Urinary metabolic phenotyping for Alzheimer's disease

Differential DNA methylation associated with delayed cerebral ischemia after aneurysmal subarachnoid hemorrhage: a systematic review

Recent studies suggest that differential DNA methylation could play a role in the mechanism of cerebral vasospasm (CVS) and delayed cerebral ischemia (DCI) after aneurysmal subarachnoid hemorrhage (aSAH). Considering the significance of this matter and a lack of effective prophylaxis against DCI, we aim to summarize the current state of knowledge regarding their associations with DNA methylation and identify the gaps for a future trial. PubMed MEDLINE, Scopus, and Web of Science were searched by two authors in three waves for relevant DNA methylation association studies in DCI after aSAH. PRISMA checklist was followed for a systematic structure. STROBE statement was used to assess the quality and risk of bias within studies. This research was funded by the National Science Centre, Poland (grant number 2021/41/N/NZ2/00844). Of 70 records, 7 peer-reviewed articles met the eligibility criteria. Five studies used a candidate gene approach, three were epigenome-wide association studies (EWAS), one utilized bioinformatics of the previous EWAS, with two studies using more than one approach. Methylation status of four cytosine-guanine dinucleotides (CpGs) related to four distinct genes (ITPR3, HAMP, INSR, CDHR5) have been found significantly or suggestively associated with DCI after aSAH. Analysis of epigenetic clocks yielded significant association of lower age acceleration with radiological CVS but not with DCI. Hub genes for hypermethylation (VHL, KIF3A, KIFAP3, RACGAP1, OPRM1) and hypomethylation (ALB, IL5) in DCI have been indicated through bioinformatics analysis. As none of the CpGs overlapped across the studies, meta-analysis was not applicable. The identified methylation sites might potentially serve as a biomarker for early diagnosis of DCI after aSAH in future. However, a lack of overlapping results prompts the need for large-scale multicenter studies. Challenges and prospects are discussed.

Urine biomarkers for Alzheimer's disease: A new opportunity for wastewater-based epidemiology?

While Alzheimer's disease (AD) diagnosis, management, and care have become priorities for healthcare providers and researcher's worldwide due to rapid population aging, epidemiologic surveillance efforts are currently limited by costly, invasive diagnostic procedures, particularly in low to middle income countries (LMIC). In recent years, wastewater-based epidemiology (WBE) has emerged as a promising tool for public health assessment through detection and quantification of specific biomarkers in wastewater, but applications for non-infectious diseases such as AD remain limited. This early review seeks to summarize AD-related biomarkers and urine and other peripheral biofluids and discuss their potential integration to WBE platforms to guide the first prospective efforts in the field. Promising results have been reported in clinical settings, indicating the potential of amyloid β, tau, neural thread protein, long non-coding RNAs, oxidative stress markers and other dysregulated metabolites for AD diagnosis, but questions regarding their concentration and stability in wastewater and the correlation between clinical levels and sewage circulation must be addressed in future studies before comprehensive WBE systems can be developed.

Non-invasive Biomarkers for Early Detection of Alzheimer's Disease: a New-Age Perspective

Alzheimer's disease (AD) is a progressive neurodegenerative disorder that primarily affects the elderly population. It gradually leads to memory loss, loss of thinking ability, and an overall cognitive decline. However, exhaustive literature is available to suggest that pathological changes in the brain occur decades before the first clinical symptoms appear. This review provides insight into the non-invasive biomarkers for early detection of AD that have been successfully studied in populations across the globe. These biomarkers have been detected in the blood, saliva, breath, and urine samples. Retinal imaging techniques are also reported. In this study, PubMed and Google scholar were the databases employed using keywords "Alzheimer's disease," "neurodegeneration," "non-invasive biomarkers," "early diagnosis," "blood-based biomarkers," and "preclinical AD," among others. The evaluation of these biomarkers will provide early diagnosis of AD in the preclinical stages due to their positive correlation with brain pathology in AD. Early diagnosis with reliable and timely intervention can effectively manage this disease.

LC-MS-based urine metabolomics analysis of chronic subdural hematoma for biomarker discovery

BACKGROUND

Chronic subdural hematoma (CSDH) is one of the most common neurosurgical diseases with atypical manifestations. The aim of this study was to utilize urine metabolomics to explore potential biomarkers for the diagnosis and prognosis of CSDH.

