The role of brain cholesterol metabolism in Alzheimer’s disease (AD) remains unclear. Peripheral and brain cholesterol levels are largely independent due to the impermeability of the blood brain barrier (BBB), highlighting the importance of studying the role of brain cholesterol homeostasis in AD. We first tested whether metabolite markers of brain cholesterol biosynthesis and catabolism were altered in AD and associated with AD pathology using linear mixed-effects models in two brain autopsy samples from the Baltimore Longitudinal Study of Aging (BLSA) and the Religious Orders Study (ROS). We next tested whether genetic regulators of brain cholesterol biosynthesis and catabolism were altered in AD using the ANOVA test in publicly available brain tissue transcriptomic datasets. Finally, using regional brain transcriptomic data, we performed genome-scale metabolic network modeling to assess alterations in cholesterol biosynthesis and catabolism reactions in AD. We show that AD is associated with pervasive abnormalities in cholesterol biosynthesis and catabolism. Using transcriptomic data from Parkinson’s disease (PD) brain tissue samples, we found that gene expression alterations identified in AD were not observed in PD, suggesting that these changes may be specific to AD. Our results suggest that reduced de novo cholesterol biosynthesis may occur in response to impaired enzymatic cholesterol catabolism and efflux to maintain brain cholesterol levels in AD. This is accompanied by the accumulation of nonenzymatically generated cytotoxic oxysterols. Our results set the stage for experimental studies to address whether abnormalities in cholesterol metabolism are plausible therapeutic targets in AD. This further highlights the importance of studying the role of brain cholesterol homeostasis in AD pathogenesis.While several epidemiological studies suggest that midlife hypercholesterolemia is associated with an increased risk of Alzheimer’s disease (AD), the role of brain cholesterol metabolism in AD remains unclear. The impermeability of cholesterol to the blood brain barrier (BBB) ensures that brain concentrations of cholesterol are largely independent of peripheral tissues3 and statin use5 in AD have suggested that cholesterol metabolism may have an impact on amyloid-β aggregation and neurotoxicity as well as tau pathology7. Other studies have addressed the molecular mechanisms underlying the relationship between brain cholesterol metabolism and AD pathogenesis8. These studies have generally implicated oxysterols, the main breakdown product of cholesterol catabolism, as plausible mediators of this relationship9. Few studies have however tested the role of both brain cholesterol biosynthesis and catabolism in AD across multiple aging cohorts. A comprehensive understanding of cholesterol metabolism may uncover therapeutic targets as suggested by emerging evidence that modulation of brain cholesterol levels may be a promising drug target10.Prior epidemiologic work examining the relationship between hypercholesterolemiaIn this study, we utilized targeted and quantitative metabolomics to measure brain tissue concentrations of both biosynthetic precursors of cholesterol as well as oxysterols, which represent BBB-permeable products of cholesterol catabolism, in samples from participants in two well-characterized cohorts—the Baltimore Longitudinal Study of Aging (BLSA) and the Religious Orders Study (ROS). We additionally utilized publicly available transcriptomic datasets in AD and control (CN) brain tissue samples to study differences in regional expression of genes regulating reactions within de novo cholesterol biosynthesis and catabolism pathways. Finally, we mapped regional brain transcriptome data on genome-scale metabolic networks to compare flux activity of reactions representing de novo cholesterol biosynthesis and catabolism between AD and CN samples.Are brain metabolite markers of cholesterol biosynthesis and catabolism altered in AD and associated with severity of AD pathology in two demographically distinct cohorts of older individuals?Are the genetic regulators of cholesterol biosynthesis and catabolism altered in brain regions vulnerable to AD pathology and are these alterations specific to AD or represent non-specific characteristics related to neurodegeneration in other diseases such as Parkinson’s disease (PD)?Are predicted metabolic flux activity through reactions within cholesterol biosynthesis and catabolism altered in brain regions vulnerable to AD pathology and are these alterations specific to AD?We addressed the following key questions in this study:APOE ε4 carrier status, statin use, and postmortem interval (PMI). AD samples were more likely White (race) compared to CN samples. The three groups varied significantly in the severity of neuritic plaques (CERAD scores) with the AD group showing the highest pathology, ASY intermediate, and CN with the lowest levels of pathology. CN, as expected, differed from AD in the severity of neurofibrillary tangles (Braak scores), with AD group showing the highest and CN the lowest levels of pathology.Table APOE ε4 carrier status, statin use, and PMI. Persons with AD were significantly older at death compared to both ASY and CN samples and were more likely female (sex) compared to CN. CN, as expected, differed from ASY and AD in the severity of neuritic plaque pathology (CERAD scores) and neurofibrillary tangle pathology (Braak scores) with the AD group showing the highest pathology, ASY being intermediate and CN with the lowest severity of each pathology.In the ROS sample, the three groups did not vary significantly in race, APOE ε4 carrier status, CERAD scores, or Braak scores. BLSA AD samples compared to ROS AD samples were significantly younger at age of onset, had a longer disease duration, lower percentage females, less likely to use statins, and had a longer PMI. BLSA ASY samples did not vary from ROS ASY samples. BLSA CN samples were significantly lower percentage White (race).Table P < 0.001). We additionally observed that lower lanosterol concentration in the MFG was significantly associated with higher neuritic plaque burden (P = 0.012) and higher neurofibrillary tangle pathology (P < 0.001). Brain tissue concentration of free cholesterol was not associated with disease status, neuritic plaque burden (CERAD score), or neurofibrillary tangle pathology (Braak score).In pooled primary analyses  Table , we obseP < 0.05) between any other metabolites related to de novo cholesterol biosynthesis and either disease status or severity of AD pathology.In cohort-specific secondary analyses (Supplementary Table P = 0.011). We additionally observed that lower 24S-hydroxycholesterol in the ITG was significantly associated with higher neuritic plaque burden (P = 0.006) and lower 24S-hydroxycholesterol concentration in both the ITG (P = 0.002) and MFG (P = 0.042) were