Surprisal Analysis Characterizes the Free Energy Time Course of Cancer Cells Undergoing Epithelial-To- Mesenchymal Transition

Surprisal analysis characterizes the free energy time course of cancer cells undergoing epithelial-to- mesenchymal transition

Sohila Zadrana, Rameshkumar Arumugamb, Harvey Herschmanc, Michael E. Phelpsc, and R. D. Levineb,c,d,1

aInstitute for Molecular Medicine, David Geffen School of Medicine, University of California, Los Angeles, CA 90095; bThe Fritz Haber Research Center, The Hebrew University, Jerusalem 91904, Israel; and cDepartment of Molecular and Medical Pharmacology, David Geffen School of Medicine, and dDepartment of Chemistry and Biochemistry, University of California, Los Angeles, CA 90095

Contributed by R. D. Levine, August 3, 2014 (sent for review May 19, 2014)

The epithelial-to-mesenchymal transition (EMT) initiates the invasive consider constraints responsible for deviations from this state. In and metastatic behavior of many epithelial cancers. Mechanisms principle, one should identify the stable state from first biophysical underlying EMT are not fully known. Surprisal analysis of mRNA time principles. However, for systems of such molecular complexity, we course data from lung and pancreatic cancer cells stimulated to must apply computational abilities beyond what is currently undergo TGF-β1–induced EMT identifies two phenotypes. Examina- available. We therefore identify the stable state by using available tion of the time course for these phenotypes reveals that EMT re- experimental data. We then have a baseline for our second task, examining deviations from the stable state. programming is a multistep process characterized by initiation, “ ” maturation, and stabilization stages that correlate with changes in We use the term state in the chemistry and physics sense, cell metabolism. Surprisal analysis characterizes the free energy time meaning everything we need to specify to predict how the system course of the expression levels throughout the transition in terms of will change in response to a small modulation of its circum- stances. By “state” here, we mean “thermodynamic state.” A0.1-M, two state variables. The landscape of the free energy changes during 100-mL aqueous saline solution at room temperature and pres- the EMT for the lung cancer cells shows a stable intermediate state. sure at rest is in a thermodynamic state. We can predict its os- Existing data suggest this is the previously proposed maturation motic pressure, but we cannot account for the Brownian motion stage. Using a single-cell ATP assay, we demonstrate that the TGF- of individual ions; a mechanical state is needed to specify the β – 1 induced EMT for lung cancer cells, particularly during the matura- position and velocity of all the ions and water molecules. A tion stage, coincides with a metabolic shift resulting in increased cy- thermodynamic description is much more parsimonious; many tosolic ATP levels. Surprisal analysis also characterizes the absolute different mechanical states will give rise to essentially the same expression levels of the mRNAs and thereby examines the homeosta- value for osmotic pressure. Here we describe the change in time of sis of the transcription system during EMT. the thermodynamic state (state) of the transcription system during an EMT. We identify variables, analogous to the osmotic pressure, free energy landscape | transcriptions expression profile | that characterize the transcription system state. The change in maximal entropy | cellular thermodynamics | microarray value of these variables describes the transition. By analogy to thermodynamics, we refer to these as the state variables. he epithelial-to-mesenchymal transition (EMT) is a cellular We suggest that quantification of transcripts according to their transition critical for several normal biological events, in- free energy (rather than fold changes) has distinct value. Pre- T viously we considered the stability of the minimal free energy cluding embryonic development and wound healing. EMT has state during cellular processes (13). This thermodynamic con- been investigated most exhaustively in cancer progression. An sideration enables an approach that may be used to make state- evoked EMT in epithelial cancer cells induces gene expression ments about alternative cellular states and predictions about how changes that result in loss of adhesive properties and acquisition targeted perturbations to a biological system might influence that of mesenchymal cell traits associated with tumor progression and system (17, 18). metastasis, e.g., increased cellular motility, migration, and invasion (1, 2). Significance Cultured epithelial tumor cells may be induced by several alternative stimuli to undergo an EMT, leading to acquisition of mesenchymal properties (3). Transforming growth factors (TGFs) Epithelial-to-mesenchymal transition (EMT) has been investi- are the most widely used EMT inducers. Microarray expression gated extensively in cancer progression. Tumor cells induced to profiling enables identification of TGF-β1 EMT-induced molec- undergo an EMT acquire mesenchymal, invasive properties. We ular alterations and mechanisms (4). apply methods developed in chemical reaction dynamics to Gene expression transcriptional profiling is a major tool in characterize the changing transcriptional profile during an EMT analyzing induced changes in cells, yet interpretation of micro- and trace the temporal unfolding of the transition on a free array experiments is faced with challenges (5–7). Here we use an energy landscape. The analysis identifies three EMT stages, in- alternative approach, surprisal analysis (8), to better understand, cluding passage through an intermediate, low-energy matura- characterize, and represent gene expression differences critical tion state. The uphill energy requirements during the ascent

