Omics sciences for systems biology in Alzheimer's disease: State-of-the-art of the evidence

Unravelling disease complexity: integrative analysis of multi-omic data in clinical research

INTRODUCTION

A holistic view on biological systems is today a reality with the application of multi-omic technologies. These technologies allow the profiling of genome, epigenome, transcriptome, proteome, metabolome as well as newly emerging 'omes.' While the multiple layers of data accumulate, their integration and reconciliation in a single system map is a cumbersome exercise that faces many challenges. Application to human health and disease requires large sample sizes, robust methodologies and high-quality standards.

AREAS COVERED

We review the different methods used to integrate multi-omics, as recent ones including artificial intelligence. With proteomics as an anchor technology, we then present selected applications of its data combination with other omics layers in clinical research, mainly covering literature from the last five years in the Scopus and/or PubMed databases.

EXPERT OPINION

Multi-omics is powerful to comprehensively type molecular layers and link them to phenotype. Yet, technologies and data are very diverse and still strategies and methodologies to properly integrate these modalities are needed.

Clinical prediction study on the risk of atrial fibrillation in hypertensive patients based on metabolism, inflammation, and gender differences

This study aimed to explore the risk factors for atrial fibrillation (AF) within one year after discharge in hypertensive patients and to construct a corresponding predictive model. This single-center, retrospective study included 566 patients admitted with hypertension. Patients were divided into two groups: those who developed AF within one year after discharge and those who did not. Variables were selected for multivariate regression analysis using univariate regression and variance inflation factor (VIF) analysis. Subgroup analysis was performed by gender to explore the predictive value of the variables, and a nomogram was constructed. The total sample was randomly divided into a training set and a validation set (7:3 ratio). The discrimination and calibration of the predictive model were evaluated using receiver operating characteristic (ROC) and calibration curves. Patients who developed AF within one year had significantly higher levels of white blood cells (WBC), neutrophils (NEUT), lymphocytes (LYMPH), creatinine (Scr), fasting blood glucose (FBG), triglycerides (TG), lipoprotein(a) [Lp(a)], glycated hemoglobin (HbA1c), neutrophil-to-lymphocyte ratio (NLR), and triglyceride-glucose (TyG) index compared to those who did not (P < 0.05). Males, smokers, and diabetic patients were more prevalent in the AF group (P < 0.05). Logistic regression analysis showed that male gender, Lp(a), HbA1c, NLR, and the TyG index were independent predictors of AF within one year after discharge in hypertensive patients. The nomogram constructed showed an area under the ROC curve (AUC) of 0.793 in the training set and 0.740 in the validation set. The calibration curves indicated good fit (P = 0.726 in the training set; P = 0.489 in the validation set). Male, Lp(a), HbA1c, NLR, and the TyG index are independent risk factors for AF within one year of discharge in hypertensive patients. The nomogram model constructed has high predictive accuracy. This study suggests that individualized management strategies should be employed based on these risk factors in clinical practice.

Differential Transcriptional Programs Reveal Modular Network Rearrangements Associated with Late-Onset Alzheimer's Disease

Alzheimer's disease (AD) is a complex, genetically heterogeneous disorder. The diverse phenotypes associated with AD result from interactions between genetic and environmental factors, influencing multiple biological pathways throughout disease progression. Network-based approaches offer a way to assess phenotype-specific states. In this study, we calculated key network metrics to characterize the network transcriptional structure and organization in LOAD, focusing on genes and pathways implicated in AD pathology within the dorsolateral prefrontal cortex (DLPFC). Our findings revealed disease-specific coexpression markers associated with diverse metabolic functions. Additionally, significant differences were observed at both the mesoscopic and local levels between AD and control networks, along with a restructuring of gene coexpression and biological functions into distinct transcriptional modules. These results show the molecular reorganization of the transcriptional program occurring in LOAD, highlighting specific adaptations that may contribute to or result from cellular responses to pathological stressors. Our findings may support the development of a unified model for the causal mechanisms of AD, suggesting that its diverse manifestations arise from multiple pathways working together to produce the disease's complex clinical patho-phenotype.

Histone Deacetylation in Alzheimer's Diseases (AD); Hope or Hype

Histone acetylation is the process by which histone acetyltransferases (HATs) add an acetyl group to the N-terminal lysine residues of histones, resulting in a more open chromatin structure. Histone acetylation tends to increase gene expression more than methylation does. In the central nervous system (CNS), histone acetylation is essential for controlling the expression of genes linked to cognition and learning. Histone deacetylases (HDACs), "writing" enzymes (HATs), and "reading" enzymes with bromodomains that identify and localize to acetylated lysine residues are responsible for maintaining histone acetylation. By giving animals HDAC inhibitors (HDACis), it is possible to intentionally control the ratios of "writer" and "eraser" activity, which will change the acetylation of histones. In addition to making the chromatin more accessible, these histone acetylation alterations re-allocate the targeting of "readers," including the transcriptional co-activators, cAMP response element-binding protein (CBP), and bromodomain-containing protein 4 (Brd4) in the CNS. Conclusive evidence has shown that HDACs slow down the progression of Alzheimer's disease (AD) by reducing the amount of histone acetylation, decreasing the activity of genes linked to memory, supporting cognitive decline and Amyloid beta (Aβ) protein accumulation, influencing aberrant tau phosphorylation, and promoting the emergence of neurofibrillary tangles (NFTs). In this review, we have covered the therapeutic targets and functions of HDACs that might be useful in treating AD.

Uncovering the epigenetic regulatory clues of PRRT1 in Alzheimer's disease: a strategy integrating multi-omics analysis with explainable machine learning

BACKGROUND

Alzheimer's disease (AD) is a complex neurodegenerative disorder with a largely unexplored epigenetic landscape.

OBJECTIVE

This study employs an innovative approach that integrates multi-omics analysis and explainable machine learning to explore the epigenetic regulatory mechanisms underlying the epigenetic signature of PRRT1 implicated in AD.

METHODS

Through comprehensive DNA methylation and transcriptomic profiling, we identified distinct epigenetic signatures associated with gene PRRT1 expression in AD patient samples compared to healthy controls. Utilizing interpretable machine learning models and ELMAR analysis, we dissected the complex relationships between these epigenetic signatures and gene expression patterns, revealing novel regulatory elements and pathways. Finally, the epigenetic mechanisms of these genes were investigated experimentally.

RESULTS

This study identified ten epigenetic signatures, constructed an interpretable AD diagnostic model, and utilized various bioinformatics methods to create an epigenomic map. Subsequently, the ELMAR R package was used to integrate multi-omics data and identify the upstream transcription factor MAZ for PRRT1. Finally, experiments confirmed the interaction between MAZ and PRRT1, which mediated apoptosis and autophagy in AD.

CONCLUSION

This study adopts a strategy that integrates bioinformatics analysis with molecular experiments, providing new insights into the epigenetic regulatory mechanisms of PRRT1 in AD and demonstrating the importance of explainable machine learning in elucidating complex disease mechanisms.

Analysis of Metabolic Risk Factors for Microcirculation Disorders Post-Percutaneous Coronary Intervention and Predictive Model Construction: A Study on Patients with Unstable Angina

Background

This study aimed to analyze the metabolic risk factors for microcirculation disorders in patients with unstable angina (UA) after percutaneous coronary intervention (PCI), evaluating their predictive value for developing microcirculation disorders.

Methods

A single-center retrospective study design was used, which included 553 patients with UA who underwent PCI. The angiographic microcirculatory resistance (AMR) index was calculated based on coronary angiography data. Patients were divided into two groups according to their post-PCI AMR values: a post-PCI AMR ≤2.50 group and a post-PCI AMR >2.50 group. Variables were included in the multivariate regression model through univariate regression and variance inflation factor (VIF) screening. Subgroup analyses were conducted by sex to further evaluate the predictive value of selected variables in the overall sample. The total sample was randomly split into a 7:3 ratio for the training and validation sets. A nomogram based on the training sets was then constructed to visualize these predictions. The discrimination and calibration of the prediction model were evaluated using the receiver operating characteristic (ROC) curve and calibration curve.

