Miles A.
Miller
a,
Layla
Barkal
a,
Karen
Jeng
a,
Andreas
Herrlich
b,
Marcia
Moss
c,
Linda G.
Griffith
a and
Douglas A.
Lauffenburger
*a
aDepartment of Biological Engineering, Massachusetts Institute of Technology, Cambridge, MA 02139. E-mail: lauffen@mit.edu
bWhitehead Institute for Biomedical Research, Cambridge, MA 02139
cBiozyme, Inc., USA
First published on 23rd December 2010
Matrix metalloproteinases (MMPs) and A Disintegrin and Metalloproteinases (ADAMs) are two related protease families that play key roles in matrix remodeling and growth factor ligand shedding. Directly ascertaining the proteolytic activities of particular MMPs and ADAMs in physiological environments in a non-invasive, real-time, multiplex manner remains a challenge. This work describes Proteolytic Activity Matrix Analysis (PrAMA), an integrated experimental measurement and mathematical analysis framework for simultaneously determining the activities of particular enzymes in complex mixtures of MMPs and ADAMs. The PrAMA method interprets dynamic signals from panels of moderately specific FRET-based polypeptide protease substrates to deduce a profile of specific MMP and ADAM proteolytic activities. Deconvolution of signals from complex mixtures of proteases is accomplished using prior data on individual MMP/ADAM cleavage signatures for the substrate panel measured with purified enzymes. We first validate PrAMA inference using a compendium of roughly 4000 measurements involving known mixtures of purified enzymes and substrates, and then demonstrate application to the live-cell response of wildtype, ADAM10−/−, and ADAM17−/− fibroblasts to phorbol ester and ionomycin stimulation. Results indicate PrAMA can distinguish closely related enzymes from each other with high accuracy, even in the presence of unknown background proteolytic activity. PrAMA offers a valuable tool for applications ranging from live-cellin vitro assays to high-throughput inhibitor screening with complex enzyme mixtures. Moreover, our approach may extend to other families of proteases, such as caspases and cathepsins, that also can lack highly-specific substrates.
Insight, innovation, integrationMetalloproteinases (MPs) impact diverse biological processes, and extensive post-translational regulations coupled with a complex repertoire of MP substrate preferences have hindered understanding of MP biology. Although methods exist to study MP activity, they typically face a trade-off between invasiveness and specificity. Here, we present a combined mathematical and experimental framework to measure real-time MP activity non-invasively, demonstrating capability for discerning specific MP activities from complex mixtures and biological samples. We develop and validate the approach by analyzing an extensive compendium of measurements using purified MPs, and apply it to several well-studied cell lines. This approach can adapt to a broad range of applications, and we show how one can a priori design studies of this type to meet particular objectives. |
Closely related to MMPs, ADAM (A Disintegrin and Metalloproteinase) enzymes are metalloproteinases (MPs) within the Metzincin family that are mostly bound at the cell surface.16 At least 13 ADAMs existing in humans have intact metalloproteinase domains and proteolytic activity.14 ADAMs mediate various cellular behaviors including migration, adhesion,17 proliferation,15 and apoptosis.16,18 Similar to MMPs, ADAM family enzymes are found throughout the body and support diverse physiological processes such as development15 and angiogenesis.5,16 Likewise, ADAMs can become dysregulated in a variety of diseases and play roles in pathologies including cancer,1,18 inflammatory bowel disease, and asthma.16 Current research suggests that ADAMs have a narrower repertoire of substrates compared to MMPs, and principally function to shed the ectodomain of surface-bound proteins such as growth factor ligands, growth factor receptors, cell adhesion molecules, and cytokine receptors.14,15
Three key properties of MP biology have created the need for methods that directly observe protease activity in a specific, non-invasive, real-time, and multiplex manner. First, the extensive amount of post-translational modification and regulatory mechanisms controlling MP proteolytic activity make direct activity measurements a valuable and complementary addition to common methods of assessing protein function, such as western blotting, immunohistochemistry, and genetic manipulation.19–22 Second, the plethora of endogenous substrates cleaved by certain MPs, the context dependency of endogenous substrate cleavage, and the overlapping endogenous substrate specificity of closely related MPs make it difficult to quantitatively match the contributions of specific proteases to global observations of endogenous substrate degradation.11,19,23 Quantitative determination of selected protease activities would complement measurements that focus on endogenous substrate cleavage, thereby facilitating attempts to match particular proteolytic activities to patterns of substrate degradation. Third, cyclical feedback interactions and compensatory mechanisms among closely related MPs can severely complicate the interpretation of protease network interactions.15,24,25 Non-invasive and multiplexed measurement of MP activity would allow for the assessment of protease network interactions without artificially biasing the underlying network structure.
