Targeted metabolomics profiles are strongly correlated with nutritional patterns in women

Nutrition plays an important role in human metabolism and health. Metabolomics is a promising tool for clinical, genetic and nutritional studies. A key question is to what extent metabolomic profiles reflect nutritional patterns in an epidemiological setting. We assessed the relationship between metabolomic profiles and nutritional intake in women from a large cross-sectional community study. Food frequency questionnaires (FFQs) were applied to 1,003 women from the TwinsUK cohort with targeted metabolomic analyses of serum samples using the Biocrates Absolute-IDQ? Kit p150 (163 metabolites). We analyzed seven nutritional parameters: coffee intake, garlic intake and nutritional scores derived from the FFQs summarizing fruit and vegetable intake, alcohol intake, meat intake, hypo-caloric dieting and a “traditional English” diet. We studied the correlation between metabolite levels and dietary intake patterns in the larger population and identified for each trait between 14 and 20 independent monozygotic twins pairs discordant for nutritional intake and replicated results in this set. Results from both analyses were then meta-analyzed. For the metabolites associated with nutritional patterns, we calculated heritability using structural equation modelling. 42 metabolite nutrient intake associations were statistically significant in the discovery samples (Bonferroni P < 4 × 10^?5) and 11 metabolite nutrient intake associations remained significant after validation. We found the strongest associations for fruit and vegetables intake and a glycerophospholipid (Phosphatidylcholine diacyl C38:6, P = 1.39 × 10^?9) and a sphingolipid (Sphingomyeline C26:1, P = 6.95 × 10^?13). We also found significant associations for coffee (confirming a previous association with C10 reported in an independent study), garlic intake and hypo-caloric dieting. Using the twin study design we find that two thirds the metabolites associated with nutritional patterns have a significant genetic contribution, and the remaining third are solely environmentally determined. Our data confirm the value of metabolomic studies for nutritional epidemiologic research.     

Global metabolic profiling to model biological processes of aging in twins

Aging is intimately linked to system‐wide metabolic changes that can be captured in blood. Understanding biological processes of aging in humans could help maintain a healthy aging trajectory and promote longevity. We performed untargeted plasma metabolomics quantifying 770 metabolites on a cross‐sectional cohort of 268 healthy individuals including 125 twin pairs covering human lifespan (from 6 months to 82 years). Unsupervised clustering of metabolic profiles revealed 6 main aging trajectories throughout life that were associated with key metabolic pathways such as progestin steroids, xanthine metabolism, and long‐chain fatty acids. A random forest (RF) model was successful to predict age in adult subjects (≥16 years) using 52 metabolites (R² = .97). Another RF model selected 54 metabolites to classify pediatric and adult participants (out‐of‐bag error = 8.58%). These RF models in combination with correlation network analysis were used to explore biological processes of healthy aging. The models highlighted established metabolites, like steroids, amino acids, and free fatty acids as well as novel metabolites and pathways. Finally, we show that metabolic profiles of twins become more dissimilar with age which provides insights into nongenetic age‐related variability in metabolic profiles in response to environmental exposure.     

Metabolites of puerarin identified by liquid chromatography tandem mass spectrometry: Similar metabolic profiles in liver and intestine of rats

