Deterministic reprogramming of neutrophils within tumors.

Neutrophils are increasingly recognized as key players in the tumor immune response and are associated with poor clinical outcomes. Despite recent advances characterizing the diversity of neutrophil states in cancer, common trajectories and mechanisms governing the ontogeny and relationship between these neutrophil states remain undefined. Here, we demonstrate that immature and mature neutrophils that enter tumors undergo irreversible epigenetic, transcriptional, and proteomic modifications to converge into a distinct, terminally differentiated dcTRAIL-R1+ state. Reprogrammed dcTRAIL-R1+ neutrophils predominantly localize to a glycolytic and hypoxic niche at the tumor core and exert pro-angiogenic function that favors tumor growth. We found similar trajectories in neutrophils across multiple tumor types and in humans, suggesting that targeting this program may provide a means of enhancing certain cancer immunotherapies.

Network analysis of large-scale ImmGen and Tabula Muris datasets highlights metabolic diversity of tissue mononuclear phagocytes.

The diversity of mononuclear phagocyte (MNP) subpopulations across tissues is one of the key physiological characteristics of the immune system. Here, we focus on understanding the metabolic variability of MNPs through metabolic network analysis applied to three large-scale transcriptional datasets: we introduce (1) an ImmGen MNP open-source dataset of 337 samples across 26 tissues; (2) a myeloid subset of ImmGen Phase I dataset (202 MNP samples); and (3) a myeloid mouse single-cell RNA sequencing (scRNA-seq) dataset (51,364 cells) assembled based on Tabula Muris Senis. To analyze such large-scale datasets, we develop a network-based computational approach, genes and metabolites (GAM) clustering, for unbiased identification of the key metabolic subnetworks based on transcriptional profiles. We define 9 metabolic subnetworks that encapsulate the metabolic differences within MNP from 38 different tissues. Obtained modules reveal that cholesterol synthesis appears particularly active within the migratory dendritic cells, while glutathione synthesis is essential for cysteinyl leukotriene production by peritoneal and lung macrophages.

CD163+ cytokine-producing cDC2 stimulate intratumoral type 1 T cell responses in HPV16-induced oropharyngeal cancer.

BACKGROUND: Human papillomavirus (HPV)-associated oropharyngeal squamous cell carcinoma (OPSCC) is a distinct clinical entity with a much better prognosis after (chemo)radiotherapy than HPV-negative OPSCC, especially in patients with a concomitant intratumoral HPV-specific and type-1 cytokine-oriented T cell response. However, knowledge on the type of myeloid cells and their coordination with intratumoral T cells and influence on patient outcome in OPSCC is lacking. METHODS: We analyzed the presence of intratumoral myeloid cells and their relationship to tumor-infiltrating T cells and patient outcome in a well-described cohort of HPV16+ patients with OPSCC using multispectral immunofluorescence, flow cytometry and functional analyses. RESULTS: We show that the tumor microenvironment of HPV16+ OPSCC tumors with such an ongoing HPV16-specific T cell response is highly infiltrated with a newly defined CD163+ cytokine-producing subset of conventional dendritic cell type 2 (cDC2), called DC3. These CD163+ cDC2 predominantly stimulated type 1 T cell polarization and produced high levels of interleukin-12 (IL-12) and IL-18, required for IFNγ and IL-22 production by T cells after cognate antigen stimulation. Tumor-infiltration with these CD163+ cDC2 positively correlated with the infiltration by Tbet+ and tumor-specific T cells, and with prolonged survival. CONCLUSIONS: These data suggest an important role for intratumoral CD163+ cDC2 in stimulating tumor-infiltrating T cells to exert their antitumor effects.

RIG-I Activation by a Designer Short RNA Ligand Protects Human Immune Cells against Dengue Virus Infection without Causing Cytotoxicity.

Virus-derived double-stranded RNA (dsRNA) molecules containing a triphosphate group at the 5' end are natural ligands of retinoic acid-inducible gene I (RIG-I). The cellular pathways and proteins induced by RIG-I are an essential part of the innate immune response against viral infections. Starting from a previously published RNA scaffold (3p10L), we characterized an optimized small dsRNA hairpin (called 3p10LG9, 25 nucleotides [nt] in length) as a highly efficient RIG-I activator. Dengue virus (DENV) infection in cell lines and primary human skin cells could be prevented and restricted through 3p10LG9-mediated activation of RIG-I. This antiviral effect was RIG-I and interferon signal dependent. The effect was temporary and was reversed above a saturating concentration of RIG-I ligand. This finding revealed an effective feedback loop that controls potentially damaging inflammatory effects of the RIG-I response, at least in immune cells. Our results show that the small RIG-I activator 3p10LG9 can confer short-term protection against DENV and can be further explored as an antiviral treatment in humans.IMPORTANCE Short hairpin RNA ligands that activate RIG-I induce antiviral responses in infected cells and prevent or control viral infections. Here, we characterized a new short hairpin RNA molecule with high efficacy in antiviral gene activation and showed that this molecule is able to control dengue virus infection. We demonstrate how structural modifications of minimal RNA ligands can lead to increased potency and a wider window of RIG-I-activating concentrations before regulatory mechanisms kick in at high concentrations. We also show that minimal RNA ligands induce an effective antiviral response in human skin dendritic cells and macrophages, which are the target cells of initial infection after the mosquito releases virus into the skin. Using short hairpin RNA as RIG-I ligands could therefore be explored as antiviral therapy.