While many useful methodologies currently exist to study MPs, unfortunately none simultaneously allow for direct, non-invasive, multiplex, real-time measurements of specific protease activity. In general, existing methods such as zymography, activity based probes, and mass-spectrometry based methods all must choose between invasiveness, specificity, and throughput.10,26–30 Synthetic polypeptide protease substrates have been extensively developed to directly assess MP activity in a non-invasive and real-time manner.20,31–34 These substrates typically consist of a fluorescence resonance energy transfer (FRET) donor and quencher fluorophore that are separated by a 3–10 amino acid linker containing a protease cleavage motif. Upon cleavage of the polypeptide linker, the donor fluorophore separates from the quencher and fluorescence increases. Protease activity dynamics can then be tracked by observing the change in fluorescence over time. Similarly to many endogenous MP substrates, MP FRET-substrates are generally cleaved by multiple closely related proteases.32,35,36 Several strategies, including positional scanning of synthetic combinatorial libraries35,37 and directed evolution using phage display38 have attempted to optimize substrate sequences such that they are more selectively cleaved by a specific protease. Combinations of multiple substrates and inhibitors have also been implemented to increase specificity.39 These strategies often succeed at distinguishing between two or a few proteases, but cross-reactivity nevertheless remains problematic in more complex mixtures.35,40,41
This work describes an approach we term ‘Proteolytic Activity Matrix Analysis’ (PrAMA) as a method of using panels of FRET-substrates to infer a dynamic, quantitative, and specific profile of MMP and ADAM proteolytic activities. PrAMA ascertains specific protease activity by deconvoluting from measurements derived from relatively non-specific FRET-substrates, employing prior knowledge of individual MMP/ADAM cleavage signatures ascertained using purified enzymes. This approach allows PrAMA to elucidate particular enzyme activities from cleavage signatures obtained in complex samples containing multiple proteases. The integrated experimental measurement and mathematical analysis framework exploits the advantages of FRET-substrates, which support non-invasive real-time measurements of live-cell activity, while addressing their problems of limited specificity and multiplexing. Peptide library microarrays have been previously implemented to assess global patterns of protease activity and infer specific protease activity.42 Nevertheless, PrAMA's novel combination of mathematical and experimental methodologies allows for greater quantification of protease activity, lower expense, and higher throughput compared to microarray-based approaches. Ultimately, PrAMA fills a niche that complements many other current methods of assaying MP activity and substrate degradation. This niche is especially important for multivariate network-level analysis, where the ability to simultaneously measure multiple MP activities in a non-invasive, real-time, and relatively high-throughput manner confers the greatest benefits. We anticipate that such network-level approaches will be valuable for designing clinical trials focused on MMPs and for illuminating unintended consequences of the many trials that have failed in the last decade.11,43
We present a compendium on the order of 4000 measurements involving mixtures of FRET-substrates and purified recombinant MPs, and use these measurements to both construct the PrAMA inference parameters and test the limits of PrAMA inference accuracy. A priori determination of the PrAMA inference parameters can predict optimal subsets of substrates for distinguishing particular MPs from each other. We demonstrate PrAMA as being capable of accurately inferring MP activity even in the presence of background protease activities. Finally, we apply PrAMA to assess the live-cell proteolytic response of wildtype, ADAM10−/−, and ADAM17−/− mouse embryonic fibroblasts (MEFs) to phorbol ester and ionomycin stimulation. Overall, this work presents the foundation, validation, and theoretical analysis of a general methodology that has potential applications ranging from systems biology to in vitroinhibitor screening.
As a fourth buffer, we spiked purified MMP into the conditioned media from the MDA-MB-231 cell line, which is an estrogen receptor negative (ER−) breast cancer cell line derived from the pleural effusion of a breast cancer patient. We obtained these cells from the American Type Culture Collection (ATCC, Manassas, VA) and routinely cultured them at 37 °C, 5% CO2, in DMEM supplemented with 10% foetal calf serum, 100 U ml−1penicillin, 100 μg ml−1streptomycin, 4 mM L-glutamine, and 4.5 g L−1D-glucose. We collected cell supernatant under the following conditions: cells were grown to 80% confluency in 10 cm tissue-culture treated polystyrene plates obtained from Corning Life Sciences (Lowell, MA), serum-starved for 4 h in basal media consisting of DMEM supplemented with penicillin/streptomycin, and stimulated with basal media supplemented with either 10 ng ml−1TNFα or 10 ng ml−1epidermal growth factor (EGF). Supernatant was collected at 12 h, spun down at 200 g for 5 min to remove debris, and immediately flash-frozen. For FRET-substrate assays involving this supernatant, final reactions were composed of a 2:
1
:
1 mixture of 20 μM substrate diluted from 5 mM DMSO stock into phosphate buffered saline, 4 nM active MMP7 diluted in ‘MMP buffer’, and thawed supernatant.
