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The International Journal of Biochemistry & Cell Biology 43 (2011) 1052–1058

Contents lists available at ScienceDirect

The International Journal of Biochemistry& Cell Biology

journa l homepage: www.e lsev ier .com/ locate /b ioce l

etabolism and cell shape in cancer: A fractal analysis

abrizio D’Anselmia,c, Mariacristina Valeriob,g, Alessandra Cucinac, Luca Gallid, Sara Proietti e,imona Dinicolae, Alessia Pasqualato f, Cesare Manetti g, Giulia Riccih,lessandro Giuliani i, Mariano Bizzarri e,∗

ASI, Italian Space Agency, Roma, ItalyArea of Molecular Medicine Scientific Director’s Office, Italian National Cancer Institute “Regina Elena”, Roma, ItalyDept. of Surgery “Pietro Valdoni” - University La Sapienza, Roma, ItalyAdvanced Computer Systems A.C.S. S.p.A., Roma, ItalyDept. of Experimental Medicine - University La Sapienza, Roma, ItalyDept. of Basic and Applied Medical Science. University “G. D’Annunzio”, Chieti-Pescara, ItalyDept. of Chemistry - University La Sapienza, Roma, ItalyDept. of Experimental Medicine - Second University, Naples, ItalyEnvironment and Health Department, Istituto Superiore di Sanita’, Roma, Italy

r t i c l e i n f o

rticle history:vailable online 9 May 2010

eywords:ractal morphologyhenotype reversionending energyumour metabolomeorphogenetic field

a b s t r a c t

Fractal analysis in cancer cell investigation provided meaningful insights into the relationship betweenmorphology and phenotype. Some reports demonstrated that changes in cell shape precede and triggerdramatic modifications in both gene expression and enzymatic function. Nonetheless, metabolomic pat-tern in cells undergoing shape changes have been not still reported. Our study was aimed to investigateif modifications in cancer cell morphology are associated to relevant transition in tumour metabolome,analyzed by nuclear magnetic resonance spectroscopy and principal component analysis. MCF-7 andMDA-MB-231 breast cancer cells, exposed to an experimental morphogenetic field, undergo a dramaticchange in their membrane profiles. Both cell lines recover a more rounded shape, loosing spindle andinvasive protrusions, acquiring a quite “normal” morphology. This result, quantified by fractal analysis,shows that normalized bending energy (a global shape characterization expressing the amount of energyneeded to transform a specific shape into its lowest energy state) decreases after 48 h. Later on, a sig-nificant shift from a high to a low glycolytic phenotype was observed on both cell lines: glucose flux

begins to drop off at 48 h, leading to reduced lactate accumulation, and fatty acids and citrate synthesisslow-down after 72 h. Moreover, de novo lipidogenesis is inhibited and nucleotide synthesis is reduced,as indicated by the positive correlation between glucose and formate. In conclusion, these data indicatethat the reorganization of cell membrane architecture, induced by environmental cues, is followed by a

tumo

relevant transition of the

. Introduction

The tumour metabolome – mainly characterized by the gly-olytic phenotype – confers to the evolving cancer cell populationn advantage and contributes to tissue invasion and metastasis

preading (De Berardinis et al., 2008). However the glycolytic phe-otype is not confined to cancer cells: embryonic tissues, as well asighly proliferating cells, like lymphocytes, share a similar pattern

Abbreviations: EMF, experimental morphogenetic field; NBE, normalized bend-ng energy; PCA, principal component analysis; NMR, nuclear magnetic resonance.∗ Corresponding author at: Dept. of Experimental Medicine, University La

apienza, viale Regina Elena 324, 00161 Roma, Italy.el.: +39 06 49766606; fax: +39 06 49766603.

E-mail address: mariano.bizzarri@uniroma1.it (M. Bizzarri).

357-2725/$ – see front matter © 2010 Elsevier Ltd. All rights reserved.oi:10.1016/j.biocel.2010.05.002

ur metabolome, suggesting cells undergo a dramatic phenotypic reversion.

© 2010 Elsevier Ltd. All rights reserved.

