Challenges in LC-MS-based metabolomics for Alzheimer's disease early detection: targeted approaches versus untargeted approaches

Pathology-specific lipid alterations with triacylglycerol as a potential biomarker in Focal cortical dysplasia (FCD) and Mesial Temporal Lobe Epilepsy with Hippocampal Sclerosis (MTLE-HS)

Focal Cortical Dysplasia (FCD) & Mesial Temporal Lobe Epilepsy-Hippocampal Sclerosis (MTLE-HS) are two common pathologies of drug-resistant focal epilepsy (DRE). Inappropriate localization of the epileptogenic zones (EZs) in FCD is a significant contributing factor to the unsatisfactory surgical results observed in FCD cases. Currently, no molecular or cellular indicators are available which can aid in identifying the epileptogenic zones (EZs) in FCD. Phospholipid modifications in healthy and malignant tumour tissues have been documented and used to demarcate tumour boundaries. The objective of this research was to analyze and evaluate the lipid profiles in a manner that takes into account the specific disease and subtype. The technique of liquid chromatography and tandem mass spectrometry was utilized to detect changes in lipids in surgically resected brain samples from patients with FCD and MTLE-HS, in comparison to non-epileptic controls. Significant upregulation of TAGs was seen in both FCD and MTLE-HS. Additionally, the levels of triglycerides in the plasma of peripheral blood were measured in patients with FCD, MTLE-HS, and healthy individuals as controls. These findings suggest that employing distinct lipid mass spectra could be an effective method for identifying the EZs in FCD. The unique lipid mass spectra of cortical tissues from patients with FCD can be utilized for real-time surgical guidance. Additionally, the plasma triglyceride (TAG) level has the potential to act as a biomarker once validated on a larger cohort.

Serum metabolic profiling analysis of chronic gastritis and gastric cancer by untargeted metabolomics

Chronic gastritis (CG), particularly when associated with Helicobacter pylori (H. pylori) infection, is a significant precursor to gastric cancer (GC), a leading cause of cancer-related deaths worldwide. The persistent inflammation in CG, driven by factors such as H. pylori, induces oxidative stress and DNA damage in gastric epithelial cells, which can lead to malignant transformation. Atrophic gastritis, a form of CG, can be categorized into autoimmune and H. pylori-associated types, both of which increase the risk of GC development, particularly when compounded by external factors like smoking and dietary habits. This manuscript explores the pathophysiological mechanisms underlying CG and its progression to GC, highlighting the critical role of metabolomics in advancing our understanding of these processes. Metabolomics, the comprehensive study of metabolites, offers a novel approach to identifying biomarkers that could facilitate early detection and improve the accuracy of GC diagnosis and prognosis. The analysis of metabolic alterations, particularly in glucose, lipid, and amino acid metabolism, reveals distinct biochemical pathways associated with the progression from benign gastritis to malignancy. Integrating metabolomic profiling with traditional diagnostic methods can revolutionize GC management, enabling more personalized treatment strategies and improving clinical outcomes. However, significant challenges remain, including the need to validate biomarkers across diverse populations and standardize metabolomic techniques. Future research should address these challenges to fully realize the potential of metabolomics in early GC detection and treatment, ultimately aiming to reduce the global burden of this deadly disease.

Advancing personalized medicine: Integrating statistical algorithms with omics and nano-omics for enhanced diagnostic accuracy and treatment efficacy

Medical laboratory services enable precise measurement of thousands of biomolecules and have become an inseparable part of high-quality healthcare services, exerting a profound influence on global health outcomes. The integration of omics technologies into laboratory medicine has transformed healthcare, enabling personalized treatments and interventions based on individuals' distinct genetic and metabolic profiles. Interpreting laboratory data relies on reliable reference values. Presently, population-derived references are used for individuals, risking misinterpretation due to population heterogeneity, and leading to medical errors. Thus, personalized references are crucial for precise interpretation of individual laboratory results, and the interpretation of omics data should be based on individualized reference values. We reviewed recent advancements in personalized laboratory medicine, focusing on personalized omics, and discussed strategies for implementing personalized statistical approaches in omics technologies to improve global health and concluded that personalized statistical algorithms for interpretation of omics data have great potential to enhance global health. Finally, we demonstrated that the convergence of nanotechnology and omics sciences is transforming personalized laboratory medicine by providing unparalleled diagnostic precision and innovative therapeutic strategies.