We determined active site concentrations by comparing observed cleavage rates to previously published catalytic efficiencies for the same substrates in either ‘MMP buffer’ or ‘ADAM buffer’.32,44 In some cases we performed active site titration with GM6001 to either confirm this comparison or to substitute it when comparison was unavailable. Activity data for active site titrations were fit to the Morrison equation using non-linear least squares curve-fitting (see below). We normalized substrate concentration to a positive control, comprised of 10 μM substrate incubated with 0.5% trypsin and 0.2% EDTA (Sigma). Almost all experiments were performed in technical triplicate, except for the MMP7 dilution series and the experiments involving cell supernatant, which both were performed in technical duplicate. For the experiments in triplicate, we excluded clear outliers in a few cases (<10% of all triplicates) using Dixon's Q-test with a 90% threshold. We performed all experiments at 37 °C. In general, readings measured fluorescence approximately every half-hour for roughly five hours. All experimental data are provided in the ESI.‡ We conducted all computational work using Matlab (2009a, MathWorks, Natick, MA).
dFobs/dt = V0(F0 − Fp)/[S]0 − kdFobs | (1) |
dFp/dt = V0(F0 − Fp)/[S]0 | (2) |
Fobs(t) = F0V0(e−V0t/[S]0)(kd[S]0 − V0)−1 + Ae−kdt | (3) |
A = ekdT0F0[(1 − e−V0T0/[S]0) − V0(e−V0T0/[S]0)(kd[S]0 − V0)−1] | (4) |
We fit model parameters in several steps. First, we subtract the signal of a negative control (FRET-substrate only) from all other signals. The maximum fluorescence in the positive control, which is generally at the first time point, indicates F0. We determine kd from the negative slope of the log-transformed positive control . We obtain the remaining two parameters (V0 and T0) by non-linear curve-fitting. In several cases, we explicitly measured T0 and compared model fitting with and without explicitly defining that parameter. Results indicate that V0 inference remains consistent regardless of whether T0 is inferred or measured. In cases where [E] is known (i.e. not blinded), we calculate Ci,j by the following relation: Ci,j = V0/([Si]0[Ej]).
V0,i = [Si]0∑Ci,j[Ej] | (5) |
![]() | (6) |
VT0 = [S]0CET | (7) |
Various methods can be applied to solve eqn (7). In this work, we implement a non-negative least squares algorithm combined with inference sensitivity analysis. We employ sensitivity analysis to quantitatively determine robustness to experimental error and to tune inference sensitivity and specificity. PrAMA inference involves three main procedures once V0 and C have been measured. First, we perform a bootstrapping scheme of randomly generating an ensemble of 1000 cleavage vectors Vs0. Sampling is from a log-normal distribution with μ = V0 and a standard deviation representative of the average variance between the experimentally observed and PrAMA expected cleavage rates obtained from PrAMA validation sets of data. Second, we use least squares to solve eqn (7) for every Vs0 in the sampling ensemble, and compute the mean and standard deviation of the ensemble inference results E. In some cases, we added artificial error to C for each iteration of the bootstrapping scheme, representative of experimentally observed parameter uncertainty. This additional process did not significantly improve PrAMA inference, however, and was excluded unless stated otherwise.
As the third step, we apply a robustness filter to the inference results to tune specificity. This filter, termed the σT threshold, roughly defines specific protease activities as significant if they are inferred in more than a certain percentage of the ensemble of inferences. We scale σT as a fraction of the inference standard deviation that is subtracted from the mean inference value. For example, setting σT = 1.0 roughly defines protease activity as significant if observed in at least 84% of the ensemble inference results.
![]() | ||
Fig. 1 PrAMA overview. Blue indicates PrAMA development & construction, red indicates PrAMA implementation, and grey indicates experimental preparation & procedure. |
![]() | ||
Fig. 2 Modeling protease cleavage kinetics and specificity signatures. (A) A typical time-lapse fluorimetry output for a single enzyme (0.5 nM MMP10) and substrate 13 in MMP buffer. The data is fit with both linear and decay-depletion kinetic models. The decay-depletion model almost perfectly overlays the data. (B) Inferred kinetic rates (V0/[S]0) of trypsin cleaving substrate 7 over a range of concentrations. (C) Decay-depletion model simulations of substrate cleavage using the following parameters: F0 = 104FLU, kcat/Km = 105 M−1s−1, [E] = 1 nM, [S] = 10 μM. (D) Hierarchical biclustering of observed cleavage efficiencies for various ADAMs and MMPs. Cleavage parameters were averaged over several concentrations. (E,F) PrAMA inference of individual MMPs at different concentrations (E, ∼0.5 nM vs. ∼0.05 nM) and buffers (F, MMP minimal media vs.MEBM). Each column represents an individual PrAMA experiment. The abscissa indicates the actual enzyme present, and the ordinate indicates the inferred MMP present. Each PrAMA output (each column) is normalized to have a total signal of 1. |
Several factors make photobleaching a significant issue in this work. We conduct protease activity assays using fluorescein-based FRET substrates over long (often >5 h) time scales. Fluorescein is relatively sensitive to photobleaching, and long time scales further amplify photo-sensitivity effects. In this application we typically read fluorescence every 15–30 min and observe resultant first-order decay constants (kd values) as high as 10−4 s−1. Computational simulations using the decay-depletion model demonstrate how significantly decay can influence the observed fluorescence (Fig. 2C). Our results indicate that even the small amount of photobleaching incurred with infrequent plate-reader measurements may result in a several-fold decrease in fluorescence after hours have elapsed.
