Systems Approaches towards Molecular Profiling of Human Immunity

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Review

Systems Approaches towards Molecular Profiling of Human Immunity Julie G. Burel,1,2,3 Simon H. Apte,1,3 and Denise L. Doolan1,2,* Systems immunology integrates cutting-edge technologies with bioinformatics to comprehensively interrogate the immune response to infection at an organismal level. Here, we review studies that have leveraged transcriptomic, genomic, proteomic, and metabolomic approaches towards the identification of cells, molecules, and pathways implicated in host–pathogen interactions. We discuss the potential of single cell technologies for the study of human immune responses and, in this context, we advocate that systems immunology provides a conceptual and methodological framework to harness these approaches to address longstanding questions of fundamental and applied immunology. Recognizing that the field is still in its infancy, we also discuss current limitations of systems immunology, as well as the need for validation of key findings for the discipline to fulfill its promise. Introduction Systems immunology is a relatively recent discipline enabled by technical and conceptual advances that have arisen in the two decades since the birth of the genomics era. These advances, which allow for high-throughput quantitative and qualitative molecular studies of very large data sets, have triggered the ‘omics revolution’. Systems biology has emerged as an interdisciplinary field to deal with the bioinformatic, computational, and mathematical modeling of complex biological systems based on these large-scale data sets. Systems immunology falls under the larger umbrella of systems biology, and aims for an in-depth and integrated understanding of the structure and function of the immune system. Systems immunology has revolutionized the comprehensive evaluation of human immune responses to a level of detail previously restricted to mouse models [1]. The discipline encompasses data collection at transcriptomic, genomic, epigenomic, proteomic, and metabolomic levels, and integrates these technologies with bioinformatics and computational science to comprehensively interrogate the immune response to perturbation at a systems level. Intrinsic to the systems immunology approach are conceptual advances that include a change in thinking from the contemporary reductionist view to a more holistic view; and recognition, that an organism's immune response in toto is the integration of a multitude of cellular and molecular components that cannot be represented by any individual component in isolation. Systems immunology thus provides a conceptual and technological framework for new approaches to answer fundamental immunological questions without the bias of current dogma. As an example, vaccines developed to date have undoubtedly benefited public health, but they have focused on the low hanging fruit of mimicking the immunity induced by natural exposure. However, this approach is unlikely to work against diseases for which natural immunity is

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Trends Specific miRNA and transcriptomic gene expression signatures [16_TD$IF]have been associated with pathogen resistance or susceptibility. Furthermore, transcriptomic signatures can be predictive of vaccine efficacy, and specific epigenetic signatures that inform vaccineinduced immunity have been identified. Technological advances, such as single cell RNA sequencing (scRNA-seq) and cytometry by time of flight (CyToF), coupled with high-throughput approaches, allow the study of rare cell populations. Tools linking DNA sequence with gene expression, such[17_TD$IF] as chromatin immunoprecipitation (ChIP)-seq and SNP analyses as well as expression quantitative trait loci (eQTL) have enabled the identification of functional disease-associated loci. Antigen–receptor repertoire analyses enable investigation of the human T cell receptor (TCR) and antibody repertoires in the context of infection or vaccination. Potential therapeutically actionable host–pathogen interactions have been revealed by protein interactomics. The field is benefitting from a growing number of consortia aimed at crossinstitutional collaboration and public data sharing.

1 Infectious Diseases Programme, QIMR Berghofer Medical Research Institute, Brisbane, Australia 2 School of Medicine, University of Queensland, Brisbane, Australia 3 Co-first authors.

*Correspondence: [email protected] (D.L. Doolan).

http://dx.doi.org/10.1016/j.it.2015.11.006 © 2015 Elsevier Ltd. All rights reserved.

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insufficient (including the ‘big three’, namely HIV/AIDS, tuberculosis, and malaria). The development of successful vaccines against such intractable diseases requires a more comprehensive understanding of natural immune responses and, in our view, systems immunology provides a framework to develop this understanding. There is an emerging recognition that studies of controlled human infection or experimental vaccination of humans provide unprecedented opportunities for the application of a systems immunology approach to develop a more comprehensive understanding of host–pathogen immune responses. For example, controlled human malaria infections (CHMI) using Plasmodium spp. sporozoites [2,3] or parasitized erythrocytes [4] provide the opportunity to collect specimens from humans at defined time points following first exposure to the pathogen under well-controlled experimental conditions. These samples represent unique and valuable resources for the study of human immunity to malaria using systems immunology. Specimens from clinical trials where candidate vaccines fail efficacy testing are also a valuable resource that can also be interrogated to understand the reasons for vaccine failure [5]. The current research emphasis on translational studies in humans in preference to animal models is timely. [18_TD$IF]The rapid growth of open-source integrative software and publicly accessible databases along with consortiums and interdisciplinary collaborations allows for an intellectual and technical synergy that will facilitate the application of systems approaches to understand human immunity (Figure 1, Key Figure).

