the basis of a biomarker panel for stratifying dementia participants in future clinical trials.

Dietary nitrate and brain health. Too much ado about nothing or a solution for dementia prevention?

Dementia is a significant public health priority with approximately 55 million cases worldwide, and this number is predicted to quadruple by 2050. Adherence to a healthy diet and achieving optimal nutritional status are vital strategies to improve brain health. The importance of this area of research has been consolidated into the new term ‘nutritional psychiatry’. Dietary nitrate, closely associated with the intake of fruits and vegetables, is a compound that is increased in dietary patterns such as the Mediterranean and MIND diets and has protective effects on cognition and brain health. Nitrate is characterised by a complex metabolism and is the precursor of the nitrate–nitrite–nitric oxide (NO) pathway contributing to systemic NO generation. A higher intake of dietary nitrate has been linked to protective effects on vascular outcomes including blood pressure and endothelial function. However, the current evidence supporting the protective effects of dietary nitrate on brain health is less convincing. This article aims to provide a critical appraisal of the current evidence for dietary nitrate supplementation for improving brain health and provide suggestions for future research.

Artificial Intelligence Predictor for Alzheimer’s Disease Trained on Blood Transcriptome: The Role of Oxidative Stress

Alzheimer’s disease (AD) is an incurable neurodegenerative disease diagnosed by clinicians through healthcare records and neuroimaging techniques. These methods lack sensitivity and specificity, so new antemortem non-invasive strategies to diagnose AD are needed. Herein, we designed a machine learning predictor based on transcriptomic data obtained from the blood of AD patients and individuals without dementia (non-AD) through an 8 × 60 K microarray. The dataset was used to train different models with different hyperparameters. The support vector machines method allowed us to reach a Receiver Operating Characteristic score of 93% and an accuracy of 89%. High score levels were also achieved by the neural network and logistic regression methods. Furthermore, the Gene Ontology enrichment analysis of the features selected to train the model along with the genes differentially expressed between the non-AD and AD transcriptomic profiles shows the “mitochondrial translation” biological process to be the most interesting. In addition, inspection of the KEGG pathways suggests that the accumulation of β-amyloid triggers electron transport chain impairment, enhancement of reactive oxygen species and endoplasmic reticulum stress. Taken together, all these elements suggest that the oxidative stress induced by β-amyloid is a key feature trained by the model for the prediction of AD with high accuracy.

Multi-Omics Integrative Approach of Extracellular Vesicles: A Future Challenging Milestone

In the era of multi-omic sciences, dogma on singular cause-effect in physio-pathological processes is overcome and system biology approaches have been providing new perspectives to see through. In this context, extracellular vesicles (EVs) are offering a new level of complexity, given their role in cellular communication and their activity as mediators of specific signals to target cells or tissues. Indeed, their heterogeneity in terms of content, function, origin and potentiality contribute to the cross-interaction of almost every molecular process occurring in a complex system. Such features make EVs proper biological systems being, therefore, optimal targets of omic sciences. Currently, most studies focus on dissecting EVs content in order to either characterize it or to explore its role in various pathogenic processes at transcriptomic, proteomic, metabolomic, lipidomic and genomic levels. Despite valuable results being provided by individual omic studies, the categorization of EVs biological data might represent a limit to be overcome. For this reason, a multi-omic integrative approach might contribute to explore EVs function, their tissue-specific origin and their potentiality. This review summarizes the state-of-the-art of EVs omic studies, addressing recent research on the integration of EVs multi-level biological data and challenging developments in EVs origin.

