Gene expression microarrays: glimpses of the immunological genome

Tumor-infiltrating lymphocytes as a prognostic and predictive factor for Melanoma

INTRODUCTION

Tumor-infiltrating lymphocytes (TILs) have been investigated as prognostic factors in melanoma. Recent advancements in assessing the tumor microenvironment in the setting of more widespread use of immune checkpoint blockade have reignited interest in identifying predictive biomarkers. This review examines the function and significance of TILs in cutaneous melanoma, evaluating their potential as prognostic and predictive markers.

AREAS COVERED

A literature search was conducted on papers covering tumor infiltrating lymphocytes in cutaneous melanoma available online in PubMed and Web of Science from inception to 1 December 2023, supplemented by citation searching. This article encompasses the assessment of TILs, the role of TILs in the immune microenvironment, TILs as a prognostic factor, TILs as a predictive factor for immunotherapy response, and clinical applications of TILs in the treatment of cutaneous melanoma.

EXPERT OPINION

Tumor-infiltrating lymphocytes play a heterogeneous role in cutaneous melanoma. While they have historically been associated with improved survival, their status as independent prognostic or predictive factors remains uncertain. Novel methods of TIL assessment, such as determination of TIL subtypes and molecular signaling, demonstrate potential for predicting therapeutic response. Further, while their clinical utility in risk-stratification in melanoma treatment shows promise, a lack of consensus data hinders standardized application.

Dissecting the Immune System through Gene Regulation

The immune system plays a dual role in human health, functioning both as a protector against pathogens and, at times, as a contributor to disease. This feature emphasizes the importance to uncover the underlying causes of its malfunctions, necessitating an in-depth analysis in both pathological and physiological conditions to better understand the immune system and immune disorders. Recent advances in scientific technology have enabled extensive investigations into gene regulation, a crucial mechanism governing cellular functionality. Studying gene regulatory mechanisms within the immune system is a promising avenue for enhancing our understanding of immune cells and the immune system as a whole. The gene regulatory mechanisms, revealed through various methodologies, and their implications in the field of immunology are discussed in this chapter.

The Multi-Dimensional Biomarker Landscape in Cancer Immunotherapy

The field of immuno-oncology is now at the forefront of cancer care and is rapidly evolving. The immune checkpoint blockade has been demonstrated to restore antitumor responses in several cancer types. However, durable responses can be observed only in a subset of patients, highlighting the importance of investigating the tumor microenvironment (TME) and cellular heterogeneity to define the phenotypes that contribute to resistance as opposed to those that confer susceptibility to immune surveillance and immunotherapy. In this review, we summarize how some of the most widely used conventional technologies and biomarkers may be useful for the purpose of predicting immunotherapy outcomes in patients, and discuss their shortcomings. We also provide an overview of how emerging single-cell spatial omics may be applied to further advance our understanding of the interactions within the TME, and how these technologies help to deliver important new insights into biomarker discovery to improve the prediction of patient response.

Transcriptomic Profiling of the Adaptive and Innate Immune Responses of Atlantic Salmon to Renibacterium salmoninarum Infection

Bacterial Kidney Disease (BKD), which is caused by a Gram-positive, intracellular bacterial pathogen (Renibacterium salmoninarum), affects salmonids including Atlantic salmon (Salmo salar). However, the transcriptome response of Atlantic salmon to BKD remained unknown before the current study. We used a 44K salmonid microarray platform to characterise the global gene expression response of Atlantic salmon to BKD. Fish (~54 g) were injected with a dose of R. salmoninarum (H-2 strain, 2 × 10⁸ CFU per fish) or sterile medium (control), and then head kidney samples were collected at 13 days post-infection/injection (dpi). Firstly, infection levels of individuals were determined through quantifying the R. salmoninarum level by RNA-based TaqMan qPCR assays. Thereafter, based on the qPCR results for infection level, fish (n = 5) that showed no (control), higher (H-BKD), or lower (L-BKD) infection level at 13 dpi were subjected to microarray analyses. We identified 6,766 and 7,729 differentially expressed probes in the H-BKD and L-BKD groups, respectively. There were 357 probes responsive to the infection level (H-BKD vs. L-BKD). Several adaptive and innate immune processes were dysregulated in R. salmoninarum-infected Atlantic salmon. Adaptive immune pathways associated with lymphocyte differentiation and activation (e.g., lymphocyte chemotaxis, T-cell activation, and immunoglobulin secretion), as well as antigen-presenting cell functions, were shown to be differentially regulated in response to BKD. The infection level-responsive transcripts were related to several mechanisms such as the JAK-STAT signalling pathway, B-cell differentiation and interleukin-1 responses. Sixty-five microarray-identified transcripts were subjected to qPCR validation, and they showed the same fold-change direction as microarray results. The qPCR-validated transcripts studied herein play putative roles in various immune processes including pathogen recognition (e.g., tlr5), antibacterial activity (e.g., hamp and camp), regulation of immune responses (e.g., tnfrsf11b and socs1), T-/B-cell differentiation (e.g., ccl4, irf1 and ccr5), T-cell functions (e.g., rnf144a, il13ra1b and tnfrsf6b), and antigen-presenting cell functions (e.g., fcgr1). The present study revealed diverse immune mechanisms dysregulated by R. salmoninarum in Atlantic salmon, and enhanced the current understanding of Atlantic salmon response to BKD. The identified biomarker genes can be used for future studies on improving the resistance of Atlantic salmon to BKD.

Profiling the transcriptome response of Atlantic salmon head kidney to formalin-killed Renibacterium salmoninarum

Renibacterium salmoninarum is a Gram-positive, intracellular bacterial pathogen that causes Bacterial Kidney Disease (BKD) in Atlantic salmon (Salmo salar). The host transcriptomic response to this immune-suppressive pathogen remains poorly understood. To identify R. salmoninarum-responsive genes, Atlantic salmon were intraperitoneally injected with a low (5 × 10⁵ cells/kg, Low-Rs) or high (5 × 10⁷ cells/kg; High-Rs) dose of formalin-killed R. salmoninarum bacterin or phosphate-buffered saline (PBS control); head kidney samples were collected before and 24 h after injection. Using 44K microarray analysis, we identified 107 and 345 differentially expressed probes in response to R. salmoninarum bacterin (i.e. High-Rs vs. PBS control) by Significance Analysis of Microarrays (SAM) and Rank Products (RP), respectively. Twenty-two microarray-identified genes were subjected to qPCR assays, and 17 genes were confirmed as being significantly responsive to the bacterin. There was an up-regulation in expression of genes playing putative roles as immune receptors and antimicrobial effectors. Genes with putative roles as pathogen recognition (e.g. clec12b and tlr5) or immunoregulatory (e.g. tnfrsf6b and tnfrsf11b) receptors were up-regulated in response to R.salmoninarum bacterin. Also, chemokines and a chemokine receptor showed opposite regulation [up-regulation of effectors (i.e. ccl13 and ccl) and down-regulation of cxcr1] in response to the bacterin. The present study identified and validated novel biomarker genes (e.g. ctsl1, lipe, cldn4, ccny) that can be used to assess Atlantic salmon response to R. salmoninarum, and will be valuable in the development of tools to combat BKD.