Results

The post-PCI AMR >2.50 group had a higher percentage of females, increased incidence of diabetes, and elevated fasting blood glucose (FBG), glycated hemoglobin (HbA1c), triglyceride (TG), total cholesterol (TC), low-density lipoprotein cholesterol (LDL-C), very low-density lipoprotein cholesterol (VLDL-C), and lipoprotein(a) (Lp(a)) levels (p < 0.05). Logistic regression analysis identified HbA1c, TG, LDL-C, and Lp(a) as independent predictors of elevated AMR post-PCI after adjusting for confounders. Subgroup analysis confirmed no significant interaction between the model and sex (p > 0.05). A nomogram was constructed based on the training set, with the area under the curve (AUC) for the ROC of 0.824 in the training set and 0.746 in the validation set. The calibration curves showed a good fit (training set: p = 0.219; validation set: p = 0.258).

Conclusions

HbA1c, TG, LDL-C, and Lp(a) levels are independent risk factors for microcirculation disorders in patients with UA post-PCI. The constructed nomogram provides good predictive accuracy.

Proteostasis disruption and senescence in Alzheimer's disease pathways to neurodegeneration

Alzheimer's Disease (AD) is a progressive neurological disease associated with behavioral abnormalities, memory loss, and cognitive impairment that cause major causes of dementia in the elderly. The pathogenetic processes cause complex effects on brain function and AD progression. The proper protein homeostasis, or proteostasis, is critical for cell health. AD causes the buildup of misfolded proteins, particularly tau and amyloid-beta, to break down proteostasis, such aggregates are toxic to neurons and play a critical role in AD pathogenesis. The rise of cellular senescence is accompanied by aging, marked by irreversible cell cycle arrest and the release of pro-inflammatory proteins. Senescent cell build-up in the brains of AD patients exacerbates neuroinflammation and neuronal degeneration. These cells senescence-associated secretory phenotype (SASP) also disturbs the brain environment. When proteostasis failure and cellular senescence coalesce, a cycle is generated that compounds each other. While senescent cells contribute to proteostasis breakdown through inflammatory and degradative processes, misfolded proteins induce cellular stress and senescence. The principal aspects of the neurodegenerative processes in AD are the interaction of cellular senescence and proteostasis failure. This review explores the interconnected roles of proteostasis disruption and cellular senescence in the pathways leading to neurodegeneration in AD.

Redefining Roles: A Paradigm Shift in Tryptophan-Kynurenine Metabolism for Innovative Clinical Applications

The tryptophan-kynurenine (KYN) pathway has long been recognized for its essential role in generating metabolites that influence various physiological processes. Traditionally, these metabolites have been categorized into distinct, often opposing groups, such as pro-oxidant versus antioxidant, excitotoxic/neurotoxic versus neuroprotective. This dichotomous framework has shaped much of the research on conditions like neurodegenerative and neuropsychiatric disorders, as well as cancer, where metabolic imbalances are a key feature. The effects are significantly influenced by various factors, including the concentration of metabolites and the particular cellular milieu in which they are generated. A molecule that acts as neuroprotective at low concentrations may exhibit neurotoxic effects at elevated levels. The oxidative equilibrium of the surrounding environment can alter the function of KYN from an antioxidant to a pro-oxidant. This narrative review offers a comprehensive examination and analysis of the contemporary understanding of KYN metabolites, emphasizing their multifaceted biological functions and their relevance in numerous physiological and pathological processes. This underscores the pressing necessity for a paradigm shift in the comprehension of KYN metabolism. Understanding the context-dependent roles of KYN metabolites is vital for novel therapies in conditions like Alzheimer's disease, multiple sclerosis, and cancer. Comprehensive pathway modulation, including balancing inflammatory signals and enzyme regulation, offers promising avenues for targeted, effective treatments.

GREMI: An Explainable Multi-Omics Integration Framework for Enhanced Disease Prediction and Module Identification

Multi-omics integration has demonstrated promising performance in complex disease prediction. However, existing research typically focuses on maximizing prediction accuracy, while often neglecting the essential task of discovering meaningful biomarkers. This issue is particularly important in biomedicine, as molecules often interact rather than function individually to influence disease outcomes. To this end, we propose a two-phase framework named GREMI to assist multi-omics classification and explanation. In the prediction phase, we propose to improve prediction performance by employing a graph attention architecture on sample-wise co-functional networks to incorporate biomolecular interaction information for enhanced feature representation, followed by the integration of a joint-late mixed strategy and the true-class-probability block to adaptively evaluate classification confidence at both feature and omics levels. In the interpretation phase, we propose a multi-view approach to explain disease outcomes from the interaction module perspective, providing a more intuitive understanding and biomedical rationale. We incorporate Monte Carlo tree search (MCTS) to explore local-view subgraphs and pinpoint modules that highly contribute to disease characterization from the global-view. Extensive experiments demonstrate that the proposed framework outperforms state-of-the-art methods in seven different classification tasks, and our model effectively addresses data mutual interference when the number of omics types increases. We further illustrate the functional- and disease-relevance of the identified modules, as well as validate the classification performance of discovered modules using an independent cohort.

Deciphering the role of miRNAs in Alzheimer's disease: Predictive targeting and pathway modulation - A systematic review

Alzheimer's Disease (AD), a multifaceted neurodegenerative disorder, is increasingly understood through the regulatory lens of microRNAs (miRNAs). This review comprehensively examines the pivotal roles of miRNAs in AD pathogenesis, shedding light on their influence across various pathways. We delve into the biogenesis and mechanisms of miRNAs, emphasizing their significant roles in brain function and regulation. The review then navigates the complex landscape of AD pathogenesis, identifying key genetic, environmental, and molecular factors, with a focus on hallmark pathological features like amyloid-beta accumulation and tau protein hyperphosphorylation. Central to our discussion is the intricate involvement of miRNAs in these processes, highlighting their altered expression patterns in AD and subsequent functional implications, from amyloid-beta metabolism to tau pathology, neuroinflammation, oxidative stress, and synaptic dysfunction. The predictive analysis of miRNA targets using computational methods, complemented by experimental validations, forms a crucial part of our discourse, unraveling the contributions of specific miRNAs to AD. Moreover, we explore the therapeutic potential of miRNAs as biomarkers and in miRNA-based interventions, while addressing the challenges in translating these findings into clinical practice. This review aims to enhance understanding of miRNAs in AD, offering a foundation for future research directions and novel therapeutic strategies.

Transcriptomic alterations in APP/PS1 mice astrocytes lead to early postnatal axon initial segment structural changes

Alzheimer´s disease (AD) is characterized by neuronal function loss and degeneration. The integrity of the axon initial segment (AIS) is essential to maintain neuronal function and output. AIS alterations are detected in human post-mortem AD brains and mice models, as well as, neurodevelopmental and mental disorders. However, the mechanisms leading to AIS deregulation in AD and the extrinsic glial origin are elusive. We studied early postnatal differences in AIS cellular/molecular mechanisms in wild-type or APP/PS1 mice and combined neuron-astrocyte co-cultures. We observed AIS integrity alterations, reduced ankyrinG expression and shortening, in APP/PS1 mice from P21 and loss of AIS integrity at 21 DIV in wild-type and APP/PS1 neurons in the presence of APP/PS1 astrocytes. AnkyrinG decrease is due to mRNAs and protein reduction of retinoic acid synthesis enzymes Rdh1 and Aldh1b1, as well as ADNP (Activity-dependent neuroprotective protein) in APP/PS1 astrocytes. This effect was mimicked by wild-type astrocytes expressing ADNP shRNA. In the presence of APP/PS1 astrocytes, wild-type neurons AIS is recovered by inhibition of retinoic acid degradation, and Adnp-derived NAP peptide (NAPVSIPQ) addition or P2X7 receptor inhibition, both regulated by retinoic acid levels. Moreover, P2X7 inhibitor treatment for 2 months impaired AIS disruption in APP/PS1 mice. Our findings extend current knowledge on AIS regulation, providing data to support the role of astrocytes in early postnatal AIS modulation. In conclusion, AD onset may be related to very early glial cell alterations that induce AIS and neuronal function changes, opening new therapeutic approaches to detect and avoid neuronal function loss.

