Bacterial uptake entails successive, increasingly strong associations between receptors on the surface of bacteria and hosts. Even with genetically identical cells grown in the same environment, there are vast differences in the number of bacteria entering any given cell. To gain insight into this variability, we examined uptake dynamics of Escherichia coli engineered to express the invasin surface receptor from Yersinia, which enables uptake via mammalian host B1-integrins. Surprisingly, we found that the uptake probability of a single bacterium follows a simple power-law dependence on the concentration of integrins. Furthermore, the value of a power-law parameter depends on the particular host-bacterium pair but not on bacterial concentration. This power-law captures the complex, variable processes underlying bacterial invasion while also enabling differentiation of cell lines.
A detailed but unwieldy mechanistic model describing individual host-pathogen receptor binding events is captured by a simple power-law dependence on the concentration of the host receptors. The power-law parameters capture characteristics of the host-bacterium pair interaction and can differentiate host cell lines. This study has important implications for understanding the accuracy and precision of therapeutics employing receptor-mediated transport of materials to mammalian hosts.
Pathogenic bacteria such as Yersinia pseudotuberculosis and Listeria monocytogenes exploit a latent phagocytic activity in gut epithelia to pass into deeper tissues that are optimal for survival and proliferation . Particularly well studied is the 'zipper' phagocytic process used by Yersi-nia: Envelopment of a bacterium by a host membrane is aided by successive binding events between bacterial surface receptors called invasins and cognate Bl-integrins exposed on the surface of host intestinal multifold cells (M-cells) in the gut lumen [2,3].
Contact does not require active alterations to host actin cytoskeleton but rather hinges on strong associations between bacterial receptors and host ligands. In contrast, the creation and resolution of the phagocytic cup is a dynamic process involving numerous signal transduction and structural genes coordinated in receptor clustering, membrane phospho-lipid redistribution, and cytoskeletal reorganization  (81 Table). A particularly critical aspect is the high affinity of invasin for Bl-integrins, which promotes phagocytosis by increasing cell-surface adhesion  and outcompetes extracellular matrix proteins for enough host integrins  to sustain bacterial engulfment.
Past studies have reported that this variability manifests as bimodal uptake dynamics, where a fraction of mammalian cells take up bacteria while the other fraction is devoid of bacteria [7—10]. This property is not limited to cultured experiments since Yersinia introduced into the intestines of mice are often found in islands of M-cells surrounded by bacteria-free regions and that the number of bacteria in infected cells spans a wide range . Such differences may arise from the stochastic nature of cellular interactions [12,13]. Also, the uptake may have resulted from preexisting, long-lived differences in host cell properties (e.g. cell surface, signal transduction network, cytoskeleton) that have stochastic  and deterministic  origins.
Here, to characterize the fundamental property of bacterial uptake, we employ kinetic modeling and experiments that distill a simple power-law, relating uptake probability—the amount of bacteria per host cell scaled by the bacteria concentration—with host receptor levels. Our study demonstrates that a simplified model of successive binding events that occur in the Zipper mechanism is sufficient to generate an ultrasensitive, threshold-dependent response to host receptors. Thus, minute cell-cell differences in host receptors are amplified into large differences in uptake. We describe how different hosts and bacterial strains translate into different power-law parameters which serves as the basis of a novel, operational definition of cell type.
In particular, we engineered non-pathogenic E. coli to express invasin from Yersinia pseudotuberculosis  (Materials and Methods). Invasin-mediated bacterial entry Via binding With b1-integrins is one of the best-studied Zipper-mechanism systems and has been used in a number of biological and potential clinical applications [7,9,18,19]. Remarkably, binding of invasins to mammalian host receptors is sufficient to facilitate entry into non-phagocytic mammalian cells. In fact, non-pathogenic bacteria eXpressing invasins  or even beads coated with invasins  can enter into mammalian host cells. Thus, the entry dynamics of this engineered bacterial system can be attributed to the interactions between invasin and host receptors. To track and quantify uptake in individual hosts, we engineered E. coli to constitutively express a green fluorescent protein (GFP). In each experiment, the engineered bacteria were co-cultured with HeLa cells for 90 minutes in well-mixed conditions to mitigate the effects of heterogeneity in the bacterial population. In addition, the mixture was co-incubated in the presence of sublethal gentamicin before washing and measurement (SIA—SlC Fig). This gentamicin treatment served two purposes by inhibiting bacterial growth: First, it reduced differences between bacterial numbers and our calculation of M01 that could arise during co-culture. Second, it minimized alterations in GFP signals due to bacterial growth. Consistent with observations reported in the literature [18,22], fluorescence microscopy confirmed drastic cell-cell variability in bacterial uptake by HeLa cells (Fig 1A). Some cells were devoid of bacteria, whereas the others each contained a wide range. This property was consistent with flow cytometry measurements (Fig 1B): At intermediate bacterial concentrations, a bimodal distribution of GFP fluorescence arises where within a single population there exist both uninfected cells (i.e. low mode) and infected cells (i.e. high mode). Even within the infected subpopulation, there was drastic variability in fluorescence, indicating a wide range of bacterial numbers in individual cells. We note that the characteristics of bacterial uptake depended on the bacterial multiplicity of infection (M01) as well as the host cell type (Fig 1C). Uptake in all the cell lines we tested, most of which are cancer models, was positively correlated with MOI but the amount of uptake was quite variable.
