The SARS-CoV-2 spike glycoprotein interacts with MAO-B and impairs mitochondrial energetics.

SARS-CoV-2 infection is associated with both acute and post-acute neurological symptoms. Emerging evidence suggests that SARS-CoV-2 can alter mitochondrial metabolism, suggesting that changes in brain metabolism may contribute to the development of acute and post-acute neurological complications. Monoamine oxidase B (MAO-B) is a flavoenzyme located on the outer mitochondrial membrane that catalyzes the oxidative deamination of monoamine neurotransmitters. Computational analyses have revealed high similarity between the SARS-CoV-2 spike glycoprotein receptor binding domain on the ACE2 receptor and MAO-B, leading to the hypothesis that SARS-CoV-2 spike glycoprotein may alter neurotransmitter metabolism by interacting with MAO-B. Our results empirically establish that the SARS-CoV-2 spike glycoprotein interacts with MAO-B, leading to increased MAO-B activity in SH-SY5Y neuron-like cells. Common to neurodegenerative disease pathophysiological mechanisms, we also demonstrate that the spike glycoprotein impairs mitochondrial bioenergetics, induces oxidative stress, and perturbs the degradation of depolarized aberrant mitochondria through mitophagy. Our findings also demonstrate that SH-SY5Y neuron-like cells expressing the SARS-CoV-2 spike protein were more susceptible to MPTP-induced necrosis, likely necroptosis. Together, these results reveal novel mechanisms that may contribute to SARS-CoV-2-induced neurodegeneration.

Choline metabolism underpins macrophage IL-4 polarization and RELMα up-regulation in helminth infection.

Type 2 cytokines like IL-4 are hallmarks of helminth infection and activate macrophages to limit immunopathology and mediate helminth clearance. In addition to cytokines, nutrients and metabolites critically influence macrophage polarization. Choline is an essential nutrient known to support normal macrophage responses to lipopolysaccharide; however, its function in macrophages polarized by type 2 cytokines is unknown. Using murine IL-4-polarized macrophages, targeted lipidomics revealed significantly elevated levels of phosphatidylcholine, with select changes to other choline-containing lipid species. These changes were supported by the coordinated up-regulation of choline transport compared to naïve macrophages. Pharmacological inhibition of choline metabolism significantly suppressed several mitochondrial transcripts and dramatically inhibited select IL-4-responsive transcripts, most notably, Retnla. We further confirmed that blocking choline metabolism diminished IL-4-induced RELMα (encoded by Retnla) protein content and secretion and caused a dramatic reprogramming toward glycolytic metabolism. To better understand the physiological implications of these observations, naïve or mice infected with the intestinal helminth Heligmosomoides polygyrus were treated with the choline kinase α inhibitor, RSM-932A, to limit choline metabolism in vivo. Pharmacological inhibition of choline metabolism lowered RELMα expression across cell-types and tissues and led to the disappearance of peritoneal macrophages and B-1 lymphocytes and an influx of infiltrating monocytes. The impaired macrophage activation was associated with some loss in optimal immunity to H. polygyrus, with increased egg burden. Together, these data demonstrate that choline metabolism is required for macrophage RELMα induction, metabolic programming, and peritoneal immune homeostasis, which could have important implications in the context of other models of infection or cancer immunity.

Generation of the human iPSC lines AKOSi011-A carrying the mutation p.Pro65Ser/p.Asp35T and AKOSi012-A, carrying the mutation p.Tyr231His, derived from FAHN patient fibroblasts.

Fatty acid hydroxylase-associated neurodegeneration (FAHN) is a hereditary neurodegenerative disease caused by mutations in the FA2H gene. Patients show a wide range of neurological symptoms and an abnormal myelination. Here we describe the generation of the human induced pluripotent stem cell (hiPSC) lines AKOSi011-A and AKOSi012-A, derived from FAHN-patient fibroblasts, carrying the compound heterozygous mutation p.Pro65Ser/p.Asp35Tyr and the homozygous mutation p.Tyr231His, respectively. The hiPSC lines were generated using a non-integrating Sendai virus. The obtained hiPSCs show an unobtrusive karyotype, carry the mutations of the original fibroblasts, express pluripotency markers and can differentiate into cells of the three germ layers.