Analyses of Gene Expression Gene Expression Profiling Gene expression profiling studies are traditionally performed using whole-transcriptome microarrays or RNA sequencing. For the past decade, microarrays capable of simultaneously measuring the expression of large numbers of genes in specific cell populations have been considered the gold standard for transcriptomic analyses. More recently, next-generation sequencing (NGS) approaches that allow for rapid genome-wide sequencing have gained[1_TD$IF] popularity. Both methodologies are now commercialized and are becoming increasingly cost-effective for routine laboratory applications. Typically, transcriptomic studies are performed on whole blood or isolated cell populations [e. g., from peripheral blood mononuclear cell (PBMC) samples], and differentially expressed genes are compared during the course of infection or vaccination to highlight key mechanisms involved in protection. The seminal report on the successful application of transcriptomic approaches to vaccine research studied the hitherto unknown immune mechanisms driving protection for the yellow fever vaccine YF-17D. Using whole-transcriptome and computational approaches, the authors identified transcriptomic signatures in PBMC from vaccinated individuals that could predict the magnitude of the CD8+[15_TD$IF] T cell immune response to challenge with up to 90% accuracy [6]. The identified gene signature also gave insight into the biological mechanisms of protection mediated by YF-17D, highlighting the role of innate immunity through[2_TD$IF] Toll-like receptor 7 (TLR7) activation and stress-induced responses [6]. Another study showed that the immunogenicity (antibody titer) of the inactivated trivalent seasonal influenza vaccine could be predicted with up to 90% accuracy by a gene signature in PBMCs [5]. Other examples of the application of transcriptomics in vaccinology include a study profiling gene expression in PBMC from individuals vaccinated against smallpox, which revealed notable differences between low versus high vaccine responders [7]; and a study identifying distinct kinetics of transcriptional responses kinetics of immune response following vaccination with pneumococcal or influenza vaccines, highlighting the importance of sampling time [19_TD$IF]points [8].

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Key Figure

Approaches for Molecular Profiling of Immune Responses to Infection or Immunization

(A) Hypothesis

(B) Data collecon under perturbaon Infecon immunizaon

Genome level (C) Data integraon Network analysis Transcript modules Single cell profiling data analysis Machine learning tools

miRNAs

Epigenecs SNPs IncRNAs mRNAs

Transcripon factors Phosphorylaon

Post-translaonal level

Transcriponal level

Cytokines Chemokines

Translaonal level

Metabolites Anbodies

Figure 1. Hypothesis-driven questions can be addressed by the integration of data reflecting many cellular processes. Transcriptional profiling approaches (epigenetic modifications and transcription of genes into mRNA), as well as approaches to measure protein translation and post-translational modifications of proteins, can be integrated with metabolomics approaches aimed at profiling the metabolic status of the cell. For the study of the immune response, these data can be complemented by antigen–receptor repertoire analyses and the profiling of [7_TD$IF]cytokines, [8_TD$IF]chemokines, and cell surface markers that provide information about the developmental or activation status of the cell. Bioinformatics and additional tools for data integration and analyses are then applied to inform and direct further questions. Abbreviations: lncRNA, long noncoding RNA; SNP, single nucleotide polymorphisms.

Transcriptomic approaches to study human immunity have been also used in the context of natural infection. One study identified distinct gene expression signatures associated with differential susceptibility to HIV of CD4+ T cells with different pathogen and/or antigen specificities [9]. Gene profiling of PBMC using microarrays highlighted common signaling pathways differentially regulated in individuals experimentally or naturally infected with Plasmodium, including TLR signaling, phagocytosis, inflammation, and apoptosis [10]. In the context of bacterial infection, microarray analysis of whole blood from patients with active tuberculosis versus healthy controls identified a specific gene signature for active tuberculosis dominated by neutrophil-driven interferon (IFN) signaling, thereby emphasizing a previously underestimated role for type I IFN in tuberculosis pathogenesis [11]. These examples all utilized redundant transcriptomic analysis, where the full transcriptome was analyzed. This approach remains expensive, requires a further validation step for genes of interest (traditionally RT-qPCR), and requires a relatively large amount of starting material, which is problematic for rare cell populations. Also, for some applications, a targeted panel of genes rather than the complete transcriptome is sufficient to address a given question. Hence, there

