Immunological Genome Project and systems immunology

Transcription factor networks directing the development, function, and evolution of innate lymphoid effectors

Mammalian lymphoid immunity is mediated by fast and slow responders to pathogens. Fast innate lymphocytes are active within hours after infections in mucosal tissues. Slow adaptive lymphocytes are conventional T and B cells with clonal antigen receptors that function days after pathogen exposure. A transcription factor (TF) regulatory network guiding early T cell development is at the core of effector function diversification in all innate lymphocytes, and the kinetics of immune responses is set by developmental programming. Operational units within the innate lymphoid system are not classified by the types of pathogen-sensing machineries but rather by discrete effector functions programmed by regulatory TF networks. Based on the evolutionary history of TFs of the regulatory networks, fast effectors likely arose earlier in the evolution of animals to fortify body barriers, and in mammals they often develop in fetal ontogeny prior to the establishment of fully competent adaptive immunity.

Network representations of immune system complexity

The mammalian immune system is a dynamic multiscale system composed of a hierarchically organized set of molecular, cellular, and organismal networks that act in concert to promote effective host defense. These networks range from those involving gene regulatory and protein–protein interactions underlying intracellular signaling pathways and single‐cell responses to increasingly complex networks of in vivo cellular interaction, positioning, and migration that determine the overall immune response of an organism. Immunity is thus not the product of simple signaling events but rather nonlinear behaviors arising from dynamic, feedback‐regulated interactions among many components. One of the major goals of systems immunology is to quantitatively measure these complex multiscale spatial and temporal interactions, permitting development of computational models that can be used to predict responses to perturbation. Recent technological advances permit collection of comprehensive datasets at multiple molecular and cellular levels, while advances in network biology support representation of the relationships of components at each level as physical or functional interaction networks. The latter facilitate effective visualization of patterns and recognition of emergent properties arising from the many interactions of genes, molecules, and cells of the immune system. We illustrate the power of integrating ‘omics’ and network modeling approaches for unbiased reconstruction of signaling and transcriptional networks with a focus on applications involving the innate immune system. We further discuss future possibilities for reconstruction of increasingly complex cellular‐ and organism‐level networks and development of sophisticated computational tools for prediction of emergent immune behavior arising from the concerted action of these networks. WIREs Syst Biol Med 2015, 7:13–38. doi: 10.1002/wsbm.1288

This article is categorized under: 1

Analytical and Computational Methods > Computational Methods

2

Laboratory Methods and Technologies > Macromolecular Interactions, Methods

Quantification of cell identity from single-cell gene expression profiles

The definition of cell identity is a central problem in biology. While single-cell RNA-seq provides a wealth of information regarding cell states, better methods are needed to map their identity, especially during developmental transitions. Here, we use repositories of cell type-specific transcriptomes to quantify identities from single-cell RNA-seq profiles, accurately classifying cells from Arabidopsis root tips and human glioblastoma tumors. We apply our approach to single cells captured from regenerating roots following tip excision. Our technique exposes a previously uncharacterized transient collapse of identity distant from the injury site, demonstrating the biological relevance of a quantitative cell identity index.

Unravelling adverse reactions to NSAIDs using systems biology

We introduce the reader to systems biology, using adverse drug reactions (ADRs), specifically hypersensitivity reactions to multiple non-steroidal anti-inflammatory drugs (NSAIDs), as a model. To disentangle the different processes that contribute to these reactions - from drug intake to the appearance of symptoms - it will be necessary to create high-throughput datasets. Just as crucial will be the use of systems biology to integrate and make sense of them. We review previous work using systems biology to study related pathologies such as asthma/allergy, and NSAID metabolism. We show examples of their application to NSAIDs-hypersensitivity using current datasets. We describe breakthroughs in high-throughput technology and speculate on their use to improve our understanding of this and other drug-induced pathologies.

Genomic expression profiling of NK cells in health and disease

NK cells are important components of innate and adaptive immunity. Functionally, they play key roles in host defense against tumors and infectious pathogens. Within the past few years, genomic-scale experiments have provided us with a plethora of gene expression data that reveal an extensive molecular and biological map underlying gene expression programs. In order to better explore and take advantage of existing datasets, we review here the genomic expression profiles of NK cells and their subpopulations in resting or stimulated states, in diseases, and in different organs; moreover, we contrast these expression data to those of other lymphocytes. We have also compiled a comprehensive list of genomic profiling studies of both human and murine NK cells in this review.

Visualisation of the T cell differentiation programme by Canonical Correspondence Analysis of transcriptomes

Background

Currently, in the era of post-genomics, immunology is facing a challenging problem to translate mutant phenotypes into gene functions based on high-throughput data, while taking into account the classifications and functions of immune cells, which requires new methods.