Exploring the multifaceted therapeutic mechanism of Schisanlactone E (XTS) in APP/PS1 mouse model of Alzheimer's disease through multi-omics analysis

Background

Schisanlactone E, also known as XueTongSu (XTS), is an active compound extracted from the traditional Tujia medicine Kadsura heteroclita ("XueTong"). Recent studies highlight its anti-inflammatory and antioxidant properties, yet the mechanisms of XTS's therapeutic effects on Alzheimer's disease (AD) are unclear. This study aims to elucidate the therapeutic efficacy and mechanisms of XTS in AD.

Methods

Ten C57BL/6 mice were assigned to the control group (NC), and twenty APP/PS1 transgenic mice were randomly divided into the model group (M) (10 mice) and the XTS treatment group (Tre) (10 mice). After an acclimatization period of 7 days, intraperitoneal injections were administered over a 60-day treatment period. The NC and M groups received saline, while the Tre group received XTS at 2 mg/kg. Learning and memory abilities were assessed using the Morris Water Maze (MWM) test. Histopathological changes were evaluated using hematoxylin and eosin (HE) and Nissl staining, and immunofluorescence was used to assess pathological products and glial cell activation. Cytokine levels (IL-1β, IL-6, TNF-α) in the hippocampus were quantified by qPCR. 16S rDNA sequencing analyzed gut microbiota metabolic alterations, and metabolomic analysis was performed on cortical samples. The KEGG database was used to analyze the regulatory mechanisms of XTS in AD treatment.

Results

XTS significantly improved learning and spatial memory in APP/PS1 mice and ameliorated histopathological changes, reducing Aβ plaque aggregation and glial cell activation. XTS decreased the expression of inflammatory cytokines IL-1β, IL-6, and TNF-α. It also enhanced gut microbiota diversity, notably increasing Akkermansia species, and modulated levels of metabolites such as isosakuranetin, 5-KETE, 4-methylcatechol, and sphinganine. Pathway analysis indicated that XTS regulated carbohydrate metabolism, neuroactive ligand-receptor interactions, and alanine, aspartate, and glutamate metabolism, mitigating gut microbiota dysbiosis and metabolic disturbances.

Conclusion

XTS ameliorates cognitive deficits, pathological changes, and inflammatory responses in APP/PS1 mice. It significantly modulates the gut microbiota, particularly increasing Akkermansia abundance, and influences levels of key metabolites in both the gut and brain. These findings suggest that XTS exerts anti-AD effects through the microbial-gut-brain axis (MGBA).

CSF amino acid profiles in ICV-streptozotocin-induced sporadic Alzheimer's disease in male Wistar rat: a metabolomics and systems biology perspective

Alzheimer's disease (AD) is an increasingly important public health concern due to the increasing proportion of older individuals within the general population. The impairment of processes responsible for adequate brain energy supply primarily determines the early features of the aging process. Restricting brain energy supply results in brain hypometabolism prior to clinical symptoms and is anatomically and functionally associated with cognitive impairment. The present study investigated changes in metabolic profiles induced by intracerebroventricular-streptozotocin (ICV-STZ) in an AD-like animal model. To this end, male Wistar rats received a single injection of STZ (3 mg·kg-1) by ICV (2.5 μL into each ventricle for 5 min on each side). In the second week after receiving ICV-STZ, rats were tested for cognitive performance using the Morris Water Maze test and subsequently prepared for positron emission tomography (PET) to confirm AD-like symptoms. Tandem Mass Spectrometry (MS/MS) analysis was used to detect amino acid changes in cerebrospinal fluid (CFS) samples. Our metabolomics study revealed a reduction in the concentrations of various amino acids (alanine, arginine, aspartic acid, glutamic acid, glycine, isoleucine, methionine, phenylalanine, proline, serine, threonine, tryptophane, tyrosine, and valine) in CSF of ICV-STZ-treated animals as compared to controls rats. The results of the current study indicate amino acid levels could potentially be considered targets of nutritional and/or pharmacological interventions to interfere with AD progression.