to the EMT (4, 9). past maturation correlate with an increase in ATP production. A CHEMISTRY Surprisal analysis assumes that if a system can decrease its free stable cell machinery whose performance remains unperturbed energy, it will do so spontaneously unless constrained. If a system underlies the EMT. does not attain its minimal free energy state, surprisal analysis seeks to recognize the constraints that prevent a reduction in en- Author contributions: H.H., M.E.P., and R.D.L. designed research; S.Z., R.A., and R.D.L. ergy; surprisal analysis identifies the main constraints on a system performed research; S.Z. contributed new reagents/analytic tools; S.Z., R.A., H.H., M.E.P., that has the thermodynamic potential to change spontaneously but and R.D.L. analyzed data; and S.Z., H.H., M.E.P., and R.D.L. wrote the paper. is restrained from doing so (10). The authors declare no conflict of interest. As in physics and chemistry (11, 12), surprisal analysis in biology 1To whom correspondence should be addressed. Email: [email protected]. – “ ” (13 16) argues that the stable state of the system, the state of This article contains supporting information online at www.pnas.org/lookup/suppl/doi:10. SYSTEMS BIOLOGY minimal free energy, must first be specified. Only then can we 1073/pnas.1414714111/-/DCSupplemental.

www.pnas.org/cgi/doi/10.1073/pnas.1414714111 PNAS | September 9, 2014 | vol. 111 | no. 36 | 13235–13240 Downloaded by guest on September 23, 2021 o Constraints for biological processes are not static; they may Xi is the expression level of transcript i in the balance state. λαðtÞ change in response to cellular and environmental perturbations is the state variable for phenotype α at time t. mRNA expression (19). Stipulating the premise of minimal free energy subject to level during EMT is well described by the two state variables, constraints, a change to the constraints will be followed by a α = 1,2. α = 1 is the leading term of the deviation. Giα is the time- move of the system toward a new constrained state. At any given independent contribution of gene i to the phenotype α remaining time, a state of minimal free energy is unique and stable—stable the same throughout the transition. in the technical sense that at that time, all other states that are An implication of the analysis is a correlation between the consistent with the constraints will have a higher free energy. transcript’s thermodynamic stability and its expression level. This Surprisal analysis ascertains whether, at a given time, a state can correlation is most reliable when deviations from the balanced o o be described as one of minimal free energy subject to constraint. state are not large. Then, XiðtÞ ≈ Xi . Xi is the expression level at If the description is appropriate, the state at that time is ther- thermodynamic equilibrium—the balance state of minimal free modynamically stable. We also want to characterize a state(s) energy. Therefore, the more stable the gene (i.e., the lower its that is kinetically stable. This presents a stronger requirement; free energy) the higher its expression level in the balance state. states at nearby time points must have a higher free energy. For The surprisal itself at each point in time is defined as the (log- example, we show below that the (starting) epithelial state is arithmic) deviation from the balance state. stable both thermodynamically and kinetically. When transcription system constraints change, the system may The Balance State During EMT for TGF-β1–Treated A549 Human Lung move to a new constrained state, one with a different free energy Cancer Cells. Surprisal analysis was applied to the gene microarray value. If the free energy increases, the constraints must do work expression profiles collected, in triplicates, at time points 0, 0.5, on the transcription system; the process will not follow unless 1, 2, 4, 8, 16, 24, and 72 h during an EMT-initiated TGF-β1 work is done to induce the change. With a constraint change, free stimulation of A549 cells (22). The first step in EMT surprisal energy also may decrease. The practical difference is that when analysis is to identify a balance state common to epithelial and free energy declines, the state change may occur spontaneously and mesenchymal states and throughout the transition. This balance the state is not kinetically stable. state is the reference point at which all cellular processes are At the molecular level, state changes are reflected in differ- assumed balanced in forward and reverse directions and for ential transcript abundance. Surprisal analysis characterizes bi- which there is no net change in the surprisal system. It therefore ological transitions by considering independent phenotypes; each