Signed Distance Correlation (SiDCo): an online implementation of distance correlation and partial distance correlation for data-driven network analysis.

MOTIVATION: There is a need for easily accessible implementations that measure the strength of both linear and non-linear relationships between metabolites in biological systems as an approach for data-driven network development. While multiple tools implement linear Pearson and Spearman methods, there are no such tools that assess distance correlation. RESULTS: We present here SIgned Distance COrrelation (SiDCo). SiDCo is a GUI platform for calculation of distance correlation in omics data, measuring linear and non-linear dependencies between variables, as well as correlation between vectors of different lengths, e.g. different sample sizes. By combining the sign of the overall trend from Pearson's correlation with distance correlation values, we further provide a novel "signed distance correlation" of particular use in metabolomic and lipidomic analyses. Distance correlations can be selected as one-to-one or one-to-all correlations, showing relationships between each feature and all other features one at a time or in combination. Additionally, we implement "partial distance correlation," calculated using the Gaussian Graphical model approach adapted to distance covariance. Our platform provides an easy-to-use software implementation that can be applied to the investigation of any dataset. AVAILABILITY AND IMPLEMENTATION: The SiDCo software application is freely available at https://complimet.ca/sidco. Supplementary help pages are provided at https://complimet.ca/sidco. Supplementary Material shows an example of an application of SiDCo in metabolomics.

Machine Learning and Hybrid Methods for Metabolic Pathway Modeling.

Computational cell metabolism models seek to provide metabolic explanations of cell behavior under different conditions or following genetic alterations, help in the optimization of in vitro cell growth environments, or predict cellular behavior in vivo and in vitro. In the extremes, mechanistic models can include highly detailed descriptions of a small number of metabolic reactions or an approximate representation of an entire metabolic network. To date, all mechanistic models have required details of individual metabolic reactions, either kinetic parameters or metabolic flux, as well as information about extracellular and intracellular metabolite concentrations. Despite the extensive efforts and the increasing availability of high-quality data, required in vivo data are not available for the majority of known metabolic reactions; thus, mechanistic models are based primarily on ex vivo kinetic measurements and limited flux information. Machine learning approaches provide an alternative for derivation of functional dependencies from existing data. The increasing availability of metabolomic and lipidomic data, with growing feature coverage as well as sample set size, is expected to provide new data options needed for derivation of machine learning models of cell metabolic processes. Moreover, machine learning analysis of longitudinal data can lead to predictive models of cell behaviors over time. Conversely, machine learning models trained on steady-state data can provide descriptive models for the comparison of metabolic states in different environments or disease conditions. Additionally, inclusion of metabolic network knowledge in these analyses can further help in the development of models with limited data.This chapter will explore the application of machine learning to the modeling of cell metabolism. We first provide a theoretical explanation of several machine learning and hybrid mechanistic machine learning methods currently being explored to model metabolism. Next, we introduce several avenues for improving these models with machine learning. Finally, we provide protocols for specific examples of the utilization of machine learning in the development of predictive cell metabolism models using metabolomic data. We describe data preprocessing, approaches for training of machine learning models for both descriptive and predictive models, and the utilization of these models in synthetic and systems biology. Detailed protocols provide a list of software tools and libraries used for these applications, step-by-step modeling protocols, troubleshooting, as well as an overview of existing limitations to these approaches.

The clinical spectrum of SMA-PME and in vitro normalization of its cellular ceramide profile.

