Democratizing systems immunology with modular transcriptional repertoire analyses

Interactions between the tumor and the blood systemic response of breast cancer patients

Although systemic immunity is critical to the process of tumor rejection, cancer research has largely focused on immune cells in the tumor microenvironment. To understand molecular changes in the patient systemic response (SR) to the presence of BC, we profiled RNA in blood and matched tumor from 173 patients. We designed a system (MIxT, Matched Interactions Across Tissues) to systematically explore and link molecular processes expressed in each tissue. MIxT confirmed that processes active in the patient SR are especially relevant to BC immunogenicity. The nature of interactions across tissues (i.e. which biological processes are associated and their patterns of expression) varies highly with tumor subtype. For example, aspects of the immune SR are underexpressed proportionally to the level of expression of defined molecular processes specific to basal tumors. The catalog of subtype-specific interactions across tissues from BC patients provides promising new ways to tackle or monitor the disease by exploiting the patient SR.

We present a novel system (MIxT) to identify genes and pathways in the primary tumor that are tightly linked to genes and pathways in the patient systemic response (SR). These results suggest new ways to tackle and monitor the disease by looking outside the tumor and exploiting the patient SR.

A human tissue-based functional assay platform to evaluate the immune function impact of small molecule inhibitors that target the immune system

While the immune system is essential for the maintenance of the homeostasis, health and survival of humans, aberrant immune responses can lead to chronic inflammatory and autoimmune disorders. Pharmacological modulation of drug targets in the immune system to ameliorate disease also carry a risk of immunosuppression that could lead to adverse outcomes. Therefore, it is important to understand the 'immune fingerprint' of novel therapeutics as they relate to current and, clinically used immunological therapies to better understand their potential therapeutic benefit as well as immunosuppressive ability that might lead to adverse events such as infection risks and cancer. Since the mechanistic investigation of pharmacological modulators in a drug discovery setting is largely compound- and mechanism-centric but not comprehensive in terms of immune system impact, we developed a human tissue based functional assay platform to evaluate the impact of pharmacological modulators on a range of innate and adaptive immune functions. Here, we demonstrate that it is possible to generate a qualitative and quantitative immune system impact of pharmacological modulators, which might help better understand and predict the benefit-risk profiles of these compounds in the treatment of immune disorders.

Nasopharyngeal Microbiota, Host Transcriptome, and Disease Severity in Children with Respiratory Syncytial Virus Infection

RATIONALE

Respiratory syncytial virus (RSV) is the leading cause of acute lower respiratory tract infections and hospitalizations in infants worldwide. Known risk factors, however, incompletely explain the variability of RSV disease severity, especially among healthy children. We postulate that the severity of RSV infection is influenced by modulation of the host immune response by the local bacterial ecosystem.

OBJECTIVES

To assess whether specific nasopharyngeal microbiota (clusters) are associated with distinct host transcriptome profiles and disease severity in children less than 2 years of age with RSV infection.

METHODS

We characterized the nasopharyngeal microbiota profiles of young children with mild and severe RSV disease and healthy children by 16S-rRNA sequencing. In parallel, using multivariable models, we analyzed whole-blood transcriptome profiles to study the relationship between microbial community composition, the RSV-induced host transcriptional response, and clinical disease severity.

MEASUREMENTS AND MAIN RESULTS

We identified five nasopharyngeal microbiota clusters characterized by enrichment of either Haemophilus influenzae, Streptococcus, Corynebacterium, Moraxella, or Staphylococcus aureus. RSV infection and RSV hospitalization were positively associated with H. influenzae and Streptococcus and negatively associated with S. aureus abundance, independent of age. Children with RSV showed overexpression of IFN-related genes, independent of the microbiota cluster. In addition, transcriptome profiles of children with RSV infection and H. influenzae- and Streptococcus-dominated microbiota were characterized by greater overexpression of genes linked to Toll-like receptor and by neutrophil and macrophage activation and signaling.

CONCLUSIONS

Our data suggest that interactions between RSV and nasopharyngeal microbiota might modulate the host immune response, potentially affecting clinical disease severity.

Omics studies: their use in diagnosis and reclassification of SLE and other systemic autoimmune diseases

Omics studies of systemic autoimmune diseases (SADs) in general, and SLE in particular, have delivered isolated information from transcriptome, epigenome, genome, cytokine and metabolome analyses. Such analyses have resulted in the identification of disease susceptibility genes and the description of IFN expression signatures, allowing extensive insight into the mechanisms of disease and the development of new therapies. Access to such technologies allows the recognition of patterns of disease at a pathway level, thereby, to reclassify SLE and other SADs and to develop new therapeutics from a personalized perspective. The use of omic information allows the discovery of correlative patterns involving drugs not currently suspected to be of value in SADs. In this review, we summarize the omics findings for SLE and propose ways of using the data for the identification of new biomarkers, finding new drugs and reclassifying patients not only with SLE, but also with other SADs.

Modular transcriptional repertoire analyses identify a blood neutrophil signature as a candidate biomarker for lupus nephritis

Objective

LN is a severe complication of SLE. Non-invasive biomarkers are needed for identifying patients at risk of a renal flare, for differentiating proliferative from non-proliferative forms and for assessing prognoses for LN.

Methods

We assessed the link between blood transcriptional signatures and LN using blood samples from patients with biopsy-proven LN, extra-renal SLE flares or quiescent SLE. Healthy controls, and control patients with glomerular diseases or bacterial sepsis were included. Modular repertoire analyses from microarray data were confirmed by PCR.

Results

A modular neutrophil signature (upregulation of module M5.15) was present in 65% of SLE patients and was strongly associated with LN. M5.15 activity was stronger in LN than in extra-renal flares (88 vs 17%). M5.15 was neither correlated to IFN modules, nor to SLEDAI or anti-dsDNA antibodies, but moderately to CS dose. M5.15 activity was associated with severity of LN, was stronger when proliferative, and decreased in patients responding to treatment. M5.15 activation was not caused by higher CS dose because it correlated only moderately to neutrophil count and was also observed among quiescent patients. Among quiescent patients, those with a past history of LN had higher M5.15 activity (50 vs 8%). M5.15 activation was present in patients with bacterial sepsis or ANCA-associated vasculitis, but not in patients with other glomerular diseases. Overall, M5.15 activation was associated with past, present or future flares of LN.

Conclusion

Modular neutrophil signature could be a biomarker for stratifying LN risk and for monitoring its response to treatment.

Trial registration

ClinicalTrials.gov, http://clinicaltrials.gov , NCT00920114.

Meta-analysis of cell- specific transcriptomic data using fuzzy c-means clustering discovers versatile viral responsive genes

BACKGROUND

Despite advances in the gene-set enrichment analysis methods; inadequate definitions of gene-sets cause a major limitation in the discovery of novel biological processes from the transcriptomic datasets. Typically, gene-sets are obtained from publicly available pathway databases, which contain generalized definitions frequently derived by manual curation. Recently unsupervised clustering algorithms have been proposed to identify gene-sets from transcriptomics datasets deposited in public domain. These data-driven definitions of the gene-sets can be context-specific revealing novel biological mechanisms. However, the previously proposed algorithms for identification of data-driven gene-sets are based on hard clustering which do not allow overlap across clusters, a characteristic that is predominantly observed across biological pathways.