is of central concern to demonstrate by analysis of expression phenotype is a set of characteristic genes as well as a separate data that the balance state is invariant throughout the transition. and independent constraint on the state. Each phenotype is a The results for the A549 cell balance state at different times constraint that prevents the system from spontaneously decreasing after TGF-β1 treatment are shown in Fig. 1. The expression level in free energy. of transcript i in the balance state at time t after treatment is o The state change with time is described by the weight of the Xi ðtÞ = expð−λ0ðtÞ Gi0Þ. We expect that the level in the balanced different phenotypes, where each weight changes with time in its state will not depend on the time point, but this is not assumed in own way. These time-dependent weights characterize the state; our procedure; analyses at different points in time validate this they are the state variables. Surprisal analysis of transcriptome data point of principle. λ0ðtÞGi0 is the free energy (in thermal energy determines the time dependence of the state variables (20). Gen- units, RT) of transcript i. Consequently, transcripts with the erally few in number, these variables identify the state at any time. lowest (i.e., most negative) free energy are those with the highest To provide a molecular-level interpretation, surprisal analysis levels in the balance state. mRNAs identified with the lowest free shows how individual transcript levels are related to phenotypes: energy, listed in Table S1, are those most correlated with cellular each phenotype is a set of transcripts. For any given phenotype, networks that regulate and maintain cellular homeostasis ma- each transcript has a weight. This weight is time independent. chinery. Our analysis illustrates the robustness of cellular ho- Transcripts that constitute a phenotype all change with time in meostasis during EMT (13, 16). the same way; this common change is described by the state A graphical representation of free energy time variations for the variable for that phenotype. The change in time for the expres- most stable genes is shown in Fig. 1A. The heat map shows sion levels of individual transcripts is represented as the sum over (negative) free energy for the 20 transcripts that have the highest the contributions from the changes of the different phenotypes. weight in the balance state vs. time. The most stable transcripts are at the top, and they hardly vary with time. Fig. 1B shows the state Results and Discussion variable of the stable state, λ0ðtÞ , vs. time. λ0ðtÞ variation with Surprisal analysis was applied as described previously (13–15, time is less than 0.1%, less than the SD of the state variable. Fig. 21). The input expression level of mRNA i at time t, XiðtÞ,is 1B establishes the balance state time invariance during the EMT. expressed as a sum of terms; here two, representing deviations Of course, biochemical changes during an EMT may change from the balance state. Each deviation term (i.e., phenotype) is the transcript free energies. To fully validate balance state sta- a product of a time-dependent weight of the phenotype; the state bility, we therefore must show that EMT-induced changes are variable and the time-independent contribution of the transcript small compared with the transcript free energies of the stable to that phenotype: state. Fig. 1C shows the individual transcript free energies (in

state variable the contribution state variable the contribution of phenotype 1 of transcript i of phenotype 2 of transcript i at time t to phenotype 1 at time t to phenotype 2 zffl}|ffl{ z}|{ zffl}|ffl{ z}|{ o ln XiðtÞ = ln Xi − λ1ðtÞ Gi1 + λ2ðtÞ Gi2 : |fflfflfflffl{zfflfflfflffl} |fflffl{zfflffl} |fflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflffl{zfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflffl} measured balanced state the two deviation terms from the balanced state expression level expression level of transcript i at time t of transcript i of transcript i at time t

13236 | www.pnas.org/cgi/doi/10.1073/pnas.1414714111 Zadran et al. Downloaded by guest on September 23, 2021 during the first few hours following TGF-β1treatmentinA549 cells are down-regulated during the subsequent time, ending in the mesenchymal state at ∼24 h. These data demonstrate that the first constraint provides a signature that distinguishes the two EMT end states; the phenotype that corresponds to this largest deviation distinguishes the epithelial and mesenchymal cellular states. Be- cause of this first constraint, the mesenchymal free energy state is higher than for the epithelial state. Note that most transcripts have a negligible contribution to this phenotype (Fig. S1).