Gut instincts: Unveiling the connection between gut microbiota and Alzheimer's disease

Alzheimer's disease (AD) is a neurodegenerative disorder marked by neuroinflammation and gradual cognitive decline. Recent research has revealed that the gut microbiota (GM) plays an important role in the pathogenesis of AD through the microbiota-gut-brain axis. However, the mechanism by which GM and microbial metabolites alter brain function is not clearly understood. GM dysbiosis increases the permeability of the intestine, alters the blood-brain barrier permeability, and elevates proinflammatory mediators causing neurodegeneration. This review article introduced us to the composition and functions of GM along with its repercussions of dysbiosis in relation to AD. We also discussed the importance of the gut-brain axis and its role in communication. Later we focused on the mechanism behind gut dysbiosis and the progression of AD including neuroinflammation, oxidative stress, and changes in neurotransmitter levels. Furthermore, we highlighted recent developments in AD management, such as microbiota-based therapy, dietary interventions like prebiotics, probiotics, and fecal microbiota transplantation. Finally, we concluded with challenges and future directions in AD research based on GM.

Can Small Molecules Provide Clues on Disease Progression in Cerebrospinal Fluid from Mild Cognitive Impairment and Alzheimer's Disease Patients?

Alzheimer's disease (AD) is a complex and multifactorial neurodegenerative disease, which is currently diagnosed via clinical symptoms and nonspecific biomarkers (such as Aβ1-42, t-Tau, and p-Tau) measured in cerebrospinal fluid (CSF), which alone do not provide sufficient insights into disease progression. In this pilot study, these biomarkers were complemented with small-molecule analysis using non-target high-resolution mass spectrometry coupled with liquid chromatography (LC) on the CSF of three groups: AD, mild cognitive impairment (MCI) due to AD, and a non-demented (ND) control group. An open-source cheminformatics pipeline based on MS-DIAL and patRoon was enhanced using CSF- and AD-specific suspect lists to assist in data interpretation. Chemical Similarity Enrichment Analysis revealed a significant increase of hydroxybutyrates in AD, including 3-hydroxybutanoic acid, which was found at higher levels in AD compared to MCI and ND. Furthermore, a highly sensitive target LC-MS method was used to quantify 35 bile acids (BAs) in the CSF, revealing several statistically significant differences including higher dehydrolithocholic acid levels and decreased conjugated BA levels in AD. This work provides several promising small-molecule hypotheses that could be used to help track the progression of AD in CSF samples.

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

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

Danggui Shaoyao San: comprehensive modulation of the microbiota-gut-brain axis for attenuating Alzheimer's disease-related pathology

Background: Alzheimer's disease (AD), an age-associated neurodegenerative disorder, currently lacks effective clinical therapeutics. Traditional Chinese Medicine (TCM) holds promising potential in AD treatment, exemplified by Danggui Shaoyao San (DSS), a TCM formulation. The precise therapeutic mechanisms of DSS in AD remain to be fully elucidated. This study aims to uncover the therapeutic efficacy and underlying mechanisms of DSS in AD, employing an integrative approach encompassing gut microbiota and metabolomic analyses. Methods: Thirty Sprague-Dawley (SD) rats were allocated into three groups: Blank Control (Con), AD Model (M), and Danggui Shaoyao San (DSS). AD models were established via bilateral intracerebroventricular injections of streptozotocin (STZ). DSS was orally administered at 24 g·kg-1·d-1 (weight of raw herbal materials) for 14 days. Cognitive functions were evaluated using the Morris Water Maze (MWM) test. Pathological alterations were assessed through hematoxylin and eosin (HE) staining. Bloodstream metabolites were characterized, gut microbiota profiled through 16S rDNA sequencing, and cortical metabolomics analyzed. Hippocampal proinflammatory cytokines (IL-1β, IL-6, TNF-α) were quantified using RT-qPCR, and oxidative stress markers (SOD, CAT, GSH-PX, MDA) in brain tissues were measured with biochemical assays. Results: DSS identified a total of 1,625 bloodstream metabolites, predominantly Benzene derivatives, Carboxylic acids, and Fatty Acyls. DSS significantly improved learning and spatial memory in AD rats and ameliorated cerebral tissue pathology. The formulation enriched the probiotic Ligilactobacillus, modulating metabolites like Ophthalmic acid (OA), Phosphocreatine (PCr), Azacridone A, Inosine, and NAD. DSS regulated Purine and Nicotinate-nicotinamide metabolism, restoring balance in the Candidatus Saccharibacteria-OA interplay and stabilizing gut microbiota-metabolite homeostasis. Additionally, DSS reduced hippocampal IL-1β, IL-6, TNF-α expression, attenuating the inflammatory state. It elevated antioxidative enzymes (SOD, CAT, GSH-PX) while reducing MDA levels, indicating diminished oxidative stress in AD rat brains. Conclusion: DSS addresses AD pathology through multifaceted mechanisms, encompassing gut microbiome regulation, specific metabolite modulation, and the mitigation of inflammation and oxidative stress within the brain. This holistic intervention through the Microbial-Gut-Brain Axis (MGBA) underscores DSS's potential as an integrative therapeutic agent in combatting AD.