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has been a growing interest in the commercial development and application of high-throughput multiplex gene expression systems, such as the Fluidigm or nCounter systems, which focus on a specific panel of target genes (either standard panels or custom designed). These systems will become more cost-effective with increased demand. Single Cell Gene Expression Profiling Recent technological advances in the field of transcriptomics, such as those described above, can also be applied to single cell gene profiling [12]. Gene expression studies at the single cell level have thus far highlighted the fact that individual cells from an apparently homogenous population (such as effector or memory cells, or antigen-specific cells) can display high heterogeneity at the mRNA level [13]. For example, using the multiplex high-throughput RT-qPCR Fluidigm system for single cell gene expression profiling, Flatz et al. were able to distinguish qualitatively different antigen-specific CD8+ T cells induced by different DNA vaccine preparations [14]. Similarly, single cell gene expression profiling using Fluidigm revealed new insights into the fate of CD8+ T cells effector and memory subsets during bacterial infection that were masked when the analysis was performed on pooled cells [15]. More recently, RNA sequencing at the single cell level (single cell RNA sequencing, scRNA-seq) has been achieved, with potential application to the study of rare cell populations by profiling individual cells from an immune subset of interest to identify similar cell types by grouping the resultant expression profiles using clustering analysis (i.e., K means clustering, hierarchical clustering, and principal component analysis) [16]. scRNA-seq applied to study the dendritic cell response to lipopolysaccharide (LPS) in mice revealed that most genes have a heterogeneous expression pattern within single cells, while belonging to the same cell subset [17]. More recently, this technology has been dramatically improved with the use of barcoded beads coupled with droplet microfluidics, allowing high-throughput single cell RNA sequencing of thousands of cells in a single batch [18,19]. Noncoding RNA Profiling Recent advances in whole-genome sequencing combined with the creation of collaborative data sharing projects, such as the Functional Annotation of the Mammalian Genome (FANTOM) [20] or the Encyclopedia of DNA Elements (ENCODE) [21] consortiums, have revealed a very large number of noncoding RNAs in the transcriptome of mammalian cells. miRNAs are short noncoding RNA molecules that mediate post-transcriptional gene regulation. They derive from both host and pathogen, and are discovered using small RNA sequencing (RNAseq) or profiled using commercially available miRNA arrays; selected relevant miRNAs are then validated using RT-qPCR. miRNA profiling of plasma and/or serum has discriminated between healthy individuals and individuals infected with bacterial or viral pathogens, including Mycobacterium tuberculosis [22] and hepatitis B virus [23]. Potential new targets for therapeutic intervention have been identified; for example, miRNA profiling of HIV identified two miRNAs that were differentially expressed in the plasma of HIV-infected individuals and associated with reduced viral replication [24]. miRNA expression profile and function in specific immune cell subsets during infection have been also investigated; in HIV infection, the miRNA expression profile in CD4+ T cells was sufficient to discriminate between healthy controls, HIV-infected elite controllers, and HIV chronically infected individuals [25]. Long noncoding RNAs (lncRNAs) are noncoding RNAs of at least 200 nucleotides in length that are abundant in all major human cell types and can act as transcriptional regulators, posttranscriptional regulators, or mediators of epigenetic modifications [26]. LncRNA profiling studies are typically based on data generated from RNA sequencing to identify lncRNAs of interest, followed by RT-qPCR for validation. Such investigations have highlighted that lncRNAs

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can regulate numerous aspects of innate and adaptive immune responses, including differentiation and activation of CD4+[20_TD$IF] and CD8+ T cells, regulation of innate immune response genes, downstream modulation of TLR responses or cytokine production, and VDJ recombination in TCR and immunoglobulin (Ig) receptors in T cells and B cells [26,27]. Epigenetics Epigenetics refers to either heritable or stable long-term changes in gene activity and expression that result in a change in phenotype without a change in genotype. Epigenetic modifications are traditionally measured by chromatin immunoprecipitation (ChIP) to purify histones and DNAse hypersensitivity to study DNA accessibility. Recently, these techniques have been combined with NGS technologies to improve the sensitivity and specificity of detection of epigenetic modifications. The importance of epigenetic modifications in modulation of immune cell function has been recently demonstrated. For example, ChIP analysis of circulating monocytes before and after Bacille Calmette-Guerin (BCG) immunization showed that the vaccine induced epigenetic reprogramming in specific cytokine promoters that was associated with enhanced innate immune function and maintained up to 3 months following vaccination [28]. In another study, specific epigenetic signatures in the promoter regions of cytokine genes defined the capacity of influenza virus-specific CD8+[21_TD$IF] T cells to produce IFN-g and tumor necrosis factor (TNF) [29]. Also, epigenetic signatures in signal transducer and activator of transcription (STAT) promoters have been implicated in controlling T helper cell differentiation, suggesting that drugs that specifically target epigenetic modifications may direct the fate and cytokine capacity of T cells [30]. Genetic Polymorphisms, SNPs, and Expression Quantitative Trait Loci Rapid advances in cost-effective NGS technologies over the past decade have resulted in comprehensive databases of host and pathogen genome sequences that can be mined to relate genomic information to immune function. In particular, genome-wide association studies (GWAS) aim to identify single nucleotide polymorphisms (SNPs) associated with a specific disease or clinical outcome. Genomic approaches have shown that heterogeneity among individuals in immune responses induced by vaccination or natural infection can be partially explained by specific combinations of individual genetic polymorphisms [31,32]. Also, susceptibility to tuberculosis infection and disease progression have been associated with polymorphisms in innate immune genes, such as those encoding pattern recognition receptors, cytokines, and chemokines [33]. However, GWAS does not inform the underlying biological processes. High-throughput functional assays, such as CHIP assays followed by sequencing (ChIP-seq), which link DNA sequence with gene expression, can be used to identify regions that are functional, such as transcription factor binding sites. Most recently, there is growing interest in expression quantitative trait loci (eQTLs; SNPs that influence gene expression) as a tool for the functional understanding of GWAS results, since many disease-associated loci map to regulatory regions rather than protein-coding regions [34].