OBJECTIVE: The objectives of this study were to define the clinical and biochemical spectrum of spinal muscular atrophy with progressive myoclonic epilepsy (SMA-PME) and to determine if aberrant cellular ceramide accumulation could be normalized by enzyme replacement. METHODS: Clinical features of 6 patients with SMA-PME were assessed by retrospective chart review, and a literature review of 24 previously published cases was performed. Leukocyte enzyme activity of acid ceramidase was assessed with a fluorescence-based assay. Skin fibroblast ceramide content and was assessed by high performance liquid chromatography, electrospray ionization tandem mass spectroscopy. Enzyme replacement was assessed using recombinant human acid ceramidase (rhAC) in vitro. RESULTS: The six new patients showed the hallmark features of SMA-PME, with variable initial symptom and age of onset. Five of six patients carried at least one of the recurrent SMA-PME variants observed in two specific codons of ASAH1. A review of 30 total cases revealed that patients who were homozygous for the most common c.125C > T variant presented in the first decade of life with limb-girdle weakness as the initial symptom. Sensorineural hearing loss was associated with the c.456A > C variant. Leukocyte acid ceramidase activity varied from 4.1%-13.1% of controls. Ceramide species in fibroblasts were detected and total cellular ceramide content was elevated by 2 to 9-fold compared to controls. Treatment with rhAC normalized ceramide profiles in cultured fibroblasts to control levels within 48 h. INTERPRETATION: This study details the genotype-phenotype correlations observed in SMA-PME and shows the impact of rhAC to correct the abnormal cellular ceramide profile in cells.

Dementia with Lewy bodies post-mortem brains reveal differentially methylated CpG sites with biomarker potential.

Dementia with Lewy bodies (DLB) is a common form of dementia with known genetic and environmental interactions. However, the underlying epigenetic mechanisms which reflect these gene-environment interactions are poorly studied. Herein, we measure genome-wide DNA methylation profiles of post-mortem brain tissue (Broadmann area 7) from 15 pathologically confirmed DLB brains and compare them with 16 cognitively normal controls using Illumina MethylationEPIC arrays. We identify 17 significantly differentially methylated CpGs (DMCs) and 17 differentially methylated regions (DMRs) between the groups. The DMCs are mainly located at the CpG islands, promoter and first exon regions. Genes associated with the DMCs are linked to "Parkinson's disease" and "metabolic pathway", as well as the diseases of "severe intellectual disability" and "mood disorders". Overall, our study highlights previously unreported DMCs offering insights into DLB pathogenesis with the possibility that some of these could be used as biomarkers of DLB in the future.

METAbolomics data Balancing with Over-sampling Algorithms (META-BOA): an online resource for addressing class imbalance.

MOTIVATION: Class imbalance, or unequal sample sizes between classes, is an increasing concern in machine learning for metabolomic and lipidomic data mining, which can result in overfitting for the over-represented class. Numerous methods have been developed for handling class imbalance, but they are not readily accessible to users with limited computational experience. Moreover, there is no resource that enables users to easily evaluate the effect of different over-sampling algorithms. RESULTS: METAbolomics data Balancing with Over-sampling Algorithms (META-BOA) is a web-based application that enables users to select between four different methods for class balancing, followed by data visualization and classification of the sample to observe the augmentation effects. META-BOA outputs a newly balanced dataset, generating additional samples in the minority class, according to the user's choice of Synthetic Minority Over-sampling Technique (SMOTE), Borderline-SMOTE (BSMOTE), Adaptive Synthetic (ADASYN) or Random Over-Sampling Examples (ROSE). To present the effect of over-sampling on the data META-BOA further displays both principal component analysis and t-distributed stochastic neighbor embedding visualization of data pre- and post-over-sampling. Random forest classification is utilized to compare sample classification in both the original and balanced datasets, enabling users to select the most appropriate method for their further analyses. AVAILABILITY AND IMPLEMENTATION: META-BOA is available at https://complimet.ca/meta-boa. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Differences in Sex-Specific Frequency of Glucocerebrosidase Variant Carriers and Familial Parkinsonism.