Puerarin is a major active ingredient of Pueraria Radix. Puerarin may exert its medicinal functions in part via its metabolites. In this study, we identified these metabolites to better understand and elucidate puerarin's metabolic pathway. Puerarin was intravenously administered to rats and then metabolites in plasma samples were identified by rapid resolution liquid chromatography electrospray ionization-collision induced dissociation tandem mass spectrometry (RRLC-ESI-CID–MS/MS). Chromatography was conducted on a Zorbax SB C18 column (2.1 × 100 mm, 1.8 μm) at 30 °C, with a gradient mobile phase consisting of 0.05% formic acid and acetonitrile, a flow rate of 0.2 mL min^?1, and a total run time of 14 min. MS/MS acquisition parameters were as follows: positive ionization mode, dry gas: nitrogen, 10 L min^?1, dry temperature: 350 °C, nebulizer: 40 psi, capillary: ?3500 V, scan range: 250–800. The autoMS, manual, or multiple reaction monitoring mode was selected as required. Two glucuronidated metabolites of puerarin (M1 and M2) were detected. M1 and M2 are presumed to be puerarin-7-O-glucuronide and puerarin-4′-O-glucuronide, respectively, and M2 likely is suspected to be the major metabolite because it represented the predominate peak. Kinetic studies of metabolites demonstrated that M1 and M2 were detected in rat plasma at 5 min after intravenous administration of puerarin, the levels of M1 and M2 then reached their peaks at 10–15 and 15–30 min, respectively. The metabolic profiles were similar in rat liver and intestine investigated by in situ liver and intestine perfusion, indicating that no metabolic regioselectivity of puerarin occurs in the two organs.     

Intracellular CHO Cell Metabolite Profiling Reveals Steady‐State Dependent Metabolic Fingerprints in Perfusion Culture

Perfusion cell culture processes allow the steady‐state culture of mammalian cells at high viable cell density, which is beneficial for overall product yields and homogeneity of product quality in the manufacturing of therapeutic proteins. In this study, the extent of metabolic steady state and the change of the metabolite profile between different steady states of an industrial Chinese hamster ovary (CHO) cell line producing a monoclonal antibody (mAb) was investigated in stirred tank perfusion bioreactors. Matrix‐assisted laser desorption/ionization time of flight mass spectrometry (MALDI‐TOF‐MS) of daily cell extracts revealed more than a hundred peaks, among which 76 metabolites were identified by tandem MS (MS/MS) and high resolution Fourier transform ion cyclotron resonance (FT‐ICR) MS. Nucleotide ratios (Uridine (U)‐ratio, nucleotide triphosphate (NTP)‐ratio and energy charge (EC)) and multivariate analysis of all features indicated a consistent metabolite profile for a stable culture performed at 40 × 10⁶ cells/mL over 26 days of culture. Conversely, the reactor was operated continuously so as to reach three distinct steady states one after the other at 20, 60, and 40 × 10⁶ cells/mL. In each case, a stable metabolite profile was achieved after an initial transient phase of approximately three days at constant cell density when varying between these set points. Clear clustering according to cell density was observed by principal component analysis, indicating steady‐state dependent metabolite profiles. In particular, varying levels of nucleotides, nucleotide sugar, and lipid precursors explained most of the variance between the different cell density set points. © 2016 American Institute of Chemical Engineers Biotechnol. Prog., 33:879–890, 2017     

Untargeted metabolic profiling of <Emphasis Type="Italic">Eucalyptus</Emphasis> spp. leaf oils using comprehensive two-dimensional gas chromatography with high resolution mass spectrometry: Expanding the metabolic coverage

Introduction

Chromatography with mass spectrometry (MS) is a technique of choice for metabolomic analysis of plant extracts. Single dimension gas chromatography (1DGC) with MS leads to poorly resolved metabolites of complex Eucalyptus spp. leaf oil secondary metabolites and consequently limited metabolic coverage of secondary compounds. Multidimensional chromatography with high resolution MS can contribute to advances in this field.

Objectives

Deeper insight into metabolite composition and variation for Eucalyptus spp. leaf oils through systematic untargeted metabolic profiling using comprehensive two-dimensional GC (GC?×?GC) with high resolution time-of-flight MS (accTOFMS), using generalised processes for metabolite identification.

Methods

GC?×?GC separation used cryogenic modulation, with standard length polar first dimension and short fast analysis non-polar ²D columns. Compound tentative identification incorporated ¹D and ²D retention information, retention indices, mass spectrum matching, and accurate mass MS data. Global metabolic profiles were interpreted through 2D contour plots and chemometric analysis.