(Wang et al., 1976). Moreover, cancer cell metabolism is signifi-cantly affected by cell cycle phase and confluence or sub-confluenceculture conditions, displaying high plasticity to adapt in presenceof adverse microenvironmental conditions (Tomassini et al., 2006).These data suggest that tumour metabolome might be consid-ered a dynamic reversible phenotypic trait, likely governed by thenon-linear interplays of several both genomic and non-genomicfactors: epigenome, nutrient availability, oxygen and blood supply,stiffness and diffusion gradients shaping the microenvironmentalconstraints. On the other hand, it is reasonable to infer that themodification of microenvironmental cues, could influence tumourmetabolism in order to force cancer cells loose (partly or entirely)

their malignant features.

Physical – extracellular matrix stiffness, microgravity – as wellas chemical microenvironmental factors, have been reported toinduce relevant morphological changes on cell architecture (Chen

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F. D’Anselmi et al. / The International Journal o

t al., 1997). In turn, cell shape transition has proven to precedend to influence significantly both gene expression and enzy-atic reactions (Carmeliet and Bouillon, 1999; Boonstra, 1999).oreover, by controlling the cellular environment with micro-

abricated patterning, studies on mammary epithelial cell tissueorphogenesis demonstrated to modify nuclear organization and

ubsequently modulate cellular and tissue phenotype (Lelièvret al., 1998). Furthermore, microenvironmental-induced shapehanges in chondrocyte nuclei correlate with collagen synthesisThomas et al., 2002) or changes in cartilage composition and den-ity (Guilak, 1995).

Therefore, as clearly stated by Ingber (1999), cell shape shoulde considered as “[. . .] the most critical determinant of cell function. . .] cell shape per se appears to govern how individual cells willespond to chemical signals (soluble mitogens and insoluble ECMolecules) in their local microenvironment.”Cell shape modifications are thought to ‘integrate’ a wide set of

timuli and to drive cell switching between different cell fates, i.e.ifferent phenotypes. Broadly speaking, cell shape could be vieweds the “structural” result of the interacting influences betweenoth internal and external constraints. Moreover, quantitativehape descriptors (i.e. fractal dimension), possess thermodynamicseaning and they could provide insights into the complexity score

f the observed system. In this context, cell shape is thought to behe morphological expression of an integrated system, and a spe-ific morphology could be assigned to every functional cell state:n other words, every phenotype or differentiated cell possesses

well-defined shape, described by both classic morphological asell thermodynamics parameters.

Yet, an understandable link between shape and metabolic orenomic function has been never proposed. This is partly due to theimited knowledge about how biochemical reactions are associatedo the cytoskeleton (i.e. the internal topology of structures-linkedeactions), and, on the other hand, to a lack of a standardized andide-accepted measure of cell shape complexity.

A quantitative method that lends itself particularly useful forharacterizing complex irregular structures is fractal analysis. Non-uclidean objects are better described by fractal geometry, whichas the ability to quantify the irregularity and complexity of objectsith a measurable value called the “fractal dimension”. Fractalimension differs from our intuitive notion of dimension (i.e. topo-

ogical dimension) in what can be considered as a non-integeralue, and more irregular and complex an object is, more highers its fractal dimension in relation to its topological dimensionMandelbrott, 1982). Fractal geometry is well suited to quantifyhose morphological features which pathologists have long usedand are still using today!) in a qualitative way to describe malig-ancies. So far, during the last decade, several reviews of fractaleasures application in pathology have outlined that fractal analy-

is could provide reliable and helpful information (Losa et al., 2002;aish and Jain, 2000; Cross, 1997).

Our study was intended to investigate if modifications in can-er cell morphology are associated to relevant transition in tumouretabolomic profile, analyzed by means of nuclear magnetic reso-

ance (NMR) spectroscopy and flux principal component analysisPCA) (exometabolome study).