MicroRNA-4722-5p and microRNA-615-3p serve as potential biomarkers for Alzheimer's disease

The aim of the present study was to investigate the expression levels of microRNA(miR)-4722-5p and miR-615-3p in Alzheimer's disease (AD) and their diagnostic value. Blood samples were collected from 33 patients with AD and 33 healthy controls, and an β-amyloid (Aβ)25-35-induced PC12 cell model was also established. The relative mRNA expression levels of miR-4722-5p and miR-615-3p were detected using reverse transcription-quantitative PCR. The correlations between the mRNA expression levels of the two miRNAs and the mini-mental state examination (MMSE) scores were analyzed, and the receiver operating characteristic curve was used to assess the diagnostic value of miR-4722-5p and miR-615-3p in AD. Functional enrichment analysis of the miRNA target genes was performed using The Database for Annotation, Visualization and Integrated Discovery database and the R language analysis package. The mRNA expression levels of miR-4722-5p and miR-615-3p were increased in patients with AD and the Aβ25-35-induced PC12 cell model. The mRNA expression levels of miR-4722-5p and miR-615-3p were negatively correlated with MMSE scores, and the combination of the two miRNAs for AD had an improved diagnostic value than that of each miRNA alone. The results of Gene Ontology (GO) enrichment analysis showed that the target genes of miR-4722-5p were found in the cytoplasm and cytosol, and were mainly involved in protein folding and cell division. The molecular functions included protein binding and GTPase activator activity. The results of Kyoto Encyclopedia of Genes and Genomes analysis showed that miR-4722-5p was associated with the regulation of dopaminergic synapses and mTOR signaling pathways. GO enrichment analysis also revealed that the target genes of miR-615-3p were located in the nucleus and cytoplasm, were involved in the regulation of transcription and protein phosphorylation, and were associated with protein binding, metal ion binding and transcription factor activity. The target genes of miR-615-3p played important roles in the regulation of the Ras and FoxO signaling pathways. In conclusion, miR-4722-5p and miR-615-3p may be potential biomarkers in the early diagnosis of AD.

Potential Applications of Artificial Intelligence in Clinical Trials for Alzheimer’s Disease

Clinical trials for Alzheimer’s disease (AD) face multiple challenges, such as the high screen failure rate and the even allocation of heterogeneous participants. Artificial intelligence (AI), which has become a potent tool of modern science with the expansion in the volume, variety, and velocity of biological data, offers promising potential to address these issues in AD clinical trials. In this review, we introduce the current status of AD clinical trials and the topic of machine learning. Then, a comprehensive review is focused on the potential applications of AI in the steps of AD clinical trials, including the prediction of protein and MRI AD biomarkers in the prescreening process during eligibility assessment and the likelihood stratification of AD subjects into rapid and slow progressors in randomization. Finally, this review provides challenges, developments, and the future outlook on the integration of AI into AD clinical trials.

Medicine-Food Herbs against Alzheimer’s Disease: A Review of Their Traditional Functional Features, Substance Basis, Clinical Practices and Mechanisms of Action

Alzheimer’s disease (AD) is a progressive, neurodegenerative disorder that currently has reached epidemic proportions among elderly populations around the world. In China, available traditional Chinese medicines (TCMs) that organically combine functional foods with medicinal values are named “Medicine Food Homology (MFH)”. In this review, we focused on MFH varieties for their traditional functional features, substance bases, clinical uses, and mechanisms of action (MOAs) for AD prevention and treatment. We consider the antiAD active constituents from MFH species, their effects on in vitro/in vivo AD models, and their drug targets and signal pathways by summing up the literature via a systematic electronic search (SciFinder, PubMed, and Web of Science). In this paper, several MFH plant sources are discussed in detail from in vitro/in vivo models and methods, to MOAs. We found that most of the MFH varieties exert neuroprotective effects and ameliorate cognitive impairments by inhibiting neuropathological signs (Aβ-induced toxicity, amyloid precursor protein, and phosphorylated Tau immunoreactivity), including anti-inflammation, antioxidative stress, antiautophagy, and antiapoptosis, etc. Indeed, some MFH substances and their related phytochemicals have a broad spectrum of activities, so they are superior to simple single-target drugs in treating chronic diseases. This review can provide significant guidance for people’s healthy lifestyles and drug development for AD prevention and treatment.