Multiple sclerosis genomic map implicates peripheral immune cells and microglia in susceptibility

We analyzed genetic data of 47,429 multiple sclerosis (MS) and 68,374 control subjects and established a reference map of the genetic architecture of MS that includes 200 autosomal susceptibility variants outside the major histocompatibility complex (MHC), one chromosome X variant, and 32 variants within the extended MHC. We used an ensemble of methods to prioritize 551 putative susceptibility genes that implicate multiple innate and adaptive pathways distributed across the cellular components of the immune system. Using expression profiles from purified human microglia, we observed enrichment for MS genes in these brain-resident immune cells, suggesting that these may have a role in targeting an autoimmune process to the central nervous system, although MS is most likely initially triggered by perturbation of peripheral immune responses.

Immune Profiling and Precision Medicine in Systemic Lupus Erythematosus

Systemic lupus erythematosus (SLE) is an autoimmune disorder with a wide range of clinical symptoms. Enormous progress has been made in the immunological and genetic understanding of SLE. However, the biology of disease heterogeneity in SLE has remained largely unexplored. Human immune profiling studies, helped by recent technological advances especially in single-cell and “omics” analyses, are now shedding light on the cellular and molecular basis of clinical symptoms and disease flares in individual patients. Peripheral blood immunophenotyping analysis with flow cytometry or mass cytometry are identifying responsible cell subsets and markers characteristic of disease heterogeneity. Transcriptome analysis is discovering molecular networks responsible for disease activity, disease subtype and future relapse. In this review, we summarize recent advances in the immune profiling analysis of SLE patients and discuss how they will be used for future precision medicine.

Proteinase 3 Limits the Number of Hematopoietic Stem and Progenitor Cells in Murine Bone Marrow

Hematopoietic stem and progenitor cells (HSPCs) undergo self-renewal and differentiation to guarantee a constant supply of short-lived blood cells. Both intrinsic and extrinsic factors determine HSPC fate, but the underlying mechanisms remain elusive. Here, we report that Proteinase 3 (PR3), a serine protease mainly confined to granulocytes, is also expressed in HSPCs. PR3 deficiency intrinsically suppressed cleavage and activation of caspase-3, leading to expansion of the bone marrow (BM) HSPC population due to decreased apoptosis. PR3-deficient HSPCs outcompete the long-term reconstitution potential of wild-type counterparts. Collectively, our results establish PR3 as a physiological regulator of HSPC numbers. PR3 inhibition is a potential therapeutic target to accelerate and increase the efficiency of BM reconstitution during transplantation.

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Proteinase 3 (PR3) is expressed in hematopoietic stem and progenitor cells (HSPCs)

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Deficiency of PR3 leads to expansion of HSPCs in murine bone marrow

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PR3 regulates spontaneous HSPC apoptosis by cleaving and activating caspase-3

Transcriptome profiling of antiviral immune and dietary fatty acid dependent responses of Atlantic salmon macrophage-like cells

BACKGROUND

Due to the limited availability and high cost of fish oil in the face of increasing aquaculture production, there is a need to reduce usage of fish oil in aquafeeds without compromising farm fish health. Therefore, the present study was conducted to determine if different levels of vegetable and fish oils can alter antiviral responses of salmon macrophage-like cells (MLCs). Atlantic salmon (Salmo salar) were fed diets containing 7.4% (FO7) or 5.1% (FO5) fish oil. These diets were designed to be relatively low in EPA + DHA (i.e. FO7: 1.41% and FO5: 1%), but near the requirement level, and resulting in comparable growth. Vegetable oil (i.e. rapeseed oil) was used to balance fish oil in experimental diets. After a 16-week feeding trial, MLCs isolated from fish in these dietary groups were stimulated by a viral mimic (dsRNA: pIC) for 6 h (qPCR assay) and 24 h (microarray and qPCR assays).

RESULTS

The fatty acid composition of head kidney leukocytes varied between the two dietary groups (e.g. higher 20:5n-3 in the FO7 group). Following microarray assays using a 44K salmonid platform, Rank Products (RP) analysis showed 14 and 54 differentially expressed probes (DEP) (PFP < 0.05) between the two diets in control and pIC groups (FO5 vs. FO7), respectively. Nonetheless, Significance Analysis of Microarrays (SAM, FDR < 0.05) identified only one DEP between pIC groups of the two diets. Moreover, we identified a large number (i.e. 890 DEP in FO7 and 1128 DEP in FO5 overlapping between SAM and RP) of pIC-responsive transcripts, and several of them were involved in TLR-/RLR-dependent and cytokine-mediated pathways. The microarray results were validated as significantly differentially expressed by qPCR assays for 2 out of 9 diet-responsive transcripts and for all of the 35 selected pIC-responsive transcripts.

CONCLUSION

Fatty acid-binding protein adipocyte (fabp4) and proteasome subunit beta type-8 (psmb8) were significantly up- and down-regulated, respectively, in the MLCs of fish fed the diet with a lower level of fish oil, suggesting that they are important diet-responsive, immune-related biomarkers for future studies. Although the different levels of dietary fish and vegetable oils involved in this study affected the expression of some transcripts, the immune-related pathways and functions activated by the antiviral response of salmon MLCs in both groups were comparable overall. Moreover, the qPCR revealed transcripts responding early to pIC (e.g. lgp2, map3k8, socs1, dusp5 and cflar) and time-responsive transcripts (e.g. scarb1-a, csf1r, traf5a, cd80 and ctsf) in salmon MLCs. The present study provides a comprehensive picture of the putative molecular pathways (e.g. RLR-, TLR-, MAPK- and IFN-associated pathways) activated by the antiviral response of salmon MLCs.