![]() | ||
Fig. 3 A compendium of cleavage signatures from purified proteases and protease mixtures. Hierarchical biclustering organizes reaction mixtures by their corresponding cleavage signatures, which consist of cleavage rates (s−1) of substrates (shown in columns) by various protease mixtures (shown in rows). Within each signature V0, the Z-score indicates deviation from the mean cleavage rate, following variance-standardization. Three arrays accompany each subplot A–C. The left array describes the buffer (indicated by white) that corresponds to each cleavage signature, aligned by row with the other two arrays. The middle array describes MMP concentrations, and corresponds by row with the adjacent cleavage signatures. Experiment groupings are as follows: (A) all single MMP experiments, (B) all single and mixture MMP experiments, and (C) all single and mixture experiments involving ADAMs. |
RM = (CTRDC)−1 | (8) |
![]() | ||
Fig. 4 Parameter matrix error analysis. (A) Model error covariance matrix RM. (B) Relative model error matrix, RrM. (C) Median off-target PrAMA inference error as a function of the number of MMPs considered in the parameter matrix, averaged over all possible combinations of MMP subsets. (D,E) Average target and off-target inference error as functions of the synthetic measurement error, when all MMPs (D) or only MMPs 1 & 10 (E) are considered in the parameter matrix. (C–E) Parameter matrix C was constructed using enzymes at ∼0.5 nM. σT = 0 for all results here. (C) Cleavage signatures were obtained at ∼0.05 nM. |
The diagonal elements of RM represent on-target model uncertainties, while off-diagonal elements indicate off-target model error. To emphasize the relative amounts of on- and off-target uncertainty, we subtract the diagonal elements of RM from their respective rows to produce a “relative” model uncertainty matrix, RrM (Fig. 4B). Large positive values in RrM indicate the potential for high cross-reactivity in PrAMA inference. For example, the RrM rows for MMPs 9, 12, and 13 have large elements corresponding to MMPs 3 and 7. This suggests that signals from MMPs 9, 12, and 13 are likely to be mistakenly inferred as MMPs 3 and 7. We experimentally tested such cross-reactivity by performing PrAMA inference on MMP signals using different C configurations. We tested all combinations of MMPs considered by C, and performed PrAMA to infer MMP activity from individual enzyme mixtures (Fig. 4C). Results indicate that indeed MMPs 9, 12, and 13 have high cross-reactivity with other MMPs. MMPs 1, 2, 3, and 7 are inferred with the greatest specificity. Encouragingly, results suggest that inference cross-reactivity is relatively independent of the number of MMPs considered, past a certain point. For most MMPs, there is hardly any increase in average cross-reactivity when increasing the number of MMPs considered from 6 to 10. For MMP13, the effects of high pairwise uncertainty become diluted when more MMPs are considered, and total cross reactivity actually decreases. To test cross-reactivity as a function of experimental variability, we simulated PrAMA by inferring MMP activity from cleavage signatures generated from the columns of C, but with increasing artificial amounts of multiplicative error added to the simulated cleavage signatures. Fig. 4D shows the average results from 1000 iterations of these simulations for MMPs 1 and 10, when all MMPs are considered in the C parameter matrix. In agreement with RM and RrM, MMP1 has higher on-target error, while MMP10 has high cross-reactivity. A priori analysis of RM can suggest potential protease inhibitors to add or biophysical separation techniques to apply in order to eliminate the number of MMPs considered in a given sample. Both on- and off-target error significantly decrease when MMPs 1 and 10, which according to RM have relatively low cross-reactivity, are the only two proteases considered in PrAMA inference. Ultimately this analysis (a) reveals the potential need for additional FRET-substrates with certain specificities, (b) suggests which MMPs can be accurately and simultaneously measured in a given sample, and (c) suggests the potential use of inhibitors or supplementary experimental methods to achieve greater inference resolution. For example, RM analysis suggests the potential need for additional substrates that better distinguish MMP9 from MMP7. Although general non-specificity may be difficult to eliminate, substrates could be designed to minimize particular cross-reactivity through a bioinformatic analysis of cleavage motifs52,53 or through targeted directed evolution methods.54,55
![]() | ||