Revisiting APP secretases: an overview on the holistic effects of retinoic acid receptor stimulation in APP processing

Alzheimer's disease (AD) is the leading cause of dementia worldwide and is characterized by the accumulation of the β-amyloid peptide (Aβ) in the brain, along with profound alterations in phosphorylation-related events and regulatory pathways. The production of the neurotoxic Aβ peptide via amyloid precursor protein (APP) proteolysis is a crucial step in AD development. APP is highly expressed in the brain and is complexly metabolized by a series of sequential secretases, commonly denoted the α-, β-, and γ-cleavages. The toxicity of resulting fragments is a direct consequence of the first cleaving event. β-secretase (BACE1) induces amyloidogenic cleavages, while α-secretases (ADAM10 and ADAM17) result in less pathological peptides. Hence this first cleavage event is a prime therapeutic target for preventing or reverting initial biochemical events involved in AD. The subsequent cleavage by γ-secretase has a reduced impact on Aβ formation but affects the peptides' aggregating capacity. An array of therapeutic strategies are being explored, among them targeting Retinoic Acid (RA) signalling, which has long been associated with neuronal health. Additionally, several studies have described altered RA levels in AD patients, reinforcing RA Receptor (RAR) signalling as a promising therapeutic strategy. In this review we provide a holistic approach focussing on the effects of isoform-specific RAR modulation with respect to APP secretases and discuss its advantages and drawbacks in subcellular AD related events.

Multiomics Analysis of Structural Magnetic Resonance Imaging of the Brain and Cerebrospinal Fluid Metabolomics in Cognitively Normal and Impaired Adults

INTRODUCTION

Integrating brain imaging with large scale omics data may identify novel mechanisms of mild cognitive impairment (MCI) and early Alzheimer's disease (AD). We integrated and analyzed brain magnetic resonance imaging (MRI) with cerebrospinal fluid (CSF) metabolomics to elucidate metabolic mechanisms and create a "metabolic map" of the brain in prodromal AD.

METHODS

In 145 subjects (85 cognitively normal controls and 60 with MCI), we derived voxel-wise gray matter volume via whole-brain structural MRI and conducted high-resolution untargeted metabolomics on CSF. Using a data-driven approach consisting of partial least squares discriminant analysis, a multiomics network clustering algorithm, and metabolic pathway analysis, we described dysregulated metabolic pathways in CSF mapped to brain regions associated with MCI in our cohort.

RESULTS

The multiomics network algorithm clustered metabolites with contiguous imaging voxels into seven distinct communities corresponding to the following brain regions: hippocampus/parahippocampal gyrus (three distinct clusters), thalamus, posterior thalamus, parietal cortex, and occipital lobe. Metabolic pathway analysis indicated dysregulated metabolic activity in the urea cycle, and many amino acids (arginine, histidine, lysine, glycine, tryptophan, methionine, valine, glutamate, beta-alanine, and purine) was significantly associated with those regions (P < 0.05).

CONCLUSION

By integrating CSF metabolomics data with structural MRI data, we linked specific AD-susceptible brain regions to disrupted metabolic pathways involving nitrogen excretion and amino acid metabolism critical for cognitive function. Our findings and analytical approach may extend drug and biomarker research toward more multiomics approaches.

Targeted Metabolomic Analysis in Alzheimer's Disease Plasma and Brain Tissue in Non-Hispanic Whites

BACKGROUND

Metabolites are biological compounds reflecting the functional activity of organs and tissues. Understanding metabolic changes in Alzheimer's disease (AD) can provide insight into potential risk factors in this multifactorial disease and suggest new intervention strategies or improve non-invasive diagnosis.

OBJECTIVE

In this study, we searched for changes in AD metabolism in plasma and frontal brain cortex tissue samples and evaluated the performance of plasma measurements as biomarkers.

METHODS

This is a case-control study with two tissue cohorts: 158 plasma samples (94 AD, 64 controls; Texas Alzheimer's Research and Care Consortium - TARCC) and 71 postmortem cortex samples (35 AD, 36 controls; Banner Sun Health Research Institute brain bank). We performed targeted mass spectrometry analysis of 630 compounds (106 small molecules: UHPLC-MS/MS, 524 lipids: FIA-MS/MS) and 232 calculated metabolic indicators with a metabolomic kit (Biocrates MxP® Quant 500).