RESULTS

We developed a pipeline using fuzzy-C-means (FCM) soft clustering approach to identify gene-sets which recapitulates topological characteristics of biological pathways. Specifically, we apply our pipeline to derive gene-sets from transcriptomic data measuring response of monocyte derived dendritic cells and A549 epithelial cells to influenza infections. Our approach apply Ward's method for the selection of initial conditions, optimize parameters of FCM algorithm for human cell-specific transcriptomic data and identify robust gene-sets along with versatile viral responsive genes.

CONCLUSION

We validate our gene-sets and demonstrate that by identifying genes associated with multiple gene-sets, FCM clustering algorithm significantly improves interpretation of transcriptomic data facilitating investigation of novel biological processes by leveraging on transcriptomic data available in the public domain. We develop an interactive 'Fuzzy Inference of Gene-sets (FIGS)' package (GitHub: https://github.com/Thakar-Lab/FIGS ) to facilitate use of of pipeline. Future extension of FIGS across different immune cell-types will improve mechanistic investigation followed by high-throughput omics studies.

Understanding Human Autoimmunity and Autoinflammation Through Transcriptomics

Transcriptomics, the high-throughput characterization of RNAs, has been instrumental in defining pathogenic signatures in human autoimmunity and autoinflammation. It enabled the identification of new therapeutic targets in IFN-, IL-1- and IL-17-mediated diseases. Applied to immunomonitoring, transcriptomics is starting to unravel diagnostic and prognostic signatures that stratify patients, track molecular changes associated with disease activity, define personalized treatment strategies, and generally inform clinical practice. Herein, we review the use of transcriptomics to define mechanistic, diagnostic, and predictive signatures in human autoimmunity and autoinflammation. We discuss some of the analytical approaches applied to extract biological knowledge from high-dimensional data sets. Finally, we touch upon emerging applications of transcriptomics to study eQTLs, B and T cell repertoire diversity, and isoform usage.

The Numerical Predominance and Large Transcriptome Differences of Neutrophils in Peripheral Blood Together Inevitably Account for a Reported Pulmonary Tuberculosis Signature

Previous transcriptomic analysis revealed a 393-transcript signature (PTBsig), which is dominated by interferon inducible genes, in whole blood of pulmonary tuberculosis (PTB) patients. Comparisons with a limited set of interferon-driven genes among separated monocytes, CD4+ T cells, CD8+ T cells, and neutrophils indicated that the signature is due to changes in neutrophils, the overwhelmingly predominant cell type. By extending the analysis to the entire 393 transcripts of PTBsig and by switching the cell proportions between separated monocytes, CD4+ T cells, CD8+ T cells, and neutrophils, we create putative PTBsig for whole blood (pPTBsig) in which CD4+ or CD8+ T cells or monocytes predominated or in which the cell proportions were unchanged. These putative signatures are then compared to the actual reported PTBsig. We show that, because of their predominance in peripheral blood and their larger transcriptional responses, neutrophils were indeed almost exclusively responsible for PTBsig. We caution that the functional significance of changes in other cell types might escape notice in transcriptome analysis that is based upon whole blood.

Longitudinal monitoring of gene expression in ultra-low-volume blood samples self-collected at home

Blood transcriptional profiles could serve as biomarkers of clinical changes in subjects at-risk for or diagnosed with diabetes. However, transcriptional variation over time is poorly understood due to the impracticality of frequent longitudinal phlebotomy in large patient cohorts. We have developed a novel transcriptome assessment method that could be applied to fingerstick blood samples self-collected by study volunteers. Fifteen μL of blood from a fingerstick yielded sufficient RNA to analyse > 176 transcripts by high-throughput quantitative polymerase chain reaction (PCR). We enrolled 13 subjects with type 1 diabetes and 14 controls to perform weekly collections at home for a period of 6 months. Subjects returned an average of 24 of 26 total weekly samples, and transcript data were obtained successfully for > 99% of samples returned. A high degree of correlation between fingerstick data and data from a standard 3 mL venipuncture sample was observed. Increases in interferon-stimulated gene expression were associated with self-reported respiratory infections, indicating that real-world transcriptional changes can be detected using this assay. In summary, we show that longitudinal monitoring of gene expression is feasible using ultra-low-volume blood samples self-collected by study participants at home, and can be used to monitor changes in gene expression frequently over extended periods.

Validation of Immune Cell Modules in Multicellular Transcriptomic Data

Numerous gene signatures, or modules have been described to evaluate the immune cell composition in transcriptomes of multicellular tissue samples. However, significant diversity in module gene content for specific cell types is associated with heterogeneity in their performance. In order to rank modules that best reflect their purported association, we have generated the modular discrimination index (MDI) score that assesses expression of each module in the target cell type relative to other cells. We demonstrate that MDI scores predict modules that best reflect independently validated differences in cellular composition, and correlate with the covariance between cell numbers and module expression in human blood and tissue samples. Our analyses demonstrate that MDI scores provide an ordinal summary statistic that reliably ranks the accuracy of gene expression modules for deconvolution of cell type abundance in transcriptional data.

Modular transcriptional repertoire and MicroRNA target analyses characterize genomic dysregulation in the thymus of Down syndrome infants

Trisomy 21-driven transcriptional alterations in human thymus were characterized through gene coexpression network (GCN) and miRNA-target analyses. We used whole thymic tissue--obtained at heart surgery from Down syndrome (DS) and karyotipically normal subjects (CT)--and a network-based approach for GCN analysis that allows the identification of modular transcriptional repertoires (communities) and the interactions between all the system's constituents through community detection. Changes in the degree of connections observed for hierarchically important hubs/genes in CT and DS networks corresponded to community changes. Distinct communities of highly interconnected genes were topologically identified in these networks. The role of miRNAs in modulating the expression of highly connected genes in CT and DS was revealed through miRNA-target analysis. Trisomy 21 gene dysregulation in thymus may be depicted as the breakdown and altered reorganization of transcriptional modules. Leading networks acting in normal or disease states were identified. CT networks would depict the "canonical" way of thymus functioning. Conversely, DS networks represent a "non-canonical" way, i.e., thymic tissue adaptation under trisomy 21 genomic dysregulation. This adaptation is probably driven by epigenetic mechanisms acting at chromatin level and through the miRNA control of transcriptional programs involving the networks' high-hierarchy genes.

Comparative Systems Analyses Reveal Molecular Signatures of Clinically tested Vaccine Adjuvants

A better understanding of the mechanisms of action of human adjuvants could inform a rational development of next generation vaccines for human use. Here, we exploited a genome wide transcriptomics analysis combined with a systems biology approach to determine the molecular signatures induced by four clinically tested vaccine adjuvants, namely CAF01, IC31, GLA-SE and Alum in mice. We report signature molecules, pathways, gene modules and networks, which are shared by or otherwise exclusive to these clinical-grade adjuvants in whole blood and draining lymph nodes of mice. Intriguingly, co-expression analysis revealed blood gene modules highly enriched for molecules with documented roles in T follicular helper (TFH) and germinal center (GC) responses. We could show that all adjuvants enhanced, although with different magnitude and kinetics, TFH and GC B cell responses in draining lymph nodes. These results represent, to our knowledge, the first comparative systems analysis of clinically tested vaccine adjuvants that may provide new insights into the mechanisms of action of human adjuvants.

