A Critical, Nonlinear Threshold Dictates Bacterial Invasion and Initial Kinetics During Influenza

Abstract

Secondary bacterial infections increase morbidity and mortality of influenza A virus (IAV) infections. Bacteria are able to invade due to virus-induced depletion of alveolar macrophages (AMs), but this is not the only contributing factor. By analyzing a kinetic model, we uncovered a nonlinear initial dose threshold that is dependent on the amount of virus-induced AM depletion. The threshold separates the growth and clearance phenotypes such that bacteria decline for dose-AM depletion combinations below the threshold, stay constant near the threshold, and increase above the threshold. In addition, the distance from the threshold correlates to the growth rate. Because AM depletion changes throughout an IAV infection, the dose requirement for bacterial invasion also changes accordingly. Using the threshold, we found that the dose requirement drops dramatically during the first 7d of IAV infection. We then validated these analytical predictions by infecting mice with doses below or above the predicted threshold over the course of IAV infection. These results identify the nonlinear way in which two independent factors work together to support successful post-influenza bacterial invasion. They provide insight into coinfection timing, the heterogeneity in outcome, the probability of acquiring a coinfection, and the use of new therapeutic strategies to combat viral-bacterial coinfections.

Host‐pathogen kinetics during influenza infection and coinfection: insights from predictive modeling

“…Uniquely identifying each parameter in a model has been challenging 57,58 but has not limited the predictive capability. 41,44 The standard viral kinetic model has seven unknown parameters (β, p, k, c, δ, V 0 , and T 0 (see Figure 2)). In most studies, the values of the eclipse phase parameter (k) and initial target cells (T 0 ) are fixed because their values can be calculated.…”

“…Importantly, correlated parameters do not inhibit the accuracy of the parameter estimates or the insight gained from the model. 41,44 Knowledge about correlated parameters should not encourage fixing parameters or fitting combinations of parameters because this may inadvertently skew the results and lead to important information being lost.…”

“…111 ing influenza virus infection (discussed below). 41,44,113 Examining the host-pathogen feedbacks during influenza-bacterial coinfection first required a quantitative description of a pneumococcal infection. 64…”

“…The models have successfully quantified and predicted the viral load kinetics from clinical and experimental infections, [12][13][14][15][16][17][18][19][20][21][22][23][24][25] the symptoms that arise during infection, 12,13 the dynamics and efficiency of different host immune responses, 14,17,18,[25][26][27][28][29][30][31][32][33][34] the effect of different viral and host factors, 15,16,[20][21][22]35 the efficacy and design of vaccines and antiviral therapies, 14,[19][20][21][22][36][37][38][39][40] and the mechanisms of coinfection between influenza viruses and other viruses or bacteria. [41][42][43]…”

Mathematical modeling of within‐host Zika virus dynamics

“…The period of time before infected cells start producing virus is referred to as the eclipse phase since the presence of these cells cannot be observed in the viral load data.The amount of time that cells spend in the eclipse phase determines the form of the equation describing the rate of change of I 1 . A commonly made assumption is that transition from I 1 to I 2 occurs at rate kI 1 , where k is a constant 22,30,68,69. This is equivalent to assuming the amount of time it takes before an infected cell starts producing virus is exponentially distributed, giving a model in which the singleODE for I and the ODE for V in Equation 1 is replaced byIn this model, the average time spent in the eclipse phase is 1/k.…”