Immune cell profiling in cancer: molecular approaches to cell-specific identification

The immune system has many important regulatory roles in cancer development and progression. Given the emergence of effective immune therapies against many cancers, reliable predictors of response are needed. One method of determining response is by evaluating immune cell populations from treated and untreated tumor samples. The amount of material obtained from tumor biopsies can be limited; therefore, gene-based or protein-based analyses may be attractive because they require minimal tissue. Cell-specific signatures are being analyzed with use of the latest technologies, including NanoString’s nCounter technology, intracellular staining flow cytometry, cytometry by time-of-flight, RNA-Seq, and barcoding antibody-based protein arrays. These signatures provide information about the contributions of specific types of immune cells to bulk tumor samples. To date, both tumor tissue and immune cells have been analyzed for molecular expression profiles that can assess genes and proteins that are specific to immune cells, yielding results of varying specificity. Here, we discuss the importance of profiling tumor tissue and immune cells to identify immune-cell-associated genes and proteins and specific gene profiles of immune cells. We also discuss the use of these signatures in cancer treatment and the challenges faced in molecular expression profiling of immune cell populations.

Confirmation of five novel susceptibility loci for systemic lupus erythematosus (SLE) and integrated network analysis of 82 SLE susceptibility loci

We recently identified ten novel SLE susceptibility loci in Asians and uncovered several additional suggestive loci requiring further validation. This study aimed to replicate five of these suggestive loci in a Han Chinese cohort from Hong Kong, followed by meta-analysis (11,656 cases and 23,968 controls) on previously reported Asian and European populations, and to perform bioinformatic analyses on all 82 reported SLE loci to identify shared regulatory signatures. We performed a battery of analyses for these five loci, as well as joint analyses on all 82 SLE loci. All five loci passed genome-wide significance: MYNN (rs10936599, Pmeta = 1.92 × 10-13, OR = 1.14), ATG16L2 (rs11235604, Pmeta = 8.87 × 10 -12, OR = 0.78), CCL22 (rs223881, Pmeta = 5.87 × 10-16, OR = 0.87), ANKS1A (rs2762340, Pmeta = 4.93 × 10-15, OR = 0.87) and RNASEH2C (rs1308020, Pmeta = 2.96 × 10-19, OR = 0.84) and co-located with annotated gene regulatory elements. The novel loci share genetic signatures with other reported SLE loci, including effects on gene expression, transcription factor binding, and epigenetic characteristics. Most (56%) of the correlated (r2 > 0.8) SNPs from the 82 SLE loci were implicated in differential expression (9.81 × 10-198 < P < 5 × 10-3) of cis-genes. Transcription factor binding sites for p53, MEF2A and E2F1 were significantly (P < 0.05) over-represented in SLE loci, consistent with apoptosis playing a critical role in SLE. Enrichment analysis revealed common pathways, gene ontology, protein domains, and cell type-specific expression. In summary, we provide evidence of five novel SLE susceptibility loci. Integrated bioinformatics using all 82 loci revealed that SLE susceptibility loci share many gene regulatory features, suggestive of conserved mechanisms of SLE etiopathogenesis.

Update on the genetic architecture of rheumatoid arthritis

Human genetic studies into rheumatoid arthritis (RA) have uncovered more than 100 genetic loci associated with susceptibility to RA and have refined the RA-association model for HLA variants. The majority of RA-risk variants are highly shared across multiple ancestral populations and are located in noncoding elements that might have allele-specific regulatory effects in relevant tissues. Emerging multi-omics data, high-density genotype data and bioinformatic approaches are enabling researchers to use RA-risk variants to identify functionally relevant cell types and biological pathways that are involved in impaired immune processes and disease phenotypes. This Review summarizes reported RA-risk loci and the latest insights from human genetic studies into RA pathogenesis, including how genetic data has helped to identify currently available drugs that could be repurposed for patients with RA and the role of genetics in guiding the development of new drugs.

Omics Approaches for the Study of Adaptive Immunity to Staphylococcus aureus and the Selection of Vaccine Candidates

Staphylococcus aureus is a dangerous pathogen both in hospitals and in the community. Due to the crisis of antibiotic resistance, there is an urgent need for new strategies to combat S. aureus infections, such as vaccination. Increasing our knowledge about the mechanisms of protection will be key for the successful prevention or treatment of S. aureus invasion. Omics technologies generate a comprehensive picture of the physiological and pathophysiological processes within cells, tissues, organs, organisms and even populations. This review provides an overview of the contribution of genomics, transcriptomics, proteomics, metabolomics and immunoproteomics to the current understanding of S. aureus‑host interaction, with a focus on the adaptive immune response to the microorganism. While antibody responses during colonization and infection have been analyzed in detail using immunoproteomics, the full potential of omics technologies has not been tapped yet in terms of T-cells. Omics technologies promise to speed up vaccine development by enabling reverse vaccinology approaches. In consequence, omics technologies are powerful tools for deepening our understanding of the “superbug” S. aureus and for improving its control.

High-density genotyping of immune-related loci identifies new SLE risk variants in individuals with Asian ancestry

Systemic lupus erythematosus (SLE) has a strong but incompletely understood genetic architecture. We conducted an association study with replication in 4,478 SLE cases and 12,656 controls from six East Asian cohorts to identify new SLE susceptibility loci and better localize known loci. We identified ten new loci and confirmed 20 known loci with genome-wide significance. Among the new loci, the most significant locus was GTF2IRD1-GTF2I at 7q11.23 (rs73366469, Pmeta = 3.75 × 10(-117), odds ratio (OR) = 2.38), followed by DEF6, IL12B, TCF7, TERT, CD226, PCNXL3, RASGRP1, SYNGR1 and SIGLEC6. We identified the most likely functional variants at each locus by analyzing epigenetic marks and gene expression data. Ten candidate variants are known to alter gene expression in cis or in trans. Enrichment analysis highlights the importance of these loci in B cell and T cell biology. The new loci, together with previously known loci, increase the explained heritability of SLE to 24%. The new loci share functional and ontological characteristics with previously reported loci and are possible drug targets for SLE therapeutics.

Genetic evidence that intratumoral T-cell proliferation and activation are associated with recurrence and survival in patients with resected colorectal liver metastases

Though immune responses correlate with prognosis in primary colorectal cancer, the role of tumor immunity in metastatic disease is less clear. We hypothesized that patient survival and tumor recurrence correlate with transcriptional evidence of lymphocyte proliferation/activation in resected colorectal cancer liver metastases (CRLM). Microarray gene analysis was performed on liver tumor specimens from 96 patients who underwent resection for CRLM. A Cox proportional hazards model identified genes associated with overall survival (OS) and recurrence-free survival (RFS). Conventional gene ontology (GO) enrichment analysis ranked biologically relevant processes. Survival probabilities of prioritized processes were assessed. Protein expression was validated with immunohistochemistry in an independent set of patients. GO analysis identified and ranked unique biologic processes that correlated with survival. Genes that specifically functioned in the biologic process of "T-cell proliferation" were significant predictors of OS (P = 0.01), and both "T-cell proliferation" and "activation" were highly associated with RFS (P ≤ 0.01). Analysis of genes in these GO categories identified increased TNFSF14/LIGHT expression to be most associated with improved OS and RFS (P ≤ 0.0006). Immunohistochemistry of an independent validation set of CRLM confirmed that both increased tumor-infiltrating lymphocytes (TIL) and higher LIGHT expression on TILs were associated with improved OS and RFS. Differential expression of genes involved in T-cell proliferation/activation was associated with survival outcomes in a large number of surgical patients who underwent resection of CRLM. These biologic functions determined by GO analysis of the tumor microenvironment have identified specific immune-related genes that may be involved in an antitumor immune response.