Results

Here we propose a novel application of a multidimensional analysis, Canonical Correspondence Analysis (CCA), to reveal the molecular characteristics of undefined cells in terms of cellular differentiation programmes by analysing two transcriptomic datasets. Using two independent datasets, whether RNA-seq or microarray data, CCA successfully visualised the cross-level relationships between genes, cells, and differentiation programmes, and thereby identified the immunological features of mutant cells (Gata3-KO T cells and Stat3-KO T cells) in a data-oriented manner. With a new concept, differentiation variable, CCA provides an automatic classification of cell samples, which had a high sensitivity and a comparable performance to other classification methods. In addition, we elaborate how CCA results can be interpreted, and reveal the features of CCA in comparison with other visualisation techniques.

Conclusions

CCA is a visualisation tool with a classification ability to reveal the cross-level relationships of genes, cells and differentiation programmes. This can be used for characterising the functional defect of cells of interest (e.g. mutant cells) in the context of cellular differentiation. The proposed approach fits with common hypothesis-oriented studies in immunology, and can be used for a wide range of molecular and genomic studies on cellular differentiation mechanisms.

Electronic supplementary material

The online version of this article (doi:10.1186/1471-2164-15-1028) contains supplementary material, which is available to authorized users.

Systems-level analysis of innate immunity

Systems-level analysis of biological processes strives to comprehensively and quantitatively evaluate the interactions between the relevant molecular components over time, thereby enabling development of models that can be employed to ultimately predict behavior. Rapid development in measurement technologies (omics), when combined with the accessible nature of the cellular constituents themselves, is allowing the field of innate immunity to take significant strides toward this lofty goal. In this review, we survey exciting results derived from systems biology analyses of the immune system, ranging from gene regulatory networks to influenza pathogenesis and systems vaccinology.

Current transcriptomics in pig immunity research

Swine performance in the face of disease challenge is becoming progressively more important. To improve the pig’s robustness and resilience against pathogens through selection, a better understanding of the genetic and epigenetic factors in the immune response is required. This review highlights results from the most recent transcriptome research, and the meta-analyses performed, in the context of pig immunity. A technological overview is given including wholegenome microarrays, immune-specific arrays, small-scale high-throughput expression methods, high-density tiling arrays, and next generation sequencing (NGS). Although whole genome microarray techniques will remain complementary to NGS for some time in domestic species, research will transition to sequencing-based methods due to cost-effectiveness and the extra information that such methods provide. Furthermore, upcoming high-throughput epigenomic studies, which will add greatly to our knowledge concerning the impact of epigenetic modifications on pig immune response, are listed in this review. With emphasis on the insights obtained from transcriptomic analyses for porcine immunity, we also discuss the experimental design in pig immunity research and the value of the newly published porcine genome assembly in using the pig as a model for human immune response. We conclude by discussing the importance of establishing community standards to maximize the possibility of integrative computational analyses, such as was clearly beneficial for the human ENCODE project.

ImmuCo: a database of gene co-expression in immune cells

Current gene co-expression databases and correlation networks do not support cell-specific analysis. Gene co-expression and expression correlation are subtly different phenomena, although both are likely to be functionally significant. Here, we report a new database, ImmuCo (http://immuco.bjmu.edu.cn), which is a cell-specific database that contains information about gene co-expression in immune cells, identifying co-expression and correlation between any two genes. The strength of co-expression of queried genes is indicated by signal values and detection calls, whereas expression correlation and strength are reflected by Pearson correlation coefficients. A scatter plot of the signal values is provided to directly illustrate the extent of co-expression and correlation. In addition, the database allows the analysis of cell-specific gene expression profile across multiple experimental conditions and can generate a list of genes that are highly correlated with the queried genes. Currently, the database covers 18 human cell groups and 10 mouse cell groups, including 20 283 human genes and 20 963 mouse genes. More than 8.6 × 10⁸ and 7.4 × 10⁸ probe set combinations are provided for querying each human and mouse cell group, respectively. Sample applications support the distinctive advantages of the database.

Reassessment of murine APOBEC1 as a retrovirus restriction factor in vivo

APOBEC1 is a cytidine deaminase involved in cholesterol metabolism that has been linked to retrovirus restriction, analogous to the evolutionarily-related APOBEC3 proteins. In particular, murine APOBEC1 was shown to inhibit Friend retrovirus (FV) in vitro, generating high levels of C-to-T and G-to-A mutations. These observations raised the possibility that FV infection might be altered in APOBEC1-null mice. To examine this question directly, we infected wild-type and APOBEC1-null mice with FV complex and evaluated acute infection levels. Surprisingly, APOBEC1-null mice exhibited similar cellular infection levels and plasma viremia relative to wild-type mice. Moreover, next-generation sequencing analyses revealed that in contrast to APOBEC3, APOBEC1 did not enhance retroviral C-to-T and G-to-A mutational frequencies in genomic DNA. Thus, APOBEC1 neither inhibited nor significantly drove the molecular evolution of FV in vivo. Our findings reinforce that not all retrovirus restriction factors characterized as potent in vitro may be functionally relevant in vivo.