Hallmarks of improved immunological responses in the vaccination of more physically active elderly females.

Physical inactivity is one of the leading contributors to worldwide morbidity and mortality. The elderly are particularly susceptible since the features of physical inactivity overlap with the outcomes of natural aging - including the propensity to develop cardiovascular diseases, cancer, diabetes mellitus, sarcopenia and cognitive impairment. The age-dependent loss of immune function, or immunosenescence, refers to the progressive depletion of primary immune resources and is linked to the development of many of these conditions. Immunosenescence is primarily driven by chronic immune activation and physical activity interventions have demonstrated the potential to reduce the risk of complications in the elderly by modulating inflammation and augmenting the immune system. Since poor vaccination outcome is a hallmark of immunosenescence, the assessment of vaccine efficacy provides a window to study the immunological effects of regular physical activity. Using an accelerator-based study, we demonstrate in a Singaporean Chinese cohort that elderly women (n=56) who walk more after vaccination display greater post-vaccination expansion of monocytes and plasmablasts in peripheral blood. Active elderly female participants also demonstrated lower baseline levels of IP-10 and Eotaxin, and the upregulation of genes associated with monocyte/macrophage phagocytosis. We further describe postive correlations between the monocyte response and the post-vaccination H1N1 HAI titres of participants. Finally, active elderly women reveal a higher induction of antibodies against Flu B in their 18-month second vaccination follow-up. Altogether, our data are consistent with better immunological outcomes in those who are more physically active and highlight the pertinent contribution of monocyte activity.

Automated Identification of Core Regulatory Genes in Human Gene Regulatory Networks.

Human gene regulatory networks (GRN) can be difficult to interpret due to a tangle of edges interconnecting thousands of genes. We constructed a general human GRN from extensive transcription factor and microRNA target data obtained from public databases. In a subnetwork of this GRN that is active during estrogen stimulation of MCF-7 breast cancer cells, we benchmarked automated algorithms for identifying core regulatory genes (transcription factors and microRNAs). Among these algorithms, we identified K-core decomposition, pagerank and betweenness centrality algorithms as the most effective for discovering core regulatory genes in the network evaluated based on previously known roles of these genes in MCF-7 biology as well as in their ability to explain the up or down expression status of up to 70% of the remaining genes. Finally, we validated the use of K-core algorithm for organizing the GRN in an easier to interpret layered hierarchy where more influential regulatory genes percolate towards the inner layers. The integrated human gene and miRNA network and software used in this study are provided as supplementary materials (S1 Data) accompanying this manuscript.

Gene network inference using continuous time Bayesian networks: a comparative study and application to Th17 cell differentiation.

BACKGROUND: Dynamic aspects of gene regulatory networks are typically investigated by measuring system variables at multiple time points. Current state-of-the-art computational approaches for reconstructing gene networks directly build on such data, making a strong assumption that the system evolves in a synchronous fashion at fixed points in time. However, nowadays omics data are being generated with increasing time course granularity. Thus, modellers now have the possibility to represent the system as evolving in continuous time and to improve the models' expressiveness. RESULTS: Continuous time Bayesian networks are proposed as a new approach for gene network reconstruction from time course expression data. Their performance was compared to two state-of-the-art methods: dynamic Bayesian networks and Granger causality analysis. On simulated data, the methods comparison was carried out for networks of increasing size, for measurements taken at different time granularity densities and for measurements unevenly spaced over time. Continuous time Bayesian networks outperformed the other methods in terms of the accuracy of regulatory interactions learnt from data for all network sizes. Furthermore, their performance degraded smoothly as the size of the network increased. Continuous time Bayesian networks were significantly better than dynamic Bayesian networks for all time granularities tested and better than Granger causality for dense time series. Both continuous time Bayesian networks and Granger causality performed robustly for unevenly spaced time series, with no significant loss of performance compared to the evenly spaced case, while the same did not hold true for dynamic Bayesian networks. The comparison included the IRMA experimental datasets which confirmed the effectiveness of the proposed method. Continuous time Bayesian networks were then applied to elucidate the regulatory mechanisms controlling murine T helper 17 (Th17) cell differentiation and were found to be effective in discovering well-known regulatory mechanisms, as well as new plausible biological insights. CONCLUSIONS: Continuous time Bayesian networks were effective on networks of both small and large size and were particularly feasible when the measurements were not evenly distributed over time. Reconstruction of the murine Th17 cell differentiation network using continuous time Bayesian networks revealed several autocrine loops, suggesting that Th17 cells may be auto regulating their own differentiation process.