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.

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.

Identification of a human neonatal immune-metabolic network associated with bacterial infection

Understanding how human neonates respond to infection remains incomplete. Here, a system-level investigation of neonatal systemic responses to infection shows a surprisingly strong but unbalanced homeostatic immune response; developing an elevated set-point of myeloid regulatory signalling and sugar-lipid metabolism with concomitant inhibition of lymphoid responses. Innate immune-negative feedback opposes innate immune activation while suppression of T-cell co-stimulation is coincident with selective upregulation of CD85 co-inhibitory pathways. By deriving modules of co-expressed RNAs, we identify a limited set of networks associated with bacterial infection that exhibit high levels of inter-patient variability. Whereas, by integrating immune and metabolic pathways, we infer a patient-invariant 52-gene-classifier that predicts bacterial infection with high accuracy using a new independent patient population. This is further shown to have predictive value in identifying infection in suspected cases with blood culture-negative tests. Our results lay the foundation for future translation of host pathways in advancing diagnostic, prognostic and therapeutic strategies for neonatal sepsis.

Infection remains a leading cause of morbidity and mortality in neonates worldwide. Here the authors report disproportionate immune stimulatory, co-inhibitory and metabolic pathway responses that specifically mark bacterial infection and can be used to predict sepsis in neonatal patients at the first clinical signs of infection.

Regulation of gene expression in autoimmune disease loci and the genetic basis of proliferation in CD4+ effector memory T cells

Genome-wide association studies (GWAS) and subsequent dense-genotyping of associated loci identified over a hundred single-nucleotide polymorphism (SNP) variants associated with the risk of rheumatoid arthritis (RA), type 1 diabetes (T1D), and celiac disease (CeD). Immunological and genetic studies suggest a role for CD4-positive effector memory T (CD⁺ T_EM) cells in the pathogenesis of these diseases. To elucidate mechanisms of autoimmune disease alleles, we investigated molecular phenotypes in CD4⁺ effector memory T cells potentially affected by these variants. In a cohort of genotyped healthy individuals, we isolated high purity CD4⁺ T_EM cells from peripheral blood, then assayed relative abundance, proliferation upon T cell receptor (TCR) stimulation, and the transcription of 215 genes within disease loci before and after stimulation. We identified 46 genes regulated by cis-acting expression quantitative trait loci (eQTL), the majority of which we detected in stimulated cells. Eleven of the 46 genes with eQTLs were previously undetected in peripheral blood mononuclear cells. Of 96 risk alleles of RA, T1D, and/or CeD in densely genotyped loci, eleven overlapped cis-eQTLs, of which five alleles completely explained the respective signals. A non-coding variant, rs389862^A, increased proliferative response (p = 4.75×10⁻⁸). In addition, baseline expression of seventeen genes in resting cells reliably predicted proliferative response after TCR stimulation. Strikingly, however, there was no evidence that risk alleles modulated CD4⁺ T_EM abundance or proliferation. Our study underscores the power of examining molecular phenotypes in relevant cells and conditions for understanding pathogenic mechanisms of disease variants.

Genome-wide association studies have identified hundreds of genetic variants associated to autoimmune diseases. To understand the mechanisms and pathways affected by these variants, follow-up studies of molecular phenotypes and functions are required. Given the diversity of cell types and specialization of functions within the immune system, it is crucial that such studies focus on specific and relevant cell types. Here, we studied genetic and cellular traits of CD4-positive effector memory T (CD4⁺ T_EM) cells, which are particularly important in the onset of rheumatoid arthritis, celiac disease, and type 1 diabetes. In a cohort of healthy individuals, we purified CD4⁺ T_EM cells, assayed genome-wide single nucleotide polymorphisms (SNPs), abundance of CD4⁺ T_EM cells in blood, proliferation upon T cell receptor stimulation, and 215 gene transcripts in resting and stimulated states. We found that expression levels of 46 genes were regulated by nearby SNPs, including disease-associated SNPs. Many of these expression quantitative trait loci were not previously seen in studies of more heterogeneous peripheral blood cells. We demonstrated that relative abundance and proliferative response of CD4⁺ T_EM cells varied in the population, however disease alleles are unlikely to confer risk by modulating these traits in this cell type.

The past, present, and future of immune repertoire biology - the rise of next-generation repertoire analysis

T and B cell repertoires are collections of lymphocytes, each characterized by its antigen-specific receptor. We review here classical technologies and analysis strategies developed to assess immunoglobulin (IG) and T cell receptor (TR) repertoire diversity, and describe recent advances in the field. First, we describe the broad range of available methodological tools developed in the past decades, each of which answering different questions and showing complementarity for progressive identification of the level of repertoire alterations: global overview of the diversity by flow cytometry, IG repertoire descriptions at the protein level for the identification of IG reactivities, IG/TR CDR3 spectratyping strategies, and related molecular quantification or dynamics of T/B cell differentiation. Additionally, we introduce the recent technological advances in molecular biology tools allowing deeper analysis of IG/TR diversity by next-generation sequencing (NGS), offering systematic and comprehensive sequencing of IG/TR transcripts in a short amount of time. NGS provides several angles of analysis such as clonotype frequency, CDR3 diversity, CDR3 sequence analysis, V allele identification with a quantitative dimension, therefore requiring high-throughput analysis tools development. In this line, we discuss the recent efforts made for nomenclature standardization and ontology development. We then present the variety of available statistical analysis and modeling approaches developed with regards to the various levels of diversity analysis, and reveal the increasing sophistication of those modeling approaches. To conclude, we provide some examples of recent mathematical modeling strategies and perspectives that illustrate the active rise of a "next-generation" of repertoire analysis.

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.