Results

Strategies for metabolite screening and identification using GC?×?GC-accTOFMS were proposed. Considerably more components are detected and recognised than for 1DGC. Structured 2D molecular composition chromatographic patterns aid identification. ca. 400 metabolites were detected, 183 compounds were identified or tentatively identified, representing between 50.8–90.0% of the total ion count, comprising various chemical families. PCA revealed discriminating metabolites, allowing chemotaxonomic classification of species.

Conclusion

Expansion of metabolic coverage by using GC?×?GC-accTOFMS, and detailed 2D metabolic fingerprints of E. polybractea, E. citriodora, E. radiata and E. globulus leaf oils were established. This high resolution analytical platform, and identification strategy can be adapted to metabolic analysis of other plant extracts.

Graphical abstract

Phytoconstituents of four Australian eucalypt leaf oils were profiled using high resolution GC?×?GC-accurate mass TOFMS. Two-dimensional plots illustrated significant expansion of metabolic coverage. PCA discriminated metabolites of the eucalypts.

Open image in new window

     

Proteomics of the milk fat globule membrane from Camelus dromedarius

Camel milk has been widely characterized with regards to casein and whey proteins. However, in camelids, almost nothing is known about the milk fat globule membrane (MFGM), the membrane surrounding fat globules in milk. The purpose of this study was thus to identify MFGM proteins from Camelus dromedarius milk. Major MFGM proteins (namely, fatty acid synthase, xanthine oxidase, butyrophilin, lactadherin, and adipophilin) already evidenced in cow milk were identified in camel milk using MS. In addition, a 1D‐LC‐MS/MS approach led us to identify 322 functional groups of proteins associated with the camel MFGM. Dromedary MFGM proteins were then classified into functional categories using DAVID (the Database for Annotation, Visualization, and Integrated Discovery) bioinformatics resources. More than 50% of MFGM proteins from camel milk were found to be integral membrane proteins (mostly belonging to the plasma membrane), or proteins associated to the membrane. Enriched GO terms associated with MFGM proteins from camel milk were protein transport (p‐value = 1.73 × 10^?14), translation (p‐value = 1.08 × 10^?11), lipid biosynthetic process (p‐value = 6.72 × 10^?10), hexose metabolic process (p‐value = 1.89 × 10^?04), and actin cytoskeleton organization (p‐value = 2.72 × 10^?04). These findings will help to contribute to a better characterization of camel milk. Identified MFGM proteins from camel milk may also provide new insight into lipid droplet formation in the mammary epithelial cell.     

New Phytotoxic Metabolites from Pestalotiopsis sp. HC02, a Fungus Residing in Chondracris rosee Gut

Two new phytotoxic γ‐lactones, pestalotines A and B ( 1 and 2 , resp.), along with 4‐oxo‐4H‐pyran‐3‐acetic acid ( 3 ) and 6‐hydroxyramulosin (=3,4,4a,5,6,7‐hexahydro‐6,8‐dihydroxy‐3‐methyl‐1H‐2‐benzopyran‐1‐one; 4 ), were isolateded from the culture of Pestalotiopsis sp. HC02, a fungus residing in the Chondracris rosee gut. Structures of the new metabolites were elucidated on the basis of their IR, NMR, and MS data. Pestalotines A and B ( 1 and 2 , resp.) significantly inhibited the radical growth of Echinochloa crusgalli with IC₅₀ values of 1.85×10^?4 and 2.50×10^?4 M , respectively, comparable to that of 2‐(2,4‐dichlorophenoxy)acetic acid (0.94×10^?4 M ) used as a positive control.     

