Metabolic network failures in Alzheimer's disease: A biochemical road map

Emerging genetic complexity and rare genetic variants in neurodegenerative brain diseases

Knowledge of the molecular etiology of neurodegenerative brain diseases (NBD) has substantially increased over the past three decades. Early genetic studies of NBD families identified rare and highly penetrant deleterious mutations in causal genes that segregate with disease. Large genome-wide association studies uncovered common genetic variants that influenced disease risk. Major developments in next-generation sequencing (NGS) technologies accelerated gene discoveries at an unprecedented rate and revealed novel pathways underlying NBD pathogenesis. NGS technology exposed large numbers of rare genetic variants of uncertain significance (VUS) in coding regions, highlighting the genetic complexity of NBD. Since experimental studies of these coding rare VUS are largely lacking, the potential contributions of VUS to NBD etiology remain unknown. In this review, we summarize novel findings in NBD genetic etiology driven by NGS and the impact of rare VUS on NBD etiology. We consider different mechanisms by which rare VUS can act and influence NBD pathophysiology and discuss why a better understanding of rare VUS is instrumental for deriving novel insights into the molecular complexity and heterogeneity of NBD. New knowledge might open avenues for effective personalized therapies.

Alzheimer's disease: An evolving understanding of noradrenergic involvement and the promising future of electroceutical therapies

Alzheimer's disease (AD) poses a significant global health concern over the next several decades. Multiple hypotheses have been put forth that attempt to explain the underlying pathophysiology of AD. Many of these are briefly reviewed here, but to-date no disease-altering therapy has been achieved. Despite this, recent work expanding on the role of noradrenergic system dysfunction in both the pathogenesis and symptomatic exacerbation of AD has shown promise. The role norepinephrine (NE) plays in AD remains complicated but pre-tangle tau has consistently been shown to arise in the locus coeruleus (LC) of patients with AD decades before symptom onset. The current research reviewed here indicates NE can facilitate neuroprotective and memory-enhancing effects through β adrenergic receptors, while α2A adrenergic receptors may exacerbate amyloid toxicity through a contribution to tau hyperphosphorylation. AD appears to involve a disruption in the balance between these two receptors and their various subtypes. There is also a poorly characterized interplay between the noradrenergic and cholinergic systems. LC deterioration leads to maladaptation in the remaining LC-NE system and subsequently inhibits cholinergic neuron function, eventually leading to the classic cholinergic disruption seen in AD. Understanding AD as a dysfunctional noradrenergic system, provides new avenues for the use of advanced neural stimulation techniques to both study and therapeutically target the earliest stages of neuropathology. Direct LC stimulation and non-invasive vagus nerve stimulation (VNS) have both demonstrated potential use as AD therapeutics. Significant work remains, though, to better understand the role of the noradrenergic system in AD and how electroceuticals can provide disease-altering treatments.

Therapies for Alzheimer's disease: a metabolic perspective

Alzheimer's disease (AD) is one of the most common forms of dementia in the elderly. Currently, there are over 50 million cases of dementia worldwide and it is expected that it will reach 136 million by 2050. AD is described as a neurodegenerative disease that gradually compromises memory and learning capacity. Patients often exhibit brain glucose hypometabolism and are more susceptible to develop type 2 diabetes or insulin resistance in comparison with age-matched controls. This suggests that there is a link between both pathologies. Glucose metabolism and the tricarboxylic acid cycle are tightly related to mitochondrial performance and energy production. Impairment of both these pathways can evoke oxidative damage on mitochondria and key proteins linked to several hallmarks of AD. Glycation is also another type of post-translational modification often reported in AD, which might impair the function of proteins that participate in metabolic pathways thought to be involved in this illness. Despite needing further research, therapies based on insulin treatment, usage of anti-diabetes drugs or some form of dietary intervention, have shown to be promising therapeutic approaches for AD in its early stages of progression and will be unveiled in this paper.

Quantitative proteomic analysis of the frontal cortex in Alzheimer's disease

Alzheimer's disease (AD) is a chronic neurodegenerative disease characterized by intracellular formation of neurofibrillary tangles and extracellular deposition of β-amyloid protein (Aβ) in the extracellular matrix. The pathogenesis of AD has not yet been fully elucidated and little is known about global alterations in the brain proteome that are related to AD. To identify and quantify such AD-related changes in the brain, we employed a tandem mass tags approach coupled to high-resolution mass spectrometry. We compared the proteomes of frontal cortex from AD patients with corresponding age-matched brain samples. Liquid chromatography-mass spectrometry/MS analysis carried out on an Orbitrap Fusion Lumos Tribrid mass spectrometer led to identification of 8,066 proteins. Of these, 432 proteins were observed to be significantly altered (>1.5 fold) in their expression in AD brains. Proteins whose abundance was previously known to be altered in AD were identified including secreted phosphoprotein 1 (SPP1), somatostatin (SST), SPARC-related modular calcium binding 1 (SMOC1), dual specificity phosphatase 26 (DUSP26), and neuronal pentraxin 2 (NPTX2). In addition, we identified several novel candidates whose association with AD has not been previously described. Of the novel molecules, we validated chromogranin A (CHGA), inner membrane mitochondrial protein (IMMT) and RAS like proto-oncogene A (RALA) in an additional set of 20 independent brain samples using targeted parallel reaction monitoring mass spectrometry assays. The differentially expressed proteins discovered in our study, once validated in larger cohorts, should help discern the pathogenesis of AD.

Data-driven biomarker analysis using computational omics approaches to assess neurodegenerative disease progression

The complexity of biological systems suggests that current definitions of molecular dysfunctions are essential distinctions of a complex phenotype. This is well seen in neurodegenerative diseases (ND), such as Alzheimer's disease (AD) and Parkinson's disease (PD), multi-factorial pathologies characterized by high heterogeneity. These challenges make it necessary to understand the effectiveness of candidate biomarkers for early diagnosis, as well as to obtain a comprehensive mapping of how selective treatment alters the progression of the disorder. A large number of computational methods have been developed to explain network-based approaches by integrating individual components for modeling a complex system. In this review, high-throughput omics methodologies are presented for the identification of potent biomarkers associated with AD and PD pathogenesis as well as for monitoring the response of dysfunctional molecular pathways incorporating multilevel clinical information. In addition, principles for efficient data analysis pipelines are being discussed that can help address current limitations during the experimental process by increasing the reproducibility of benchmarking studies.

Early signature in the blood lipidome associated with subsequent cognitive decline in the elderly: A case-control analysis nested within the Three-City cohort study

BACKGROUND

Brain lipid metabolism appears critical for cognitive aging, but whether alterations in the lipidome relate to cognitive decline remains unclear at the system level.

METHODS

We studied participants from the Three-City study, a multicentric cohort of older persons, free of dementia at time of blood sampling, and who provided repeated measures of cognition over 12 subsequent years. We measured 189 serum lipids from 13 lipid classes using shotgun lipidomics in a case-control sample on cognitive decline (matched on age, sex and level of education) nested within the Bordeaux study center (discovery, n = 418). Associations with cognitive decline were investigated using bootstrapped penalized regression, and tested for validation in the Dijon study center (validation, n = 314).

FINDINGS

Among 17 lipids identified in the discovery stage, lower levels of the triglyceride TAG50:5, and of four membrane lipids (sphingomyelin SM40:2,2, phosphatidylethanolamine PE38:5(18:1/20:4), ether-phosphatidylethanolamine PEO34:3(16:1/18:2), and ether-phosphatidylcholine PCO34:1(16:1/18:0)), and higher levels of PCO32:0(16:0/16:0), were associated with greater odds of cognitive decline, and replicated in our validation sample.

INTERPRETATION

These findings indicate that in the blood lipidome of non-demented older persons, a specific profile of lipids involved in membrane fluidity, myelination, and lipid rafts, is associated with subsequent cognitive decline.

FUNDING

The complete list of funders is available at the end of the manuscript, in the Acknowledgement section.

Plasma metabolomics of presymptomatic PSEN1-H163Y mutation carriers: a pilot study

BACKGROUND AND OBJECTIVE

PSEN1-H163Y carriers, at the presymptomatic stage, have reduced ¹⁸ FDG-PET binding in the cerebrum of the brain (Scholl et al., Neurobiol Aging 32:1388-1399, 2011). This could imply dysfunctional energy metabolism in the brain. In this study, plasma of presymptomatic PSEN1 mutation carriers was analyzed to understand associated metabolic changes.

