Protein kinase C-delta (PKCδ), a marker of inflammation and tuberculosis disease progression in humans, is important for optimal macrophage killing effector functions and survival in mice.

This corrects the article DOI: 10.1038/mi.2017.68.

Protein kinase C-delta (PKCδ), a marker of inflammation and tuberculosis disease progression in humans, is important for optimal macrophage killing effector functions and survival in mice.

We previously demonstrated that protein kinase C-δ (PKCδ) is critical for immunity against Listeria monocytogenes, Leishmania major, and Candida albicans infection in mice. However, the functional relevance of PKCδ during Mycobacterium tuberculosis (Mtb) infection is unknown. PKCδ was significantly upregulated in whole blood of patients with active tuberculosis (TB) disease. Lung proteomics further revealed that PKCδ was highly abundant in the necrotic and cavitory regions of TB granulomas in multidrug-resistant human participants. In murine Mtb infection studies, PKCδ-/- mice were highly susceptible to tuberculosis with increased mortality, weight loss, exacerbated lung pathology, uncontrolled proinflammatory cytokine responses, and increased mycobacterial burdens. Moreover, these mice displayed a significant reduction in alveolar macrophages, dendritic cells, and decreased accumulation of lipid bodies (lungs and macrophages) and serum fatty acids. Furthermore, a peptide inhibitor of PKCδ in wild-type mice mirrored lung inflammation identical to infected PKCδ-/- mice. Mechanistically, increased bacterial growth in macrophages from PKCδ-/- mice was associated with a decline in killing effector functions independent of phagosome maturation and autophagy. Taken together, these data suggest that PKCδ is a marker of inflammation during active TB disease in humans and required for optimal macrophage killing effector functions and host protection during Mtb infection in mice.

Quantifying gene network connectivity in silico: scalability and accuracy of a modular approach.

Large, complex data sets that are generated from microarray experiments, create a need for systematic analysis techniques to unravel the underlying connectivity of gene regulatory networks. A modular approach, previously proposed by Kholodenko and co-workers, helps to scale down the network complexity into more computationally manageable entities called modules. A functional module includes a gene's mRNA, promoter and resulting products, thus encompassing a large set of interacting states. The essential elements of this approach are described in detail for a three-gene model network and later extended to a ten-gene model network, demonstrating scalability. The network architecture is identified by analysing in silico steady-state changes in the activities of only the module outputs, communicating intermediates, that result from specific perturbations applied to the network modules one at a time. These steady-state changes form the system response matrix, which is used to compute the network connectivity or network interaction map. By employing a known biochemical network, the accuracy of the modular approach and its sensitivity to key assumptions are evaluated.