Quantitative proteomic profiling reveals photosynthesis responsible for inoculum size dependent variation in Chlorella sorokiniana

High density cultivation is essential to industrial production of biodiesel from microalgae, which involves in variations of micro‐environment around individual cells, including light intensity, nutrition distribution, other abiotic stress and so on. To figure out the main limit factor in high inoculum cultivation, a quantitative proteomic analysis (iTRAQ‐on‐line 2‐D nano‐LC/MS) in a non‐model green microalga, Chlorella sorokiniana, under different inoculum sizes was conducted. The resulting high‐quality proteomic dataset consisted of 695 proteins. Using a cutoff of P < 0.05, 241 unique proteins with differential expression levels were identified between control and different inoculum sizes. Functional analysis showed that proteins participating in photosynthesis (light reaction) and Calvin cycle (carbon reaction pathway) had highest expression levels under inoculum size of 1 × 10⁶ cells mL^?1, and lowest levels under 1 × 10⁷ cells mL^?1. Canonical correlation analysis of the photosynthesis related proteins and metabolites biomarkers showed that a good correlation existed between them (canonical coefficient was 0.987), suggesting photosynthesis process greatly affected microalgae biodiesel productivity and quality. Proteomic study of C. sorokiniana under different illuminations was also conducted to confirm light intensity as a potential limit factor of high inoculum size. Nearly two thirds of proteins showed up‐regulation under the illumination of 70–110 µmol m^?2 s^?1, compared to those of 40 µmol m^?2 s^?1. This result suggested that by elegantly adjusting light conditions, high cell density cultivation and high biodiesel production might be achieved. Biotechnol. Bioeng. 2013; 110: 773–784. © 2012 Wiley Periodicals, Inc.     

Photobioreactor design for isotopic non‐stationary 13C‐metabolic flux analysis (INST 13C‐MFA) under photoautotrophic conditions

Adaptive metabolic behavior of photoautotrophic microorganisms toward genetic and environmental perturbations can be interpreted in a quantitative depiction of carbon flow through a biochemical reaction network using isotopic non‐stationary ¹³C‐metabolic flux analysis (INST ¹³C‐MFA). To evaluate ¹³C‐metabolic flux maps for Chlamydomonas reinhardtii, an original experimental framework was designed allowing rapid, reliable collection of high‐quality isotopomer data against time. It involved (i) a short‐time ¹³C labeling injection device based on mixing control in a torus‐shaped photobioreactor with plug‐flow hydrodynamics allowing a sudden step‐change in the ¹³C proportion in the substrate feed and (ii) a rapid sampling procedure using an automatic fast filtration method coupled to a manual rapid liquid nitrogen quenching step. ¹³C‐substrate labeling enrichment was controlled through the total dissolved inorganic carbon concentration in the pulsed solution. First results were obtained from steady‐state continuous culture measurements allowing the characterization of the kinetics of label incorporation into light‐limited growing cells cultivated in a photobioreactor operating at the maximal biomass productivity for an incident photon flux density of 200 µmol m^?2 s^?1. ¹³C label incorporation was measured for 21 intracellular metabolites using IC‐MS/MS in 58 samples collected across a labeling experiment duration of 7 min. The fastest labeling rate was observed for 2/3‐phosphoglycerate with an apparent isotopic stationary state reached after 300 s. The labeling rate was consistent with the optimized mixing time of about 4.9 s inside the reactor and the shortest reliable sampling period assessed at 5 s. Biotechnol. Bioeng. 2012; 109: 3030–3040. © 2012 Wiley Periodicals, Inc.     

Intra- and inter-metabolite correlation spectroscopy of tomato metabolomics data obtained by liquid chromatography-mass spectrometry and nuclear magnetic resonance