METHODS

Seventy-seven healthy controls and ninety-two patients with CSDH were enrolled in our study. In total, 261 urine samples divided into the discovery group and validation group were analyzed by LC-MS. The statistical analysis and functional annotation were applied to discover potential biomarker panels and altered metabolic pathways.

RESULTS

A total of 53 differential metabolites were identified in this study. And the urinary metabolic profiles showed apparent separation between patients and controls. Further functional annotation showed that the differential metabolites were associated with lipid metabolism, fatty acid metabolism, amino acid metabolism, biotin metabolism, steroid hormone biosynthesis, and pentose and glucuronate interconversions. Moreover, one panel of Capryloylglycine, cis-5-Octenoic acid, Ethisterone, and 5,6-DiHETE showed good predictive performance in the diagnosis of CSDH, with an AUC of 0.89 in discovery group and an AUC of 0.822 in validation group. Another five metabolites (Trilobinol, 3'-Hydroxyropivacaine, Ethisterone, Arginyl-Proline, 5-alpha-Dihydrotestosterone glucuronide) showed the levels of them returned to a healthy state after surgery, showing good possibility to monitor the recovery of CSDH patients.

CONCLUSION AND CLINICAL RELEVANCE

The findings of the study revealed urine metabolomic differences between CSDH and controls. The potentially diagnostic and prognostic biomarker panels of urine metabolites were established, and functional analysis demonstrated deeper metabolic disorders of CSDH, which might conduce to improve early diagnose of CSDH clinically.

Effects of Exercise on Urinary AD7c-NTP (Alzheimer-Associated Neuronal Thread Protein) Levels and Cognitive Function Among Active Korean Elderly: A Randomized Controlled Trial

Background

Alzheimer-associated neuronal thread protein (AD7c-NTP) has been demonstrated to have high diagnostic accuracy in differentiating Alzheimer's disease (AD) patients from healthy individuals. However, it is yet unclear whether exercise can lower the level of AD7c-NTP in urine among active Korean elderly.

Objective

To assess the effect of exercise on AD7c-ntp levels in urine and cognitive function among active Korean elderly.

Methods

In total, 40 Korean elderly (≥65 years) were divided into Active Control group (CG, n = 10), Aerobic exercise group (AG, n = 18), and combined Resistance/Aerobic exercise group (RAG, n = 12). A total of 12 weeks of exercise intervention was implemented. At week 0 and 12, cognitive performance (Korean Mini-Mental State Examination, Korean-Color Word Stroop test), grip strength, and body composition (muscle mass and body fat percentage) were measured. Also, a morning urine sample was obtained from each subject. The level of AD7c-NTP was measured using competitive enzyme-linked immunosorbent assay (ELISA).

Results

After 12 weeks of exercise intervention, there was a significant difference of AD7c-NTP levels between RAG and CG (p = 0.026), AG and CG (p = 0.032), respectively. Furthermore, the AD7c-NTP levels in urine showed negative correlation with K-MMSE scores (r = -0.390, p = 0.013) and grip strength (r = -0.376, p = 0.017), among all participants after exercise intervention.

Conclusions

This is the first study to investigate urine biomarker through exercise intervention. In future stuides, participants who have low cognitive function and low activity levels need to be recruited to observe more significant 'Exercise' effect.

Urine metabolomics phenotyping and urinary biomarker exploratory in mild cognitive impairment and Alzheimer's disease

Introduction

Alzheimer's disease is a prevalent disease with a heavy global burden and is suggested to be a metabolic disease in the brain in recent years. The metabolome is considered to be the most promising phenotype which reflects changes in genetic, transcript, and protein profiles as well as environmental effects. Aiming to obtain a comprehensive understanding and convenient diagnosis of MCI and AD from another perspective, researchers are working on AD metabolomics. Urine is more convenient which could reflect the change of disease at an earlier stage. Thus, we conducted a cross-sectional study to investigate novel diagnostic panels.

Methods

We first enrolled participants from China-Japan Friendship Hospital from April 2022 to November 2022, collected urine samples and conducted an LC-MS/MS analysis. In parallel, clinical data were collected and clinical examinations were performed. After statistical and bioinformatics analyzes, significant risk factors and differential urinary metabolites were determined. We attempt to investigate diagnostic panels based on machine learning including LASSO and SVM.

