Multi-Omic analyses characterize the ceramide/sphingomyelin pathway as a therapeutic target in Alzheimer's disease

Disruption of sphingomyelin synthase 2 gene alleviates cognitive impairment in a mouse model of Alzheimer's disease

The membrane raft accommodates the key enzymes synthesizing amyloid β (Aβ). One of the two characteristic components of the membrane raft, cholesterol, is well known to promote the key enzymes that produce amyloid-β (Aβ) and exacerbate Alzheimer's disease (AD) pathogenesis. Given that the raft is a physicochemical platform for the sound functioning of embedded bioactive proteins, the other major lipid component sphingomyelin may also be involved in AD. Here we knocked out the sphingomyelin synthase 2 gene (SMS2) in 3xTg AD model mice by hybridization, yielding SMS2KO mice (4S mice). The novel object recognition test in 9/10-month-old 4S mice showed that cognitive impairment in 3xTg mice was alleviated by SMS2KO, though performance in the Morris water maze (MWM) was not improved. The tail suspension test detected a depressive trait in 4S mice, which may have hindered the manifestation of performance in the wet, stressful environment of MWM. In the hippocampal CA1, hyperexcitability in 3xTg was also found alleviated by SMS2KO. In the hippocampal dentate gyrus of 4S mice, the number of neurons positive with intracellular Aβ or its precursor proteins, the hallmark of young 3xTg mice, is reduced to one-third, suggesting an SMS2KO-led suppression of syntheses of those peptides in the dentate gyrus. Although we previously reported that large-conductance calcium-activated potassium (BK) channels are suppressed in 3xTg mice and their recovery relates to cognitive amelioration, no changes occurred by hybridization. Sphingomyelin in the membrane raft may serve as a novel target for AD drugs.

Circulating sphingolipids and subclinical brain pathology: the cardiovascular health study

Background

Sphingolipids are implicated in neurodegeneration and neuroinflammation. We assessed the potential role of circulating ceramides and sphingomyelins in subclinical brain pathology by investigating their association with brain magnetic resonance imaging (MRI) measures and circulating biomarkers of brain injury, neurofilament light chain (NfL) and glial fibrillary acidic protein (GFAP) in the Cardiovascular Health Study (CHS), a large and intensively phenotyped cohort of older adults.

Methods

Brain MRI was offered twice to CHS participants with a mean of 5 years between scans, and results were available from both time points in 2,116 participants (mean age 76 years; 40% male; and 25% APOE ε4 allele carriers). We measured 8 ceramide and sphingomyelin species in plasma samples and examined the associations with several MRI, including worsening grades of white matter hyperintensities and ventricular size, number of brain infarcts, and measures of brain atrophy in a subset with quantitative measures. We also investigated the sphingolipid associations with serum NfL and GFAP.

Results

In the fully adjusted model, higher plasma levels of ceramides and sphingomyelins with a long (16-carbon) saturated fatty acid were associated with higher blood levels of NfL [β = 0.05, false-discovery rate corrected P (PFDR) = 0.004 and β = 0.06, PFDR = < 0.001, respectively]. In contrast, sphingomyelins with very long (20- and 22-carbon) saturated fatty acids tended to have an inverse association with levels of circulating NfL. In secondary analyses, we found an interaction between ceramide d18:1/20:0 and sex (P for interaction = <0.001), such that ceramide d18:1/20:0 associated with higher odds for infarcts in women [OR = 1.26 (95%CI: 1.07, 1.49), PFDR = 0.03]. We did not observe any associations with GFAP blood levels, white matter grade, ventricular grade, mean bilateral hippocampal volume, or total brain volume.

Conclusion

Overall, our comprehensive investigation supports the evidence that ceramides and sphingomyelins are associated with increased aging brain pathology and that the direction of association depends on the fatty acid attached to the sphingosine backbone.