. Materials and methods

.1. Cell culture and EMF

MCF-7 and MDA-MB-231 human breast carcinoma cell linesECACC, Sigma–Aldrich, St. Louis, MO, USA) were cultured in Dul-ecco modified Eagle’s medium (DMEM, Catalog no. EC B7501L;uroclone Ltd., Cramlington, UK), supplemented with 10% fetal

hemistry & Cell Biology 43 (2011) 1052–1058 1053

calf serum (FCS, Euroclone), non-essential aminoacids and antibi-otics (penicillin 100 IU/ml, streptomycin 100 �g/ml, gentamycin200 �g/ml, all from Euroclone). MCF-7 cells used for experimentswere from passages 30 to 40, and MDA-MB-231 cells were frompassages 5 to 15. The different number of passages for the twocell lines used in our experiments simply represents the passagesperformed in our laboratory from the purchase of each cell line.

EMF (experimental morphogenetic field) is a set of proteinsextracted from chicken egg’s albumen, easily soluble in culturemedia. EMF is now patented. We dissolved EMF in DMEM to reacha final concentration of 10% of the volume. Cells were cultured for168 h in DMEM + 10% FCS, as a control condition, and DMEM + 10%FCS + 10% EMF as experimental arm.

2.2. Cell proliferation assay

MCF-7 and MDA-MB-231 cells were seeded in 12-well cultureplates (Falcon, Becton Dickinson Labware) at a concentration of2 × 104 cells/well in DMEM + 10% FCS. The following day, the cellswere refed with DMEM + 10% FCS and DMEM + 10% FCS + EMF 10%.The plates were incubated for 168 h at 37 ◦C in an atmosphere of5% CO2. Every day the cells were trypsinized and centrifuged, andcell pellets were resuspended in PBS. Cell count was performed bya particle count and size analyzer (Beckman Coulter, Inc. Fuller-ton, CA, USA) and by a Thoma hemocytometer. Three replicatewells were used for each data point, and the experiments wereperformed six times.

2.3. Optical microscopy

Both MCF-7 and MDA-MB-231 cells were grown on six welltissue culture plate (Becton Dickinson Labware, Franklin Lake, NJ,USA) and were photographed every 24 h with Nikon Coolpix 995digital camera coupled with Nikon Eclipse TS100 optical micro-scope (Nikon Corporation, Japan). Photomicrographs at differentmagnifications, were saved as TIFF files and used for NormalizedBending Energy (NBE) analysis.

2.4. Electron microscopy

MCF-7 and MDA cells, cultured with or without EMF, were fixedin 2.5% glutaraldehyde in 0.1 M cacodylate buffer (pH 7.4), post-fixed in 1% OsO4 in Zetterquist buffer, de-hydrated in ethanol,and embedded in epoxy resin. Ultrathin sections were contrastedin aqueous uranyl-acetate and lead-hydroxide, studied and pho-tographed by a Hitachi 7000 Transmission Electron Microscope(Hitachi, Tokyo, Japan).

2.5. Shape analysis

Shape analysis via NBE computation of nuclear and cell mem-brane of breast cancer cells has been performed in a semi-automaticway. First, cell membranes have been manually segmenteddelineating the initial contour; secondly, accurate membrane seg-mentation is automatically performed by using an Active ContourModel (“Snake”) (Sebri et al., 2007). Snake models basically con-sist of a curve, which can dynamically conform to object shapes inresponse to internal and external forces. These forces could be seenas the result of a functional global minimization processor based onlocal information. The conventional Snake model has some limita-tion, in that the initial contour must be placed close to the objectto prevent it from converging to a local minimum. In order to over-

come this problem, the Gradient Vector Flow GVF-Snake method(Xu and Prince, 1998) has been used, which, besides having a largecapturing range, is currently also the most efficient one in termsof precision, for the tracking of breast cancer cells (Fig. 1). The

1054 F. D’Anselmi et al. / The International Journal of Biochemistry & Cell Biology 43 (2011) 1052–1058

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ig. 1. Examples of breast cancer cell membrane segmentation using the GVF-Snakeells in control and (D) in EMF condition after 96 h.

BE shape descriptor has been computed considering 36 cell mem-ranes, randomly chosen from both the MCF-7 and MDA-MB-231reast cancer cell lines for each experimental time. The compu-ations were performed by in-house software developed at ACSAdvanced Computer Systems, S.p.A., Rome, Italy).

