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Macrophages are known to show highly dynamic responses to diverse stimuli, and have been implicated in metabolic syndrome and related diseases. Genome-wide association studies have identified many single-nucleotide polymorphisms (SNPs) associated with these common conditions, largely in non-coding regions; how these SNPs influence disease processes remains largely unknown. We hypothesized that examining RNA expression and chromatin state in macrophages across diverse activation conditions would elucidate the roles of macrophages in mediating the effects of disease-linked SNPs on the development of disease. We measured gene expression via RNAseq and histone 3 lysine 27 acetylation via ChIPseq as an indicator of promoter/enhancer activity in human blood monocyte-derived macrophages stimulated in vitro with a range of inflammatory and disease-relevant stimuli. Over 11,000 genes and 43,000 regulatory elements show condition-dependent activity in this dataset. Functional enrichment in modules of co-expressed transcripts confirmed expected stimuli-specific biology and reveal potentially novel relationships between distinct stimulation states. Correlation of regulatory element activity with module gene expression shows broad inflammatory/anti-inflammatory separation, and metabolic-trait associated SNPs tend to be embedded within regulatory elements whose activity is associated with both non-inflammatory and, surprisingly, some anti-viral macrophage gene modules. Ongoing work includes using these data to infer the transcriptional networks underlying these states. This study was supported by the Intramural Research Program of the National Institute of Allergy and Infectious Diseases, NIH.

Inflammation is common in virtually all diseases. Since immune cells travel through the blood to inflamed tissues, peripheral blood reflects inflammation at distant sites. However, blood biomarkers are hard to detect in global gene expression data since blood is not the site of perturbation and statistical corrections for multiple testing reduce sensitivity. We hypothesized that combining transcriptional signatures from inflamed tissue and machine learning would be useful to derive blood biomarkers. We first tested this in the mouse model of influenza infection. We identified a set of early genes in the lungs associated with lethality after infection. We then tested their differential abundance early in the blood of mice infected with a range of influenza doses. We applied elastic net to build a biomarker able to predict disease outcome (4 classes: early lethal, late lethal, non-lethal vs. non-infected; accuracy on training and validation sets: 91% and 79%). We next confirmed our hypothesis in lung cancer and breast cancer in humans. We identified sets of genes associated with tumors but not with tumor-free tissues and tested their differential abundance in the blood of patients compared to healthy controls. Using elastic net, we built two blood biomarkers to classify subjects according to disease status (case vs. control; accuracy on training and validation sets in breast cancer: 92% and 67%; in lung cancer: 89% and 78%). In conclusion, combining inflamed tissue signatures and machine learning, we were able to derive blood biomarkers for three different diseases in two different species. We expect that this novel strategy can be applied in other diseases as well. This work was supported by the Intramural Research Program of NIAID, NIH.

The mammalian gut is colonized by trillions of microorganisms termed the “microbiota”, which have a mutually beneficial relationship with their host. In normal individuals, the gut microbiota matures after birth to a state of balanced commensalism that is marked by the absence of adverse inflammation. Both innate lymphoid cells (ILCs) and antigen-specific conventional T cells contribute to containment and clearance of microbial pathogens. But how these two major lymphoid cell populations help shape the mature commensal (non-pathogenic) microbiome and maintain tissue homeostasis has not been determined. Using advanced multiplex quantitative imaging methods, here we show that in the absence of adaptive lymphocytes, IL-23 induced by specific commensal bacteria such as Segmented Filamentous Bacteria (SFB) persistently activates RORgt+ group 3 innate lymphoid cells (ILC3s) in the ileum to produce IL-22, which induces STAT3 activation in virtually all epithelial cells, contributing to production of molecules such as anti-microbial peptides that protect the tissue from microbial damage. The distinct roles of ILCs in handling gut microbes play out in normal mice during development. The pSTAT3 signature is absent after birth, which is followed by microbial colonization and strong ILC3 activation and an extensive epithelial pSTAT3 signature upon weaning. This innate immune activity is subsequently extinguished as adaptive CD4+ T cell immunity develops in response to the expanding commensal burden. Our findings provide new insights into how innate and adaptive lymphocytes sequentially operate during normal development to establish steady state commensalism.

Toll-like receptors (TLRs) expressed on macrophages are ligated in various combinations by diverse pathogen-associated molecular patterns (PAMPs), but how mixtures of signals through these receptors are integrated to mediate distinct biological responses to different pathogens remains unknown. By stimulating macrophages using a wide matrix of TLR2/TLR4 concentration combinations, we identified conditions resulting in non-linear responses. Transcriptomic and RNAi analyses identified negative regulators induced in a TLR-specific manner that regulated cytokine production dynamics and chemokine milieus across the combinatorial matrix. TLR4 ligand concentration explained much of the variation in production of many chemokines. However, combinations of sub-maximal amounts of either single ligand produced a zone of non-linear responses with sustained production of select inflammatory mediators, due to lack of negative regulation induced only at higher individual ligand concentrations. A similar sustained response was observed for Gram-positive bacteria, as compared to a panel of Gram-negative species, which express high amounts of the TLR4 ligand LPS. The novel TLR4-specific negative regulators we describe thus aid the innate immune system in discriminating Gram-negative from Gram-positive species, supporting qualitatively distinct inflammatory responses to these classes of bacteria. This research was supported by the Intramural Research Program of the NIH, NIAID