Decoding dendritic cell function through module and network analysis

Immune function of miR-214 and its application prospects as molecular marker

MicroRNAs are a class of evolutionary conserved non-coding small RNAs that play key regulatory roles at the post-transcriptional level. In recent years, studies have shown that miR-214 plays an important role in regulating several biological processes such as cell proliferation and differentiation, tumorigenesis, inflammation and immunity, and it has become a hotspot in the miRNA field. In this review, the regulatory functions of miR-214 in the proliferation, differentiation and functional activities of immune-related cells, such as dendritic cells, T cells and NK cells, were briefly reviewed. Also, the mechanisms of miR-214 involved in tumor immunity, inflammatory regulation and antivirus were discussed. Finally, the value and application prospects of miR-214 as a molecular marker in inflammation and tumor related diseases were analyzed briefly. We hope it can provide reference for further study on the mechanism and application of miR-214.

Characterization of Dendritic Cell Subsets Through Gene Expression Analysis

Dendritic cells (DCs) are immune sentinels of the body and play a key role in the orchestration of the communication between the innate and the adaptive immune systems. DCs can polarize innate and adaptive immunity toward a variety of functions, sometimes with opposite roles in the overall control of immune responses (e.g., tolerance or immunosuppression versus immunity) or in the balance between various defense mechanisms promoting the control of different types of pathogens (e.g., antiviral versus antibacterial versus anti-worm immunity). These multiple DC functions result both from the plasticity of individual DC to exert different activities and from the existence of various DC subsets specialized in distinct functions. Functional genomics represents a powerful, unbiased, approach to better characterize these two levels of DC plasticity and to decipher its molecular regulation. Indeed, more and more experimental immunologists are generating high-throughput data in order to better characterize different states of DC based, for example, on their belonging to a specific subpopulation and/or on their exposure to specific stimuli and/or on their ability to exert a specific function. However, the interpretation of this wealth of data is severely hampered by the bottleneck of their bioinformatics analysis. Indeed, most experimental immunologists lack advanced computational or bioinformatics expertise and do not know how to translate raw gene expression data into potential biological meaning. Moreover, subcontracting such analyses is generally disappointing or financially not sustainable, since companies generally propose canonical analysis pipelines that are often unadapted for the structure of the data to analyze or for the precise type of questions asked. Hence, there is an important need of democratization of the bioinformatics analyses of gene expression profiling studies, in order to accelerate interpretation of the results by the researchers at the origin of the research project, of the data and who know best the underlying biology. This chapter will focus on the analysis of DC subset transcriptomes as measured by microarrays. We will show that simple bioinformatics procedures, applied one after the other in the framework of a pipeline, can lead to the characterization of DC subsets. We will develop two tutorials based on the reanalysis of public gene expression data. The first tutorial aims at illustrating a strategy for establishing the identity of DC subsets studied in a novel context, here their in vitro generation in cultures of human CD34(+) hematopoietic progenitors. The second tutorial aims at illustrating how to perform a posteriori bioinformatics analyses in order to evaluate the risk of contamination or of improper identification of DC subsets during preparation of biological samples, such that this information is taken into account in the final interpretation of the data and can eventually help to redesign the sampling strategy.

Systems approaches to unravel innate immune cell diversity, environmental plasticity and functional specialization

Innate immune cells are generated through central and peripheral differentiation pathways, and receive multiple signals from tissue microenvironment. The complex interplay between immune cell state and environmental signals is crucial for the adaptation and efficient response to pathogenic threats. Here, we discuss how systems biology approaches have brought global view and high resolution to the characterization of (1) immune cell diversity, (2) phenotypic, transcriptional and functional changes in response to environmental signals, (3) integration of multiple stimuli. We will mostly focus on systems level studies in dendritic cells and macrophages. Generalization of these approaches should elucidate innate immune cell diversity and plasticity, and may be used in the human to generate hypothesis on cell filiation and novel strategies for immunotherapy.

Tailored immunity by skin antigen-presenting cells

Skin vaccination aims at targeting epidermal and dermal antigen-presenting cells (APCs), indeed many subsets of different origin endowed with various functions populate the skin. The idea that the skin could represent a particularly potent site to induce adaptive and protective immune response emerged after the success of vaccinia virus vaccination by skin scarification. Recent advances have shown that multiple subsets of APCs coexist in the skin and participate in immunity to infectious diseases. Induction of an adaptive immune response depends on the initial recognition and capture of antigens by skin APCs and their transport to lymphoid organs. Innovative strategies of vaccination have thus been developed to target skin APCs for tailored immunity, hence this review will discuss recent insights into skin APC subsets characterization and how they can shape adaptive immune responses.