Novelties on Neuroinflammation in Alzheimer's Disease-Focus on Gut and Oral Microbiota Involvement

Recent studies underscore the role of gut and oral microbiota in influencing neuroinflammation through the microbiota-gut-brain axis, including in Alzheimer's disease (AD). This review aims to provide a comprehensive synthesis of recent findings on the involvement of gut and oral microbiota in the neuroinflammatory processes associated with AD, emphasizing novel insights and therapeutic implications. This review reveals that dysbiosis in AD patients' gut and oral microbiota is linked to heightened peripheral and central inflammatory responses. Specific bacterial taxa, such as Bacteroides and Firmicutes in the gut, as well as Porphyromonas gingivalis in the oral cavity, are notably altered in AD, leading to significant changes in microglial activation and cytokine production. Gut microbiota alterations are associated with increased intestinal permeability, facilitating the translocation of endotoxins like lipopolysaccharides (LPS) into the bloodstream and exacerbating neuroinflammation by activating the brain's toll-like receptor 4 (TLR4) pathways. Furthermore, microbiota-derived metabolites, including short-chain fatty acids (SCFAs) and amyloid peptides, can cross the blood-brain barrier and modulate neuroinflammatory responses. While microbial amyloids may contribute to amyloid-beta aggregation in the brain, certain SCFAs like butyrate exhibit anti-inflammatory properties, suggesting a potential therapeutic avenue to mitigate neuroinflammation. This review not only highlights the critical role of microbiota in AD pathology but also offers a ray of hope by suggesting that modulating gut and oral microbiota could represent a novel therapeutic strategy for reducing neuroinflammation and slowing disease progression.

Advancements in Single-Cell RNA Sequencing and Spatial Transcriptomics for Central Nervous System Disease

The incidence of central nervous system (CNS) disease has persistently increased over the last several years. There is an urgent need for effective methods to improve the cure rates of CNS disease. However, the precise molecular basis underlying the development and progression of major CNS diseases remains elusive. A complete molecular map will contribute to research on CNS disease treatment strategies. Emerging technologies such as single-cell RNA sequencing (scRNA-seq) and Spatial Transcriptomics (ST) are potent tools for exploring the molecular complexity, cell heterogeneity, and functional specificity of CNS disease. scRNA-seq and ST can provide insights into the disease at cellular and spatial transcription levels. This review presents a survey of scRNA-seq and ST studies on CNS diseases, such as chronic neurodegenerative diseases, acute CNS injuries, and others. These studies offer novel perspectives in treating and diagnosing CNS diseases by discovering new cell types or subtypes associated with the disease, proposing new pathophysiological mechanisms, uncovering novel therapeutic targets, and identifying putative biomarkers.

Integrating amyloid and tau imaging with proteomics and genomics in Alzheimer's disease

Alzheimer's disease (AD) is the most common neurodegenerative disease and is characterized by the aggregation of β-amyloid (Aβ) and tau in the brain. Breakthroughs in disease-modifying treatments targeting Aβ bring new hope for the management of AD. But to effectively modify and someday even prevent AD, a better understanding is needed of the biological mechanisms that underlie and link Aβ and tau in AD. Developments of high-throughput omics, including genomics, proteomics, and transcriptomics, together with molecular imaging of Aβ and tau with positron emission tomography (PET), allow us to discover and understand the biological pathways that regulate the aggregation and spread of Aβ and tau in living humans. The field of integrated omics and PET studies of Aβ and tau in AD is growing rapidly. We here provide an update of this field, both in terms of biological insights and in terms of future clinical implications of integrated omics-molecular imaging studies.

AutoFocus: a hierarchical framework to explore multi-omic disease associations spanning multiple scales of biomolecular interaction

Recent advances in high-throughput measurement technologies have enabled the analysis of molecular perturbations associated with disease phenotypes at the multi-omic level. Such perturbations can range in scale from fluctuations of individual molecules to entire biological pathways. Data-driven clustering algorithms have long been used to group interactions into interpretable functional modules; however, these modules are typically constrained to a fixed size or statistical cutoff. Furthermore, modules are often analyzed independently of their broader biological context. Consequently, such clustering approaches limit the ability to explore functional module associations with disease phenotypes across multiple scales. Here, we introduce AutoFocus, a data-driven method that hierarchically organizes biomolecules and tests for phenotype enrichment at every level within the hierarchy. As a result, the method allows disease-associated modules to emerge at any scale. We evaluated this approach using two datasets: First, we explored associations of biomolecules from the multi-omic QMDiab dataset (n = 388) with the well-characterized type 2 diabetes phenotype. Secondly, we utilized the ROS/MAP Alzheimer's disease dataset (n = 500), consisting of high-throughput measurements of brain tissue to explore modules associated with multiple Alzheimer's Disease-related phenotypes. Our method identifies modules that are multi-omic, span multiple pathways, and vary in size. We provide an interactive tool to explore this hierarchy at different levels and probe enriched modules, empowering users to examine the full hierarchy, delve into biomolecular drivers of disease phenotype within a module, and incorporate functional annotations.

Omics approaches to unravel insecticide resistance mechanism in Bemisia tabaci (Gennadius) (Hemiptera: Aleyrodidae)

Bemisia tabaci (Gennadius) whitefly (BtWf) is an invasive pest that has already spread worldwide and caused major crop losses. Numerous strategies have been implemented to control their infestation, including the use of insecticides. However, prolonged insecticide exposures have evolved BtWf to resist these chemicals. Such resistance mechanism is known to be regulated at the molecular level and systems biology omics approaches could shed some light on understanding this regulation wholistically. In this review, we discuss the use of various omics techniques (genomics, transcriptomics, proteomics, and metabolomics) to unravel the mechanism of insecticide resistance in BtWf. We summarize key genes, enzymes, and metabolic regulation that are associated with the resistance mechanism and review their impact on BtWf resistance. Evidently, key enzymes involved in the detoxification system such as cytochrome P450 (CYP), glutathione S-transferases (GST), carboxylesterases (COE), UDP-glucuronosyltransferases (UGT), and ATP binding cassette transporters (ABC) family played key roles in the resistance. These genes/proteins can then serve as the foundation for other targeted techniques, such as gene silencing techniques using RNA interference and CRISPR. In the future, such techniques will be useful to knock down detoxifying genes and crucial neutralizing enzymes involved in the resistance mechanism, which could lead to solutions for coping against BtWf infestation.

Genetically predicted 1091 blood metabolites and 309 metabolite ratios in relation to risk of type 2 diabetes: a Mendelian randomization study

Background

Metabolic dysregulation represents a defining characteristic of Type 2 diabetes (T2DM). Nevertheless, there remains an absence of substantial evidence establishing a direct causal link between circulating blood metabolites and the promotion or prevention of T2DM. In addressing this gap, we employed Mendelian randomization (MR) analysis to investigate the potential causal association between 1,091 blood metabolites, 309 metabolite ratios, and the occurrence of T2DM.

Methods

Data encompassing single-nucleotide polymorphisms (SNPs) for 1,091 blood metabolites and 309 metabolite ratios were extracted from a Canadian Genome-wide association study (GWAS) involving 8,299 participants. To evaluate the causal link between these metabolites and Type 2 diabetes (T2DM), multiple methods including Inverse Variance Weighted (IVW), Weighted Median, MR Egger, Weighted Mode, and Simple Mode were employed. p-values underwent correction utilizing False Discovery Rates (FDR). Sensitivity analyses incorporated Cochran's Q test, MR-Egger intercept test, MR-PRESSO, Steiger test, leave-one-out analysis, and single SNP analysis. The causal effects were visualized via Circos plot, forest plot, and scatter plot. Furthermore, for noteworthy, an independent T2DM GWAS dataset (GCST006867) was utilized for replication analysis. Metabolic pathway analysis of closely correlated metabolites was conducted using MetaboAnalyst 5.0.