Distinct transcriptional changes in non-small cell lung cancer patients associated with multi-antigenic RNActive® CV9201 immunotherapy

We recently completed a phase I/IIa trial of RNActive® CV9201, a novel mRNA-based therapeutic vaccine targeting five tumor-associated antigens in non-small cell lung cancer (NSCLC) patients. The aim of the study presented here was to comprehensively analyze changes in peripheral blood during the vaccination period and to generate hypotheses facilitating the identification of potential biomarkers correlating with differential clinical outcomes post RNActive® immunotherapy. We performed whole-genome expression profiling in a subgroup of 22 stage IV NSCLC patients before and after initiation of treatment with CV9201. Utilizing an analytic approach based on blood transcriptional modules (BTMs), a previously described, sensitive tool for blood transcriptome data analysis, patients segregated into two major clusters based on transcriptional changes post RNActive® treatment. The first group of patients was characterized by the upregulation of an expression signature associated with myeloid cells and inflammation, whereas the other group exhibited an expression signature associated with T and NK cells. Patients with an enrichment of T and NK cell modules after treatment compared to baseline exhibited significantly longer progression-free and overall survival compared to patients with an upregulation of myeloid cell and inflammatory modules. Notably, these gene expression signatures were mutually exclusive and inversely correlated. Furthermore, our findings correlated with phenotypic data derived by flow cytometry as well as the neutrophil-to-lymphocyte ratio. Our study thus demonstrates non-overlapping, distinct transcriptional profiles correlating with survival warranting further validation for the development of biomarker candidates for mRNA-based immunotherapy.

Immunosenescence-Related Transcriptomic and Immunologic Changes in Older Individuals Following Influenza Vaccination

The goal of annual influenza vaccination is to reduce mortality and morbidity associated with this disease through the generation of protective immune responses. The objective of the current study was to examine markers of immunosenescence and identify immunosenescence-related differences in gene expression, gene regulation, cytokine secretion, and immunologic changes in an older study population receiving seasonal influenza A/H1N1 vaccination. Surprisingly, prior studies in this cohort revealed weak correlations between immunosenescence markers and humoral immune response to vaccination. In this report, we further examined the relationship of each immunosenescence marker (age, T cell receptor excision circle frequency, telomerase expression, percentage of CD28⁻ CD4⁺ T cells, percentage of CD28⁻ CD8⁺ T cells, and the CD4/CD8 T cell ratio) with additional markers of immune response (serum cytokine and chemokine expression) and measures of gene expression and/or regulation. Many of the immunosenescence markers indeed correlated with distinct sets of individual DNA methylation sites, miRNA expression levels, mRNA expression levels, serum cytokines, and leukocyte subsets. However, when the individual immunosenescence markers were grouped by pathways or functional terms, several shared biological functions were identified: antigen processing and presentation pathways, MAPK, mTOR, TCR, BCR, and calcium signaling pathways, as well as key cellular metabolic, proliferation and survival activities. Furthermore, the percent of CD4⁺ and/or CD8⁺ T cells lacking CD28 expression also correlated with miRNAs regulating clusters of genes known to be involved in viral infection. Integrated (DNA methylation, mRNA, miRNA, and protein levels) network biology analysis of immunosenescence-related pathways and genesets identified both known pathways (e.g., chemokine signaling, CTL, and NK cell activity), as well as a gene expression module not previously annotated with a known function. These results may improve our ability to predict immune responses to influenza and aid in new vaccine development, and highlight the need for additional studies to better define and characterize immunosenescence.

Concise gene signature for point-of-care classification of tuberculosis

There is an urgent need for new tools to combat the ongoing tuberculosis (TB) pandemic. Gene expression profiles based on blood signatures have proved useful in identifying genes that enable classification of TB patients, but have thus far been complex. Using real‐time PCR analysis, we evaluated the expression profiles from a large panel of genes in TB patients and healthy individuals in an Indian cohort. Classification models were built and validated for their capacity to discriminate samples from TB patients and controls within this cohort and on external independent gene expression datasets. A combination of only four genes distinguished TB patients from healthy individuals in both cross‐validations and on separate validation datasets with very high accuracy. An external validation on two distinct cohorts using a real‐time PCR setting confirmed the predictive power of this 4‐gene tool reaching sensitivity scores of 88% with a specificity of around 75%. Moreover, this gene signature demonstrated good classification power in HIV⁺ populations and also between TB and several other pulmonary diseases. Here we present proof of concept that our 4‐gene signature and the top classifier genes from our models provide excellent candidates for the development of molecular point‐of‐care TB diagnosis in endemic areas.

Preservation of epithelial cell barrier function and muted inflammation in resistance to allergic rhinoconjunctivitis from house dust mite challenge

BACKGROUND

An emerging paradigm holds that resistance to the development of allergic diseases, including allergic rhinoconjunctivitis, relates to an intact epithelial/epidermal barrier during early childhood. Conceivably, the immunologic and genomic footprint of this resistance is preserved in nonatopic, nonallergic adults and is unmasked during exposure to an aeroallergen.

OBJECTIVE

The aim of this study was to obtain direct support of the epithelial/epidermal barrier model for allergic rhinoconjunctivitis.

METHODS

Twenty-three adults allergic to house dust mites (HDMs) (M+) and 15 nonsensitive, nonallergic (M-) participants completed 3-hour exposures to aerosolized HDM (Dermatophagoides pteronyssinus) powder on 4 consecutive days in an allergen challenge chamber. We analyzed: (1) peripheral blood leukocyte levels and immune responses; and (2) RNA sequencing-derived expression profiles of nasal cells, before and after HDM exposure.

RESULTS

On HDM challenge: (1) only M+ persons developed allergic rhinoconjunctivitis symptoms; and (2) peripheral blood leukocyte levels/responses and gene expression patterns in nasal cells were largely concordant between M+ and M- participants; gross differences in these parameters were not observed at baseline (pre-exposure). Two key differences were observed. First, peripheral blood CD4⁺ and CD8⁺ T-cell activation levels initially decreased in M- participants versus increased in M+ participants. Second, in M- compared with M+ participants, genes that promoted epidermal/epithelial barrier function (eg, filament-aggregating protein [filaggrin]) versus inflammation (eg, chemokines) and innate immunity (interferon) were upregulated versus muted, respectively.

CONCLUSION

An imprint of resistance to HDM challenge in nonatopic, nonallergic adults was muted T-cell activation in the peripheral blood and inflammatory response in the nasal compartment, coupled with upregulation of genes that promote epidermal/epithelial cell barrier function.

Association of RNA Biosignatures With Bacterial Infections in Febrile Infants Aged 60 Days or Younger

IMPORTANCE

Young febrile infants are at substantial risk of serious bacterial infections; however, the current culture-based diagnosis has limitations. Analysis of host expression patterns ("RNA biosignatures") in response to infections may provide an alternative diagnostic approach.