METHODS

We analyzed plasma from noncarriers (NC, n = 8) and presymptomatic PSEN1-H163Y mutation carriers (MC, n = 6) via untargeted metabolomics using gas and liquid chromatography coupled with mass spectrometry, which identified 1199 metabolites. All the metabolites were compared between MC and NC using univariate analysis, as well as correlated with the ratio of Aβ_{1-42/A β 1-40} , using Spearman's correlation. Altered metabolites were subjected to Ingenuity Pathway Analysis (IPA).

RESULTS

Based on principal component analysis the plasma metabolite profiles were divided into dataset A and dataset B. In dataset A, when comparing between presymptomatic MC and NC, the levels of 79 different metabolites were altered. Out of 79, only 14 were annotated metabolites. In dataset B, 37 metabolites were significantly altered between presymptomatic MC and NC and nine metabolites were annotated. In both datasets, annotated metabolites represent amino acids, fatty acyls, bile acids, hexoses, purine nucleosides, carboxylic acids, and glycerophosphatidylcholine species. 1-docosapentaenoyl-GPC was positively correlated, uric acid and glucose were negatively correlated with the ratio of plasma Aβ_1-42 /Aβ_1-40 (P < 0.05).

INTERPRETATION

This study finds dysregulated metabolite classes, which are changed before the disease symptom onset. Also, it provides an opportunity to compare with sporadic Alzheimer's Disease. Observed findings in this study need to be validated in a larger and independent Familial Alzheimer's Disease (FAD) cohort.

Multi-omics integration in biomedical research - A metabolomics-centric review

Recent advances in high-throughput technologies have enabled the profiling of multiple layers of a biological system, including DNA sequence data (genomics), RNA expression levels (transcriptomics), and metabolite levels (metabolomics). This has led to the generation of vast amounts of biological data that can be integrated in so-called multi-omics studies to examine the complex molecular underpinnings of health and disease. Integrative analysis of such datasets is not straightforward and is particularly complicated by the high dimensionality and heterogeneity of the data and by the lack of universal analysis protocols. Previous reviews have discussed various strategies to address the challenges of data integration, elaborating on specific aspects, such as network inference or feature selection techniques. Thereby, the main focus has been on the integration of two omics layers in their relation to a phenotype of interest. In this review we provide an overview over a typical multi-omics workflow, focusing on integration methods that have the potential to combine metabolomics data with two or more omics. We discuss multiple integration concepts including data-driven, knowledge-based, simultaneous and step-wise approaches. We highlight the application of these methods in recent multi-omics studies, including large-scale integration efforts aiming at a global depiction of the complex relationships within and between different biological layers without focusing on a particular phenotype.

Metabolomic Biomarkers Are Associated With Area of the Pons in Fragile X Premutation Carriers at Risk for Developing FXTAS

Fragile X-associated tremor/ataxia syndrome (FXTAS) is a late adult-onset neurodegenerative disorder that affects movement and cognition in male and female carriers of a premutation allele (55-200 CGG repeats; PM) in the fragile X mental retardation (FMR1) gene. It is currently unknown how the observed brain changes are associated with metabolic signatures in individuals who develop the disorder over time. The primary objective of this study was to investigate the correlation between longitudinal changes in the brain (area of the pons, midbrain, and MCP width) and the changes in the expression level of metabolic biomarkers of early diagnosis and progression of FXTAS in PM who, as part of an ongoing longitudinal study, emerged into two distinct categories. These included those who developed symptoms of FXTAS (converters, CON) at subsequent visits and those who did not meet the criteria of diagnosis (non-converters, NCON) and were compared to age-matched healthy controls (HC). We assessed CGG repeat allele size by Southern Blot and PCR analysis. Magnetic Resonance Imaging (MRIs) acquisition was obtained on a 3T Siemens Trio scanner and metabolomic profile was obtained by ultra-performance liquid chromatography, accurate mass spectrometer, and an Orbitrap mass analyzer. Our findings indicate that differential metabolite levels are linked with the area of the pons between healthy control and premutation groups. More specifically, we observed a significant association of ceramides and mannonate metabolites with a decreased area of the pons, both at visit 1 (V1) and visit 2 (V2) only in the CON as compared to the NCON group suggesting their potential role in the development of the disorder. In addition, we found a significant correlation of these metabolic signatures with the FXTAS stage at V2 indicating their contribution to the progression and pathogenesis of FXTAS. Interestingly, these metabolites, as part of lipid and sphingolipid lipids pathways, provide evidence of the role that their dysregulation plays in the development of FXTAS and inform us as potential targets for personalized therapeutic development.

The role of peripheral fatty acids as biomarkers for Alzheimer's disease and brain inflammation

Alzheimer's disease (AD) is a complex and heterogeneous neurodegenerative disease. A wide range of techniques have been proposed to facilitate early diagnosis of AD, including biomarkers from the cerebrospinal fluid and blood. Although phosphorylated tau and amyloid beta are amongst the most promising biomarkers of AD, other peripheral biomarkers have been identified and in this review we synthesize the current knowledge on circulating fatty acids. Fatty acids are involved in different biological process including neurotransmission and inflammation. Interestingly, some fatty acids appear to be modulated during disease progression, including long chain saturated fatty acids, and polyunsaturated fatty acids, such as docosahexaenoic acid . However, discrepant results have been reported in the literature and there is the need for further validation and method standardization. Nonetheless, our literature review suggests that fatty acid analyses could potentially provide a valuable source of data to further inform the pathology and progression of AD.

Plasma Sphingomyelins in Late-Onset Alzheimer's Disease

BACKGROUND

Altered plasma levels of sphingolipids, including sphingomyelins (SM), have been found in mouse models of Alzheimer's disease (AD) and in AD patient plasma samples.

OBJECTIVE

This study assesses fourteen plasma SM species in a late-onset AD (LOAD) patient cohort (n = 138).

METHODS

Specimens from control, preclinical, and symptomatic subjects were analyzed using targeted mass-spectrometry-based metabolomic methods.

RESULTS

Total plasma SM levels were not significantly affected by age or cognitive status. However, one metabolite that has been elevated in manifest AD in several recent studies, SM OHC14:1, was reduced significantly in pre-clinical AD and MCI relative to normal controls.

CONCLUSION

We recommend additional comprehensive plasma lipidomics in experimental and clinical biospecimens related to LOAD that might advance the utility of plasma sphingomyelin levels in molecular phenotyping and interpretations of pathobiological mechanisms.

Low Liver Enzymes and Risk of Dementia: The Atherosclerosis Risk in Communities (ARIC) Study

BACKGROUND

Low levels of alanine aminotransferase (ALT) and aspartate aminotransferase (AST) in the low physiologic range, surrogate markers for reduced liver metabolic function, are associated with cerebral hypometabolism, impairment in neurotransmitter production and synaptic maintenance, and a higher prevalence of dementia. It is unknown whether a prospective association exists between low liver enzyme levels and incident dementia.

OBJECTIVE

To determine whether low levels of ALT and AST are associated with higher risk of incident dementia.

METHODS

Plasma ALT and AST were measured on 10,100 study participants (mean age 63.2 years, 55% female, 22% black) in 1996-1998. Dementia was ascertained from comprehensive neuropsychological assessments, annual contact, and medical record surveillance. Cox proportional hazards regression was used to estimate the association.

RESULTS

During a median follow-up of 18.3 years (maximum 21.9 years), 1,857 individuals developed dementia. Adjusted for demographic factors, incidence rates of dementia were higher at the lower levels of ALT and AST. Compared to the second quintile, ALT values <10th percentile were associated with a higher risk of dementia (hazard ratio [HR] 1.34, 95% CI 1.08-1.65). The corresponding HR was 1.22 (0.99-1.51) for AST.

CONCLUSION

Plasma aminotransferases <10th percentile of the physiologic range at mid-life, particularly ALT, were associated with greater long-term risk of dementia, advocating for attention to the putative role of hepatic function in the pathogenesis of dementia.