Results

The IVW analysis method utilized in this study revealed 88 blood metabolites and 37 metabolite ratios demonstrating a significant causal relationship with T2DM (p < 0.05). Notably, strong causal associations with T2DM were observed for specific metabolites: 1-linoleoyl-GPE (18:2) (IVW: OR:0.930, 95% CI: 0.899-0.962, p = 2.16 × 10-5), 1,2-dilinoleoyl-GPE (18:2/18:2) (IVW: OR:0.942, 95% CI: 0.917-0.968, p = 1.64 × 10-5), Mannose (IVW: OR:1.133, 95% CI: 1.072-1.197, p = 1.02 × 10-5), X-21829 (IVW: OR:1.036, 95% CI: 1.036-1.122, p = 9.44 × 10-5), and Phosphate to mannose ratio (IVW: OR:0.870, 95% CI: 0.818-0.926, p = 1.29 × 10-5, FDR = 0.008). Additionally, metabolic pathway analysis highlighted six significant pathways associated with T2DM development: Valine, leucine and isoleucine biosynthesis, Phenylalanine metabolism, Glycerophospholipid metabolism, Alpha-Linolenic acid metabolism, Sphingolipid metabolism, and Alanine, aspartate, and glutamate metabolism.

Conclusion

This study identifies both protective and risk-associated metabolites that play a causal role in the development of T2DM. By integrating genomics and metabolomics, it presents novel insights into the pathogenesis of T2DM. These findings hold potential implications for early screening, preventive measures, and treatment strategies for T2DM.

Tackling neurodegeneration in vitro with omics: a path towards new targets and drugs

Drug discovery is a generally inefficient and capital-intensive process. For neurodegenerative diseases (NDDs), the development of novel therapeutics is particularly urgent considering the long list of late-stage drug candidate failures. Although our knowledge on the pathogenic mechanisms driving neurodegeneration is growing, additional efforts are required to achieve a better and ultimately complete understanding of the pathophysiological underpinnings of NDDs. Beyond the etiology of NDDs being heterogeneous and multifactorial, this process is further complicated by the fact that current experimental models only partially recapitulate the major phenotypes observed in humans. In such a scenario, multi-omic approaches have the potential to accelerate the identification of new or repurposed drugs against a multitude of the underlying mechanisms driving NDDs. One major advantage for the implementation of multi-omic approaches in the drug discovery process is that these overarching tools are able to disentangle disease states and model perturbations through the comprehensive characterization of distinct molecular layers (i.e., genome, transcriptome, proteome) up to a single-cell resolution. Because of recent advances increasing their affordability and scalability, the use of omics technologies to drive drug discovery is nascent, but rapidly expanding in the neuroscience field. Combined with increasingly advanced in vitro models, which particularly benefited from the introduction of human iPSCs, multi-omics are shaping a new paradigm in drug discovery for NDDs, from disease characterization to therapeutics prediction and experimental screening. In this review, we discuss examples, main advantages and open challenges in the use of multi-omic approaches for the in vitro discovery of targets and therapies against NDDs.

Personalized medicine in old age psychiatry and Alzheimer's disease

Elderly patients show us unfolded lives with unique individual characteristics. An increasing life span is associated with increasing physical and mental disease burden. Alzheimer's disease (AD) is an increasing challenge in old age. AD cannot be cured but it can be treated. The complexity of old age and AD offer targets for personalized medicine (PM). Targets for stratification of patients, detection of patients at risk for AD or for future targeted therapy are plentiful and can be found in several omic-levels.

Memory circuits in dementia: The engram, hippocampal neurogenesis and Alzheimer's disease

Here, we provide an in-depth consideration of our current understanding of engrams, spanning from molecular to network levels, and hippocampal neurogenesis, in health and Alzheimer's disease (AD). This review highlights novel findings in these emerging research fields and future research directions for novel therapeutic avenues for memory failure in dementia. Engrams, memory in AD, and hippocampal neurogenesis have each been extensively studied. The integration of these topics, however, has been relatively less deliberated, and is the focus of this review. We primarily focus on the dentate gyrus (DG) of the hippocampus, which is a key area of episodic memory formation. Episodic memory is significantly impaired in AD, and is also the site of adult hippocampal neurogenesis. Advancements in technology, especially opto- and chemogenetics, have made sophisticated manipulations of engram cells possible. Furthermore, innovative methods have emerged for monitoring neurons, even specific neuronal populations, in vivo while animals engage in tasks, such as calcium imaging. In vivo calcium imaging contributes to a more comprehensive understanding of engram cells. Critically, studies of the engram in the DG using these technologies have shown the important contribution of hippocampal neurogenesis for memory in both health and AD. Together, the discussion of these topics provides a holistic perspective that motivates questions for future research.

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.

A critical appraisal of blood-based biomarkers for Alzheimer's disease

Biomarkers that predict the clinical onset of Alzheimer's disease (AD) enable the identification of individuals in the early, preclinical stages of the disease. Detecting AD at this point may allow for more effective therapeutic interventions and optimized enrollment for clinical trials of novel drugs. The current biological diagnosis of AD is based on the AT(N) classification system with the measurement of brain deposition of amyloid-β (Aβ) ("A"), tau pathology ("T"), and neurodegeneration ("N"). Diagnostic cut-offs for Aβ1-42, the Aβ1-42/Aβ1-40 ratio, tau and hyperphosphorylated-tau concentrations in cerebrospinal fluid have been defined and may support AD clinical diagnosis. Blood-based biomarkers of the AT(N) categories have been described in the AD continuum. Cross-sectional and longitudinal studies have shown that the combination of blood biomarkers tracking neuroaxonal injury (neurofilament light chain) and neuroinflammatory pathways (glial fibrillary acidic protein) enhance sensitivity and specificity of AD clinical diagnosis and improve the prediction of AD onset. However, no international accepted cut-offs have been identified for these blood biomarkers. A kit for blood Aβ1-42/Aβ1-40 is commercially available in the U.S.; however, it does not provide a diagnosis, but simply estimates the risk of developing AD. Although blood-based AD biomarkers have a great potential in the diagnostic work-up of AD, they are not ready for the routine clinical use.

New approach for early and specific Alzheimer disease diagnosis from different plasma biomarkers

BACKGROUND

Alzheimer Disease (AD) is a complex pathology, in which several biochemical pathways could be involved. Therefore, the development of clinical studies combining different nature biomarkers in an AD diagnosis approach is required. Specifically, the present study evaluated blood biomarkers from different molecular pathways (epigenomics, lipid metabolism, lipid peroxidation), to obtain an early and specific AD diagnosis approach.

METHODS

The participants were classified into early AD (n = 53), and non-AD (healthy controls, other dementias) (n = 83). Blood samples were collected and biochemical determinations (microRNAs, lipids, lipid peroxidation compounds) were carried out by quantitative PCR and liquid chromatography coupled to mass spectrometry, respectively. Then, a logistic regression model with a Bayesian variable selection procedure was developed.

RESULTS

The Bayesian variable selection procedure for microRNAs did not show any relevant variable. Therefore, microRNA biomarkers were excluded. So, the developed model considered only lipids and lipid peroxidation compounds. The corresponding selected variables were age, 18:0 LPC, PGE2, isoprostanes and, isofurans. The validated model (by leave-one-out cross-validation) provided satisfactory diagnosis indexes (AUC 0.83, Sensitivity 87 %, Specificity 79 %).

