Publisher’s version / Version de l'éditeur:
Vous avez des questions? Nous pouvons vous aider. Pour communiquer directement avec un auteur, consultez la
première page de la revue dans laquelle son article a été publié afin de trouver ses coordonnées. Si vous n’arrivez pas à les repérer, communiquez avec nous à PublicationsArchive-ArchivesPublications@nrc-cnrc.gc.ca.
Questions? Contact the NRC Publications Archive team at
PublicationsArchive-ArchivesPublications@nrc-cnrc.gc.ca. If you wish to email the authors directly, please see the first page of the publication for their contact information.
https://publications-cnrc.canada.ca/fra/droits
L’accès à ce site Web et l’utilisation de son contenu sont assujettis aux conditions présentées dans le site LISEZ CES CONDITIONS ATTENTIVEMENT AVANT D’UTILISER CE SITE WEB.
Symposium on Biological Complexity: Emerging Concepts and Trends [Proceedings], 2010-11-27
READ THESE TERMS AND CONDITIONS CAREFULLY BEFORE USING THIS WEBSITE.
https://nrc-publications.canada.ca/eng/copyright
NRC Publications Archive Record / Notice des Archives des publications du CNRC :
https://nrc-publications.canada.ca/eng/view/object/?id=cfe3c146-9dc4-4c0d-bfc9-07d318fb9010 https://publications-cnrc.canada.ca/fra/voir/objet/?id=cfe3c146-9dc4-4c0d-bfc9-07d318fb9010
NRC Publications Archive
Archives des publications du CNRC
This publication could be one of several versions: author’s original, accepted manuscript or the publisher’s version. / La version de cette publication peut être l’une des suivantes : la version prépublication de l’auteur, la version acceptée du manuscrit ou la version de l’éditeur.
Access and use of this website and the material on it are subject to the Terms and Conditions set forth at
Metabolomics Analysis of Breast Cell Lines and Corresponding Microvesicules
Cuperlovic-Culf, Miroslava; Griffiths, S.; Ghosh, A.; Chute, I.; Culf, A.; Touaibia, M.; Lewis, S. E.; Ouellette, R.
Miroslava Čuperlović-Culf
1,4, S. Griffiths
2, A. Ghosh
2, I. Chute
2, A. Culf
2,4, M. Touaibia
3, S.E.Lewis
5and R. Ouellette
21
National Research Council of Canada, Institute for Information Technology, Moncton, NB. Canada
2
Atlantic Cancer Research Institute, Moncton, NB, Canada
3
Department of Chemistry and Biochemistry, University of Moncton, Moncton, NB, Canada
4
Department of Chemistry and Biochemistry, Mount Allison University, Sackville, NB,Canada
5
New England Peptide Gardner MA USA
Institute for Information
Technology
Metabolomics Analysis of Breast Cell Lines and Corresponding Microvesicles
Salk, 2010
Acknowledgements: NMR instrument has been obtained from funding provided by CFI.
Financial support from NRC Cluster program and ACOOA are greatly appreciated.
References:
1.Cuperlovic-Culf, M; et al. DDT 15:610-621 (2010).
2.Cuperlovic-Culf, M; et al. Magn Res Chem, 47: S96-S104 (2009).
3.Cuperlovic-Culf, M. in Cancer Systems Biology. Chapman & Hall/CRC 2010. 4.Tusher VG, et al. Proc Natl Acad Sci USA. 98:5116–5121 (2001).
5.Cocucci, E. et al. Trends Cell Biol 19:43-51 (2009).