Circadian changes in Alzheimer's disease: Neurobiology, clinical problems, and therapeutic opportunities

The understanding of Alzheimer's disease (AD) pathophysiology is an active area of research, and the traditional focus on hippocampus, amyloid and tau protein, and memory impairment has been expanded with components like neuroinflammation, insulin resistance, and circadian rhythm alterations. The bidirectional vicious cycle of neuroinflammation and neurodegeneration on a molecular level may cause functional deficits already long before the appearance of overt clinical symptoms. Located at the crossroads of metabolic, circadian, and hormonal signaling, the hypothalamus has been identified as another brain region affected by AD pathophysiology. Current findings on hypothalamic dysfunction open a broader horizon for studying AD pathogenesis and offer new opportunities for diagnosis and therapy. While treatments with cholinomimetics and memantine form a first line of pharmacological treatment, additional innovative research is pursued toward the development of antiinflammatory, growth factor, or antidiabetic types of medication. Following recent epidemiological data showing associations of AD incidence with modern societal and "life-style"-related risk factors, also nonpharmacological interventions, including sleep optimization, are being developed and some have been shown to be beneficial. Circadian aspects in AD are relevant from a pathophysiological standpoint, but they can also have an important role in pharmacologic and nonpharmacologic interventions, and appropriate timing of sleep, meals, and medication may boost therapeutic efficacy.

Predictive network modeling in human induced pluripotent stem cells identifies key driver genes for insulin responsiveness

Insulin resistance (IR) precedes the development of type 2 diabetes (T2D) and increases cardiovascular disease risk. Although genome wide association studies (GWAS) have uncovered new loci associated with T2D, their contribution to explain the mechanisms leading to decreased insulin sensitivity has been very limited. Thus, new approaches are necessary to explore the genetic architecture of insulin resistance. To that end, we generated an iPSC library across the spectrum of insulin sensitivity in humans. RNA-seq based analysis of 310 induced pluripotent stem cell (iPSC) clones derived from 100 individuals allowed us to identify differentially expressed genes between insulin resistant and sensitive iPSC lines. Analysis of the co-expression architecture uncovered several insulin sensitivity-relevant gene sub-networks, and predictive network modeling identified a set of key driver genes that regulate these co-expression modules. Functional validation in human adipocytes and skeletal muscle cells (SKMCs) confirmed the relevance of the key driver candidate genes for insulin responsiveness.

Insulin resistance is characterized by a defective response (“resistance”) to normal insulin concentrations to uptake the glucose present in the blood, and is the underlying condition that leads to type 2 diabetes (T2D) and increases the risk of cardiovascular disease. It is estimated that 25–33% of the US population are insulin resistant enough to be at risk of serious clinical consequences. For more than a decade, large population studies have tried to discover the genes that participate in the development of insulin resistance, but without much success. It is now increasingly clear that the complex genetic nature of insulin resistance requires novel approaches centered in patient specific cellular models. To fill this gap, we have generated an induced pluripotent stem cell (iPSC) library from individuals with accurate measurements of insulin sensitivity, and performed gene expression and key driver analyses. Our work demonstrates that iPSCs can be used as a revolutionary technology to model insulin resistance and to discover key genetic drivers. Moreover, they can develop our basic knowledge of the disease, and are ultimately expected to increase the therapeutic targets to treat insulin resistance and type 2 diabetes.

Blood-based Biomarkers of Alzheimer's Disease: The Long and Winding Road

BACKGROUND

Blood-based biomarkers can be very useful in formulating new diagnostic and treatment proposals in the field of dementia, especially in Alzheimer's disease (AD). However, due to the influence of several factors on the reproducibility and reliability of these markers, their clinical use is still very uncertain. Thus, up-to-date knowledge about the main blood biomarkers that are currently being studied is extremely important in order to discover clinically useful and applicable tools, which could also be used as novel pharmacological strategies for the AD treatment.

METHODS

A narrative review was performed based on the current candidates of blood-based biomarkers for AD to show the main results from different studies, focusing on their clinical applicability and association with AD pathogenesis.

OBJECTIVE

The aim of this paper was to carry out a literature review on the major blood-based biomarkers for AD, connecting them with the pathophysiology of the disease.

RESULTS

Recent advances in the search of blood-based AD biomarkers were summarized in this review. The biomarkers were classified according to the topics related to the main hallmarks of the disease such as inflammation, amyloid, and tau deposition, synaptic degeneration and oxidative stress. Moreover, molecules involved in the regulation of proteins related to these hallmarks were described, such as non-coding RNAs, neurotrophins, growth factors and metabolites. Cells or cellular components with the potential to be considered as blood-based AD biomarkers were described in a separate topic.

CONCLUSION

A series of limitations undermine new discoveries on blood-based AD biomarkers. The lack of reproducibility of findings due to the small size and heterogeneity of the study population, different analytical methods and other assay conditions make longitudinal studies necessary in this field to validate these structures, especially when considering a clinical evaluation that includes a broad panel of these potential and promising blood-based biomarkers.

Targeted metabolomics study of early pathological features in hippocampus of triple transgenic Alzheimer's disease male mice

Alzheimer's disease (AD) is a serious neurodegenerative disease in people of age 65 or above. The detailed etiology and pathogenesis of AD have not been elucidated yet. In this study, the hippocampi of 2- and 6-month-old triple transgenic Alzheimer's disease male mice and age-sex-matched wild-type (WT) mice were analyzed by using targeted metabolomics approach. Compared with WT mice, 24 and 60 metabolites were found with significant differences in 2- and 6-month-old AD mice. Among these, 14 metabolites were found common while 10 metabolites showed consistent variable trends in both groups. These differential metabolites are found associated with amino acid, lipid, vitamin, nucleotide-related base, neurotransmitter and energy metabolisms, and oxidative stress. The results suggest that these differential metabolites might play a critical role in AD pathophysiology, and may serve as potential biomarkers for AD. Moreover, the results highlight the involvement of abnormal purine, pyrimidine, arginine, and proline metabolism, along with glycerophospholipid metabolism in early pathology of AD. For the first time, several differential metabolites are found to be associated with AD in this study. Targeted metabolomics can be used for rapid and accurate quantitative analysis of specific target metabolites associated with AD.

An In Vitro Model to Investigate the Role of Helicobacter pylori in Type 2 Diabetes, Obesity, Alzheimer's Disease and Cardiometabolic Disease

Helicobacter pylori (Hp) is a Gram-negative bacterium colonizing the human stomach. Nuclear Magnetic Resonance (NMR) analysis of intracellular human gastric carcinoma cells (MKN-28) incubated with the Hp cell filtrate (Hpcf) displays high levels of amino acids, including the branched chain amino acids (BCAA) isoleucine, leucine, and valine. Polymerase chain reaction (PCR) Array Technology shows upregulation of mammalian Target Of Rapamycin Complex 1 (mTORC1), inflammation, and mitochondrial dysfunction. The review of literature indicates that these traits are common to type 2 diabetes, obesity, Alzheimer’s diseases, and cardiometabolic disease. Here, we demonstrate how Hp may modulate these traits. Hp induces high levels of amino acids, which, in turn, activate mTORC1, which is the complex regulating the metabolism of the host. A high level of BCAA and upregulation of mTORC1 are, thus, directly regulated by Hp. Furthermore, Hp modulates inflammation, which is functional to the persistence of chronic infection and the asymptomatic state of the host. Finally, in order to induce autophagy and sustain bacterial colonization of gastric mucosa, the Hp toxin VacA localizes within mitochondria, causing fragmentation of these organelles, depletion of ATP, and oxidative stress. In conclusion, our in vitro disease model replicates the main traits common to the above four diseases and shows how Hp may potentially manipulate them.

Proceedings from the Fourth International Symposium on σ-2 Receptors: Role in Health and Disease

The σ-2 receptor (S2R) complex has been implicated in CNS disorders ranging from anxiety and depression to neurodegenerative disorders such as Alzheimer's disease (AD). The proteins comprising the S2R complex impact processes including autophagy, cholesterol synthesis, progesterone signaling, lipid membrane-bound protein trafficking, and receptor stabilization at the cell surface. While there has been much progress in understanding the role of S2R in cellular processes and its potential therapeutic value, a great deal remains unknown. The International Symposium on Sigma-2 Receptors is held in conjunction with the annual Society for Neuroscience (SfN) conference to promote collaboration and advance the field of S2R research. This review summarizes updates presented at the Fourth International Symposium on Sigma-2 Receptors: Role in Health and Disease, a Satellite Symposium held at the 2019 SfN conference. Interdisciplinary members of the S2R research community presented both previously published and preliminary results from ongoing studies of the role of S2R in cellular metabolism, the anatomic and cellular expression patterns of S2R, the relationship between S2R and amyloid β (Aβ) in AD, the role of S2R complex protein PGRMC1 in health and disease, and the efforts to design new S2R ligands for the purposes of research and drug development. The proceedings from this symposium are reported here as an update on the field of S2R research, as well as to highlight the value of the symposia that occur yearly in conjunction with the SfN conference.