.6. Metabolomic analysis

For metabolomic analysis both MCF-7 and MDA-MB-231 cellsere grown on INTEGRIDTM (150 mm × 25 mm) tissue culture dish

Becton Dickinson Labware, Franklin Lake, NJ, USA). Cells weretopped at 48, 72 and 96 h; the medium for each time point wasollected and stored at −80 ◦C. Medium dried samples were dis-olved in 600 �l of 1 mM TSP [sodium salt of 3-(trimethylsilyl)ropionic-2,2,3,3-d4 acid] solution in D2O PBS buffer (pH 7.4) tovoid chemical-shift changes due to pH variation.

2D 1H J-resolved (JRES) NMR spectra were acquired on a00 MHz DRX Bruker Avance spectrometer (Bruker Biospin, Rhe-

nstetten, Germany), using a double spin echo sequence with 8ransients per increment for 32 increments. After 2D JRES pro-essing, the 1D skyline projections exported were aligned andhen reduced into spectral bins with widths ranging from 0.01o 0.03 ppm. The total spectral area (excluding water signal) wasormalized to unity. NMR data was processed using Bruker’s XWIN-MR software and custom-written MATLAB code (Version 7.0; TheathWorks, Natick, MA). All data were expressed in terms of net

alances, i.e. as difference between the single time points (48, 72nd 96 h) and time 0, allowing the analysis of fluxes, representinghe actual utilization of the substrate. Each 142 bins spectrum cane considered as a vector of 142 dimensions, being each dimen-

ion the Y value at each specific X (ppm) location. In the casef N samples, the matrix having the spectra as rows (statisticalnits) and the 142 Y values as variables (columns) constituted thetarting data matrix collecting all the relevant information. This

od. (A) MCF-7 cells in control and (B) in EMF condition after 96 h. (C) MDA-MB-231

matrix undergoes a principal component analysis (PCA) procedurein order to extract the main features of the data set in terms ofbetween variables correlation structure: each extracted compo-nent points to a direction of variation maximizing the betweenvariables correlation and thus correspondent to a given metabolicpathway. Between groups differences on principal componentscores were considered as statistically significant at p < 0.05 in thet-test. PCA was conducted using PLS Toolbox (Version 5.2; Eigen-vector Research, Manson, WA) within MATLAB. Inferential analysiswas performed by using GraphPad Instat software (GraphPad Soft-ware, Inc., San Diego, CA, USA).

3. Results

3.1. Cell proliferation

Both MCF-7 and MDA-MB-231 control cells display high rate ofproliferation. A slightly, even if significant decrease in proliferativetrend was observed in EMF-treated cells, after the first 48 h (datanot shown).

3.2. Shape analysis

MCF-7 and MDA-MB-231 cells growing in an experimental mor-phogenetic field progressively undergo dramatic changes of cellmembrane shape. After 48 h, membrane profiles change, evolvinginto a more rounded shape, loosing spindle and invasive protru-sions. NBE values while not changing in MCF-7 control cells, areslightly decreasing in MDA-MB-231 control samples. On the con-trary, a dramatic reduction was observed for NBE levels in treated

samples. Differences were statistically significant starting from 72 hand 120 h for MDA-MB-231 and MCF-7, respectively (Table 1). Evenif the differences, above mentioned, are not statistically significant,they were already emerging at 48 h. Indeed, NBE profiles of cell

F. D’Anselmi et al. / The International Journal of Biochemistry & Cell Biology 43 (2011) 1052–1058 1055

Fig. 2. Mean normalized bending energy values (calculated for cell membrane) inMCF-7 cell line computed at different experimental time in controls and treated(EMF) conditions. The error bars refer to the standard error of the mean NBE values.

Fig. 3. Mean normalized bending energy values (calculated for cell membrane) inMtv

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DA-MB-231 cell line computed at different experimental time in controls andreated (EMF) conditions. The error bars refer to the standard error of the mean NBEalues.

embrane MCF-7 and MDA-MB-231 (Figs. 2 and 3) show that cellhape in treated samples undergoes a phase-transition between 48nd 72 h, as indicated by the high variance values.