BACKGROUND: Although men and women with the LRRK2 G2019S variant appear to be equally likely to have Parkinson's disease (PD), the sex-distribution among glucocerebrosidase (GBA) variant carriers with PD, including limited to specific variant severities of GBA, is not well understood. Further, the sex-specific genetic contribution to PD without a known genetic variant is controversial. OBJECTIVES: To better understand sex differences in genetic contribution to PD, especially sex-specific frequencies among GBA variant carriers with PD (GBA PD) and LRRK2-G2019S variant carriers with PD (LRRK2 PD). METHODS: We assess differences in the sex-specific frequency in GBA PD, including in subsets of GBA variant severity, LRRK2 PD, and idiopathic PD in an Ashkenazi Jewish cohort with PD. Further, we expand prior work evaluating differences in family history of parkinsonism. RESULTS: Both idiopathic PD (267/420 men, 63.6%) (P < 0.001) and GBA PD overall (64/107, 59.8%) (P = 0.042) were more likely to be men, whereas no difference was seen in LRRK2 PD (50/99, 50.5%) and LRRK2/GBA PD (5/10, 50%). However, among GBA PD probands, severe variant carriers were more likely to be women (15/19 women, 79.0%) (P = 0.005), whereas mild variant carriers (44/70 men, 62.9%) (P = 0.039) and risk-variant carriers (15/17 men, 88.2%) (P = 0.001) were more likely to be men. CONCLUSIONS: Our study demonstrates that the male-sex predominance present in GBA PD overall was not consistent across GBA variant severities, and a female-sex predominance was present among severe GBA variant carriers. Therefore, research and trial designs for PD should consider sex-specific differences, including across GBA variant severities. © 2022 International Parkinson and Movement Disorder Society.

BATL: Bayesian annotations for targeted lipidomics.

MOTIVATION: Bioinformatic tools capable of annotating, rapidly and reproducibly, large, targeted lipidomic datasets are limited. Specifically, few programs enable high-throughput peak assessment of liquid chromatography-electrospray ionization tandem mass spectrometry data acquired in either selected or multiple reaction monitoring modes. RESULTS: We present here Bayesian Annotations for Targeted Lipidomics, a Gaussian naïve Bayes classifier for targeted lipidomics that annotates peak identities according to eight features related to retention time, intensity, and peak shape. Lipid identification is achieved by modeling distributions of these eight input features across biological conditions and maximizing the joint posterior probabilities of all peak identities at a given transition. When applied to sphingolipid and glycerophosphocholine selected reaction monitoring datasets, we demonstrate over 95% of all peaks are rapidly and correctly identified. AVAILABILITY AND IMPLEMENTATION: BATL software is freely accessible online at https://complimet.ca/batl/ and is compatible with Safari, Firefox, Chrome and Edge. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Relative Ratios of Human Seasonal Coronavirus Antibodies Predict the Efficiency of Cross-Neutralization of SARS-CoV-2 Spike Binding to ACE2.

BACKGROUND: Antibodies raised against human seasonal coronaviruses (sCoVs), which are responsible for the common cold, are known to cross-react with SARS-CoV-2 antigens. This prompts questions about their protective role against SARS-CoV-2 infections and COVID-19 severity. However, the relationship between sCoVs exposure and SARS-CoV-2 correlates of protection are not clearly identified. METHODS: We performed a cross-sectional analysis of cross-reactivity and cross-neutralization to SARS-CoV-2 antigens (S-RBD, S-trimer, N) using pre-pandemic sera from four different groups: pediatrics and adolescents, individuals 21 to 70 years of age, older than 70 years of age, and individuals living with HCV or HIV. Data was then further analysed using machine learning to identify predictive patterns of neutralization based on sCoVs serology. FINDINGS: Antibody cross-reactivity to SARS-CoV-2 antigens varied between 1.6% and 15.3% depending on the cohort and the isotype-antigen pair analyzed. We also show a range of neutralizing activity (0-45%) with median inhibition ranging from 17.6 % to 23.3 % in serum that interferes with SARS-CoV-2 spike attachment to ACE2 independently of age group. While the abundance of sCoV antibodies did not directly correlate with neutralization, we show that neutralizing activity is rather dependent on relative ratios of IgGs in sera directed to all four sCoV spike proteins. More specifically, we identified antibodies to NL63 and OC43 as being the most important predictors of neutralization. INTERPRETATION: Our data support the concept that exposure to sCoVs triggers antibody responses that influence the efficiency of SARS-CoV-2 spike binding to ACE2, which may potentially impact COVID-19 disease severity through other latent variables. FUNDING: This study was supported by a grant by the CIHR (VR2 -172722) and by a grant supplement by the CITF, and by a NRC Collaborative R&D Initiative Grant (PR031-1).