A trivial eXplanation is that variability in uptake reflects a wide, bimodal distribution of host Bl-integrins or GFP signals in bacteria. However, immunolabeling experiments revealed a relatively narrow, unimodal distribution of Bl-integrins across HeLa cells (SID Fig). Similarly, GFP signals in bacteria showed a tight and unimodal distribution (82 Fig). Hence, we hypothesized that the dynamics arising from zipper-like interactions may be responsible for generating the observed bimodal uptake from a unimodal Bl-integrin distribution.
First, invasin-integrin interactions are inherently cooperative  as sequential binding events are increasingly likely due to host-pathogen proximity and the stabilizing role of invasin-integrin clustering . Second, bacteria must maintain a minimum number of invasin-integrin interactions to remain stably attached to host cells.
Furthermore, rather than explicitly describing every single receptor binding event, we divide the uptake process into three reversible stages (i.e. the 3-stage model). Initially, bacteria bind weakly to a small number of Bl-integrins (state B1) and these initial interactions increase the avidity of subsequent binding events. In the second stage, weakly-bound bacteria can adhere in a more stable fashion by interacting with an intermediate number of Bl-integrins (state Bm), which we assume is sufficient for successful adherence even after repeated washing. In the final stage, a maximal number of Bl-integrin interactions can be formed (state 13,), representing the saturation of invasin receptors and a state that corresponds to bacterial engulfment and internalization [2,3]. Thus, we define bacterial uptake in this study is as the sum of bacteria achieving at least the intermediate binding state (i.e. sum of Bm and 13,) to represent both stably-adhered and internalized bacteria. In fact, immunostaining of a single infected host cell with anti-LPS shows both stably-adhered and fully internalized bacteria (S3 Fig). We note that the number of Bl-interactions depend on the amount of invasins expressed in bacteria cells, with successful adherence and internalization likely requiring a large number of interactions. Thus, rather than modeling each binding event explicitly to match experimental data, our goals with the 3-stage and the full model are to explore the conceptual basis for the experimentally observed variability in bacterial uptake. Indeed, the qualitative behaviors of our full model, in which each binding step is explicitly modeled, are not especially dependent upon the nominal values of intermediate (m = 10) or maximal number (n = 100) of interactions.
Consistent With experimental measurements, our model predicts that both the mean uptake per cell and the fraction of the population infected increase With the bacterial MOI (Figs 2B and 84A). Simulation results show that these behaviors arise from a threshold relationship between uptake and host receptor number. In particular, host cells with fewer than threshold numbers of receptors do not take up bacteria; above a threshold, uptake is positively correlated with the number of host receptors. Since we assume a bimolecular binding reaction between bacteria and mammalian receptors for the initial binding step, a higher bacterial concentration would require a lower mammalian receptor concentration to enable bacterial uptake. Thus, bacterial concentration modulates overall uptake while reducing the minimal threshold of host receptors. For example, uptake only occurs in cells with more than 6- 105 Bl-integrins at 25 MOI (Fig 2B; dark green) whereas only 1-105 Bl-integrins are needed at 1000 MOI (Fig 2B; brown).
For a given host receptor concentration, our model predicts that the bacterial uptake increases linearly (in log-log scale) with the bacterial concentration (Fig 2B, inset). This property could be expected as we have assumed independent binding of bacteria to receptors. In other words, from the perspective of a single bacterium, its uptake would solely depend on the host receptor concentration.