Analyses of Protein Expression Flow Cytometry Multiparametric flow cytometry studies have highlighted the importance of the quality of immune responses in protective immune responses to infection or immunization. Several studies have associated polyfunctional T cells with immunity to infections, including Leishmania major [35], Mycobacterium tuberculosis [36], and HIV [37], although this correlation remains controversial for some pathogens [38]. T cell polyfunctionality is thus a key parameter evaluated in studies of host–pathogen immunity. Recent advances in flow cytometry now allow for the simultaneous detection of more than 20 parameters in a single cell [39], greatly expanding the panel of potential immune correlates.

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Most recently, technological advances associated with flow cytometry and mass spectrometry have resulted in the development of the cytometry by time of flight technology (CyTOF, also called mass cytometry). CyTOF combines flow cytometry with mass spectroscopy and can be used to analyze more than 60 parameters in one cell [40]. Unlike flow cytometry, which uses antibodies labeled with fluorochromes, antibodies in CyTOF are conjugated with heavy metal isotopes that can be detected with a mass spectrometer. The elegant study by Newell et al. described the use of CyTOF to decipher the phenotypic and functional complexity of memory CD8+ T cell to several viral pathogens [40]. Flow cytometry can also be used for the high-throughput simultaneous measurement of multiple cytokines in body fluids and culture supernatants using commercially available multiplexed bead-based assays [41]. Applications include the identification of biomarkers of infection, or of resistance or susceptibility to disease. Such studies have identified specific cytokine signatures in whole blood or blood fractions that correlated with parasite burden in malaria [42] or clinical outcome following smallpox vaccination [43]. Flow cytometry can also be used to investigate protein phosphorylation, which is an important regulatory event in signal transduction pathways. This method, termed ‘phosphoflow’, allows detailed analysis of the phosphorylation status of various proteins, such as transcription factors, in distinct cell subsets using a combination of phospho-epitope and lineage-specific antibodies [44]. Applications of phosphoflow include the assessment of the basal phosphorylation state of transcription factors, as well as the responsiveness of specific cell populations to a given perturbation, such as exposure to cytokines or drugs. Thus, phosphoflow may be a useful tool to monitor immune responses after infection or immunization [44,45]. For example, Lee et al. identified a defect in Granulocyte-macrophage colony-stimulating factor (GM-CSF)driven STAT5 phosphorylation in the monocytes of HIV1-infected individuals [46], providing new research targets for therapeutic intervention. More recently, Bendall et al. used single cell mass cytometry coupled with phosphoflow to comprehensively compare the responsiveness of more than 30 immune cell subsets to specific stimuli, such as recombinant cytokines or drugs [47]. Antigen–Receptor Repertoire Analyses TCR Repertoire Analysis The low frequency of antigen-specific T cells in the peripheral blood and the highly polymorphic nature of the major histocompatibility complex (MHC) have hindered such studies in the past, but technical advances now enable investigation of the TCR repertoire in humans in the context of infection or vaccination. Typically, the TCR repertoire is interrogated by single cell TCR sequencing of antigen-reactive T cell populations [48]. TCR sequencing from antigen-specific T cell populations has revealed that the TCR repertoire diversity in humans is much greater than expected, and that heterogeneity of the TCR repertoire is retained with age despite an overall reduction in diversity [49]. Although the exact antigen specificity of a T cell cannot yet be predicted from its TCR sequence [48], TCR repertoire analyses can provide a glimpse of potential TCR ligands associated with antigen-specific T cells. For example, single cell TCR sequencing of recently activated circulating CD8+[3_TD$IF] T cells isolated from patients with celiac disease after gluten exposure revealed highly TCR convergent motifs that are likely to be associated with common antigen specificity [50]. Additionally, single cell TCR sequencing combined with yeast clone libraries displaying a vast array of MHC–peptide complexes is predicted to be a powerful strategy to identify TCR ligands from TCR sequences [48]. More recently, the combination of epitope-specific tetramer staining with the CyTOF technology has allowed the identification of new epitopes recognized by rotavirus specific CD8+ T cells circulating in healthy individuals [51]. This technique is predicted to considerably enhance our knowledge of T cell repertoires and T cell epitopes [48].

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Table 1. Common Systems Immunology Tools and Methods[9_TD$IF]. Category

Name

Description

Features

Accessibility

Refs

Network analysis

Ingenuity Pathway Analysis

Licensed software to integrate molecular profiling data with manually curated biomolecular interactions from the literature

Encompasses all known biological interactions and functional annotations between genes, proteins, complexes, cells, tissues, drugs[10_TD$IF], and diseases

http://www.ingenuity.com/products/ipa

[5]

Cytoscape

Open-source software to integrate molecular profiling data with publically available biomolecular interactions data sets

Large number of extensions available, including: CluePedia for pathway analysis from gene, protein, and miRNA data sets and ClueGo for functionally grouped gene ontology/metabolic pathway network analysis

http://www.cytoscape.org/

[77–80]

Innate DB

Public database regrouping the innate immunity interactome. Manually curated

Includes network visualization, pathway analysis, and orthologous interaction construction

http://www.innatedb.com/

[81,82]

Human Disease Network

Public dataset of relationships between human diseases: examines relationship between genes and disease