OBJECTIVE

To assess whether RNA biosignatures can distinguish febrile infants aged 60 days or younger with and without serious bacterial infections.

DESIGN, SETTING, AND PARTICIPANTS

Prospective observational study involving a convenience sample of febrile infants 60 days or younger evaluated for fever (temperature >38° C) in 22 emergency departments from December 2008 to December 2010 who underwent laboratory evaluations including blood cultures. A random sample of infants with and without bacterial infections was selected for RNA biosignature analysis. Afebrile healthy infants served as controls. Blood samples were collected for cultures and RNA biosignatures. Bioinformatics tools were applied to define RNA biosignatures to classify febrile infants by infection type.

EXPOSURE

RNA biosignatures compared with cultures for discriminating febrile infants with and without bacterial infections and infants with bacteremia from those without bacterial infections.

MAIN OUTCOMES AND MEASURES

Bacterial infection confirmed by culture. Performance of RNA biosignatures was compared with routine laboratory screening tests and Yale Observation Scale (YOS) scores.

RESULTS

Of 1883 febrile infants (median age, 37 days; 55.7% boys), RNA biosignatures were measured in 279 randomly selected infants (89 with bacterial infections-including 32 with bacteremia and 15 with urinary tract infections-and 190 without bacterial infections), and 19 afebrile healthy infants. Sixty-six classifier genes were identified that distinguished infants with and without bacterial infections in the test set with 87% (95% CI, 73%-95%) sensitivity and 89% (95% CI, 81%-93%) specificity. Ten classifier genes distinguished infants with bacteremia from those without bacterial infections in the test set with 94% (95% CI, 70%-100%) sensitivity and 95% (95% CI, 88%-98%) specificity. The incremental C statistic for the RNA biosignatures over the YOS score was 0.37 (95% CI, 0.30-0.43).

CONCLUSIONS AND RELEVANCE

In this preliminary study, RNA biosignatures were defined to distinguish febrile infants aged 60 days or younger with vs without bacterial infections. Further research with larger populations is needed to refine and validate the estimates of test accuracy and to assess the clinical utility of RNA biosignatures in practice.

A systems approach to renal inflammation in SLE

Lupus disease and its complications including lupus nephritis (LN) are very disabling and significantly impact the quality of life and longevity of patients. Broadly immunosuppressive treatments do not always provide the expected clinical benefits and have significant side effects that contribute to patient morbidity. In the era of systems biology, new strategies are being deployed integrating diverse sources of information (molecular and clinical) so as to identify individual disease specificities and select less aggressive treatments. In this review, we summarize integrative approaches linking molecular disease profiles (mainly tissue transcriptomics) and clinical phenotypes. The main goals are to better understand the pathogenesis of lupus nephritis, to identify the risk factors for renal flare and to find the predictors of both short and long-term clinical outcome. Identification of common key drivers and additional patient-specific key drivers can open the door to improved and individualized therapy to prevent and treat LN.

Rhinovirus Detection in Symptomatic and Asymptomatic Children: Value of Host Transcriptome Analysis

RATIONALE

Rhinoviruses (RVs) are a major cause of symptomatic respiratory tract infection in all age groups. However, RVs can frequently be detected in asymptomatic individuals.

OBJECTIVES

To evaluate the ability of host transcriptional profiling to differentiate between symptomatic RV infection and incidental detection in children.

METHODS

Previously healthy children younger than 2 years old (n = 151) were enrolled at four study sites and classified into four clinical groups: RV- healthy control subjects (n = 37), RV+ asymptomatic subjects (n = 14), RV+ outpatients (n = 30), and RV+ inpatients (n = 70). Host responses were analyzed using whole-blood RNA transcriptional profiles.

MEASUREMENTS AND MAIN RESULTS

RV infection induced a robust transcriptional signature, which was validated in three independent cohorts and by quantitative real-time polymerase chain reaction with high prediction accuracy. The immune profile of symptomatic RV infection was characterized by overexpression of innate immunity and underexpression of adaptive immunity genes, whereas negligible changes were observed in asymptomatic RV+ subjects. Unsupervised hierarchical clustering identified two main clusters of subjects. The first included 93% of healthy control subjects and 100% of asymptomatic RV+ subjects, and the second comprised 98% of RV+ inpatients and 88% of RV+ outpatients. Genomic scores of healthy control subjects and asymptomatic RV+ children were similar and significantly lower than those of RV+ inpatients and outpatients (P < 0.0001).

CONCLUSIONS

Symptomatic RV infection induced a robust and reproducible transcriptional signature, whereas identification of RV in asymptomatic children was not associated with significant systemic transcriptional immune responses. Transcriptional profiling represents a useful tool to discriminate between active infection and incidental virus detection.

Transcriptomic evidence for modulation of host inflammatory responses during febrile Plasmodium falciparum malaria

Identifying molecular predictors and mechanisms of malaria disease is important for understanding how Plasmodium falciparum malaria is controlled. Transcriptomic studies in humans have so far been limited to retrospective analysis of blood samples from clinical cases. In this prospective, proof-of-principle study, we compared whole-blood RNA-seq profiles at pre-and post-infection time points from Malian adults who were either asymptomatic (n = 5) or febrile (n = 3) during their first seasonal PCR-positive P. falciparum infection with those from malaria-naïve Dutch adults after a single controlled human malaria infection (n = 5). Our data show a graded activation of pathways downstream of pro-inflammatory cytokines, with the highest activation in malaria-naïve Dutch individuals and significantly reduced activation in malaria-experienced Malians. Newly febrile and asymptomatic infections in Malians were statistically indistinguishable except for genes activated by pro-inflammatory cytokines. The combined data provide a molecular basis for the development of a pyrogenic threshold as individuals acquire immunity to clinical malaria.

Profiling networks of distinct immune-cells in tumors

Background

It is now clearly evident that cancer outcome and response to therapy is guided by diverse immune-cell activity in tumors. Presently, a key challenge is to comprehensively identify networks of distinct immune-cell signatures present in complex tissue, at higher-resolution and at various stages of differentiation, activation or function. This is particularly so for closely related immune-cells with diminutive, yet critical, differences.

Results

To predict networks of infiltrated distinct immune-cell phenotypes at higher resolution, we explored an integrated knowledge-based approach to select immune-cell signature genes integrating not only expression enrichment across immune-cells, but also an automatic capture of relevant immune-cell signature genes from the literature. This knowledge-based approach was integrated with resources of immune-cell specific protein networks, to define signature genes of distinct immune-cell phenotypes. We demonstrate the utility of this approach by profiling signatures of distinct immune-cells, and networks of immune-cells, from metastatic melanoma patients who had undergone chemotherapy. The resultant bioinformatics strategy complements immunohistochemistry from these tumors, and predicts both tumor-killing and immunosuppressive networks of distinct immune-cells in responders and non-responders, respectively. The approach is also shown to capture differences in the immune-cell networks of BRAF versus NRAS mutated metastatic melanomas, and the dynamic changes in resistance to targeted kinase inhibitors in MAPK signalling.