Metabolomics and computational analysis of the role of monoamine oxidase activity in delirium and SARS-COV-2 infection.

Delirium is an acute change in attention and cognition occurring in ~ 65% of severe SARS-CoV-2 cases. It is also common following surgery and an indicator of brain vulnerability and risk for the development of dementia. In this work we analyzed the underlying role of metabolism in delirium-susceptibility in the postoperative setting using metabolomic profiling of cerebrospinal fluid and blood taken from the same patients prior to planned orthopaedic surgery. Distance correlation analysis and Random Forest (RF) feature selection were used to determine changes in metabolic networks. We found significant concentration differences in several amino acids, acylcarnitines and polyamines linking delirium-prone patients to known factors in Alzheimer's disease such as monoamine oxidase B (MAOB) protein. Subsequent computational structural comparison between MAOB and angiotensin converting enzyme 2 as well as protein-protein docking analysis showed that there potentially is strong binding of SARS-CoV-2 spike protein to MAOB. The possibility that SARS-CoV-2 influences MAOB activity leading to the observed neurological and platelet-based complications of SARS-CoV-2 infection requires further investigation.

Identification of pannexin 1-regulated genes, interactome, and pathways in rhabdomyosarcoma and its tumor inhibitory interaction with AHNAK.

Rhabdomyosarcoma (RMS), the most common soft tissue sarcoma in children, is an aggressive cancer with a poor prognosis. Despite current management, the 5-year survival rate for patients with metastatic RMS is ∼30%; underscoring the need to develop better treatment strategies. We have recently reported that pannexin 1 (PANX1) levels are downregulated in RMS and that restoring its expression inhibits RMS progression. Here, we have surveyed and characterized the molecular changes induced by PANX1 re-expression in RMS. We cataloged transcriptomic changes in this context by RNA sequencing. At the protein level, we unveiled PANX1 interactors using BioID, complemented by co-immunoprecipitation coupled to high-performance liquid chromatography/electrospray ionization tandem mass spectrometry performed in PANX1-enriched fractions. Using these data, we generated searchable public databases for the PANX1 interactome and changes to the RMS transcriptome occurring when PANX1 expression is restored. STRING network analyses revealed a PANX1 interactome involving plasma membrane and cytoskeleton-associated proteins including the previously undescribed interactor AHNAK. Indeed, AHNAK knockdown abrogated the PANX1-mediated reduction in RMS cell viability and migration. Using these unbiased approaches, we bring insight to the mechanisms by which PANX1 inhibits RMS progression, identifying the cell migration protein AHNAK as a key modifier of PANX1-mediated changes in RMS malignant properties.

Computational Identification of Human Biological Processes and Protein Sequence Motifs Putatively Targeted by SARS-CoV-2 Proteins Using Protein-Protein Interaction Networks.

While the COVID-19 pandemic is causing important loss of life, knowledge of the effects of the causative SARS-CoV-2 virus on human cells is currently limited. Investigating protein-protein interactions (PPIs) between viral and host proteins can provide a better understanding of the mechanisms exploited by the virus and enable the identification of potential drug targets. We therefore performed an in-depth computational analysis of the interactome of SARS-CoV-2 and human proteins in infected HEK 293 cells published by Gordon et al. (Nature2020, 583, 459-468) to reveal processes that are potentially affected by the virus and putative protein binding sites. Specifically, we performed a set of network-based functional and sequence motif enrichment analyses on SARS-CoV-2-interacting human proteins and on PPI networks generated by supplementing viral-host PPIs with known interactions. Using a novel implementation of our GoNet algorithm, we identified 329 Gene Ontology terms for which the SARS-CoV-2-interacting human proteins are significantly clustered in PPI networks. Furthermore, we present a novel protein sequence motif discovery approach, LESMoN-Pro, that identified 9 amino acid motifs for which the associated proteins are clustered in PPI networks. Together, these results provide insights into the processes and sequence motifs that are putatively implicated in SARS-CoV-2 infection and could lead to potential therapeutic targets.