Differential protein network analysis of the immune cell lineage

Recently, the Immunological Genome Project (ImmGen) completed the first phase of the goal to understand the molecular circuitry underlying the immune cell lineage in mice. That milestone resulted in the creation of the most comprehensive collection of gene expression profiles in the immune cell lineage in any model organism of human disease. There is now a requisite to examine this resource using bioinformatics integration with other molecular information, with the aim of gaining deeper insights into the underlying processes that characterize this immune cell lineage. We present here a bioinformatics approach to study differential protein interaction mechanisms across the entire immune cell lineage, achieved using affinity propagation applied to a protein interaction network similarity matrix. We demonstrate that the integration of protein interaction networks with the most comprehensive database of gene expression profiles of the immune cells can be used to generate hypotheses into the underlying mechanisms governing the differentiation and the differential functional activity across the immune cell lineage. This approach may not only serve as a hypothesis engine to derive understanding of differentiation and mechanisms across the immune cell lineage, but also help identify possible immune lineage specific and common lineage mechanism in the cells protein networks.

Heterogeneity in immune responses: from populations to single cells

The mammalian immune system is tasked with protecting the host against a broad range of threats. Understanding how immune populations leverage cellular diversity to achieve this breadth and flexibility, particularly during dynamic processes such as differentiation and antigenic response, is a core challenge that is well suited for single cell analysis. Recent years have witnessed transformative and intersecting advances in nanofabrication and genomics that enable deep profiling of individual cells, affording exciting opportunities to study heterogeneity in the immune response at an unprecedented scope. In light of these advances, here we review recent work exploring how immune populations generate and leverage cellular heterogeneity at multiple molecular and phenotypic levels. Additionally, we highlight opportunities for single cell technologies to shed light on the causes and consequences of heterogeneity in the immune system.

Data integration in the era of omics: current and future challenges

To integrate heterogeneous and large omics data constitutes not only a conceptual challenge but a practical hurdle in the daily analysis of omics data. With the rise of novel omics technologies and through large-scale consortia projects, biological systems are being further investigated at an unprecedented scale generating heterogeneous and often large data sets. These data-sets encourage researchers to develop novel data integration methodologies. In this introduction we review the definition and characterize current efforts on data integration in the life sciences. We have used a web-survey to assess current research projects on data-integration to tap into the views, needs and challenges as currently perceived by parts of the research community.

Influenza A immunomics and public health omics: the dynamic pathway interplay in host response to H1N1 infection

Towards unraveling the influenza A (H1N1) immunome, this work aims at constructing the murine host response pathway interactome. To accomplish that, an ensemble of dynamic and time-varying Gene Regulatory Network Inference methodologies was recruited to set a confident interactome based on mouse time series transcriptome data (day 1-day 60). The proposed H1N1 interactome demonstrated significant transformations among activated and suppressed pathways in time. Enhanced interplay was observed at day 1, while the maximal network complexity was reached at day 8 (correlated with viral clearance and iBALT tissue formation) and one interaction was present at day 40. Next, we searched for common interactivity features between the murine-adapted PR8 strain and other influenza A subtypes/strains. For this, two other interactomes, describing the murine host response against H5N1 and H1N1pdm, were constructed, which in turn validated many of the observed interactions (in the period day 1-day 7). The H1N1 interactome revealed the role of cell cycle both in innate and adaptive immunity (day 1-day 14). Also, pathogen sensory pathways (e.g., RIG-I) displayed long-lasting association with cytokine/chemokine signaling (until day 8). Interestingly, the above observations were also supported by the H5N1 and H1N1pdm models. It also elucidated the enhanced coupling of the activated innate pathways with the suppressed PPAR signaling to keep low inflammation until viral clearance (until day 14). Further, it showed that interactions reflecting phagocytosis processes continued long after the viral clearance and the establishment of adaptive immunity (day 8-day 40). Additionally, interactions involving B cell receptor pathway were evident since day 1. These results collectively inform the emerging field of public health omics and future clinical studies aimed at deciphering dynamic host responses to infectious agents.

Beyond the transcriptome: completion of act one of the Immunological Genome Project

The Immunological Genome Consortium has generated a public resource (www.immgen.org) that provides a compendium of gene expression profiles of ∼270 leukocyte subsets in the mouse. This effort established carefully standardized operating procedures that resulted in a transcriptional dataset of unprecedented comprehensiveness and quality. The findings have been detailed recently in a series of publications providing molecular insights into the development, heterogeneity, and/or function of these cellular lineages and distinct subpopulations. Here, we review the key findings of these studies, highlighting what has been gained and how the knowledge can be used to accelerate progress toward a comprehensive understanding of the immune system.

Computational deconvolution: extracting cell type-specific information from heterogeneous samples

The quanta unit of the immune system is the cell, yet analyzed samples are often heterogeneous with respect to cell subsets which can mislead result interpretation. Experimentally, researchers face a difficult choice whether to profile heterogeneous samples with the ensuing confounding effects, or a priori focus on a few cell subsets of interest, potentially limiting new discoveries. An attractive alternative solution is to extract cell subset-specific information directly from heterogeneous samples via computational deconvolution techniques, thereby capturing both cell-centered and whole system level context. Such approaches are capable of unraveling novel biology, undetectable otherwise. Here we review the present state of available deconvolution techniques, their advantages and limitations, with a focus on blood expression data and immunological studies in general.