associated with higher neurofibrillary pathology. Higher 7α-hydroxycholesterol concentration in the ITG was significantly associated with higher neuritic plaque burden (P = 0.027).In pooled primary analyses  Table , we obseP = 0.036 and 0.048, respectively). Lower 7α-hydroxycholesterol concentration in the MFG in ROS was significantly associated with higher neuritic plaque burden (FDR-adjusted P = 0.037). Lower 4β-hydroxycholesterol concentration in the MFG in ROS was also significantly associated with higher neuritic plaque burden (FDR-adjusted P = 0.029).In cohort-specific secondary analyses . Higher 7-ketocholesterol and 7β-hydroxycholesterol concentrations in the ITG were also associated with higher neuritic plaque burden .In pooled primary analyses  Table , we obseP = 0.039 and P = 0.039, respectively).In cohort-specific secondary analyses  in AD in 9 out of 20 genes. The majority of significantly altered genes (eight out of nine) showed lower gene expression in AD relative to CN (AD < CN) in the ERC and/or hippocampus. We observed no significant associations in the control region .As indicated in the Fig. P value <0.05) in five out of ten genes. Three had higher gene expression in AD compared to CN (AD > CN) and two had lower gene expression in AD relative to CN (AD < CN) in the ERC and/or hippocampus. We observed no significant associations in the control region .For cholesterol catabolism (enzymatic), we observed significantly altered gene expression  in the ERC. We observed no significant associations in the control region . Significant genes are included in Fig. P value <0.05) in PD compared to CN in the substantia nigra  altered in AD in the ERC and/or hippocampus including 15 within the de novo cholesterol biosynthesis pathway  and 1 within the cholesterol catabolism (enzymatic) pathway. The majority of reactions within the de novo cholesterol biosynthesis pathway (14/15) were decreased in AD compared to CN. Within the cholesterol catabolism (enzymatic) pathway, 1/1 was increased in the AD hippocampus. The majority of reactions within the control region—visual cortex—(15/16) were not significantly different between AD and CN. Reactions related to cholesterol esterification were not predicted to be significantly altered between the two groups in any of the brain regions examined. Supplementary Table In Table pathway 1/15 were Genome-scale metabolic network modeling in PD samples relative to CN (in the substantia nigra) of the 16 reactions that were significantly altered in AD did not reveal any significantly altered reactions  as well as exposure to statin therapy and PMI. Converging results from these two independent cohorts, therefore, suggest that our observations on dysregulation of cholesterol homeostasis likely reflect fundamental features of AD pathogenesis.We additionally assessed whether our results were specific to AD by performing identical analyses on gene expression data in a non-AD neurodegenerative disease, by using brain tissue samples from PD patients. We found that gene expression of enzymes regulating de novo cholesterol biosynthesis and catabolism are not altered in the substantia nigra in PD suggesting that these changes may be relatively specific to brain regions vulnerable to AD pathology.We observed significantly lower levels of the principal cholesterol precursor, lanosterol in AD as well as significant associations between lower lanosterol concentrations and greater severity of both neuritic plaque burden and neurofibrillary pathology. The de novo synthesis of cholesterol from acetyl CoA occurs through a series of enzymatic reactions in the mevalonate pathway Fig.  that fir11. Similarly, increasing levels of lanosterol have been shown to reduce neuronal atrophy and improve motor deficits in a mouse model of Huntington’s disease as well as the survival of striatal cells overexpressing Huntingtin protein12. Our finding that this cholesterol precursor is depleted in the AD brain in regions vulnerable to pathology suggests that mitochondrial function and autophagy may be disrupted leading to neurodegeneration. Interestingly, the accumulation of mutant amyloid precursor protein (APP) and Aβ in hippocampal neurons has been shown to induce impairment in both mitochondrial structure and function as well as defective mitophagy and autophagy with synaptic loss13.Our finding that AD is associated with reduced synthesis of lanosterol can be interpreted in the context of recent evidence that biosynthetic precursors of cholesterol may exert neuroprotective roles. Lanosterol has been shown to induce mitochondrial uncoupling and autophagy, promoting neuronal survival in a mouse model of Parkinson’s disease16. Some prior reports have however observed higher brain cholesterol concentrations in AD. Xiong et al. assayed both free and esterified cholesterol levels in a small sample of AD and CN brains using enzymatic methods and reported higher concentrations in AD17. However, it is unclear whether these assays were performed in specific brain regions or measured total brain cholesterol concentrations. In a smaller study, Cuttler et al. reported higher concentrations of cholesterol in the MFG in AD samples relative to CN18.Despite a decrease in brain tissue concentration of lanosterol, we found no significant differences in absolute concentrations of free cholesterol in AD. Our results are broadly consistent with prior studies that have measured free cholesterol in the AD brainIn order to further assess whether de novo cholesterol biosynthesis is altered in AD, we tested differences in gene expression of enzymatic regulators of these reactions between AD and CN samples in the hippocampus, ERC, and visual cortex.20.Broadly, we observed a significant reduction in expression of several genes catalyzing reactions in de novo cholesterol biosynthesis in the hippocampus and ERC in AD, while no alterations were detected in the visual cortex. These included genes encoding enzymes catalyzing reactions leading to the synthesis of the earliest cholesterol precursors Fig. , includi21. Our findings, therefore, suggest that key biochemical reactions determining the biosynthetic fates of both cholesterol and non-sterol isoprenoids are impaired in AD. These results add to growing evidence implicating perturbations in isoprenoid metabolism in AD pathogenesis22. Until recently, dysregulation in isoprenoid metabolism has received relatively little attention in comparison to cholesterol metabolism in the pathogenesis of AD. The isoprenoids, FPP, and geranylgeranyl pyrophosphate (GGPP) participate in prenylation reactions—an important post-translational modification of several proteins including the small GTPases, which serve as molecular switches in numerous signaling pathways relevant to AD23. Interestingly, in a previous proteomics study performed in the same BLSA samples as in our current report, we showed reduced levels of