CONCLUSION

The developed model included biomarkers from different pathways (lipid metabolism, oxidative stress), achieving a promising approach to early, specific and, minimally invasive AD diagnosis. Nevertheless, further work to validate clinically these preliminary results with an external cohort is required. Also, the integration of different compounds coming from several biochemical pathways could constitute a relevant research field for the development of AD therapeutic targets.

Digging into the intrinsic capacity concept: Can it be applied to Alzheimer's disease?

Historically, aging research has largely centered on disease pathology rather than promoting healthy aging. The World Health Organization's (WHO) policy framework (2015-2030) underscores the significance of fostering the contributions of older individuals to their families, communities, and economies. The WHO has introduced the concept of intrinsic capacity (IC) as a key metric for healthy aging, encompassing five primary domains: locomotion, vitality, sensory, cognitive, and psychological. Past AD research, constrained by methodological limitations, has focused on single outcome measures, sidelining the complexity of the disease. Our current scientific milieu, however, is primed to adopt the IC concept. This is due to three critical considerations: (I) the decline in IC is linked to neurocognitive disorders, including AD, (II) cognition, a key component of IC, is deeply affected in AD, and (III) the cognitive decline associated with AD involves multiple factors and pathophysiological pathways. Our study explores the application of the IC concept to AD patients, offering a comprehensive model that could revolutionize the disease's diagnosis and prognosis. There is a dearth of information on the biological characteristics of IC, which are a result of complex interactions within biological systems. Employing a systems biology approach, integrating omics technologies, could aid in unraveling these interactions and understanding IC from a holistic viewpoint. This comprehensive analysis of IC could be leveraged in clinical settings, equipping healthcare providers to assess AD patients' health status more effectively and devise personalized therapeutic interventions in accordance with the precision medicine paradigm. We aimed to determine whether the IC concept could be extended from older individuals to patients with AD, thereby presenting a model that could significantly enhance the diagnosis and prognosis of this disease.

Epigenetic Alterations in Alzheimer's Disease: Impact on Insulin Signaling and Advanced Drug Delivery Systems

Alzheimer's disease (AD) is a neurodegenerative condition that predominantly affects the hippocampus and the entorhinal complex, leading to memory lapse and cognitive impairment. This can have a negative impact on an individual's behavior, speech, and ability to navigate their surroundings. AD is one of the principal causes of dementia. One of the most accepted theories in AD, the amyloid β (Aβ) hypothesis, assumes that the buildup of the peptide Aβ is the root cause of AD. Impaired insulin signaling in the periphery and central nervous system has been considered to have an effect on the pathophysiology of AD. Further, researchers have shifted their focus to epigenetic mechanisms that are responsible for dysregulating major biochemical pathways and intracellular signaling processes responsible for directly or indirectly causing AD. The prime epigenetic mechanisms encompass DNA methylation, histone modifications, and non-coding RNA, and are majorly responsible for impairing insulin signaling both centrally and peripherally, thus leading to AD. In this review, we provide insights into the major epigenetic mechanisms involved in causing AD, such as DNA methylation and histone deacetylation. We decipher how the mechanisms alter peripheral insulin signaling and brain insulin signaling, leading to AD pathophysiology. In addition, this review also discusses the need for newer drug delivery systems for the targeted delivery of epigenetic drugs and explores targeted drug delivery systems such as nanoparticles, vesicular systems, networks, and other nano formulations in AD. Further, this review also sheds light on the future approaches used for epigenetic drug delivery.

CoQ10 and Mitochondrial Dysfunction in Alzheimer's Disease

The progress in understanding the pathogenesis and treatment of Alzheimer's disease (AD) is based on the recognition of the primary causes of the disease, which can be deduced from the knowledge of risk factors and biomarkers measurable in the early stages of the disease. Insights into the risk factors and the time course of biomarker abnormalities point to a role for the connection of amyloid beta (Aβ) pathology, tau pathology, mitochondrial dysfunction, and oxidative stress in the onset and development of AD. Coenzyme Q10 (CoQ10) is a lipid antioxidant and electron transporter in the mitochondrial electron transport system. The availability and activity of CoQ10 is crucial for proper mitochondrial function and cellular bioenergetics. Based on the mitochondrial hypothesis of AD and the hypothesis of oxidative stress, the regulation of the efficiency of the oxidative phosphorylation system by means of CoQ10 can be considered promising in restoring the mitochondrial function impaired in AD, or in preventing the onset of mitochondrial dysfunction and the development of amyloid and tau pathology in AD. This review summarizes the knowledge on the pathophysiology of AD, in which CoQ10 may play a significant role, with the aim of evaluating the perspective of the pharmacotherapy of AD with CoQ10 and its analogues.

TEMINET: A Co-Informative and Trustworthy Multi-Omics Integration Network for Diagnostic Prediction

Advancing the domain of biomedical investigation, integrated multi-omics data have shown exceptional performance in elucidating complex human diseases. However, as the variety of omics information expands, precisely perceiving the informativeness of intra- and inter-omics becomes challenging due to the intricate interrelations, thus presenting significant challenges in the integration of multi-omics data. To address this, we introduce a novel multi-omics integration approach, referred to as TEMINET. This approach enhances diagnostic prediction by leveraging an intra-omics co-informative representation module and a trustworthy learning strategy used to address inter-omics fusion. Considering the multifactorial nature of complex diseases, TEMINET utilizes intra-omics features to construct disease-specific networks; then, it applies graph attention networks and a multi-level framework to capture more collective informativeness than pairwise relations. To perceive the contribution of co-informative representations within intra-omics, we designed a trustworthy learning strategy to identify the reliability of each omics in integration. To integrate inter-omics information, a combined-beliefs fusion approach is deployed to harmonize the trustworthy representations of different omics types effectively. Our experiments across four different diseases using mRNA, methylation, and miRNA data demonstrate that TEMINET achieves advanced performance and robustness in classification tasks.

Characterization of cerebellar amyloid-β deposits in Alzheimer disease

Cerebellar amyloid-β (Aβ) plaques are a component of the diagnostic criteria used in Thal staging and ABC scoring for Alzheimer disease (AD) neuropathologic change. However, Aβ deposits in this anatomic compartment are unique and under-characterized; and their relationship with other pathological findings are largely undefined. In 73 cases of pure or mixed AD with an A3 score in the ABC criteria, parenchymal (plaques) and vascular (cerebral amyloid angiopathy [CAA]) cerebellar Aβ-42 deposits were characterized with respect to localization, morphology, density, and intensity. Over 85% of cases demonstrated cerebellar Aβ-42 parenchymal staining that correlated with a Braak stage V-VI/B3 score (p < 0.01). Among the 63 with cerebellar Aβ-42 deposits, a diffuse morphology was observed in 75% of cases, compact without a central dense core in 32%, and compact with a central dense core in 16% (all corresponding to plaques evident on hematoxylin and eosin staining). Cases with Purkinje cell (PC) loss showed higher proportions of PC layer Aβ-42 staining than cases without PC loss (88% vs 44%, p = 0.02), suggesting a link between Aβ-42 deposition and PC damage. Among all 73 cases, CAA was observed in the parenchymal vessels of 19% of cases and in leptomeningeal vessels in 44% of cases.

RUPE-phospho: Rapid Ultrasound-Assisted Peptide-Identification-Enhanced Phosphoproteomics Workflow for Microscale Samples

Global phosphoproteome profiling can provide insights into cellular signaling and disease pathogenesis. To achieve comprehensive phosphoproteomic analyses with minute quantities of material, we developed a rapid and sensitive phosphoproteomics sample preparation strategy based on ultrasound. We found that ultrasonication-assisted digestion can significantly improve peptide identification by 20% due to the generation of longer peptides that can be detected by mass spectrometry. By integrating this rapid ultrasound-assisted peptide-identification-enhanced proteomic method (RUPE) with streamlined phosphopeptide enrichment steps, we established RUPE-phospho, a fast and efficient strategy to characterize protein phosphorylation in mass-limited samples. This approach dramatically reduces the sample loss and processing time: 24 samples can be processed in 3 h; 5325 phosphosites, 4549 phosphopeptides, and 1888 phosphoproteins were quantified from 5 μg of human embryonic kidney (HEK) 293T cell lysate. In addition, 9219 phosphosites were quantified from 1-2 mg of OCT-embedded mouse brain with 120 min streamlined RUPE-phospho workflow. RUPE-phospho facilitates phosphoproteome profiling for microscale samples and will provide a powerful tool for proteomics-driven precision medicine research.