This curve summarizes how uptake depends on host receptor number. In essence, increasing bacterial MOI extends the uptake curve into a lower range of host receptor concentrations, as evidenced by a downward shift in the mean. Importantly, the sensitive threshold relationship and unified uptake trajectory were recapitulated by a Zipper model explicitly describing all 100 binding/unbinding steps, indicating these behaviors are not specific to the simpler 3-stage model (S4B Fig).
Indeed, the inset in Fig 2D show a fit by the equation logP = B'logR—logKDeff, where Band KDefic are fitted parameters. Sequential perturbation of Zipper model parameters shows that these changes can be mapped to changes in both power-law parameters (84C Fig). In particular, changes to the dissociation constants for binding of B and B1 to receptors had the greatest effect. Altogether this analysis shows that a simple power-law provides a concise summary of zipper-mediated uptake.
Highly cooperative binding processes can often be approximated by the Hill equation , in which the fraction of particle binding as a function of the log of the input follows a characteristic sigmoidal trajectory. Zipper model simulations reveal that uptake probability as a function of receptor behaves in this characteristic fashion according to P 2 RB / (KDeff + RB) (Fig 2D). A power-law arises if the receptor concentration is much lower than the effective dissociation constant (i.e. KDeff >> RB). A large KDefir can arise from the reversible, multi-step nature of uptake that makes achieving the uptake state less likely than if it were a single binding event.
To this end, we used a non-activating antibody to fluorescently label Bl-integrins [25,26]. In principle, labeling With antibody may inhibit bacterial uptake by serving as competitive inhibitor. To examine impact of antibody on uptake, we also extended the model to account for sequestration of Bl-integrins (SSA Fig). Modeling (85B Fig) and experiments (85C and 85D Fig) reveal that indeed, antibody addition could result in reduced uptake (over 40% reduction at > = 1.5 ug/mL). We chose a relatively low concentration of antibody (0.15 ug/mL) that reports the relative amount of receptors on host cells while not having a severe effect on bacterial uptake (SSE Fig). While antibody-bound receptors may not be directly involved in bacterial uptake, we reasoned that, at a sufficiently low concentration, the antibody-bound receptors may be used as a proxy reporter for the relative amount of Bl-integrins.
In the absence of induction, bacteria carrying pBACr-AraIm/ cannot infect host cells, which was reported previously  and observed by us by co-incubating HeLa cells with uninduced bacteria (S6A—S6C Fig). We further showed that bacterial uptake increased with increasing concentrations of invasin induction (S6D—S6G Fig). Further validating the construct, our qPCR experiments showed significantly increased invasin expression in arabinose-induced bacteria compared to uninduced bacteria (S7 Fig). Consistent with previous modeling (Fig 2B), collected data suggested an increase in the fraction and overall amount of uptake with bacterial MOI.
In each population, single-cell measurements revealed a positive correlation between the infected host cells and the corresponding level of Bl-integrins (Fig 3C). With increasing bacterial MOI, the major axes of GFP+ subpopulations were shifted towards higher GFP levels, consistent with an overall increase in uptake, and towards lower Bl-integrin levels, indicative of a reduced threshold of receptors required for uptake. When GFP values were scaled with their respective MOI for the fraction of infected host cells, the curves collapsed into a single linear line that has high correlation (Fig 3D), similar to our previous simulation results (Fig 2C). This striking observation indicates that the correlation between the uptake and host receptor levels follows a power law as predicted by model.
A final prediction of our model is that the location of probability curves could be modulated by varying the invasin KDefic (Fig 3E). Indeed, when the bacterial MOI was fixed at 200, increasing arabinose shifted the scaled GFP values to higher GFP and lower host receptor levels (Fig 3F).
Changes in many kinetic parameters associated with the Zipper mechanism can be mapped to changes in the power-law parameters (Fig 4A; simulated data in 84C Fig). In a typical cell, changes in Zipper-mechanism parameters reflect the changes in either the bacteria or the host cells. For instance, the initial binding rate (ka1) between bacteria and host cells is affected by factors including eXpression levels of invasin and Bl-integrins along with the prevalence of [3 chain integrins in host cells. For the same bacterial strain, different host cell types would likely lead to different kinetic parameters for the Zipper mechanisms, and thus different corresponding power-law parameters. If this notion is correct, the power-law parameters can be used as a quantitative phenotype for a host cell type in a specific growth environment.