Nuclear magnetic resonance (NMR) and liquid chromatography-mass spectrometry (LCMS) are frequently used as technological platforms for metabolomics applications. In this study, the metabolic profiles of ripe fruits from 50 different tomato cultivars, including beef, cherry and round types, were recorded by both ¹H NMR and accurate mass LC-quadrupole time-of-flight (QTOF) MS. Different analytical selectivities were found for these both profiling techniques. In fact, NMR and LCMS provided complementary data, as the metabolites detected belong to essentially different metabolic pathways. Yet, upon unsupervised multivariate analysis, both NMR and LCMS datasets revealed a clear segregation of, on the one hand, the cherry tomatoes and, on the other hand, the beef and round tomatoes. Intra-method (NMR–NMR, LCMS–LCMS) and inter-method (NMR–LCMS) correlation analyses were performed enabling the annotation of metabolites from highly correlating metabolite signals. Signals belonging to the same metabolite or to chemically related metabolites are among the highest correlations found. Inter-method correlation analysis produced highly informative and complementary information for the identification of metabolites, even in de case of low abundant NMR signals. The applied approach appears to be a promising strategy in extending the analytical capacities of these metabolomics techniques with regard to the discovery and identification of biomarkers and yet unknown metabolites. Electronic supplementary material  The online version of this article (doi:) contains supplementary material, which is available to authorized users.     

A surface plasmon resonance assay for characterisation and epitope mapping of anti‐GLP‐1 antibodies

The incretin hormone glucagon‐like peptide‐1 (GLP‐1) has been subject to substantial pharmaceutical research regarding the treatment of type 2 diabetes mellitus. However, quantification of GLP‐1 levels remains complicated due to the low circulation concentration and concurrent existence of numerous metabolites, homologous peptides, and potentially introduced GLP‐1 receptor agonists. Surface plasmon resonance (SPR) facilitates real‐time monitoring allowing a more detailed characterisation of the interaction compared with conventional enzyme‐linked immunosorbent assays (ELISA). In this paper, we describe the development of the first SPR assays for characterisation of anti‐GLP‐1 antibodies for ELISA purposes. Binding responses were obtained on covalently immobilised anti‐GLP‐1 antibodies at 12°C, 25°C, and 40°C and fitted to a biomolecular (1:1) interaction model showing association rates of 1.01 × 10³ to 4.54 × 10³ M^?1 s^?1 and dissociation rates of 3.56 × 10^?5 to 1.56 × 10^?3 s^?1 leading to affinities of 35.2 to 344 nM, depending on the temperature. Determination of thermodynamic properties revealed an enthalpy driven interaction (ΔH < ΔS < 0) with higher affinities at lower temperatures due to the formation and stabilisation of hydrogen bonds within the binding site primarily composed of polar amino acids (ΔC_p < 0). Pair‐wise epitope mapping was performed on captured anti‐GLP‐1 antibodies followed by subsequent interaction with GLP‐1 (7‐36) and other anti‐GLP‐1 antibodies. A global evaluation of every binding response led to an epitope map elucidating the potential of various anti‐GLP‐1 antibody pairs for sandwich ELISA and hence pinpointing the optimal antibody combinations. The SPR assays proved capable of providing vital information for ELISA development endorsing it as a useful optimisation tool.     

Genetics Meets Metabolomics: A Genome-Wide Association Study of Metabolite Profiles in Human Serum

The rapidly evolving field of metabolomics aims at a comprehensive measurement of ideally all endogenous metabolites in a cell or body fluid. It thereby provides a functional readout of the physiological state of the human body. Genetic variants that associate with changes in the homeostasis of key lipids, carbohydrates, or amino acids are not only expected to display much larger effect sizes due to their direct involvement in metabolite conversion modification, but should also provide access to the biochemical context of such variations, in particular when enzyme coding genes are concerned. To test this hypothesis, we conducted what is, to the best of our knowledge, the first GWA study with metabolomics based on the quantitative measurement of 363 metabolites in serum of 284 male participants of the KORA study. We found associations of frequent single nucleotide polymorphisms (SNPs) with considerable differences in the metabolic homeostasis of the human body, explaining up to 12% of the observed variance. Using ratios of certain metabolite concentrations as a proxy for enzymatic activity, up to 28% of the variance can be explained (p-values 10⁻¹⁶ to 10⁻²¹). We identified four genetic variants in genes coding for enzymes (FADS1, LIPC, SCAD, MCAD) where the corresponding metabolic phenotype (metabotype) clearly matches the biochemical pathways in which these enzymes are active. Our results suggest that common genetic polymorphisms induce major differentiations in the metabolic make-up of the human population. This may lead to a novel approach to personalized health care based on a combination of genotyping and metabolic characterization. These genetically determined metabotypes may subscribe the risk for a certain medical phenotype, the response to a given drug treatment, or the reaction to a nutritional intervention or environmental challenge.     