Alzheimer`s disease mortality in the United States: Cross-sectional analysis of county-level socio-environmental factors

BACKGROUND

Geographical disparities in mortality among Alzheimer`s disease (AD) patients have been reported and complex sociodemographic and environmental determinants of health (SEDH) may be contributing to this variation. Therefore, we aimed to explore high-risk SEDH factors possibly associated with all-cause mortality in AD across US counties using machine learning (ML) methods.

METHODS

We performed a cross-sectional analysis of individuals ≥65 years with any underlying cause of death but with AD in the multiple causes of death certificate (ICD-10,G30) between 2016 and 2020. Outcomes were defined as age-adjusted all-cause mortality rates (per 100,000 people). We analyzed 50 county-level SEDH and Classification and Regression Trees (CART) was used to identify specific county-level clusters. Random Forest, another ML technique, evaluated variable importance. CART`s performance was validated using a "hold-out" set of counties.

RESULTS

Overall, 714,568 individuals with AD died due to any cause across 2,409 counties during 2016-2020. CART identified 9 county clusters associated with an 80.1% relative increase of mortality across the spectrum. Furthermore, 7 SEDH variables were identified by CART to drive the categorization of clusters, including High School Completion (%), annual Particulate Matter 2.5 Level in Air, live births with Low Birthweight (%), Population under 18 years (%), annual Median Household Income in US dollars ($), population with Food Insecurity (%), and houses with Severe Housing Cost Burden (%).

CONCLUSION

ML can aid in the assimilation of intricate SEDH exposures associated with mortality among older population with AD, providing opportunities for optimized interventions and resource allocation to reduce mortality among this population.

Artificial intelligence for neurodegenerative experimental models

INTRODUCTION

Experimental models are essential tools in neurodegenerative disease research. However, the translation of insights and drugs discovered in model systems has proven immensely challenging, marred by high failure rates in human clinical trials.

METHODS

Here we review the application of artificial intelligence (AI) and machine learning (ML) in experimental medicine for dementia research.

RESULTS

Considering the specific challenges of reproducibility and translation between other species or model systems and human biology in preclinical dementia research, we highlight best practices and resources that can be leveraged to quantify and evaluate translatability. We then evaluate how AI and ML approaches could be applied to enhance both cross-model reproducibility and translation to human biology, while sustaining biological interpretability.

DISCUSSION

AI and ML approaches in experimental medicine remain in their infancy. However, they have great potential to strengthen preclinical research and translation if based upon adequate, robust, and reproducible experimental data.

HIGHLIGHTS

There are increasing applications of AI in experimental medicine. We identified issues in reproducibility, cross-species translation, and data curation in the field. Our review highlights data resources and AI approaches as solutions. Multi-omics analysis with AI offers exciting future possibilities in drug discovery.

Cardiovascular Disease and Alzheimer's Disease: The Heart-Brain Axis

Cardiovascular disease (CVD) remains one of the leading causes of morbidity and mortality in aging adults across the United States. Prior studies indicate that the presence of atherosclerosis, the pathogenic basis of CVD, is linked with dementias. Alzheimer's disease (AD) and AD-related dementias are a major public health challenge in the United States. Recent studies indicate that ≈3.7 million Americans ≥65 years of age had clinical AD in 2017, with projected increases to 9.3 million by 2060. Treatment options for AD remain limited. Development of disease-modifying therapies are challenging due, in part, to the long preclinical window of AD. The preclinical incubation period of AD starts in midlife, providing a critical window for identification and optimization of AD risk factors. Studies link AD with CVD risk factors such as hypertension, inflammation, and dyslipidemia. Both AD and CVD are progressive diseases with decades-long development periods. CVD can clinically manifest several years earlier than AD, making CVD and its risk factors a potential predictor of future AD. The current review focuses on the state of literature on molecular and metabolic pathways modulating the heart-brain axis underlying the potential association of midlife CVD risk factors and their effect on AD and related dementias. Further, we explore potential CVD/dementia preventive strategies during the window of opportunity in midlife and the future of research in the field in the multiomics and novel biomarker use era.

Deep learning analysis of UPLC-MS/MS-based metabolomics data to predict Alzheimer's disease

OBJECTIVE

Metabolic biomarkers can potentially inform disease progression in Alzheimer's disease (AD). The purpose of this study is to identify and describe a new set of diagnostic biomarkers for developing deep learning (DL) tools to predict AD using Ultra Performance Liquid Chromatography Mass Spectrometry (UPLC-MS/MS)-based metabolomics data.

METHODS

A total of 177 individuals, including 78 with AD and 99 with cognitive normal (CN), were selected from the Alzheimer's Disease Neuroimaging Initiative (ADNI) cohort along with 150 metabolomic biomarkers. We performed feature selection using the Least Absolute Shrinkage and Selection Operator (LASSO). The H2O DL function was used to build multilayer feedforward neural networks to predict AD.

RESULTS

The LASSO selected 21 metabolic biomarkers. To develop DL models, the 21 biomarkers identified by LASSO were imported into the H2O package. The data was split into 70% for training and 30% for validation. The best DL model with two layers and 18 neurons achieved an accuracy of 0.881, F1-score of 0.892, and AUC of 0.873. Several metabolomic biomarkers involved in glucose and lipid metabolism, in particular bile acid metabolites, were associated with APOE-ε4 allele and clinical biomarkers (Aβ42, tTau, pTau), cognitive assessments [the Alzheimer's Disease Assessment Scale-cognitive subscale 13 (ADAS13), the Mini-Mental State Examination (MMSE)], and hippocampus volume.

CONCLUSIONS

This study identified a new set of diagnostic metabolomic biomarkers for developing DL tools to predict AD. These biomarkers may help with early diagnosis, prognostic risk stratification, and/or early treatment interventions for patients at risk for AD.

The association of circulating endocannabinoids with neuroimaging and blood biomarkers of neuro-injury

BACKGROUND

Preclinical studies highlight the importance of endogenous cannabinoids (endocannabinoids; eCBs) in neurodegeneration. Yet, prior observational studies focused on limited outcome measures and assessed only few eCB compounds while largely ignoring the complexity of the eCB system. We examined the associations of multiple circulating eCBs and eCB-like molecules with early markers of neurodegeneration and neuro-injury and tested for effect modification by sex.

METHODS

This exploratory cross-sectional study included a random sample of 237 dementia-free older participants from the Framingham Heart Study Offspring cohort who attended examination cycle 9 (2011-2014), were 65 years or older, and cognitively healthy. Forty-four eCB compounds were quantified in serum, via liquid chromatography high-resolution mass spectrometry. Linear regression models were used to examine the associations of eCB levels with brain MRI measures (i.e., total cerebral brain volume, gray matter volume, hippocampal volume, and white matter hyperintensities volume) and blood biomarkers of Alzheimer's disease and neuro-injury (i.e., total tau, neurofilament light, glial fibrillary acidic protein and Ubiquitin C-terminal hydrolase L1). All models were adjusted for potential confounders and effect modification by sex was examined.

RESULTS

Participants mean age was 73.3 ± 6.2 years, and 40% were men. After adjustment for potential confounders and correction for multiple comparisons, no statistically significant associations were observed between eCB levels and the study outcomes. However, we identified multiple sex-specific associations between eCB levels and the various study outcomes. For example, high linoleoyl ethanolamide (LEA) levels were related to decreased hippocampal volume among men and to increased hippocampal volume among women (β ± SE =  - 0.12 ± 0.06, p = 0.034 and β ± SE = 0.08 ± 0.04, p = 0.026, respectively).