Results

Fifty-seven AD patients, 43 MCI patients and 62 CN subjects were enrolled. A total of 2,140 metabolites were identified among which 125 significantly differed between the AD and CN groups, including 46 upregulated ones and 79 downregulated ones. In parallel, there were 93 significant differential metabolites between the MCI and CN groups, including 23 upregulated ones and 70 downregulated ones. AD diagnostic panel (30 metabolites+ age + APOE) achieved an AUC of 0.9575 in the test set while MCI diagnostic panel (45 metabolites+ age + APOE) achieved an AUC of 0.7333 in the test set. Atropine, S-Methyl-L-cysteine-S-oxide, D-Mannose 6-phosphate (M6P), Spiculisporic Acid, N-Acetyl-L-methionine, 13,14-dihydro-15-keto-tetranor Prostaglandin D2, Pyridoxal 5'-Phosphate (PLP) and 17(S)-HpDHA were considered valuable for both AD and MCI diagnosis and defined as hub metabolites. Besides, diagnostic metabolites were weakly correlated with cognitive functions.

Discussion

In conclusion, the procedure is convenient, non-invasive, and useful for diagnosis, which could assist physicians in differentiating AD and MCI from CN. Atropine, M6P and PLP were evidence-based hub metabolites in AD.

NMR-based metabolomics in Alzheimer's disease research: a review

Alzheimer's disease (AD) is a progressive neurodegenerative disorder and represents the most common cause of dementia in the elderly population worldwide. Currently, there is no cure for AD, and the continuous increase in the number of susceptible individuals poses one of the most significant emerging threats to public health. However, the molecular pathways involved in the onset and progression of AD are not fully understood. This information is crucial for developing less invasive diagnostic instruments and discovering novel potential therapeutic targets. Metabolomics studies the complete ensemble of endogenous and exogenous metabolites present in biological specimens and may provide an interesting approach to identify alterations in multiple biochemical processes associated with AD onset and evolution. In this mini review, we summarize the results from metabolomic studies conducted using nuclear magnetic resonance (NMR) spectroscopy on human biological samples (blood derivatives, cerebrospinal fluid, urine, saliva, and tissues) from AD patients. We describe the metabolic alterations identified in AD patients compared to controls and to patients diagnosed with mild cognitive impairment (MCI). Moreover, we discuss the challenges and issues associated with the application of NMR-based metabolomics in the context of AD research.

Non-invasive biomarkers for mild cognitive impairment and Alzheimer's disease

Alzheimer's disease is the most common type of dementia in the elderly. It is a progressive degenerative disorder that may begin to develop up to 15 years before clinical symptoms appear. The identification of early biomarkers is crucial to enable a prompt diagnosis and to start effective interventions. In this work, we conducted a metabolomic study using proton Nuclear Magnetic Resonance (1H NMR) spectroscopy in serum samples from patients with neuropathologically confirmed Alzheimer's disease (AD, n = 51), mild cognitive impairment (MCI, n = 27), and cognitively healthy controls (HC, n = 50) to search for metabolites that could be used as biomarkers. Patients and controls underwent yearly clinical follow-ups for up to six years. MCI group included samples from three subgroups of subjects with different disease progression rates. The first subgroup included subjects that remained clinically stable at the MCI stage during the period of study (stable MCI, S-MCI, n = 9). The second subgroup accounted for subjects which were diagnosed with MCI at the moment of blood extraction, but progressed to clinical dementia in subsequent years (MCI-to-dementia, MCI-D, n = 14). The last subgroup was composed of subjects that had been diagnosed as dementia for the first time at the moment of sample collection (incipient dementia, Incp-D, n = 4). Partial Least Square Discriminant Analysis (PLS-DA) models were developed. Three models were obtained, one to discriminate between AD and HC samples with high sensitivity (93.75%) and specificity (94.75%), another model to discriminate between AD and MCI samples (100% sensitivity and 82.35% specificity), and a last model to discriminate HC and MCI with lower sensitivity and specificity (67% and 50%). Differences within the MCI group were further studied in an attempt to determine those MCI subjects that could develop AD-type dementia in the future. The relative concentration of metabolites, and metabolic pathways were studied. Alterations in the pathways of alanine, aspartate and glutamate metabolism, pantothenate and CoA biosynthesis, and beta-alanine metabolism, were found when HC and MCI- D patients were compared. In contrast, no pathway was found disturbed in the comparison of S-MCI with HC groups. These results highlight the potential of 1H NMR metabolomics to support the diagnosis of dementia in a less invasive way, and set a starting point for the study of potential biomarkers to identify MCI or HC subjects at risk of developing AD in the future.