Stable isotope resolved metabolomics of lung cancer in a SCID mouse model

We have determined the time course of [U-¹³C]-glucose utilization and transformations in SCID mice via bolus injection of the tracer in the tail vein. Incorporation of ¹³C into metabolites extracted from mouse blood plasma and several tissues (lung, heart, brain, liver, kidney, and skeletal muscle) were profiled by NMR and GC–MS, which helped ascertain optimal sampling times for different target tissues. We found that the time for overall optimal ¹³C incorporation into tissue was 15–20 min but with substantial differences in ¹³C labeling patterns of various organs that reflected their specific metabolism. Using this stable isotope resolved metabolomics (SIRM) approach, we have compared the ¹³C metabolite profile of the lungs in the same mouse with or without an orthotopic lung tumor xenograft established from human PC14PE6 lung adenocarcinoma cells. The ¹³C metabolite profile shows considerable differences in [U-¹³C]-glucose transformations between the two lung tissues, demonstrating the feasibility of applying SIRM to investigate metabolic networks of human cancer xenograft in the mouse model.     

Stimulatory role of smoke–water and karrikinolide on the photosynthetic pigment and phenolic contents of micropropagated ‘Williams’ bananas

At low concentrations, smoke–water (SW) and smoke-derived karrikinolide (KAR₁) are compounds with potential cytokinin and auxin-like activity. Their roles on the growth, photosynthetic pigment and phenolic contents of micropropagated ‘Williams’ bananas were investigated in comparison with meta-topolin (mT). Explants were cultured on modified Murashige and Skoog basal media supplemented with either SW (1:125; 1:250; 1:500; 1:1,000; 1:2,000 dilutions) or KAR₁ at four concentrations ranging from 4.8?×?10^?22 to 3.3?×?10^?12?M. After 42?days, growth parameters were measured while the photosynthetic pigments and phenolic contents were quantified using spectrophotometric methods. Chlorophyll a, b and total carotenoid contents were significantly enhanced by KAR₁ (4.8?×?10^?22?M) and SW (1:125 and 1:1,000 dilutions). The pigments in KAR₁-treated plantlets were approximately two-fold to three-fold higher than those in the control and mT-treated plants, respectively. Total phenolic content was highest with KAR₁ at 1.0?×?10^?19?M in the leaves and 7.8?×?10^?17?M in the roots. Furthermore, KAR₁-treated plants at 1.0?×?10^?19?M yielded the highest level of total phenolics (leaves) and proanthocyanidins (roots). At 1:500 dilutions, SW stimulated the highest total flavonoid content in the leaves across all the treatments. Combining mT with either SW (1:500) or KAR₁ (4.8?×?10^?22?M) significantly increased the quantity of secondary metabolites. However, the growth parameters and pigment contents were not improved. Based on the significant role of photosynthetic pigments and phenolic compounds on the defense and survival strategies of plants, current findings will have practical significance for important processes such as acclimatization and survival of micropropagated plants. These results are also demonstrating the potential of SW and KAR₁ as an eliciting agent for secondary metabolite production.     

