ABSTRACT
The Dot/Icm system of Legionella pneumophila triggers activation of caspase-3 during early stages of infection of human macrophages, but apoptosis is delayed until late stages of infection. During early stages of infection of mouse macrophages, the organism triggers rapid caspase-1-mediated cytotoxicity, which is mediated by bacterial flagellin. However, it is not known whether caspase-1 is triggered by L. pneumophila in human macrophages or whether caspase-3 is activated in permissive or nonpermissive mouse macrophages. Using single-cell analyses, we show that the wild-type strain of L. pneumophila does not trigger caspase-1 activation throughout the intracellular infection of human monocyte-derived macrophages (hMDMs), even when the flagellated bacteria escape into the cytoplasm during late stages. Using single-cell analyses, we show that the Dot/Icm system of L. pneumophila triggers caspase-3 but not caspase-1 within permissive A/J mouse bone marrow-derived primary macrophages by 2 to 8 h, but apoptosis is delayed until late stages of infection. While L. pneumophila triggers a Dot/Icm-dependent activation of caspase-1 in nonpermissive BALB/c mouse-derived macrophages, caspase-3 is not activated at any stage of infection. We show that robust intrapulmonary replication of the wild-type strain of L. pneumophila in susceptible A/J mice is associated with late-stage Dot/Icm-dependent pulmonary apoptosis and alveolar inflammation. In the lungs of nonpermissive BALB/c mice, L. pneumophila does not replicate and does not trigger pulmonary apoptosis or alveolar inflammation. Thus, similar to hMDMs, L. pneumophila does not trigger caspase-1 but triggers caspase-3 activation during early and exponential replication in permissive A/J mouse-derived macrophages, and apoptosis is delayed until late stages of infection. The Dot/Icm type IV secretion system is essential for pulmonary apoptosis in the genetically susceptible A/J mice.
Infinity Flow: High-Throughput Single-Cell Quantification of 100s of Proteins Using Conventional Flow Cytometry and Machine Learning