Small molecule metabolites: discovery of biomarkers and therapeutic targets

Metabolic abnormalities lead to the dysfunction of metabolic pathways and metabolite accumulation or deficiency which is well-recognized hallmarks of diseases. Metabolite signatures that have close proximity to subject's phenotypic informative dimension, are useful for predicting diagnosis and prognosis of diseases as well as monitoring treatments. The lack of early biomarkers could lead to poor diagnosis and serious outcomes. Therefore, noninvasive diagnosis and monitoring methods with high specificity and selectivity are desperately needed. Small molecule metabolites-based metabolomics has become a specialized tool for metabolic biomarker and pathway analysis, for revealing possible mechanisms of human various diseases and deciphering therapeutic potentials. It could help identify functional biomarkers related to phenotypic variation and delineate biochemical pathways changes as early indicators of pathological dysfunction and damage prior to disease development. Recently, scientists have established a large number of metabolic profiles to reveal the underlying mechanisms and metabolic networks for therapeutic target exploration in biomedicine. This review summarized the metabolic analysis on the potential value of small-molecule candidate metabolites as biomarkers with clinical events, which may lead to better diagnosis, prognosis, drug screening and treatment. We also discuss challenges that need to be addressed to fuel the next wave of breakthroughs.

The Pathological Effects of Circulating Hydrophobic Bile Acids in Alzheimer's Disease

Recent clinical studies have revealed that the serum levels of toxic hydrophobic bile acids (deoxy cholic acid, lithocholic acid [LCA], and glycoursodeoxycholic acid) are significantly higher in patients with Alzheimer's disease (AD) and amnestic mild cognitive impairment (aMCI) when compared to control subjects. The elevated serum bile acids may be the result of hepatic peroxisomal dysfunction. Circulating hydrophobic bile acids are able to disrupt the blood-brain barrier and promote the formation of amyloid-β plaques through enhancing the oxidation of docosahexaenoic acid. Hydrophobic bile acid may find their ways into the neurons via the apical sodium-dependent bile acid transporter. It has been shown that hydrophobic bile acids impose their pathological effects by activating farnesoid X receptor and suppressing bile acid synthesis in the brain, blocking NMDA receptors, lowering brain oxysterol levels, and interfering with 17β-estradiol actions such as LCA by binding to E2 receptors (molecular modelling data exclusive to this paper). Hydrophobic bile acids may interfere with the sonic hedgehog signaling through alteration of cell membrane rafts and reducing brain 24(S)-hydroxycholesterol. This article will 1) analyze the pathological roles of circulating hydrophobic bile acids in the brain, 2) propose therapeutic approaches, and 3) conclude that consideration be given to reducing/monitoring toxic bile acid levels in patients with AD or aMCI, prior/in combination with other treatments.

Investigating neurological symptoms of infectious diseases like COVID-19 leading to a deeper understanding of neurodegenerative disorders such as Parkinson's disease