Enhancing the functional content of eukaryotic protein interaction networks

Protein interaction networks are a promising type of data for studying complex biological systems. However, despite the rich information embedded in these networks, these networks face important data quality challenges of noise and incompleteness that adversely affect the results obtained from their analysis. Here, we apply a robust measure of local network structure called common neighborhood similarity (CNS) to address these challenges. Although several CNS measures have been proposed in the literature, an understanding of their relative efficacies for the analysis of interaction networks has been lacking. We follow the framework of graph transformation to convert the given interaction network into a transformed network corresponding to a variety of CNS measures evaluated. The effectiveness of each measure is then estimated by comparing the quality of protein function predictions obtained from its corresponding transformed network with those from the original network. Using a large set of human and fly protein interactions, and a set of over 100 GO terms for both, we find that several of the transformed networks produce more accurate predictions than those obtained from the original network. In particular, the HC.cont measure and other continuous CNS measures perform well this task, especially for large networks. Further investigation reveals that the two major factors contributing to this improvement are the abilities of CNS measures to prune out noisy edges and enhance functional coherence in the transformed networks.

A systems biology approach to the analysis of subset-specific responses to lipopolysaccharide in dendritic cells

Dendritic cells (DCs) are critical for regulating CD4 and CD8 T cell immunity, controlling Th1, Th2, and Th17 commitment, generating inducible Tregs, and mediating tolerance. It is believed that distinct DC subsets have evolved to control these different immune outcomes. However, how DC subsets mount different responses to inflammatory and/or tolerogenic signals in order to accomplish their divergent functions remains unclear. Lipopolysaccharide (LPS) provides an excellent model for investigating responses in closely related splenic DC subsets, as all subsets express the LPS receptor TLR4 and respond to LPS in vitro. However, previous studies of the LPS-induced DC transcriptome have been performed only on mixed DC populations. Moreover, comparisons of the in vivo response of two closely related DC subsets to LPS stimulation have not been reported in the literature to date. We compared the transcriptomes of murine splenic CD8 and CD11b DC subsets after in vivo LPS stimulation, using RNA-Seq and systems biology approaches. We identified subset-specific gene signatures, which included multiple functional immune mediators unique to each subset. To explain the observed subset-specific differences, we used a network analysis approach. While both DC subsets used a conserved set of transcription factors and major signalling pathways, the subsets showed differential regulation of sets of genes that ‘fine-tune’ the network Hubs expressed in common. We propose a model in which signalling through common pathway components is ‘fine-tuned’ by transcriptional control of subset-specific modulators, thus allowing for distinct functional outcomes in closely related DC subsets. We extend this analysis to comparable datasets from the literature and confirm that our model can account for cell subset-specific responses to LPS stimulation in multiple subpopulations in mouse and man.

Systems biology of host-mycobiota interactions: dissecting Dectin-1 and Dectin-2 signalling in immune cells with DC-ATLAS

Modelling the networks sustaining the fruitful coexistence between fungi and their mammalian hosts is becoming increasingly important to control emerging fungal pathogens. The C-type lectins Dectin-1 and Dectin-2 are involved in host defense mechanisms against fungal infection driving inflammatory and adaptive immune responses and complement in containing fungal burdens. Recognizing carbohydrate structures in pathogens, their engagement induces maturation of dendritic cells (DCs) into potent immuno-stimulatory cells endowed with the capacity to efficiently prime T cells. Owing to these properties, Dectin-1 and Dectin-2 agonists are currently under investigation as promising adjuvants in vaccination procedures for the treatment of fungal infection. Thus, a detailed understanding of events' cascade specifically triggered in DCs upon engagement is of great interest in translational research. Here, we summarize the current knowledge on Dectin-1 and Dectin-2 signalling in DCs highlighting similarities and differences. Detailed maps are annotated, using the Biological Connection Markup Language (BCML) data model, and stored in DC-ATLAS, a versatile resource for the interpretation of high-throughput data generated perturbing the signalling network of DCs.