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.

The spleen is an important site for generating protective immune responses against pathogens. After infection, immune cells undergo rapid reorganization to initiate and maintain localized inflammatory responses; however, the mechanisms governing this spatial and temporal cellular reorganization remain unclear. We show that the strategic position of splenic marginal zone CD169+ macrophages is vital for rapid initiation of antibacterial responses. In addition to controlling initial bacterial growth, CD169+ macrophages orchestrate a second phase of innate protection by mediating the transport of bacteria to splenic T cell zones. This compartmentalization of bacteria within the spleen was essential for driving the reorganization of innate immune cells into hierarchical clusters and for local interferon-γ production near sites of bacterial replication foci. Our results show that both phases of the antimicrobial innate immune response were dependent on CD169+ macrophages, and, in their absence, the series of events needed for pathogen clearance and subsequent survival of the host was disrupted. Our study provides insight into how lymphoid organ structure and function are related at a fundamental level.

Broad domain promoters and super enhancers are regulatory elements that govern cell-specific functions and harbor disease-associated sequence variants. These elements are characterized by distinct epigenomic profiles, such as expanded deposition of histone marks H3K27ac for super enhancers and H3K4me3 for broad domains, however little is known about how they interact with each other and the rest of the genome in three-dimensional chromatin space. Using network theory methods, we studied chromatin interactions between broad domains and super enhancers in three ENCODE cell lines (K562, MCF7, GM12878) obtained via ChIA-PET, Hi-C, and Hi-CHIP assays. In these networks, broad domains and super enhancers interact more frequently with each other compared to their typical counterparts. Network measures and graphlets revealed distinct connectivity patterns associated with these regulatory elements that are robust across cell types and alternative assays. Machine learning models showed that these connectivity patterns could effectively discriminate broad domains from typical promoters and super enhancers from typical enhancers. Finally, targets of broad domains in these networks were enriched in disease-causing SNPs of cognate cell types. Taken together these results suggest a robust and unique organization of the chromatin around broad domains and super enhancers: loci critical for pathologies and cell-specific functions.

Targeted therapies interfering with specifically one protein activity are promising strategies in the treatment of diseases like cancer. However, accumulated empirical experience has shown that targeting multiple proteins in signaling networks involved in the disease is often necessary. Thus, one important problem in biomedical research is the design and prioritization of optimal combinations of interventions to repress a pathological behavior, while minimizing side-effects. OCSANA (optimal combinations of interventions from network analysis) is a new software designed to identify and prioritize optimal and minimal combinations of interventions to disrupt the paths between source nodes and target nodes. When specified by the user, OCSANA seeks to additionally minimize the side effects that a combination of interventions can cause on specified off-target nodes. With the crucial ability to cope with very large networks, OCSANA includes an exact solution and a novel selective enumeration approach for the combinatorial interventions' problem. Availability: The latest version of OCSANA, implemented as a plugin for Cytoscape and distributed under LGPL license, is available together with source code at http://bioinfo.curie.fr/projects/ocsana.

Finding inclusion-minimal hitting sets for a given collection of sets is a fundamental combinatorial problem with applications in domains as diverse as Boolean algebra, computational biology, and data mining. Much of the algorithmic literature focuses on the problem of recognizing the collection of minimal hitting sets; however, in many of the applications, it is more important to generate these hitting sets. We survey twenty algorithms from across a variety of domains, considering their history, classification, useful features, and computational performance on a variety of synthetic and real-world inputs. We also provide a suite of implementations of these algorithms with a ready-to-use, platform-agnostic interface based on Docker containers and the AlgoRun framework, so that interested computational scientists can easily perform similar tests with inputs from their own research areas on their own computers or through a convenient Web interface.

Supplementary data are available at Bioinformatics online.

Boolean networks have long been used as models of molecular networks and play an increasingly important role in systems biology. This paper describes a software package, Polynome, offered as a web service, that helps users construct Boolean network models based on experimental data and biological input. The key feature is a discrete analog of parameter estimation for continuous models. With only experimental data as input, the software can be used as a tool for reverse-engineering of Boolean network models from experimental time course data.Comment: Web interface of the software is available at http://polymath.vbi.vt.edu/polynome