Apart from common respiratory symptoms, neurological symptoms are prevalent among patients with COVID-19. Research has shown that infection with SARS-CoV-2 accelerated alpha-synuclein aggregation, induced Lewy-body-like pathology, caused dopaminergic neuron senescence, and worsened symptoms in patients with Parkinson's disease (PD). In addition, SARS-CoV-2 infection can induce neuroinflammation and facilitate subsequent neurodegeneration in long COVID, and increase individual vulnerability to PD or parkinsonism. These findings suggest that a post-COVID-19 parkinsonism might follow the COVID-19 pandemic. In order to prevent a possible post-COVID-19 parkinsonism, this paper reviewed neurological symptoms and related findings of COVID-19 and related infectious diseases (influenza and prion disease) and neurodegenerative disorders (Alzheimer's disease, PD and amyotrophic lateral sclerosis), and discussed potential mechanisms underlying the neurological symptoms and the relationship between the infectious diseases and the neurodegenerative disorders, as well as the therapeutic and preventive implications in the neurodegenerative disorders. Infections with a relay of microbes (SARS-CoV-2, influenza A viruses, gut bacteria, etc.) and prion-like alpha-synuclein proteins over time may synergize to induce PD. Therefore, a systematic approach that targets these pathogens and the pathogen-induced neuroinflammation and neurodegeneration may provide cures for neurodegenerative disorders. Further, antiviral/antimicrobial drugs, vaccines, immunotherapies and new therapies (e.g., stem cell therapy) need to work together to treat, manage or prevent these disorders. As medical science and technology advances, it is anticipated that better vaccines for SARS-CoV-2 variants, new antiviral/antimicrobial drugs, effective immunotherapies (alpha-synuclein antibodies, vaccines for PD or parkinsonism, etc.), as well as new therapies will be developed and made available in the near future, which will help prevent a possible post-COVID-19 parkinsonism in the 21st century.

Genome-wide metabolite quantitative trait loci analysis (mQTL) in red blood cells from volunteer blood donors

The red blood cell (RBC)-Omics study, part of the larger NHLBI-funded Recipient Epidemiology and Donor Evaluation Study (REDS-III), aims to understand the genetic contribution to blood donor RBC characteristics. Previous work identified donor demographic, behavioral, genetic, and metabolic underpinnings to blood donation, storage, and (to a lesser extent) transfusion outcomes, but none have yet linked the genetic and metabolic bodies of work. We performed a genome-wide association (GWA) analysis using RBC-Omics study participants with generated untargeted metabolomics data to identify metabolite quantitative trait loci in RBCs. We performed GWA analyses of 382 metabolites in 243 individuals imputed using the 1000 Genomes Project phase 3 all-ancestry reference panel. Analyses were conducted using ProbABEL and adjusted for sex, age, donation center, number of whole blood donations in the past 2 years, and first 10 principal components of ancestry. Our results identified 423 independent genetic loci associated with 132 metabolites (p < 5×10^-8). Potentially novel locus-metabolite associations were identified for the region encoding heme transporter FLVCR1 and choline and for lysophosphatidylcholine acetyltransferase LPCAT3 and lysophosphatidylserine 16.0, 18.0, 18.1, and 18.2; these associations are supported by published rare disease and mouse studies. We also confirmed previous metabolite GWA results for associations, including N(6)-methyl-L-lysine and protein PYROXD2 and various carnitines and transporter SLC22A16. Association between pyruvate levels and G6PD polymorphisms was validated in an independent cohort and novel murine models of G6PD deficiency (African and Mediterranean variants). We demonstrate that it is possible to perform metabolomics-scale GWA analyses with a modest, trans-ancestry sample size.

Non-Invasive Nasal Discharge Fluid and Other Body Fluid Biomarkers in Alzheimer’s Disease

The key to current Alzheimer’s disease (AD) therapy is the early diagnosis for prompt intervention, since available treatments only slow the disease progression. Therefore, this lack of promising therapies has called for diagnostic screening tests to identify those likely to develop full-blown AD. Recent AD diagnosis guidelines incorporated core biomarker analyses into criteria, including amyloid-β (Aβ), total-tau (T-tau), and phosphorylated tau (P-tau). Though effective, the accessibility of screening tests involving conventional cerebrospinal fluid (CSF)- and blood-based analyses is often hindered by the invasiveness and high cost. In an attempt to overcome these shortcomings, biomarker profiling research using non-invasive body fluid has shown the potential to capture the pathological changes in the patients’ bodies. These novel non-invasive body fluid biomarkers for AD have emerged as diagnostic and pathological targets. Here, we review the potential peripheral biomarkers, including non-invasive peripheral body fluids of nasal discharge, tear, saliva, and urine for AD.