the GTPase signaling proteins, RHOB, and G protein subunit alpha i protein (GNAI1) in the frontal cortex in AD24. The role of Rho GTPases as regulators of synaptic plasticity may be especially relevant in interpreting our findings in the context of AD pathogenesis25.In addition to reduced de novo cholesterol biosynthesis through the pre-squalene mevalonate pathway in AD Fig. , we also26. Although subsequent microarray studies have reported inconsistent results on DHCR24 expression in AD, accumulating evidence suggests that DHCR24 may exert pleiotropic effects on several molecular mechanisms relevant to AD. While DHCR24 and its substrate, desmosterol play key roles in cholesterol homeostasis, DHCR24 also has reactive oxygen species (ROS)-scavenging activity and may protect against Aβ-induced neurotoxicity and apoptosis by inhibiting caspase-3 activation27. Reduced DHCR24 gene expression in regions vulnerable to AD pathology may therefore indicate greater susceptibility to ROS, Aβ-induced neurotoxicity, apoptosis, and neurodegeneration.In the post-squalene cholesterol biosynthesis pathway Fig. , we addi29. Our findings are consistent with accumulating evidence that 24S-hydroxycholesterol may play important roles as a modulator of Aβ production, tau phosphorylation and neuronal death as well as cognitive performance31.As brain cholesterol homeostasis likely reflects net effects of both cholesterol biosynthesis and catabolism, we were also interested in assessing concentrations of metabolite markers of cholesterol breakdown. We found that cholesterol breakdown through enzymatic conversion to its principal catabolic product, 24S-hydroxycholesterol Fig.  is lower32 and 7β-hydroxycholesterol, both of which were also significantly associated with severity of neuritic plaque pathology. Other nonenzymatically generated oxysterols whose concentrations were higher in AD included 5α,6α-epoxycholesterol, 5α,6β-dihydroxycholestanol, and 5β,6β-epoxycholesterol. Our results are relevant in the context of prior studies, suggesting that these oxysterol species may mediate cytotoxicity, apoptosis, oxidative stress and chronic inflammation34. While the precise mechanisms generating cytotoxic oxysterols in the brain remain to be identified, it is interesting that both APP and Aβ have been shown to oxidize cholesterol33. Furthermore, Aβ:copper complexes in lipid rafts promote the catalytic oxidation of cholesterol to generate oxysterols that may trigger hyperphosphorylation of tau and accumulation of neurofibrillary tangles36.We also observed alterations in brain tissue concentrations of nonenzymatically generated oxysterols in AD Fig. . These i16. They however did not report any significant group differences in the concentrations of cholesterol precursors, free cholesterol, or oxysterols in their study. It is likely that differences in assay methodology (GC–MS versus UHPLC–MS/MS) may account for the inconsistency between these prior findings and our current results. Testa et al.37 utilized isotope dilution gas chromatography/mass spectrometry to measure enzymatically and nonenzymatically generated oxysterol concentrations from the frontal and occipital cortices in AD (N = 13) and CN (N = 4) brains. They found that levels of several oxysterols were associated with disease progression. These prior findings are broadly consistent with our current report.One previous study utilized mass spectrometry-based assays of cholesterol precursors, free cholesterol, and oxysterols in the brain in AD in comparison to CN samples. In samples from the ROS study, Hascalovici et al. used gas chromatography–mass spectrometry (GC–MS) to assay these metabolites in the frontal cortex in AD, MCI, and CN samples38 through its conversion to 24S-hydroxycholesterol39 and plays a key role in regulating brain cholesterol levels. Inactivation of CYP46A1 has been shown to lower cholesterol efflux from the brain leading to a compensatory decrease in de novo cholesterol biosynthesis40. This compensatory reduction in cholesterol synthesis appears to be important in maintaining steady-state cholesterol levels in the brain in response to CYP46A1 inactivation. Our current results showing unaltered concentrations of free cholesterol in the brain in AD despite reduced expression of CYP46A1 may thus be mediated by a compensatory reduction in de novo cholesterol biosynthesis as suggested by reduced concentrations of lanosterol, the early biosynthetic precursor of cholesterol. Of relevance to our current findings are also previous studies that support a role for CYP46A1 beyond cholesterol turnover as 24S-hydroxycholesterol41 is a potent modulator of NMDARs which are critical for synaptic plasticity and memory42. Recent studies have suggested that CYP46A1 plays a key role in the preservation of cognitive performance during aging and may be a promising target of disease-modifying treatments for AD43. Female mice overexpressing CYP46A1 showed improved measures of spatial memory during aging, modulation of NMDA receptor activity, and improved markers of synaptic integrity44. Activation of CYP46A1 by low-dose Efavirenz, a non-nucleoside reverse transcriptase inhibitor is a therapeutic target that is currently under evaluation in a randomized clinical trial in patients with mild cognitive impairment due to AD45. An exciting finding from a recent study by van der Kant and colleagues in induced pluripotent stem cell (iPSC)-derived neurons suggests that reducing levels of cholesterol esters through activation of CYP46A1 by Efavirenz reduced both p-tau and Aβ secretion46. These results raise the exciting possibility that CYP46A1 activation and conversion of cholesterol to 24S-hydroxycholesterol39 may be a therapeutic mechanism targeting both the principal pathological processes in AD47.Our transcriptomics analyses compared gene expression levels of several enzymes regulating synthesis of oxysterols in the brain Fig. . While t39 by CYP46A1, a small fraction is esterified for storage through the enzymes sterol O-acyltransferase 1 (SOAT1)  and lecithin:cholesterol acyltransferase (LCAT)49. It is interesting that we find increased gene expression of SOAT1 in AD samples in the ERC. Inhibition of ACAT1 has received attention as a promising therapeutic target in AD and is believed to reduce amyloidogenic processing of APP by increasing the conversion of unesterified cholesterol to 24S-hydroxycholesterol39 by CYP46A150. Furthermore, polymorphisms in the SOAT1 gene have been previously associated with AD risk, brain amyloid load and CSF cholesterol concentrations51. Our finding of increased gene expression of SOAT1 in the ERC in AD suggests that it may promote the accumulation of cholesterol esters within the endoplasmic reticulum and promote amyloidogenic processing of APP.While the predominant mechanism of cholesterol elimination from the brain is through its conversion to 24S-hydroxycholesterol53. These