Plasmalogens, platelet-activating factor and beyond - Ether lipids in signaling and neurodegeneration

Glycerol-based ether lipids including ether phospholipids form a specialized branch of lipids that in mammals require peroxisomes for their biosynthesis. They are major components of biological membranes and one particular subgroup, the plasmalogens, is widely regarded as a cellular antioxidant. Their vast potential to influence signal transduction pathways is less well known. Here, we summarize the literature showing associations with essential signaling cascades for a wide variety of ether lipids, including platelet-activating factor, alkylglycerols, ether-linked lysophosphatidic acid and plasmalogen-derived polyunsaturated fatty acids. The available experimental evidence demonstrates links to several common players like protein kinase C, peroxisome proliferator-activated receptors or mitogen-activated protein kinases. Furthermore, ether lipid levels have repeatedly been connected to some of the most abundant neurological diseases, particularly Alzheimer's disease and more recently also neurodevelopmental disorders like autism. Thus, we critically discuss the potential role of these compounds in the etiology and pathophysiology of these diseases with an emphasis on signaling processes. Finally, we review the emerging interest in plasmalogens as treatment target in neurological diseases, assessing available data and highlighting future perspectives. Although many aspects of ether lipid involvement in cellular signaling identified in vitro still have to be confirmed in vivo, the compiled data show many intriguing properties and contributions of these lipids to health and disease that will trigger further research.

Big data in severe mental illness: the role of electronic monitoring tools and metabolomics

There is an increasing interest in the development of effective early detection and intervention strategies in severe mental illness (SMI). Ideally, these efforts should lead to the delineation of accurate staging models of SMI enabling personalized interventions. It is plausible that big data approaches will be instrumental in describing the developmental trajectories of SMI by facilitating the incorporation of data from multiple sources, including those pertaining to the biological make-up of affected subjects. In this review, we first aimed to offer a perspective on how big data are helping the delineation of personalized approaches in SMI, and, second, to offer a quantitative synthesis of big data approaches in metabolomics of SMI. We finally described future directions of this research area.

Spatially resolved mass spectrometry analysis of amyloid plaque-associated lipids

Over the last 10 years, considerable technical advances in mass spectrometry (MS)-based bioanalysis have enabled the investigation of lipid signatures in neuropathological structures. In Alzheimer´s Disease (AD) research, it is now well accepted that lipid dysregulation plays a key role in AD pathogenesis and progression. This review summarizes current MS-based strategies, notably MALDI and ToF-SIMS imaging as well as laser capture microdissection combined with LC-ESI-MS. It also presents recent advances to assess lipid alterations associated with Amyloid-β plaques, one of the hallmarks of AD. Collectively, these methodologies offer new opportunities for the study of lipids, thus pushing forward our understanding of their role in such a complex and still untreatable disease as AD.

The Dichotomous Role of Inflammation in the CNS: A Mitochondrial Point of View

Innate immune response is one of our primary defenses against pathogens infection, although, if dysregulated, it represents the leading cause of chronic tissue inflammation. This dualism is even more present in the central nervous system, where neuroinflammation is both important for the activation of reparatory mechanisms and, at the same time, leads to the release of detrimental factors that induce neurons loss. Key players in modulating the neuroinflammatory response are mitochondria. Indeed, they are responsible for a variety of cell mechanisms that control tissue homeostasis, such as autophagy, apoptosis, energy production, and also inflammation. Accordingly, it is widely recognized that mitochondria exert a pivotal role in the development of neurodegenerative diseases, such as multiple sclerosis, Parkinson's and Alzheimer's diseases, as well as in acute brain damage, such in ischemic stroke and epileptic seizures. In this review, we will describe the role of mitochondria molecular signaling in regulating neuroinflammation in central nervous system (CNS) diseases, by focusing on pattern recognition receptors (PRRs) signaling, reactive oxygen species (ROS) production, and mitophagy, giving a hint on the possible therapeutic approaches targeting mitochondrial pathways involved in inflammation.

Brain Branched-Chain Amino Acids in Maple Syrup Urine Disease: Implications for Neurological Disorders

Maple syrup urine disease (MSUD) is an autosomal recessive disorder caused by decreased activity of the branched-chain α-ketoacid dehydrogenase complex (BCKDC), which catalyzes the irreversible catabolism of branched-chain amino acids (BCAAs). Current management of this BCAA dyshomeostasis consists of dietary restriction of BCAAs and liver transplantation, which aims to partially restore functional BCKDC activity in the periphery. These treatments improve the circulating levels of BCAAs and significantly increase survival rates in MSUD patients. However, significant cognitive and psychiatric morbidities remain. Specifically, patients are at a higher lifetime risk for cognitive impairments, mood and anxiety disorders (depression, anxiety, and panic disorder), and attention deficit disorder. Recent literature suggests that the neurological sequelae may be due to the brain-specific roles of BCAAs. This review will focus on the derangements of BCAAs observed in the brain of MSUD patients and will explore the potential mechanisms driving neurologic dysfunction. Finally, we will discuss recent evidence that implicates the relevance of BCAA metabolism in other neurological disorders. An understanding of the role of BCAAs in the central nervous system may facilitate future identification of novel therapeutic approaches in MSUD and a broad range of neurological disorders.

Alzheimer's disease and type 2 diabetes mellitus: A systematic review of proteomic studies

Similar to dementia, the risk for developing type 2 diabetes mellitus (T2DM) increases with age, and T2DM also increases the risk for dementia, particularly Alzheimer's disease (AD). Although T2DM is primarily a peripheral disorder and AD is a central nervous system disease, both share some common features as they are chronic and complex diseases, and both show involvement of oxidative stress and inflammation in their progression. These characteristics suggest that T2DM may be associated with AD, which gave rise to a new term, type 3 diabetes (T3DM). In this study, we searched for matching peripheral proteomic biomarkers of AD and T2DM based in a systematic review of the available literature. We identified 17 common biomarkers that were differentially expressed in both patients with AD or T2DM when compared with healthy controls. These biomarkers could provide a useful workflow for screening T2DM patients at risk to develop AD.

Triheptanoin Mitigates Brain ATP Depletion and Mitochondrial Dysfunction in a Mouse Model of Alzheimer's Disease

BACKGROUND

Brain energy failure is an early pathological event associated with synaptic dysfunction in Alzheimer's disease (AD). Thus, mitigation or enhancement of brain energy metabolism may offer a therapeutic avenue. However, there is uncertainty as to what metabolic process(es) may be more appropriate to support or augment since metabolism is a multiform process such that each of the various metabolic precursors available is utilized via a specific metabolic pathway. In the brain, these pathways sustain not only a robust rate of energy production but also of carbon replenishment.

OBJECTIVE

Triheptanoin, an edible odd-chain fatty acid triglyceride, is uncommon in that it replenishes metabolites in the tricarboxylic acid cycle (TCA) cycle via anaplerosis in addition to fueling the cycle via oxidation, thus potentially leading to both carbon replenishment and enhanced mitochondrial ATP production.

METHODS

To test the hypothesis that triheptanoin is protective in AD, we supplied mice with severe brain amyloidosis (5×FAD mice) with dietary triheptanoin for four and a half months, followed by biological and biochemical experiments to examine mice metabolic as well as synaptic function.

RESULTS

Triheptanoin treatment had minimal impact on systemic metabolism and brain amyloidosis as well as tauopathy while attenuating brain ATP deficiency and mitochondrial dysfunction including respiration and redox balance in 5×FAD mice. Synaptic density, a disease hallmark, was also preserved in hippocampus and neocortex despite profound amyloid deposition. None of these effects took place in treated control mice.

CONCLUSION

These findings support the energy failure hypothesis of AD and justify investigating the mechanisms in greater depth with ultimate therapeutic intent.

Integrated lipidomics and proteomics network analysis highlights lipid and immunity pathways associated with Alzheimer's disease

BACKGROUND

There is an urgent need to understand the pathways and processes underlying Alzheimer's disease (AD) for early diagnosis and development of effective treatments. This study was aimed to investigate Alzheimer's dementia using an unsupervised lipid, protein and gene multi-omics integrative approach.

METHODS

A lipidomics dataset comprising 185 AD patients, 40 mild cognitive impairment (MCI) individuals and 185 controls, and two proteomics datasets (295 AD, 159 MCI and 197 controls) were used for weighted gene co-expression network analyses (WGCNA). Correlations of modules created within each modality with clinical AD diagnosis, brain atrophy measures and disease progression, as well as their correlations with each other, were analyzed. Gene ontology enrichment analysis was employed to examine the biological processes and molecular and cellular functions of protein modules associated with AD phenotypes. Lipid species were annotated in the lipid modules associated with AD phenotypes. The associations between established AD risk loci and the lipid/protein modules that showed high correlation with AD phenotypes were also explored.