Fig. 5 Robustness thresholds filter off-target inference. (A) Inference results for cleavage signatures obtained using MMP 8 at ∼0.05 nM and parameters obtained at ∼0.5 nM. Increasing amounts of multiplicative error was added to cleavage signatures, randomly sampled from a normal distribution with standard deviations of σ = {33, 67, 100%}. (B) Total cross-reactivity in inference results for cleavage signatures obtained using MMPs 1,2,3,7, and 8 at ∼0.5 nM and parameters obtained at ∼0.05 nM. Cross-reactivity is undefined at high sampling error and σT thresholds, when no signal falls above σT. (C,D) PrAMA inference of MMPs 9, 10, 12, and 13 at different concentrations (∼0.5 nM and ∼0.05 nM) before (C) and after (D) applying 30% sampling error and threshold σT = 2. Each column represents an individual PrAMA experiment. The abscissa indicates the actual enzyme present, and the ordinate indicates the inferred MMP present. Each PrAMA output (each column) is normalized to have a total signal of 1. |
![]() | ||
Fig. 6 PrAMA inference of enzyme mixtures. (A) Heat maps indicating PrAMA inference results using mixtures of purified enzymes. Each column corresponds to a different enzyme mixture, and rows indicate which enzymes are considered in the parameter matrix C. The top heat map shows actual enzyme activity concentrations, and the bottom two indicate inference results obtained using different σT thresholds. Each PrAMA output (each column) is normalized to have a total signal of 1. (B) ROC curve describing PrAMA accuracy for inference shown in A. True positive rate (TPR) and false positive rate (FPR) describe accuracies with which PrAMA infers whether or not an enzyme is present in the mixture. (C–D) Heat maps and ROC curves describing PrAMA inference results for mixtures involving ADAMs 10, 17, and MMP 2, 14. (E) Maximum accuracies for inference of individual enzymes in mixtures shown in A. Although 14 enzymes are considered in the parameter matrix as in A–B, here we present inference accuracy for each enzyme individually. (F) PrAMA inference results (ordinate) of MMP7 at different actual concentrations (abscissa). Inference was performed using substrates 1–16 in MMP buffer (black) and conditioned media (red). |
For each mixture, we define MP activity as significant if inferred at levels above a defined σT threshold. If that enzyme is actually present in the reaction mixture, then we label the inference for that specific MP as ‘true positive’. Receiver-operator characteristic (ROC) curves then summarize the total PrAMA inference results (Fig. 6A). Tuning the σT threshold moves inference results along the ROC curve to adjust the true positive and false positive rates. Inference accuracy, defined as the ratio (true positives + true negatives)/(total positives + total negatives), is maximally ∼90% for all mixtures, using a parameter matrix that considers the presence of all 14 MPs used in this study. Maximum accuracy for single-enzyme mixtures is slightly above 90%, while more complex triple and four-enzyme mixtures have inference accuracy of roughly 80%. We explored an alternative bootstrapping method in this work (see Methods), but found PrAMA results to be robust to the algorithm variation (Fig. S13‡). Fig. 6A shows PrAMA inference results with increasing levels of σT. Previous information regarding MP interactions, co-expression, and cellular localization partly informed the selection of MP mixture compositions. For example, mixtures involving membrane-bound ADAM enzymes also included MMP14, which is the only MMP we analyzed that is membrane-bound with a transmembrane domain. We also included MMP2 in these mixtures, as previous work indicates MMP14 activates MMP2 at the cell surface.56 We analyzed 5 double-enzyme and 10 single-enzyme mixtures involving ADAM10, ADAM17, MMP2, and MMP14. Fig. 6D presents PrAMA results using a parameter matrix that focuses on just these four enzymes. Maximum PrAMA inference accuracies for both the single and double enzyme mixtures are roughly 90%.
Using PrAMA results from the 92 enzyme mixtures shown in Fig. 6A–B, we analyzed which particular proteases PrAMA most reliably measures. We calculated inference accuracy for each of the 14 MPs as a function of the significance threshold σT. ROC curves (Fig. S14‡) and corresponding maximum accuracies (Fig. 6E) for each enzyme reveal ADAM17 to be the most reliably identified MP. Results indicate MMP7 and MMP3 are the most difficult enzymes to measure, and this agrees with previously discussed RM analysis (Fig. 4A). As a caveat, these results may be somewhat skewed by the non-random selection of protease mixture compositions. Nevertheless, results suggest that even enzymes with relatively low catalytic efficiencies for the substrates, such as ADAM12, can be assessed with high accuracy.