Dataset is publicly available as interactive map, poster[1_TD$IF], or book. Valuable resource to study gene and disease associations

http://hudine.neu.edu/

[83]

Modular analysis

Transcript modules

Analysis, interpretation and visualization of highdimensional transcriptomic data. Based on the identification of modules containing sets of interdependent transcripts related to a specific cell type, immune function, or pathology

Functionally characterizes the modular transcriptional repertoire by assigning scores to modules based on increases and decreases in transcript expression contained within the module and creating a fingerprint for each immune/clinical phenotype

Single cell analysis

SPADE

Algorithm to visualize high-dimensional single cell flow cytometry data

Visualizes highdimensional single cell data as clusters of similar cell phenotypes

http://cytospade.org/

[47,84]

viSNE

Algorithm to visualize high-dimensional single cell flow cytometry data

Allows mapping of highdimension flow data in two dimensions

http://www.c2b2.columbia.edu/danapeerlab/ html/cyt.html

[85]

MIMOSA

Algorithm for analysis of single cell flow cytometry or gene expression data

A Bayesian hierarchical mixture model for analysis of differential expression

http://www.bioconductor.org/packages/release/ bioc/html/MIMOSA.html

[86]

Compass

Algorithm to analyze high-dimensional flow cytometry data from very low frequency cell populations

Intended to relate specific changes to clinical outcome

https://github.com/RGLab/COMPASS

[87]

Machine learning tools

Algorithms to predict the behavior of the immune system to a perturbation

Numerous algorithms developed

DAMIP

[6,88]

PAM

[89]

k-nearest neighbors

[11]

Predictive analysis

[8,74]

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Table 2. Databases Publically Available for the Components of the Human Interactome[12_TD$IF]. Category

Database

Description

Accessibility

Genome

UCSC Genome browser

Database of genomic sequences and annotations from approximately 90 organisms, primarily vertebrates

http://genome.ucsc.edu/cgi-bin/ hgGateway

ENCODE

Database of all known functional DNA elements from the human genome

https://www.encodeproject.org/

TargetScan

Database for miRNA target prediction in human, mouse, worm, fish, and fly

http://www.targetscan.org/

miRBase

Database of published miRNA sequences and annotations

http://www.mirbase.org/

microRNA

Database for miRNA target prediction and expression profiles in mammalian tissues and cell lines

http://www.microrna.org

miRDB

Database for miRNA target prediction and functional annotations in human, mouse, rat, dog, and chicken

http://mirdb.org/miRDB/

starBase

Database decoding RNA–RNA and protein–RNA interaction networks from high-throughput sequencing data

http://starbase.sysu.edu.cn/

LncRBase

Database classifying all known lncRNA in human and mouse

http://bicresources.jcbose.ac. in/zhumur/lncrbase/

DAVID

Database for Annotation, Visualization and Integrated Discovery from highthroughput genomic data

http://david.abcc.ncifcrf.gov/

GEO

Gene Expression Omnibus (NCBI). Contains microarray and sequencing gene expression data as well as curated gene expression profiles

http://www.ncbi.nlm.nih.gov/geo/

MSignDB

Database of annotated gene sets to be used with the Gene Set Enrichment Analysis software

http://www.broadinstitute.org/gsea/ msigdb/index.jsp

FANTOM

Database from international research consortium encompassing functional annotation of large-scale transcriptomic data. Latest version, FANTOM5, contains data about transcripts and associated promoters, enhancers, and transcription factors in various mammalian cell lines

http://fantom.gsc.riken.jp/

STRING

Database of known and predicted direct or indirect protein–protein interactions. Integrates data from genomic and proteomic experiments, coexpression, and published literature

http://string-db.org/

DIP

Database of experimentally validated protein–protein interactions manually and computationally curated

http://dip.doe-mbi.ucla.edu/dip/Main.cgi

HPRD

Database of curated proteomic data for all known human proteins, from domain architecture to disease association

http://www.hprd.org/

BioGRID

Database of genetic and protein interactions from humans and other model organisms

http://thebiogrid.org/

Phospho.ELM

Database of experimentally [13_TD$IF]validated phosphorylations in eukaryotic proteins

http://phospho.elm.eu.org/index.html

Phosphosite

Database of post translational modifications along with biological annotations and cell signaling networks

http://www.phosphosite.org/homeAction.do

PHOSIDA

Database of phosphorylation, acetylation, and glycosylation sites of proteins along with structural and evolutionary information

http://www.phosida.com/

KEGG

Database integrating genomic, chemical, and systemic functional information into metabolism KEGG pathways

http://www.genome.jp/kegg/kegg1.html

Reactome

Database containing curated and peer-reviewed pathways of human biological processes, from metabolism to signal transduction pathways

http://www.reactome.org/

NCI Pathway Interaction Database

Database regrouping curated and peer-reviewed pathways of molecular signaling, regulatory events, and cellular processes in humans

http://pid.nci.nih.gov/

BIGG

Database of genome-scale metabolic networks from published literature. Uses standard nomenclature facilitating comparison across studies and species