findings add to growing evidence of abnormal brain bile acid metabolism and signaling in both AD and other neurodegenerative diseases55. In a recent publication using brain tissue samples from the BLSA included in this study, we reported higher concentrations of the primary bile acids, cholic acid, and chenodeoxycholic acid in AD samples relative to CN56. In addition, a study using similar iMAT-based analysis identified significant differences between AD and CN in bile acid-associated reactions using transcriptomic data across multiple cohorts and identified HSD3B7 as significantly altered in AD57. Together, these results point to a shift in cholesterol catabolism toward the enhanced synthesis of bile acids in AD and an accompanying reduction in 24S-hydroxycholesterol levels that may compromise synaptic plasticity and accelerate cognitive impairment through perturbation of NMDA receptor activity41.While regional differences in brain tissue abundance of metabolite levels and differential gene expression can provide insights into metabolic dysregulation in AD, these analyses only provide a limited view of cholesterol metabolism. They do not account for interactions between cholesterol metabolism and other biochemical pathways, consider interactions between reactions within the cholesterol biosynthesis/catabolism pathways, or identify an increase or decrease in rates of associated reactions. Therefore, to develop a systems-level overview of cholesterol metabolism in AD, we mapped regional brain transcriptomic data to a genome-scale metabolic network using iMAT in order to predict the relative activity/inactivity of reactions catalyzed by specific genetic regulators of cholesterol synthesis and catabolism. These results broadly support our interpretation that lower biosynthesis of cholesterol, as well as reduced breakdown, are characteristic biochemical abnormalities in AD. They also extend these findings by suggesting that there may be increased conversion of 24S-hydroxycholesterol to primary bile acids in the AD brain through 3β-hydroxysteroid isomerase (HSD3B7). This enzyme catalyzes the inversion of the 3β-hydroxyl group of cholesterol to the 3α-hydroxyl group of bile acids and is the converging step of bile acid synthesis through the classical, alternative, and neural pathwaysUsing targeted and quantitative metabolomics assays of brain tissue samples from two well-characterized longitudinal cohorts in combination with regional brain gene expression and metabolic network modeling, we show that AD is associated with pervasive abnormalities in cholesterol biosynthesis and catabolism. Our findings suggest a disease model where reduced de novo cholesterol biosynthesis may occur in response to impaired enzymatic cholesterol catabolism and efflux to maintain brain cholesterol levels in AD. While reduced cholesterol biosynthesis may enhance mitochondrial dysfunction and impair autophagy, reduced cholesterol conversion to 24S-hydroxycholesterol may increase amyloidogenic processing of APP, tau phosphorylation, and neuronal death. These perturbations appear to be accompanied by the accumulation of nonenzymatically generated cytotoxic oxysterols in AD that may further exacerbate oxidative damage and neuroinflammation. This model presents testable hypotheses in experimental studies that can address whether these abnormalities in cholesterol metabolism may be caused by disease or act as primary drivers of AD pathogenesis. These follow-up experimental studies are also critical to identify cholesterol metabolism-related therapeutic targets in AD.There are important limitations to our study. While we assayed the primary metabolites associated with cholesterol biosynthesis and breakdown, these represent only a subset of the total number of metabolites in these pathways. Therefore, our interpretation of the results is restricted to the metabolites that could be reliably detected and measured. Furthermore, as our metabolomic and gene expression analyses were cross-sectional, we were unable to directly test how AD progression may impact changes in metabolite concentrations or their genetic regulation.Another limitation was that the analyses of gene expression were performed on publicly available datasets in brain regions distinct from the BLSA or ROS where metabolite levels were assayed. The limited availability of tissue samples from the ERC and hippocampus in BLSA and ROS precluded metabolomics assays on these regions in our study. The regions chosen for gene expression analyses for both AD and PD were however disease-specific and symptom proximate regions  that may allow us to derive disease-specific insights about abnormal metabolism in AD.58. This makes it challenging to conclusively attribute observed metabolomic changes in our study to specific pathologies. However, our use of two independent cohorts where both AD and CN samples were defined using standardized clinical and neuropathologic diagnoses, and our inclusion of PD as a non-AD neurodegenerative disease comparison provides additional support that our findings may be specific to AD and associated pathologies.Another important consideration is that prior studies have indicated that many community-dwelling older individuals have multiple brain pathologiesWe also acknowledge that we chose to focus our attention on findings that were convergent between the BLSA and ROS cohorts as these are more likely to be related to AD-associated pathological changes. There are however divergent associations between the two cohorts that may be driven primarily by interactions between metabolic abnormalities and demographic differences such as sex and race as well as non-AD pathologies that merit further investigation.Despite these limitations, our integrated analyses of regional brain metabolomic and transcriptomic data enhance understanding of dysregulated cholesterol metabolism in AD, provide valuable opportunities to test a priori hypotheses in experimental models in future studies, and may pave the way for the discovery of treatments targeting abnormal cholesterol metabolism in AD.60.The BLSA is a prospective cohort study that began at the National Institute on Aging (NIA) in 1958Evaluations are scheduled every two years. As of 2003 participants over the age of 80 are scheduled for evaluations every year.61, including differences compared to the overall BLSA cohort62, neuropathologic assessment of AD pathology63, and clinical diagnoses at consensus case conferences64.Autopsy sample characteristics have been described in detail previouslyBLSA autopsy participants were classified into three groups based on the below criteria:n = 15) had a clinical diagnosis of either AD or MCI due to AD within 1 year of death and a CERAD pathology score of >1 .1) AD participants (n = 8) had normal cognition within 1 year of death and a CERAD pathology score < =1 . CN participants did not have significant