RESULTS

Five of the 20 identified lipid modules and five of the 17 identified protein modules were correlated with clinical AD diagnosis, brain atrophy measures and disease progression. The lipid modules comprising phospholipids, triglycerides, sphingolipids and cholesterol esters were correlated with AD risk loci involved in immune response and lipid metabolism. The five protein modules involved in positive regulation of cytokine production, neutrophil-mediated immunity, and humoral immune responses were correlated with AD risk loci involved in immune and complement systems and in lipid metabolism (the APOE ε4 genotype).

CONCLUSIONS

Modules of tightly regulated lipids and proteins, drivers in lipid homeostasis and innate immunity, are strongly associated with AD phenotypes.

Mitochondrial Oxidative and Nitrosative Stress and Alzheimer Disease

Oxidative and nitrosative stress are widely recognized as critical factors in the pathogenesis and progression of Alzheimer disease (AD) and its earlier stage, amnestic mild cognitive impairment (MCI). A major source of free radicals that lead to oxidative and nitrosative damage is mitochondria. This review paper discusses oxidative and nitrosative stress and markers thereof in the brain, along with redox proteomics, which are techniques that have been pioneered in the Butterfield laboratory. Selected biological alterations in-and oxidative and nitrosative modifications of-mitochondria in AD and MCI and systems of relevance thereof also are presented. The review article concludes with a section on the implications of mitochondrial oxidative and nitrosative stress in MCI and AD with respect to imaging studies in and targeted therapies toward these disorders. Taken together, this review provides support for the notion that brain mitochondrial alterations in AD and MCI are key components of oxidative and nitrosative stress observed in these two disorders, and as such, they provide potentially promising therapeutic targets to slow-and hopefully one day stop-the progression of AD, which is a devastating dementing disorder.

Perturbations in glycerophospholipid levels of PC12 cells after exposure to PCB95 based on targeted lipidomics analysis

Polychlorinated biphenyls (PCBs) are a group of organic chlorine chemicals that can induce various adverse health effects in animals and humans. The toxicology of PCBs is a significant public health concern because of their long-term presence in the environment. Among the 209 PCB congeners, PCB95 has been reported to be neurotoxic, however, there has been limited researches on evaluating whether and how PCB95 affects cellular lipids, the most abundant components of the brain. In this study, PCB95 was found to inhibit cell proliferation at concentrations of 0.1 μM, 2 μM and 10 μM for 120 h. Additionally, there may be a shift in apoptosis to necrosis at 2 μM PCB95 exposure for 24 h. However, lipid peroxidation was found not dominant for PCB95 exposure, especially at the concentrations of 0.1 μM and 2 μM. Because of playing vital roles in cell metabolism, 20 glycerophospholipids in PC12 cells were investigated after exposure to PCB95 for 120 h. The distinctions in the orthogonal projection to latent structures-discriminant analysis (OPLS-DA) models indicated that different concentrations of PCB95 leaded to aberrant glycerophospholipid metabolism. Based on the principles of t-test P-value < 0.05, variable importance at projection (VIP) value >1 and fold change >1, PC (14:0/14:0) and PC (16:0/14:0) were screened as potential biomarkers from all the target glycerophospholipids. This study is the first time that identifies the effects of PCB95 on specific glycerophospholipids in PC12 cells, and the observed changes in glycerophospholipids provides the basis for further evaluation of PCB95-induced neurotoxicity mechanisms.

Peripheral serum metabolomic profiles inform central cognitive impairment

The incidence of Alzheimer's disease (AD) increases with age and is becoming a significant cause of worldwide morbidity and mortality. However, the metabolic perturbation behind the onset of AD remains unclear. In this study, we performed metabolite profiling in both brain (n = 109) and matching serum samples (n = 566) to identify differentially expressed metabolites and metabolic pathways associated with neuropathology and cognitive performance and to identify individuals at high risk of developing cognitive impairment. The abundances of 6 metabolites, glycolithocholate (GLCA), petroselinic acid, linoleic acid, myristic acid, palmitic acid, palmitoleic acid and the deoxycholate/cholate (DCA/CA) ratio, along with the dysregulation scores of 3 metabolic pathways, primary bile acid biosynthesis, fatty acid biosynthesis, and biosynthesis of unsaturated fatty acids showed significant differences across both brain and serum diagnostic groups (P-value < 0.05). Significant associations were observed between the levels of differential metabolites/pathways and cognitive performance, neurofibrillary tangles, and neuritic plaque burden. Metabolites abundances and personalized metabolic pathways scores were used to derive machine learning models, respectively, that could be used to differentiate cognitively impaired persons from those without cognitive impairment (median area under the receiver operating characteristic curve (AUC) = 0.772 for the metabolite level model; median AUC = 0.731 for the pathway level model). Utilizing these two models on the entire baseline control group, we identified those who experienced cognitive decline in the later years (AUC = 0.804, sensitivity = 0.722, specificity = 0.749 for the metabolite level model; AUC = 0.778, sensitivity = 0.633, specificity = 0.825 for the pathway level model) and demonstrated their pre-AD onset prediction potentials. Our study provides a proof-of-concept that it is possible to discriminate antecedent cognitive impairment in older adults before the onset of overt clinical symptoms using metabolomics. Our findings, if validated in future studies, could enable the earlier detection and intervention of cognitive impairment that may halt its progression.

Untargeted metabolomics reveal dysregulations in sugar, methionine, and tyrosine pathways in the prodromal state of AD

INTRODUCTION

Altered metabolism may occur years before clinical manifestations of Alzheimer's disease (AD). We used untargeted metabolomics on the cerebrospinal fluid of patients with mild cognitive impairment (MCI) to uncover metabolomic derangements.

METHODS

CSF from 92 normal controls and 93 MCI underwent untargeted metabolomics using high-resolution mass spectrometry with liquid chromatography. Partial least squares discriminant analysis was used followed by metabolite annotation and pathway enrichment analysis (PES). Significant features were correlated with disease phenotypes.

RESULTS

We identified 294 features differentially expressed between the two groups and 94 were annotated. PES showed that sugar regulation (N-glycan, P = .0007; sialic acid, P = .0014; aminosugars, P = .0042; galactose, P = .0054), methionine regulation (P = .0081), and tyrosine metabolism (P = .019) pathways were differentially activated and significant features within these pathways correlated with multiple disease phenotypes.

CONCLUSION

There is a metabolic signature characterized by impairments in sugars, methionine, and tyrosine regulation in MCI. Targeting these pathways may offer new therapeutic approaches to AD.

Genetic variants and functional pathways associated with resilience to Alzheimer's disease

Approximately 30% of older adults exhibit the neuropathological features of Alzheimer's disease without signs of cognitive impairment. Yet, little is known about the genetic factors that allow these potentially resilient individuals to remain cognitively unimpaired in the face of substantial neuropathology. We performed a large, genome-wide association study (GWAS) of two previously validated metrics of cognitive resilience quantified using a latent variable modelling approach and representing better-than-predicted cognitive performance for a given level of neuropathology. Data were harmonized across 5108 participants from a clinical trial of Alzheimer's disease and three longitudinal cohort studies of cognitive ageing. All analyses were run across all participants and repeated restricting the sample to individuals with unimpaired cognition to identify variants at the earliest stages of disease. As expected, all resilience metrics were genetically correlated with cognitive performance and education attainment traits (P-values < 2.5 × 10-20), and we observed novel correlations with neuropsychiatric conditions (P-values < 7.9 × 10-4). Notably, neither resilience metric was genetically correlated with clinical Alzheimer's disease (P-values > 0.42) nor associated with APOE (P-values > 0.13). In single variant analyses, we observed a genome-wide significant locus among participants with unimpaired cognition on chromosome 18 upstream of ATP8B1 (index single nucleotide polymorphism rs2571244, minor allele frequency = 0.08, P = 2.3 × 10-8). The top variant at this locus (rs2571244) was significantly associated with methylation in prefrontal cortex tissue at multiple CpG sites, including one just upstream of ATPB81 (cg19596477; P = 2 × 10-13). Overall, this comprehensive genetic analysis of resilience implicates a putative role of vascular risk, metabolism, and mental health in protection from the cognitive consequences of neuropathology, while also providing evidence for a novel resilience gene along the bile acid metabolism pathway. Furthermore, the genetic architecture of resilience appears to be distinct from that of clinical Alzheimer's disease, suggesting that a shift in focus to molecular contributors to resilience may identify novel pathways for therapeutic targets.