Genome-wide analysis of immune system genes by expressed sequence Tag profiling

Profiling studies of mRNA and microRNA, particularly microarray-based studies, have been extensively used to create compendia of genes that are preferentially expressed in the immune system. In some instances, functional studies have been subsequently pursued. Recent efforts such as the Encyclopedia of DNA Elements have demonstrated the benefit of coupling RNA sequencing analysis with information from expressed sequence tags (ESTs) for transcriptomic analysis. However, the full characterization and identification of transcripts that function as modulators of human immune responses remains incomplete. In this study, we demonstrate that an integrated analysis of human ESTs provides a robust platform to identify the immune transcriptome. Beyond recovering a reference set of immune-enriched genes and providing large-scale cross-validation of previous microarray studies, we discovered hundreds of novel genes preferentially expressed in the immune system, including noncoding RNAs. As a result, we have established the Immunogene database, representing an integrated EST road map of gene expression in human immune cells, which can be used to further investigate the function of coding and noncoding genes in the immune system. Using this approach, we have uncovered a unique metabolic gene signature of human macrophages and identified PRDM15 as a novel overexpressed gene in human lymphomas. Thus, we demonstrate the utility of EST profiling as a basis for further deconstruction of physiologic and pathologic immune processes.

T cell aging: a review of the transcriptional changes determined from genome-wide analysis

Age carries a detrimental impact on T cell function. In the past decade, analyses of the genome-scale transcriptional changes of T cells during aging have yielded a large amount of data and provided a global view of gene expression changes in T cells from aged hosts as well as subsets of T cells accumulated with age. Here, we aim to review the changes of gene expression in thymocytes and peripheral mature T cells, as well as the subsets of T cells accumulated with age, and discuss the gene networks and signaling pathways that are altered with aging in T cells. We also discuss future direction for furthering the understanding of the molecular basis of gene expression alterations in aged T cells, which could potentially provide opportunities for gene-based clinical interventions.

The molecular basis of the memory T cell response: differential gene expression and its epigenetic regulation

How the immune system remembers a previous encounter with a pathogen and responds more efficiently to a subsequent encounter has been one of the central enigmas for immunologists for over a century. The identification of pathogen-specific memory lymphocytes that arise after an infection provided a cellular basis for immunological memory. But the molecular mechanisms of immunological memory remain only partially understood. The emerging evidence suggests that epigenetic changes have a key role in controlling the distinct transcriptional profiles of memory lymphocytes and thus in shaping their function. In this Review, we summarize the recent progress that has been made in assessing the differential gene expression and chromatin modifications in memory CD4(+) and CD8(+) T cells, and we present our current understanding of the molecular basis of memory T cell function.

Molecular pattern discovery based on penalized matrix decomposition

A reliable and precise identification of the type of tumors is crucial to the effective treatment of cancer. With the rapid development of microarray technologies, tumor clustering based on gene expression data is becoming a powerful approach to cancer class discovery. In this paper, we apply the penalized matrix decomposition (PMD) to gene expression data to extract metasamples for clustering. The extracted metasamples capture the inherent structures of samples belong to the same class. At the same time, the PMD factors of a sample over the metasamples can be used as its class indicator in return. Compared with the conventional methods such as hierarchical clustering (HC), self-organizing maps (SOM), affinity propagation (AP) and nonnegative matrix factorization (NMF), the proposed method can identify the samples with complex classes. Moreover, the factor of PMD can be used as an index to determine the cluster number. The proposed method provides a reasonable explanation of the inconsistent classifications made by the conventional methods. In addition, it is able to discover the modules in gene expression data of conterminous developmental stages. Experiments on two representative problems show that the proposed PMD-based method is very promising to discover biological phenotypes.

TREEGL: reverse engineering tree-evolving gene networks underlying developing biological lineages

Motivation: Estimating gene regulatory networks over biological lineages is central to a deeper understanding of how cells evolve during development and differentiation. However, one challenge in estimating such evolving networks is that their host cells not only contiguously evolve, but also branch over time. For example, a stem cell evolves into two more specialized daughter cells at each division, forming a tree of networks. Another example is in a laboratory setting: a biologist may apply several different drugs individually to malignant cancer cells to analyze the effects of each drug on the cells; the cells treated by one drug may not be intrinsically similar to those treated by another, but rather to the malignant cancer cells they were derived from.

Results: We propose a novel algorithm, Treegl, an ℓ₁ plus total variation penalized linear regression method, to effectively estimate multiple gene networks corresponding to cell types related by a tree-genealogy, based on only a few samples from each cell type. Treegl takes advantage of the similarity between related networks along the biological lineage, while at the same time exposing sharp differences between the networks. We demonstrate that our algorithm performs significantly better than existing methods via simulation. Furthermore we explore an application to a breast cancer dataset, and show that our algorithm is able to produce biologically valid results that provide insight into the progression and reversion of breast cancer cells.

Availability: Software will be available at http://www.sailing.cs.cmu.edu/.

Contact:epxing@cs.cmu.edu

Systems vaccinology: learning to compute the behavior of vaccine induced immunity

The goal of systems biology is to access and integrate information about the parts (e.g., genes, proteins, cells) of a biological system with a view to computing and predicting the behavior of the system. The past decade has witnessed technological revolutions in the capacity to make high throughput measurements about the behavior of genes, proteins, and cells. Such technologies are widely used in biological research and in medicine, such as toward prognosis and therapy response prediction in cancer patients. More recently, systems biology is being applied to vaccinology, with the goal of: (1) understanding the mechanisms by which vaccines stimulate protective immunity, and (2) predicting the immunogenicity or efficacy of vaccines. Here, we review the recent advances in this area, and highlight the biological and computational challenges posed.

Integrating autoimmune risk loci with gene-expression data identifies specific pathogenic immune cell subsets

Although genome-wide association studies have implicated many individual loci in complex diseases, identifying the exact causal alleles and the cell types within which they act remains greatly challenging. To ultimately understand disease mechanism, researchers must carefully conceive functional studies in relevant pathogenic cell types to demonstrate the cellular impact of disease-associated genetic variants. This challenge is highlighted in autoimmune diseases, such as rheumatoid arthritis, where any of a broad range of immunological cell types might potentially be impacted by genetic variation to cause disease. To this end, we developed a statistical approach to identify potentially pathogenic cell types in autoimmune diseases by using a gene-expression data set of 223 murine-sorted immune cells from the Immunological Genome Consortium. We found enrichment of transitional B cell genes in systemic lupus erythematosus (p = 5.9 × 10(-6)) and epithelial-associated stimulated dendritic cell genes in Crohn disease (p = 1.6 × 10(-5)). Finally, we demonstrated enrichment of CD4+ effector memory T cell genes within rheumatoid arthritis loci (p < 10(-6)). To further validate the role of CD4+ effector memory T cells within rheumatoid arthritis, we identified 436 loci that were not yet known to be associated with the disease but that had a statistically suggestive association in a recent genome-wide association study (GWAS) meta-analysis (p(GWAS) < 0.001). Even among these putative loci, we noted a significant enrichment for genes specifically expressed in CD4+ effector memory T cells (p = 1.25 × 10(-4)). These cell types are primary candidates for future functional studies to reveal the role of risk alleles in autoimmunity. Our approach has application in other phenotypes, outside of autoimmunity, where many loci have been discovered and high-quality cell-type-specific gene expression is available.