Associations Between Metabolomics Findings and Brain Hypometabolism in Mild Cognitive Impairment and Alzheimer's Disease

BACKGROUND

Alzheimer's disease (AD) is a progressive neurodegenerative disease with rising prevalence due to the aging global population. Existing methods for diagnosing AD are struggling to detect the condition in its earliest and most treatable stages. One early indicator of AD is a substantial decrease in the brain's glucose metabolism. Metabolomics can detect disturbances in biofluids, which may be advantageous for early detection of some AD-related changes. The study aims to predict brain hypometabolism in Alzheimer's disease using metabolomics findings and develop a predictive model based on metabolomic data.

METHODS

The data used in this study were acquired from the Alzheimer's Disease Neuroimaging Initiative (ADNI) project. We conducted a longitudinal study with three assessment time points to investigate the predictive power of baseline metabolomics for modeling longitudinal fluorodeoxyglucose- positron emission tomography (FDG-PET) trajectory changes in AD patients. A total of 44 participants with AD were included. The Alzheimer's Disease Assessment Scale (ADAS), the Mini-Mental State Examination (MMSE), and the Clinical Dementia Rating Scale-Sum of Boxes (CDR-SB) were used for cognitive assessments. A single global brain hypo-metabolism index was used as the outcome variable.

RESULTS

Across models, we observed consistent positive relationships between specific cholesterol esters - CE (20:3) (p = 0.005) and CE (18:3) (p = 0.0039) - and FDG-PET metrics, indicating these baseline metabolites may be valuable indicators of future PET score changes. Selected triglycerides like DG-O (16:0-20:4) also showed time-specific positive associations (p = 0.017).

CONCLUSION

This research provides new insights into the disruptions in the metabolic network linked to AD pathology. These findings could pave the way for identifying novel biomarkers and potential treatment targets for AD.

Multiplatform untargeted metabolomics

Metabolomics samples like human urine or serum contain upwards of a few thousand metabolites, but individual analytical techniques can only characterize a few hundred metabolites at best. The uncertainty in metabolite identification commonly encountered in untargeted metabolomics adds to this low coverage problem. A multiplatform (multiple analytical techniques) approach can improve upon the number of metabolites reliably detected and correctly assigned. This can be further improved by applying synergistic sample preparation along with the use of combinatorial or sequential non-destructive and destructive techniques. Similarly, peak detection and metabolite identification strategies that employ multiple probabilistic approaches have led to better annotation decisions. Applying these techniques also addresses the issues of reproducibility found in single platform methods. Nevertheless, the analysis of large data sets from disparate analytical techniques presents unique challenges. While the general data processing workflow is similar across multiple platforms, many software packages are only fully capable of processing data types from a single analytical instrument. Traditional statistical methods such as principal component analysis were not designed to handle multiple, distinct data sets. Instead, multivariate analysis requires multiblock or other model types for understanding the contribution from multiple instruments. This review summarizes the advantages, limitations, and recent achievements of a multiplatform approach to untargeted metabolomics.