In theory, we eXpect that different uptake probability dose responses would be measured at different bacterial MOI to share a given power-law trajectory. However, measurements done at low MOI values would likely be prone to fluctuations due to the stochastic nature of
Host and pathogen specific power-law parameters. (A) Mapping of 3-stage model parameters on power-law parameters. Each set of markers shows the effect of increasing a zipper model parameter over two orders of magnitude centered on a base value. Zipper model simulations were performed at MOI (500) and a power-law of the form P = RB/KDeff was fit to scaled output. Simulated data presented in S4C Fig (B) Power-law parameters for different hosts and bacterial strains. Mammalian cells were co-cultured with the indicated strain of GFP-expressing E. coli at 1000 MOI as described in (A). Raw data presented in 88D Fig the interaction between small numbers of bacteria and mammalian cells. This would then lead to greater uncertainty in the estimated power-law parameters.
In particular, we treated several cell lines with increasing MOI. And these measurements showed that the power-law parameter values stabilized with increasing MOI, which likely resulted from more accurate measurement and analysis when more cells were in the GFP+ mode (SSA—88C Fig). As such, for more extensive analysis of different cell lines, we chose a high MOI for bacterial infection.
This is reminiscent of modeling analysis showing that perturbation to Zipper model parameters modulates power-law parameter values along the same, restricted path (Fig 4A). In addition, for each host cell line, [3 and K Defic were lower for E. coli harboring pBAC-AraIm/ relative to pSCT7Im/ plasmid, possibly resulting from the lower invasin expression from pBAC-AraIm/ under these conditions.
Every set of perturbations results in quasi-parallel lines for a range of specific receptor concentrations, thus enabling the data collapse over several orders of magnitude for biologically relevant values (Fig 4A). However, as receptor concentration approaches zero, the linear fit breaks down and all lines approach a quasi-constant locus (S9A Fig). Therefore, when examining the correlation between [i and 1/ KDeff, we can construct an inverse linear regression fit (89B Fig) such that the slope and intercept of this line correspond to the locus of convergence for receptors and for probability uptake. We are therefore left with a nontrivial dependent relationship between the power-law fitted parameters, which fall along the linear correlation between [i and l/KDeffl: fl =
Bacterial uptake through this mechanism has been found to be highly variable, even within iso-genic cells grown under tightly controlled experimental conditions [18,22,28]. In general, heterogeneous behaviors have been believed to arise from the stochastic nature of biochemical reactions  and differences in the local microenvironment for each cell, for example, the placement of neighboring cells . Phagocytosis is particularly complex as it is affected by quantitative changes in host signal transduction molecules , cytoskeletal dynamics , particle receptor affinity  , host receptor density, particle size, and shape . It has been suggested that the hundreds of signaling molecules are potentially involved . The sheer complexity presents a seemingly daunting challenge for attempts to incorporate detailed molecular knowledge into a theoretical model with predictive power .
Our detailed, quantitative, single-cell measurements revealed that, in the same population of host cells, the fraction of host cells infected with bacteria increases in a monotonic fashion with the levels of host [31-integrins—irrespective of M01. Likewise, our modeling results demonstrate a positive correlation between invasin-mediated uptake of E. coli and the relative abundance of available Bl-integrins expressed by hosts. A counterintuitive, striking notion from our analysis is that uptake may be distilled into a simple empirical relation defined by two lumped parameters. Indeed, phenomenological approaches have been routinely used when detailed description is impractical, for example, oxygen binding , gene regulation , and bacterial growth . Similarly, power-law relations have a rich history in the description of scale-invariant processes in nature . Here we show that an ultrasensitive host-pathogen relationship explains a large proportion of the variation in pathogen invasion. Our results demonstrate generation of broad cell-cell variability in bacterial uptake by a Hill-like mechanism involving surface receptor interactions, though other mechanisms can contribute to this variability. For example, there could be a positive feedback in host cell cytoskeletal signaling that determine phagocytosis of bound bacteria.