On Transcript Regulation. The state variables determine the di- rection of deviation of the process from the balance state. When the state variables change sign, transcripts that were overex- pressed with reference to the balance state become underex- pressed, and vice versa. To avoid unambiguity, we rank the transcripts according to their time-independent weights, the Giαs (see phenotypes α = 0;1;2inSI Appendix). From a biological perspective, surprisal analysis weighs the impact of individual genes differently from the ranking of genes by fold change in expression. For example, the down-regulated anterior gradient-2 (AGR2) gene, a transcript that decreases in abundance, tops the EMT-specific surprisal A549-cell gene signature list (Table S2), whereas in fold-change microarray analysis, AGR2 levels do not vary significantly. However, AGR2 has Fig. 1. Stability of the balanced state of the mRNA expression in A549 cells during TGF-β1–induced EMT. (A) The heat map shows as the ordinate the logarithm of expression levels for the 20 most highly expressed transcripts of the EMT balance state. Each column is a transcript free energy at that time. λ ð Þ − oð Þ = λ ð Þ (B) 0 t vs. time, where ln Xi t 0 t Gi0. Fig. 1B is to be compared with Figs. 2B and 3B, which show variations of λ1ðtÞ and λ2ðtÞ with time. (C) Free energy of all of the transcripts in the balance state. The ordinate is the change in the free energy of that transcript during the EMT. (D) Stability of all transcripts of the balance state, and the free energy changes of all transcripts during EMT. All transcripts in the balance state are shown in red; free energy changes resulting from the EMT are shown in blue. The ordinate is the number of transcripts per bin. The width of the energy bins is 10 units of thermal energy, RT.

thermal energy units) in the balance state (abscissa) vs. the change in the free energy of that transcript (ordinate) during the EMT. No transcript increases or decreases its free energy by more than 10 units; all balance-state transcripts are stable by more than −20 units. Consequently, for no transcript does its free energy become positive (i.e., fully destabilized) during the EMT. This conclusion is central; we show it in a different graphical representation in Fig. 1D, a free energy histogram. Transcript free energy distributions in the stable state are in red. Blue bins show transcript free energy changes due to the EMT. The red bars reiterate the point that there are no transcripts in the balance state whose free energy change becomes positive. About as many genes are stabilized (have a decrease in their free energy) further during EMT as are destabilized, illustrated by the two red bars of roughly the same height.

EMT-Specific Differential Gene Expression in A549 Cells During the EMT. Each constraint identifies a transcript expression pattern unique to net ongoing biological processes in the system: a phenotype. The phenotypes are ranked such that the largest deviation from the balance state is α = 1. This phenotype has the largest value for its state variable, λ1ðtÞ; it is most responsible for the changes in free energy during the process. CHEMISTRY Free energy changes during the EMT for genes whose free energies undergo the greatest deviation (both up and down) from their balance state values are shown in Fig. 2A. Genes that Fig. 2. The EMT-specific gene signature distinguishes epithelial and mes- dominate this phenotype for the 549-cell EMT are listed in Table enchymal states in A549 cells. (A) Logarithm of expression levels for the 20 S2 in order of their contribution to free energy change. Some are uppermost and 20 most down-regulated transcripts of the first phenotype, highly up-regulated, and some are very down-regulated. These α = 1. (B) The first state variable, λ1ðtÞ, during the EMT. The λ1ðtÞ sign change genes and their weights are time independent but are associated identifies the time (∼8 h) when the transition from the epithelial to the with the first time-dependent state variable, λ1ðtÞ (Fig. 2B); all mesenchymal state occurs. This sign change is correlated with the minimum SYSTEMS BIOLOGY these genes vary with time in the same way. Genes up-regulated in free energy in A.

Zadran et al. PNAS | September 9, 2014 | vol. 111 | no. 36 | 13237 Downloaded by guest on September 23, 2021 balance state (Fig. 3A). The time dependence of the second state variable, λ2ðtÞ, exhibits a change in sign (Fig. 3B) that coincides with the epithelial state transitioning (at about 2 h) to what we call, following ref. 9, a “maturation state.” This state is a state of low free energy. The maturation state is followed by a second change (at about 24 h) out of the maturation stage and into the mesenchymal state (Fig. 3B and Fig. S2). Transition from the maturation stage is costly in free energy and might possibly offer a target for inhibiting the EMT. This second phenotype identified by surprisal analysis reveals that EMT cellular reprogramming is a multistep process that includes an intermediate low free energy state. The free energy changes reflected in the sign changes for the state variable suggest that this multistep process is correlated with changes in cell metabolism as the external source of energy. The corresponding gene signature (Table S3) for this second phenotype indeed includes an increased contribution of metabolic genes.