Multiplatform untargeted metabolomics

Metabolomics samples like human urine or serum contain upwards of a few thousand metabolites, but individual analytical techniques can only characterize a few hundred metabolites at best. The uncertainty in metabolite identification commonly encountered in untargeted metabolomics adds to this low coverage problem. A multiplatform (multiple analytical techniques) approach can improve upon the number of metabolites reliably detected and correctly assigned. This can be further improved by applying synergistic sample preparation along with the use of combinatorial or sequential non-destructive and destructive techniques. Similarly, peak detection and metabolite identification strategies that employ multiple probabilistic approaches have led to better annotation decisions. Applying these techniques also addresses the issues of reproducibility found in single platform methods. Nevertheless, the analysis of large data sets from disparate analytical techniques presents unique challenges. While the general data processing workflow is similar across multiple platforms, many software packages are only fully capable of processing data types from a single analytical instrument. Traditional statistical methods such as principal component analysis were not designed to handle multiple, distinct data sets. Instead, multivariate analysis requires multiblock or other model types for understanding the contribution from multiple instruments. This review summarizes the advantages, limitations, and recent achievements of a multiplatform approach to untargeted metabolomics.

High-Fat Diet Alters Acylcarnitine Metabolism of the Retina and Retinal Pigment Epithelium/Choroidal Tissues in Laser-Induced Choroidal Neovascularization Rat Models

SCOPE

Choroidal neovascularization (CNV) is age-related macular degeneration's (AMD) main pathological change. High-fat diet (HFD) is associated with a form of CNV; however, the specific mechanism is unclear. Mitochondrial dysfunction, characterized by abnormal acylcarnitine, occurs during metabolic screening of serum or other body tissues in AMD. This study investigates HFD's role in retinal and retinal pigment epithelium (RPE)/choroidal acylcarnitine metabolism in CNV formation.

METHODS AND RESULTS

Chow diet and HFD-BN rats are laser-treated to induce CNV. Acylcarnitine species are quantitatively characterized by ultrahigh-performance liquid chromatography-tandem mass spectrometry. Optical coherence tomography and fundus fluorescein angiography evaluate CNV severity. HFD promotes weight gain, dyslipidemia, and CNV formation. In CNV rats, few medium-chain fatty acids (MCFAs) acylcarnitine in the RPE/choroid are initially affected. When an HFD is administered to these, even MCFA acylcarnitine in the RPE/choroid is found to decline. However, in the retina, odd acylcarnitines are increased, revealing "an opposite" change within the RPE/choroid, accompanied by influencing glycolytic key enzymes. The HFD+CNV group incorporated fewer long-chain acylcarnitines, like C18:2, into the retina than controls.

CONCLUSIONS

HFD hastens choroidal neovascularization. The study comprehensively documented acylcarnitine profiles in a CNV rat model. Acylcarnitine's odd-even and carbon-chain length properties may guide future therapeutics.

Recent Review on Selected Xenobiotics and Their Impacts on Gut Microbiome and Metabolome

As it is well known, the gut is one of the primary sites in any host for xenobiotics, and the many microbial metabolites responsible for the interactions between the gut microbiome and the host. However, there is a growing concern about the negative impacts on human health induced by toxic xenobiotics. Metabolomics, broadly including lipidomics, is an emerging approach to studying thousands of metabolites in parallel. In this review, we summarized recent advancements in mass spectrometry (MS) technologies in metabolomics. In addition, we reviewed recent applications of MS-based metabolomics for the investigation of toxic effects of xenobiotics on microbial and host metabolism. It was demonstrated that metabolomics, gut microbiome profiling, and their combination have a high potential to identify metabolic and microbial markers of xenobiotic exposure and determine its mechanism. Further, there is increasing evidence supporting that reprogramming the gut microbiome could be a promising approach to the intervention of xenobiotic toxicity.

Advances in peripheral blood biomarkers of patients with Alzheimer's disease: Moving closer to personalized therapies

Recently, measurable peripheral biomarkers in the plasma of patients with Alzheimer's disease (AD) have gained considerable clinical interest. Several studies have identified one or more blood signatures that may facilitate the development of novel diagnostic and therapeutic strategies. For instance, changes in peripheral amyloid β42 (Aβ42) levels have been largely investigated in patients with AD and correlated with the progression of the pathology, although with controversial results. In addition, tumor necrosis factor α (TNFα) has been identified as an inflammatory biomarker strongly associated with AD, and several studies have consistently suggested the pharmacological targeting of TNFα to reduce systemic inflammation and prevent neurotoxicity in AD. Moreover, alterations in plasma metabolite levels appear to predict the progression of systemic processes relevant to brain functions. In this study, we analyzed the changes in the levels of Aβ42, TNFα, and plasma metabolites in subjects with AD and compared the results with those in healthy elderly (HE) subjects. Differences in plasma metabolites of patients with AD were analyzed with respect to Aβ42, TNFα, and the Mini-Mental State Examination (MMSE) score, searching for plasma signatures that changed simultaneously. In addition, the phosphorylation levels of the Tyr682 residue of the amyloid precursor protein (APP), which we previously proposed as a biomarker of AD, were measured in five HE and five AD patients, in whom the levels of Aβ42, TNFα, and two plasma lipid metabolites increased simultaneously. Overall, this study highlights the potential of combining different plasma signatures to define specific clinical phenotypes of patient subgroups, thus paving the way for the stratification of patients with AD and development of personalized approaches.