Genetic and multi-omic risk assessment of Alzheimer's disease implicates core associated biological domains

INTRODUCTION

Alzheimer's disease (AD) is the predominant dementia globally, with heterogeneous presentation and penetrance of clinical symptoms, variable presence of mixed pathologies, potential disease subtypes, and numerous associated endophenotypes. Beyond the difficulty of designing treatments that address the core pathological characteristics of the disease, therapeutic development is challenged by the uncertainty of which endophenotypic areas and specific targets implicated by those endophenotypes to prioritize for further translational research. However, publicly funded consortia driving large-scale open science efforts have produced multiple omic analyses that address both disease risk relevance and biological process involvement of genes across the genome.

METHODS

Here we report the development of an informatic pipeline that draws from genetic association studies, predicted variant impact, and linkage with dementia associated phenotypes to create a genetic risk score. This is paired with a multi-omic risk score utilizing extensive sets of both transcriptomic and proteomic studies to identify system-level changes in expression associated with AD. These two elements combined constitute our target risk score that ranks AD risk genome-wide. The ranked genes are organized into endophenotypic space through the development of 19 biological domains associated with AD in the described genetics and genomics studies and accompanying literature. The biological domains are constructed from exhaustive Gene Ontology (GO) term compilations, allowing automated assignment of genes into objectively defined disease-associated biology. This rank-and-organize approach, performed genome-wide, allows the characterization of aggregations of AD risk across biological domains.

RESULTS

The top AD-risk-associated biological domains are Synapse, Immune Response, Lipid Metabolism, Mitochondrial Metabolism, Structural Stabilization, and Proteostasis, with slightly lower levels of risk enrichment present within the other 13 biological domains.

DISCUSSION

This provides an objective methodology to localize risk within specific biological endophenotypes and drill down into the most significantly associated sets of GO terms and annotated genes for potential therapeutic targets.

Large-Scale Population-Based Studies of Blood Metabolome and Brain Health

Metabolomics technologies enable the quantification of multiple metabolomic measures simultaneously, which provides novel insights into molecular aspects of human health and disease. In large-scale, population-based studies, blood is often the preferred biospecimen. Circulating metabolome may relate to brain health either by affecting or reflecting brain metabolism. Peripheral metabolites may act at or cross the blood-brain barrier and, subsequently, influence brain metabolism, or they may reflect brain metabolism if similar pathways are engaged. Peripheral metabolites may also include those penetrating the circulation from the brain, indicating, for example, brain damage. Most brain health-related metabolomics studies have been conducted in the context of neurodegenerative disorders and cognition, but some studies have also focused on neuroimaging markers of these disorders. Moreover, several metabolomics studies of neurodevelopmental disorders have been performed. Here, we provide a brief background on the types of blood metabolites commonly assessed, and we review the literature describing the relationships between human blood metabolome (n > 50 metabolites) and brain health reported in large-scale studies (n > 500 individuals).

How do sphingosine-1-phosphate affect immune cells to resolve inflammation?

Inflammation is an important immune response of the body. It is a physiological process of self-repair and defense against pathogens taken up by biological tissues when stimulated by damage factors such as trauma and infection. Inflammation is the main cause of high morbidity and mortality in most diseases and is the physiological basis of the disease. Targeted therapeutic strategies can achieve efficient toxicity clearance at the inflammatory site, reduce complications, and reduce mortality. Sphingosine-1-phosphate (S1P), a lipid signaling molecule, is involved in immune cell transport by binding to S1P receptors (S1PRs). It plays a key role in innate and adaptive immune responses and is closely related to inflammation. In homeostasis, lymphocytes follow an S1P concentration gradient from the tissues into circulation. One widely accepted mechanism is that during the inflammatory immune response, the S1P gradient is altered, and lymphocytes are blocked from entering the circulation and are, therefore, unable to reach the inflammatory site. However, the full mechanism of its involvement in inflammation is not fully understood. This review focuses on bacterial and viral infections, autoimmune diseases, and immunological aspects of the Sphks/S1P/S1PRs signaling pathway, highlighting their role in promoting intradial-adaptive immune interactions. How S1P signaling is regulated in inflammation and how S1P shapes immune responses through immune cells are explained in detail. We teased apart the immune cell composition of S1P signaling and the critical role of S1P pathway modulators in the host inflammatory immune system. By understanding the role of S1P in the pathogenesis of inflammatory diseases, we linked the genomic studies of S1P-targeted drugs in inflammatory diseases to provide a basis for targeted drug development.