http://bigg.ucsd.edu/

HMDB

Database of human metabolome linking chemical, clinical, and biochemical data

http://www.hmdb.ca/

Transcriptome

Proteome

Post-translational modifications

Pathways

Metabolome

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Antibody Repertoire Analysis Recently, large-scale proteome-based screening of the antibody repertoire following natural or experimental exposure to target pathogens has been achieved using protein microarrays, whereby a protein chip representing the complete or partial proteome of a given pathogen is probed with sera or plasma [52]. Such analyses have yielded important information about antigens implicated in protective immune responses induced by infection or vaccination and that thus represent potential vaccine candidates, as well as novel diagnostic biomarkers. More than 30 human pathogens have now been evaluated using protein microarray technology [53], resulting in the identification of novel antigens associated with protective immunity to malaria, schistosomiasis, toxoplasmosis, chlamydia, herpes simplex virus, Q fever, and many other pathogens; as well as biomarkers for typhoid and tuberculosis. An alternative approach is to use peptide-based arrays. In one example predicting vaccine efficacy, sera from human volunteers vaccinated against seasonal influenza was probed against a CIM10K protein chip containing tens of thousands of random sequence peptides to identify an immmunosignature that could discriminate between immune and non-immune individuals [54]. The rapidly emerging technology of[2_TD$IF] B cell receptor [23_TD$IF](BCR[24_TD$IF]) sequencing (also called immunoglobulin sequencing) enables determination of the antibody repertoire [55]. BCR repertoire analysis can enhance our understanding of the effect of pathogen exposure and immune status on antibody repertoire, and facilitate identification of new vaccine targets. For example, BCR sequencing of circulating B cells in various human populations showed that both age and chronic viral infection altered the B cell repertoire [56]. Also, immunoglobulin sequencing of B cells isolated from recently immunized individuals identified vaccine-specific BCR sequences [57]. Protein–Protein Interactions Interactomics, the study of the physical interaction between molecules and their associated consequences, most commonly refers to protein–protein interactions and has usually been Box 1. Integrated Data Outputs Network analysis tools are commonly used to monitor immune responses and discover new biomarkers of infection or new targets for drugs or vaccines [90]. The combination of transcriptomics with yeast two-hybrid analysis yielded the first comprehensive map of the physical and regulatory interactions between influenza virus and human epithelial cells, identifying several RNA-binding proteins associated with virus replication [58]. Approaches integrating transcriptional profiling, genetic and small-molecule perturbations, and phosphoproteomics to decipher immune signaling networks identified regulatory networks involved in TLR signaling in dendritic cells [91,92]. Integration of miRNA and mRNA expression data sets together with miRNA target prediction software generated reliable and functional miRNA–mRNA regulatory networks [93]. Blood transcriptomic analyses using modular transcriptional analysis [74] showed that influenza and pneumococcal vaccines elicited different immune responses [8]. Analysis of complex high-dimensional single cell data has necessitated the development of complex mathematical models and algorithms. An unsupervised tree-based algorithm that visualizes high-dimensional single cell data as clusters of similar cell phenotypes, called SPADE, identified the effect of specific drugs on the signalling activity of human hematopoietic bone marrow cells [47]. Antigen-specific T cell phenotypes representing potential immune correlate of protection to HIV were identified using COMPASS software, developed to analyze high-dimensional flow cytometry data from very low frequency cell populations in relation to clinical outcome [87]. Machine learning tools based on multivariate statistical methods, such as decision trees, random forests, and artificial neural network (when the hypothesis is dependent on a nominal variable, e.g., healthy versus control), or pattern identification methods, such as principal component analysis, clustering, or association rule mining (when the hypothesis relies on a continuous/numerical variable; e.g., antigen-specific antibody titer in sera), are being increasingly used [94]. A common application is to identify from a global gene signature the minimal set of genes capable of predicting a clinical outcome; such as the discovery of gene signatures that predict the immunogenicity of yellow fever and influenza vaccines [5,6]. Machine learning tool for analysis of polychromatic flow cytometry data identified a combination of markers for three specific T cell subsets whose frequency early during HIV infection was significantly associated with disease progression [95]. For increased accuracy, different experimental data sets (protein, genes, miRNAs, etc.) can be integrated. For example, a multimodel framework predictive of responses post-influenza vaccination by integrating transcriptomic, antibody response, and cell frequency data collected before vaccination for each individual [96].

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measured with yeast two-hybrid systems. A key application of protein interactomics is to enhance our understanding of specific interactions between a pathogen of interest and the host immune system. The study of protein–protein interactions has enabled the identification of specific virus–pathogen interactions, such as for influenza [58], hepatitis C virus [59], herpes virus [60], and Epstein–Barr virus [61]. Recently, protein microarrays have been also used as a tool to monitor protein–protein interactions; for example, Tribolet et al. probed a human proteome microarray with a recombinant hookworm protein to identify the key host–pathogen protein–protein interaction, thereby revealing a new mechanism by which hookworms can control BCR signaling [62].