6.Krishnamoorthy, L. et al. Nature Chem Biol 5: 244-250 (2009)
Perturbation of cellular processes such as growth, replication and apoptosis are the ultimate outcome of oncogenesis. Across different cancers many of the triggering oncogenic mutations are clustered in pathways that are closely linked with metabolic processes. The downstream effects on the metabolism are similar across different cancers and therefore define a cancer metabolic phenotype. Altered metabolism gives cancer cells an advantage in survival and proliferation and is a necessary part of cancer development. This unique metabolic phenotype is characterized by several hallmark features including a) high glucose uptake, b) increased glycolytic activity, c) decreased mitochondrial activity for energy production, d) low bioenergetic expenditure; e) increased phospholipid turnover, altered lipid profile and increase of de novo lipid synthesis; f) increased amino acid transport and protein as well as DNA synthesis; g) increased hypoxia (a pathological condition in which the body as a whole or region of the body is deprived of adequate oxygen supply); h) increased tolerance to reactive oxygen species (ROS - highly reactive ions or small molecules with an unpaired valence shell electrons). Although it is nowadays accepted that the majority of cancers exhibit the cancer metabolic phenotype there are still significant metabolic differences between cancers subtypes that are not well understood. Recently, it has also been demonstrated that cancer cells produce large quantities of microvesicular material (MV). These MV are involved in intercellular communication, regulation of programmed cell death, modulation of immune response, inflammation, angiogenesis and coagulation. Numerous studies have begun to describe the protein and nucleic acid composition of MV. however, the metabolic content and profiles of MV has yet to be explored. Given the importance of cancer metabolic phenotype, i.e. metabolites for function and proliferation of cancers as well as the significance of metabolites in cell signaling and communication we are investigating the metabolic profiles of breast cancer cells and their corresponding MV material in order to determine metabolic characteristics associated with different subtypes of cancer. This information is a first step in the development of models of different phenotypes and application of MV as well as cancer metabolic phenotype for diagnostics, prognostics and treatment.
MCF10A MCF7 MB231 SKBR3 MCF10A MCF7 MB231 SKBR3 Microvesicles (MVs) are fragments of plasma membrane ranging from 50 nm
to 1000 nm that are shed from almost all cell types. MVs represent a newly recognized system of intercellular communications and are increasingly being considered to play a pivotal role in information transfer between cells. MVs appear to play a role in intercellular signaling through their capacity to mediate the exchange of mRNA, microRNAs, and proteins between cells. Their presence and role have been proven in several physiological and pathological processes, such as immune modulation in inflammation and pregnancy, or blood coagulation and cancer. MVs shed by tumour cells have several proposed functions all leading to promoting tumour progression by
manipulating the surrounding environment (5). They can potentially serve
as prognostic markers in different diseases, as well as possibly also present new therapeutic targets or provide tools for drug delivery. MVs represent a
heterogeneous population , differing in cellular origin, number, size and antigenic composition. In body fluids (e.g. blood) there is a high
concentrations of different MV which thus far can not be independently isolated. Thus samples of human blood, FBS or cell cultures with FBS show indistinguishable signals (see figure below). It is therefore essential to develop better understanding and description of these fascinating systems in models such as cell cultures prior to working with MVs from body fluids.
Comparison of NMR signal from MV
metabolic extract from MV obtained from human blood, FBS and cell cultures grown in the presence of FBS.
1D 1H NMR data for MV metabolites in DMSO for 4 cell lines and 5 biological replicates. The consistency between replicates is
apparent .
Major different features between 4 cell types (red dots).
Fuzzy K-means clustering of samples based on NMR metabolic profiles of MVs for four breast cell lines. Different cell types are clearly separated with crisp assignment of MCF10A and SKBR3 types. MB231 and MCF7 cell lines are also grouped separately but the second highest membership values for MB231 cells shows similarity with MCF7s.
PCA of NMR metabolic profiles for four breast cell lines based on MV profiles. Different cell types are clearly separated with clear separation of normal cell line (MCF10A) and of aggressive cell phenotypes (SKRB3) in PC3.