Neuroprotective effect of the RNS60 in a mouse model of transient focal cerebral ischemia

BACKGROUND

Stroke is a major cause of death, disability, and public health problems. Its intervention is limited to early treatment with thrombolytics and/or endovascular clot removal with mechanical thrombectomy without any available subacute or chronic neuroprotective treatments. RNS60 has reduced neuroinflammation and increased neuronal survival in several animal models of neurodegeneration and trauma. The aim here was to evaluate whether RNS60 protects the brain and cognitive function in a mouse stroke model.

METHODS

Male C57BL/6J mice were subjected to sham or ischemic stroke surgery using 60-minute transient middle cerebral artery occlusion (tMCAo). In each group, mice received blinded daily administrations of RNS60 or control fluids (PNS60 or normal saline [NS]), beginning 2 hours after surgery over 13 days. Multiple neurobehavioral tests were conducted (Neurological Severity Score [mNSS], Novel Object Recognition [NOR], Active Place Avoidance [APA], and the Conflict Variant of APA [APAc]). On day 14, cortical microvascular perfusion (MVP) was measured, then brains were removed and infarct volume, immunofluorescence of amyloid beta (Aβ), neuronal density, microglial activation, and white matter damage/myelination were measured. SPSS was used for analysis (e.g., ANOVA for parametric data; Kruskal Wallis for non-parametric data; with post-hoc analysis).

RESULTS

Thirteen days of treatment with RNS60 reduced brain infarction, amyloid pathology, neuronal death, microglial activation, white matter damage, and increased MVP. RNS60 reduced brain pathology and resulted in behavioral improvements in stroke compared to sham surgery mice (increased memory-learning in NOR and APA, improved cognitive flexibility in APAc).

CONCLUSION

RNS60-treated mice exhibit significant protection of brain tissue and improved neurobehavioral functioning after tMCAo-stroke. Additional work is required to determine mechanisms, time-window of dosing, and multiple dosing volumes durations to support clinical stroke research.

The Alzheimer's Disease Neuroimaging Initiative in the era of Alzheimer's disease treatment: A review of ADNI studies from 2021 to 2022

The Alzheimer's Disease Neuroimaging Initiative (ADNI) aims to improve Alzheimer's disease (AD) clinical trials. Since 2006, ADNI has shared clinical, neuroimaging, and cognitive data, and biofluid samples. We used conventional search methods to identify 1459 publications from 2021 to 2022 using ADNI data/samples and reviewed 291 impactful studies. This review details how ADNI studies improved disease progression understanding and clinical trial efficiency. Advances in subject selection, detection of treatment effects, harmonization, and modeling improved clinical trials and plasma biomarkers like phosphorylated tau showed promise for clinical use. Biomarkers of amyloid beta, tau, neurodegeneration, inflammation, and others were prognostic with individualized prediction algorithms available online. Studies supported the amyloid cascade, emphasized the importance of neuroinflammation, and detailed widespread heterogeneity in disease, linked to genetic and vascular risk, co-pathologies, sex, and resilience. Biological subtypes were consistently observed. Generalizability of ADNI results is limited by lack of cohort diversity, an issue ADNI-4 aims to address by enrolling a diverse cohort.