Diurnal variation of metabolites in three individual participants

The circadian system influences virtually all biological functions. Understanding the impact of circadian variation on metabolism may provide insight into mechanisms of circadian-associated disorders and guide the implementation of chrono-therapy. Previous research has reported circadian variation in 7–20% of metabolites in human blood. In this study, untargeted metabolomics profiles were measured using blood of two healthy men and one healthy woman, collected every 2 h for up to 48 h under carefully controlled conditions. The pattern of variation of each metabolite over time was examined on each participant separately, using both one- and two-order harmonic models. A total of 100 of 663 metabolites, representing all metabolite categories, showed diurnal rhythmic concentrations that exceeded the Bonferroni threshold (P < 2.5 × 10^?5). Overall, peak times of many metabolites were clustered during the afternoon-midnight, including the majority of amino acids, all peptides, all lysolipids and all phospholipids, whereas the majority of steroids peaked in the morning. We observed substantial inter-individual variation for both peak times and amplitudes in these three subjects. In conclusion, at least 15% of blood metabolites, representing a broad group of biological pathways, exhibit diurnal variation in three participants. The average peak times of most of these metabolites are clustered in morning or afternoon-midnight. Further work is needed to validate and extend this work in more individuals.     

Untargeted Plasma Metabolomics Identifies Endogenous Metabolite with Drug-like Properties in Chronic Animal Model of Multiple Sclerosis

We performed untargeted metabolomics in plasma of B6 mice with experimental autoimmune encephalitis (EAE) at the chronic phase of the disease in search of an altered metabolic pathway(s). Of 324 metabolites measured, 100 metabolites that mapped to various pathways (mainly lipids) linked to mitochondrial function, inflammation, and membrane stability were observed to be significantly altered between EAE and control (p < 0.05, false discovery rate <0.10). Bioinformatics analysis revealed six metabolic pathways being impacted and altered in EAE, including α-linolenic acid and linoleic acid metabolism (PUFA). The metabolites of PUFAs, including ω-3 and ω-6 fatty acids, are commonly decreased in mouse models of multiple sclerosis (MS) and in patients with MS. Daily oral administration of resolvin D1, a downstream metabolite of ω-3, decreased disease progression by suppressing autoreactive T cells and inducing an M2 phenotype of monocytes/macrophages and resident brain microglial cells. This study provides a proof of principle for the application of metabolomics to identify an endogenous metabolite(s) possessing drug-like properties, which is assessed for therapy in preclinical mouse models of MS.     

CpG‐related SNPs in the MS4A region have a dose‐dependent effect on risk of late–onset Alzheimer disease

CpG‐related single nucleotide polymorphisms (CGS) have the potential to perturb DNA methylation; however, their effects on Alzheimer disease (AD) risk have not been evaluated systematically. We conducted a genome‐wide association study using a sliding‐window approach to measure the combined effects of CGSes on AD risk in a discovery sample of 24 European ancestry cohorts (12,181 cases, 12,601 controls) from the Alzheimer's Disease Genetics Consortium (ADGC) and replication sample of seven European ancestry cohorts (7,554 cases, 27,382 controls) from the International Genomics of Alzheimer's Project (IGAP). The potential functional relevance of significant associations was evaluated by analysis of methylation and expression levels in brain tissue of the Religious Orders Study and the Rush Memory and Aging Project (ROSMAP), and in whole blood of Framingham Heart Study participants (FHS). Genome‐wide significant (p < 5 × 10^?8) associations were identified with 171 1.0 kb‐length windows spanning 932 kb in the APOE region (top p < 2.2 × 10^?308), five windows at BIN1 (top p = 1.3 × 10^?13), two windows at MS4A6A (top p = 2.7 × 10^?10), two windows near MS4A4A (top p = 6.4 × 10^?10), and one window at PICALM (p = 6.3 × 10^‐9). The total number of CGS‐derived CpG dinucleotides in the window near MS4A4A was associated with AD risk (p = 2.67 × 10^?10), brain DNA methylation (p = 2.15 × 10^?10), and gene expression in brain (p = 0.03) and blood (p = 2.53 × 10^?4). Pathway analysis of the genes responsive to changes in the methylation quantitative trait locus signal at MS4A4A (cg14750746) showed an enrichment of methyltransferase functions. We confirm the importance of CGS in AD and the potential for creating a functional CpG dosage‐derived genetic score to predict AD risk.