Cell Line Type Characteristics Oestradiol/ progesteron
Morphology
MCF10A Normal* Adherent cells Epithelial MCF7 Adenocarcinoma Invasive +/+ Epithelial MB231 Adenocarcinoma Invasive -/- Epithelial SKBR3 Adenocarcinoma Invasive -/- Epithelial
SAM determined major differences between MCF10A v.s. MCF7 and MB231 MV. The chemical shifts point readily to cholesterol sulfate as a “over concentrated” metabolite (green spectra)
Materials and Methods
Cell culture: All cell lines used in this study were obtained from ATCC (Manassas, VA,
USA). All media and components were purchased from Invitrogen unless otherwise noted. MCF-10a cells were grown in Dulbecco’s modified Eagle’s medium/Ham’s F12 (1:1, v/v) supplemented with 2mM L-glutamine, 1mM sodium pyruvate, 20ng/ml epidermal growth factor (Sigma Aldrich), 100 ng/ml cholera toxin (Sigma Aldrich), 0.01mg/ml bovine insulin (Sigma Aldrich), 500 ng/ml hydrocortisone (Sigma Aldrich), 5% fetal bovine serum (PAA) and penicillin/streptomycin (100U/ml and 100µg/ml, respectively). MDA-MB-231 and MCF-7 cells were grown in Dulbecco’s modified Eagle’s medium supplemented with 10% fetal bovine serum (PAA), 2mM L-glutamine and penicillin/streptomycin. SKBR-3 cells were grown in McCoy’s 5A medium supplemented with 10% fetal bovine serum (PAA) and penicillin/streptomycin. T47D cells were grown in RPMI 1640 medium supplemented with 10% fetal bovine serum (PAA) and penicillin/streptomycin.
10cm culture-treated dishes (5 replicates per cell line) were seeded with approximately 3 x 106 cells and incubated for 24 hrs at 37C and 5% CO2. Following this period, the seeding
media for all cell types was removed and replaced with FBS-free Dulbecco’s modified Eagle’s medium. The cells were then incubated for a further 24 hrs prior to harvest. Conditioned media from this period was used for microvesicle collection. Cells were harvested by scraping and rinsed with PBS before being spun down by centrifuging at 4,000 rcf for one minute. Cell pellets were kept on ice for 5 minutes before being resuspended in 1ml of 50% acetonitrile (Fisher Scientific). Cell suspensions were kept on ice for 10 minutes before being spun at 16,000 rcf for 10 minutes at 4C.
MV collection and metabolite extraction: 100 um of the proprietary reagent (Vn96,,
Patent application in process, Atlantic Cancer Research Institute) in 10ul of buffer was added to 2ml volumes of extracellular medium and incubated at 4C overnight. The tubes were centrifuged at 17000g for 10m at 4C. The supernatants were decanted to waste revealing a translucent gelatinous pellet. The pellet was resuspended in 2ml of 0.22u filtered PBS by vortex and then centrifuged again for 10min as before. The PBS wash was decanted to waste and pellets resuspended in 1ml of PBS and duplicate tubes of each of the five replicates were again centrifuged as before. The wash was decanted to waste. A final quick spin (30s) was performed to bring residual PBS to the pointed base of the 2ml micro tubes and removed by pipetteman.
NMR experimentation
Sample preparation: for cell cultures: The aqueous acetonitrile extract solution was
dried at 50C by rotary evaporation over 5 minutes. The white residue was dissolved in 0.6mL of deuterium oxide (Aldrich, 99.96 atom% 2H), pipetted into a glass NMR tube and sealed with parafilm for NMR analysis. For MV: The viscous material was dissolved in 0.6 mL of d6-DMSO (99.9 atom% 2H), pipetted into a glass NMR tube and sealed with parafilm for NMR analysis.
All 1D 1H NMR measurements were performed on Bruker UltraShield 400 using a gradient water presaturation method. Measurement for MV metabolites were obtained with 1500 scans and for cell culture metabolites 512 scans were sufficient. NMR spectra were processed using Mnova with exponential apodization (exp 2.5); normalization to the highest peak; baseline correction () and line smoothing (Savitzky-Goley procedure) as provided in Mnova.