Computational analysis of five neurodegenerative diseases reveals shared and specific genetic loci

Neurodegenerative diseases (ND) are heterogeneous disorders of the central nervous system that share a chronic and selective process of neuronal cell death. A computational approach to investigate shared genetic and specific loci was applied to 5 different ND: Amyotrophic lateral sclerosis (ALS), Alzheimer's disease (AD), Parkinson's disease (PD), Multiple sclerosis (MS), and Lewy body dementia (LBD). The datasets were analyzed separately, and then we compared the obtained results. For this purpose, we applied a genetic correlation analysis to genome-wide association datasets and revealed different genetic correlations with several human traits and diseases. In addition, a clumping analysis was carried out to identify SNPs genetically associated with each disease. We found 27 SNPs in AD, 6 SNPs in ALS, 10 SNPs in PD, 17 SNPs in MS, and 3 SNPs in LBD. Most of them are located in non-coding regions, with the exception of 5 SNPs on which a protein structure and stability prediction was performed to verify their impact on disease. Furthermore, an analysis of the differentially expressed miRNAs of the 5 examined pathologies was performed to reveal regulatory mechanisms that could involve genes associated with selected SNPs. In conclusion, the results obtained constitute an important step toward the discovery of diagnostic biomarkers and a better understanding of the diseases.

Recent advances towards mass spectrometry-based clinical lipidomics

The objective of this review is to provide a comprehensive summary of the latest methodological advancements and emerging patterns in utilizing lipidomics in clinical research.In this review, we assess the recent advancements in lipidomics methodologies that exhibit high levels of selectivity and sensitivity, capable of generating numerous molecular lipid species from limited quantities of biological matrices. The reviewed studies demonstrate that molecular lipid signatures offer new opportunities for precision medicine by providing sensitive diagnostic tools for disease prediction and monitoring. Moreover, the latest innovations in microsampling techniques have the potential to make a substantial contribution to clinical lipidomics. The review also shows that more work is needed to harmonize results across diverse lipidomics platforms and avoid significant errors in analysis and reporting. The increased implementation of internal standards and standard reference materials in analytical workflows will aid in this direction.

The Metabolome-Wide Signature of Major Depressive Disorder

Major Depressive Disorder (MDD) is an often-chronic condition with substantial molecular alterations and pathway dysregulations involved. Single metabolite, pathway and targeted metabolomics platforms have indeed revealed several metabolic alterations in depression including energy metabolism, neurotransmission and lipid metabolism. More comprehensive coverage of the metabolome is needed to further specify metabolic dysregulation in depression and reveal previously untargeted mechanisms. Here we measured 820 metabolites using the metabolome-wide Metabolon platform in 2770 subjects from a large Dutch clinical cohort with extensive depression clinical phenotyping (1101 current MDD, 868 remitted MDD, 801 healthy controls) at baseline and 1805 subjects at 6-year follow up (327 current MDD, 1045 remitted MDD, 433 healthy controls). MDD diagnosis was based on DSM-IV psychiatric interviews. Depression severity was measured with the Inventory of Depressive Symptomatology self-report. Associations between metabolites and MDD status and depression severity were assessed at baseline and at the 6-year follow-up. Metabolites consistently associated with MDD status or depression severity on both occasions were examined in Mendelian randomization (MR) analysis using metabolite (N=14,000) and MDD (N=800,000) GWAS results. At baseline, 139 and 126 metabolites were associated with current MDD status and depression severity, respectively, with 79 overlapping metabolites. Six years later, 34 out of the 79 metabolite associations were subsequently replicated. Downregulated metabolites were enriched with long-chain monounsaturated (P=6.7e-07) and saturated (P=3.2e-05) fatty acids and upregulated metabolites with lysophospholipids (P=3.4e-4). Adding BMI to the models changed results only marginally. MR analyses showed that genetically-predicted higher levels of the lysophospholipid 1-linoleoyl-GPE (18:2) were associated with greater risk of depression. The identified metabolome-wide profile of depression (severity) indicated altered lipid metabolism with downregulation of long-chain fatty acids and upregulation of lysophospholipids, for which causal involvement was suggested using genetic tools. This metabolomics signature offers a window on depression pathophysiology and a potential access point for the development of novel therapeutic approaches.