RESULTS

We discovered disturbances (FDR≤0.05) in multiple metabolic pathways in AD in both cohorts including microbiome-related metabolites with pro-toxic changes, methylhistidine metabolism, polyamines, corticosteroids, omega-3 fatty acids, acylcarnitines, ceramides, and diglycerides. In AD, plasma reveals elevated triglycerides, and cortex shows altered amino acid metabolism. A cross-validated diagnostic prediction model from plasma achieves AUC = 82% (CI95 = 75-88%); for females specifically, AUC = 88% (CI95 = 80-95%). A reduced model using 20 features achieves AUC = 79% (CI95 = 71-85%); for females AUC = 84% (CI95 = 74-92%).

CONCLUSION

Our findings support the involvement of gut environment in AD and encourage targeting multiple metabolic areas in the design of intervention strategies, including microbiome composition, hormonal balance, nutrients, and muscle homeostasis.

Applied machine learning in Alzheimer's disease research: omics, imaging, and clinical data

Alzheimer's disease (AD) remains a devastating neurodegenerative disease with few preventive or curative treatments available. Modern technology developments of high-throughput omics platforms and imaging equipment provide unprecedented opportunities to study the etiology and progression of this disease. Meanwhile, the vast amount of data from various modalities, such as genetics, proteomics, transcriptomics, and imaging, as well as clinical features impose great challenges in data integration and analysis. Machine learning (ML) methods offer novel techniques to address high dimensional data, integrate data from different sources, model the etiological and clinical heterogeneity, and discover new biomarkers. These directions have the potential to help us better manage the disease progression and develop novel treatment strategies. This mini-review paper summarizes different ML methods that have been applied to study AD using single-platform or multi-modal data. We review the current state of ML applications for five key directions of AD research: disease classification, drug repurposing, subtyping, progression prediction, and biomarker discovery. This summary provides insights about the current research status of ML-based AD research and highlights potential directions for future research.

Metabolomics Work Flow and Analytics in Systems Biology

Metabolomics is an omics approach of systems biology that involves the development and assessment of large-scale, comprehensive biochemical analysis tools for metabolites in biological systems. This review describes the metabolomics workflow and provides an overview of current analytic tools used for the quantification of metabolic profiles. We explain analytic tools such as mass spectrometry (MS), nuclear magnetic resonance (NMR) spectroscopy, ionization techniques, and approaches for data extraction and analysis.

Assessment of lipophilic fluorescence products in β-amyloid-induced cognitive decline: A parallel track in hippocampus, CSF, plasma and erythrocytes

BACKGROUND

Oxidative stress implicates in Alzheimer's disease (AD) pathophysiology, and associates with the creation of end products of free radical reactions, are known as lipophilic fluorescent products (LFPs). This study aimed to evaluate the probable parallel alterations in the spectral properties of the LFPs in the hippocampus tissues, cerebrospinal fluid (CSF), plasma, and erythrocytes during AD model induction by intra-cerebroventricular (ICV) amyloid β-protein fragment 25-35 (Aβ) injection.

METHODS

Male rats received an intra-ICV injection of Aβ. Hippocampus, CSF, plasma, and erythrocytes were harvested at 5, 14, and 21 days after Aβ injection. The fluorescent intensity of LFPs was assessed by spectrofluorimetry using synchronous fluorescence spectra 25 (SYN 25) and 50 (SYN 50) in the range of 250-500 nm. Hippocampal tissue malondialdehyde (MDA) and superoxide dismutase (SOD) were also measured. Cognitive alterations were evaluated using Morris water maze (MWM) test.

RESULTS

The parallel significant rise in the fluorescence intensity of LFPs was detected in the hippocampus, CSF, plasma, and erythrocytes, 14, and 21 days after ICV-Aβ injection. These alterations were found in both types of synchronous spectra 25, and 50, and were coincided with hippocampal cognitive decline, the MDA rise, and decrease of SOD activity. There was a positive correlation between hippocampus homogenate, and plasma or CSF rise in fluorescence intensity.

CONCLUSION

Data showed that the Aβ increased hippocampal MDA, and decreased SOD activity, led to a higher rate of oxidative products and subsequently resulted in an increase in LFPs fluorescence intensity during the development of cognitive decline. LFPs' alterations reflect a comprehensive view of tissue redox status. The fluorescence properties of LFPs indicate their composition, which may pave the way to trace the different pathological states.