Metabolomics unveils the mechanism of Bufei Huayu decoction in combination with cisplatin against non-small cell lung cancer (NSCLC)

Introduction

Bufei Huayu Decoction (BFHY) is a clinical prescription with reported efficacy in enhancing the therapeutic outcomes of chemotherapeutic agents for non-small cell lung cancer (NSCLC). However, the underlying metabolic mechanism of BFHY's action remains unexplored.

Objective

The objective of this study is to investigate the global metabolic effects of cisplatin and cisplatin plus BFHY on NSCLC.

Methods

Three groups (NSCLC, cisplatin, and cisplatin + BFHY) underwent a serum metabolomics procedure based on UHPLC-QE-MS. Then, a pathway analysis was carried out using MetaboAnalyst 3.0 to elucidate the therapeutic action routes of cisplatin and cisplatin plus BFHY in NSCLC.

Results

In the subcutaneous NSCLC model, both cisplatin and cisplatin + BFHY reduced the tumor volume and caused cell death. In comparison to cisplatin alone, cisplatin + BFHY showed a stronger tumor-suppressing impact. Furthermore, the same 16 metabolic signaling pathways were shared by the cisplatin and cisplatin + BFHY treatments. These typical metabolites are mainly involved in amino acid metabolism, lipid mobilization, nucleic acid metabolism and carbohydrate metabolites.

Conclusions

Potential biomarkers and metabolic networks of cisplatin and cisplatin + BFHY's anti-tumor actions are revealed in our investigation.

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.

Integrative multi-omics and systems bioinformatics in translational neuroscience: A data mining perspective

Bioinformatic analysis of large and complex omics datasets has become increasingly useful in modern day biology by providing a great depth of information, with its application to neuroscience termed neuroinformatics. Data mining of omics datasets has enabled the generation of new hypotheses based on differentially regulated biological molecules associated with disease mechanisms, which can be tested experimentally for improved diagnostic and therapeutic targeting of neurodegenerative diseases. Importantly, integrating multi-omics data using a systems bioinformatics approach will advance the understanding of the layered and interactive network of biological regulation that exchanges systemic knowledge to facilitate the development of a comprehensive human brain profile. In this review, we first summarize data mining studies utilizing datasets from the individual type of omics analysis, including epigenetics/epigenomics, transcriptomics, proteomics, metabolomics, lipidomics, and spatial omics, pertaining to Alzheimer's disease, Parkinson's disease, and multiple sclerosis. We then discuss multi-omics integration approaches, including independent biological integration and unsupervised integration methods, for more intuitive and informative interpretation of the biological data obtained across different omics layers. We further assess studies that integrate multi-omics in data mining which provide convoluted biological insights and offer proof-of-concept proposition towards systems bioinformatics in the reconstruction of brain networks. Finally, we recommend a combination of high dimensional bioinformatics analysis with experimental validation to achieve translational neuroscience applications including biomarker discovery, therapeutic development, and elucidation of disease mechanisms. We conclude by providing future perspectives and opportunities in applying integrative multi-omics and systems bioinformatics to achieve precision phenotyping of neurodegenerative diseases and towards personalized medicine.