Circulating ethanolamine plasmalogen indices in Alzheimer's disease: Relation to diagnosis, cognition, and CSF tau

INTRODUCTION

Altered lipid metabolism is implicated in Alzheimer's disease (AD), but the mechanisms remain obscure. Aging-related declines in circulating plasmalogens containing omega-3 fatty acids may increase AD risk by reducing plasmalogen availability.

METHODS

We measured four ethanolamine plasmalogens (PlsEtns) and four closely related phosphatidylethanolamines (PtdEtns) from the Alzheimer's Disease Neuroimaging Initiative (ADNI; n = 1547 serum) and University of Pennsylvania (UPenn; n = 112 plasma) cohorts, and derived indices reflecting PlsEtn and PtdEtn metabolism: PL-PX (PlsEtns), PL/PE (PlsEtn/PtdEtn ratios), and PBV (plasmalogen biosynthesis value; a composite index). We tested associations with baseline diagnosis, cognition, and cerebrospinal fluid (CSF) AD biomarkers.

RESULTS

Results revealed statistically significant negative relationships in ADNI between AD versus CN with PL-PX (P = 0.007) and PBV (P = 0.005), late mild cognitive impairment (LMCI) versus cognitively normal (CN) with PL-PX (P = 2.89 × 10^-5 ) and PBV (P = 1.99 × 10^-4 ), and AD versus LMCI with PL/PE (P = 1.85 × 10^-4 ). In the UPenn cohort, AD versus CN diagnosis associated negatively with PL/PE (P = 0.0191) and PBV (P = 0.0296). In ADNI, cognition was negatively associated with plasmalogen indices, including Alzheimer's Disease Assessment Scale 13-item cognitive subscale (ADAS-Cog13; PL-PX: P = 3.24 × 10^-6 ; PBV: P = 6.92 × 10^-5 ) and Mini-Mental State Examination (MMSE; PL-PX: P = 1.28 × 10^-9 ; PBV: P = 6.50 × 10^-9 ). In the UPenn cohort, there was a trend toward a similar relationship of MMSE with PL/PE (P = 0.0949). In ADNI, CSF total-tau was negatively associated with PL-PX (P = 5.55 × 10^-6 ) and PBV (P = 7.77 × 10^-6 ). Additionally, CSF t-tau/Aβ_1-42 ratio was negatively associated with these same indices (PL-PX, P = 2.73 × 10^-6 ; PBV, P = 4.39 × 10^-6 ). In the UPenn cohort, PL/PE was negatively associated with CSF total-tau (P = 0.031) and t-tau/Aβ_1-42 (P = 0.021). CSF Aβ_1-42 was not significantly associated with any of these indices in either cohort.

DISCUSSION

These data extend previous studies by showing an association of decreased plasmalogen indices with AD, mild cognitive impairment (MCI), cognition, and CSF tau. Future studies are needed to better define mechanistic relationships, and to test the effects of interventions designed to replete serum plasmalogens.

Tracking the potential involvement of metabolic disease in Alzheimer's disease-Biomarkers and beyond

There is a vast literature linking systemic metabolic conditions to dementia due to Alzheimer's disease (AD). Advances in in vivo measurements of AD neuropathology using brain imaging, cerebrospinal fluid (CSF), and/or blood biomarkers have led to research in AD that uses in vivo biomarkers as outcomes, focusing primarily on amyloid, tau, and neurodegeneration as constructs. Studies of Type 2 Diabetes Mellitus (T2DM) and AD biomarkers seem to show that T2DM is not related to amyloid deposition, but is related to neurodegeneration and tau deposition. There is a dearth of studies examining adiposity, insulin resistance, and metabolic syndrome in relation to AD biomarkers and the associations in these studies are inconsistent. Metabolomics studies have reported associations of unsaturated fatty acids with AD neuropathology at autopsy, and sphingolipids and glycerophospholipids in relation to neurodegeneration and amyloid and tau. There are other neurodegenerative diseases, such as Lewy body disease that may overlap with AD, and specific biomarkers for these pathologies are being developed and should be integrated into AD biomarker research. More longitudinal studies are needed with concurrent assessment of metabolic factors and AD biomarkers in order to improve the opportunity to assess causality. Ideally, AD biomarkers should be integrated into clinical trials of interventions that affect metabolic factors. Advances in blood-based AD biomarkers, which are less costly and invasive compared with CSF and brain imaging biomarkers, could facilitate widespread implementation of AD biomarkers in studies examining the metabolic contribution to AD.

Mini Nutritional Assessment May Identify a Dual Pattern of Perturbed Plasma Amino Acids in Patients with Alzheimer's Disease: A Window to Metabolic and Physical Rehabilitation?

Conflicting results about alterations of plasma amino acid (AA) levels are reported in subjects with Alzheimer’s disease (AD). The current study aimed to provide more homogeneous AA profiles and correlations between AAs and cognitive tests. Venous plasma AAs were measured in 54 fasting patients with AD (37 males, 17 females; 74.63 ± 8.03 yrs; 3.2 ± 1.9 yrs from symptom onset). Seventeen matched subjects without neurodegenerative symptoms (NNDS) served as a control group (C-NNDS). Patients were tested for short-term verbal memory and attention capacity and stratified for nutritional state (Mini Nutritional Assessment, MNA). Compared to C-NNDS, patients exhibited lower plasma levels of aspartic acid and taurine (p < 0.0001) and higher 3-methylhistidine (p < 0.0001), which were independent of patients’ MNA. In comparison to normonourished AD, the patients at risk of and with malnutrition showed a tendency towards lower ratios of Essential AAs/Total AAs, Branched-chain AAs/Total AAs, and Branched-chain AAs/Essential AAs. Serine and histidine were positively correlated with verbal memory and attention capacity deficits, respectively. Total AAs negatively correlated with attention capacity deficits. Stratifying patients with AD for MNA may identify a dual pattern of altered AAs, one due to AD per se and the other linked to nutritional state. Significant correlations were observed between several AAs and cognitive tests.

Integrated Metabolomic and Lipidomic Analysis Reveals the Neuroprotective Mechanisms of Bushen Tiansui Formula in an Aβ1-42-Induced Rat Model of Alzheimer's Disease

Bushen Tiansui Formula (BSTSF) is a traditional Chinese medicine prescription. It has been widely applied to treat Alzheimer's disease (AD) in the clinic; however, the mechanisms underlying its effects remain largely unknown. In this study, we used a rat AD model to study the effects of BSTSF on cognitive performance, and UPLC-MS/MS-based metabolomic and lipidomic analysis was further performed to identify significantly altered metabolites in the cerebral cortices of AD rats and determine the effects of BSTSF on the metabolomic and lipidomic profiles in the cerebral cortices of these animals. The results revealed that the levels of 47 metabolites and 30 lipids primarily associated with sphingolipid metabolism, glycerophospholipid metabolism, and linoleic acid metabolism were significantly changed in the cerebral cortices of AD rats. Among the altered lipids, ceramides, phosphatidylethanolamines, lysophosphatidylethanolamines, phosphatidylcholines, lysophosphatidylcholines, phosphatidylserines, sphingomyelins, and phosphatidylglycerols showed robust changes. Moreover, 34 differential endogenous metabolites and 21 lipids, of which the levels were mostly improved in the BSTSF treatment group, were identified as potential therapeutic targets of BSTSF against AD. Our results suggest that lipid metabolism is highly dysregulated in the cerebral cortices of AD rats, and BSTSF may exert its neuroprotective mechanisms by restoring metabolic balance, including that of sphingolipid metabolism, glycerophospholipid metabolism, alanine, aspartate, and glutamate metabolism, and D-glutamine and D-glutamate metabolism. Our data may lead to a deeper understanding of the AD-associated metabolic profile and shed new light on the mechanism underlying the therapeutic effects of BSTSF.

Metabolomics markers in Neurology: current knowledge and future perspectives for therapeutic targeting

INTRODUCTION

Metabolomics is an emerging approach providing new insights into the metabolic changes and underlying mechanisms involved in the pathogenesis of neurological disorders.

AREAS COVERED

Here, the authors present an overview of the current knowledge of metabolic profiling (metabolomics) to provide critical insight on the role of biochemical markers and metabolic alterations in neurological diseases.

EXPERT OPINION

Elucidation of characteristic metabolic alterations in neurological disorders is crucial for a better understanding of their pathogenesis, and for identifying potential biomarkers and drug targets. Nevertheless, discrepancies in diagnostic criteria, sample handling protocols, and analytical methods still affect the generalizability of current study results.