Review of Metabolomics-Based Biomarker Research for Parkinson's Disease

Parkinson's disease (PD), as the second most common neurodegenerative disease, is seriously affecting the life quality of the elderly. However, there is still a lack of efficient medical methods to diagnosis PD before apparent symptoms occur. In recent years, clinical biomarkers including genetic, imaging, and tissue markers have exhibited remarkable benefits in assisting PD diagnoses. Due to the advantages of high-throughput detection of metabolites and almost non-invasive sample collection, metabolomics research of PD is widely used for diagnostic biomarker discovery. However, there are also a few shortages for those identified biomarkers, such as the scarcity of verifications regarding the sensitivity and specificity. Thus, reviewing the research progress of PD biomarkers based on metabolomics techniques is of great significance for developing PD diagnosis. To comprehensively clarify the progress of current metabolic biomarker studies in PD, we reviewed 20 research articles regarding the discovery and validation of biomarkers for PD diagnosis from three mainstream academic databases (NIH PubMed, ISI Web of Science, and Elsevier ScienceDirect). By analyzing those materials, we summarized the metabolic biomarkers identified by those metabolomics studies and discussed the potential approaches used for biomarker verifications. In conclusion, this review provides a comprehensive and updated overview of PD metabolomics research in the past two decades and particularly discusses the validation of disease biomarkers. We hope those discussions might provide inspiration for PD biomarker discovery and verification in the future.

A review on omics-based biomarkers discovery for Alzheimer's disease from the bioinformatics perspectives: Statistical approach vs machine learning approach

Alzheimer's Disease (AD) is a neurodegenerative disease that affects cognition and is the most common cause of dementia in the elderly. As the number of elderly individuals increases globally, the incidence and prevalence of AD are expected to increase. At present, AD is diagnosed clinically, according to accepted criteria. The essential elements in the diagnosis of AD include a patients history, a physical examination and neuropsychological testing, in addition to appropriate investigations such as neuroimaging. The omics-based approach is an emerging field of study that may not only aid in the diagnosis of AD but also facilitate the exploration of factors that influence the development of the disease. Omics techniques, including genomics, transcriptomics, proteomics and metabolomics, may reveal the pathways that lead to neuronal death and identify biomolecular markers associated with AD. This will further facilitate an understanding of AD neuropathology. In this review, omics-based approaches that were implemented in studies on AD were assessed from a bioinformatics perspective. Current state-of-the-art statistical and machine learning approaches used in the single omics analysis of AD were compared based on correlations of variants, differential expression, functional analysis and network analysis. This was followed by a review of the approaches used in the integration and analysis of multi-omics of AD. The strengths and limitations of multi-omics analysis methods were explored and the issues and challenges associated with omics studies of AD were highlighted. Lastly, future studies in this area of research were justified.

Mass spectrometry-based methods for robust measurement of Alzheimer's disease biomarkers in biological fluids

Alzheimer's disease (AD) is the most common form of dementia affecting 60%-70% of people afflicted with this disease. Accurate antemortem diagnosis is urgently needed for early detection of AD to enable reliable estimation of prognosis, intervention, and monitoring of the disease. The National Institute on Aging/Alzheimer's Association sponsored the 'Research Framework: towards a biological definition of AD', which recommends using different biomarkers in living persons for a biomarker-based definition of AD regardless of clinical status. Fluid biomarkers represent one of key groups of them. Since cerebrospinal fluid (CSF) is in direct contact with brain and many proteins present in the brain can be detected in CSF, this fluid has been regarded as the best biofluid in which to measure AD biomarkers. Recently, technological advancements in protein detection made possible the effective study of plasma AD biomarkers despite their significantly lower concentrations versus to that in CSF. This and other challenges that face plasma-based biomarker measurements can be overcome by using mass spectrometry. In this review, we discuss AD biomarkers which can be reliably measured in CSF and plasma using targeted mass spectrometry coupled to liquid chromatography (LC/MS/MS). We describe progress in LC/MS/MS methods' development, emphasize the challenges, and summarize major findings. We also highlight the role of mass spectrometry and progress made in the process of global standardization of the measurement of Aβ42/Aβ40. Finally, we briefly describe exploratory proteomics which seek to identify new biomarkers that can contribute to detection of co-pathological processes that are common in sporadic AD.