A Targeted Mass Spectrometry Approach to Identify Peripheral Changes in Metabolic Pathways of Patients with Alzheimer's Disease

Alzheimer's disease (AD), a neurodegenerative disorder, is the most common cause of dementia in the elderly population. Since its original description, there has been intense debate regarding the factors that trigger its pathology. It is becoming apparent that AD is more than a brain disease and harms the whole-body metabolism. We analyzed 630 polar and apolar metabolites in the blood of 20 patients with AD and 20 healthy individuals, to determine whether the composition of plasma metabolites could offer additional indicators to evaluate any alterations in the metabolic pathways related to the illness. Multivariate statistical analysis showed that there were at least 25 significantly dysregulated metabolites in patients with AD compared with the controls. Two membrane lipid components, glycerophospholipids and ceramide, were upregulated, whereas glutamic acid, other phospholipids, and sphingolipids were downregulated. The data were analyzed using metabolite set enrichment analysis and pathway analysis using the KEGG library. The results showed that at least five pathways involved in the metabolism of polar compounds were dysregulated in patients with AD. Conversely, the lipid pathways did not show significant alterations. These results support the possibility of using metabolome analysis to understand alterations in the metabolic pathways related to AD pathophysiology.

Small molecule biomarker discovery: Proposed workflow for LC-MS-based clinical research projects

Mass spectrometry focusing on small endogenous molecules has become an integral part of biomarker discovery in the pursuit of an in-depth understanding of the pathophysiology of various diseases, ultimately enabling the application of personalized medicine. While LC-MS methods allow researchers to gather vast amounts of data from hundreds or thousands of samples, the successful execution of a study as part of clinical research also requires knowledge transfer with clinicians, involvement of data scientists, and interactions with various stakeholders. The initial planning phase of a clinical research project involves specifying the scope and design, and engaging relevant experts from different fields. Enrolling subjects and designing trials rely largely on the overall objective of the study and epidemiological considerations, while proper pre-analytical sample handling has immediate implications on the quality of analytical data. Subsequent LC-MS measurements may be conducted in a targeted, semi-targeted, or non-targeted manner, resulting in datasets of varying size and accuracy. Data processing further enhances the quality of data and is a prerequisite for in-silico analysis. Nowadays, the evaluation of such complex datasets relies on a mix of classical statistics and machine learning applications, in combination with other tools, such as pathway analysis and gene set enrichment. Finally, results must be validated before biomarkers can be used as prognostic or diagnostic decision-making tools. Throughout the study, quality control measures should be employed to enhance the reliability of data and increase confidence in the results. The aim of this graphical review is to provide an overview of the steps to be taken when conducting an LC-MS-based clinical research project to search for small molecule biomarkers.

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.

Gut-brain axis through the lens of gut microbiota and their relationships with Alzheimer's disease pathology: Review and recommendations

Alzheimer's disease (AD) is a neurodegenerative disorder that affects millions of people worldwide. Growing evidence suggests that the gut microbiome (GM) plays a pivotal role in the pathogenesis of AD through the microbiota-gut-brain axis (MGB). Alterations in GM composition and diversity have been observed in both animal models and in human patients with AD. GM dysbiosis has been implicated in increased intestinal permeability, blood-brain barrier (BBB) impairment, neuroinflammation and the development of hallmarks of AD. Further elucidation of the role of GM in AD could pave way for the development of holistic predictive methods for determining AD risk and progression of disease. Furthermore, accumulating evidence suggests that GM modulation could alleviate adverse symptoms of AD or serve as a preventive measure. In addition, increasing evidence shows that Type 2 Diabetes Mellitus (T2DM) is often comorbid with AD, with common GM alterations and inflammatory response, which could chart the development of GM-related treatment interventions for both diseases. We conclude by exploring the therapeutic potential of GM in alleviating symptoms of AD and in reducing risk. Furthermore, we also propose future directions in AD research, namely fecal microbiota transplantation (FMT) and precision medicine.

Metabolomic alterations in the blood plasma of older adults with mild cognitive impairment and Alzheimer's disease (from the Nakayama Study)