Conclusions

This integrative bioinformatics approach demonstrates that capturing the protein network signatures and ratios of distinct immune-cell in the tumor microenvironment maybe an important factor in predicting response to therapy. This may serve as a computational strategy to define network signatures of distinct immune-cells to guide immuno-pathological discovery.

Electronic supplementary material

The online version of this article (doi:10.1186/s12859-016-1141-3) contains supplementary material, which is available to authorized users.

Interferon-driven alterations of the host's amino acid metabolism in the pathogenesis of typhoid fever

Enteric fever, caused by Salmonella enterica serovar Typhi, is an important public health problem in resource-limited settings and, despite decades of research, human responses to the infection are poorly understood. In 41 healthy adults experimentally infected with wild-type S. Typhi, we detected significant cytokine responses within 12 h of bacterial ingestion. These early responses did not correlate with subsequent clinical disease outcomes and likely indicate initial host-pathogen interactions in the gut mucosa. In participants developing enteric fever after oral infection, marked transcriptional and cytokine responses during acute disease reflected dominant type I/II interferon signatures, which were significantly associated with bacteremia. Using a murine and macrophage infection model, we validated the pivotal role of this response in the expression of proteins of the host tryptophan metabolism during Salmonella infection. Corresponding alterations in tryptophan catabolites with immunomodulatory properties in serum of participants with typhoid fever confirmed the activity of this pathway, and implicate a central role of host tryptophan metabolism in the pathogenesis of typhoid fever.

Introducing a New Dimension to Molecular Disease Classifications

Systemic lupus erythematosus (SLE) is an autoimmune disease presenting with a wide range of clinical manifestations. A recent study in Cell has uncovered distinct molecular classes of pediatric SLE patients. The approach employed is remarkable in that it may enable alignment of molecular classifications of disease with the development and selection of novel treatment modalities.

A Simple Screening Approach To Prioritize Genes for Functional Analysis Identifies a Role for Interferon Regulatory Factor 7 in the Control of Respiratory Syncytial Virus Disease

Greater understanding of the functions of host gene products in response to infection is required. While many of these genes enable pathogen clearance, some enhance pathogen growth or contribute to disease symptoms. Many studies have profiled transcriptomic and proteomic responses to infection, generating large data sets, but selecting targets for further study is challenging. Here we propose a novel data-mining approach combining multiple heterogeneous data sets to prioritize genes for further study by using respiratory syncytial virus (RSV) infection as a model pathogen with a significant health care impact. The assumption was that the more frequently a gene is detected across multiple studies, the more important its role is. A literature search was performed to find data sets of genes and proteins that change after RSV infection. The data sets were standardized, collated into a single database, and then panned to determine which genes occurred in multiple data sets, generating a candidate gene list. This candidate gene list was validated by using both a clinical cohort and in vitro screening. We identified several genes that were frequently expressed following RSV infection with no assigned function in RSV control, including IFI27, IFIT3, IFI44L, GBP1, OAS3, IFI44, and IRF7. Drilling down into the function of these genes, we demonstrate a role in disease for the gene for interferon regulatory factor 7, which was highly ranked on the list, but not for IRF1, which was not. Thus, we have developed and validated an approach for collating published data sets into a manageable list of candidates, identifying novel targets for future analysis. IMPORTANCE Making the most of "big data" is one of the core challenges of current biology. There is a large array of heterogeneous data sets of host gene responses to infection, but these data sets do not inform us about gene function and require specialized skill sets and training for their utilization. Here we describe an approach that combines and simplifies these data sets, distilling this information into a single list of genes commonly upregulated in response to infection with RSV as a model pathogen. Many of the genes on the list have unknown functions in RSV disease. We validated the gene list with new clinical, in vitro, and in vivo data. This approach allows the rapid selection of genes of interest for further, more-detailed studies, thus reducing time and costs. Furthermore, the approach is simple to use and widely applicable to a range of diseases.

Personalized Immunomonitoring Uncovers Molecular Networks that Stratify Lupus Patients

Systemic lupus erythematosus (SLE) is an autoimmune disease characterized by loss of tolerance to nucleic acids and highly diverse clinical manifestations. To assess its molecular heterogeneity, we longitudinally profiled the blood transcriptome of 158 pediatric patients. Using mixed models accounting for repeated measurements, demographics, treatment, disease activity (DA), and nephritis class, we confirmed a prevalent IFN signature and identified a plasmablast signature as the most robust biomarker of DA. We detected gradual enrichment of neutrophil transcripts during progression to active nephritis and distinct signatures in response to treatment in different nephritis subclasses. Importantly, personalized immunomonitoring uncovered individual correlates of disease activity that enabled patient stratification into seven groups, supported by patient genotypes. Our study uncovers the molecular heterogeneity of SLE and provides an explanation for the failure of clinical trials. This approach may improve trial design and implementation of tailored therapies in genetically and clinically complex autoimmune diseases. PAPERCLIP.

Gene signature-based mapping of immunological systems and diseases

Background

The immune system is multifaceted, structured by diverse components that interconnect using multilayered dynamic cellular processes. Genomic technologies provide a means for investigating, at the molecular level, the adaptations of the immune system in host defense and its dysregulation in pathological conditions. A critical aspect of intersecting and investigating complex datasets is determining how to best integrate genomic data from diverse platforms and heterogeneous sample populations to capture immunological signatures in health and disease.

Result

We focus on gene signatures, representing highly enriched genes of immune cell subsets from both diseased and healthy tissues. From these, we construct a series of biomaps that illustrate the molecular linkages between cell subsets from different lineages, the connectivity between different immunological diseases, and the enrichment of cell subset signatures in diseased tissues. Finally, we overlay the downstream genes of drug targets with disease gene signatures to display the potential therapeutic applications for these approaches.

Conclusion

An in silico approach has been developed to characterize immune cell subsets and diseases based on the gene signatures that most differentiate them from other biological states. This modular ‘biomap’ reveals the linkages between different diseases and immune subtypes, and provides evidence for the presence of specific immunocyte subsets in mixed tissues. The over-represented genes in disease signatures of interest can be further investigated for their functions in both host defense and disease.

Electronic supplementary material

The online version of this article (doi:10.1186/s12859-016-1012-y) contains supplementary material, which is available to authorized users.

Characterization of Dendritic Cell Subsets Through Gene Expression Analysis

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

The human immune response to tuberculosis and its treatment: a view from the blood

The immune response upon infection with the pathogen Mycobacterium tuberculosis is poorly understood, hampering the discovery of new treatments and the improvements in diagnosis. In the last years, a blood transcriptional signature in tuberculosis has provided knowledge on the immune response occurring during active tuberculosis disease. This signature was absent in the majority of asymptomatic individuals who are latently infected with M. tuberculosis (referred to as latent). Using modular and pathway analyses of the complex data has shown, now in multiple studies, that the signature of active tuberculosis is dominated by overexpression of interferon-inducible genes (consisting of both type I and type II interferon signaling), myeloid genes, and inflammatory genes. There is also downregulation of genes encoding B and T-cell function. The blood signature of tuberculosis correlates with the extent of radiographic disease and is diminished upon effective treatment suggesting the possibility of new improved strategies to support diagnostic assays and methods for drug treatment monitoring. The signature suggested a previously under-appreciated role for type I interferons in development of active tuberculosis disease, and numerous mechanisms have now been uncovered to explain how type I interferon impedes the protective response to M. tuberculosis infection.

