BackgroundInferring the structure of gene regulatory networks (GRN) from a collection of gene expression data has many potential applications, from the elucidation of complex biological processes to the identification of potential drug targets. It is however a notoriously difficult problem, for which the many existing methods reach limited accuracy.ResultsIn this paper, we formulate GRN inference as a sparse regression problem and investigate the performance of a popular feature selection method, least angle regression (LARS) combined with stability selection, for that purpose. We introduce a novel, robust and accurate scoring technique for stability selection, which improves the performance of feature selection with LARS. The resulting method, which we call TIGRESS (for Trustful Inference of Gene REgulation with Stability Selection), was ranked among the top GRN inference methods in the DREAM5 gene network inference challenge. In particular, TIGRESS was evaluated to be the best linear regression-based method in the challenge. We investigate in depth the influence of the various parameters of the method, and show that a fine parameter tuning can lead to significant improvements and state-of-the-art performance for GRN inference, in both directed and undirected settings.ConclusionsTIGRESS reaches state-of-the-art performance on benchmark data, including both in silico and in vivo (E. coli and S. cerevisiae) networks. This study confirms the potential of feature selection techniques for GRN inference. Code and data are available on http://cbio.ensmp.fr/tigress. Moreover, TIGRESS can be run online through the GenePattern platform (GP-DREAM, http://dream.broadinstitute.org).

Tissue-resident macrophages are a diverse population of cells that perform specialized functions including sustaining tissue homeostasis and tissue surveillance. Here, we report an interstitial subset of CD169+ lung-resident macrophages that are transcriptionally and developmentally distinct from alveolar macrophages (AMs). They are primarily localized around the airways and are found in close proximity to the sympathetic nerves in the bronchovascular bundle. These nerve- and airway-associated macrophages (NAMs) are tissue resident, yolk sac derived, self-renewing, and do not require CCR2+ monocytes for development or maintenance. Unlike AMs, the development of NAMs requires CSF1 but not GM-CSF. Bulk population and single-cell transcriptome analysis indicated that NAMs are distinct from other lung-resident macrophage subsets and highly express immunoregulatory genes under steady-state and inflammatory conditions. NAMs proliferated robustly after influenza infection and activation with the TLR3 ligand poly(I:C), and in their absence, the inflammatory response was augmented, resulting in excessive production of inflammatory cytokines and innate immune cell infiltration. Overall, our study provides insights into a distinct subset of airway-associated pulmonary macrophages that function to maintain immune and tissue homeostasis.