Analyses of Metabolite Production Metabolomics is the study of the entire repertoire of small metabolites, such as sugars, amino acids, or fatty acids, contained within a cell or a biological sample, and requires metabolite extraction, isolation by chromatography, and profiling using nuclear magnetic resonance or mass spectrometry. Metabolomic studies have been used to diagnose infection with dengue virus [63], HIV [64], and active tuberculosis [65]; or to monitor the efficiency of a therapeutic intervention, such as in pulmonary tuberculosis, where a metabolite signature in urine samples collected early post-treatment correlated with the response to therapy [66]. Other studies have correlated metabolites with the immune response to infection: blood metabolomes from mice infected with lymphocytic choriomeningitis virus (LCMV) correlated with immune responses and viral clearance [67]; upregulation of fatty acid biosynthesis within infected cells by human cytomegalovirus was essential for its replication [68]; and conversion of extracellular arginine into ornithine in P. falciparum-infected erythrocytes suggested a mechanism for the hypoargininemia observed in cerebral malaria [69]. Data Integration and Analysis The objective of systems immunology is to integrate data collected independently from different omics-based studies as well as clinical (or other biologically relevant) data into a common framework to establish a global picture of the immune response at a systems level [70]. Some commonly used approaches for data integration are summarized in Table 1. Numerous Box 2. Systems Immunology: A Multidisciplinary Collaboration Systems immunology studies require diverse technological platforms for molecular profiling and associated complex bioinformatics that are beyond the scope of [14_TD$IF]a single laboratory. Hence, collaborative projects and consortia whose objective is to decipher the human immune profile in normal versus perturbed conditions and publically share data are rapidly emerging [97]. Recently, the Human Immunology Project Consortium (HIPC) program was established by the National Institute of Allergy and Infectious Diseases (NIAID) integrating several immune profiling methods to create a public resource for characterizing the human immune response to infection and vaccination [98]. Several HIPC projects are ongoing and successful outcomes have already been reported, including molecular profiling of the immune response following immunization with influenza or pneumococcal vaccines [8], as well as an immune signature predictive of the response to influenza vaccination [96]. The NIAID has also created the Systems Biology for Infectious Diseases Program to combine computational and experimental methods to develop predictive models of host–pathogen interactions and to define specific molecular and/or cellular networks that are altered following infection. Comprising four biology centers (the Tuberculosis Systems Biology Center, the Systems Virology Center, the Center for Systems Influenza, and the Center for Systems Biology for EnteroPathogens [99]), the program has defined several host–pathogen interactions critical for pathogen survival or disease pathogenesis, including influenza [100] and Mycobacterium tuberculosis [101]. Additionally, NIAID is strongly encouraging the public sharing of data generated from systems immunology studies with the creation of ImmPort, an online portali, where scientists can deposit the raw data from their published studies, along with all the source code and other information necessary to reproduce the figures and the bioinformatic analysis associated with the study [102]. This portal can be used by the scientific community to reproduce and validate the published analyses and figures to ensure maximal transparency, as well as to enable new data analysis and interpretation by using different bioinformatics analysis tools or combining various data sets generated from independent studies [102]. Finally, the Immunological Genome Consortium is a collaborative work between immunologists and bioinformaticians on deep molecular phenotyping of immune cells and genetic regulatory networks within the mouse [103]. Already, this project has significantly enhanced our understanding of the genetic regulations between immune cell lineages [104]. All the available data generated from this consortium are publicly available online through ImmGen's browserii

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algorithms and software packages have been developed for systems immunology-based data analysis; commonly used tools are summarized in Table 2. Some examples of integrated data output using these tools are provided in Box 1.

Outstanding Questions

Data generated from systems immunology studies are often compiled and made publically available through online databases (Table 2). Additionally, there is an increasing number of systems immunology-based consortiums and collaborative research projects that integrate various technological platforms (Box 2), since it is not feasible for a single laboratory to have all techniques, tools, infrastructure, and expertise required for comprehensive molecular immune profiling.

What functional and/or biological readouts are relevant for translation of systems immunology findings to therapeutic intervention?

Concluding Remarks Despite decades of intensive research, there remains a paucity of knowledge regarding human immune responses to the most significant human pathogens. Not coincidentally, over this period, we have consistently failed to develop efficacious vaccines against many infections, including the three most serious infectious diseases (HIV/AIDS, tuberculosis, and malaria). These failures seem hard to reconcile with the enormous technological advances that have occurred in the field of biomedical research over the past two decades. We believe the dominant reductionist approach and an overemphasis on mechanistic studies in mice are impeding rather than facilitating a more holistic understanding of human immune responses and may help to explain some of our failures thus far. Systems immunology provides a conceptual and technological framework for new approaches to understand human immunity. However, as a relatively young discipline, systems immunology also faces a number of conceptual and technical challenges (see Outstanding Questions). First, most studies involving humans are restricted to sampling the peripheral blood, and parameters measured in this tissue may not accurately reflect other sites. Without more invasive procedures in human volunteers, we are restricted to comparing these findings with those in more amenable animal models. For example, humanized mice containing functioning human genes, cells, tissues, and/or organs are gaining in popularity as an appealing animal model of human disease since defects identified in the earlier mice are being continually rectified [71,72]. Second, antigen-specific cells might account for as little as 0.01% of the total lymphocytes within the blood, and molecular changes in antigen-specific cells and description of biologically relevant signatures could be masked by experimental noise from nonspecific cells within the total population. This limitation could be addressed by sorting specific lymphocyte subsets displaying a phenotype or function related to the outcome of interest either ex vivo or following specific antigen stimulation [5,11]. Indeed, Shen-Orr et al. established the feasibility of deconvoluting in silico a gene expression profile from a mixed cell population sample relative to the frequency of each cell type [73]. Alternatively, transcript modules can be used to decipher changes in specific cell subsets within bulk population data sets [74].