Microbial-derived metabolites as a risk factor of age-related cognitive decline and dementia

A consequence of our progressively ageing global population is the increasing prevalence of worldwide age-related cognitive decline and dementia. In the absence of effective therapeutic interventions, identifying risk factors associated with cognitive decline becomes increasingly vital. Novel perspectives suggest that a dynamic bidirectional communication system between the gut, its microbiome, and the central nervous system, commonly referred to as the microbiota-gut-brain axis, may be a contributing factor for cognitive health and disease. However, the exact mechanisms remain undefined. Microbial-derived metabolites produced in the gut can cross the intestinal epithelial barrier, enter systemic circulation and trigger physiological responses both directly and indirectly affecting the central nervous system and its functions. Dysregulation of this system (i.e., dysbiosis) can modulate cytotoxic metabolite production, promote neuroinflammation and negatively impact cognition. In this review, we explore critical connections between microbial-derived metabolites (secondary bile acids, trimethylamine-N-oxide (TMAO), tryptophan derivatives and others) and their influence upon cognitive function and neurodegenerative disorders, with a particular interest in their less-explored role as risk factors of cognitive decline.

A Nuclear Magnetic Resonance Spectroscopy Method in Characterization of Blood Metabolomics for Alzheimer's Disease

There is currently a crucial need for improved diagnostic techniques and targeted treatment methods for Alzheimer's disease (AD), a disease which impacts millions of elderly individuals each year. Metabolomic analysis has been proposed as a potential methodology to better investigate and understand the progression of this disease. In this report, we present our AD metabolomics results measured with high resolution magic angle spinning (HRMAS) nuclear magnetic resonance (NMR) on human blood plasma samples obtained from AD and non-AD subjects. Our study centers on developments of AD and non-AD metabolomics differentiating models with procedures of quality assurance (QA) and quality control (QC) through pooled samples. Our findings suggest that analysis of blood plasma samples using HRMAS NMR has the potential to differentiate between diseased and healthy subjects, which has important clinical implications for future improvements in AD diagnosis methodologies.

Multivariate analysis of NMR-based metabolomic data

Nuclear magnetic resonance (NMR) spectroscopy allows for simultaneous detection of a wide range of metabolites and lipids. As metabolites act together in complex metabolic networks, they are often highly correlated, and optimal biological insight is achieved when using methods that take the correlation into account. For this reason, latent-variable-based methods, such as principal component analysis and partial least-squares discriminant analysis, are widely used in metabolomic studies. However, with increasing availability of larger population cohorts, and a shift from analysis of spectral data to using quantified metabolite levels, both more traditional statistical approaches and alternative machine learning methods have become more widely used. This review aims at providing an overview of the current state-of-the-art multivariate methods for the analysis of NMR-based metabolomic data as well as alternative methods, highlighting their strengths and limitations.

Structure Elucidation and Mitigation of Endogenous Interferences in LC-MS-Based Metabolic Profiling of Urine

Liquid chromatography-mass spectrometry (LC-MS) is the main workhorse of metabolomics owing to its high degree of analytical sensitivity and specificity when measuring diverse chemistry in complex biological samples. LC-MS-based metabolic profiling of human urine, a biofluid of primary interest for clinical and biobank studies, is not widely considered to be compromised by the presence of endogenous interferences and is often accomplished using a simple "dilute-and-shoot" approach. Yet, it is our experience that broad obscuring signals are routinely observed in LC-MS metabolic profiles and represent interferences that lack consideration in the relevant metabolomics literature. In this work, we chromatographically isolated the interfering metabolites from human urine and unambiguously identified them via de novo structure elucidation as two separate proline-containing dipeptides: N,N,N-trimethyl-l-alanine-l-proline betaine (l,l-TMAP) and N,N-dimethyl-l-proline-l-proline betaine (l,l-DMPP), the latter reported here for the first time. Offline LC-MS/MS, magnetic resonance mass spectrometry (MRMS), and nuclear magnetic resonance (NMR) spectroscopy were essential components of this workflow for the full chemical and spectroscopic characterization of these metabolites and for establishing the coexistence of cis and trans isomers of both dipeptides in solution. Analysis of these definitive structures highlighted intramolecular ionic interactions as responsible for slow interconversion between these isomeric forms resulting in their unusually broad elution profiles. Proposed mitigation strategies, aimed at increasing the quality of LC-MS-based urine metabolomics data, include modification of column temperature and mobile-phase pH to reduce the chromatographic footprint of these dipeptides, thereby reducing their interfering effect on the underlying metabolic profiles. Alternatively, sample dilution and internal standardization methods may be employed to reduce or account for the observed effects of ionization suppression on the metabolic profile.