Data analysis
Data preprocessing including data organization, removal of undesired areas and (binning to 0.0098ppm bin size) as well as data presentation was performed with Matlab. PCA as well as fuzzy K-means cluster analysis were also performed under Matlab platform as described previously (2). Feature selection was done with Significance analysis for microarrays method (4). Spectra for suggested differentially present metabolites are obtained from Human Metabolomics Database (www.hmdb.ca).
MCF10A MCF7 MB231
1D 1H NMR data for cell cultures metabolite extracts for 3 cell lines and 5 biological replicates. The consistency between
replicates is apparent. Cancer cell lines have large choline peak
SAM determined major differences between MCF10A v.s. MCF7 and MB231 cell line metabolic profiles. The largest difference is due to the strong choline signal in cancer cell lines. However peaks corresponding to cholesterol sulfate, determined to be major component in MV analysis are also present (see green arrow and spectra below)
PCA of NMR metabolic profiles for 3 breast cell lines. Different cell types are clearly separated with majordistinction between normal cell line (MCF10A) and of cancer cell phenotypes (MCF7 and MB231) in both PC1 vs PC2 and PC1 vs PC3 plots.
Fuzzy K-means clustering of samples based on NMR metabolic profiles for three breast cell lines. Different cell types are clearly separated with crisp assignment of MCF10A. MB231 and MCF7 cell lines are also grouped separately but the second highest
membership values for MB231 cells shows similarity with MCF7s.
Choline
Mean, binned MCF10A
Mean binned MCF7, MB231
MCF10A
MCF7
MB231 Metabolomics represents a global quantitative assessment of metabolites within
a biological system. The metabolic analysis of cell cultures has many potential applications and advantages to currently utilized methods for cell line testing. Metabolite concentrations represent sensitive markers of both genomic and phenotypic changes. Consequently, the development of robust metabolomic platforms will greatly facilitate various applications of cell cultures including for example the understanding of the in vitro and in vivo actions of drugs and aid in their rapid incorporation into novel therapeutic settings. Additionally, metabolomics analysis of cell lines provides information, either independently or in conjunction with other omics measurements, essential for system level analysis and modelling of biological systems (1).
Metabolic changes are widely accepted as one of the major, ubiquitous characteristics of cancers with some understanding of the major properties of cancer metabolic phenotype and its connection with the other carcenogenic alterations (3).
However, the understanding, description and a model of specific steps as well as metabolic differences in different types and subtypes of cancers is not in place.
In this work we are exploring metabolic differences between subtypes of breast cancers cells as well as corresponding microvesicular material. The initial analysis used qualitative metabolic measurements and was aimed at determining major difference between the subtypes.
Glycolysis TCA Lipid biosynthesis Protein Biosynthesis translation Nucleic acids biosynthesis C-Myc Lactate Akt SREBP1 HIF1a P53 O2 mTOR Ras ERK1/2 Conclusions
a)It is possible to distinguish different subtypes of breast cancer from both cell culture metabolic profiles and MV material metabolic profiles using either PCA or FKM analysis;
b)From qualitative NMR data it is possible to determine likely metabolic differences between groups of samples;
c)Major difference between cancer and normal samples in cell data is due to changes in choline as has been observed previously; in MV profiles major difference appears to be due to cholesterol sulfate (or related molecule);
d)The cholesterol sulfate appears to be “over-concentrated” in normal cells and the corresponding MV material; this is not the case for other differentially present metabolites with choline not present at all in MVs.
e)V96 reagent appears to provide a rapid and reproducible mean for the enrichment of vesicular material.
Future work will focus on:
a)Quantification of metabolic information and network analysis of the results independently and in combination with other omics measurements in order to determine pathways leading to differential metabolite presence;
b)Establishment of tools for separation of different types of MV material;
c)Separate analysis of membrane and intracellular and intravesicular metabolic profiles.
HIV daughter particles being shed from and infected T-cell (6).
