CD4 T Cell-Derived IFN-γ Plays a Minimal Role in Control of Pulmonary Mycobacterium tuberculosis Infection and Must Be Actively Repressed by PD-1 to Prevent Lethal Disease

IFN-γ–producing CD4 T cells are required for protection against Mycobacterium tuberculosis (Mtb) infection, but the extent to which IFN-γ contributes to overall CD4 T cell-mediated protection remains unclear. Furthermore, it is not known if increasing IFN-γ production by CD4 T cells is desirable in Mtb infection. Here we show that IFN-γ accounts for only ~30% of CD4 T cell-dependent cumulative bacterial control in the lungs over the first six weeks of infection, but >80% of control in the spleen. Moreover, increasing the IFN-γ–producing capacity of CD4 T cells by ~2 fold exacerbates lung infection and leads to the early death of the host, despite enhancing control in the spleen. In addition, we show that the inhibitory receptor PD-1 facilitates host resistance to Mtb by preventing the detrimental over-production of IFN-γ by CD4 T cells. Specifically, PD-1 suppressed the parenchymal accumulation of and pathogenic IFN-γ production by the CXCR3+KLRG1-CX3CR1- subset of lung-homing CD4 T cells that otherwise mediates control of Mtb infection. Therefore, the primary role for T cell-derived IFN-γ in Mtb infection is at extra-pulmonary sites, and the host-protective subset of CD4 T cells requires negative regulation of IFN-γ production by PD-1 to prevent lethal immune-mediated pathology.

In our experiments, Mtb cell numbers in the lung or spleen were continuously increasing over time. To estimate the relative efficacy at which IFN-γ-producing CD4 T cells suppress Mtb growth we used a simple mathematical model. In the model the bacterial population grows exponentially in the presence of immunity in accord with equation (see Figure S1): where g is the net accumulation rate of bacteria in the absence of immunity and k is the rate at which Mtb growth is suppressed by immunity. In general, both rates g and k could be (and are likely to be) time-dependent but our results were not strongly dependent on the assumption of the constant rates g and k (see below). Starting with B 0 bacteria, the number of bacteria at time t in the presence of immunity is If rates g and k were time-dependent, then the difference (g −k) should be treated as an average g − k = g − k over the time period (0, t). The impact of the immunity on Mtb growth could be calculated in several different ways.
The most straighforward way was to estimate the rate of Mtb growth as the slope in the change in natural logarithm of Mtb counts over time. Then the efficacy of the immune response would be given by the difference in the slopes in the presence and absence of immunity, normalized by the Mtb growth rate in the absence of immunity. This would result in the estimate for immune response efficacy as k/g. However, because of noisy measurements of Mtb counts in lungs of individual mice this method led to relatively imprecise measurements of immune response-mediated suppression efficacy (results not shown). Another method was to calculate the total area under the curve describing bacterial counts over time. Area under the curve tended to be a more robust measure of growth because it was averaged over all the mice used in experiments. There are at least three different ways of how area under the Mtb counts curve could be calculated. First was to calculate the average under the log-transformed Mtb counts. Using eqn. (S.2) this area-under-cuver (AUC) quantity is given by The relative efficacy of the immune response at reducing bacterial numbers is then calculated as the ratio of the difference in cumulative number of CFUs over cumulative CFU in the absence of immunity for the infection of duration T Efficacy A ≈ k g if the ratio B 0 /T is sufficiently small. Note that extending the initial model (eqn. (S.1)) with time-dependent rates g and k could still allow to calculate the average efficacy of the immune response, =k/ḡ withḡ andk being the average growth and suppression rates over the infection.
Second was to calculate the total cumulative number of bacteria N and and then compare the logarithms of cumulative numer in the absence or presence of immunity. The cumulative number of bacteria by time t is given by (S.5) and the relative efficacy of the immune response in suppressing bacterial accumulation is then Efficacy N ≈ k g if T is sufficiently large and g k. This method requires more assumptions and is not easily extentable to time-dependent rates g and k (results not shown).
Third and final method was to calculate the efficacy by using the cumulative number of bacteria over the course of infection, Nt = N (0,t)−N (k,t) . This method did not lead to an accurate estimate of the relative efficacy of the immune response at suppressive bacterial growth (results not shown). Taken together, out of three methods involving analysis of the total number of bacteria over the course of infection the first method (eqn. (S.4)) was the most robust method to estimate the impact of immunity on bacterial growth.
To calculate the efficacy of CD4 T cell-derived IFN-γ in suppression of Mtb growth in the lung and spleen we estimated the total area under the log-transformed CFUs over the course of infection (eqn. (S.3)). As we performed three different types of experiments, we calculated three different AUCs ( Figure S1): A 0 was the cumulative Mtb counts in the absence of T cells, A g was the cumulative Mtb counts in RAG1-deficient hosts reconsitituted with IFN-γ-deficient CD4 T cells, and A T was the cumulative Mtb counts in RAG1-deficient hosts reconsituted with wild-type CD4 T cells. By dividing the total suppression efficacy of CD4 T cells (k) into IFN-γ-dependent (k γ ) and IFN-γ-independent efficacy (k i , so k = k γ + k i ), a simple model (eqn. (S.1)) predicted the following expressions for cumulative Mtb counts and the relative efficacy of IFN-γ in suppressing Mtb growth after a simple algebra is given by (see Figure S1) Note that because the denominator in eqn. (S.8) involves the difference in two areas under the curve, the calculation of efficacy is independent of the assumption of a small product 2B 0 /T as this term disappears in the difference. Thus, experimental ratio of differences in AUC given eqn. (S.8) provides a reasonable estimate of the relative efficacy of CD4 T cell-derived IFN-γ in suppressing Mtb growth. Therefore, it was used in the main text.