Growth environment and organ specific variation in in-vitro cytoprotective activities of Picea mariana in PC12 cells exposed to glucose toxicity: a plant used for treatment of diabetes symptoms by the Cree of Eeyou Istchee (Quebec, Canada).

BACKGROUND: The Cree of Eeyou Istchee (James Bay area of northern Quebec) suffer from a high rate of diabetes and its complications partly due to the introduction of the western lifestyle within their culture. As part of a search for alternative medicine based on traditional practice, this project evaluates the biological activity of Picea mariana (Mill.) Britton, Sterns & Poggenb. needle, bark, and cone, in preventing glucose toxicity to PC12-AC cells in vitro (a diabetic neurophathy model) and whether habitat and growth environment influence this activity. METHODS: Three different organs (needle, bark, and cone) of P. mariana were collected at different geographical locations and ecological conditions and their 80% ethanolic extracts were prepared. Extracts were then tested for their ability to protect PC12-AC cells from hyperglycaemic challenge at physiologically relevant concentrations of 0.25, 0.5, 1.0 and 2.0 µg/mL. Folin-Ciocalteu method was used to determine the total phenolic content of P. mariana extracts. RESULTS: All extracts were well-tolerated in vitro exhibiting LD50 of 25 µg/mL or higher. Extracts from all tested organs showed a cytoprotective concentration-dependent response. Furthermore, the cytoprotective activity was habitat- and growth environment-dependent with plants grown in bog or forest habitats in coastal or inland environments exhibiting different cytoprotective efficacies. These differences in activity correlated with total phenolic content but not with antioxidant activity. In addition, this paper provides the first complete Ultra-Performance Liquid Chromatography-quadrupole time-of-flight (UPLC-QTOF) mass spectrometry analysis of Picea mariana's bark, needles and cones. CONCLUSIONS: Together, these results provide further understanding of the cytoprotective activity of Canadian boreal forest plants identified by the Cree healers of Eeyou Istchee in a cell model of diabetic neuropathy. Their activity is relevant to diabetic peripheral neuropathic complications and shows that their properties can be optimized by harvesting in optimal growth environments.

Dysregulated Lipid Metabolism and Its Role in α-Synucleinopathy in Parkinson's Disease.

Parkinson's disease (PD) is the second most common neurodegenerative disease, the main pathological hallmark of which is the accumulation of α-synuclein (α-syn) and the formation of filamentous aggregates called Lewy bodies in the brainstem, limbic system, and cortical areas. Lipidomics is a newly emerging field which can provide fresh insights and new answers that will enhance our capacity for early diagnosis, tracking disease progression, predicting critical endpoints, and identifying risk in pre-symptomatic persons. In recent years, lipids have been implicated in many aspects of PD pathology. Biophysical and lipidomic studies have demonstrated that α-syn binds preferentially not only to specific lipid families but also to specific molecular species and that these lipid-protein complexes enhance its interaction with synaptic membranes, influence its oligomerization and aggregation, and interfere with the catalytic activity of cytoplasmic lipid enzymes and lysosomal lipases, thereby affecting lipid metabolism. The genetic link between aberrant lipid metabolism and PD is even more direct, with mutations in GBA and SMPD1 enhancing PD risk in humans and loss of GALC function increasing α-syn aggregation and accumulation in experimental murine models. Moreover, a number of lipidomic studies have reported PD-specific lipid alterations in both patient brains and plasma, including alterations in the lipid composition of lipid rafts in the frontal cortex. A further aspect of lipid dysregulation promoting PD pathogenesis is oxidative stress and inflammation, with proinflammatory lipid mediators such as platelet activating factors (PAFs) playing key roles in arbitrating the progressive neurodegeneration seen in PD linked to α-syn intracellular trafficking. Lastly, there are a number of genetic risk factors of PD which are involved in normal lipid metabolism and function. Genes such as PLA2G6 and SCARB2, which are involved in glycerophospholipid and sphingolipid metabolism either directly or indirectly are associated with risk of PD. This review seeks to describe these facets of metabolic lipid dysregulation as they relate to PD pathology and potential pathomechanisms involved in disease progression, while highlighting incongruous findings and gaps in knowledge that necessitate further research.