[Genetics of contact allergy]

The genetics of contact allergy (CA) is still only partly understood, despite decades of research. This might be due to inadequately defined phenotypes used in the past. Therefore we suggested studying an extreme phenotype, namely, polysensitization (sensitization to 3 or more unrelated allergens). Another approach to unravel the genetics of CA has been the study of candidate genes. In this review, we summarize studies on the associations between genetic variation (e.g. SNPs) in certain candidate genes and CA. The following polymorphisms and mutations were studied: (1) filaggrin, (2) N-acetyltransferase (NAT1 and 2), (3) glutathione-S-transferase (GST M and T), (4) manganese superoxide dismutase, (5) angiotensin-converting enzyme (ACE), (6) tumor necrosis factor (TNF), and (7) interleukin-16 (IL16). The polymorphisms of NAT1/2, GST M/T, ACE, TNF, and IL16 were shown to be associated with an increased risk of CA. In one of our studies, the increased risk conferred by the TNF and IL16 polymorphisms was confined to polysensitized individuals. Other relevant candidate genes may be identified by studying diseases related to CA in terms of clinical symptoms, a more general pathology (inflammation) and possibly an overlapping genetic background, such as irritant contact dermatitis.

Genetic factors in contact allergy--review and future goals

The genetics of contact allergy are still only partly understood, despite decades of research; this might be a consequence of inadequately defined phenotypes used in the past. A recommendation is to study an extreme phenotype, namely, polysensitization (sensitization to three or more unrelated allergens). Another approach to unravel the genetics of contact allergy is the study of candidate genes. In this review, we summarize studies on the associations between genetic variation (e.g. single-nucleotide polymorphisms) in certain candidate genes and contact allergy. Polymorphisms and mutations affecting the following proteins were studied: (i) filaggrin; (ii) N-acetyltransferase (NAT) 1 and 2; (iii) glutathione-S-transferase (GST) M and T; (iv) manganese superoxide dismutase; (v) angiotensin-converting enzyme (ACE); (vi) tumour necrosis factor (TNF); and (vii) interleukin-16 (IL-16). The polymorphisms of NAT1, NAT2, GSTM, GSTT, ACE, TNF and IL-16 were shown to be associated with an increased risk of contact allergy. In one of our studies, the increased risk conferred by the TNF and IL-16 polymorphisms was confined to polysensitized individuals. Other relevant candidate genes may be identified by studying diseases related to contact allergy in terms of clinical symptoms, a more general pathology (inflammation), and possibly an overlapping genetic background, such as irritant contact dermatitis.

Photodynamic therapy: illuminating the road from cell death towards anti-tumour immunity

Photodynamic therapy (PDT) utilizes the destructive power of reactive oxygen species generated via visible light irradiation of a photosensitive dye accumulated in the cancerous tissue/cells, to bring about their obliteration. PDT activates multiple signalling pathways in cancer cells, which could give rise to all three cell death modalities (at least in vitro). Simultaneously, PDT is capable of eliciting various effects in the tumour microenvironment thereby affecting the tumour-associated/-infiltrating immune cells and by extension, leading to infiltration of various immune cells (e.g. neutrophils) into the treated site. PDT is also associated to the activation of different immune phenomena, e.g. acute-phase response, complement cascade and production of cytokines/chemokines. It has also come to light that, PDT is capable of activating 'anti-tumour adaptive immunity' in both pre-clinical as well as clinical settings. Although the ability of PDT to induce 'anti-cancer vaccine effect' is still debatable, yet it has been shown to be capable of inducing exposure/release of certain damage-associated molecular patterns (DAMPs) like HSP70. Therefore, it seems that PDT is unique among other approved therapeutic procedures in generating a microenvironment suitable for development of systemic anti-tumour immunity. Apart from this, recent times have seen the emergence of certain promising modalities based on PDT like-photoimmunotherapy and PDT-based cancer vaccines. This review mainly discusses the effects exerted by PDT on cancer cells, immune cells as well as tumour microenvironment in terms of anti-tumour immunity. The ability of PDT to expose/release DAMPs and the future perspectives of this paradigm have also been discussed.

Methods for transcriptomic analyses of the porcine host immune response: application to Salmonella infection using microarrays

Technological developments in both the collection and analysis of molecular genetic data over the past few years have provided new opportunities for an improved understanding of the global response to pathogen exposure. Such developments are particularly dramatic for scientists studying the pig, where tools to measure the expression of tens of thousands of transcripts, as well as unprecedented data on the porcine genome sequence, have combined to expand our abilities to elucidate the porcine immune system. In this review, we describe these recent developments in the context of our work using primarily microarrays to explore gene expression changes during infection of pigs by Salmonella. Thus while the focus is not a comprehensive review of all possible approaches, we provide links and information on both the tools we use as well as alternatives commonly available for transcriptomic data collection and analysis of porcine immune responses. Through this review, we expect readers will gain an appreciation for the necessary steps to plan, conduct, analyze and interpret the data from transcriptomic analyses directly applicable to their research interests.

Comprehensive expression profiles of genes for protein tyrosine phosphatases in immune cells

The phosphorylation and dephosphorylation of signaling molecules play a crucial role in various cellular processes, including immune responses. To date, the global expression profile of protein tyrosine phosphatases (PTPs) in various immune cells has not been described. With the RefDIC (Reference Genomics Database of Immune Cells) database compiled by RIKEN (Rikagaku Kenkyusho), we examined the expression patterns of PTP-encoding genes in mice and identified between 57 and 64 PTP-encoding genes (depending on cutoff values) that were commonly expressed in immune cells. Cells of different lineages contained additional, unique PTP-encoding genes, which resulted in a total of 58 to 76 genes. Compared with cells from nonimmune tissues, immune cells exhibited enhanced expression of the genes encoding 8 PTP-encoding genes, including Ptprc, Ptpn6, and Ptpn22, but had barely detectable expression of 11 PTP-encoding genes, including Ptprd and Tns1. Each immune cell lineage had between 2 and 18 PTP-encoding genes expressed at relatively high or low extents relative to the average expression among immune cells; for example, Ptprj in B cells, Dusp3 in macrophages, Ptpro in dendritic cells, and Ptprg in mast cells. These PTPs potentially play important roles in each cell lineage, and our analysis provides insight for future functional studies.