Though simple, the power-law description of uptake is robust in describing invasin-mediated bacterial uptake by mammalian cells, and the extracted power-law parameters can be useful in distinguishing pairs of E. coli strains (expressing varying levels of invasion) and mammalian cell lines. This represents a complementary, simple and efficient means to characterize cell physiology. Each perturbation to the system produces a unique set of power-law parameters; a library of these parameters can be collected for easily accessible sample analysis. Also, this notion can be extended to other pathogens and particles that utilize a multi-step, reversible binding process such as viruses. Similar to bacterial uptake by host cells, viruses also employ receptor-mediated endocytosis to use biological machinery in the host cells for them to replicate . This behavior plays an important role in viral infections including Influenza and Rubella . By providing simple predictions for multi-step receptor-mediated uptake, the power-law description can enable us to capture cellular dynamics without identifying interconnected processes involved in each step.
GFP expression plasmid pTetGFP was created by fusing a PCR-derived fragment of the gfp gene into pPROTet.E using the KpnI and BamHI enzyme digestion sites. To construct the plasmid pSCT7InV, the invasin gene was PCR amplified from pR1203  With BamHI and EcoRI enzyme digestion sites at the end and inserted into similar sites of a plasmid that contained a
Cells were grown overnight at 37°C in Luria-Bertani (LB) and diluted in Dulbecco’s modified eagle medium (D-MEM; GIBCO® Cat. No. 31053—036) supplemented with 2% L-glutaminine (Cat. No. 25030—164), 1% sodium pyruvate (Cat. No. 11360—070), and 0.02% bovine growth serum (BGS) until their absorbance reading (Abs600) on a plate reader was approximately 1.0. At this absorbance, the number of bacteria in luL of the culture was approximately 2.0 X 106.
Prior to co-culture with bacteria, cells were seeded in 6-well plates at approximately 2.0 X 105/well, and were allowed to grow overnight in D-MEM with 10% BGS. Typically, bacterial overnight cultures were co-incubated with mammalian host cells in the presence of gentamicin (50pg/ml)
Then the co-cultured cells were rigorously washed twice with phosphate buffered saline (PBS) to remove bacteria in suspension, trypsinized to detach mammalian host cells for flow cytometry, fixed with PBS supplemented with 1% formaldehyde, 0.33% BGS and 0.001% of sodium pyruvate and assayed for their GFP signals by flow cytome-try (FACSCanto II, Becton Dickinson). For fluorescent microscopy (DMI6000 microscope, Leica), cells were imaged immediately after rigorous washing with PBS twice. For quantification of Bl-integrin levels, mammalian cells were pre-incubated with a monoclonal antibody (Cat No. MAB2253, Millipore) followed by detection with anti-mouse antibody conjugated to Alexa647 (Cat No. A21237, Invitrogen).
Sub-culturing were performed with 100-fold dilution in LB with appropriate antibiotics (Kan and Cm) and induced with 0.1% arabinose when appropriate. Induced and uninduced samples were cultured for additional 5 hours at 37°C, then RNA samples were isolated from the cell cultures using RNeasy Mini Kit (QIAGEN, cat. no. 74104) in combination with RNAprotect Bacteria Reagent (QIAGEN, cat. no. 76506). Using Power SYBR Green RNA-to-CT l-Step Kit (Life Technologies, cat. no.4389986), qPCR was conducted with primers targeting invasin (forward primer: CTCACTCAATGGTGGGC-GAT; reverse primer: CATACCAAGGAGCCAGCCAA) and FFH was used as a reference gene for normalization (forward primer: TGTGACGAATAGAGAGCGCC; reverse primer: GGCCAATACGGCAAAAGCAT). The standard curve method for relative quantification was used.
On the following day, HeLa cells were then infected with invasin-expressing E. coli at an MOI of 200 by plate rotation at an rpm of 50, in a 37°C, 5% C02 humidified incubator for 90min. After infection, HeLa cell were washed twice with phosphoate buffered saline (PBS) and fixed with 3% formaldehyde/ 0.025% glutaraldehyde at room temperature for 10 min. HeLa cells were then blocked with 5% BSA in PBS for 30 min at room temperature and stained with anti-LPS E. coli (Abcam). Secondary antibody conjugated to Alexa Fluor 555 (Life technologies), and Hoechst 33342 (Life Technologies) where then incubated for 30min.