amyloid pathology at autopsy and cognitive impairment during life.2) Cognitively normal (CN) participants (n = 6) had a clinical diagnosis of normal cognition within 1 year of death and a CERAD pathology score >1 . ASY participants had characteristic AD brain pathology (neuritic plaques and neurofibrillary tangles) and did not have cognitive impairment during life. This group has been described previously65. Table 3) Asymptomatic AD (ASY) participants  had a final consensus clinical diagnosis of AD and an NIA-Reagan score of intermediate or high likelihood of AD. NIA-Reagan criteria are based on both neuritic plaques (CERAD score) and neurofibrillary tangles (Braak score)71.1) AD participants (n = 22) had a clinical diagnosis of no cognitive impairment (NCI) and an NIA-Reagan score of low likelihood or no AD) and therefore free of significant AD-specific pathology at death and cognitive impairment during life.2) CN participants (n = 18) had a final consensus clinical diagnosis of NCI and an NIA-Regan score of intermediate or high likelihood of AD. Table 3) ASY participants n = 8 had a fAll ROS participants provided written informed consent and the study was approved by an Institutional Review Board of Rush University Medical Center. Participants signed an Anatomical Gift Act for organ donation and a repository consent to allow their data and biospecimens to be shared.n = 25 and CN: n = 52), hippocampus (AD: n = 29 and CN: n = 56), and visual cortex (used as a control region) (AD: n = 18 and CN: n = 12).We accessed gene expression data on AD and CN samples from publicly available microarray datasets (Gene Expression Omnibus (GEO): GSE48350 and GSE5281). Both datasets included gene expression data acquired on the Affymetrix U133 Plus2 array platform from 23 brain regions. We analyzed the data from the entorhinal cortex .We accessed gene expression data on Parkinson’s disease (PD) and CN samples from publicly available microarray datasets (GEO: GSE20292 and GSE20114). Both datasets included gene expression data acquired on the Affymetrix (U133A and U133 Plus2 arrays) platform. We analyzed data from the substantia nigra, the brain region primarily impacted by PD-specific neuropathologySimilar to analyses in AD vs CN, we used GEO data for regional brain gene expression and for genome-scale metabolic network modeling and reported pooled results combining both GEO datasets.74. Sample extraction and storage have been described previously75.For brain tissue samples in both BLSA and ROS, we performed targeted metabolomics on two a priori specified regions: the inferior temporal gyrus (ITG) and the middle frontal gyrus (MFG). These two regions were selected as regions vulnerable to β-amyloid and tau deposition, respectivelyBrain tissue samples (up to 80 mg) were homogenized with 85/15 ethanol phosphate buffer 1:3 (mg tissue/µL solvent w/v) using a Precellys  device and centrifuged . In total, 20 µL sample homogenate supernatant was placed on the 96-well plate Biocrates kit filter plate with prior placed oxysterol-specific stable isotope-labeled internal standards , concentration range 0.5–100 µM), dried under nitrogen for 5 min. In all, 14 d6 or d7 deuterium-labeled internal standards appropriate to each of the 14 analytes were used. Free oxysterols were extracted from the sample homogenate supernatant (dried for 30 min under nitrogen) with 100 µL methanol +0.01% BHT by filter plate shaking (20 min at 600 rpm) and centrifugation  into the capture plate. 30 µL Milli-Q water was added to each sample extract and carefully shaken for 5 min at 500 rpm.®  instrument with electrospray ionization (ESI) were used for free oxysterol assays. The 96-well plate allows the analysis of up to 75 samples, 1 blank sample, 3 zero samples , calibrators 1–7, and three quality control levels  of human plasma-based materials. Quantitation was performed with deuterium-labeled internal standards for each analyte (mix produced based on Avanti Polar Lipids standards) and 7-point external calibration. The individual calibrators for each analyte are designed in relation to their physiological ranges. Supplementary Table 76. Analytical intra- and interday/batch precision expressed by the coefficients of variation (CV) using this methodology were <15% (intra-/interday/batch) for all analytes. Batch effects were controlled and adjusted for using MetIDQ software-implemented normalization procedure.Ultrahigh-Performance Liquid Chromatography-tandem mass spectrometry (UHPLC–MS/MS) with multiple reaction monitoring (MRM)-based detection in positive mode using a SCIEX API 5500 QTRAP21. All metabolites met the inclusion criteria described below .De novo cholesterol biosynthesis: 24,25-dihydrolanosterol, 7-dehydrocholesterol, desmosterol, lanosterol, and free cholesterol.Cholesterol catabolism (enzymatic): 27-hydroxycholesterol, 4β-hydroxycholesterol, 24S-hydroxycholesterol, and 7α-hydroxycholesterol*.Cholesterol catabolism (non-enzymatic): 5α,6α-epoxycholesterol, 5α,6β-dihydroxycholestanol, 5β,6β-epoxycholesterol, 7-ketocholesterol, and 7β-hydroxycholesterol.We measured metabolite concentrations across three categories related to cholesterol homeostasis77.*7α-hydroxycholesterol can be generated both enzymatically and nonenzymatically64.In BLSA, evaluation of cognitive status including dementia diagnosis has been described in detail previously79.In ROS, evaluation of cognitive status including dementia diagnosis has been described in detail previouslyDe novo cholesterol biosynthesisCholesterol catabolism (enzymatic): representing oxysterol biosynthesis from the enzymatic conversion of cholesterolCholesterol esterificationWe first identified an a priori list of genes known to encode enzymes across three categories related to cholesterol homeostasis:82. We chose the visual cortex as a control region. We tested for differential gene expression in an a priori list of 31 genes known to encode enzymes regulating cholesterol biosynthesis, catabolism (enzymatic), and esterification reactions. Expression levels of these genes were also used in genome-scale metabolic network modeling using Integrative Metabolic Analysis Tool (iMAT)83 .We examined differential gene expression of these genes in AD vs CN samples in three brain regions. We chose the hippocampus and ERC as the accumulation of pathology in these regions is thought to trigger the onset of AD symptomsWe then examined differential gene expression in the substantia nigra of PD compared CN samples. This analysis was restricted to genes that were significantly differentially expressed in the AD compared CN samples with the goal of testing whether gene expression differences in AD were disease-specific or related to non-specific changes associated with neurodegeneration. Expression levels of these genes in the substantia nigra in PD compared to CN samples were also used in genome-scale metabolic network modeling using