Moreover, starting from 72 h, MCF-7 cells treated with EMF wereble to form both tight (absent in control cells) and gap-junctions,s confirmed by electron microscopy (Fig. 4).

.3. Metabolomic analysis of MCF-7 cells

PCA extracted seven relevant components, together explainingbout 75% of the total variability of the system. To compare all thepecific pairs of control and treated groups a t-test was appliedo the component scores. The discrimination between control andreated groups was achieved in the first two PCs at each experi-

ental time and in the PC3 and PC4 at 48 and 96 h as well as in PC6t 72 and 96 h (Table 2).

The analysis of the major order parameters present in theata, namely PC1 and PC2 (21% and 19% of variation explained,espectively), allowed us to determine the metabolite pattern dis-

riminating the two groups (Fig. 5, Table 3).

PC1 most correlated variables were net balances of fatty acids,-hydroxybutyrate and acetate (these entering with negative load-

ng). This profile points, in EMF-treated MCF-7 cells, to a reduced

able 1ormalized bending energy shape descriptor t-test probability, comparing control versxperimental time (threshold p < 0.05).

Experimental time (h)

0 24 48 72

MCF-7 0.8766 0.5432 0.7345 0.9789MDA-MB-231 0.92874 0.1973 0.4773 0.0164

(grey arrow, left panel) and tight junctions (white arrows, right panel) are shown inphotomicrographs.

de novo lipidogenesis and a greater utilization of these metabo-lites for energetic purposes, compared to control ones, particularlyat 48 h (differences in PC1 scores: 3.1 at 48 h, 0.5 at 72 h, 0.9 at96 h). Starting from 72 h, the discrimination between control andtreated samples is carried out by PC2, pointing to a shift froma high glycolytic phenotype to a metabolism in which energyrequirements are mainly fulfilled by glutamine and fatty acids con-

sumption. Indeed, PC2 points to a lower consumption of glucose– with reduced lactate release – and a greater utilization of glu-tamine for biosynthetic purposes in EMF-treated cells, particularlyafter 72 h (differences in PC2 scores: 0.6 at 48 h, 2.1 at 72 h, 2.2 at

us treated cells values for MCF-7 and MDA-MB-231 breast cancer lines for each

96 120 144 168

0.0890 0.0322 0.0383 0.00210.0000 0.0000 0.0000 0.0000

1056 F. D’Anselmi et al. / The International Journal of Biochemistry & Cell Biology 43 (2011) 1052–1058

Table 2t-Test comparing control versus treated cells (MCF7). In parentheses the percent of variance explained by each principal component is reported (threshold p < 0.05).

Experimental time (h) PC1 (21%) PC2 (19%) PC3 (11%) PC4 (9%) PC5 (6%) PC6 (5%) PC7 (4%)

48 <0.00001 0.0003 0.021 0.0002 0.169 0.424 0.87872 0.0007 <0.00001 0.614 0.338 0.420 0.002 0.97396 <0.00001 <0.00001 0.0002 0.0001 0.720 <0.00001 0.697

Table 3Most correlated regions of 1H NMR spectra to PC1 and PC2.

PC1 PC2

ppma Factor loadingb Metabolitec ppma Factor loadingb Metabolitec

0.89, 1.29, 2.75 0.81 Lipid (c) 3.23, 3.41, 3.71, 3.75, 3.78, 3.83, 3.95, 4.66, 5.23 0.75 Glucose (c)1.20 −0.81 3-Hydroxybutyrate (c) 1.47 −0.67 Alanine (p)1.92 −0.73 Acetate (c) 2.14, 2.43 −0.80 Glutamine (c)

8.45 0.80 Formate (p)

a Mid-spectral integral region, i.e. 3.26 represents ppm 3.23–3.27; only spectral regions containing only one metabolite are reported.b Values are given as mean of loadings obtained for all spectral regions of each metabolite.c Consumption and production are indicated as (c) and (p), respectively.

Fig. 5. Overview of the PCA model built on the NMR dataset of medium samplescos

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Fig. 6. Overview of the PCA model built on the NMR dataset of medium samplescollected from control and EMF-treated MDA-MB-231 cells at 48, 72 and 96 h. The

ollected from control and EMF-treated MCF-7 cells at 48, 72 and 96 h. The score plot

f the first two components (PC1 versus PC2) showing differences among groups ishown.