Exposure of ultrafine particulate matter causes glutathione redox imbalance in the hippocampus: A neurometabolic susceptibility to Alzheimer's pathology

Particulate matter (PM) exposure is related to an increased risk of sporadic Alzheimer's disease (AD), the pathogenesis of which is explained by chronic neurometabolic disturbance. Therefore, PM-induced alterations in neurometabolism might herald AD. We aimed to identify brain region-specific changes in metabolic pathways associated with ultrafine particle (UFP) exposure and to determine whether such metabolic alterations are linked to susceptibility to AD. We constructed UFP exposure chambers and generated UFP by the pyrolysis method, which produces no toxic oxidized by-products of combustion, such as NOx and CO. Twenty male C57BL6 mice (11-12 months old) were exposed either to UFP or room air in the chambers for 3 weeks. One week following completion of UFP exposure, regional brain tissues, including the olfactory bulb, cortex, hippocampus, and cerebellum, were obtained and analyzed by metabolomics based on GC-MS and LC-MS, western blot analysis, and immunohistochemistry. Our results demonstrated that the metabolomic phenotype was distinct within the 4 different anatomical regions following UFP exposure. The highest level of metabolic change was identified in the hippocampus, a vulnerable region involved in AD pathogenesis. In this region, one of the key changes was perturbed redox homeostasis via alterations in the methionine-glutathione pathway. UFP exposure also induced oxidative stress and neuroinflammation, and importantly, increased Alzheimer's beta-amyloid levels in the hippocampus. These results suggest that inhaled UFP-induced perturbation in hippocampal redox homeostasis has a role in the pathogenesis of AD. Therefore, chronic exposure to UFP should be regarded as a cumulative environmental risk factor for sporadic AD.

Serum triglycerides in Alzheimer disease: Relation to neuroimaging and CSF biomarkers

OBJECTIVE

To investigate the association of triglyceride (TG) principal component scores with Alzheimer disease (AD) and the amyloid, tau, neurodegeneration, and cerebrovascular disease (A/T/N/V) biomarkers for AD.

METHODS

Serum levels of 84 TG species were measured with untargeted lipid profiling of 689 participants from the Alzheimer's Disease Neuroimaging Initiative cohort, including 190 cognitively normal older adults (CN), 339 with mild cognitive impairment (MCI), and 160 with AD. Principal component analysis with factor rotation was used for dimension reduction of TG species. Differences in principal components between diagnostic groups and associations between principal components and AD biomarkers (including CSF, MRI and [¹⁸F]fluorodeoxyglucose-PET) were assessed with a generalized linear model approach. In both cases, the Bonferroni method of adjustment was used to correct for multiple comparisons.

RESULTS

The 84 TGs yielded 9 principal components, 2 of which, consisting of long-chain, polyunsaturated fatty acid-containing TGs (PUTGs), were significantly associated with MCI and AD. Lower levels of PUTGs were observed in MCI and AD compared to CN. PUTG principal component scores were also significantly associated with hippocampal volume and entorhinal cortical thickness. In participants carrying the APOE ε4 allele, these principal components were significantly associated with CSF β-amyloid_1-42 values and entorhinal cortical thickness.

CONCLUSION

This study shows that PUTG component scores were significantly associated with diagnostic group and AD biomarkers, a finding that was more pronounced in APOE ε4 carriers. Replication in independent larger studies and longitudinal follow-up are warranted.

Large-scale proteomic analysis of Alzheimer's disease brain and cerebrospinal fluid reveals early changes in energy metabolism associated with microglia and astrocyte activation

Our understanding of Alzheimer's disease (AD) pathophysiology remains incomplete. Here we used quantitative mass spectrometry and coexpression network analysis to conduct the largest proteomic study thus far on AD. A protein network module linked to sugar metabolism emerged as one of the modules most significantly associated with AD pathology and cognitive impairment. This module was enriched in AD genetic risk factors and in microglia and astrocyte protein markers associated with an anti-inflammatory state, suggesting that the biological functions it represents serve a protective role in AD. Proteins from this module were elevated in cerebrospinal fluid in early stages of the disease. In this study of >2,000 brains and nearly 400 cerebrospinal fluid samples by quantitative proteomics, we identify proteins and biological processes in AD brains that may serve as therapeutic targets and fluid biomarkers for the disease.

Breath Metabolomics Provides an Accurate and Noninvasive Approach for Screening Cirrhosis, Primary, and Secondary Liver Tumors

Hepatocellular carcinoma (HCC) and secondary liver tumors, such as colorectal cancer liver metastases are significant contributors to the overall burden of cancer‐related morality. Current biomarkers, such as alpha‐fetoprotein (AFP) for HCC, result in too many false negatives, necessitating noninvasive approaches with improved sensitivity. Volatile organic compounds (VOCs) detected in the breath of patients can provide valuable insight into disease processes and can differentiate patients by disease status. Here, we investigate whether 22 VOCs from the breath of 296 patients can distinguish those with no liver disease (n = 54), cirrhosis (n = 30), HCC (n = 112), pulmonary hypertension (n = 49), or colorectal cancer liver metastases (n = 51). This work extends previous studies by evaluating the ability for VOC signatures to differentiate multiple diseases in a large cohort of patients. Pairwise disease comparisons demonstrated that most of the VOCs tested are present in significantly different relative abundances (false discovery rate P < 0.1), indicating broad impacts on the breath metabolome across diseases. A predictive model developed using random forest machine learning and cross validation classified patients with 85% classification accuracy and 75% balanced accuracy. Importantly, the model detected HCC with 73% sensitivity compared with 53% for AFP in the same cohort. An added value of this approach is that influential VOCs in the predictive model may provide insight into disease etiology. Acetaldehyde and acetone, both of which have roles in tumor promotion, were considered important VOCs for differentiating disease groups in the predictive model and were increased in patients with cirrhosis and HCC compared to patients with no liver disease (false discovery rate P < 0.1). Conclusion: The use of machine learning and breath VOCs shows promise as an approach to develop improved, noninvasive screening tools for chronic liver disease and primary and secondary liver tumors.

Predicting MCI progression with FDG-PET and cognitive scores: a longitudinal study

BACKGROUND

Mild cognitive impairment (MCI) is an intermediate stage between normal aging and dementia. Studies on MCI progression are important for Alzheimer's disease (AD) prevention. 18F fluoro-deoxy-glucose positron emission tomography (FDG-PET) has been proven to be a powerful tool for measuring cerebral glucose metabolism. In this study, we proposed a classification framework for MCI prediction with both baseline and multiple follow-up FDG-PET scans as well as cognitive scores of 33 progressive MCI (pMCI) patients and 46 stable MCI (sMCI) patients from the Alzheimer's Disease Neuroimaging Initiative (ADNI).

METHOD

First, PET images were normalized using the Yakushev normalization procedure and registered to the Brainnetome Atlas (BNA). The average metabolic intensities of brain regions were defined as static features. Dynamic features were the intensity variation between baseline and the other three time points and change ratios with the intensity obtained at baseline considered as reference. Mini-mental State Examination (MMSE) scores and Alzheimer's disease Assessment Scale-Cognitive section (ADAS-cog) scores of each time point were collected as cognitive features. And F-score was applied for feature selection. Finally, support vector machine (SVM) with radial basis function (RBF) kernel was used for the three above features.

RESULTS

Dynamic features showed the best classification performance in accuracy of 88.61% than static features (accuracy of 78.48%). And the combination of cognitive features and dynamic features improved the classification performance in specificity of 95.65% and Area Under Curve (AUC) of 0.9308.

CONCLUSION

Our results reported that dynamic features are more representative in longitudinal research for MCI prediction work. And dynamic features and cognitive scores complementarily enhance the classification performance in specificity and AUC. These findings may predict the disease course and clinical changes in individuals with mild cognitive impairment.

Metabolomics and incident dementia in older Chinese adults: The Shanghai Aging Study

INTRODUCTION

Metabolomics provide a promising tool to understand the pathogenesis and to identify novel biomarkers of dementia. This study aimed to determine circulating metabolites associated with incident dementia in a Chinese cohort, and whether a selected metabolite panel could predict dementia.

METHODS

Thirty-eight metabolites in baseline serum were profiled by nuclear magnetic resonance in 1440 dementia-free participants followed 5 years in the Shanghai Aging Study.