The EMT Free Energy Landscape. At any time t during an EMT, the work done to bring the system to its current state can be com- puted from the surprisal analysis (10, 19). Two important constraints for the A549 TGF-β1–induced EMT have been identified; therefore, there are two variables in a plot of the free energy change during the EMT. We chose as the two variables the state variables λ1ðtÞ and λ2ðtÞ (Fig. 4). After exiting the epithelial state, the system dissipates energy as it moves to a lower minimal energy, the maturation state. However, work must be done to drive the system from the maturation state into the mesenchymal state. Surprisal analysis shows that this EMT proceeds through a low- energy, intermediate maturation state. It is not the case that a trough invariably must be traversed between two states; a path also might exist in which energy is first required and subsequently released. Careful examination of Fig. 4 shows that here, too, there are barriers to cross. Transition from the epithelial state is over a small barrier, showing that the epithelial state is stable not only at a given time but also for small changes in time. Similarly, the transition into the mesenchymal state also crosses a small barrier. In summary, the EMT involves three states—the epithelial, maturation, and mesenchymal states—that not only are thermodynamically

Fig. 3. Surprisal analysis reveals an intermediate maturation stage during TGF-β1–induced EMT in A549 cells. (A) Free energy for the 20 uppermost and 20 most down-regulated transcripts of the second phenotype, α = 2. Be- tween ∼2 and 24 h, the free energy switches signs. We identify this interval with a maturation stage (16). (B) During this maturation stage, the second state variable, λ2ðtÞ, has a sign that differs from that before or after entering the maturation stage; the first sign change coincides with the epithelial state entering the maturation stage, the second sign change coincides with the exit from the maturation stage into the mesenchymal state.

the greatest free energy contribution to the first deviation from the balance state. Note also that AGR2 has been implicated as a major player in other studies when observed in proteomic data (23). Near the bottom of the list of phenotype 1 is an up-regulated gene by fold-increase analysis, serine protease inhibitor 2/ protease-nexin 1 (SERPINE2) (Table S2, opposite column). AGR2 and the plasminogen activator SERPINE1 illustrate the resolving power of surprisal analysis compared with purely statistical methods. SERPINE2 is known to greatly enhance the local invasiveness of pancreatic tumors, accompanied by a massive increase in ECM production in the invasive tumors (24). The list of genes shown in Table S2 is representative of phenotype 1, the phenotype that distinguishes the epithelial and Fig. 4. The free energy landscape for the TGF-β1–induced EMT in A549 cells. mesenchymal states. The state variable sign change of this phe- At every point in time, the energetic state of the cell undergoing the EMT notype can be seen in Fig. 2B. may be characterized by two time-dependent state variables, λ1ðtÞ and λ ðtÞ. The path of the transition is along the orange arrows and proceeds β – 2 TGF- 1 Stimulated A549-Cell EMT Proceeds Through a Maturation through a stable intermediate, the maturation point. Energy is released in Stage. Surprisal analysis of TGF-β1 EMT-induced changes in transition from the epithelial state to the maturation point and consumed A549 mRNA levels shows a second leading deviation from the from the maturation point to the higher-in-energy mesenchymal state.

13238 | www.pnas.org/cgi/doi/10.1073/pnas.1414714111 Zadran et al. Downloaded by guest on September 23, 2021 stable but also are kinetically stable, that is, stable against small changes in time. As in traditional chemical kinetics, there is a barrier between any two connected, kinetically stable states.

Error Analysis: Additional Constraints. The TGF-β1–induced EMT A549 microarray study (www.ncbi.nlm.nih.gov/sites/GDSbrowser? acc=GDS3710) was conducted in triplicate, permitting evaluation of error bars on the state variables (21). The balance state is robust; error bars are negligible (Fig. 1B). Error bars on the potential λ1ðtÞ of the first constraint also are small (Fig. 2B). Error bars on the potential λ2ðtÞ of the second constraint are more significant (Fig. 3B). Surprisal analysis for this experiment determines a third constraint and a corresponding state variable λ3ðtÞ (Fig. S3). However, error bars for δ λ3ðtÞ are large; the error bars are com- parable to the value of the variable itself, i.e., δ λ3ðtÞ ≈ λ3ðtÞ at all time points. Consequently, the value zero is within the range of possibility for λ3ðtÞ. The state variable measures the importance of the constraint as judged by the search for a minimal free energy state. When δ λ3ðtÞ ≈ λ3ðtÞ, we arrive at the same state, regardless of whether the third constraint is included in the search for this minimum. In such cases, the imposition of a third (or any higher) constraint is not supported.