6 h). Moreover, the positive correlation as for PC2 between glucosend formate, suggests an inhibition of de novo nucleotide synthesisn EMF-treated cells. The absence of correlation between glutaminend lactate shows the former is preferentially driven along anabolicathways in EMF-treated samples.

.4. Metabolomic analysis of MDA-MB-231 cells

For MDA-MB-231 cells, five principal components (PCs) werextracted, together explaining the 80% of the total variance. A t-test,pplied to the component scores to compare control and treatedells, highlighted significant differences between the two groupsn the first four PCs at each experimental time and on the PC5 at8 and 96 h (Table 4).

Analysis of the PC1/PC2 plane (Fig. 6) showed that PC1 is byar the major order parameter present in the data (42% of vari-nce explained) and corresponds to the core energy metabolism asvident from its positive loading (correlation coefficient betweenriginal variable and component) with glucose utilization and itsegative loading with lactate (see Table 5).

This correlation structure implies the samples having higher PC1cores correspond to those samples with a lower use of glucose; onhe contrary, those ones with lower scores are the statistical unitsndowed with the higher glucose utilization and lactate produc-

score plot of the first two components (PC1 versus PC2) showing differences amonggroups is shown. The major metabolic difference between control and treated groupsat 96 h is highlighted by the black line.

tion. As evident in Fig. 6, the by far maximal difference betweencontrol and treated groups is at the 96 h point, where control sam-ples display much higher glucose consumption (high glycolyticphenotype). In the other time points too, control samples showconsistently lower values of PC1 with respect to treated samples,but differences are noticeably lower. This emerges by the aver-age differences in PC1 scores between control and treated groupsat different times (0.6 at 48 h, 1.0 at 72 h, 2.6 at 96 h). Moreover,after 72 h, PC2 scores obtained from EMF-treated cells, displayeda clear metabolomic reversion, mainly characterized by reducedglycolytic fluxes that, concomitantly with a reduced citrate bio-conversion, lead to increased �-oxidation rates and reduced fattyacids synthesis.

4. Discussion

Microenvironmental factors and proteins extracted from bothembryonic and maternal morphogenetic fields, have been shownto inhibit cancer progression, inducing programmed cell death or

morphologic and phenotype reversion (Cucina et al., 2006; Hendrixet al., 2007). Moreover, environmental cues and physical forceshave been shown to deeply influence both cell shape and archi-tecture (Ingber, 2005). Significant changes in cell morphology have

F. D’Anselmi et al. / The International Journal of Biochemistry & Cell Biology 43 (2011) 1052–1058 1057

Table 4t-Test comparing control versus treated cells (MDA-MB-231). In parentheses the percent of variance explained by each principal component is reported (threshold p < 0.05).

Experimental time (h) PC1 (42%) PC2 (15%) PC3 (12%) PC4 (7%) PC5 (4%)

48 <0.00001 0.007 <0.00001 <0.00001 0.00372 <0.00001 <0.00001 <0.00001 <0.00001 0.32696 <0.00001 <0.00001 0.006 0.001 0.044

Table 5Most correlated regions of 1H NMR spectra to PC1 and PC2.

PC1 PC2

ppma Factor loadingb Metabolitec ppma Factor loadingb Metabolitec

3.23, 3.41, 3.71, 3.75, 3.78, 3.83, 3.95, 4.66, 5.23 0.97 Glucose (c) 0.89, 1.29, 1.59, 2.05, 2.25 0.81 Lipid (c)1.33, 4.12 −0.85 Lactate (p) 2.14 0.73 Acetoacetate (c)2.14, 2.43 −0.87 Glutamine (c) 2.65 0.76 Citrate (c)

a Mid-spectral integral region, i.e. 3.26 represents ppm 3.23–3.27; only spectral regions containing only one metabolite are reported.etabo

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b Values are given as mean of loadings obtained for all spectral regions of each mc Consumption and production are indicated as (c) and (p), respectively.