Metabolic Profiling of Neocortical Tissue Discriminates Alzheimer's Disease from Mild Cognitive Impairment, High Pathology Controls, and Normal Controls

Alzheimer's disease (AD) is the most common cause of dementia, accounting for an estimated 60-80% of cases, and is the sixth-leading cause of death in the United States. While considerable advancements have been made in the clinical care of AD, it remains a complicated disorder that can be difficult to identify definitively in its earliest stages. Recently, mass spectrometry (MS)-based metabolomics has shown significant potential for elucidation of disease mechanisms and identification of therapeutic targets as well diagnostic and prognostic markers that may be useful in resolving some of the difficulties affecting clinical AD studies, such as effective stratification. In this study, complementary gas chromatography- and liquid chromatography-MS platforms were used to detect and monitor 2080 metabolites and features in 48 postmortem tissue samples harvested from the superior frontal gyrus of male and female subjects. Samples were taken from four groups: 12 normal control (NC) patients, 12 cognitively normal subjects characterized as high pathology controls (HPC), 12 subjects with nonspecific mild cognitive impairment (MCI), and 12 subjects with AD. Multivariate statistics informed the construction and cross-validation (p < 0.01) of partial least squares-discriminant analysis (PLS-DA) models defined by a nine-metabolite panel of disease markers (lauric acid, stearic acid, myristic acid, palmitic acid, palmitoleic acid, and four unidentified mass spectral features). Receiver operating characteristic analysis showed high predictive accuracy of the resulting PLS-DA models for discrimination of NC (97%), HPC (92%), MCI (∼96%), and AD (∼96%) groups. Pathway analysis revealed significant disturbances in lysine degradation, fatty acid metabolism, and the degradation of branched-chain amino acids. Network analysis showed significant enrichment of 11 enzymes, predominantly within the mitochondria. The results expand basic knowledge of the metabolome related to AD and reveal pathways that can be targeted therapeutically. This study also provides a promising basis for the development of larger multisite projects to validate these candidate markers in readily available biospecimens such as blood to enable the effective screening, rapid diagnosis, accurate surveillance, and therapeutic monitoring of AD. All raw mass spectrometry data have been deposited to MassIVE (data set identifier MSV000087165).

Artificial Intelligence for Alzheimer's Disease: Promise or Challenge?

Decades of experimental and clinical research have contributed to unraveling many mechanisms in the pathogenesis of Alzheimer's disease (AD), but the puzzle is still incomplete. Although we can suppose that there is no complete set of puzzle pieces, the recent growth of open data-sharing initiatives collecting lifestyle, clinical, and biological data from AD patients has provided a potentially unlimited amount of information about the disease, far exceeding the human ability to make sense of it. Moreover, integrating Big Data from multi-omics studies provides the potential to explore the pathophysiological mechanisms of the entire biological continuum of AD. In this context, Artificial Intelligence (AI) offers a wide variety of methods to analyze large and complex data in order to improve knowledge in the AD field. In this review, we focus on recent findings and future challenges for AI in AD research. In particular, we discuss the use of Computer-Aided Diagnosis tools for AD diagnosis and the use of AI to potentially support clinical practices for the prediction of individual risk of AD conversion as well as patient stratification in order to finally develop effective and personalized therapies.

Return of individual research results: What do participants prefer and expect?