BubbleGUM: automatic extraction of phenotype molecular signatures and comprehensive visualization of multiple Gene Set Enrichment Analyses

Background

Recent advances in the analysis of high-throughput expression data have led to the development of tools that scaled-up their focus from single-gene to gene set level. For example, the popular Gene Set Enrichment Analysis (GSEA) algorithm can detect moderate but coordinated expression changes of groups of presumably related genes between pairs of experimental conditions. This considerably improves extraction of information from high-throughput gene expression data. However, although many gene sets covering a large panel of biological fields are available in public databases, the ability to generate home-made gene sets relevant to one’s biological question is crucial but remains a substantial challenge to most biologists lacking statistic or bioinformatic expertise. This is all the more the case when attempting to define a gene set specific of one condition compared to many other ones. Thus, there is a crucial need for an easy-to-use software for generation of relevant home-made gene sets from complex datasets, their use in GSEA, and the correction of the results when applied to multiple comparisons of many experimental conditions.

Result

We developed BubbleGUM (GSEA Unlimited Map), a tool that allows to automatically extract molecular signatures from transcriptomic data and perform exhaustive GSEA with multiple testing correction. One original feature of BubbleGUM notably resides in its capacity to integrate and compare numerous GSEA results into an easy-to-grasp graphical representation. We applied our method to generate transcriptomic fingerprints for murine cell types and to assess their enrichments in human cell types. This analysis allowed us to confirm homologies between mouse and human immunocytes.

Conclusions

BubbleGUM is an open-source software that allows to automatically generate molecular signatures out of complex expression datasets and to assess directly their enrichment by GSEA on independent datasets. Enrichments are displayed in a graphical output that helps interpreting the results. This innovative methodology has recently been used to answer important questions in functional genomics, such as the degree of similarities between microarray datasets from different laboratories or with different experimental models or clinical cohorts. BubbleGUM is executable through an intuitive interface so that both bioinformaticians and biologists can use it. It is available at http://www.ciml.univ-mrs.fr/applications/BubbleGUM/index.html.

Electronic supplementary material

The online version of this article (doi:10.1186/s12864-015-2012-4) contains supplementary material, which is available to authorized users.

Blood Interferon Signatures Putatively Link Lack of Protection Conferred by the RTS,S Recombinant Malaria Vaccine to an Antigen-specific IgE Response

Malaria remains a major cause of mortality and morbidity worldwide. Progress has been made in recent years with the development of vaccines that could pave the way towards protection of hundreds of millions of exposed individuals. Here we used a modular repertoire approach to re-analyze a publically available microarray blood transcriptome dataset monitoring the response to malaria vaccination. We report the seminal identification of interferon signatures in the blood of subjects on days 1, 3 and 14 following administration of the third dose of the RTS,S recombinant malaria vaccine. These signatures at day 1 correlate with protection, and at days 3 and 14 to susceptibility to subsequent challenge of study subjects with live parasites. In addition we putatively link the decreased abundance of interferon-inducible transcripts observed at days 3 and 14 post-vaccination with the elicitation of an antigen-specific IgE response in a subset of vaccine recipients that failed to be protected by the RTS,S vaccine. Furthermore, profiling of antigen-specific levels of IgE in a Mozambican cohort of malaria-exposed children vaccinated with RTS,S identified an association between elevated baseline IgE levels and subsequent development of naturally acquired malaria infection during follow up. Taken together these findings warrant further investigation of the role of antigen-specific IgE in conferring susceptibility to malaria infection.

Profiling the host response to malaria vaccination and malaria challenge

A vaccine for malaria is urgently required. The RTS,S vaccine represents major progress, but is only partially effective. Development of the next generation of highly effective vaccines requires elucidation of the protective immune response. Immunity to malaria is known to be complex, and pattern-based approaches such as global gene expression profiling are ideal for understanding response to vaccination and protection against disease. The availability of experimental sporozoite challenge in humans to test candidate malaria vaccines offers a precious opportunity unavailable for other current targets of vaccine research such as HIV, tuberculosis and Ebola. However, a limited number of transcriptional profiling studies in the context of malaria vaccine research have been published to date. This review outlines the background, existing studies, limits and opportunities for gene expression studies to accelerate malaria vaccine research.

Age gene expression and coexpression progressive signatures in peripheral blood leukocytes

Both cellular senescence and organismic aging are known to be dynamic processes that start early in life and progress constantly during the whole life of the individual. In this work, with the objective of identifying signatures of age-related progressive change at the transcriptomic level, we have performed a whole-genome gene expression analysis of peripheral blood leukocytes in a group of healthy individuals with ages ranging from 14 to 93 years. A set of genes with progressively changing gene expression (either increase or decrease with age) has been identified and contextualized in a coexpression network. A modularity analysis has been performed on this network and biological-term and pathway enrichment analyses have been used for biological interpretation of each module. In summary, the results of the present work reveal the existence of a transcriptomic component that shows progressive expression changes associated to age in peripheral blood leukocytes, highlighting both the dynamic nature of the process and the need to complement young vs. elder studies with longitudinal studies that include middle aged individuals. From the transcriptional point of view, immunosenescence seems to be occurring from a relatively early age, at least from the late 20s/early 30s, and the 49-56 year old age-range appears to be critical. In general, the genes that, according to our results, show progressive expression changes with aging are involved in pathogenic/cellular processes that have classically been linked to aging in humans: cancer, immune processes and cellular growth vs. maintenance.

Omic personality: implications of stable transcript and methylation profiles for personalized medicine

Background

Personalized medicine is predicated on the notion that individual biochemical and genomic profiles are relatively constant in times of good health and to some extent predictive of disease or therapeutic response. We report a pilot study quantifying gene expression and methylation profile consistency over time, addressing the reasons for individual uniqueness, and its relation to N = 1 phenotypes.

Methods

Whole blood samples from four African American women, four Caucasian women, and four Caucasian men drawn from the Atlanta Center for Health Discovery and Well Being study at three successive 6-month intervals were profiled by RNA-Seq, miRNA-Seq, and Illumina Methylation 450 K arrays. Standard regression approaches were used to evaluate the proportion of variance for each type of omic measure among individuals, and to quantify correlations among measures and with clinical attributes related to wellness.

Results

Longitudinal omic profiles were in general highly consistent over time, with an average of 67 % variance in transcript abundance, 42 % in CpG methylation level (but 88 % for the most differentiated CpG per gene), and 50 % in miRNA abundance among individuals, which are all comparable to 74 % variance among individuals for 74 clinical traits. One third of the variance could be attributed to differential blood cell type abundance, which was also fairly stable over time, and a lesser amount to expression quantitative trait loci (eQTL) effects. Seven conserved axes of covariance that capture diverse aspects of immune function explained over half of the variance. These axes also explained a considerable proportion of individually extreme transcript abundance, namely approximately 100 genes that were significantly up-regulated or down-regulated in each person and were in some cases enriched for relevant gene activities that plausibly associate with clinical attributes. A similar fraction of genes had individually divergent methylation levels, but these did not overlap with the transcripts, and fewer than 20 % of genes had significantly correlated methylation and gene expression.

