Fig 1.
Cell-cell variability in invasin-mediated bacterial uptake.
(A) Microscope image of phase and GFP signals from HeLa human cervical cancer cells co-cultured with E. coli harboring plasmids directing expression of (left) GFP only or (center, right) GFP and invasin (pSCT7Inv) at a multiplicity of infection (MOI) of 100 per mammalian cell for 60 minutes in the presence of gentamicin. Image on right is a magnification of boxed region. (B and C) Flow cytometry signals from E. coli (GFP/Invasin) incubated with (B) HeLa cells or (C) the indicated cell line as described in (A).
Fig 2.
Zipper-mediated uptake can be described by a power-law.
(A) Schematic of a 3-stage zipper model. Free bacteria (B) reversibly bind to β1-integrins in three stages: Unstable, singly bound (B1); stable attachment via a minimal number of interactions (Bm); and a maximal number of interactions (Bn). Uptake is defined as the sum of Bm and Bn. (B) Simulated bacterial uptake per cell as a function of total β1-integrins ranging from 105 to 106 per cell for various MOI. Lines indicate the linear dependence of bacterial uptake on receptor concentration and circles indicate mean bacterial uptake in the infected host cells for each bacterial MOI. Inset shows linear fit to means. (C) The dependence of the uptake probability by a single bacterium on the host receptor concentration is independent of the bacterial concentration. Note that the uptake curves for larger MOI span a larger region of the same trajectory. The uptake probability is defined as the overall bacterial uptake normalized with respect to the total bacterial MOI. Inset shows power-law fit to means. (D) Power-law dependence arises in a limiting case of a more general Hill-type dose response. The zipper mechanism is a cooperative, multistep, and reversible process well-described by a Hill function. A power-law is a limiting case when the receptor concentration is less than the scaled effective dissociation constant i.e. R<<KDeff1/β (blue shaded region). This condition is met when a highly cooperative, multistep, reversible process reduces the likelihood of finding bacteria in intermediate states. This regime corresponds to the linear region in a log-log plot (shaded region in inset).
Fig 3.
Uptake of bacteria is independent of one another.
(A) Flow cytometry measurements of bacterial uptake at various MOI. HeLa cells were labeled with β1-integrin antibody for 60 minutes, washed, co-incubated with GFP-expressing E. coli grown in 0.1% arabinose to induce high-level of invasin expression from the pBACr-AraInv plasmid, along with fluorescent-conjugated secondary antibody (Alexa647) for 90 minutes prior to washing. Scatter plots show the relationship between uptake (Log GFP) and β1-integrins (Log Alexa647) in individual cells. Each uptake distribution for MOI>0 is bimodal with infected cells (GFP+ mode) coexisting with those devoid of bacteria (GFP- mode). The two modes are designated by a Gaussian mixture model (see (B)), each mode parameterized by a mean and a standard deviation. Colored and black points indicate GFP+ and GFP- subpopulations, respectively. (B) Processing of data for MOI = 50 in (A). The expectation-maximization (EM) algorithm was used to probabilistically assign cells into either GFP- sub-population (black) or GFP+ sub-population (red). Histograms summarize data from each respective channel; Principle components represented by ellipse and major axis superimposed on scatter plot (circle). The half-length of the major axis (i.e. from the center to a vertex) represents twice the standard deviation in that direction. (C) Correlation between uptake and host receptor levels. Using flow cytometry data in (A), each line approximates the dependence of the bacterial uptake in the GFP+ subpopulation on the host receptor concentration for the corresponding MOI. The length of each line is 4 times the standard deviation along the major axis. Mean indicated by circle. Inset shows linear fit on means. (D) Uptake probability. Using flow cytometry data in (A), GFP values were scaled by their respective MOI and then processed as in (C). These curves approximate the dependence of the uptake probability per bacterium on the host receptor concentrations. (E) Simulations showing the dependence of uptake on the host receptor number per cell at bacterial MOI of 250 for two values of the invasin dissociation constant (KD = krB1/kfB1). (F) Experimental modulation of invasin expression. HeLa cells were incubated with E. coli (GFP/Invasin) grown in the indicated concentrations of arabinose to vary invasin expression, which leads to enhanced binding of bacteria to the host receptors. Shown are the major axes and mean of GFP+ subpopulations scaled by their respective MOI.
Fig 4.
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 = Rβ/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 S8D Fig