RESULTS

Higher serum levels of glutamine and O-acetyl-glycoproteins were associated with increased risk of dementia, whereas glutamate, tyrosine, acetate, glycine, and phenylalanine were negatively related to incident dementia. A panel of five metabolites selected by least absolute shrinkage and selection operator within cross-validation regression analysis could predict incident dementia with an area under the receiver-operating characteristic curve of 0.72.

DISCUSSION

We identified seven candidate serum metabolic biomarkers for dementia. These findings and the underlying biological mechanisms need to be further replicated and elucidated in future studies.

Metabolic correlates of prevalent mild cognitive impairment and Alzheimer's disease in adults with Down syndrome

INTRODUCTION

Disruption of metabolic function is a recognized feature of late onset Alzheimer's disease (LOAD). We sought to determine whether similar metabolic pathways are implicated in adults with Down syndrome (DS) who have increased risk for Alzheimer's disease (AD).

METHODS

We examined peripheral blood from 292 participants with DS who completed baseline assessments in the Alzheimer's Biomarkers Consortium-Down Syndrome (ABC-DS) using untargeted mass spectrometry (MS). Our sample included 38 individuals who met consensus criteria for AD (DS-AD), 43 who met criteria for mild cognitive impairment (DS-MCI), and 211 who were cognitively unaffected and stable (CS).

RESULTS

We measured relative abundance of 8,805 features using MS and 180 putative metabolites were differentially expressed (DE) among the groups at false discovery rate-corrected q< 0.05. From the DE features, a nine-feature classifier model classified the CS and DS-AD groups with receiver operating characteristic area under the curve (ROC AUC) of 0.86 and a two-feature model classified the DS-MCI and DS-AD groups with ROC AUC of 0.88. Metabolite set enrichment analysis across the three groups suggested alterations in fatty acid and carbohydrate metabolism.

DISCUSSION

Our results reveal metabolic alterations in DS-AD that are similar to those seen in LOAD. The pattern of results in this cross-sectional DS cohort suggests a dynamic time course of metabolic dysregulation which evolves with clinical progression from non-demented, to MCI, to AD. Metabolomic markers may be useful for staging progression of DS-AD.

Skyline for Small Molecules: A Unifying Software Package for Quantitative Metabolomics

Vendor-independent software tools for quantification of small molecules and metabolites are lacking, especially for targeted analysis workflows. Skyline is a freely available, open-source software tool for targeted quantitative mass spectrometry method development and data processing with a 10 year history supporting six major instrument vendors. Designed initially for proteomics analysis, we describe the expansion of Skyline to data for small molecule analysis, including selected reaction monitoring, high-resolution mass spectrometry, and calibrated quantification. This fundamental expansion of Skyline from a peptide-sequence-centric tool to a molecule-centric tool makes it agnostic to the source of the molecule while retaining Skyline features critical for workflows in both peptide and more general biomolecular research. The data visualization and interrogation features already available in Skyline, such as peak picking, chromatographic alignment, and transition selection, have been adapted to support small molecule data, including metabolomics. Herein, we explain the conceptual workflow for small molecule analysis using Skyline, demonstrate Skyline performance benchmarked against a comparable instrument vendor software tool, and present additional real-world applications. Further, we include step-by-step instructions on using Skyline for small molecule quantitative method development and data analysis on data acquired with a variety of mass spectrometers from multiple instrument vendors.

Sphingolipids in food and their critical roles in human health

Sphingolipids (SLs) are ubiquitous structural components of cell membranes and are essential for cell functions under physiological conditions or during disease progression. Abundant evidence supports that SLs and their metabolites, including ceramide (Cer), ceramide-1-phosphate (C1P), sphingosine (So), sphingosine-1-phosphate (S1P), are signaling molecules that regulate a diverse range of cellular processes and human health. However, there are limited reviews on the emerging roles of exogenous dietary SLs in human health. In this review, we discuss the ubiquitous presence of dietary SLs, highlighting their structures and contents in foodstuffs, particularly in sea foods. The digestion and metabolism of dietary SLs is also discussed. Focus is given to the roles of SLs in both the etiology and prevention of diseases, including bacterial infection, cancers, neurogenesis and neurodegenerative diseases, skin integrity, and metabolic syndrome (MetS). We propose that dietary SLs represent a "functional" constituent as emerging strategies for improving human health. Gaps in research that could be of future interest are also discussed.

Fingerprinting Alzheimer's Disease by 1H Nuclear Magnetic Resonance Spectroscopy of Cerebrospinal Fluid

In this study, we sought for a cerebrospinal fluid (CSF) metabolomic fingerprint in Alzheimer's disease (AD) patients characterized, according to the clinical picture and CSF AD core biomarkers (Aβ₄₂, p-tau, and t-tau), both at pre-dementia (mild cognitive impairment due to AD, MCI-AD) and dementia stages (ADdem) and in a group of patients with a normal CSF biomarker profile (non-AD) using untargeted ¹H nuclear magnetic resonance (NMR) spectroscopy-based metabolomics. This is a retrospective study based on two independent cohorts: a Dutch cohort, which comprises 20 ADdem, 20 MCI-AD, and 20 non-AD patients, and an Italian cohort, constituted by 14 ADdem and 12 non-AD patients. ¹H NMR CSF spectra were analyzed using OPLS-DA. Metabolomic fingerprinting in the Dutch cohort provides a significant discrimination (86.1% accuracy) between ADdem and non-AD. MCI-AD patients show a good discrimination with respect to ADdem (70.0% accuracy) but only slight differences when compared with non-AD (59.6% accuracy). Acetate, valine, and 3-hydroxyisovalerate result to be altered in ADdem patients. Valine correlates with cognitive decline at follow-up (R = 0.53, P = 0.0011). The discrimination between ADdem and non-AD was confirmed in the Italian cohort. The CSF metabolomic fingerprinting shows a signature characteristic of ADdem patients with respect to MCI-AD and non-AD patients.

Altered Plasma Acylcarnitines and Amino Acids Profile in Spinocerebellar Ataxia Type 7

Spinocerebellar ataxia type 7 (SCA7), a neurodegenerative disease characterized by cerebellar ataxia and retinal degeneration, is caused by an abnormal CAG repeat expansion in the ATXN7 gene coding region. The onset and severity of SCA7 are highly variable between patients, thus identification of sensitive biomarkers that accurately diagnose the disease and monitoring its progression are needed. With the aim of identified SCA7-specific metabolites with clinical relevance, we report for the first time, to the best of our knowledge, a metabolomics profiling of circulating acylcarnitines and amino acids in SCA7 patients. We identified 21 metabolites with altered levels in SCA7 patients and determined two different sets of metabolites with diagnostic power. The first signature of metabolites (Valine, Leucine, and Tyrosine) has the ability to discriminate between SCA7 patients and healthy controls, while the second one (Methionine, 3-hydroxytetradecanoyl-carnitine, and 3-hydroxyoctadecanoyl-carnitine) possess the capability to differentiate between early-onset and adult-onset patients, as shown by the multivariate model and ROC analyses. Furthermore, enrichment analyses of metabolic pathways suggest alterations in mitochondrial function, energy metabolism, and fatty acid beta-oxidation in SCA7 patients. In summary, circulating SCA7-specific metabolites identified in this study could serve as effective predictors of SCA7 progression in the clinics, as they are sampled in accessible biofluid and assessed by a relatively simple biochemical assay.

Sex and APOE ε4 genotype modify the Alzheimer's disease serum metabolome

Late-onset Alzheimer’s disease (AD) can, in part, be considered a metabolic disease. Besides age, female sex and APOE ε4 genotype represent strong risk factors for AD that also give rise to large metabolic differences. We systematically investigated group-specific metabolic alterations by conducting stratified association analyses of 139 serum metabolites in 1,517 individuals from the AD Neuroimaging Initiative with AD biomarkers. We observed substantial sex differences in effects of 15 metabolites with partially overlapping differences for APOE ε4 status groups. Several group-specific metabolic alterations were not observed in unstratified analyses using sex and APOE ε4 as covariates. Combined stratification revealed further subgroup-specific metabolic effects limited to APOE ε4+ females. The observed metabolic alterations suggest that females experience greater impairment of mitochondrial energy production than males. Dissecting metabolic heterogeneity in AD pathogenesis can therefore enable grading the biomedical relevance for specific pathways within specific subgroups, guiding the way to personalized medicine.

Sex and the APOE ε4 genotype are important risk factors for late-onset Alzheimer’s disease. In the current study, the authors investigate how sex and APOE ε4 genotype modify the association between Alzheimer’s disease biomarkers and metabolites in serum.