DMS as an orthogonal separation to LC/ESI/MS/MS for quantifying isomeric cerebrosides in plasma and cerebrospinal fluid.

Cerebrosides, including glucosylceramides (GlcCers) and galactosylceramides (GalCers), are important membrane components of animal cells with deficiencies resulting in devastating lysosomal storage disorders. Their quantification is essential for disease diagnosis and a better understanding of disease mechanisms. The simultaneous quantification of GlcCer and GalCer isomers is, however, particularly challenging due to their virtually identical structures. To address this challenge, we developed a new LC/MS-based method using differential ion mobility spectrometry (DMS) capable of rapidly and reproducibly separating and quantifying isomeric cerebrosides in a single run. We show that this LC/ESI/DMS/MS/MS method exhibits robust quantitative performance within an analyte concentration range of 2.8-355 nM. We further report the simultaneous quantification of nine GlcCers (16:0, 18:0, 20:0, 22:0, 23:0, 24:1, 24:0, 25:0, and 26:0) and five GalCers (16:0, 22:0, 23:0, 24:1, and 24:0) molecular species in human plasma, as well as six GalCers (18:0, 22:0, 23:0, 24:1, 24:0 and 25:0) and two GlcCers (24:1 and 24:0) in human cerebrospinal fluid. Our method expands the potential of DMS technology in the field of glycosphingolipid analysis for both biomarker discovery and drug screening by enabling the unambiguous assignment and quantification of cerebroside lipid species in biological samples.

Distinct disruptions in Land's cycle remodeling of glycerophosphocholines in murine cortex mark symptomatic onset and progression in two Alzheimer's disease mouse models.

Changes in glycerophosphocholine metabolism are observed in Alzheimer's disease; however, it is not known whether these metabolic disruptions are linked to cognitive decline. Here, using unbiased lipidomic approaches and direct biochemical assessments, we profiled Land's cycle lipid remodeling in the hippocampus, frontal cortex, and temporal-parietal-entorhinal cortices of human amyloid beta precursor protein (ΑßPP) over-expressing mice. We identified a cortex-specific hypo-metabolic signature at symptomatic onset and a cortex-specific hyper-metabolic signature of Land's cycle glycerophosphocholine remodeling over the course of progressive behavioral decline. When N5 TgCRND8 and ΑßPPSwe /PSIdE9 mice first exhibited deficits in the Morris Water Maze, levels of lyso-phosphatidylcholines, LPC(18:0/0:0), LPC(16:0/0:0), LPC(24:6/0:0), LPC(25:6/0:0), the lyso-platelet-activating factor (PAF), LPC(O-18:0/0:0), and the PAF, PC(O-22:6/2:0), declined as a result of reduced calcium-dependent cytosolic phospholipase A2 α (cPLA2 α) activity in all cortices but not hippocampus. Chronic intermittent hypoxia, an environmental risk factor that triggers earlier learning memory impairment in ΑßPPSwe /PSIdE9 mice, elicited these same metabolic changes in younger animals. Thus, this lipidomic signature of phenoconversion appears age-independent. By contrast, in symptomatic N5 TgCRND8 mice, cPLA2 α activity progressively increased; overall Lyso-phosphatidylcholines (LPC) and LPC(O) and PC(O-18:1/2:0) levels progressively rose. Enhanced cPLA2 α activity was only detected in transgenic mice; however, age-dependent increases in the PAF acetylhydrolase 1b α1 to α2 expression ratio, evident in both transgenic and non-transgenic mice, reduced PAF hydrolysis thereby contributing to PAF accumulation. Taken together, these data identify distinct age-independent and age-dependent disruptions in Land's cycle metabolism linked to symptomatic onset and progressive behavioral decline in animals with pre-existing Αß pathology. OPEN SCIENCE BADGES: This article has received a badge for *Open Materials* because it provided all relevant information to reproduce the study in the manuscript. The complete Open Science Disclosure form for this article can be found at the end of the article. More information about the Open Practices badges can be found at https://cos.io/our-services/open-science-badges/.