een obtained in 3D culture of cancer cells (Kenny and Bissell,003); however, this is the first report in which on a 2D culture,morphogenetic field, constituted by an experimental set of pro-

eins extracted from egg albumen, induces both phenotype andorphology reversion in breast cancer cells.Cell morphology in EMF-treated samples undergoes signifi-

ant changes, evolving from a spindle to a round profile. Fractaleasures demonstrated cell shape acquires a “less-dissipative”

rchitecture in terms of reduced levels of NBE. The “curvegram”btained by using digital signal processing gives a multiscale rep-esentation of the curvature, providing an useful resource forranslation and rotation-invariant shape classification, as well asmean of deriving quantitative information about the complexityf the shapes being investigated (Cesar and Costa, 1997). Bend-ng Energy is a global shape characterization correspondent to themount of energy needed to transform the specific shape undernalysis into its lowest energy state (i.e. a circle) (Bowie and Young,977). Bending energy is thus proportional to the fractal dimensionf the studied object that in turn has to do with the amount of com-lexity. We can consider fractal dimension in much the same wayhat thermodynamics might view intensive measures as tempera-ure. Therefore, NBE provides an understandable link between cell

orphology and thermodynamics features of the system (Smith etl., 1996).

In our study, membrane profiles of control cancer cells exhibitigh basal NBE values. In MCF-7 control samples, after 96 h, noignificant changes were recorded in NBE levels, whereas in MDA-B-231 control cells, a slight reduction is observed after 72 h. In

he latter case, it could be argued that the high population den-ity reached by confluent cells exerts a physical constraint, limitinghe “protrusive” phenotype and determining a more “compact” cell

orphology (crowding effect).On the contrary, EMF treatment induces in both cancer cell

ines an impressive morphological modification and a significanteduction of NBE, starting from 48 h. These effects emerged earliern MDA-MB-321 than in MCF7 breast cancer cells. Indeed MCF7re widely recognized to present only some malignant features,howing a low aggressive behaviour. These data suggest that morevident are the morphological abnormalities, more sensitive arehe cells to the effects triggered by environmental cues.

The experimental morphogenetic field induces an overall reor-anization of cell architecture leading to a shape “normalization”,

haracterized by rounded, smooth profiles, and reduced levels ofissipative energy. It is tempting to speculate modifications likehat produce several consequences on the overall system, leadingo changes in cell to cell relationships. Bending Energy is known

lite.

to be inversely correlated with surface tensions, and surface ten-sions are reflective of intercellular adhesive intensities (Foty et al.,1994). Therefore, it can be assumed that, while reducing bend-ing energy, EMF treatment increases intercellular adhesion forces.This aspect is noteworthy, considering that surface tensions influ-ence both embryonic and tumour cell spreading (Foty et al., 1996).Modification in bending energy highlights the transition from a“protrusive” towards a compact shape, the former associated to aninvasive pattern (Rohrschneider et al., 2007). Hence, we hypoth-esize that reversion of tumour shape towards more “physiologic”fractal dimension, implies a reduced morphologic instability andincreased cell connectivity. This statement is further supportedby the re-establishment of both tight and gap-junctions (absentin control cells), observed in MCF-7 EMF-treated cells. It is wellknown that gap junctional intercellular structures integrate andmodulate both cellular and microenvironmental signals in orderto inhibit proliferation and enhancing differentiating processes(Trosko et al., 2004). It could be therefore argued that some ofthe modifications observed in tumour metabolism, could be partlyattributed to the de novo reconstituted communication betweencells.

It is noteworthy that current cytotoxic anticancer treatmentsinduce a significant increase in cell shape fractal dimension and“may unwittingly contribute to tumour morphologic instability andconsequent tissue invasion” (Cristini et al., 2005). Indeed, whereasmild chemotherapeutic regimens did not modify tumour frac-tal dimension, intensive cytotoxic chemotherapy increases fractaland NBE values, and enhances tissue disorder and chaotic tumourbehaviour, eventually promoting selection of more malignant phe-notypes (Ferreira et al., 2003).