We performed PrAMA inference on mixtures containing various concentrations of MMP7 to ascertain PrAMA's ability to quantitatively infer MMP activity, in addition to simply inferring whether or not an enzyme is present (Fig. 6F). We used 16 substrates and considered 10 MMPs (i.e., constructed a 16 × 10 matrix C) in this analysis, and tested 7 concentrations ranging from 0.01 nM to 1 nM. In all cases, PrAMA inferred MMP7 activity with 100% specificity. Furthermore, PrAMA detected quantitative differences in protease activity with high accuracy. R2 = 0.98 for a log-log plot that describes inferred MMP7 activity as a function of its actual concentration. PrAMA has less success in quantifying absolute differences in activity among multiple MPs (Fig. S15A–B‡), in part due to the fact that the relationship between MMP concentration and observed protease activityV0 is enzyme-specific and can deviate from linearity (Fig. S15C and S16‡). In general, we observe the recombinant MMPs employed in this work to be less efficient at higher concentrations. Enzyme concentration effects on proteolytic activity may be due to issues such as non-specific protein adsorption and aggregation. To test this hypothesis, we added increasing concentrations of Brij 35 to the reaction buffer (Fig. S17A‡). Although Brij can decrease proteolytic efficiency, our results suggest that Brij improves assay linearity perhaps by decreasing non-specific aggregation at higher enzyme concentrations (Fig. S17B‡), which has been observed for other secreted proteins.57 Even when nonlinear relationships between MMP concentration and observed protease activityV0 exist, PrAMA inference does not seem to distort these relationships (Fig. S15C‡). Consequently, quantitative comparisons of individual protease activities from one experimental sample to another can still be accurately made.
![]() | ||
Fig. 7 Using PrAMA with background protease activity and protease inhibitors . (A) The figure legend indicates which MMPs are considered in the parameter matrix. The corresponding ROC curves describe PrAMA inference accuracy for mixtures that contain MMPs and ADAMs that are both considered and ignored in the parameter matrix. (B) Inference for a triple-enzyme mixture involving MMP3, MMP7, and MMP9, with or without invoking information from an MMP9 inhibitor. For inference with inhibitor, p < 0.001. |
We also used PrAMA to infer protease activity over a background of conditioned media from the breast cancer cell line MDA-MB-231 (Fig. 6F). We added recombinant, active MMP7 to supernatant collected 12 h after stimulating cells with EGF and the inflammatory cytokine TNFα. We considered 10 MMPs in the parameter matrix C, and ultimately were able to identify MMP7 protease activity with 100% specificity. PrAMA did not detect any additional MMP activity in these samples.
![]() | ||
Fig. 8 Live cell inference of PMA-stimulated MP activity. PrAMA was conducted using three cell lines (WT, ADAM10−/−, and ADAM17−/− MEFs) and 7 total substrates, tracking substrate cleavage up to two hours after adding substrate. (A) Time-lapse fluorimetry for 4 of the 7 total substrates used in this experiment. (B) Inferred substrate cleavage rates (V0/[S]0), corresponding by row to the time-courses shown in A and using all four time-points in A for the inference. Stars indicate p < 0.05, comparing between cleavage rates for the control and stimulated conditions. (C) Parameter matrix used in this experiment, with each column divided by its Euclidean norm. (D) PrAMA inference results for the increased activity caused by PMA stimulation, using significance threshold σT = 1.4. No significant increase in MMP2, MMP14, or ADAM10 activity was detected at this threshold. For MMP9 and ADAM17, all inferred differences were statistically significant (p < 0.05). For all subplots, error bars indicate standard deviation of three biological replicates. |
We also applied PrAMA to assess the protease-activity response to ionomycin treatment. We used six substrates to ascertain activities of the same five proteases considered in the PMA example, again using wildtype, ADAM10−/−, and ADAM17−/− MEF cells (Fig. 9). All substrates show a statistically significant increase in cleavage upon IM stimulation within 2 h (p < 0.05), even in the mutant cell lines. In contrast to PMA stimulation, PrAMA results suggest that IM stimulates MMP9 & ADAM10 activity. In all three cell lines, PrAMA did not detect an increase in MMP2, MMP14, or ADAM17 activity at the same level of significance as detected for ADAM10 and MMP9 (zero at the significance level corresponding to results in Fig. 9D). Encouragingly, ADAM10−/− cells show a >90% decrease in inferred ADAM10 activity compared to wildtype cells. Again, remaining ADAM10 signal may be attributed to other proteases with similar substrate preference. Interestingly, the knockout cell lines seem to exhibit heightened general proteolytic response to IM stimulation compared to the wildtype cells (Fig. 9A–B). However, PrAMA analysis suggests that this increased activity likely arises from proteases other than ADAM10 or ADAM17. Fig. 9C shows that wildtype cells cleave the good ADAM substrates (6 & 9) at a greater relative rate compared to the knockout cell lines, even if the absolute cleavage rate is lower. Consequently, PrAMA infers the knockout cells to exhibit stronger MMP9 rather than ADAM activity.