Newer data platforms offer increased opportunity to share multidimensional health data with research participants, but the preferences of participants for which data to receive and how is evolving. Our objective is to describe the preferences and expectations of participants for the return of individual research results within Project Baseline Health Study (PBHS). The PBHS is an ongoing, multicenter, longitudinal cohort study with data from four initial enrollment sites. PBHS participants are recruited from the general population along with groups enriched for heart disease and cancer disease risk. Cross-sectional data on return of results were collected in 2017-2018 from an (1) in-person enrollment survey (n = 1,890), (2) benchmark online survey (n = 1,059), and (3) participant interviews (n = 21). The main outcomes included (1) preferences for type of information to be added next to returned results, (2) participant plans for sharing returned results with a non-study clinician, and (3) choice to opt-out of receiving genetic results. Results were compared by sociodemographic characteristics. Enrollment and benchmark survey respondents were 57.1% and 53.5% female, and 60.0% and 66.2% white, respectively. Participants preferred the following data types be added to returned results in the future: genetics (29.9%), heart imaging, (16.4%), study watch (15.8%), and microbiome (13.3%). Older adults (OR 0.60, 95% CI: 0.41-0.87) were less likely to want their genetic results returned next. Forty percent of participants reported that they would not share all returned results with their non-study clinicians. Black (OR 0.64, 95% CI 0.43-0.95) and Asian (OR 0.47, 95% CI 0.30-0.73) participants were less likely, and older participants more likely (OR 1.45-1.61), to plan to share all results with their clinician than their counterparts. At enrollment, 5.8% of participants opted out of receiving their genetics results. The study showed that substantial heterogeneity existed in participant's preferences and expectations for return of results, and variations were related to sociodemographic characteristics.

Identification of Cysteine Protease Inhibitor CST2 as a Potential Biomarker for Colorectal Cancer

Additional biomarkers for the development and progression of colorectal cancer (CRC) remain to be identified. Hence, the current study aimed to identify potential diagnostic markers for CRC. Analyses of cysteine protease inhibitor [cystatins (CSTs)] expression in CRC samples and its correlation with cancer stage or survival in patients with CRC demonstrated that CRC tissues had greater CST1 and CST2 mRNA expression compared to noncancerous adjacent tissues, while higher CST2 mRNA expression in CRC tissues was correlated with advanced stages and disease-free survival in patients with CRC, encouraging further exploration on the role of CST2 in CRC. Through an online database search and tissue microarray (TMA), we confirmed that CRC samples had higher CST2 expression compared to noncancerous adjacent tissue or normal colorectal tissues at both the mRNA and protein levels. TMA also revealed that colorectal adenoma, CRC, and metastatic CRC tissues exhibited a significantly increased CST2 protein expression. Accordingly, survival analysis demonstrated that the increase in CST2 protein expression was correlated with shorter overall survival of patients with CRC. Moreover, our results found a significant upregulation of CST2 in multiple cancer tissues. Taken together, these findings suggest the potential role of CST2 as a diagnostic and prognostic biomarker for CRC.

Disease-specific interactome alterations via epichaperomics: the case for Alzheimer's disease

The increasingly appreciated prevalence of complicated stressor-to-phenotype associations in human disease requires a greater understanding of how specific stressors affect systems or interactome properties. Many currently untreatable diseases arise due to variations in, and through a combination of, multiple stressors of genetic, epigenetic, and environmental nature. Unfortunately, how such stressors lead to a specific disease phenotype or inflict a vulnerability to some cells and tissues but not others remains largely unknown and unsatisfactorily addressed. Analysis of cell- and tissue-specific interactome networks may shed light on organization of biological systems and subsequently to disease vulnerabilities. However, deriving human interactomes across different cell and disease contexts remains a challenge. To this end, this opinion article links stressor-induced protein interactome network perturbations to the formation of pathologic scaffolds termed epichaperomes, revealing a viable and reproducible experimental solution to obtaining rigorous context-dependent interactomes. This article presents our views on how a specialized 'omics platform called epichaperomics may complement and enhance the currently available conventional approaches and aid the scientific community in defining, understanding, and ultimately controlling interactome networks of complex diseases such as Alzheimer's disease. Ultimately, this approach may aid the transition from a limited single-alteration perspective in disease to a comprehensive network-based mindset, which we posit will result in precision medicine paradigms for disease diagnosis and treatment.