Shape “normalization” was accompanied by a meaningfulmetabolomic shift in cancer cells exposed to EMF. Glycolyticfluxes were reduced, concomitantly with a decrease in lactate,glutathione, glutamine and other compounds. Namely for EMF-treated MDA-MB-231 cells, when cell proliferation slows-downand cell shape reaches a new stable configuration with low valuesof Bending Energy, cancer cells undergo a metabolomic rever-sion, characterized by the inhibition of both lipidogenesis andde novo nucleotide synthesis. It is well known that cancer cellsshare an increased glutamine use (McKeehan, 1982). However,in EMF-treated cells, glutaminolysis increase does not correlatewith a simultaneous increase in lactate, nor to an increase in fatty

acid synthesis. Keeping in mind that proliferation is slightly inhib-ited in EMF-treated cells, these results outline that glutaminolysisincreased rates can not be explained by proliferative needs: thisimplies the treated cells devote a higher portion of chemical energy

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058 F. D’Anselmi et al. / The International Journal o

o a different anabolic work. Indeed, excess of glutamine is prefer-ntially transformed into proteins and does not appear as lactate.his interpretation is given a proof of concept by the observationf the development of differentiating pathways (the increase of-cadherin and �-casein protein biosynthesis) and differentiatedtructures (ducts and hollow acini, mainly in MCF-7 cells) in treatedells at later times (96–168 h) (data not shown).

Our results should be related to a previously published reportMeadows et al., 2008), in which glucose uptake was measured inuman normal mammary epithelial 48R cells and MCF-7 cells. Gly-olytic fluxes have been correlated with biomass, cell morphologynd medium exposed surface, demonstrating that both morpho-ogical traits and medium exposed surface were the main drivingorce of glucose uptake in cells.

It is worth noting that shape modification leads to a less-issipative architecture, as it is showed by the significant reduction

n NBE values. So far, fractal measures enable to emphasize the linketween cell morphology and thermodynamics. According to therigogine–Wiame theory of development (Prigogine and Wiame,946), during carcinogenesis, a living system constitutively devi-tes from a steady state trajectory. This deviation is accompanied byn increase in the system dissipation function (� ) at the expense ofoupled processes in other parts of the organism, where � = q0 + qglmeaning, respectively, q0 oxygen consumption and qgl glycolysisntensity). The metabolomic data collected in this study displayed aignificant reduction in glycolysis activity, with unchanged valuesf oxygen consumption. Therefore in EMF condition � decreasedignificantly, until a stable state was attained, characterized by ainimum in the rate of energy dissipation (principle of minimum

nergy dissipation) (Zotin and Zotin, 1997). Thereby, the reductionn both dissipative function and NBE levels, suggests cell systemchieves, after shape transition, a new thermodynamic stable state.his behaviour is exactly the opposite of what happens in grow-ng cancer cells and experimentally observed in tumour controlamples.

Pursuing this perspective, the “Warburg effect” (i.e. high gly-olytic fluxes and high glucose anaerobic metabolism) should note longer considered as a “linear” consequence of gene deregulationr an adaptation to hypoxia, but a “system property” of cancer cells,nfluenced by both internal and microenvironmental constraintsCascante et al., 2002). Cell energy metabolism changes along dif-erent cell cycle phases, being more “dissipative” during woundealing, fast growth (specifically during embryonic development),nd cancer progression. Keeping in mind that thermodynamicissipative function is correlated with both glucose metabolismnd cell shape, we suggest that the latter could interfere withetabolic pathways. Cell shape is known to influence several gene-

egulatory pathways through architectural rearrangement, therebyepresenting a relevant independent factor controlling tissue fatend cell commitment to quiescence, apoptosis or proliferation. Ourata demonstrated that the morphogenetic field is able in induc-

ng dramatic changes in breast cancer cell shape expressed byractal measures. Consequently, meaningful changes in “tumour

etabolome” were observed by NMR-spectroscopy and PCA fluxnalysis. These data indicate cell shape “normalization” is asso-iated with a reversion in tumour metabolic phenotype. Further

etabolomic studies are clearly warranted to better correlateetabolism and shape morphology, and to handle these two set

f parameters into an integrated dynamical description of tumourell biology.

hemistry & Cell Biology 43 (2011) 1052–1058

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