In silico modeling of cancer cell dissemination and metastasis.

Metastasis is the main cause of cancer-related death. It is surprising then that the exact nature of metastasis-the process by which cancer cells leave the primary tumor to reach distant organs, and resume proliferation-is not fully understood. Moreover, the different conditions under which the immune system can either promote or suppress metastasis are only now beginning to be uncovered. In recent years, our understanding of metastasis as a genocentric, cell-autonomous process has shifted toward a systemic model in which interactions between cancer cells and their surrounding microenvironments lead to dissemination and metastasis. In silico modeling of the various steps involved in metastasis can help provide an understanding of how tumor properties emerge from the complex interplays between tumor cells and their microenvironment. In silico models can also be useful in identifying the selective forces that favor the outcomes of cancer cells with metastatic potential.

Selection of Mesenchymal-Like Metastatic Cells in Primary Tumors - An in silico Investigation.

In order to metastasize, cancer cells must undergo phenotypic transition from an anchorage-dependent form to a motile form via a process referred to as epithelial to mesenchymal transition. It is currently unclear whether metastatic cells emerge late during tumor progression by successive accumulation of mutations, or whether they derive from distinct cell populations already present during the early stages of tumorigenesis. Similarly, the selective pressures that drive metastasis are poorly understood. Selection of cancer cells with increased proliferative capacity and enhanced survival characteristics may explain how some transformations promote a metastatic phenotype. However, it is difficult to explain how cancer cells that disseminate can emerge due to such selective pressure, since these cells usually remain dormant for prolonged periods of time. In the current study, we have used in silico modeling and simulation to investigate the hypothesis that mesenchymal-like cancer cells evolve during the early stages of primary tumor development, and that these cells exhibit survival and proliferative advantages within the tumor microenvironment. In an agent-based tumor microenvironment model, cancer cell agents with distinct sets of attributes governing nutrient consumption, proliferation, apoptosis, random motility, and cell adhesion were allowed to compete for space and nutrients. These simulation data indicated that mesenchymal-like cancer cells displaying high motility and low adhesion proliferate more rapidly and display a survival advantage over epithelial-like cancer cells. Furthermore, the presence of mesenchymal-like cells within the primary tumor influences the macroscopic properties, emergent morphology, and growth rate of tumors.

A database of annotated promoters of genes associated with common respiratory and related diseases.

Many genes have been implicated in the pathogenesis of common respiratory and related diseases (RRDs), yet the underlying mechanisms are largely unknown. Differential gene expression patterns in diseased and healthy individuals suggest that RRDs affect or are affected by modified transcription regulation programs. It is thus crucial to characterize implicated genes in terms of transcriptional regulation. For this purpose, we conducted a promoter analysis of genes associated with 11 common RRDs including allergic rhinitis, asthma, bronchiectasis, bronchiolitis, bronchitis, chronic obstructive pulmonary disease, cystic fibrosis, emphysema, eczema, psoriasis, and urticaria, many of which are thought to be genetically related. The objective of the present study was to obtain deeper insight into the transcriptional regulation of these disease-associated genes by annotating their promoter regions with transcription factors (TFs) and TF binding sites (TFBSs). We discovered many TFs that are significantly enriched in the target disease groups including associations that have been documented in the literature. We also identified a number of putative TFs/TFBSs that appear to be novel. The results of our analysis are provided in an online database that is freely accessible to researchers at http://www.respiratorygenomics.com. Promoter-associated TFBS information and related genomic features, such as histone modification sites, microsatellites, CpG islands, and SNPs, are graphically summarized in the database. Users can compare and contrast underlying mechanisms of specific RRDs relative to candidate genes, TFs, gene ontology terms, micro-RNAs, and biological pathways for the conduct of metaanalyses. This database represents a novel, useful resource for RRD researchers.

Jmjd3 contributes to the control of gene expression in LPS-activated macrophages.

Jmjd3, a JmjC family histone demethylase, is induced by the transcription factor NF-kB in response to microbial stimuli. Jmjd3 erases H3K27me3, a histone mark associated with transcriptional repression and involved in lineage determination. However, the specific contribution of Jmjd3 induction and H3K27me3 demethylation to inflammatory gene expression remains unknown. Using chromatin immunoprecipitation-sequencing we found that Jmjd3 is preferentially recruited to transcription start sites characterized by high levels of H3K4me3, a marker of gene activity, and RNA polymerase II (Pol_II). Moreover, 70% of lipopolysaccharide (LPS)-inducible genes were found to be Jmjd3 targets. Although most Jmjd3 target genes were unaffected by its deletion, a few hundred genes, including inducible inflammatory genes, showed moderately impaired Pol_II recruitment and transcription. Importantly, most Jmjd3 target genes were not associated with detectable levels of H3K27me3, and transcriptional effects of Jmjd3 absence in the window of time analysed were uncoupled from measurable effects on this histone mark. These data show that Jmjd3 fine-tunes the transcriptional output of LPS-activated macrophages in an H3K27 demethylation-independent manner.