iMAT. These analyses were also restricted to reactions that were significantly less active or more active in AD compared CN in the ERC, hippocampus, or visual cortex and tested whether metabolic reactions predicted to be altered in AD were also altered in PD compared to CN samples in the substantia nigra.85. Values that were indicated as less than the limit of detection (LOD) were imputed as the LOD threshold value divided by 2. Because missing values indicated as less than LOD ( < LOD) are not missing at random (NMAR), we used metabolite-specific LOD threshold data to impute a value for metabolites that had < =30% of values missing. We have included the percentage of missing values by brain region across metabolites as well as metabolites that were excluded from analyses based on the 30% threshold in Supplementary Table Metabolites with >30% of values missing were excluded from all analyses. This threshold is consistent with our prior studiesFor statistical tests, we used an alpha-level of 0.05 as the threshold for statistical significance. Each metabolite tested in this study represented a hypothesis developed a priori based on its established role in specific biochemical pathways as well as a priori-defined brain regions vulnerable to AD pathology.APOE genotype, statin use, CERAD scores, Braak scores, and postmortem interval (PMI) across AD, CN, and ASY groups within studies as well as across studies .We first tested for differences in age at death, sex, race, P < 0.05. Divergent associations—i.e., where linear associations between metabolite concentration and disease status/ pathology in ROS and BLSA were in a different direction—were not pooled and are included as cohort-specific secondary analyses in Supplementary Tables. As these secondary results represent divergent associations in cohort-specific models, we report significant associations using the Benjamini–Hochberg false discovery rate (FDR) < 0.0586 to correct for the total number of metabolite comparisons within each a priori specified biochemical pathway/cluster.Second, in primary analyses, we tested whether brain tissue concentrations of metabolites differed across AD, CN, and ASY groups  and were associated with severity of AD pathology  in the ITG and MFG. For models with AD pathology, we examined the association between metabolite levels and CERAD and Braak scores independent of disease status . We first visualized linear associations between metabolite concentrations and our predictors of interest: disease status  : de novo cholesterol biosynthesis, cholesterol catabolism (enzymatic), and cholesterol catabolism (non-enzymatic). Log2-transformed metabolite concentration was used as the dependent variable, disease status  as the main fixed effect, sex, and age at death as covariates, within-subject covariance structure was modeled as unstructured, and variance was estimated using Huber-White robust variance estimates. We used the same approach to model CERAD and Braak pathology scores substituting pathology for disease status in the model. Significant associations are indicated in Table Similar to our previous metabolomics analyses87 with the Brainarray ENTREZG (version 22) custom CDF88. In order to test for differences between AD and CN in the pooled GEO datasets, we used the R package limma89 to test each gene univariately, controlling for sex, age, and batch. We used FDR86 (P < 0.05) to adjust for multiple comparisons accounting for all 20,414 genes on the Affymetrix U133 Plus2.0 array used in both GEO datasets. We highlighted significant (FDR-corrected) genes that were differentially expressed in AD vs CN samples across all three brain regions: hippocampus, ERC, and visual cortex (control region). In a heatmap  and first normalized the samples using Robust Multi-array Average (RMA)limma89 and FDR correction) to test for differences between PD and CN samples, controlling for batch. As one of the PD datasets analyzed (GSE20141) did not include sex or age information, these covariates were not included in this analysis.We performed similar analyses for brain gene expression data from the substantia nigra comparing PD vs CN using GEO datasets GSE20292 and GSE20141; Brainarray ENTREZG (version 24) was used to normalize samples. The goal of this analysis was to test whether differential gene expression observed in AD was similar in a non-AD neurodegenerative disease. We, therefore, restricted these analyses to significant genes that were differentially expressed in AD vs CN analyses. We used identical analyses  network, Human-GEM 1.3.292 to create personalized metabolic networks for all (1) AD vs CN samples from the hippocampus, ERC, and primary visual cortex regions across the GSE5281 and GSE48350 datasets; and (2) PD vs CN samples from the substantia nigra across the GSE20292 and GSE20141datasets.Using regional brain gene expression data, we additionally performed genome-scale metabolic network modeling, a computational framework to predict fluxes through multiple metabolic reactions93. This is achieved by writing mass balances around each intracellular metabolite considering the rates (fluxes) of reactions that consume and produce that metabolite94. The mass balances are solved as a set of linear equations through optimization to predict fluxes through model reactions in a given condition. Subcellular localization  is also taken into account in this approach for enzymes located in multiple cellular compartments by defining separate reactions for each compartment. Our human GEM network included 13417 reactions associated with 3628 genes iMAT is one of the commonly preferred algorithms for metabolic network modeling because it does not require any additional information such as the measured rates of some reactions. iMAT uses an optimization algorithm to maximize the consistency between flux activity predictions from the metabolic network and the gene expression data such that reactions associated with highly expressed genes are pushed to carry a non-zero flux  in the metabolic network while reactions associated with lowly expressed genes are pushed to be inactive. Separately for each of the two GEO datasets, gene expression data across all samples were combined to identify the 25th and 75th percentiles of expression. These were used to determine whether the expression of a specific gene within any given sample was either lowly expressed , highly expressed  or moderately expressed . Only genes that were lowly or highly expressed contributed to iMAT predictions of whether a reaction was transcriptionally downregulated  or upregulated   to ensure their activity: glucose and oxygen uptake rates and active macromolecule synthesis rate had their lower bounds set to 0.01, 0.01, and 0.0001, respectively, leaving all the other reaction rates unconstrained in the simulations. Therefore, these three reactions were always active in all personalized models generated