A Comprehensive Machine Learning Framework for the Exact Prediction of the Age of Onset in Familial and Sporadic Alzheimer's Disease

Machine learning (ML) algorithms are widely used to develop predictive frameworks. Accurate prediction of Alzheimer's disease (AD) age of onset (ADAOO) is crucial to investigate potential treatments, follow-up, and therapeutic interventions. Although genetic and non-genetic factors affecting ADAOO were elucidated by other research groups and ours, the comprehensive and sequential application of ML to provide an exact estimation of the actual ADAOO, instead of a high-confidence-interval ADAOO that may fall, remains to be explored. Here, we assessed the performance of ML algorithms for predicting ADAOO using two AD cohorts with early-onset familial AD and with late-onset sporadic AD, combining genetic and demographic variables. Performance of ML algorithms was assessed using the root mean squared error (RMSE), the R-squared (R2), and the mean absolute error (MAE) with a 10-fold cross-validation procedure. For predicting ADAOO in familial AD, boosting-based ML algorithms performed the best. In the sporadic cohort, boosting-based ML algorithms performed best in the training data set, while regularization methods best performed for unseen data. ML algorithms represent a feasible alternative to accurately predict ADAOO with little human intervention. Future studies may include predicting the speed of cognitive decline in our cohorts using ML.

An integrative multi-omics approach reveals new central nervous system pathway alterations in Alzheimer's disease

BACKGROUND

Multiple pathophysiological processes have been described in Alzheimer's disease (AD). Their inter-individual variations, complex interrelations, and relevance for clinical manifestation and disease progression remain poorly understood. We hypothesize that specific molecular patterns indicating both known and yet unidentified pathway alterations are associated with distinct aspects of AD pathology.

METHODS

We performed multi-level cerebrospinal fluid (CSF) omics in a well-characterized cohort of older adults with normal cognition, mild cognitive impairment, and mild dementia. Proteomics, metabolomics, lipidomics, one-carbon metabolism, and neuroinflammation related molecules were analyzed at single-omic level with correlation and regression approaches. Multi-omics factor analysis was used to integrate all biological levels. Identified analytes were used to construct best predictive models of the presence of AD pathology and of cognitive decline with multifactorial regression analysis. Pathway enrichment analysis identified pathway alterations in AD.

RESULTS

Multi-omics integration identified five major dimensions of heterogeneity explaining the variance within the cohort and differentially associated with AD. Further analysis exposed multiple interactions between single 'omics modalities and distinct multi-omics molecular signatures differentially related to amyloid pathology, neuronal injury, and tau hyperphosphorylation. Enrichment pathway analysis revealed overrepresentation of the hemostasis, immune response, and extracellular matrix signaling pathways in association with AD. Finally, combinations of four molecules improved prediction of both AD (protein 14-3-3 zeta/delta, clusterin, interleukin-15, and transgelin-2) and cognitive decline (protein 14-3-3 zeta/delta, clusterin, cholesteryl ester 27:1 16:0 and monocyte chemoattractant protein-1).

CONCLUSIONS

Applying an integrative multi-omics approach we report novel molecular and pathways alterations associated with AD pathology. These findings are relevant for the development of personalized diagnosis and treatment approaches in AD.

Tissue Microbiome Associated With Human Diseases by Whole Transcriptome Sequencing and 16S Metagenomics

In recent years, a substantial number of tissue microbiome studies have been published, mainly due to the recent improvements in the minimization of microbial contamination during whole transcriptome analysis. Another reason for this trend is due to the capability of next-generation sequencing (NGS) to detect microbiome composition even in low biomass samples. Several recent studies demonstrate a significant role for the tissue microbiome in the development and progression of cancer and other diseases. For example, the increase of the abundance of Proteobacteria in tumor tissues of the breast has been revealed by gene expression analysis. The link between human papillomavirus infection and cervical cancer has been known for some time, but the relationship between the microbiome and breast cancer (BC) is more novel. There are also recent attempts to investigate the possible link between the brain microbiome and the cognitive dysfunction caused by neurological diseases. Such studies pointing to the role of the brain microbiome in Huntington’s disease (HD) and Alzheimer’s disease (AD) suggest that microbial colonization is a risk factor. In this review, we aim to summarize the studies that associate the tissue microbiome, rather than gut microbiome, with cancer and other diseases using whole-transcriptome analysis, along with 16S rRNA analysis. After providing several case studies for each relationship, we will discuss the potential role of transcriptome analysis on the broader portrayal of the pathophysiology of the breast, brain, and vaginal microbiome.