Global gene expression changes in type 1 diabetes: insights into autoimmune response in the target organ and in the periphery

Type 1 diabetes (T1D) is an autoimmune disease caused by the selective destruction of the insulin-producing β cells. Research into the pathogenesis of T1D has been hindered by the lack of detection of the autoimmune process during the asymptomatic period and by the inaccessibility to the target tissue. Therefore current understanding of the immunological phenomena that take place in the pancreas of the patients is very limited and much of the current knowledge on T1D has been obtained using animal models. Microarray technology and bioinformatics allow the comparison of the gene expression profile - transcriptome - in normal and pathological conditions, creating a global picture of altered processes. Microarray experiments have defined new transcriptional alterations associated with several autoimmune diseases, and are focused on the identification of specific biomarkers. In this review we summarize current data on gene expression profiles in T1D from an immunological point of view. Reported transcriptome studies have been performed in T1D patients and Non-Obese Diabetic mouse models analyzing peripheral blood, lymphoid organs and pancreas/islets. In the periphery, the distinctive profiles are inflammatory pathways inducible by IL-1β and IFNs that can help in the identification of new biomarkers. In the target organ, a remarkable finding is the overexpression of inflammatory and innate immune response genes and the active autoimmune response at longstanding stages, contrary to the pre-existing concept of acute autoimmune process in T1D.

Integrating genomic signatures for immunologic discovery

Understanding heterogeneity in adaptive immune responses is essential to dissect pathways of memory B and T cell differentiation and to define correlates of protective immunity. Traditionally, immunologists have deconvoluted this heterogeneity with flow cytometry--with combinations of markers to define signatures that represent specific lineages, differentiation states, and functions. Genome-scale technologies have become widely available and provide the ability to define expression signatures--sets of genes--that represent discrete biological properties of cell populations. Because genomic signatures can serve as surrogates of a phenotype, function, or cell state, they can integrate phenotypic information between experiments, cell types, and species. Here, we discuss how integration of well-defined expression signatures across experimental conditions together with functional analysis of their component genes could provide new opportunities to dissect the complexity of the adaptive immune response and map the immune response to vaccines and pathogens.

Gene expression profiles identify inflammatory signatures in dendritic cells

Dendritic cells (DCs) constitute a heterogeneous group of antigen-presenting leukocytes important in activation of both innate and adaptive immunity. We studied the gene expression patterns of DCs incubated with reagents inducing their activation or inhibition. Total RNA was isolated from DCs and gene expression profiling was performed with oligonucleotide microarrays. Using a supervised learning algorithm based on Random Forest, we generated a molecular signature of inflammation from a training set of 77 samples. We then validated this molecular signature in a testing set of 38 samples. Supervised analysis identified a set of 44 genes that distinguished very accurately between inflammatory and non inflammatory samples. The diagnostic performance of the signature genes was assessed against an independent set of samples, by qRT-PCR. Our findings suggest that the gene expression signature of DCs can provide a molecular classification for use in the selection of anti-inflammatory or adjuvant molecules with specific effects on DC activity.

Gene expression profiles for the human pancreas and purified islets in type 1 diabetes: new findings at clinical onset and in long-standing diabetes

Type 1 diabetes (T1D) is caused by the selective destruction of the insulin-producing beta cells of the pancreas by an autoimmune response. Due to ethical and practical difficulties, the features of the destructive process are known from a small number of observations, and transcriptomic data are remarkably missing. Here we report whole genome transcript analysis validated by quantitative reverse transcription-polymerase chain reaction (qRT-PCR) and correlated with immunohistological observations for four T1D pancreases (collected 5 days, 9 months, 8 and 10 years after diagnosis) and for purified islets from two of them. Collectively, the expression profile of immune response and inflammatory genes confirmed the current views on the immunopathogenesis of diabetes and showed similarities with other autoimmune diseases; for example, an interferon signature was detected. The data also supported the concept that the autoimmune process is maintained and balanced partially by regeneration and regulatory pathway activation, e.g. non-classical class I human leucocyte antigen and leucocyte immunoglobulin-like receptor, subfamily B1 (LILRB1). Changes in gene expression in islets were confined mainly to endocrine and neural genes, some of which are T1D autoantigens. By contrast, these islets showed only a few overexpressed immune system genes, among which bioinformatic analysis pointed to chemokine (C-C motif) receptor 5 (CCR5) and chemokine (CXC motif) receptor 4) (CXCR4) chemokine pathway activation. Remarkably, the expression of genes of innate immunity, complement, chemokines, immunoglobulin and regeneration genes was maintained or even increased in the long-standing cases. Transcriptomic data favour the view that T1D is caused by a chronic inflammatory process with a strong participation of innate immunity that progresses in spite of the regulatory and regenerative mechanisms.

Pomelo II: finding differentially expressed genes

Pomelo II (http://pomelo2.bioinfo.cnio.es) is an open-source, web-based, freely available tool for the analysis of gene (and protein) expression and tissue array data. Pomelo II implements: permutation-based tests for class comparisons (t-test, ANOVA) and regression; survival analysis using Cox model; contingency table analysis with Fisher's exact test; linear models (of which t-test and ANOVA are especial cases) that allow additional covariates for complex experimental designs and use empirical Bayes moderated statistics. Permutation-based and Cox model analysis use parallel computing, which permits taking advantage of multicore CPUs and computing clusters. Access to, and further analysis of, additional biological information and annotations (PubMed references, Gene Ontology terms, KEGG and Reactome pathways) are available either for individual genes (from clickable links in tables and figures) or sets of genes. The source code is available, allowing for extending and reusing the software. A comprehensive test suite is also available, and covers both the user interface and the numerical results. The possibility of including additional covariates, parallelization of computation, open-source availability of the code and comprehensive testing suite make Pomelo II a unique tool.

From transcriptome analysis to immunogenomics: current status and future direction

In 1994, we pioneered a complementary DNA (cDNA) sequencing project that aimed to predict the primary structures of unknown human proteins. Although our cDNA project was focused on the sequencing of large cDNAs, the following cDNA sequencing projects conducted by other groups have more extensively characterized mammalian transcriptome. In parallel, many groups have made a tremendous amount of effort to develop various resources for functional human genomics. In this context, to demonstrate the power of functional genomic approaches in practice, we have applied them for a comprehensive understanding of the immune system, which we term 'immunogenomics'. This mini-review first describes the historical background of our cDNA project and then provides perspectives on the present and future of immunogenomics based on our experiences.

The Immunological Genome Project: networks of gene expression in immune cells

The Immunological Genome Project combines immunology and computational biology laboratories in an effort to establish a complete 'road map' of gene-expression and regulatory networks in all immune cells.