CONCLUSIONS

Circulating eCBs may play a role in neuro-injury and may explain sex differences in susceptibility to accelerated brain aging. Particularly, our results highlight the possible involvement of eCBs from the N-acyl amino acids and fatty acid ethanolamide classes and suggest specific novel fatty acid compounds that may be implicated in brain aging. Furthermore, investigation of the eCBs contribution to neurodegenerative disease such as Alzheimer's disease in humans is warranted, especially with prospective study designs and among diverse populations, including premenopausal women.

Integration of plasma and CSF metabolomics with CSF proteomic reveals novel associations between lipid mediators and central nervous system vascular and energy metabolism

Integration of the omics data, including metabolomics and proteomics, provides a unique opportunity to search for new associations within metabolic disorders, including Alzheimer's disease. Using metabolomics, we have previously profiled oxylipins, endocannabinoids, bile acids, and steroids in 293 CSF and 202 matched plasma samples from AD cases and healthy controls and identified both central and peripheral markers of AD pathology within inflammation-regulating cytochrome p450/soluble epoxide hydrolase pathway. Additionally, using proteomics, we have identified five cerebrospinal fluid protein panels, involved in the regulation of energy metabolism, vasculature, myelin/oligodendrocyte, glia/inflammation, and synapses/neurons, affected in AD, and reflective of AD-related changes in the brain. In the current manuscript, using metabolomics-proteomics data integration, we describe new associations between peripheral and central lipid mediators, with the above-described CSF protein panels. Particularly strong associations were observed between cytochrome p450/soluble epoxide hydrolase metabolites, bile acids, and proteins involved in glycolysis, blood coagulation, and vascular inflammation and the regulators of extracellular matrix. Those metabolic associations were not observed at the gene-co-expression level in the central nervous system. In summary, this manuscript provides new information regarding Alzheimer's disease, linking both central and peripheral metabolism, and illustrates the necessity for the "omics" data integration to uncover associations beyond gene co-expression.

Applications of multi-omics analysis in human diseases

Multi-omics usually refers to the crossover application of multiple high-throughput screening technologies represented by genomics, transcriptomics, single-cell transcriptomics, proteomics and metabolomics, spatial transcriptomics, and so on, which play a great role in promoting the study of human diseases. Most of the current reviews focus on describing the development of multi-omics technologies, data integration, and application to a particular disease; however, few of them provide a comprehensive and systematic introduction of multi-omics. This review outlines the existing technical categories of multi-omics, cautions for experimental design, focuses on the integrated analysis methods of multi-omics, especially the approach of machine learning and deep learning in multi-omics data integration and the corresponding tools, and the application of multi-omics in medical researches (e.g., cancer, neurodegenerative diseases, aging, and drug target discovery) as well as the corresponding open-source analysis tools and databases, and finally, discusses the challenges and future directions of multi-omics integration and application in precision medicine. With the development of high-throughput technologies and data integration algorithms, as important directions of multi-omics for future disease research, single-cell multi-omics and spatial multi-omics also provided a detailed introduction. This review will provide important guidance for researchers, especially who are just entering into multi-omics medical research.

Multiomics profiling of human plasma and cerebrospinal fluid reveals ATN-derived networks and highlights causal links in Alzheimer's disease

INTRODUCTION

This study employed an integrative system and causal inference approach to explore molecular signatures in blood and CSF, the amyloid/tau/neurodegeneration [AT(N)] framework, mild cognitive impairment (MCI) conversion to Alzheimer's disease (AD), and genetic risk for AD.

METHODS

Using the European Medical Information Framework (EMIF)-AD cohort, we measured 696 proteins in cerebrospinal fluid (n = 371), 4001 proteins in plasma (n = 972), 611 metabolites in plasma (n = 696), and genotyped whole-blood (7,778,465 autosomal single nucleotide epolymorphisms, n = 936). We investigated associations: molecular modules to AT(N), module hubs with AD Polygenic Risk scores and APOE4 genotypes, molecular hubs to MCI conversion and probed for causality with AD using Mendelian randomization (MR).

RESULTS

AT(N) framework associated with protein and lipid hubs. In plasma, Proprotein Convertase Subtilisin/Kexin Type 7 showed evidence for causal associations with AD. AD was causally associated with Reticulocalbin 2 and sphingomyelins, an association driven by the APOE isoform.

DISCUSSION

This study reveals multi-omics networks associated with AT(N) and causal AD molecular candidates.

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.

Multidimensional biomarkers for multiple system atrophy: an update and future directions

Multiple system atrophy (MSA) is a fatal progressive neurodegenerative disease. Biomarkers are urgently required for MSA to improve the diagnostic and prognostic accuracy in clinic and facilitate the development and monitoring of disease-modifying therapies. In recent years, significant research efforts have been made in exploring multidimensional biomarkers for MSA. However, currently few biomarkers are available in clinic. In this review, we systematically summarize the latest advances in multidimensional biomarkers for MSA, including biomarkers in fluids, tissues and gut microbiota as well as imaging biomarkers. Future directions for exploration of novel biomarkers and promotion of implementation in clinic are also discussed.

Multiplexed Gene Engineering Based on dCas9 and gRNA-tRNA Array Encoded on Single Transcript

Simultaneously, multiplexed genome engineering and targeting multiple genomic loci are valuable to elucidating gene interactions and characterizing genetic networks that affect phenotypes. Here, we developed a general CRISPR-based platform to perform four functions and target multiple genome loci encoded in a single transcript. To establish multiple functions for multiple loci targets, we fused four RNA hairpins, MS2, PP7, com and boxB, to stem-loops of gRNA (guide RNA) scaffolds, separately. The RNA-hairpin-binding domains MCP, PCP, Com and λN22 were fused with different functional effectors. These paired combinations of cognate-RNA hairpins and RNA-binding proteins generated the simultaneous, independent regulation of multiple target genes. To ensure that all proteins and RNAs are expressed in one transcript, multiple gRNAs were constructed in a tandemly arrayed tRNA (transfer RNA)-gRNA architecture, and the triplex sequence was cloned between the protein-coding sequences and the tRNA-gRNA array. By leveraging this system, we illustrate the transcriptional activation, transcriptional repression, DNA methylation and DNA demethylation of endogenous targets using up to 16 individual CRISPR gRNAs delivered on a single transcript. This system provides a powerful platform to investigate synthetic biology questions and engineer complex-phenotype medical applications.

Advances and applications of fluids biomarkers in diagnosis and therapeutic targets of Alzheimer's disease

AIMS

Alzheimer's disease (AD) is a neurodegenerative disease with challenging early diagnosis and effective treatments due to its complex pathogenesis. AD patients are often diagnosed after the appearance of the typical symptoms, thereby delaying the best opportunity for effective measures. Biomarkers could be the key to resolving the challenge. This review aims to provide an overview of application and potential value of AD biomarkers in fluids, including cerebrospinal fluid, blood, and saliva, in diagnosis and treatment.

METHODS

A comprehensive search of the relevant literature was conducted to summarize potential biomarkers for AD in fluids. The paper further explored the biomarkers' utility in disease diagnosis and drug target development.

RESULTS

Research on biomarkers mainly focused on amyloid-β (Aβ) plaques, Tau protein abnormal phosphorylation, axon damage, synaptic dysfunction, inflammation, and related hypotheses associated with AD mechanisms. Aβ₄₂ , total Tau (t-Tau), and phosphorylated Tau (p-Tau), have been endorsed for their diagnostic and predictive capability. However, other biomarkers remain controversial. Drugs targeting Aβ have shown some efficacy and those that target BACE1 and Tau are still undergoing development.

CONCLUSION

Fluid biomarkers hold considerable potential in the diagnosis and drug development of AD. However, improvements in sensitivity and specificity, and approaches for managing sample impurities, need to be addressed for better diagnosis.