The parameters are obtained either from the literature or fitted from experimental data. Numerical simulations and data analysis were performed using Matlab (Natick, MA). The detailed models are described in the supporting text (81 Text) and tables (SI—$4 Tables).
in a plate reader at 37°C in lml Dulbecco’s minimal media supplemented with 10% bovine growth serum (BGS) with the indicated number of bacteria. (B) Flow cytometry of bacterial GFP associated with mammalian cells. HeLa cells were co-incubated with E. coli (GFP/Invasin) at a multiplicity of infection (MOI) of 2000 in the absence or presence of 50 ug/mL gentamicin for the indicated amount of time prior to measurement. (C) Statistical summary of data from (B). The expectation-maximization (EM) algorithm was applied to GFP signals in order to assign cells to either a GFP+ subpopulation (colored) or a GFP- subpopulation. (D) Flow cytometry detection of surface Bl-integrins. HeLa cells incubated With either control antibody (actin) or antibody against Bl-integrin along With fluorescent secondary antibody (Alexa647).
Flow cytometry data for GFP expression. pBacr-AraInv and pTetGFP plasmids were introduced into ToplOF' E. coli, enabling the control of Invasin expression through addition of arabinose and control of GFP expression through ATC, respectively. ToplOF’ cells containing only pBacr-AraInv were used for control. Cultures were grown overnight at 37°C in LB for 16 hours and subcultures were performed with lOO-fold dilution from overnight culture. (A) The control strain was grown without arabinose induction for comparison, and ToplOF’ containing pBacr-AraInv and pTetGFP was either (B) uninduced, or (C) induced with. 1% arabinose and 100ng/mlATC to induce Invasin and GFP. The mean expression level of GFP in the induced sample (C) is approximately three magnitudes higher than that measured in the uninduced sample (B). Also, the basal level expression in the cells containing only pBacr-AraInv (A) is also at least two magnitudes smaller than that in the induced sample (C). All three panels in the figure show tight and unimodal distributions of
HeLa cells, infected with E. coli expressing invasin and GFP at an MOI of 200 for 90 minutes, were washed with PBS twice, fixed with paraformaldehyde, and immunostained with an anti-LPS antibody to identify bacterial cell wall (red color) and Hoechst to identify DNA (blue color). In the merged image, yellow dots (indicated by white arrows) represent GFP-expressing bacteria that were immunostained in red, suggesting that they are surface-bound. Green dots represent fully internalized bacteria that were prevented from immunostaining by the mammalian cell wall. The presence of both green and yellow bacteria suggests that invasin-expressing bacteria can adhere and invade into mammalian host cells. (TIF)
(A) Simulations using a 3-stage zipper model with a log-normal input distribution (top; n = 2000) of receptor numbers at various MOI (bottom). (B) Simulations using a 100-stage zipper model. (Left) A 100-stage zipper model that explicitly describes each bacterial binding state produces qualitatively the same results as does the 3-stage model (compare with Fig 2B). Horizontal axis indicates total number of host receptors per cell. Uptake is defined as the total amount of bacteria bound to at least 10 or more host receptors. Mean indicated by circles. (Right) Similar to the prediction by the 3-stage model, the 100-stage model also predicts that an approximate power-law captures the dependence of the uptake probability by a single bacterium on the concentration of the host receptors. The uptake probabilities are calculated by scaling data on left with the respective MOI. (C) Mapping of kinetic parameters associated with the 3-stage model to power-law parameters. Zipper model simulations were performed and the uptake was calculated for M01 (500). A power-law of the form P = RB/KDeff was fit to data for different values of each zipper model parameter. The zipper model parameters are normalized with respect to their base values (S4 Table).