Dietary soy, pork and chicken proteins induce distinct nitrogen metabolism in rat liver

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Dietary amino acid composition affected nitrogen metabolism in rat liver.

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Amino acid metabolizing enzymes were downregulated by meat protein diets.

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Ribosome assembly and protein synthesis were regulated by diet by mTOR pathway.

Diets have been shown to alter metabolism and gene expression. However, few data are available about changes in gene expression in liver after intake of different meat protein diets. This work aimed to explore the long-term effects of protein source on liver metabolic enzymes. Rats were fed protein diets for 90 days to study whether intake of chicken and pork protein diets promoted gene expression involved in hepatic metabolism. Liver proteome profiles were measured by iTRAQ labeling and LC–ESI–MS/MS. Chicken protein diet induced higher level of serum amino acids in rats than soy protein. Amino acid metabolizing enzymes were downregulated by pork and chicken protein diets compared with soy protein diet. Intake of meat protein diets downregulated enzymes involved in protein synthesis, disulfide bond formation, signal peptide addition, transport, localization, degradation and glycosylation modification, but upregulated enzymes involved in prolyl cis–trans isomerization for protein synthesis. Protein diets from different sources affected the amino acid supply, and further influenced ribosome assembly and protein synthesis through mTOR signaling pathway.

Alterations in Glycerolipid and Fatty Acid Metabolic Pathways in Alzheimer's Disease Identified by Urinary Metabolic Profiling: A Pilot Study

An easily accessible and non-invasive biomarker for the early detection of Alzheimer's disease (AD) is needed. Evidence suggests that metabolic dysfunction underlies the pathophysiology of AD. While urine is a non-invasively collectable biofluid and a good source for metabolomics analysis, it is not yet widely used for this purpose. This small-scale pilot study aimed to examine whether the metabolic profile of urine from AD patients reflects the metabolic dysfunction reported to underlie AD pathology, and to identify metabolites that could distinguish AD patients from cognitively healthy controls. Spot urine of 18 AD patients (AD group) and 18 age- and sex-matched, cognitively normal controls (control group) were analyzed by mass spectrometry (MS). Capillary electrophoresis time-of-flight MS and liquid chromatography–Fourier transform MS were used to cover a larger range of molecules with ionic as well as lipid characteristics. A total of 304 ionic molecules and 81 lipid compounds of 12 lipid classes were identified. Of these, 26 molecules showed significantly different relative concentrations between the AD and control groups (Wilcoxon's rank-sum test). Moreover, orthogonal partial least-squares discriminant analysis revealed significant discrimination between the two groups. Pathway searches using the KEGG database, and pathway enrichment and topology analysis using Metaboanalyst software, suggested alterations in molecules relevant to pathways of glycerolipid and glycerophospholipid metabolism, thermogenesis, and caffeine metabolism in AD patients. Further studies of urinary metabolites will contribute to the early detection of AD and understanding of its pathogenesis.

1H-NMR-based metabolic profiling identifies non-invasive diagnostic and predictive urinary fingerprints in 5q spinal muscular atrophy

Background

5q spinal muscular atrophy (SMA) is a disabling and life-limiting neuromuscular disease. In recent years, novel therapies have shown to improve clinical outcomes. Yet, the absence of reliable biomarkers renders clinical assessment and prognosis of possibly already affected newborns with a positive newborn screening result for SMA imprecise and difficult. Therapeutic decisions and stratification of individualized therapies remain challenging, especially in symptomatic children. The aim of this proof-of-concept and feasibility study was to explore the value of ¹H-nuclear magnetic resonance (NMR)-based metabolic profiling in identifying non-invasive diagnostic and prognostic urinary fingerprints in children and adolescents with SMA.

Results

Urine samples were collected from 29 treatment-naïve SMA patients (5 pre-symptomatic, 9 SMA 1, 8 SMA 2, 7 SMA 3), 18 patients with Duchenne muscular dystrophy (DMD) and 444 healthy controls. Using machine-learning algorithms, we propose a set of prediction models built on urinary fingerprints that showed potential diagnostic value in discriminating SMA patients from controls and DMD, as well as predictive properties in separating between SMA types, allowing predictions about phenotypic severity. Interestingly, preliminary results of the prediction models suggest additional value in determining biochemical onset of disease in pre-symptomatic infants with SMA identified by genetic newborn screening and furthermore as potential therapeutic monitoring tool.