Alzheimer's disease (AD) is a progressive disease, and the number of AD patients is increasing every year as the population ages. One of the pathophysiological mechanisms of AD is thought to be the effect of metabolomic abnormalities. There have been several studies of metabolomic abnormalities of AD, and new biomarkers are being investigated. Metabolomic studies have been attracting attention, and the aim of this study was to identify metabolomic biomarkers associated with AD and mild cognitive impairment (MCI). Of the 927 participants in the Nakayama Study conducted in Iyo City, Ehime Prefecture, 106 were selected for this study as Control (n = 40), MCI (n = 26), and AD (n = 40) groups, matched by age and sex. Metabolomic comparisons were made across the three groups. Then, correlations between metabolites and clinical symptoms were examined. The blood mRNA levels of the ornithine metabolic enzymes were also measured. Of the plasma metabolites, significant differences were found in ornithine, uracil, and lysine. Ornithine was significantly decreased in the AD group compared to the Control and MCI groups (Control vs. AD: 97.2 vs. 77.4; P = 0.01, MCI vs. AD: 92.5 vs. 77.4; P = 0.02). Uracil and lysine were also significantly decreased in the AD group compared to the Control group (uracil, Control vs. AD: 272 vs. 235; P = 0.04, lysine, Control vs. AD: 208 vs. 176; P = 0.03). In the total sample, the MMSE score was significantly correlated with lysine, ornithine, thymine, and uracil. The Barthel index score was significantly correlated with lysine. The instrumental activities of daily living (IADL) score were significantly correlated with lysine, betaine, creatine, and thymine. In the ornithine metabolism pathway, the spermine synthase mRNA level was significantly decreased in AD. Ornithine was decreased, and mRNA expressions related to its metabolism were changed in the AD group compared to the Control and MCI groups, suggesting an association between abnormal ornithine metabolism and AD. Increased betaine and decreased methionine may also have the potential to serve as markers of higher IADL in elderly persons. Plasma metabolites may be useful for predicting the progression of AD.

Therapeutic effect and mechanism of Anemarrhenae Rhizoma on Alzheimer’s disease based on multi-platform metabolomics analyses

Anemarrhenae Rhizoma (AR) has multiple pharmacological activities to prevent and treat Alzheimer’s disease (AD). However, the effect and its molecular mechanism are not elucidated clear. This study aims to evaluate AR’s therapeutic effect and mechanism on AD model rats induced by D-galactose and AlCl₃ with serum metabolomics. Behavior study, histopathological observations, and biochemical analyses were applied in the AD model assessment. Gas chromatography-mass spectrometry (GC-MS) and liquid chromatography-mass spectrometry (LC-QTOF/MS) were combined with multivariate statistical analysis to identify potential biomarkers of AD and evaluate the therapeutic effect of AR on AD from the perspective of metabolomics. A total of 49 biomarkers associated with the AD model were identified by metabolomics, and pathway analysis was performed to obtain the metabolic pathways closely related to the model. With the pre-treatment of AR, 32 metabolites in the serum of AD model rats were significantly affected by AR compared with the AD model group. The regulated metabolites affected by AR were involved in the pathway of arginine biosynthesis, arginine and proline metabolism, ether lipid metabolism, glutathione metabolism, primary bile acid biosynthesis, and steroid biosynthesis. These multi-platform metabolomics analyses were in accord with the results of behavior study, histopathological observations, and biochemical analyses. This study explored the therapeutic mechanism of AR based on multi-platform metabolomics analyses and provided a scientific basis for the application of AR in the prevention and treatment of AD.

Application of Sebum Lipidomics to Biomarkers Discovery in Neurodegenerative Diseases

Lipidomics is strategic in the discovery of biomarkers of neurodegenerative diseases (NDDs). The skin surface lipidome bears the potential to provide biomarker candidates in the detection of pathological processes occurring in distal organs. We investigated the sebum composition to search diagnostic and, possibly, prognostic, biomarkers of Alzheimer’s disease (AD) and Parkinson’s disease (PD). The observational study included 64 subjects: 20 characterized as “probable AD with documented decline”, 20 as “clinically established PD”, and 24 healthy subjects (HS) of comparable age. The analysis of sebum by GCMS and TLC retrieved the amounts (µg) of 41 free fatty acids (FFAs), 7 fatty alcohols (FOHs), vitamin E, cholesterol, squalene, and total triglycerides (TGs) and wax esters (WEs). Distributions of sebum lipids in NDDs and healthy conditions were investigated with multivariate ANOVA-simultaneous component analysis (ASCA). The deranged sebum composition associated with the PD group showed incretion of most composing lipids compared to HS, whereas only two lipid species (vitamin E and FOH14:0) were discriminant of AD samples and presented lower levels than HS sebum. Thus, sebum lipid biosynthetic pathways are differently affected in PD and AD. The characteristic sebum bio-signatures detected support the value of sebum lipidomics in the biomarkers search in NDDs.