![]() | ||
Fig. 9 Live cell inference of ionomycin stimulated MP activity. PrAMA was conducted using three cell lines (WT, ADAM10−/−, and ADAM17−/− MEFs) and 6 total substrates, tracking substrate cleavage up to two hours after adding substrate. (A) Time-lapse fluorimetry for 3 of the 6 total substrates used in this experiment. (B) Inferred substrate cleavage rates (V0/[S]0), corresponding by row to the time-courses shown in A and using all time-points in A for the inference. Stars indicate p < 0.05, comparing between cleavage rates for the control and stimulated conditions. (C) Observed cleavage vector V0 (columns), normalized to have total signal of 1, for each of the three cell lines. (D) PrAMA inference results for the increased activity caused by IM stimulation, using significance threshold σT = 1.4. No significant increase in MMP2, MMP14, or ADAM17 activity was detected at this threshold. For MMP9 and ADAM10, all inferred differences were statistically significant (p < 0.05). For all subplots, error bars indicate standard deviation of three biological replicates. |
Ultimately, PrAMA results that show ADAM17 to be activated in response to PMA agree with multiple other reports in the literature.19,20,25 Furthermore, PrAMA indications that IM stimulates ADAM10 activity also agree with previous literature.19 This work complements these previous reports by observing specific ADAM17 and ADAM10 activity in a non-invasive, real-time manner, without resorting to pharmacological or genetic perturbations.
min det(RM) = max det(CTRDC) | (9) |
![]() | ||
Fig. 10 Optimal substrates selection improves PrAMA accuracy. (A,B) ROC curves describing PrAMA accuracy for double-enzyme experiments involving MMPs 1, 2, 3, 7, and 8, where PrAMA uses a worst (A) or best (B) subset of the substrates, as defined in the text. (C–F) Area under the ROC curve (AUROC) as a function of the number of best (black) or worst (red) substrates, for various sets of PrAMA experiments: single (C), double (D), and triple (E) enzyme mixtures involving MMPs 1, 2, 3, 7, and 8; (F) double-enzyme mixtures involving ADAMs 10 and 17. |
Synthetic polypeptide protease substrates have been developed for an increasingly wide range of enzymes. Within the last few years several FRET-substrates have been designed with some specificity, thereby supporting their application in complex biological samples.20,31,32 Nevertheless, cross-reactivity with closely related MPs and distantly-related, but much more non-specific, proteases can still complicate the interpretation of FRET-substrate activity assays. As an example, several FRET-substrates with some specificity for ADAM17 have recently been developed with a sequence based on the ADAM17 cleavage site on pro-TNFα.31,32,62,63 Multiple recent reports employ these ADAM17 FRET-substrates, even though they have documented cross-reactivity with related MPs.35,64 At least six MPs have been recognized to cleave endogenous pro-TNFα, in some cases at the same site.32,65,66 Such non-specificity complicates interpretation of the observed FRET-substrate cleavage, especially when comparing multiple correlated MP activities in the same biological sample.64 Several MPs cleave many of the same synthetic and endogenous substrates that ADAM17 cleaves, suggesting the repertoire of ADAM17 substrates could be a subset of the repertoire for more promiscuous MPs (e.g., MMP14) or non-specific proteases like plasmin.67 At least to some degree, this situation is conceivable not just for ADAM17 but for a variety of MPs, and would make identifying truly specific substrates impossible.
Although MPs have been extensively studied for decades, no method yet exists to assay multiple protease activities in real-time with high specificity and non-invasiveness. One explanation partly accounting for this fact is that the ubiquitous regulatory interactions, diverse substrates, and distinct roles played by closely related MPs have only recently become fully appreciated. Both MMPs and ADAMs engage in regulatory networks controlled by cyclical feedback interactions. For example, ADAMs participate in an autocrine positive feedback loop in mammary epithelial cells: EGFR transactivation stimulates Erk activity, which in turn stimulates ADAM shedding of EGF ligands, further activating EGFR.69 In this situation, common methods of ascertaining the influence of ADAM activity on EGFR signaling, such as by applying a protease inhibitor or siRNA treatment, can both disrupt the underlying feedback interactions and potentially create compensatory reactions whereby closely related ADAMs modify their activity to accommodate perturbations.15,24,25,68 As another example, many MMPs activate themselves and one another. Such interactions can create positive feedback interactions that allow, for example, an initiating MMP activation event to trigger further protease activation.70 We predict non-invasive, multiplexed, real-time, and specific measurements of MP activity will be critical towards understanding the complex regulatory mechanisms underlying MP networks.