Conclusions

This study provides preliminary evidence for the use of ¹H-NMR-based urinary metabolic profiling as diagnostic and prognostic biomarker in spinal muscular atrophy.

Supplementary Information

The online version contains supplementary material available at 10.1186/s13023-021-02075-x.

Role of Oxidative Damage in Alzheimer's Disease and Neurodegeneration: From Pathogenic Mechanisms to Biomarker Discovery

Alzheimer’s disease (AD) is a neurodegenerative disorder accounting for over 50% of all dementia patients and representing a leading cause of death worldwide for the global ageing population. The lack of effective treatments for overt AD urges the discovery of biomarkers for early diagnosis, i.e., in subjects with mild cognitive impairment (MCI) or prodromal AD. The brain is exposed to oxidative stress as levels of reactive oxygen species (ROS) are increased, whereas cellular antioxidant defenses are decreased. Increased ROS levels can damage cellular structures or molecules, leading to protein, lipid, DNA, or RNA oxidation. Oxidative damage is involved in the molecular mechanisms which link the accumulation of amyloid-β and neurofibrillary tangles, containing hyperphosphorylated tau, to microglia response. In this scenario, microglia are thought to play a crucial role not only in the early events of AD pathogenesis but also in the progression of the disease. This review will focus on oxidative damage products as possible peripheral biomarkers in AD and in the preclinical phases of the disease. Particular attention will be paid to biological fluids such as blood, CSF, urine, and saliva, and potential future use of molecules contained in such body fluids for early differential diagnosis and monitoring the disease course. We will also review the role of oxidative damage and microglia in the pathogenesis of AD and, more broadly, in neurodegeneration.

Urinary metabolomic changes and microbiotic alterations in presenilin1/2 conditional double knockout mice

Background

Given the clinical low efficient treatment based on mono-brain-target design in Alzheimer’s disease (AD) and an increasing emphasis on microbiome-gut-brain axis which was considered as a crucial pathway to affect the progress of AD along with metabolic changes, integrative metabolomic signatures and microbiotic community profilings were applied on the early age (2-month) and mature age (6-month) of presenilin1/2 conditional double knockout (PS cDKO) mice which exhibit a series of AD-like phenotypes, comparing with gender and age-matched C57BL/6 wild-type (WT) mice to clarify the relationship between microbiota and metabolomic changes during the disease progression of AD.

Materials and methods

Urinary and fecal samples from PS cDKO mice and gender-matched C57BL/6 wild-type (WT) mice both at age of 2 and 6 months were collected. Urinary metabolomic signatures were measured by the gas chromatography-time-of-flight mass spectrometer, as well as 16S rRNA sequence analysis was performed to analyse the microbiota composition at both ages. Furthermore, combining microbiotic functional prediction and Spearman’s correlation coefficient analysis to explore the relationship between differential urinary metabolites and gut microbiota.

Results

In addition to memory impairment, PS cDKO mice displayed metabolic and microbiotic changes at both of early and mature ages. By longitudinal study, xylitol and glycine were reduced at both ages. The disturbed metabolic pathways were involved in glycine, serine and threonine metabolism, glyoxylate and dicarboxylate metabolism, pentose and glucuronate interconversions, starch and sucrose metabolism, and citrate cycle, which were consistent with functional metabolic pathway predicted by the gut microbiome, including energy metabolism, lipid metabolism, glycan biosynthesis and metabolism. Besides reduced richness and evenness in gut microbiome, PS cDKO mice displayed increases in Lactobacillus, while decreases in norank_f_Muribaculaceae, Lachnospiraceae_NK4A136_group, Mucispirillum, and Odoribacter. Those altered microbiota were exceedingly associated with the levels of differential metabolites.

Conclusions

The urinary metabolomics of AD may be partially mediated by the gut microbiota. The integrated analysis between gut microbes and host metabolism may provide a reference for the pathogenesis of AD.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12967-021-03032-9.