EMT Time Scales. The time course of the TGF-β1–elicited A549- cell EMT is shown through the time dependence of the state variables λ1ðtÞ and λ2ðtÞ (Figs. 2B and 3B). The TGF-β1–induced progression from the epithelial to the mesenchymal state lasts ∼24 h. At ∼8 h, the switch from the epithelial to the mesenchymal state occurs [shown by the sign change for λ1ðtÞ, Fig. 2B and Fig. S1]. Somewhat earlier, at ∼4 h, the maturation phase begins [as shown in the first sign change for λ2ðtÞ (Fig. 3B)]. The energy release along the transition to maturation occurs rapidly and quite early; in contrast, the energy buildup from the maturation point to the mesenchymal end state is far slower [λ2ðtÞ, Fig. 3B], extending beyond the 24-h time point. The energy buildup is complete by 72 h; without mRNA expression level data between 24–72 h, it is not possible to conclude when the transition to the mesenchymal state is finished.

An Increase in Cytosolic ATP Levels Correlates with the Maturation Stage of the TGF-β1–Induced A549-Cell EMT. TGF-β1A549treat- ment induces mesenchymal-like protrusions and filopodia at 4 and 8h(Fig.5A). The TGF-β1 maturation stage of this EMT begins when the second state variable, λ2ðtÞ, is positive (Fig. 3B). A single- cell enhanced-acceptor fluorescence (EAF)-based ATP biosensor (25, 26) demonstrates that individual cell ATP levels in the mesenchymal state appear higher than in cells in the epithelial state (Fig. 5B), consistent with the idea that the energy required to bring the cells from the maturation point to the mesenchymal state is greater than that for the epithelial state (Fig. 3B and Fig. S1). The data (Figs. 2–4 and Fig. S1) suggest that the minimum free energy change in the TGF-β1–driven EMT, the maturation point, occurs ∼8 h after TGF-β1 application, and that work input is required to drive an energetically dependent transition from the maturation point to the mesenchymal state. By using both single-cell (Fig. 5C) and bulk-cell ATP measurements (Fig. 5D), increased cytosolic ATP levels are observed at 8 h. Transition Fig. 5. Cytosolic ATP levels during the EMT. (A) EMT is induced in A549 cells from the maturation state to the mesenchymal state occurs at with TGF-β1. At the times indicated, cells were collected for analysis. (B)At ∼20 h (Fig. 3B); elevated ATP levels are observed at 20 h (Fig. 5 the times shown, the cells were imaged by phase contrast microscopy (20×). C and D), when cells have left the maturation state and entered Cellular fasciculation is present within 4 h; an increase in filopodia migratory

the mesenchymal state. The data suggest that ATP levels reflect, CHEMISTRY protrusions occurs in 8 h. (C) EAF-based single-cell ATP biosensor analysis and possibly may drive, the maturation process. of A549 cells following TGF-β1treatment.(Upper) Donor fluorescence was monitored (GFP, pseudocolored red for better contrast). (Lower)Fluorescence The Balance State During EMT for TGF-β1–Treated Pancreatic Cancer images are overlaid on bright-field images. n = 4 across three independent Cells. Surprisal analysis also was applied to microarray data for cultures. (D) Quantification of EAF-based ATP biosensor donor signal. GFP β fluorescence was monitored in individual cells at the times shown (n = 5, three cultured Panc-1 epithelial cells 48 h after TGF- 1 treatment, cells per field); data are means ± SEM of five independent experiments. *P < when the cells attained a mesenchymal state (27). The balance 0.05, **P < 0.01, using Student t test. (E) Whole-cell ATP levels were moni- state of Panc-1 cells, like that of A549 cells, was unaffected by tored with a modified luciferin/luciferase-based ATP assay at the time points TGF-β1 treatment (Fig. S2A). Like A549 cells, ribosomal genes shown (n = 5, three cells per field); data are means ± SEM of five independent and structural proteins for Panc-1 cells have the greatest negative SYSTEMS BIOLOGY experiments. *P < 0.05, **P < 0.01, using Student t test. free energy (Table S1). When free energy changes for the most