Conclusions

People express an “omic personality” consisting of peripheral blood transcriptional and epigenetic profiles that are constant over the course of a year and reflect various types of immune activity. Baseline genomic profiles can provide a window into the molecular basis of traits that might be useful for explaining medical conditions or guiding personalized health decisions.

Electronic supplementary material

The online version of this article (doi:10.1186/s13073-015-0209-4) contains supplementary material, which is available to authorized users.

Utilizing population variation, vaccination, and systems biology to study human immunology

The move toward precision medicine has highlighted the importance of understanding biological variability within and across individuals in the human population. In particular, given the prevalent involvement of the immune system in diverse pathologies, an important question is how much and what information about the state of the immune system is required to enable accurate prediction of future health and response to medical interventions. Towards addressing this question, recent studies using vaccination as a model perturbation and systems-biology approaches are beginning to provide a glimpse of how natural population variation together with multiplexed, high-throughput measurement and computational analysis can be used to uncover predictors of immune response quality in humans. Here I discuss recent developments in this emerging field, with emphasis on baseline correlates of vaccination responses, sources of immune-state variability, as well as relevant features of study design, data generation, and computational analysis.

Transcriptomic analysis of mononuclear phagocyte differentiation and activation

Monocytes and macrophages differentiate from progenitor cells under the influence of colony-stimulating factors. Genome-scale data have enabled the identification of the sets of genes that are associated with specific functions and the mechanisms by which thousands of genes are regulated in response to pathogen challenge. In large datasets, it is possible to identify large sets of genes that are coregulated with the transcription factors that regulate them. They include macrophage-specific genes, interferon-responsive genes, early inflammatory genes, and those associated with endocytosis. Such analyses can also extract macrophage-associated signatures from large cancer tissue datasets. However, cluster analysis provides no support for a signature that distinguishes macrophages from antigen-presenting dendritic cells, nor the classification of macrophage activation states as classical versus alternative, or M1 versus M2. Although there has been a focus on a small subset of lineage-enriched transcription factors, such as PU.1, more than half of the transcription factors in the genome can be expressed in macrophage lineage cells under some state of activation, and they interact in a complex network. The network architecture is conserved across species, but many of the target genes evolve rapidly and differ between mouse and human. The data and publication deluge related to macrophage biology require the development of new analytical tools and ways of presenting information in an accessible form.

More or less

Current laboratory investigation of patients with suspected immunodeficiency or immune-mediated disease includes extensive analysis of lymphocyte subsets, T-cell receptor use, antibody profiles, cytokine profiles and genetic polymorphisms of relevant genes, all in all: big data. Clinical immunology clearly has entered the omics era, generating more and more data.

An interactive web application for the dissemination of human systems immunology data

BACKGROUND

Systems immunology approaches have proven invaluable in translational research settings. The current rate at which large-scale datasets are generated presents unique challenges and opportunities. Mining aggregates of these datasets could accelerate the pace of discovery, but new solutions are needed to integrate the heterogeneous data types with the contextual information that is necessary for interpretation. In addition, enabling tools and technologies facilitating investigators' interaction with large-scale datasets must be developed in order to promote insight and foster knowledge discovery.

METHODS

State of the art application programming was employed to develop an interactive web application for browsing and visualizing large and complex datasets. A collection of human immune transcriptome datasets were loaded alongside contextual information about the samples.

RESULTS

We provide a resource enabling interactive query and navigation of transcriptome datasets relevant to human immunology research. Detailed information about studies and samples are displayed dynamically; if desired the associated data can be downloaded. Custom interactive visualizations of the data can be shared via email or social media. This application can be used to browse context-rich systems-scale data within and across systems immunology studies. This resource is publicly available online at [Gene Expression Browser Landing Page ( https://gxb.benaroyaresearch.org/dm3/landing.gsp )]. The source code is also available openly [Gene Expression Browser Source Code ( https://github.com/BenaroyaResearch/gxbrowser )].

CONCLUSIONS

We have developed a data browsing and visualization application capable of navigating increasingly large and complex datasets generated in the context of immunological studies. This intuitive tool ensures that, whether taken individually or as a whole, such datasets generated at great effort and expense remain interpretable and a ready source of insight for years to come.

Community structure analysis of transcriptional networks reveals distinct molecular pathways for early- and late-onset temporal lobe epilepsy with childhood febrile seizures

Age at epilepsy onset has a broad impact on brain plasticity and epilepsy pathomechanisms. Prolonged febrile seizures in early childhood (FS) constitute an initial precipitating insult (IPI) commonly associated with mesial temporal lobe epilepsy (MTLE). FS-MTLE patients may have early disease onset, i.e. just after the IPI, in early childhood, or late-onset, ranging from mid-adolescence to early adult life. The mechanisms governing early (E) or late (L) disease onset are largely unknown. In order to unveil the molecular pathways underlying E and L subtypes of FS-MTLE we investigated global gene expression in hippocampal CA3 explants of FS-MTLE patients submitted to hippocampectomy. Gene coexpression networks (GCNs) were obtained for the E and L patient groups. A network-based approach for GCN analysis was employed allowing: i) the visualization and analysis of differentially expressed (DE) and complete (CO) - all valid GO annotated transcripts - GCNs for the E and L groups; ii) the study of interactions between all the system's constituents based on community detection and coarse-grained community structure methods. We found that the E-DE communities with strongest connection weights harbor highly connected genes mainly related to neural excitability and febrile seizures, whereas in L-DE communities these genes are not only involved in network excitability but also playing roles in other epilepsy-related processes. Inversely, in E-CO the strongly connected communities are related to compensatory pathways (seizure inhibition, neuronal survival and responses to stress conditions) while in L-CO these communities harbor several genes related to pro-epileptic effects, seizure-related mechanisms and vulnerability to epilepsy. These results fit the concept, based on fMRI and behavioral studies, that early onset epilepsies, although impacting more severely the hippocampus, are associated to compensatory mechanisms, while in late MTLE development the brain is less able to generate adaptive mechanisms, what has implications for epilepsy management and drug discovery.

Using Transcriptional Signatures to Assess Immune Cell Function: From Basic Mechanisms to Immune-Related Disease

Assessing human immune response remains a challenge as it involves multiple cell types in specific tissues. The use of microarray-based expression profiling as a tool for assessing the immune response has grown increasingly over the past decade. Transcriptome analyses provide investigators with a global perspective of the complex molecular and cellular events that unfold during the development of an immune response. In this review, we will detail the broad use of gene expression profiling to decipher the complexity of immune responses from disease biomarkers identification to cell activation, polarisation or functional specialisation. We will also describe how such data-driven strategies revealed the flexibility of immune function with common and specific transcriptional programme under multiple stimuli.