How can outcomes of systems immunology studies be validated using functional assays?

How do researchers deal with practical limitations regarding inaccessibility of key immune organs and cell populations thought to be critical in host– pathogen immunity? Are immune cells circulating in the blood truly representative of the immune response to infection? What is the relevance of studies on bulk cell population versus specific cell subsets? Do bulk population analyses mask the identification of molecular changes occurring in specific immune cell populations that are activated during infection and/or immunization? What is the best time point for data collection, and how do researchers deal with sampling in relation to the kinetics of immune response post infection or post vaccination? How can biologically relevant questions be formulated computationally to ensure the correct output? Is it possible to standardize systems immunology studies? How should researchers from isolated laboratories be integrated into large consortiums to ensure widespread access to multidisciplinary resources and expertise so that the discipline of systems immunology is not exclusive?

Third, given the multidisciplinary collaborative nature of the field, standardized immune assays and metrics are critical to ensure reproducibility and robustness of findings. Variation in reagents, sample handling, instrument set-up, or data analysis across laboratories can otherwise result in misleading outcomes [75]. The widespread use of standard panels, such as the eight-color panel for immunophenotyping of PBMC subsets using flow cytometry [76], central ‘immune monitoring cores’, and consortium-based collaborative arrangements are positive steps [25_TD$IF]on this path (Box 2). Standardization of data analysis is also an important consideration, since the use of ‘in-house’ bioinformatic tools for data interpretation may challenge analysis of data sets generated by different institutions. Data-sharing strategies, such as ImmPort, can partially address that issue by publishing the source code specifically used for each data analysis (Box 2).

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Fourth, a major challenge is to extract and validate meaningful information from very large multidimensional data sets with a complexity that cannot be interpreted using traditional graphical outputs and statistic tests. Thus, systems immunology data must be carefully integrated with computational biology and informatics, and researchers must correctly formulate a question that can be asked computationally [75]. Finally, a major hurdle is how to validate the findings. Whereas most contemporary immunological questions are asked using a reductionist approach, experimental validation is mostly achieved along the same reductionist lines. As an example: an experiment suggests that gene A drives expression of protein A, which interacts with protein B inducing a phenotypic change measurable at the cellular level. This experiment could be validated to the satisfaction of most peers by using any number of the tools now available to interrupt this process; for example, stopping the interaction between A and B and looking for differences in phenotype. However, validation of experiments on the systems scale, where we ask questions such as ‘how is disease outcome affected by the complex interactions of multitudes of A and B’, presents difficult challenges. The integration of data using complex computing algorithms is an integral component of systems immunology, yet most immunologists are not mathematicians, computer programmers, or statisticians, nor do we propose that systems immunologists should be; however, it will be critical that integration methods are transparent and that researchers validate their results across different platforms and with different data sets. Finally, where possible researchers should test their conclusions in independent experiments, such as the seminal studies of Querec and colleagues investigating responses to the [26_TD$IF]YF-17D yellow fever vaccine [6] and Nakaya and colleagues investigating responses to seasonal influenza vaccines [5]. Both used a systems immunology approach to generate predictive rules for immune responses to the vaccines by utilizing the DAMIP algorithm to first interrogate random samples as a training group, then evaluated the generated rules by applying the rules to all of the samples in the original trial; then validated the predictive power of the rules in a new independent trial. Of particular note, Nakaya et al. were able to move back to the reductionist approach in a knockout mouse model to validate an unexpected finding in the human trials that suggested a critical role for the enzyme calcium/calmodulin-dependent protein kinase type IV (CaMKIV) in regulation of antibody responses. Described in this review are specific opportunities for the utilization of systems immunology. Indeed, numerous studies provide evidence that fundamental molecular components of the host response when exposed to a foreign pathogen can be dissected and potential pathways or molecules for targeted intervention can be identified. Specific outcomes include: (i) identifying mechanisms involved in pathogen resistance or susceptibility; (ii) identifying important cell types or immune mediators; (iii) predicting the immunogenicity or efficacy of vaccines in the human population; (iv) informing the mechanism by which a vaccine stimulates protective immunity; (v) identifying targets of pathogen-specific protection that may represent excellent subunit vaccine candidates; and (vi) diagnosing pathogen infection. These studies provide experimental support for the concept that systems immunology offers a powerful framework to unravel the natural complexity of the human immunity. Acknowledgments J.G.B. was supported by an International Research Tuition Award from the University of Queensland. D.L.D. is supported by an NHMRC Principal Research Fellowship. The authors acknowledge research support from the Australian National Health and Medical Research Council (NHMRC).

Resources i

[4_TD$IF] https://immport.niaid.nih.gov

ii

www.immgen.org/

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