and differences in the predicted reaction activities between AD and CN (or PD and CN) were only due to differences in the set of highly and lowly expressed reactions for each sample as well as the consequent change in the mass-balance-based metabolite routes in the network.In mapping the expression data on reactions associated with multiple genes, the maximum gene expression value among the multiple genes was assigned to that reaction for isoenzymes , and the minimum gene expression value among the multiple genes was assigned to that reaction for enzyme complexes (where multiple genes are required to code for an enzyme)P < 0.05) altered between AD and CN . We performed similar analyses in PD compared to CN samples in the substantia nigra. The goal of this analysis was to test whether reactions that were significantly altered in AD were similarly altered in a non-AD neurodegenerative disease. We, therefore, restricted these analyses to reactions that were significantly less active or more active in AD compared to CN in the ERC, hippocampus, or visual cortex.After reactions were predicted by iMAT as either inactive or active, within each disease group (AD and CN) Fig. 95.Simulations were performed in MATLAB R2018a using Gurobi optimizer and the iMAT implementation available under COBRA ToolboxIn order to enhance the interpretability of our metabolite, gene expression, and metabolic network modeling results, we visualize results in pathway figures Fig.  includinFurther information on research design is available in the Reporting SummarySupplementary InformationSupplementary Data 1


DcAOX1 and DcAOX2a genes in carrot tap root growth and in response to cold stress, their expression was analyzed in two experiments: during root growth for 13 weeks and in response to a cold challenge trial of 7 days, in both cases using different carrot cultivars. Carrot root growth is initially characterized by an increase in length, followed by a strong increase in weight. DcAOX2a presented the highest expression levels during the initial stages of root growth for all cultivars, but DcAOX1 showed no particular trend in expression. Cold stress had a negative impact on root growth, and generally up-regulated DcAOX2a with no consistent effect on DcAOX1. The identification of cis-acting regulatory elements (CAREs) located at the promoters of both genes showed putative sequences involved in cold stress responsiveness, as well as growth. However, DcAOX2a promoter presented more CAREs related to hormonal pathways, including abscisic acid and gibberellins synthesis, than DcAOX1. These results point to a dual role of DcAOX2a on carrot tap root secondary growth and cold stress response.Alternative oxidase (AOX) is a key enzyme of the alternative respiration, known to be involved in plant development and in response to various stresses. To verify the role of  The plant mitochondrial electron transport chain (ETC) is branched so that electrons in the ubiquinone pool can pass to oxygen via the usual cytochrome (cyt) pathway  or via alternative oxidase (AOX) . ElectroAOX can play a role in response to stress and on the maintenance of cellular and mitochondrial homeostasis , and numAOX genes have been also associated with response upon beneficial microorganism interactions, both at host plant and symbiont microorganism sides . Accessions DCAR_028361 and DCAR_021859 were used to retrieve the complete full length genomic sequence including upstream region in which the promoters of DcAOX1 and DcAOX2 genes are included. The freely available New PLACE tool—A Database of Plant Cis-acting Regulatory DNA elements (https://www.dna.affrc.go.jp/PLACE/?action=newplace) [accessed on 17 January 2018] [http://bioinformatics.psb.ugent.be/webtools/plantcare/html/ [accessed on 16 January 2018]   and Planry 2018]  were usecis-elements were mapped at the promoter sequence using CLC Main Workbench 7.5.1 software .The positions of the identified DcAOX2a with the highest expression during the initial stages of root growth for all carrot cultivars, contrarily to DcAOX1 that showed no specific trend in expression. Similar results were observed in carrot plants submitted to cold stress with DcAOX2a generally up-regulated, and no consistent changes on DcAOX1. Cold stress responsiveness as well as growth related CAREs were identified in both DcAOX genes. However, DcAOX2a promoter sequence presented more CAREs related to hormonal pathways, giving indications about its putative role on both, carrot tap root secondary growth and cold stress response.Our work revealed DcAOX2a appears as a promising target for molecular marker development focused on selection of highly resilient carrot genotypes to be used in breeding programs.In view of these results,


Dear EditorIn-depth understanding of physiology course can be acquired if students are provided opportunity to identify patho-physiology via clinical practicum  . SimulaObservational, cross sectional study commenced for 150 first year medical students of 2017-18 batch. The theoretical aspect of the ECG was taught during 3hrs lecture within three days, followed by 3hrs of practical session in the Labdhi (Skilled Lab) center and physiology lab of the Smt.B.K.Shah Medical institute & Research center, Sumandeep Vidyapeeth University.A pre-test was conducted after theory lectures and post test after completion of the practical. Students were divided into 6 groups (25 students in each). ECGs presentations for normal and various cardiac and metabolic disorders were conducted on “Heartsim 200” mannequins. Clinical case series as practical practice sheets were discussed after the lab session, separately. Students perception for corrective implementation of mannequin based teaching was collected, by using a feedback survey questionnaire, based on five points Likert scale with two open-ended questions. Three Focus group discussions (FGD) were conducted (8 students in each group) after the session.Present study shows statistical significance  and middle level of correlation , between pre and post test result of participants (N=97) implying enhanced learning outcome with this teaching approach. These results are aligned with that of the study conducted by other researcher , participants enjoyed applied basis for learning of physiology (11.02). Lowest rank for questions “The concepts learned during these sessions may not be useful in near future” (3.53) and “Every student of the class was actively engaged and involved for this program” (6.06). Gender variability was observed for some of the questions.The study concludes that this teaching method encourages students to be in a “near real live setting environment” and have better clinical conceptual understanding of the basic science topic taught. Simulations based sessions are organizational skills which will prepare students for real patient setting without compromising the patient care and safety.