From genetic correlations of Alzheimer's disease to classification with artificial neural network models

Sporadic Alzheimer's disease (AD) is a complex neurological disorder characterized by many risk loci with potential associations with different traits and diseases. AD, characterized by a progressive loss of neuronal functions, manifests with different symptoms such as decline in memory, movement, coordination, and speech. The mechanisms underlying the onset of AD are not always fully understood, but involve a multiplicity of factors. Early diagnosis of AD plays a central role as it can offer the possibility of early treatment, which can slow disease progression. Currently, the methods of diagnosis are cognitive testing, neuroimaging, or cerebrospinal fluid analysis that can be time-consuming, expensive, invasive, and not always accurate. In the present study, we performed a genetic correlation analysis using genome-wide association statistics from a large study of AD and UK Biobank, to examine the association of AD with other human traits and disorders. In addition, since hippocampus, a part of cerebral cortex could play a central role in several traits that are associated with AD; we analyzed the gene expression profiles of hippocampus of AD patients applying 4 different artificial neural network models. We found 65 traits correlated with AD grouped into 9 clusters: medical conditions, fluid intelligence, education, anthropometric measures, employment status, activity, diet, lifestyle, and sexuality. The comparison of different 4 neural network models along with feature selection methods on 5 Alzheimer's gene expression datasets showed that the simple basic neural network model obtains a better performance (66% of accuracy) than other more complex methods with dropout and weight regularization of the network.

Integrative metabolomics science in Alzheimer's disease: Relevance and future perspectives

Alzheimer's disease (AD) is determined by various pathophysiological mechanisms starting 10-25 years before the onset of clinical symptoms. As multiple functionally interconnected molecular/cellular pathways appear disrupted in AD, the exploitation of high-throughput unbiased omics sciences is critical to elucidating the precise pathogenesis of AD. Among different omics, metabolomics is a fast-growing discipline allowing for the simultaneous detection and quantification of hundreds/thousands of perturbed metabolites in tissues or biofluids, reproducing the fluctuations of multiple networks affected by a disease. Here, we seek to critically depict the main metabolomics methodologies with the aim of identifying new potential AD biomarkers and further elucidating AD pathophysiological mechanisms. From a systems biology perspective, as metabolic alterations can occur before the development of clinical signs, metabolomics - coupled with existing accessible biomarkers used for AD screening and diagnosis - can support early disease diagnosis and help develop individualized treatment plans. Presently, the majority of metabolomic analyses emphasized that lipid metabolism is the most consistently altered pathway in AD pathogenesis. The possibility that metabolomics may reveal crucial steps in AD pathogenesis is undermined by the difficulty in discriminating between the causal or epiphenomenal or compensatory nature of metabolic findings.

Integrating Omics Data in Genome-Scale Metabolic Modeling: A Methodological Perspective for Precision Medicine

Recent advancements in omics technologies have generated a wealth of biological data. Integrating these data within mathematical models is essential to fully leverage their potential. Genome-scale metabolic models (GEMs) provide a robust framework for studying complex biological systems. GEMs have significantly contributed to our understanding of human metabolism, including the intrinsic relationship between the gut microbiome and the host metabolism. In this review, we highlight the contributions of GEMs and discuss the critical challenges that must be overcome to ensure their reproducibility and enhance their prediction accuracy, particularly in the context of precision medicine. We also explore the role of machine learning in addressing these challenges within GEMs. The integration of omics data with GEMs has the potential to lead to new insights, and to advance our understanding of molecular mechanisms in human health and disease.