Transcriptional impact of Aire varies with cell type

Aire promotes T cell tolerance by inducing the expression of a broad swath of genes encoding peripheral tissue antigens (PTAs) in medullary epithelial cells (MECs) of the thymus. The exact mechanism used in inducing this ectopic transcription remains obscure. To address this issue, we generated transgenic mice expressing Aire in pancreatic islet beta cells. Gene-expression profiling of such islets revealed that Aire can have a significant impact on transcription in these cells, mainly inducing, but also repressing, transcript levels in a manner comparable with its influence on MECs. The exact transcripts affected differed in MECs and beta cells, with limited overlap between the two sets of Aire-modulated genes. We propose that Aire promotes ectopic gene expression by a generic mechanism that does not depend on any particular characteristics or transcription mechanisms operating in MECs, whereas the cellular environment does govern which genes are actually susceptible to Aire regulation.

A modular analysis framework for blood genomics studies: application to systemic lupus erythematosus

The analysis of patient blood transcriptional profiles offers a means to investigate the immunological mechanisms relevant to human diseases on a genome-wide scale. In addition, such studies provide a basis for the discovery of clinically relevant biomarker signatures. We designed a strategy for microarray analysis that is based on the identification of transcriptional modules formed by genes coordinately expressed in multiple disease data sets. Mapping changes in gene expression at the module level generated disease-specific transcriptional fingerprints that provide a stable framework for the visualization and functional interpretation of microarray data. These transcriptional modules were used as a basis for the selection of biomarkers and the development of a multivariate transcriptional indicator of disease progression in patients with systemic lupus erythematosus. Thus, this work describes the implementation and application of a methodology designed to support systems-scale analysis of the human immune system in translational research settings.

Inferring differentiation pathways from gene expression

Motivation: The regulation of proliferation and differentiation of embryonic and adult stem cells into mature cells is central to developmental biology. Gene expression measured in distinguishable developmental stages helps to elucidate underlying molecular processes. In previous work we showed that functional gene modules, which act distinctly in the course of development, can be represented by a mixture of trees. In general, the similarities in the gene expression programs of cell populations reflect the similarities in the differentiation path.

Results: We propose a novel model for gene expression profiles and an unsupervised learning method to estimate developmental similarity and infer differentiation pathways. We assess the performance of our model on simulated data and compare it with favorable results to related methods. We also infer differentiation pathways and predict functional modules in gene expression data of lymphoid development.

Conclusions: We demonstrate for the first time how, in principal, the incorporation of structural knowledge about the dependence structure helps to reveal differentiation pathways and potentially relevant functional gene modules from microarray datasets. Our method applies in any area of developmental biology where it is possible to obtain cells of distinguishable differentiation stages.

Availability: The implementation of our method (GPL license), data and additional results are available at http://algorithmics.molgen.mpg.de/Supplements/InfDif/

Contact:filho@molgen.mpg.de, schliep@molgen.mpg.de

Supplementary information:Supplementary data is available at Bioinformatics online.

Genomics and the immune system

While the hereditary information encoded in the Watson-Crick base pairing of genomes is largely static within a given individual, access to this information is controlled by dynamic mechanisms. The human genome is pervasively transcribed, but the roles played by the majority of the non-protein-coding genome sequences are still largely unknown. In this review we focus on insights to gene transcriptional regulation by placing special emphasis on genome-wide approaches, and on how non-coding RNAs, which derive from global transcription of the genome, in turn control gene expression. We review recent progress in the field with highlights on the immune system.

New perspectives for large-scale repertoire analysis of immune receptors

In vertebrates, the world of antigenic motifs is matched to large populations of lymphocytes through specific recognition of an epitope by a given receptor unique to a lymphocyte clone. The concept of immune repertoire was proposed to describe this diversity of lymphocyte receptors - Ig and TCR - required by the network of interactions. The immune repertoires became useful tools to describe lymphocyte and receptor populations through the development of the immune system and in pathological situations. Recently, the development of mass technologies made possible a comprehensive survey of immune repertoires at the genome, transcript and protein levels, and some of these techniques have been already adapted to TCR and Ig repertoire analyses. Such approaches generate very big datasets, which necessitates complex and multi-parametric annotations in dedicated databases. They also require new analysis methods, leading to the integration of structure and dynamics of the immune repertoires, at different time scales (immune response, development of the individual, evolution of the species). Such methods may be extended to the analysis of new classes of adaptive-like receptors, which were recently discovered in different invertebrates and in agnathans. Ultimately, they may allow a parallel monitoring of pathogen and immune repertoires addressing the reciprocal influences that decide for the host survival or death. In this review, we first study the characteristics of Ig and TCR repertoires, and we examine several systematic approaches developed for the analysis of these "classical" immune repertoires at different levels. We then consider examples of the recent developments of modeling and statistical analysis, and we discuss their relevance and their importance for the study of the immune diversity. An extended view of immune repertoires is proposed, integrating the diversity of other receptors involved in immune recognition. Also, we discuss how repertoire studies could link pathogen variation and immune diversity to reveal regulatory patterns and rules driving their co-diversification race.

Novel insights into the relationships between dendritic cell subsets in human and mouse revealed by genome-wide expression profiling

Genome-wide expression profiling of mouse and human leukocytes reveal conserved transcriptional programs of plasmacytoid or conventional dendritic cell subsets.

Background

Dendritic cells (DCs) are a complex group of cells that play a critical role in vertebrate immunity. Lymph-node resident DCs (LN-DCs) are subdivided into conventional DC (cDC) subsets (CD11b and CD8α in mouse; BDCA1 and BDCA3 in human) and plasmacytoid DCs (pDCs). It is currently unclear if these various DC populations belong to a unique hematopoietic lineage and if the subsets identified in the mouse and human systems are evolutionary homologs. To gain novel insights into these questions, we sought conserved genetic signatures for LN-DCs and in vitro derived granulocyte-macrophage colony stimulating factor (GM-CSF) DCs through the analysis of a compendium of genome-wide expression profiles of mouse or human leukocytes.

Results

We show through clustering analysis that all LN-DC subsets form a distinct branch within the leukocyte family tree, and reveal a transcriptomal signature evolutionarily conserved in all LN-DC subsets. Moreover, we identify a large gene expression program shared between mouse and human pDCs, and smaller conserved profiles shared between mouse and human LN-cDC subsets. Importantly, most of these genes have not been previously associated with DC function and many have unknown functions. Finally, we use compendium analysis to re-evaluate the classification of interferon-producing killer DCs, lin^-CD16⁺HLA-DR⁺ cells and in vitro derived GM-CSF DCs, and show that these cells are more closely linked to natural killer and myeloid cells, respectively.

Conclusion

Our study provides a unique database resource for future investigation of the evolutionarily conserved molecular pathways governing the ontogeny and functions of leukocyte subsets, especially DCs.