Zadran et al. PNAS | September 9, 2014 | vol. 111 | no. 36 | 13239 Downloaded by guest on September 23, 2021 up- and down-regulated genes in the epithelial and mesenchymal triplicate. Samples were assayed using Affymetrix HG_U133_plus_2 arrays with states, relative to the balance state, are evaluated, a definitive 54,675 probe sets, following standard techniques. “switch” is observed (Fig. S4B). EMT in Pancreatic Adenocarcinoma Cells. Expression profiles are publically Gene Ontogeny Analysis of the Two Major Phenotypes for A549 Cells available at www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE23952 by Zadran During the EMT. SI Appendix et al. (26), who treated human Panc-1 pancreatic adenocarcinoma cells with In , we outline and apply a synergistic β procedure for analysis of microarray data. Gene ontogeny (GO) 5ng/mLTGF- for 0 and 48 h to induce EMT. The experiment was performed in analysis (28) is applied separately to each of the phenotypes that triplicate. Samples were assayed using Affymetrix HG_U133_plus_2 arrays. surprisal analysis identifies as relevant for the transition and to the gene list of the balance state (Figs. S5–S7). GO analysis of the ATP Imaging and Measurement. A549 cells, from D. Shackelford, UCLA, Los Angeles CA, were cultured and maintained as adherent monolayers in second constraint suggests that the critical cellular functions that DMEM (Cellgro) supplemented with 5% (vol/vol) FBS (Omega Scientific) and dominate this phenotypic state are functions involved in receptor 100 U/mL penicillin and streptomycin (Gibco) in 5% (vol/vol) CO at 37 °C. binding mechanisms (Fig. S7). We suggest that this phenotype 2 integrates information from the cell environment, through recep- Single-Cell ATP Imaging. An EAF-based single-cell ATP biosensor was gener- tors and subsequent ligand-dependent signaling pathways, into its ated as described previously (24, 25). A549 cells were plated on laminin- cellular decision-making processes. It is expected that during an coated glass-bottom dishes and transfected with FuGENE6 transfection re- EMT, the ability of cells to assimilate environmental cues is critical agent (Roche Diagnostics) and the EAF-based ATP biosensor. Cells were as they take on a mesenchymal-like state and acquire migratory subjected to confocal imaging 2–3 d after transfection. Wide-field obser- properties (29, 30). vations used a Nikon TE2000-PFS inverted microscope (Nikon Instruments); In conclusion, we applied surprisal analysis to characterize and cells were maintained at 37 °C with a continuous supply of a 95% air and 5% elucidate gene expression behavior responsible for an EMT. We carbon dioxide mixture, by using a stage-top incubator. Untreated A549 cells identified an intermediate state in the process and observed expressing the EAF-based ATP biosensor initially were monitored via con- a correlation of metabolic shifts with progress through the EMT. focal microscopy for donor (GFP) and acceptor (YFP) emission. After TGF-β1 Surprisal analysis can monitor cell phenotype-specific thermo- (5 ng/mL) treatment, EAF behavior in single cells was monitored over 72 h. dynamic behavior, trace the change of state with time on a free The experiment was repeated in triplicate across three independent cul- energy landscape, and provide an increased understanding of cell tures. Image analysis used ImageJ. transitions and cellular state-dependent processes. Surprisal analysis also begins to provide fundamental insights into the mecha- Luciferase-Based ATP Analysis. Cellular ATP also was monitored with a mod- nisms that regulate the unbalanced processes—by identification of ified luciferase/luciferin-based assay (24). “process-specifying” genes. Surprisal analysis thus suggests that the GO Enrichment Analysis. GO analysis was conducted as described previously state variables are potential drug targets. − (ref. 27 and http://cbl-gorilla.cs.technion.ac.il). Threshold P value was 10 11. Experimental Procedures ACKNOWLEDGMENTS. We thank the Institute of Molecular Medicine and EMT in Lung Adenocarcinoma Cells. Expression profiles are available publically the David Geffen School of Medicine at the University of California, Los = at www.ncbi.nlm.nih.gov/sites/GDSbrowser?acc GDS3710 and ref. 22. Human Angeles, for support to S.Z. This work was supported by a Prostate Cancer A549 lung adenocarcinoma cells were treated with 5 ng/mL TGF-β for 0, 0.5, 1, Foundation Creativity Award to R.D.L. and European Commission FP7 Future 2, 4, 8, 16, 24, and 72 h to induce EMT. The experiment was performed in and Emerging Technologies–Open Project BAMBI 618024 (to R.A. and R.D.L.).

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