Improved Protocol for Reproducible Human Cortical Organoids Reveals Early Alterations in Metabolism with MAPT Mutations

Cerebral cortical-enriched organoids derived from human pluripotent stem cells (hPSCs) are valuable models for studying neurodevelopment, disease mechanisms, and therapeutic development. However, recognized limitations include the high variability of organoids across hPSC donor lines and experimental replicates. We report a 96-slitwell method for efficient, scalable, reproducible cortical organoid production. When hPSCs were cultured with controlled-release FGF2 and an SB431542 concentration appropriate for their TGFBR1 / ALK5 expression level, organoid cortical patterning and reproducibility were significantly improved. Well-patterned organoids included 16 neuronal and glial subtypes by single cell RNA sequencing (scRNA-seq), frequent neural progenitor rosettes and robust BCL11B+ and TBR1+ deep layer cortical neurons at 2 months by immunohistochemistry. In contrast, poorly-patterned organoids contain mesendoderm-related cells, identifiable by negative QC markers including COL1A2 . Using this improved protocol, we demonstrate increased sensitivity to study the impact of different MAPT mutations from patients with frontotemporal dementia (FTD), revealing early changes in key metabolic pathways.

Functional roles of sphingolipids in immunity and their implication in disease

Sphingolipids, which are components of cellular membranes and organ tissues, can be synthesized or degraded to modulate cellular responses according to environmental cues, and the balance among the different sphingolipids is important for directing immune responses, regardless of whether they originate, as intra- or extracellular immune events. Recent progress in multiomics-based analyses and methodological approaches has revealed that human health and diseases are closely related to the homeostasis of sphingolipid metabolism, and disease-specific alterations in sphingolipids and related enzymes can be prognostic markers of human disease progression. Accumulating human clinical data from genome-wide association studies and preclinical data from disease models provide support for the notion that sphingolipids are the missing pieces that supplement our understanding of immune responses and diseases in which the functions of the involved proteins and nucleotides have been established. In this review, we analyze sphingolipid-related enzymes and reported human diseases to understand the important roles of sphingolipid metabolism. We discuss the defects and alterations in sphingolipid metabolism in human disease, along with functional roles in immune cells. We also introduce several methodological approaches and provide summaries of research on sphingolipid modulators in this review that should be helpful in studying the roles of sphingolipids in preclinical studies for the investigation of experimental and molecular medicines.

Very-long-chain fatty acids induce glial-derived sphingosine-1-phosphate synthesis, secretion, and neuroinflammation

VLCFAs (very-long-chain fatty acids) are the most abundant fatty acids in myelin. Hence, during demyelination or aging, glia are exposed to higher levels of VLCFA than normal. We report that glia convert these VLCFA into sphingosine-1-phosphate (S1P) via a glial-specific S1P pathway. Excess S1P causes neuroinflammation, NF-κB activation, and macrophage infiltration into the CNS. Suppressing the function of S1P in fly glia or neurons, or administration of Fingolimod, an S1P receptor antagonist, strongly attenuates the phenotypes caused by excess VLCFAs. In contrast, elevating the VLCFA levels in glia and immune cells exacerbates these phenotypes. Elevated VLCFA and S1P are also toxic in vertebrates based on a mouse model of multiple sclerosis (MS), experimental autoimmune encephalomyelitis (EAE). Indeed, reducing VLCFA with bezafibrate ameliorates the phenotypes. Moreover, simultaneous use of bezafibrate and fingolimod synergizes to improve EAE, suggesting that lowering VLCFA and S1P is a treatment avenue for MS.

Repurposing drugs against Alzheimer's disease: can the anti-multiple sclerosis drug fingolimod (FTY720) effectively tackle inflammation processes in AD?