Telomerase action at a DSB can interfere with conservative mechanisms of DNA repair and results in loss of distal sequences but may prevent additional nucleolytic resection and/or chromosome rearrangement through formation of a functional telomere . Here, we review studies of spontaneous and induced DSBs in the yeast Saccharomyces cerevisiae that shed light on mechanisms that negatively regulate de novo telomere addition, in particular how the cell prevents telomerase action at DSBs while facilitating elongation of critically short telomeres. Much of our understanding comes from the use of perfect artificial telomeric tracts to “seed” de novo telomere addition. However, endogenous sequences that are enriched in thymine and guanine nucleotides on one strand (TG-rich) but do not perfectly match the telomere consensus sequence can also stimulate unusually high frequencies of telomere formation following a DSB. These observations suggest that some internal sites may fully or partially escape mechanisms that normally negatively regulate de novo telomere addition.Telomeres, repetitive sequences located at the ends of most eukaryotic chromosomes, provide a mechanism to replenish terminal sequences lost during DNA replication, limit nucleolytic resection, and protect chromosome ends from engaging in double-strand break (DSB) repair. The ribonucleoprotein telomerase contains an RNA subunit that serves as the template for the synthesis of telomeric DNA. While telomere elongation is typically primed by a 3′ overhang at existing chromosome ends, telomerase can act upon internal non-telomeric sequences. Such  Most linear eukaryotic chromosomes terminate in protein-bound repetitive sequences termed telomeres. Telomere sequences are highly repetitive and contain a thymine and guanine-rich (TG-rich)-rich 3′ terminating strand that extends beyond the 5′ strand to create a single-stranded (ss) overhang. Telomeres protect chromosome ends from nucleolytic resection (thereby preventing checkpoint activation) and provide a mechanism to counteract progressive loss of terminal sequences during DNA replication [reviewed in Saccharomyces cerevisiae, extension by telomerase, C-strand fill-in, and telomere protection are coordinated by the CST (Cdc13-Stn1-Ten1) complex (In the budding yeast  complex . Cdc13 b complex . Stn1 an complex . CST fun complex . The “ca complex . Recent  complex .de novo telomere addition (dnTA). DnTA can be developmentally regulated. For example, ciliated protozoa undergo programmed mass genome fragmentation during macronuclear development where each newly formed linear fragment acquires de novo telomeres , chromosome/telomere capture, or elomeres . In otheelomeres  and α-thelomeres , are attcis- and trans-acting factors that promote genome stability  events are recovered and analyzed to determine where the chromosome break resolved . Importatability .The second strategy to study dnTA involves generation of an induced DSB, most predominantly using the homothallic switching (HO) endonuclease . StrainsThe HO cleavage system has an advantage over the GCR assay because the site of the initiating break is known. Even in the absence of a seed sequence, dnTA events can occur at the HO site, but they are also observed many kilobases internal, implying that telomerase can act after extensive 5′ end resection . Such evGiven the potential for telomerase to compete with the DNA repair machinery at DSBs, it is not surprising that multiple mechanisms inhibit dnTA. These mechanisms fall into two classes: (1) mechanisms that spatially or temporally separate telomerase from DSBs and (2) mechanisms that alter the action of telomerase at a DSB. Examples of the first class include observations that telomerase is sequestered in the nucleolus in response to DSBs  and thatRRD1 eliminates dnTA at TG tracts of fewer than 11 bp, consistent with a requirement for Cdc13 association at such sequences. While Mec1-dependent phosphorylation of Cdc13 is detected in response to DNA damage, bulk levels of phosphorylated Cdc13 do not increase upon deletion of PPH3 or RRD1, suggesting that dephosphorylation may specifically occur at DSBs. Consistent with this idea, Pph3 accumulates at HO-induced breaks  kinase, directly phosphorylates Cdc13 at serine 306, thereby preventing the accumulation of Cdc13 at DSBs . Mec1 acd breaks .1–3 nucleotides is puzzling because Cdc13 binding requires 11 bases of TG-rich ssDNA. How can phosphorylation of Cdc13 influence its association with a sequence to which it is not predicted to directly bind? One possibility is that Cdc13 associates, albeit with lower affinity, to shorter TG tracts. While several positions of the 11-base Cdc13 binding site are critical , single mutations are tolerated in the rest of the binding site with minimal consequences for affinity  . Remarka1–3 sequences of varying lengths integrated adjacent to an HO cleavage site, the Durocher lab systematically probed how Pif1 function is influenced by the telomeric character of a DSB  does not affect the threshold, suggesting that phosphorylation cannot account for this distinction  were transformed into yeast, addition of yeast TGstrategy . Ciliatestrategy . Finallystrategy . These rSites of Repair-associated Telomere Addition, or SiRTAs. SiRTAs on chromosomes 5 and 9 contain a ∼10–20-bp “Core” sequence that is the direct target of telomere addition and a similarly sized “Stim” sequence  that, while rarely the site of telomere addition, strongly enhances the frequency of dnTA , the Ku80 protein, a component of the Ku heterodimer that binds DSBs and is required for non-homologous end joining, promotes dnTA through interaction with the RNA component of telomerase. In the absence of the Yku80–telomerase RNA interaction, nearly all dnTA events identified by GCR assay are within the chromosome 5 SiRTA described above, highlighting that SiRTAs utilize a pathway independent of this association . The SiRgulation . However1–3 threshold required for Pif1 resistance, consistent with a simple competition between Cdc13-mediated recruitment and Pif1-mediated removal of telomerase. The Durocher lab disfavors this model, since artificial recruitment of telomerase (by fusion of Cdc13 to Est1 or Est2) does not render a short (18-bp) TG1–3 tract resistant to Pif1  through which Pif1 regulates dnTA. Mutations reducing the Cdc13–Est1 interaction increase the TG to Pif1 . Howeverresponse . Neverth34 tract (likely in complex with Stn1 and Ten1) is sufficient to block resection, while the TG18 tract is subjected to resection in a manner dependent (perhaps indirectly) on Pif1. Since Cdc13 binding and dnTA increase when resection is inhibited (18) and short telomere (TG34).Pif1 enhances resection at uncapped telomeres (telomeres lacking Cdc13) in conjunction with Exonuclease 1 (Exo1) . Streckenhibited , dispariIn vitro, Pif1 requires an ss gap of ≥56 bases to dissociate telomerase and longer gaps facilitate more efficient removal (34 binds sufficient Cdc13 to inhibit 5′ end resection (as observed for TG81 repeats), insufficient ssDNA may be generated for Pif1 loading. In contrast, regions with little or no ability to bind Cdc13 would be rapidly resected, facilitating Pif1 association  correlates well with the minimal region required for Pif1 loading . Furtherde novo telomere addition, but outstanding questions remain. Future studies must address whether the effects of Cdc13 binding on Pif1 function and/or 5′ end resection at sequences immediately adjacent to an HO cleavage site (described above) are similar at SiRTAs, where Cdc13 binding sites are revealed only after significant and ongoing resection. Likewise, while the role of Cdc13 at SiRTAs is well established, how Cdc13 associates with noncanonical sequences during resection and whether such binding is affected by association with binding partners must be addressed. SiRTAs may provide a “back-up” mechanism to facilitate chromosome healing by telomerase when other pathways have failed. Tests of this hypothesis will require genome-wide identification of SiRTAs and analysis of their evolutionary conservation. While the identity of the human ortholog of Cdc13 remains unclear (de novo telomere addition observed in some disease states may require the human Ctc1/Stn1/Ten1 complex (Much progress has been made in understanding how endogenous TG-rich sequences stimulate  unclear , results complex  and/or P complex , a telomRH, KN, and KF wrote the manuscript. RH designed the graphics. All authors contributed to the article and approved the submitted version.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.