(A) Relationship between bacterial binding and amount of surface Bl-integrins immunolabeling. Abbreviations: A-R—antibody-receptor complex. (B) Simulations. Relationship between uptake and AR on host cells in the presence of the indicated amount of antibody for Bl-integrins. (C) Flow cytom-etry measurements of bacterial uptake in HeLa cells in the presence of increasing concentration of the antibody. HeLa cells were pre-incubated for 60 minutes with the indicated amount of [31-integrin antibody and subsequently co-cultured with GFP-expressing E. coli also Invasin from the plasmid pSCT7Inv at 500 M01 and secondary antibody (Alexa647) for an additional 90 minutes prior to washing and measurement. The EM algorithm was used to assign cells into the GFP+ mode (colored). (D) Statistical summary of flow cytometry data in (C). Statistics from the EM algorithm were used to extract the mean (circle) and major axes (lines) of the GFP+ mode. Numbers beside the means indicate inhibition calculated as the mean GFP signal relative to the mean GFP signal at the lowest antibody concentration (GFP0 = 1.5e-6 ug/mL) (i.e. [GFP—GFPo]/GFPO). (E) Simulations. Cell receptors are pre-incubated with 0.15 ug/mL of antibody for 60 minutes of simulation time followed by 90 minutes with the indicated MOI of bacteria. (Left) Relationship between bacterial uptake and A-R. (Right) Relationship between probability (uptake/MOI) and AR calculated from simulated data. These results show that the dependence of bacterial uptake on the concentration of antibody-bound receptors is qualitatively the same as the dependence on total receptor (Fig 2B and 2C). (F) Flow cytometry demonstrating that qualitative relationship between uptake and host receptors is recapitulated with a different bacterial strain. Experiments were performed as described in SSC Fig in the presence of 0.15 ug/mL of Bl-integrin antibody and the indicated MOI of GFP-expressing E. coli also expressing INV from the plasmid pSCT7Inv. EM algorithm was used to assign cells to the GFP+ subpopulation (colored). (G) Fractional uptake. For each MOI sample in (F), data points in GFP+ mode within 2.5 standard deviations of the mean Alexa647 signal were extracted, placed into 15 contiguous bins, and the fraction of GFP+ cells were plotted. (H) Uptake and probability. Shown for each MOI sample in (F) are the (left) major axes and mean or (right) GFP values scaled by bacterial MOI.
HeLa cells (A) were co-cultured with bacteria harboring pTetGFP alone (B), pTetGFP and pBACr-AraInV without arabinose induction (C), or with induction at the indicated concentrations of arabinose (D-G). The blue line denotes auto-fluo-rescent GFP signal from HeLa cells, and is overlaid for comparison with GFP signals in other conditions (shown in red). S6A—S6C Fig show similar GFP distributions, suggesting that bacteria without invasin expression cannot invade HeLa cell and that arabinose induction is required for bacterial uptake. 86D and 86E Fig show the dependence of bacterial uptake on
Quantification of invasin expression induced by arabi-nose. ToplOF’ cells containing pBACr-AraINV and pTetGFP were either uninduced or induced with 0.1% arabinose and harvested after 5 hours. qPCR was conducted with primers targeting invasin and FFH was used as a reference gene for normalization. The fold change in the average expression level of invasin was then calculated using the standard curve method for relative quantification. The average expression level of invasin in the induced sample was 25 times greater than that in the uninduced sample. The average level of invasin expression is cal-S8 Fig. Uptake in different host cells types using different bacterial strains. (A) Flow cytom-etry. Mammalian cells were labeled with Bl-integrin antibody for 60 minutes, washed, then co-incubated with GFP-expressing E. coli also expressing Invasin from pBAC-AraInV (cultured in 0.1% arabinose) at the indicated MOI along with fluorescent-conjugated secondary antibody (Alexa647) for 90 minutes prior to washing. Scatter plots show the relationship between uptake (Log GFP) and Bl-integrins (Log Alexa647) in individual cells. (B) Statistical summary of data in (A). Shown are the major axes and mean of GFP+ subpopulations from (A). (C) Power-law parameters for calculated from samples presented in (A) and data in (B). (D) Flow cytometry. Indicated mammalian cells were co-cultured with the indicated GFP-expressing strain of E. coli (pSCT7InV, pBACr-Aralnv) as described in (A). Shown on right are the major axes of GFP+ subpopulations scaled by their respective MOI.
(A) For each parameter, the zipper model simulates several perturbations over two orders of magnitude. This results in quasi-parallel lines consistent with the emergence of the power-law, analogous to that of Fig 2B; the spread of the distribution of each set of lines is determined by the model’s sensitivity to each respective parameter. Lines extended from the power-law simulation results no longer remain parallel away from biologically relevant receptor concentrations. As the power-law breaks down, each line approximately approaches a common point of convergence (R*, P*). The subset shown above consists of one line chosen from each perturbation. (B) Apparent linear correlation between fitted power-law parameters is defined by the quasi-constant locus (R*, P*), which determines the slope and intercept for the lines of best fit. Every point will fall along the same trajectory, as described by equation fl (TIF)
We thank Dr. I Christopher Anderson for the arabinose-inducible plasmid, and Dr. Joseph Nevins for mammalian cells.
Performed the experiments: TIL SB. Analyzed the data: TIL IW SB AIL LY. Contributed reagents/materials/ analysis tools: LY FY. Wrote the paper: TIL IW SB AIL AL FY LY. Implemented and analyzed the kinetic models: TIL IW.
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