Therapeutic approaches providing effective medication for Alzheimer's disease (AD) patients after disease onset are urgently needed. Previous studies in AD mouse models and in humans suggested that physical exercise or changed lifestyle can delay AD-related synaptic and memory dysfunctions when treatment started in juvenile animals or in elderly humans before onset of disease symptoms. However, a pharmacological treatment that can reverse memory deficits in AD patients was thus far not identified. Importantly, AD disease-related dysfunctions have increasingly been associated with neuro-inflammatory mechanisms and searching for anti-inflammatory medication to treat AD seems promising. Like for other diseases, repurposing of FDA-approved drugs for treatment of AD is an ideally suited strategy to reduce the time to bring such medication into clinical practice. Of note, the sphingosine-1-phosphate analogue fingolimod (FTY720) was FDA-approved in 2010 for treatment of multiple sclerosis patients. It binds to the five different isoforms of Sphingosine-1-phosphate receptors (S1PRs) that are widely distributed across human organs. Interestingly, recent studies in five different mouse models of AD suggest that FTY720 treatment, even when starting after onset of AD symptoms, can reverse synaptic deficits and memory dysfunction in these AD mouse models. Furthermore, a very recent multi-omics study identified mutations in the sphingosine/ceramide pathway as a risk factor for sporadic AD, suggesting S1PRs as promising drug target in AD patients. Therefore, progressing with FDA-approved S1PR modulators into human clinical trials might pave the way for these potential disease modifying anti-AD drugs.

Neuronal Prosurvival Role of Ceramide Synthase 2 by Olidogendrocyte-to-Neuron Extracellular Vesicle Transfer

Ageing is associated with notorious alterations in neurons, i.e., in gene expression, mitochondrial function, membrane degradation or intercellular communication. However, neurons live for the entire lifespan of the individual. One of the reasons why neurons remain functional in elderly people is survival mechanisms prevail over death mechanisms. While many signals are either pro-survival or pro-death, others can play both roles. Extracellular vesicles (EVs) can signal both pro-toxicity and survival. We used young and old animals, primary neuronal and oligodendrocyte cultures and neuroblastoma and oligodendrocytic lines. We analysed our samples using a combination of proteomics and artificial neural networks, biochemistry and immunofluorescence approaches. We found an age-dependent increase in ceramide synthase 2 (CerS2) in cortical EVs, expressed by oligodendrocytes. In addition, we show that CerS2 is present in neurons via the uptake of oligodendrocyte-derived EVs. Finally, we show that age-associated inflammation and metabolic stress favour CerS2 expression and that oligodendrocyte-derived EVs loaded with CerS2 lead to the expression of the antiapoptotic factor Bcl2 in inflammatory conditions. Our study shows that intercellular communication is altered in the ageing brain, which favours neuronal survival through the transfer of oligodendrocyte-derived EVs containing CerS2.

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

BACKGROUND

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

METHODS

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

RESULTS

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

CONCLUSIONS

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

Cerebral Gray and White Matter Monogalactosyl Diglyceride Levels Rise with the Progression of Alzheimer's Disease

BACKGROUND

Multiple studies have reported brain lipidomic abnormalities in Alzheimer's disease (AD) that affect glycerophospholipids, sphingolipids, and fatty acids. However, there is no consensus regarding the nature of these abnormalities, and it is unclear if they relate to disease progression.

OBJECTIVE

Monogalactosyl diglycerides (MGDGs) are a class of lipids which have been recently detected in the human brain. We sought to measure their levels in postmortem human brain and determine if these levels correlate with the progression of the AD-related traits.

METHODS

We measured MGDGs by ultrahigh performance liquid chromatography tandem mass spectrometry in postmortem dorsolateral prefrontal cortex gray matter and subcortical corona radiata white matter samples derived from three cohorts of participants: the Framingham Heart Study, the Boston University Alzheimer's Disease Research Center, and the Arizona Study of Aging and Neurodegenerative Disorders/Brain and Body Donation Program (total n = 288).

RESULTS

We detected 40 molecular species of MGDGs (including diacyl and alkyl/acyl compounds) and found that the levels of 29 of them, as well as total MGDG levels, are positively associated with AD-related traits including pathologically confirmed AD diagnosis, clinical dementia rating, Braak and Braak stage, neuritic plaque score, phospho-Tau AT8 immunostaining density, levels of phospho-Tau396 and levels of Aβ40. Increased MGDG levels were present in both gray and white matter, indicating that they are widespread and likely associated with myelin-producing oligodendrocytes-the principal cell type of white matter.

CONCLUSIONS

Our data implicate the MGDG metabolic defect as a central correlate of clinical and pathological progression in AD.