High-throughput data analysis and data integration for vaccine trials

Rational vaccine development can benefit from biomarker studies, which help to predict, optimize and evaluate the immunogenicity of vaccines and ultimately provide surrogate endpoints for vaccine trials. Systems biology approaches facilitate acquisition of both simple biomarkers and complex biosignatures. Yet, evaluation of high-throughput (HT) data requires a plethora of tools for data integration and analysis. In this review, we present an overview of methods for evaluation and integration of large amounts of data collected in vaccine trials from similar and divergent molecular HT techniques, such as transcriptomic, proteomic and metabolic profiling. We will describe a selection of relevant statistical and bioinformatic approaches that are frequently associated with systems biology. We will present data dimension reduction techniques, functional analysis approaches and methods of integrating heterogeneous HT data. Finally, we will provide a few examples of applications of these techniques in vaccine research and development.

Exploring immunological mechanisms of the whole sporozoite vaccination against P. falciparum malaria

Great progress has been made in the development of whole sporozoite vaccines including the manufacturing of cryopreserved Plasmodium falciparum sporozoites (PfSPZ) suitable for clinical application. Such whole sporozoites are being used for clinical studies of controlled human malaria infection (CHMI) as well as for evaluation of candidate vaccine approaches (both attenuated sporozoites and infectious sporozoites administered with chemoprophylaxis) and as reagents for immunology and cell biology assays. CHMI studies with whole sporozoites provide a great opportunity to better understand the intrinsic mechanisms of resistance to P. falciparum (e.g. due to sickle cell trait and other hemoglobinopathies) as well as host responses to an initial P. falciparum infection. High-level protective efficacy has been demonstrated in a small number of volunteers after intravenous (IV) inoculation of radiation-attenuated PfSPZ or in those who were exposed to live PfSPZ while on malaria chemoprophylaxis. These advances and data warrant further investigations of the immunological mechanism(s) whereby whole sporozoite inoculation elicits protective immunity in order to facilitate whole sporozoite vaccine development. The National Institute of Allergy and Infectious Diseases (NIAID) convened a workshop on Sept. 2-3, 2014 involving participation of international experts in the field of malaria vaccine development, and in basic and clinical immunology research. The workshop discussed the current understanding of host immune responses to whole malaria sporozoite inoculation, identified gaps in knowledge, resources to facilitate progress, and applicable new technologies and approaches to accelerate immunologic and vaccinologic studies and biomarker identification. This report summarizes the discussions and major conclusions from the workshop participants.

Systems vaccinology: Enabling rational vaccine design with systems biological approaches

Vaccines have drastically reduced the mortality and morbidity of many diseases. However, vaccines have historically been developed empirically, and recent development of vaccines against current pandemics such as HIV and malaria has been met with difficulty. The advent of high-throughput technologies, coupled with systems biological methods of data analysis, has enabled researchers to interrogate the entire complement of a variety of molecular components within cells, and characterize the myriad interactions among them in order to model and understand the behavior of the system as a whole. In the context of vaccinology, these tools permit exploration of the molecular mechanisms by which vaccines induce protective immune responses. Here we review the recent advances, challenges, and potential of systems biological approaches in vaccinology. If the challenges facing this developing field can be overcome, systems vaccinology promises to empower the identification of early predictive signatures of vaccine response, as well as novel and robust correlates of protection from infection. Such discoveries, along with the improved understanding of immune responses to vaccination they impart, will play an instrumental role in development of the next generation of rationally designed vaccines.

Assessment of immune status using blood transcriptomics and potential implications for global health

The immune system plays a key role in health maintenance and pathogenesis of a wide range of diseases. Leukocytes that are present in the blood convey valuable information about the status of the immune system. Blood transcriptomics, which consists in profiling blood transcript abundance on genome-wide scales, has gained in popularity over the past several years. Indeed, practicality and simplicity largely makes up for what this approach may lack in terms of cell population-level resolution. An extensive survey of the literature reveals increasingly widespread use across virtually all fields of medicine as well as across a number of different animal species, including model organisms but also animals of economical importance. Dissemination across such a wide range of disciplines holds the promise of adding a new perspective, breadth or context, to the considerable depth afforded by whole genome profiling of blood transcript abundance. Indeed, it is only through such contextualization that a truly global perspective will be gained from the use of systems approaches. Also discussed are opportunities that may arise for the fields of immunology and medicine from using blood transcriptomics as a common denominator for developing interactions and cooperation across fields of research that have traditionally been and largely remain compartmentalized. Finally, an argument is made for building immunology research capacity using blood transcriptomics platforms in low-resource and high-disease burden settings.

Superiority of transcriptional profiling over procalcitonin for distinguishing bacterial from viral lower respiratory tract infections in hospitalized adults

BACKGROUND

Distinguishing between bacterial and viral lower respiratory tract infection (LRTI) remains challenging. Transcriptional profiling is a promising tool for improving diagnosis in LRTI.

METHODS

We performed whole blood transcriptional analysis in 118 patients (median age [interquartile range], 61 [50-76] years) hospitalized with LRTI and 40 age-matched healthy controls (median age, 60 [46-70] years). We applied class comparisons, modular analysis, and class prediction algorithms to identify and validate diagnostic biosignatures for bacterial and viral LRTI.

RESULTS

Patients were classified as having bacterial (n = 22), viral (n = 71), or bacterial-viral LRTI (n = 25) based on comprehensive microbiologic testing. Compared with healthy controls, statistical group comparisons (P < .01; multiple-test corrections) identified 3376 differentially expressed genes in patients with bacterial LRTI, 2391 in viral LRTI, and 2628 in bacterial-viral LRTI. Patients with bacterial LRTI showed significant overexpression of inflammation and neutrophil genes (bacterial > bacterial-viral > viral), and those with viral LRTI displayed significantly greater overexpression of interferon genes (viral > bacterial-viral > bacterial). The K-nearest neighbors algorithm identified 10 classifier genes that discriminated between bacterial and viral LRTI with a 95% sensitivity (95% confidence interval, 77%-100%) and 92% specificity (77%-98%), compared with a sensitivity of 38% (18%-62%) and a specificity of 91% (76%-98%) for procalcitonin.

CONCLUSIONS

Transcriptional profiling is a helpful tool for diagnosis of LRTI.

A narrow repertoire of transcriptional modules responsive to pyogenic bacteria is impaired in patients carrying loss-of-function mutations in MYD88 or IRAK4

Loss of function of the kinase IRAK4 or the adaptor MyD88 in humans interrupts a pathway critical for pathogen sensing and ignition of inflammation. However, patients with loss-of-function mutations in the genes encoding these factors are, unexpectedly, susceptible to only a limited range of pathogens. We employed a systems approach to investigate transcriptome responses following in vitro exposure of patients' blood to agonists of Toll-like receptors (TLRs) and receptors for interleukin 1 (IL-1Rs) and to whole pathogens. Responses to purified agonists were globally abolished, but variable residual responses were present following exposure to whole pathogens. Further delineation of the latter responses identified a narrow repertoire of transcriptional programs affected by loss of MyD88 function or IRAK4 function. Our work introduces the use of a systems approach for the global assessment of innate immune responses and the characterization of human primary immunodeficiencies.