We anticipate that PrAMA should have broad applicability in protease biology. FRET-substrates have been extensively used for high-throughput inhibitor screening with individual purified enzymes. PrAMA would allow inhibitor screening to be performed in more complex enzyme mixtures and biological samples, and could be adapted for high-throughput in vitro functional assays of inhibitor activity. As discussed above, PrAMA is well suited for network-level analysis of in vitroprotease activity, and PrAMA can scale up and down in scope depending on the particular application. At the most basic level, PrAMA could use multiple FRET-substrates in tandem to bolster the specificity of an activity measurement for even a single protease. In other words, the parameter matrix C could be as small as (2 substrates × 1 enzyme). PrAMA can capture protease activity on a variety of time-scales, depending on the particular application. We demonstrate high sensitivity measurements that are made over the course of >5 h, and live-cell measurements can detect significant differences in cleavage within 30 min. Dynamic measurements on this short time-scale can be relevant for detecting rapid post-translational protease activation, while longer time-scale measurements have relevance, for example, to phenotypic responses that are downstream of transcriptional changes. Soluble FRET-substrates can be directly applied to both live-cells and cell lysate for protease activity measurement.31,64 Our initial experiments show that PrAMA can operate by adding individual yet distinct FRET-substrates to live-cells in a multi-well format. Furthermore, FRET-substrates with distinctive excitation/emission spectra may be simultaneously combined in the same solution for PrAMA of a single biological sample. FRET-substrates have been tethered directly to 3D substrata such as collagen,71 providing localized measurement of protease activity. For simultaneously analyzing many protease activities, the mathematical framework behind PrAMA can be applied to microarrays of peptides, for instance, that contain hundreds or thousands of FRET-peptide substrates. Previous work with peptide microarrays has demonstrated how patterns of peptidase activity can be deconvoluted using non-linear least squares in a similar manner to PrAMA, ultimately to infer the presence of specific proteases within complex biological samples.42PrAMA builds upon this previous work by using non-invasive dynamic measurements of peptide cleavage rather than static snapshots of cleavage patterns to quantitatively infer kinetic parameters (i.e., V0). The advantages of PrAMA's mathematical framework include explicitly accounting for substrate depletion & photobleaching effects, as well as readily extending to dynamic measurements of protease activity where V0 is not constant. Strategies for selecting optimal substrates depend on the application. Most commonly, optimal substrates are individually chosen by the combination of their specificity and cleavage efficiency.35,38,42 In this work, however, we employ a global optimization strategy to identify the set of substrates that combine to yield the greatest specificity and the least inference uncertainty. The principles behind PrAMA, including strategies for optimally selecting substrates, are readily extendable to other classes of enzymes, such as caspases and cathepsins. FRET-based protease substrates have been successfully applied to measuring in vitrocaspase activation. Like MPs, however, individual caspases have overlapping substrate specificity and it can be difficult to interpret which specific caspase has become activated.72 Lastly, PrAMA inference has many potential uses involving clinical samples. For example, simultaneous measurement of multiple protease activities in patient fluid samples or biopsies could reveal mechanistic insight and/or identify activity-based markers of disease state for diagnostic/prognostic use. Ultimately, this work presents an integrated mathematical and experimental framework that can be adapted and extended to a broad range of applications. We have demonstrated various methods of a priori analyzing how best to design PrAMA experiments, whether it be through choosing optimal substrates, identifying which proteases can be specifically measured with the available substrates, or understanding how to account for experimental variability.
ABP | activity based probe |
ADAM | a disintegrin and metalloproteinase |
B | background signal |
DMEM | Dulbecco's Modified Eagle Medium |
DMSO | dimethyl sulfoxide |
C i,j | catalytic efficiency for the ith substrate and jth enzyme |
C | catalytic efficiency parameter matrix |
Cha | cyclohexylalanyl |
Dabcyl | 4-(4-dimethylaminophenylazo)benzoyl |
[E] | enzyme concentration |
E | vector of enzyme activities |
EGF | epidermal growth factor |
F 0 | peak fluorescence from positive control |
F obs | observed fluorescence from product formation |
F p | fluorescence from product formation |
Fam | 5-carboxyfluorescein |
FRET | fluorescence resonance energy transfer |
GABA | γ-aminobutyric acid |
Homophe | homophenylalanyl |
IM | ionomycin |
k cat | turnover number |
k cat/Km | catalytic efficiency |
k d | photobleaching decay constant |
K m | Michaelis–Menten constant |
M–M | Michaelis–Menten |
MEBM | Mammary Epithelial Basal Medium |
MMP | matrix metalloproteinase |
MP | metalloproteinase |
PrAMA | Proteolytic Activity Matrix Analysis |
R M | model covariance error matrix |
R r M | relative model covariance error matrix |
R D | data uncertainty covariance matrix |
[S]0 | initial substrate concentration |
[S] | substrate |
σ T | significance of inference threshold |
T 0 | lag time |
TIMP | tissue inhibitor of metalloproteinase |
TNFα | tumor necrosis factor-alpha |
V 0 | initial rate of substrate cleavage |
V0 | vector of V0's for all substrates |
V s 0 | bootstrapping sample ensemble of multiple V0 |
Footnotes |
† Published as part of an Integrative Biology themed issue in honour of Mina J. Bissell: Guest Editor Mary Helen Barcellos-Hoff. |
‡ Electronic supplementary information (ESI) available: Table S1; Text S1; Fig. S1–S18; supplemental data. See DOI: 10.1039/c0ib00083c |
This journal is © The Royal Society of Chemistry 2011 |