A global view of the genetic basis of Alzheimer disease

The risk of Alzheimer disease (AD) increases with age, family history and informative genetic variants. Sadly, there is still no cure or means of prevention. As in other complex diseases, uncovering genetic causes of AD could identify underlying pathological mechanisms and lead to potential treatments. Rare, autosomal dominant forms of AD occur in middle age as a result of highly penetrant genetic mutations, but the most common form of AD occurs later in life. Large-scale, genome-wide analyses indicate that 70 or more genes or loci contribute to AD. One of the major factors limiting progress is that most genetic data have been obtained from non-Hispanic white individuals in Europe and North America, preventing the development of personalized approaches to AD in individuals of other ethnicities. Fortunately, emerging genetic data from other regions - including Africa, Asia, India and South America - are now providing information on the disease from a broader range of ethnicities. Here, we summarize the current knowledge on AD genetics in populations across the world. We predominantly focus on replicated genetic discoveries but also include studies in ethnic groups where replication might not be feasible. We attempt to identify gaps that need to be addressed to achieve a complete picture of the genetic and molecular factors that drive AD in individuals across the globe.

The Potential of NLRP3 Inflammasome as a Therapeutic Target in Neurological Diseases

NLRP3 (NLRP3: NOD-, LRR-, and pyrin domain-containing protein 3) inflammasome is the best-described inflammasome that plays a crucial role in the innate immune system and a wide range of diseases. The intimate association of NLRP3 with neurological disorders, including neurodegenerative diseases and strokes, further emphasizes its prominence as a clinical target for pharmacological intervention. However, after decades of exploration, the mechanism of NLRP3 activation remains indefinite. This review highlights recent advances and gaps in our insights into the regulation of NLRP3 inflammasome. Furthermore, we present several emerging pharmacological approaches of clinical translational potential targeting the NLRP3 inflammasome in neurological diseases. More importantly, despite small-molecule inhibitors of the NLRP3 inflammasome, we have focused explicitly on Chinese herbal medicine and botanical ingredients, which may be splendid therapeutics by inhibiting NLRP3 inflammasome for central nervous system disorders. We expect that we can contribute new perspectives to the treatment of neurological diseases.

The Effects of Amyloid-β on Metabolomic Profiles of Cardiomyocytes and Coronary Endothelial Cells

BACKGROUND

An increasing number of experimental and clinical studies show a link between Alzheimer's disease and heart diseases such as heart failure, ischemic heart disease, and atrial fibrillation. However, the mechanisms underlying the potential role of amyloid-β (Aβ) in the pathogenesis of cardiac dysfunction in Alzheimer's disease remain unknown. We have recently shown the effects of Aβ 1 - 40 and Aβ 1 - 42 on cell viability and mitochondrial function in cardiomyocytes and coronary artery endothelial cells.

OBJECTIVE

In this study, we investigated the effects of Aβ 1 - 40 and Aβ 1 - 42 on the metabolism of cardiomyocytes and coronary artery endothelial cells.

METHODS

Gas chromatography-mass spectrometry was used to analyze metabolomic profiles of cardiomyocytes and coronary artery endothelial cells treated with Aβ 1 - 40 and Aβ 1 - 42. In addition, we determined mitochondrial respiration and lipid peroxidation in these cells.

RESULTS

We found that the metabolism of different amino acids was affected by Aβ 1 - 42 in each cell type, whereas the fatty acid metabolism is consistently disrupted in both types of cells. Lipid peroxidation was significantly increased, whereas mitochondrial respiration was reduced in both cell types in response to Aβ 1 - 42.

CONCLUSION

This study revealed the disruptive effects of Aβ on lipid metabolism and mitochondria function in cardiac cells.

The foundation and architecture of precision medicine in neurology and psychiatry

Neurological and psychiatric diseases have high degrees of genetic and pathophysiological heterogeneity, irrespective of clinical manifestations. Traditional medical paradigms have focused on late-stage syndromic aspects of these diseases, with little consideration of the underlying biology. Advances in disease modeling and methodological design have paved the way for the development of precision medicine (PM), an established concept in oncology with growing attention from other medical specialties. We propose a PM architecture for central nervous system diseases built on four converging pillars: multimodal biomarkers, systems medicine, digital health technologies, and data science. We discuss Alzheimer's disease (AD), an area of significant unmet medical need, as a case-in-point for the proposed framework. AD can be seen as one of the most advanced PM-oriented disease models and as a compelling catalyzer towards PM-oriented neuroscience drug development and advanced healthcare practice.

Identification of hub proteins in cerebrospinal fluid as potential biomarkers of Alzheimer's disease by integrated bioinformatics

BACKGROUND

Alzheimer's disease (AD) is a heterogeneous neurodegenerative disease with complex pathophysiology. Therefore, the identification of novel effective fluid biomarkers is essential for Alzheimer's disease diagnosis and drug development. This study aimed to identify potential candidate hub proteins in cerebrospinal fluid for precise Alzheimer's disease diagnosis using bioinformatics methods.

METHODS

A total of 29 co-significant differentially expressed proteins were identified by differential protein expression analysis in four different cohorts. Functional enrichment analysis revealed that most of these proteins were enriched in pathways related to glycometabolism. Using the Least Absolute Shrinkage and Selection Operator (LASSO) and random forest feature selection methods, six hub proteins [14-3-3 protein zeta/delta (YWHAZ), SPARC-related modular calcium-binding protein 1 (SMOC1), aldolase A (ALDOA), pyruvate kinase isoenzyme type M2 (PKM), chitinase-3-like protein 1 (CHI3L1), and secreted phosphoprotein 1 (SPP1)] were identified.

RESULTS

These six hub proteins were upregulated in the cerebrospinal fluid of patients with Alzheimer's disease compared with cognitively unimpaired control individuals. Meanwhile, SMOC1, ALDOA, and PKM were specifically upregulated in the cerebrospinal fluid of patients with Alzheimer's disease but not in other neurodegenerative diseases. Build AD diagnostic models showed that a single hub protein or six hub proteins combination had an excellent ability to discriminate Alzheimer's disease.

CONCLUSIONS

In conclusion, our study suggests that these identified hub proteins, which are related to glycometabolism, may be potential biomarkers for further basic and clinical research in Alzheimer's disease.

Biomarkers of diagnosis, prognosis, pathogenesis, response to therapy: Convergence or divergence? Lessons from Alzheimer's disease and synucleinopathies

Alzheimer's disease (AD) is the most common disorder associated with cognitive impairment. Recent observations emphasize the pathogenic role of multiple factors inside and outside the central nervous system, supporting the notion that AD is a syndrome of many etiologies rather than a "heterogeneous" but ultimately unifying disease entity. Moreover, the defining pathology of amyloid and tau coexists with many others, such as α-synuclein, TDP-43, and others, as a rule, not an exception. Thus, an effort to shift our AD paradigm as an amyloidopathy must be reconsidered. Along with amyloid accumulation in its insoluble state, β-amyloid is becoming depleted in its soluble, normal states, as a result of biological, toxic, and infectious triggers, requiring a shift from convergence to divergence in our approach to neurodegeneration. These aspects are reflected-in vivo-by biomarkers, which have become increasingly strategic in dementia. Similarly, synucleinopathies are primarily characterized by abnormal deposition of misfolded α-synuclein in neurons and glial cells and, in the process, depleting the levels of the normal, soluble α-synuclein that the brain needs for many physiological functions. The soluble to insoluble conversion also affects other normal brain proteins, such as TDP-43 and tau, accumulating in their insoluble states in both AD and dementia with Lewy bodies (DLB). The two diseases have been distinguished by the differential burden and distribution of insoluble proteins, with neocortical phosphorylated tau deposition more typical of AD and neocortical α-synuclein deposition peculiar to DLB. We propose a reappraisal of the diagnostic approach to cognitive impairment from convergence (based on clinicopathologic criteria) to divergence (based on what differs across individuals affected) as a necessary step for the launch of precision medicine.